Granger Causality Testing with Intensive Longitudinal Data.

PubMed

Molenaar, Peter C M

2018-06-01

The availability of intensive longitudinal data obtained by means of ambulatory assessment opens up new prospects for prevention research in that it allows the derivation of subject-specific dynamic networks of interacting variables by means of vector autoregressive (VAR) modeling. The dynamic networks thus obtained can be subjected to Granger causality testing in order to identify causal relations among the observed time-dependent variables. VARs have two equivalent representations: standard and structural. Results obtained with Granger causality testing depend upon which representation is chosen, yet no criteria exist on which this important choice can be based. A new equivalent representation is introduced called hybrid VARs with which the best representation can be chosen in a data-driven way. Partial directed coherence, a frequency-domain statistic for Granger causality testing, is shown to perform optimally when based on hybrid VARs. An application to real data is provided.

Three essays on price dynamics and causations among energy markets and macroeconomic information

NASA Astrophysics Data System (ADS)

Hong, Sung Wook

This dissertation examines three important issues in energy markets: price dynamics, information flow, and structural change. We discuss each issue in detail, building empirical time series models, analyzing the results, and interpreting the findings. First, we examine the contemporaneous interdependencies and information flows among crude oil, natural gas, and electricity prices in the United States (US) through the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model, Directed Acyclic Graph (DAG) for contemporaneous causal structures and Bernanke factorization for price dynamic processes. Test results show that the DAG from residuals of out-of-sample-forecast is consistent with the DAG from residuals of within-sample-fit. The result supports innovation accounting analysis based on DAGs using residuals of out-of-sample-forecast. Second, we look at the effects of the federal fund rate and/or WTI crude oil price shock on US macroeconomic and financial indicators by using a Factor Augmented Vector Autoregression (FAVAR) model and a graphical model without any deductive assumption. The results show that, in contemporaneous time, the federal fund rate shock is exogenous as the identifying assumption in the Vector Autoregression (VAR) framework of the monetary shock transmission mechanism, whereas the WTI crude oil price return is not exogenous. Third, we examine price dynamics and contemporaneous causality among the price returns of WTI crude oil, gasoline, corn, and the S&P 500. We look for structural break points and then build an econometric model to find the consistent sub-periods having stable parameters in a given VAR framework and to explain recent movements and interdependency among returns. We found strong evidence of two structural breaks and contemporaneous causal relationships among the residuals, but also significant differences between contemporaneous causal structures for each sub-period.

A vector auto-regressive model for onshore and offshore wind synthesis incorporating meteorological model information

NASA Astrophysics Data System (ADS)

Hill, D.; Bell, K. R. W.; McMillan, D.; Infield, D.

2014-05-01

The growth of wind power production in the electricity portfolio is striving to meet ambitious targets set, for example by the EU, to reduce greenhouse gas emissions by 20% by 2020. Huge investments are now being made in new offshore wind farms around UK coastal waters that will have a major impact on the GB electrical supply. Representations of the UK wind field in syntheses which capture the inherent structure and correlations between different locations including offshore sites are required. Here, Vector Auto-Regressive (VAR) models are presented and extended in a novel way to incorporate offshore time series from a pan-European meteorological model called COSMO, with onshore wind speeds from the MIDAS dataset provided by the British Atmospheric Data Centre. Forecasting ability onshore is shown to be improved with the inclusion of the offshore sites with improvements of up to 25% in RMS error at 6 h ahead. In addition, the VAR model is used to synthesise time series of wind at each offshore site, which are then used to estimate wind farm capacity factors at the sites in question. These are then compared with estimates of capacity factors derived from the work of Hawkins et al. (2011). A good degree of agreement is established indicating that this synthesis tool should be useful in power system impact studies.

Is First-Order Vector Autoregressive Model Optimal for fMRI Data?

PubMed

Ting, Chee-Ming; Seghouane, Abd-Krim; Khalid, Muhammad Usman; Salleh, Sh-Hussain

2015-09-01

We consider the problem of selecting the optimal orders of vector autoregressive (VAR) models for fMRI data. Many previous studies used model order of one and ignored that it may vary considerably across data sets depending on different data dimensions, subjects, tasks, and experimental designs. In addition, the classical information criteria (IC) used (e.g., the Akaike IC (AIC)) are biased and inappropriate for the high-dimensional fMRI data typically with a small sample size. We examine the mixed results on the optimal VAR orders for fMRI, especially the validity of the order-one hypothesis, by a comprehensive evaluation using different model selection criteria over three typical data types--a resting state, an event-related design, and a block design data set--with varying time series dimensions obtained from distinct functional brain networks. We use a more balanced criterion, Kullback's IC (KIC) based on Kullback's symmetric divergence combining two directed divergences. We also consider the bias-corrected versions (AICc and KICc) to improve VAR model selection in small samples. Simulation results show better small-sample selection performance of the proposed criteria over the classical ones. Both bias-corrected ICs provide more accurate and consistent model order choices than their biased counterparts, which suffer from overfitting, with KICc performing the best. Results on real data show that orders greater than one were selected by all criteria across all data sets for the small to moderate dimensions, particularly from small, specific networks such as the resting-state default mode network and the task-related motor networks, whereas low orders close to one but not necessarily one were chosen for the large dimensions of full-brain networks.

Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China.

PubMed

Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian

2017-01-01

The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.

Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China

PubMed Central

Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian

2017-01-01

The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry. PMID:28459872

The dynamic correlation between policy uncertainty and stock market returns in China

NASA Astrophysics Data System (ADS)

Yang, Miao; Jiang, Zhi-Qiang

2016-11-01

The dynamic correlation is examined between government's policy uncertainty and Chinese stock market returns in the period from January 1995 to December 2014. We find that the stock market is significantly correlated to policy uncertainty based on the results of the Vector Auto Regression (VAR) and Structural Vector Auto Regression (SVAR) models. In contrast, the results of the Dynamic Conditional Correlation Generalized Multivariate Autoregressive Conditional Heteroscedasticity (DCC-MGARCH) model surprisingly show a low dynamic correlation coefficient between policy uncertainty and market returns, suggesting that the fluctuations of each variable are greatly influenced by their values in the preceding period. Our analysis highlights the understanding of the dynamical relationship between stock market and fiscal and monetary policy.

Vector Autoregressive Models and Granger Causality in Time Series Analysis in Nursing Research: Dynamic Changes Among Vital Signs Prior to Cardiorespiratory Instability Events as an Example.

PubMed

Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M

Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display interrelated vital sign changes during situations of physiological stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. The purpose of this article is to illustrate the development of patient-specific VAR models using vital sign time series data in a sample of acutely ill, monitored, step-down unit patients and determine their Granger causal dynamics prior to onset of an incident CRI. CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40-140/minute, RR = 8-36/minute, SpO2 < 85%) and persisting for 3 minutes within a 5-minute moving window (60% of the duration of the window). A 6-hour time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity, (b) appropriate lag was determined using a lag-length selection criteria, (c) the VAR model was constructed, (d) residual autocorrelation was assessed with the Lagrange Multiplier test, (e) stability of the VAR system was checked, and (f) Granger causality was evaluated in the final stable model. The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%; i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing changes in HR occurred with equal frequency (18%). Within this sample of acutely ill patients who experienced a CRI event, VAR modeling indicated that RR changes tend to occur before changes in HR and SpO2. These findings suggest that contextual assessment of RR changes as the earliest sign of CRI is warranted. Use of VAR modeling may be helpful in other nursing research applications based on time series data.

International Trade, Pollution Accumulation and Sustainable Growth: A VAR Estimation from the Pearl River Delta Region

NASA Astrophysics Data System (ADS)

Zuo, Hui; Tian, Lu

2018-03-01

In order to investigate international trade influence in the regional environment. This paper constructs a vector auto-regression (VAR) model and estimates the equations with the environment and trade data of the Pearl River Delta Region. The major mechanisms to the lag are discussed and the fit simulation of the environmental change by the international impulse is given. The result shows that impulse of pollution-intensive export deteriorates the environment continuously and impulse of such import improves it. These effects on the environment are insignificantly correlated with contemporary regional income but significantly correlative to early-stage trade feature. To a typical trade-dependent economy, both export and import have hysteresis influence in the regional environment. The lagged impulse will change environmental development in the turning point, maximal pollution level and convergence.

Exploring the transformation and upgrading of China's economy using electricity consumption data: A VAR-VEC based model

NASA Astrophysics Data System (ADS)

Zhang, Chi; Zhou, Kaile; Yang, Shanlin; Shao, Zhen

2017-05-01

Since the reforming and opening up in 1978, China has experienced a miraculous development. To investigate the transformation and upgrading of China's economy, this study focuses on the relationship between economic growth and electricity consumption of the secondary and tertiary industry in China. This paper captures the dynamic interdependencies among the related variables using a theoretical framework based on a Vector Autoregressive (VAR)-Vector Error Correction (VEC) model. Using the macroeconomic and electricity consumption data, the results show that, for secondary industry, there is only a unidirectional Granger causality from electricity consumption to Gross Domestic Product (GDP) from 1980 to 2000. However, for the tertiary industry, it only occurs that GDP Granger causes electricity consumption from 2001 to 2014. All these conclusions are verified by the impulse response function and variance decomposition. This study has a great significance to reveal the relationship between industrial electricity consumption and the pattern of economic development. Meanwhile, it further suggests that, since China joined the World Trade Organization (WTO) in 2001, the trend of the economic transformation and upgrading has gradually appeared.

Vector Autoregressive (VAR) Models and Granger Causality in Time Series Analysis in Nursing Research: Dynamic Changes Among Vital Signs Prior to Cardiorespiratory Instability Events as an Example

PubMed Central

Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M.

2016-01-01

Background Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display inter-related vital sign changes during situations of physiologic stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. Purpose The purpose of this article is to illustrate development of patient-specific VAR models using vital sign time series (VSTS) data in a sample of acutely ill, monitored, step-down unit (SDU) patients, and determine their Granger causal dynamics prior to onset of an incident CRI. Approach CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40–140/minute, RR = 8–36/minute, SpO2 < 85%) and persisting for 3 minutes within a 5-minute moving window (60% of the duration of the window). A 6-hour time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity; (b) appropriate lag was determined using a lag-length selection criteria; (c) the VAR model was constructed; (d) residual autocorrelation was assessed with the Lagrange Multiplier test; (e) stability of the VAR system was checked; and (f) Granger causality was evaluated in the final stable model. Results The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%) (i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing changes in HR occurred with equal frequency (18%). Discussion Within this sample of acutely ill patients who experienced a CRI event, VAR modeling indicated that RR changes tend to occur before changes in HR and SpO2. These findings suggest that contextual assessment of RR changes as the earliest sign of CRI is warranted. Use of VAR modeling may be helpful in other nursing research applications based on time series data. PMID:27977564

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data.

PubMed

Epskamp, Sacha; Waldorp, Lourens J; Mõttus, René; Borsboom, Denny

2018-04-16

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.

Dimension reduction of frequency-based direct Granger causality measures on short time series.

PubMed

Siggiridou, Elsa; Kimiskidis, Vasilios K; Kugiumtzis, Dimitris

2017-09-01

The mainstream in the estimation of effective brain connectivity relies on Granger causality measures in the frequency domain. If the measure is meant to capture direct causal effects accounting for the presence of other observed variables, as in multi-channel electroencephalograms (EEG), typically the fit of a vector autoregressive (VAR) model on the multivariate time series is required. For short time series of many variables, the estimation of VAR may not be stable requiring dimension reduction resulting in restricted or sparse VAR models. The restricted VAR obtained by the modified backward-in-time selection method (mBTS) is adapted to the generalized partial directed coherence (GPDC), termed restricted GPDC (RGPDC). Dimension reduction on other frequency based measures, such the direct directed transfer function (dDTF), is straightforward. First, a simulation study using linear stochastic multivariate systems is conducted and RGPDC is favorably compared to GPDC on short time series in terms of sensitivity and specificity. Then the two measures are tested for their ability to detect changes in brain connectivity during an epileptiform discharge (ED) from multi-channel scalp EEG. It is shown that RGPDC identifies better than GPDC the connectivity structure of the simulated systems, as well as changes in the brain connectivity, and is less dependent on the free parameter of VAR order. The proposed dimension reduction in frequency measures based on VAR constitutes an appropriate strategy to estimate reliably brain networks within short-time windows. Copyright © 2017 Elsevier B.V. All rights reserved.

On The Value at Risk Using Bayesian Mixture Laplace Autoregressive Approach for Modelling the Islamic Stock Risk Investment

NASA Astrophysics Data System (ADS)

Miftahurrohmah, Brina; Iriawan, Nur; Fithriasari, Kartika

2017-06-01

Stocks are known as the financial instruments traded in the capital market which have a high level of risk. Their risks are indicated by their uncertainty of their return which have to be accepted by investors in the future. The higher the risk to be faced, the higher the return would be gained. Therefore, the measurements need to be made against the risk. Value at Risk (VaR) as the most popular risk measurement method, is frequently ignore when the pattern of return is not uni-modal Normal. The calculation of the risks using VaR method with the Normal Mixture Autoregressive (MNAR) approach has been considered. This paper proposes VaR method couple with the Mixture Laplace Autoregressive (MLAR) that would be implemented for analysing the first three biggest capitalization Islamic stock return in JII, namely PT. Astra International Tbk (ASII), PT. Telekomunikasi Indonesia Tbk (TLMK), and PT. Unilever Indonesia Tbk (UNVR). Parameter estimation is performed by employing Bayesian Markov Chain Monte Carlo (MCMC) approaches.

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

ERIC Educational Resources Information Center

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

Does health promote economic growth? Portuguese case study: from dictatorship to full democracy.

PubMed

Morgado, Sónia Maria Aniceto

2014-07-01

This paper revisits the debate on health and economic growth (Deaton in J Econ Lit 51:113-158, 2003) focusing on the Portuguese case by testing the relationship between growth and health. We test Portuguese insights, using time series data from 1960 to 2005, taking into account different variables (life expectancy, labour, capital, infant mortality) and considering the years that included major events on the political scene, such as the dictatorship and a closed economy (1960-1974), a revolution (1974) and full democracy and an open economy (1975-2005), factors that influence major economic, cultural, social and politic indicators. Therefore the analysis is carried out adopting Lucas' (J Monet Econ 22(1):3-42, 1988) endogenous growth model that considers human capital as one factor of production, it adopts a VAR (vector autoregressive) model to test the causality between growth and health. Estimates based on the VAR seem to confirm that economic growth influences the health process, but health does not promote growth, during the period under study.

Coupling detrended fluctuation analysis of Asian stock markets

NASA Astrophysics Data System (ADS)

Wang, Qizhen; Zhu, Yingming; Yang, Liansheng; Mul, Remco A. H.

2017-04-01

This paper uses the coupling detrended fluctuation analysis (CDFA) method to investigate the multifractal characteristics of four Asian stock markets using three stock indices: stock price returns, trading volumes and the composite index. The results show that coupled correlations exist among the four stock markets and the coupled correlations have multifractal characteristics. We then use the chi square (χ2) test to identify the sources of multifractality. For the different stock indices, the contributions of a single series to multifractality are different. In other words, the contributions of each country to coupled correlations are different. The comparative analysis shows that the research on the combine effect of stock price returns and trading volumes may be more comprehensive than on an individual index. By comparing the strength of multifractality for original data with the residual errors of the vector autoregression (VAR) model, we find that the VAR model could not be used to describe the dynamics of the coupled correlations among four financial time series.

Dedollarization in Turkey after decades of dollarization: A myth or reality?

NASA Astrophysics Data System (ADS)

Metin-Özcan, Kıvılcım; Us, Vuslat

2007-11-01

The paper analyzes dollarization in the Turkish economy given the evidence on dedollarization signals. On conducting a Vector Autoregression (VAR) model, the empirical evidence suggests that dollarization has mostly been shaped by macroeconomic imbalances as measured by exchange rate depreciation volatility, inflation volatility and expectations. Furthermore, the generalized impulse response function (IRF) analysis, in addition to the analysis of variance decomposition (VDC) gives support to the notion that dollarization seems to sustain its persistent nature, thus hysteresis still prevails. Hence, unfavorable macroeconomic conditions apparently contribute to dollarization while dollarization itself contains inertia. Furthermore, dedollarization that presumably started after 2001 has lost headway after May 2006. Thus, it seems too early to conclude that dollarization changed its route to dedollarization.

Ecological Momentary Assessments and Automated Time Series Analysis to Promote Tailored Health Care: A Proof-of-Principle Study.

PubMed

van der Krieke, Lian; Emerencia, Ando C; Bos, Elisabeth H; Rosmalen, Judith Gm; Riese, Harriëtte; Aiello, Marco; Sytema, Sjoerd; de Jonge, Peter

2015-08-07

Health promotion can be tailored by combining ecological momentary assessments (EMA) with time series analysis. This combined method allows for studying the temporal order of dynamic relationships among variables, which may provide concrete indications for intervention. However, application of this method in health care practice is hampered because analyses are conducted manually and advanced statistical expertise is required. This study aims to show how this limitation can be overcome by introducing automated vector autoregressive modeling (VAR) of EMA data and to evaluate its feasibility through comparisons with results of previously published manual analyses. We developed a Web-based open source application, called AutoVAR, which automates time series analyses of EMA data and provides output that is intended to be interpretable by nonexperts. The statistical technique we used was VAR. AutoVAR tests and evaluates all possible VAR models within a given combinatorial search space and summarizes their results, thereby replacing the researcher's tasks of conducting the analysis, making an informed selection of models, and choosing the best model. We compared the output of AutoVAR to the output of a previously published manual analysis (n=4). An illustrative example consisting of 4 analyses was provided. Compared to the manual output, the AutoVAR output presents similar model characteristics and statistical results in terms of the Akaike information criterion, the Bayesian information criterion, and the test statistic of the Granger causality test. Results suggest that automated analysis and interpretation of times series is feasible. Compared to a manual procedure, the automated procedure is more robust and can save days of time. These findings may pave the way for using time series analysis for health promotion on a larger scale. AutoVAR was evaluated using the results of a previously conducted manual analysis. Analysis of additional datasets is needed in order to validate and refine the application for general use.

Ecological Momentary Assessments and Automated Time Series Analysis to Promote Tailored Health Care: A Proof-of-Principle Study

PubMed Central

Emerencia, Ando C; Bos, Elisabeth H; Rosmalen, Judith GM; Riese, Harriëtte; Aiello, Marco; Sytema, Sjoerd; de Jonge, Peter

2015-01-01

Background Health promotion can be tailored by combining ecological momentary assessments (EMA) with time series analysis. This combined method allows for studying the temporal order of dynamic relationships among variables, which may provide concrete indications for intervention. However, application of this method in health care practice is hampered because analyses are conducted manually and advanced statistical expertise is required. Objective This study aims to show how this limitation can be overcome by introducing automated vector autoregressive modeling (VAR) of EMA data and to evaluate its feasibility through comparisons with results of previously published manual analyses. Methods We developed a Web-based open source application, called AutoVAR, which automates time series analyses of EMA data and provides output that is intended to be interpretable by nonexperts. The statistical technique we used was VAR. AutoVAR tests and evaluates all possible VAR models within a given combinatorial search space and summarizes their results, thereby replacing the researcher’s tasks of conducting the analysis, making an informed selection of models, and choosing the best model. We compared the output of AutoVAR to the output of a previously published manual analysis (n=4). Results An illustrative example consisting of 4 analyses was provided. Compared to the manual output, the AutoVAR output presents similar model characteristics and statistical results in terms of the Akaike information criterion, the Bayesian information criterion, and the test statistic of the Granger causality test. Conclusions Results suggest that automated analysis and interpretation of times series is feasible. Compared to a manual procedure, the automated procedure is more robust and can save days of time. These findings may pave the way for using time series analysis for health promotion on a larger scale. AutoVAR was evaluated using the results of a previously conducted manual analysis. Analysis of additional datasets is needed in order to validate and refine the application for general use. PMID:26254160

A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization

DOE PAGES

Zhao, Junbo; Wang, Shaobu; Mili, Lamine; ...

2018-01-08

Here, this paper develops a robust power system state estimation framework with the consideration of measurement correlations and imperfect synchronization. In the framework, correlations of SCADA and Phasor Measurements (PMUs) are calculated separately through unscented transformation and a Vector Auto-Regression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model with robustly estimated parameters using projection statistics approach. The latter takes into account the temporal and spatial correlations of PMU measurements and provides redundant measurements to suppress bad data and mitigate imperfect synchronization. In case where the SCADAmore » and PMU measurements are not time synchronized, either the forecasted PMU measurements or the prior SCADA measurements from the last estimation run are leveraged to restore system observability. Then, a robust generalized maximum-likelihood (GM)-estimator is extended to integrate measurement error correlations and to handle the outliers in the SCADA and PMU measurements. Simulation results that stem from a comprehensive comparison with other alternatives under various conditions demonstrate the benefits of the proposed framework.« less

A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization

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

Zhao, Junbo; Wang, Shaobu; Mili, Lamine

Here, this paper develops a robust power system state estimation framework with the consideration of measurement correlations and imperfect synchronization. In the framework, correlations of SCADA and Phasor Measurements (PMUs) are calculated separately through unscented transformation and a Vector Auto-Regression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model with robustly estimated parameters using projection statistics approach. The latter takes into account the temporal and spatial correlations of PMU measurements and provides redundant measurements to suppress bad data and mitigate imperfect synchronization. In case where the SCADAmore » and PMU measurements are not time synchronized, either the forecasted PMU measurements or the prior SCADA measurements from the last estimation run are leveraged to restore system observability. Then, a robust generalized maximum-likelihood (GM)-estimator is extended to integrate measurement error correlations and to handle the outliers in the SCADA and PMU measurements. Simulation results that stem from a comprehensive comparison with other alternatives under various conditions demonstrate the benefits of the proposed framework.« less

Multiscale analysis of information dynamics for linear multivariate processes.

PubMed

Faes, Luca; Montalto, Alessandro; Stramaglia, Sebastiano; Nollo, Giandomenico; Marinazzo, Daniele

2016-08-01

In the study of complex physical and physiological systems represented by multivariate time series, an issue of great interest is the description of the system dynamics over a range of different temporal scales. While information-theoretic approaches to the multiscale analysis of complex dynamics are being increasingly used, the theoretical properties of the applied measures are poorly understood. This study introduces for the first time a framework for the analytical computation of information dynamics for linear multivariate stochastic processes explored at different time scales. After showing that the multiscale processing of a vector autoregressive (VAR) process introduces a moving average (MA) component, we describe how to represent the resulting VARMA process using statespace (SS) models and how to exploit the SS model parameters to compute analytical measures of information storage and information transfer for the original and rescaled processes. The framework is then used to quantify multiscale information dynamics for simulated unidirectionally and bidirectionally coupled VAR processes, showing that rescaling may lead to insightful patterns of information storage and transfer but also to potentially misleading behaviors.

Recursive regularization for inferring gene networks from time-course gene expression profiles

PubMed Central

Shimamura, Teppei; Imoto, Seiya; Yamaguchi, Rui; Fujita, André; Nagasaki, Masao; Miyano, Satoru

2009-01-01

Background Inferring gene networks from time-course microarray experiments with vector autoregressive (VAR) model is the process of identifying functional associations between genes through multivariate time series. This problem can be cast as a variable selection problem in Statistics. One of the promising methods for variable selection is the elastic net proposed by Zou and Hastie (2005). However, VAR modeling with the elastic net succeeds in increasing the number of true positives while it also results in increasing the number of false positives. Results By incorporating relative importance of the VAR coefficients into the elastic net, we propose a new class of regularization, called recursive elastic net, to increase the capability of the elastic net and estimate gene networks based on the VAR model. The recursive elastic net can reduce the number of false positives gradually by updating the importance. Numerical simulations and comparisons demonstrate that the proposed method succeeds in reducing the number of false positives drastically while keeping the high number of true positives in the network inference and achieves two or more times higher true discovery rate (the proportion of true positives among the selected edges) than the competing methods even when the number of time points is small. We also compared our method with various reverse-engineering algorithms on experimental data of MCF-7 breast cancer cells stimulated with two ErbB ligands, EGF and HRG. Conclusion The recursive elastic net is a powerful tool for inferring gene networks from time-course gene expression profiles. PMID:19386091

Non performing loans (NPLs) in a crisis economy: Long-run equilibrium analysis with a real time VEC model for Greece (2001-2015)

NASA Astrophysics Data System (ADS)

Konstantakis, Konstantinos N.; Michaelides, Panayotis G.; Vouldis, Angelos T.

2016-06-01

As a result of domestic and international factors, the Greek economy faced a severe crisis which is directly comparable only to the Great Recession. In this context, a prominent victim of this situation was the country's banking system. This paper attempts to shed light on the determining factors of non-performing loans in the Greek banking sector. The analysis presents empirical evidence from the Greek economy, using aggregate data on a quarterly basis, in the time period 2001-2015, fully capturing the recent recession. In this work, we use a relevant econometric framework based on a real time Vector Autoregressive (VAR)-Vector Error Correction (VEC) model, which captures the dynamic interdependencies among the variables used. Consistent with international evidence, the empirical findings show that both macroeconomic and financial factors have a significant impact on non-performing loans in the country. Meanwhile, the deteriorating credit quality feeds back into the economy leading to a self-reinforcing negative loop.

The Empirical Relationship between Mining Industry Development and Environmental Pollution in China.

PubMed

Li, Gerui; Lei, Yalin; Ge, Jianping; Wu, Sanmang

2017-03-02

This study uses a vector autoregression (VAR) model to analyze changes in pollutants among different mining industries and related policy in China from 2001 to 2014. The results show that: (1) because the pertinence of standards for mining waste water and waste gas emissions are not strong and because the maximum permissible discharge pollutant concentrations in these standards are too high, ammonia nitrogen and industrial sulfur dioxide discharges increased in most mining industries; (2) chemical oxygen demand was taken as an indicator of sewage treatment in environmental protection plans; hence, the chemical oxygen demand discharge decreased in all mining industries; (3) tax reduction policies, which are only implemented in coal mining and washing and extraction of petroleum and natural gas, decreased the industrial solid waste discharge in these two mining industries.

Aviation Trainer Technology Test Plan. Volume II. Software Development

DTIC Science & Technology

1991-11-25

feild values in new node *Ieg>X=x newg->X = Y newg->Len =len; newg->Help =help; newg->Ignore = ignore; newg->Format = format; newg->Validation - NULL;I... vector : North long varVFE; /* F-16A velocity vector : East long varVFU; /* F-16A velocity vector : Up */ long varH; /* plane heading */ long varC; /* plane...31\\\\ETH523.sys" parmsdr.args=getds(); parmsdr.non7=OxOO; /*save interrupt vector for future restoration */ cSavvecso; rc=getdso; rc=cInitParameters

The Impact of United States Monetary Policy in the Crude Oil futures market

NASA Astrophysics Data System (ADS)

Padilla-Padilla, Fernando M.

This research examines the empirical impact the United States monetary policy, through the federal fund interest rate, has on the volatility in the crude oil price in the futures market. Prior research has shown how macroeconomic events and variables have impacted different financial markets within short and long--term movements. After testing and decomposing the variables, the two stationary time series were analyzed using a Vector Autoregressive Model (VAR). The empirical evidence shows, with statistical significance, a direct relationship when explaining crude oil prices as function of fed fund rates (t-1) and an indirect relationship when explained as a function of fed fund rates (t-2). These results partially address the literature review lacunas within the topic of the existing implication monetary policy has within the crude oil futures market.

The Empirical Relationship between Mining Industry Development and Environmental Pollution in China

PubMed Central

Li, Gerui; Lei, Yalin; Ge, Jianping; Wu, Sanmang

2017-01-01

This study uses a vector autoregression (VAR) model to analyze changes in pollutants among different mining industries and related policy in China from 2001 to 2014. The results show that: (1) because the pertinence of standards for mining waste water and waste gas emissions are not strong and because the maximum permissible discharge pollutant concentrations in these standards are too high, ammonia nitrogen and industrial sulfur dioxide discharges increased in most mining industries; (2) chemical oxygen demand was taken as an indicator of sewage treatment in environmental protection plans; hence, the chemical oxygen demand discharge decreased in all mining industries; (3) tax reduction policies, which are only implemented in coal mining and washing and extraction of petroleum and natural gas, decreased the industrial solid waste discharge in these two mining industries. PMID:28257126

Identifying the impacts of climate on the regional transport of haze pollution and inter-cities correspondence within the Yangtze River Delta.

PubMed

Xiao, Hang; Huang, Zhongwen; Zhang, Jingjing; Zhang, Huiling; Chen, Jinsheng; Zhang, Han; Tong, Lei

2017-09-01

Regional haze pollution has become an important environmental issue in the Yangtze River Delta (YRD) region. Regional transport and inter-influence of PM 2.5 among cities occurs frequently as a result of the subtropical monsoon climate. Backward trajectory statistics indicated that a north wind prevailed from October to March, while a southeast wind predominated from May to September. The temporal relationships of carbon and nitrogen isotopes among cities were dependent on the prevailing wind direction. Regional PM 2.5 pollution was confirmed in the YRD region by means of significant correlations and similar cyclical characteristics of PM 2.5 among Lin'an, Ningbo, Nanjing and Shanghai. Granger causality tests of the time series of PM 2.5 values indicate that the regional transport of haze pollutants is governed by prevailing wind direction, as the PM 2.5 concentrations from upwind area cities generally influence that of the downwind cities. Furthermore, stronger correlation coefficients were identified according to monsoon pathways. To clarify the impacts of the monsoon climate, a vector autoregressive (VAR) model was introduced. Variance decomposition in the VAR model also indicated that the upwind area cities contributed significantly to PM 2.5 in the downwind area cities. Finally, we attempted to predict daily PM 2.5 concentrations in each city based on the VAR model using data from all cities and obtained fairly reasonable predictions. These indicate that statistical methods of the Granger causality test and VAR model have the potential to evaluate inter-influence and the relative contribution of PM 2.5 among cities, and to predict PM 2.5 concentrations as well. Copyright © 2017 Elsevier Ltd. All rights reserved.

The temporal dynamics of cortisol and affective states in depressed and non-depressed individuals.

PubMed

Booij, Sanne H; Bos, Elisabeth H; de Jonge, Peter; Oldehinkel, Albertine J

2016-07-01

Cortisol levels have been related to mood disorders at the group level, but not much is known about how cortisol relates to affective states within individuals over time. We examined the temporal dynamics of cortisol and affective states in depressed and non-depressed individuals in daily life. Specifically, we addressed the direction and timing of the effects, as well as individual differences. Thirty depressed and non-depressed participants (aged 20-50 years) filled out questionnaires regarding their affect and sampled saliva three times a day for 30 days in their natural environment. They were pair-matched on age, gender, smoking behavior and body mass index. The multivariate time series (T=90) of every participant were analyzed using vector autoregressive (VAR) modeling to assess lagged effects of cortisol on affect, and vice versa. Contemporaneous effects were assessed using the residuals of the VAR models. Impulse response function analysis was used to examine the timing of effects. For 29 out of 30 participants, a VAR model could be constructed. A significant relationship between cortisol and positive or negative affect was found for the majority of the participants, but the direction, sign, and timing of the relationship varied among individuals. This approach proves to be a valuable addition to traditional group designs, because our results showed that daily life fluctuations in cortisol can influence affective states, and vice versa, but not in all individuals and in varying ways. Future studies may examine whether these individual differences relate to susceptibility for or progression of mood disorders. Copyright © 2016 Elsevier Ltd. All rights reserved.

On the relationship between health, education and economic growth: Time series evidence from Malaysia

NASA Astrophysics Data System (ADS)

Khan, Habib Nawaz; Razali, Radzuan B.; Shafei, Afza Bt.

2016-11-01

The objectives of this paper is two-fold: First, to empirically investigate the effects of an enlarged number of healthy and well-educated people on economic growth in Malaysia within the Endogeneous Growth Model framework. Second, to examine the causal links between education, health and economic growth using annual time series data from 1981 to 2014 for Malaysia. Data series were checked for the time series properties by using ADF and KPSS tests. Long run co-integration relationship was investigated with the help of vector autoregressive (VAR) method. For short and long run dynamic relationship investigation vector error correction model (VECM) was applied. Causality analysis was performed through Engle-Granger technique. The study results showed long run co-integration relation and positively significant effects of education and health on economic growth in Malaysia. The reported results also confirmed a feedback hypothesis between the variables in the case of Malaysia. The study results have policy relevance of the importance of human capital (health and education) to the growth process of the Malaysia. Thus, it is suggested that policy makers focus on education and health sectors for sustainable economic growth in Malaysia.

SCoT: a Python toolbox for EEG source connectivity.

PubMed

Billinger, Martin; Brunner, Clemens; Müller-Putz, Gernot R

2014-01-01

Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT-a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with the MVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR) models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting. We demonstrate basic usage of SCoT on motor imagery (MI) data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1) brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2) offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT.

SCoT: a Python toolbox for EEG source connectivity

PubMed Central

Billinger, Martin; Brunner, Clemens; Müller-Putz, Gernot R.

2014-01-01

Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT—a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with the MVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR) models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting. We demonstrate basic usage of SCoT on motor imagery (MI) data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1) brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2) offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT. PMID:24653694

Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market

NASA Astrophysics Data System (ADS)

Gong, Pu; Weng, Yingliang

2016-01-01

This paper generalizes a recently proposed spatial autoregressive model and introduces a spatiotemporal model for forecasting stock returns. We support the view that stock returns are affected not only by the absolute values of factors such as firm size, book-to-market ratio and momentum but also by the relative values of factors like trading volume ranking and market capitalization ranking in each period. This article studies a new method for constructing stocks' reference groups; the method is called quartile method. Applying the method empirically to the Shanghai Stock Exchange 50 Index, we compare the daily volatility forecasting performance and the out-of-sample forecasting performance of Value-at-Risk (VaR) estimated by different models. The empirical results show that the spatiotemporal model performs surprisingly well in terms of capturing spatial dependences among individual stocks, and it produces more accurate VaR forecasts than the other three models introduced in the previous literature. Moreover, the findings indicate that both allowing for serial correlation in the disturbances and using time-varying spatial weight matrices can greatly improve the predictive accuracy of a spatial autoregressive model.

Equivalent Dynamic Models.

PubMed

Molenaar, Peter C M

2017-01-01

Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series. It also is shown that dynamic factor models with lagged factor loadings are not equivalent to the currently popular state-space models, and that restriction of attention to the latter type of models may yield invalid results. The known equivalent vector autoregressive model types, standard and structural, are given a new interpretation in which they are conceived of as the extremes of an innovating type of hybrid vector autoregressive models. It is shown that consideration of hybrid models solves many problems, in particular with Granger causality testing.

Business cycles and fertility dynamics in the United States: a vector autoregressive model.

PubMed

Mocan, N H

1990-01-01

"Using vector-autoregressions...this paper shows that fertility moves countercyclically over the business cycle....[It] shows that the United States fertility is not governed by a deterministic trend as was assumed by previous studies. Rather, fertility evolves around a stochastic trend. It is shown that a bivariate analysis between fertility and unemployment yields a procyclical picture of fertility. However, when one considers the effects on fertility of early marriages and the divorce behavior as well as economic activity, fertility moves countercyclically." excerpt

Forecasting the value-at-risk of Chinese stock market using the HARQ model and extreme value theory

NASA Astrophysics Data System (ADS)

Liu, Guangqiang; Wei, Yu; Chen, Yongfei; Yu, Jiang; Hu, Yang

2018-06-01

Using intraday data of the CSI300 index, this paper discusses value-at-risk (VaR) forecasting of the Chinese stock market from the perspective of high-frequency volatility models. First, we measure the realized volatility (RV) with 5-minute high-frequency returns of the CSI300 index and then model it with the newly introduced heterogeneous autoregressive quarticity (HARQ) model, which can handle the time-varying coefficients of the HAR model. Second, we forecast the out-of-sample VaR of the CSI300 index by combining the HARQ model and extreme value theory (EVT). Finally, using several popular backtesting methods, we compare the VaR forecasting accuracy of HARQ model with other traditional HAR-type models, such as HAR, HAR-J, CHAR, and SHAR. The empirical results show that the novel HARQ model can beat other HAR-type models in forecasting the VaR of the Chinese stock market at various risk levels.

The Disparate Labor Market Impacts of Monetary Policy

ERIC Educational Resources Information Center

Carpenter, Seth B.; Rodgers, William M., III

2004-01-01

Employing two widely used approaches to identify the effects of monetary policy, this paper explores the differential impact of policy on the labor market outcomes of teenagers, minorities, out-of-school youth, and less-skilled individuals. Evidence from recursive vector autoregressions and autoregressive distributed lag models that use…

Projecting county pulpwood production with historical production and macro-economic variables

Treesearch

Consuelo Brandeis; Dayton M. Lambert

2014-01-01

We explored forecasting of county roundwood pulpwood produc-tion with county-vector autoregressive (CVAR) and spatial panelvector autoregressive (SPVAR) methods. The analysis used timberproducts output data for the state of Florida, together with a set ofmacro-economic variables. Overall, we found the SPVAR specifica-tion produced forecasts with lower error rates...

Measuring daily Value-at-Risk of SSEC index: A new approach based on multifractal analysis and extreme value theory

NASA Astrophysics Data System (ADS)

Wei, Yu; Chen, Wang; Lin, Yu

2013-05-01

Recent studies in the econophysics literature reveal that price variability has fractal and multifractal characteristics not only in developed financial markets, but also in emerging markets. Taking high-frequency intraday quotes of the Shanghai Stock Exchange Component (SSEC) Index as example, this paper proposes a new method to measure daily Value-at-Risk (VaR) by combining the newly introduced multifractal volatility (MFV) model and the extreme value theory (EVT) method. Two VaR backtesting techniques are then employed to compare the performance of the model with that of a group of linear and nonlinear generalized autoregressive conditional heteroskedasticity (GARCH) models. The empirical results show the multifractal nature of price volatility in Chinese stock market. VaR measures based on the multifractal volatility model and EVT method outperform many GARCH-type models at high-risk levels.

Time-series panel analysis (TSPA): multivariate modeling of temporal associations in psychotherapy process.

PubMed

Ramseyer, Fabian; Kupper, Zeno; Caspar, Franz; Znoj, Hansjörg; Tschacher, Wolfgang

2014-10-01

Processes occurring in the course of psychotherapy are characterized by the simple fact that they unfold in time and that the multiple factors engaged in change processes vary highly between individuals (idiographic phenomena). Previous research, however, has neglected the temporal perspective by its traditional focus on static phenomena, which were mainly assessed at the group level (nomothetic phenomena). To support a temporal approach, the authors introduce time-series panel analysis (TSPA), a statistical methodology explicitly focusing on the quantification of temporal, session-to-session aspects of change in psychotherapy. TSPA-models are initially built at the level of individuals and are subsequently aggregated at the group level, thus allowing the exploration of prototypical models. TSPA is based on vector auto-regression (VAR), an extension of univariate auto-regression models to multivariate time-series data. The application of TSPA is demonstrated in a sample of 87 outpatient psychotherapy patients who were monitored by postsession questionnaires. Prototypical mechanisms of change were derived from the aggregation of individual multivariate models of psychotherapy process. In a 2nd step, the associations between mechanisms of change (TSPA) and pre- to postsymptom change were explored. TSPA allowed a prototypical process pattern to be identified, where patient's alliance and self-efficacy were linked by a temporal feedback-loop. Furthermore, therapist's stability over time in both mastery and clarification interventions was positively associated with better outcomes. TSPA is a statistical tool that sheds new light on temporal mechanisms of change. Through this approach, clinicians may gain insight into prototypical patterns of change in psychotherapy. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Short-term climate change impacts on Mara basin hydrology

NASA Astrophysics Data System (ADS)

Demaria, E. M.; Roy, T.; Valdés, J. B.; Lyon, B.; Valdés-Pineda, R.; Serrat-Capdevila, A.; Durcik, M.; Gupta, H.

2017-12-01

The predictability of climate diminishes significantly at shorter time scales (e.g. decadal). Both natural variability as well as sampling variability of climate can obscure or enhance climate change signals in these shorter scales. Therefore, in order to assess the impacts of climate change on basin hydrology, it is important to design climate projections with exhaustive climate scenarios. In this study, we first create seasonal climate scenarios by combining (1) synthetic precipitation projections generated from a Vector Auto-Regressive (VAR) model using the University of East Anglia Climate Research Unit (UEA-CRU) data with (2) seasonal trends calculated from 31 models in the Coupled Model Intercomparison Project Phase 5 (CMIP). The seasonal climate projections are then disaggregated to daily level using the Agricultural Modern-Era Retrospective Analysis for Research and Applications (AgMERRA) data. The daily climate data are then bias-corrected and used as forcings to the land-surface model, Variable Infiltration Capacity (VIC), to generate different hydrological projections for the Mara River basin in East Africa, which are then evaluated to study the hydrologic changes in the basin in the next three decades (2020-2050).

Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data.

PubMed

de Haan-Rietdijk, Silvia; Voelkle, Manuel C; Keijsers, Loes; Hamaker, Ellen L

2017-01-01

The Experience Sampling Method is a common approach in psychological research for collecting intensive longitudinal data with high ecological validity. One characteristic of ESM data is that it is often unequally spaced, because the measurement intervals within a day are deliberately varied, and measurement continues over several days. This poses a problem for discrete-time (DT) modeling approaches, which are based on the assumption that all measurements are equally spaced. Nevertheless, DT approaches such as (vector) autoregressive modeling are often used to analyze ESM data, for instance in the context of affective dynamics research. There are equivalent continuous-time (CT) models, but they are more difficult to implement. In this paper we take a pragmatic approach and evaluate the practical relevance of the violated model assumption in DT AR(1) and VAR(1) models, for the N = 1 case. We use simulated data under an ESM measurement design to investigate the bias in the parameters of interest under four different model implementations, ranging from the true CT model that accounts for all the exact measurement times, to the crudest possible DT model implementation, where even the nighttime is treated as a regular interval. An analysis of empirical affect data illustrates how the differences between DT and CT modeling can play out in practice. We find that the size and the direction of the bias in DT (V)AR models for unequally spaced ESM data depend quite strongly on the true parameter in addition to data characteristics. Our recommendation is to use CT modeling whenever possible, especially now that new software implementations have become available.

Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data

PubMed Central

de Haan-Rietdijk, Silvia; Voelkle, Manuel C.; Keijsers, Loes; Hamaker, Ellen L.

2017-01-01

The Experience Sampling Method is a common approach in psychological research for collecting intensive longitudinal data with high ecological validity. One characteristic of ESM data is that it is often unequally spaced, because the measurement intervals within a day are deliberately varied, and measurement continues over several days. This poses a problem for discrete-time (DT) modeling approaches, which are based on the assumption that all measurements are equally spaced. Nevertheless, DT approaches such as (vector) autoregressive modeling are often used to analyze ESM data, for instance in the context of affective dynamics research. There are equivalent continuous-time (CT) models, but they are more difficult to implement. In this paper we take a pragmatic approach and evaluate the practical relevance of the violated model assumption in DT AR(1) and VAR(1) models, for the N = 1 case. We use simulated data under an ESM measurement design to investigate the bias in the parameters of interest under four different model implementations, ranging from the true CT model that accounts for all the exact measurement times, to the crudest possible DT model implementation, where even the nighttime is treated as a regular interval. An analysis of empirical affect data illustrates how the differences between DT and CT modeling can play out in practice. We find that the size and the direction of the bias in DT (V)AR models for unequally spaced ESM data depend quite strongly on the true parameter in addition to data characteristics. Our recommendation is to use CT modeling whenever possible, especially now that new software implementations have become available. PMID:29104554

Circular Conditional Autoregressive Modeling of Vector Fields.

PubMed

Modlin, Danny; Fuentes, Montse; Reich, Brian

2012-02-01

As hurricanes approach landfall, there are several hazards for which coastal populations must be prepared. Damaging winds, torrential rains, and tornadoes play havoc with both the coast and inland areas; but, the biggest seaside menace to life and property is the storm surge. Wind fields are used as the primary forcing for the numerical forecasts of the coastal ocean response to hurricane force winds, such as the height of the storm surge and the degree of coastal flooding. Unfortunately, developments in deterministic modeling of these forcings have been hindered by computational expenses. In this paper, we present a multivariate spatial model for vector fields, that we apply to hurricane winds. We parameterize the wind vector at each site in polar coordinates and specify a circular conditional autoregressive (CCAR) model for the vector direction, and a spatial CAR model for speed. We apply our framework for vector fields to hurricane surface wind fields for Hurricane Floyd of 1999 and compare our CCAR model to prior methods that decompose wind speed and direction into its N-S and W-E cardinal components.

Circular Conditional Autoregressive Modeling of Vector Fields*

PubMed Central

Modlin, Danny; Fuentes, Montse; Reich, Brian

2013-01-01

As hurricanes approach landfall, there are several hazards for which coastal populations must be prepared. Damaging winds, torrential rains, and tornadoes play havoc with both the coast and inland areas; but, the biggest seaside menace to life and property is the storm surge. Wind fields are used as the primary forcing for the numerical forecasts of the coastal ocean response to hurricane force winds, such as the height of the storm surge and the degree of coastal flooding. Unfortunately, developments in deterministic modeling of these forcings have been hindered by computational expenses. In this paper, we present a multivariate spatial model for vector fields, that we apply to hurricane winds. We parameterize the wind vector at each site in polar coordinates and specify a circular conditional autoregressive (CCAR) model for the vector direction, and a spatial CAR model for speed. We apply our framework for vector fields to hurricane surface wind fields for Hurricane Floyd of 1999 and compare our CCAR model to prior methods that decompose wind speed and direction into its N-S and W-E cardinal components. PMID:24353452

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

Santini, Danilo J.; Poyer, David A.

Vector error correction (VEC) was used to test the importance of a theoretical causal chain from transportation fuel cost to vehicle sales to macroeconomic activity. Real transportation fuel cost was broken into two cost components: real gasoline price (rpgas) and real personal consumption of gasoline and other goods (gas). Real personal consumption expenditure on vehicles (RMVE) represented vehicle sales. Real gross domestic product (rGDP) was used as the measure of macroeconomic activity. The VEC estimates used quarterly data from the third quarter of 1952 to the first quarter of 2014. Controlling for the financial causes of the recent Great Recession,more » real homeowners’ equity (equity) and real credit market instruments liability (real consumer debt, rcmdebt) were included. Results supported the primary hypothesis of the research, but also introduced evidence that another financial path through equity is important, and that use of the existing fleet of vehicles (not just sales of vehicles) is an important transport-related contributor to macroeconomic activity. Consumer debt reduction is estimated to be a powerful short-run force reducing vehicle sales. Findings are interpreted in the context of the recent Greene, Lee, and Hopson (2012) (hereafter GLH) estimation of the magnitude of three distinct macroeconomic damage effects that result from dependence on imported oil, the price of which is manipulated by the Organization of Petroleum Exporting Countries (OPEC). The three negative macroeconomic impacts are due to (1) dislocation (positive oil price shock), (2) high oil price levels, and (3) a high value of the quantity of oil imports times an oil price delta (cartel price less competitive price). The third of these is the wealth effect. The VEC model addresses the first two, but the software output from the model (impulse response plots) does not isolate them. Nearly all prior statistical tests in the literature have used vector autoregression (VAR) and autoregressive distributed lag models that considered effects of oil price changes, but did not account for effects of oil price levels. Gasoline prices were rarely examined. The tests conducted in this report evaluate gasoline instead of oil.« less

Firm performance and the role of environmental management.

PubMed

Lundgren, Tommy; Zhou, Wenchao

2017-12-01

This paper analyzes the interactions between three dimensions of firm performance - productivity, energy efficiency, and environmental performance - and especially sheds light on the role of environmental management. In this context, environmental management is investments to reduce environmental impact, which may also affect firm competitiveness, in terms of change in productivity, and spur more (or less) efficient use of energy. We apply data envelopment analysis (DEA) technique to calculate the Malmquist firm performance indexes, and a panel vector auto-regression (VAR) methodology is utilized to investigate the dynamic and causal relationship between the three dimensions of firm performance and environmental investment. Main results show that energy efficiency and environmental performance are integrated, and energy efficiency and productivity positively reinforce each other, signifying the cost saving property of more efficient use of energy. Hence, increasing energy efficiency, as advocated in many of today's energy policies, could capture multiple benefits. The results also show that improved environmental performance and environmental investments constrain next period productivity, a result that would be in contrast with the Porter hypothesis and strategic corporate social responsibility; both concepts conveying the notion that pro-environmental management can boost productivity and competitiveness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Oil price fluctuations and the Gulf Cooperation Council (GCC) countries, 1960--2004

NASA Astrophysics Data System (ADS)

Alotaibi, Bader

The dissertation examines the effect of oil price fluctuations on GCC economies for the period 1960-2004. The objective of chapter two is to investigate whether oil price fluctuations have asymmetric effects on GDP growth. Does a negative oil price shock have merely an opposite effect as does a positive price shock or are there differences in degrees? Many past studies have examined asymmetries between oil prices and output growth in oil importing countries. A fixed effect model is used. We find that negative oil price shocks dominate positive shocks. The objective of chapter three is to investigate the impact of oil price shocks on real exchange rates and price levels. A structural Vector Autoregression (VAR) model for each country is used containing three and four variables in the first and second specifications, respectively. Oil price shocks are found to be not only important but persistent. In most countries, supply shocks play larger roles than do demand shocks. Nominal shocks have only short-run effects on the real exchange rate and the price level. The objective of chapter four is to investigate fluctuations in budget and trade deficits. Do agents smooth over income shocks due to fluctuations in oil prices or do oil price shocks have large effects? Also, are the budget and trade deficits causally related? If so, what direction does this causal relation take? Many studies have considered links between budget and trade deficits but most have been conducted for countries where oil is not a major concern. A VAR model containing three variables for each country is used. Oil price shocks are found to be persistent. Also, the results support the twin deficits hypothesis. Budget deficit shocks cause deterioration in the trade deficits in GCC countries.

Application of multivariate autoregressive spectrum estimation to ULF waves

NASA Technical Reports Server (NTRS)

Ioannidis, G. A.

1975-01-01

The estimation of the power spectrum of a time series by fitting a finite autoregressive model to the data has recently found widespread application in the physical sciences. The extension of this method to the analysis of vector time series is presented here through its application to ULF waves observed in the magnetosphere by the ATS 6 synchronous satellite. Autoregressive spectral estimates of the power and cross-power spectra of these waves are computed with computer programs developed by the author and are compared with the corresponding Blackman-Tukey spectral estimates. The resulting spectral density matrices are then analyzed to determine the direction of propagation and polarization of the observed waves.

Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

NASA Astrophysics Data System (ADS)

Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

2012-06-01

SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

Analysis of Factors Influencing PM2.5 in Beijing: A Microcosmic and Dynamic Perspective for Sustainable Development

NASA Astrophysics Data System (ADS)

Wang, Yani; Wang, Jun; Tao, Guiping

2017-12-01

Haze pollution has become a hot issue concerned with the process of modernization and one serious problem requiring urgent solution, especially in Beijing. PM2.5 is the main reason causing haze and its harm. Although there has been research centering on factors affecting PM2.5, little attention has been devoted to the microcosmic and dynamic effects on it. Vector auto-regression (VAR) mode is applied in this study to explore the interaction between PM2.5, PM10, SO2, CO and NO2. Results of Granger causality tests tell that there exists causal relationship between PM10, SO2, CO, NO2 and PM2.5. Impulse response functions (IRFs) show that the response of PM2.5 to a shock in CO is positive and large in the short period, while the reaction of PM2.5 to a shock in SO2 increases over time. Meanwhile, variance decomposition indicate that PM2.5 is more closely related to CO in the short term while SO2’ influence accounts for a higher proportion in the long run. The findings provide a novel perspective to analyze the factors influencing PM2.5 dynamically and contribute to a better understanding of haze and its relationship with sustainable development.

Spatio-temporal genetic variation of the biting midge vector species Culicoides imicola (Ceratopogonidae) Kieffer in France.

PubMed

Jacquet, Stéphanie; Huber, Karine; Guis, Hélène; Setier-Rio, Marie-Laure; Goffredo, Maria; Allène, Xavier; Rakotoarivony, Ignace; Chevillon, Christine; Bouyer, Jérémy; Baldet, Thierry; Balenghien, Thomas; Garros, Claire

2016-03-11

Introduction of vector species into new areas represents a main driver for the emergence and worldwide spread of vector-borne diseases. This poses a substantial threat to livestock economies and public health. Culicoides imicola Kieffer, a major vector species of economically important animal viruses, is described with an apparent range expansion in Europe where it has been recorded in south-eastern continental France, its known northern distribution edge. This questioned on further C. imicola population extension and establishment into new territories. Studying the spatio-temporal genetic variation of expanding populations can provide valuable information for the design of reliable models of future spread. Entomological surveys and population genetic approaches were used to assess the spatio-temporal population dynamics of C. imicola in France. Entomological surveys (2-3 consecutive years) were used to evaluate population abundances and local spread in continental France (28 sites in the Var department) and in Corsica (4 sites). We also genotyped at nine microsatellite loci insects from 3 locations in the Var department over 3 years (2008, 2010 and 2012) and from 6 locations in Corsica over 4 years (2002, 2008, 2010 and 2012). Entomological surveys confirmed the establishment of C. imicola populations in Var department, but indicated low abundances and no apparent expansion there within the studied period. Higher population abundances were recorded in Corsica. Our genetic data suggested the absence of spatio-temporal genetic changes within each region but a significant increase of the genetic differentiation between Corsican and Var populations through time. The lack of intra-region population structure may result from strong gene flow among populations. We discussed the observed temporal variation between Corsica and Var as being the result of genetic drift following introduction, and/or the genetic characteristics of populations at their range edge. Our results suggest that local range expansion of C. imicola in continental France may be slowed by the low population abundances and unsuitable climatic and environmental conditions.

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm.

PubMed

Jafari, Masoumeh; Salimifard, Maryam; Dehghani, Maryam

2014-07-01

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Kumaraswamy autoregressive moving average models for double bounded environmental data

NASA Astrophysics Data System (ADS)

Bayer, Fábio Mariano; Bayer, Débora Missio; Pumi, Guilherme

2017-12-01

In this paper we introduce the Kumaraswamy autoregressive moving average models (KARMA), which is a dynamic class of models for time series taking values in the double bounded interval (a,b) following the Kumaraswamy distribution. The Kumaraswamy family of distribution is widely applied in many areas, especially hydrology and related fields. Classical examples are time series representing rates and proportions observed over time. In the proposed KARMA model, the median is modeled by a dynamic structure containing autoregressive and moving average terms, time-varying regressors, unknown parameters and a link function. We introduce the new class of models and discuss conditional maximum likelihood estimation, hypothesis testing inference, diagnostic analysis and forecasting. In particular, we provide closed-form expressions for the conditional score vector and conditional Fisher information matrix. An application to environmental real data is presented and discussed.

Combined effect of seaweed (Sargassum wightii) and Bacillus thuringiensis var. israelensis on the coastal mosquito,Anopheles sundaicus, in Tamil Nadu, India

USDA-ARS?s Scientific Manuscript database

Studies were made of the extract of Sargassum wightii combined with Bacillus thuringiensis var. israelensis (Bti) for control of the malaria vector Anopheles sundaicus. Treatment of mosquito larvae with 0.001% S. wightii extract indicated median lethal concentrations (LC50) of 88, 73, 134, 156, and...

[Susceptibility of Aedes aegypti (L.) strains from Havana to a Bacillus thuringiensis var. israelensis].

PubMed

Menéndez Díaz, Zulema; Rodríguez Rodríguez, Jinnay; Gato Armas, René; Companioni Ibañez, Ariamys; Díaz Pérez, Manuel; Bruzón Aguila, Rosa Yirian

2012-01-01

the integration of chemical and biological methods is one of the strategies for the vector control, due to the existing environmental problems and the concerns of the community as a result of the synthetic organic insecticide actions. The bacterium called Bacillus thuringiensis var. israelensis in liquid formulation has been widely used in the vector control programs in several countries and has shown high efficacy at lab in Cuba. to determine the susceptibility of Aedes aegypti collected in the municipalities of La Habana province to Bacillus thuringiensis var. israelensis. fifteen Aedes aegypti strains, one from each municipality, were used including larvae and pupas collected in 2010 and one reference strain known as Rockefeller. The aqueous formulation of Bacillus thuringiensis var. israelensis (Bactivec, Labiofam, Cuba) was used. The bioassays complied with the World Health Organization guidelines for use of bacterial larvicides in the public health sector. The larval mortality was read after 24 hours and the results were processed by the statistical system SPSS (11.0) through Probit analysis. the evaluated mosquito strains showed high susceptibility to biolarvicide, there were no significant differences in LC50 values of Ae. aegypti strains, neither in the comparison of these values with those of the reference strain. the presented results indicate that the use of Bacillus thuringiensis var. israelensis continues to be a choice for the control of Aedes aegypti larval populations in La Habana province.

40 CFR 180.1154 - CryIA(c) and CryIC derived delta-endotoxins of Bacillus thuringiensis var. kurstaki encapsulated...

Code of Federal Regulations, 2010 CFR

2010-07-01

...-endotoxins of Bacillus thuringiensis var. kurstaki encapsulated in killed Pseudomonas fluorescens, and the... RESIDUES IN FOOD Exemptions From Tolerances § 180.1154 CryIA(c) and CryIC derived delta-endotoxins of... plasmid and cloning vector genetic constructs. CryIA(c) and CryIC derived delta-endotoxins of Bacillus...

40 CFR 180.1154 - CryIA(c) and CryIC derived delta-endotoxins of Bacillus thuringiensis var. kurstaki encapsulated...

Code of Federal Regulations, 2011 CFR

2011-07-01

...-endotoxins of Bacillus thuringiensis var. kurstaki encapsulated in killed Pseudomonas fluorescens, and the... RESIDUES IN FOOD Exemptions From Tolerances § 180.1154 CryIA(c) and CryIC derived delta-endotoxins of... plasmid and cloning vector genetic constructs. CryIA(c) and CryIC derived delta-endotoxins of Bacillus...

40 CFR 180.1154 - CryIA(c) and CryIC derived delta-endotoxins of Bacillus thuringiensis var. kurstaki encapsulated...

Code of Federal Regulations, 2013 CFR

2013-07-01

...-endotoxins of Bacillus thuringiensis var. kurstaki encapsulated in killed Pseudomonas fluorescens, and the... RESIDUES IN FOOD Exemptions From Tolerances § 180.1154 CryIA(c) and CryIC derived delta-endotoxins of... plasmid and cloning vector genetic constructs. CryIA(c) and CryIC derived delta-endotoxins of Bacillus...

40 CFR 180.1154 - CryIA(c) and CryIC derived delta-endotoxins of Bacillus thuringiensis var. kurstaki encapsulated...

Code of Federal Regulations, 2012 CFR

2012-07-01

...-endotoxins of Bacillus thuringiensis var. kurstaki encapsulated in killed Pseudomonas fluorescens, and the... RESIDUES IN FOOD Exemptions From Tolerances § 180.1154 CryIA(c) and CryIC derived delta-endotoxins of... plasmid and cloning vector genetic constructs. CryIA(c) and CryIC derived delta-endotoxins of Bacillus...

40 CFR 180.1154 - CryIA(c) and CryIC derived delta-endotoxins of Bacillus thuringiensis var. kurstaki encapsulated...

Code of Federal Regulations, 2014 CFR

2014-07-01

...-endotoxins of Bacillus thuringiensis var. kurstaki encapsulated in killed Pseudomonas fluorescens, and the... RESIDUES IN FOOD Exemptions From Tolerances § 180.1154 CryIA(c) and CryIC derived delta-endotoxins of... plasmid and cloning vector genetic constructs. CryIA(c) and CryIC derived delta-endotoxins of Bacillus...

Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Barrett, Adam B.; Seth, Anil K.

2009-12-01

Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. Developed originally in the field of econometrics, it has since found application in a broader arena, particularly in neuroscience. More recently transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes, has gained traction in a similarly wide field. While it has been recognized that the two concepts must be related, the exact relationship has until now not been formally described. Here we show that for Gaussian variables, Granger causality and transfer entropy are entirely equivalent, thus bridging autoregressive and information-theoretic approaches to data-driven causal inference.

A comparison of a two-dimensional variational analysis method and a median filter for NSCAT ambiguity removal

NASA Astrophysics Data System (ADS)

Henderson, J. M.; Hoffman, R. N.; Leidner, S. M.; Atlas, R.; Brin, E.; Ardizzone, J. V.

2003-06-01

The ocean surface vector wind can be measured from space by scatterometers. For a set of measurements observed from several viewing directions and collocated in space and time, there will usually exist two, three, or four consistent wind vectors. These multiple wind solutions are known as ambiguities. Ambiguity removal procedures select one ambiguity at each location. We compare results of two different ambiguity removal algorithms, the operational median filter (MF) used by the Jet Propulsion Laboratory (JPL) and a two-dimensional variational analysis method (2d-VAR). We applied 2d-VAR to the entire NASA Scatterometer (NSCAT) mission, orbit by orbit, using European Centre for Medium-Range Weather Forecasts (ECMWF) 10-m wind analyses as background fields. We also applied 2d-VAR to a 51-day subset of the NSCAT mission using National Centers for Environmental Prediction (NCEP) 1000-hPa wind analyses as background fields. This second data set uses the same background fields as the MF data set. When both methods use the same NCEP background fields as a starting point for ambiguity removal, agreement is very good: Approximately only 3% of the wind vector cells (WVCs) have different ambiguity selections; however, most of the WVCs with changes occur in coherent patches. Since at least one of the selections is in error, this implies that errors due to ambiguity selection are not isolated, but are horizontally correlated. When we examine ambiguity selection differences at synoptic scales, we often find that the 2d-VAR selections are more meteorologically reasonable and more consistent with cloud imagery.

Structural Equation Modeling of Multivariate Time Series

ERIC Educational Resources Information Center

du Toit, Stephen H. C.; Browne, Michael W.

2007-01-01

The covariance structure of a vector autoregressive process with moving average residuals (VARMA) is derived. It differs from other available expressions for the covariance function of a stationary VARMA process and is compatible with current structural equation methodology. Structural equation modeling programs, such as LISREL, may therefore be…

Modelling malaria incidence by an autoregressive distributed lag model with spatial component.

PubMed

Laguna, Francisco; Grillet, María Eugenia; León, José R; Ludeña, Carenne

2017-08-01

The influence of climatic variables on the dynamics of human malaria has been widely highlighted. Also, it is known that this mosquito-borne infection varies in space and time. However, when the data is spatially incomplete most popular spatio-temporal methods of analysis cannot be applied directly. In this paper, we develop a two step methodology to model the spatio-temporal dependence of malaria incidence on local rainfall, temperature, and humidity as well as the regional sea surface temperatures (SST) in the northern coast of Venezuela. First, we fit an autoregressive distributed lag model (ARDL) to the weekly data, and then, we adjust a linear separable spacial vectorial autoregressive model (VAR) to the residuals of the ARDL. Finally, the model parameters are tuned using a Markov Chain Monte Carlo (MCMC) procedure derived from the Metropolis-Hastings algorithm. Our results show that the best model to account for the variations of malaria incidence from 2001 to 2008 in 10 endemic Municipalities in North-Eastern Venezuela is a logit model that included the accumulated local precipitation in combination with the local maximum temperature of the preceding month as positive regressors. Additionally, we show that although malaria dynamics is highly heterogeneous in space, a detailed analysis of the estimated spatial parameters in our model yield important insights regarding the joint behavior of the disease incidence across the different counties in our study. Copyright © 2017 Elsevier Ltd. All rights reserved.

Efficient transformation and expression of gfp gene in Valsa mali var. mali.

PubMed

Chen, Liang; Sun, Gengwu; Wu, Shujing; Liu, Huixiang; Wang, Hongkai

2015-01-01

Valsa mali var. mali, the causal agent of valsa canker of apple, causes great loss of apple production in apple producing regions. The pathogenic mechanism of the pathogen has not been studied extensively, thus a suitable gene marker for pathogenic invasion analysis and a random insertion of T-DNA for mutants are desirable. In this paper, we reported the construction of a binary vector pKO1-HPH containing a positive selective gene hygromycin phosphotransferase (hph), a reporter gene gfp conferring green fluorescent protein, and an efficient protocol for V. mali var. mali transformation mediated by Agrobacterium tumefaciens. A transformation efficiency up to about 75 transformants per 10(5) conidia was achieved when co-cultivation of V. mali var. mali and A. tumefaciens for 48 h in A. tumefaciens inductive medium agar plates. The insertions of hph gene and gfp gene into V. mali var. mali genome verified by polymerase chain reaction and southern blot analysis showed that 10 randomly-selected transformants exhibited a single, unique hybridization pattern. This is the first report of A. tumefaciens-mediated transformation of V. mali var mali carrying a 'reporter' gfp gene that stably and efficiently expressed in the transformed V. mali var. mali species.

Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data

PubMed Central

Tran, Truyen; Luo, Wei; Phung, Dinh; Venkatesh, Svetha

2016-01-01

Background: Modeling patient flow is crucial in understanding resource demand and prioritization. We study patient outflow from an open ward in an Australian hospital, where currently bed allocation is carried out by a manager relying on past experiences and looking at demand. Automatic methods that provide a reasonable estimate of total next-day discharges can aid in efficient bed management. The challenges in building such methods lie in dealing with large amounts of discharge noise introduced by the nonlinear nature of hospital procedures, and the nonavailability of real-time clinical information in wards. Objective Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data. Methods We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features. Results Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014. Conclusions In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments. PMID:27444059

The Response of US College Enrollment to Unexpected Changes in Macroeconomic Activity

ERIC Educational Resources Information Center

Ewing, Kris M.; Beckert, Kim A.; Ewing, Bradley T.

2010-01-01

This paper estimates the extent and magnitude of US college and university enrollment responses to unanticipated changes in macroeconomic activity. In particular, we consider the relationship between enrollment, economic growth, and inflation. A time series analysis known as a vector autoregression is estimated and impulse response functions are…

Development of EPA`s new methods to quantify vector attraction of wastewater sludges

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

Farrell, J.B.; Bhide, V.; Smith, J.E. Jr.

1996-05-01

EPA`s 1979 and 1993 sludge regulations require that sewage sludge be reduced in vector attraction before it can be applied to the land. In the 1979 regulation, satisfactory vector attraction reduction (VAR) could be demonstrated if treatment processes reduced the volatile solids content of sludge by 38%. The 1993 regulation adds two alternative test methods for aerobic sludges for determining whether VAR has been adequate. In the first method, specific oxygen uptake rate (SOUR) of the sludge must be <1.5 mg O{sub 2}/hr/g total solids, and in the second method, the additional volatile solids reduction (AVSR) that occurs when themore » sludge is further digested for 30 days must be <15%. Experimentation with the new tests is described. Comparisons among the three methods showed that the 38% VSR requirement and the SOUR test were equivalent only near 20{degree}C. The AVSR test was more conservative than either of the other tests. 18 refs., 7 figs., 3 tabs.« less

Fragmentation, integration and macroprudential surveillance of the US financial industry: Insights from network science

PubMed Central

Geraci, Marco Valerio; Béreau, Sophie; Gnabo, Jean-Yves

2018-01-01

Drawing on recent contributions inferring financial interconnectedness from market data, our paper provides new insights on the evolution of the US financial industry over a long period of time by using several tools coming from network science. Relying on a Time-Varying Parameter Vector AutoRegressive (TVP-VAR) approach on stock market returns to retrieve unobserved directed links among financial institutions, we reconstruct a fully dynamic network in the sense that connections are let to evolve through time. The financial system analysed consists of a large set of 155 financial institutions that are all the banks, broker-dealers, insurance and real estate companies listed in the Standard & Poors’ 500 index over the 1993–2014 period. Looking alternatively at the individual, then sector-, community- and system-wide levels, we show that network sciences’ tools are able to support well-known features of the financial markets such as the dramatic fall of connectivity following Lehman Brothers’ collapse. More importantly, by means of less traditional metrics, such as sectoral interface or measurements based on contagion processes, our results document the co-existence of both fragmentation and integration phases between firms independently from the sectors they belong to, and doing so, question the relevance of existing macroprudential surveillance frameworks which have been mostly developed on a sectoral basis. Overall, our results improve our understanding of the US financial landscape and may have important implications for risk monitoring as well as macroprudential policy design. PMID:29694415

Fragmentation, integration and macroprudential surveillance of the US financial industry: Insights from network science.

PubMed

Gandica, Yerali; Geraci, Marco Valerio; Béreau, Sophie; Gnabo, Jean-Yves

2018-01-01

Drawing on recent contributions inferring financial interconnectedness from market data, our paper provides new insights on the evolution of the US financial industry over a long period of time by using several tools coming from network science. Relying on a Time-Varying Parameter Vector AutoRegressive (TVP-VAR) approach on stock market returns to retrieve unobserved directed links among financial institutions, we reconstruct a fully dynamic network in the sense that connections are let to evolve through time. The financial system analysed consists of a large set of 155 financial institutions that are all the banks, broker-dealers, insurance and real estate companies listed in the Standard & Poors' 500 index over the 1993-2014 period. Looking alternatively at the individual, then sector-, community- and system-wide levels, we show that network sciences' tools are able to support well-known features of the financial markets such as the dramatic fall of connectivity following Lehman Brothers' collapse. More importantly, by means of less traditional metrics, such as sectoral interface or measurements based on contagion processes, our results document the co-existence of both fragmentation and integration phases between firms independently from the sectors they belong to, and doing so, question the relevance of existing macroprudential surveillance frameworks which have been mostly developed on a sectoral basis. Overall, our results improve our understanding of the US financial landscape and may have important implications for risk monitoring as well as macroprudential policy design.

Temporal dynamics of physical activity and affect in depressed and nondepressed individuals.

PubMed

Stavrakakis, Nikolaos; Booij, Sanne H; Roest, Annelieke M; de Jonge, Peter; Oldehinkel, Albertine J; Bos, Elisabeth H

2015-12-01

The association between physical activity and affect found in longitudinal observational studies is generally small to moderate. It is unknown how this association generalizes to individuals. The aim of the present study was to investigate interindividual differences in the bidirectional dynamic relationship between physical activity and affect, in depressed and nondepressed individuals, using time-series analysis. A pair-matched sample of 10 depressed and 10 nondepressed participants (mean age = 36.6, SD = 8.9, 30% males) wore accelerometers and completed electronic questionnaires 3 times a day for 30 days. Physical activity was operationalized as the total energy expenditure (EE) per day segment (i.e., 6 hr). The multivariate time series (T = 90) of every individual were analyzed using vector autoregressive modeling (VAR), with the aim to assess direct as well as lagged (i.e., over 1 day) effects of EE on positive and negative affect, and vice versa. Large interindividual differences in the strength, direction and temporal aspects of the relationship between physical activity and positive and negative affect were observed. An exception was the direct (but not the lagged) effect of physical activity on positive affect, which was positive in nearly all individuals. This study showed that the association between physical activity and affect varied considerably across individuals. Thus, while at the group level the effect of physical activity on affect may be small, in some individuals the effect may be clinically relevant. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Temporal relationships of emotional avoidance in a patient with anorexia nervosa--a time series analysis.

PubMed

Stroe-Kunold, Esther; Wesche, Daniela; Friederich, Hans-Christoph; Herzog, Wolfgang; Zastrow, Arne; Wild, Beate

2012-01-01

Anorexia nervosa (AN) is a serious eating disorder marked by self-induced underweight. In patients with AN, the avoidance of emotions appears to be a central feature that is reinforced during the acute state of the disorder. This single case study investigated the role of emotional avoidance of a 25-year-old patient with AN during her inpatient treatment. Throughout the course of 96 days, the patient answered questions daily about her emotional avoidance, pro-anorectic beliefs, perfectionism, and further variables on an electronic diary. The patient's daily self-assessment of emotional avoidance was described in terms of mean value, range, and variability for the various treatment phases. Temporal relationships between emotional avoidance and further variables were determined using a time series approach (vector autoregressive (VAR) modelling). Diary data reflect that the patient's ability to tolerate unpleasant emotions appeared to undergo a process of change during inpatient treatment. Results of the time series analysis indicate that the more the patient was able to deal with negative emotions on any one day (t-1), the less she would be socially avoidant, cognitively confined to food and eating, as well as feeling less secure with her AN, and less depressive on the following day (t). The findings show that for this patient emotional avoidance plays a central role in the interacting system of various psychosocial variables. Replication of these results in other patients with AN would support the recommendation to focus more on emotional regulation in the treatment of AN.

Investigating Soil Moisture Feedbacks on Precipitation With Tests of Granger Causality

NASA Astrophysics Data System (ADS)

Salvucci, G. D.; Saleem, J. A.; Kaufmann, R.

2002-05-01

Granger causality (GC) is used in the econometrics literature to identify the presence of one- and two-way coupling between terms in noisy multivariate dynamical systems. Here we test for the presence of GC to identify a soil moisture (S) feedback on precipitation (P) using data from Illinois. In this framework S is said to Granger cause P if F(Pt;At-dt)does not equal F(P;(A-S)t-dt) where F denotes the conditional distribution of P at time t, At-dt represents the set of all knowledge available at time t-dt, and (A-S)t-dt represents all knowledge available at t-dt except S. Critical for land-atmosphere interaction research is that At-dt includes all past information on P as well as S. Therefore that part of the relation between past soil moisture and current precipitation which results from precipitation autocorrelation and soil water balance will be accounted for and not attributed to causality. Tests for GC usually specify all relevant variables in a coupled vector autoregressive (VAR) model and then calculate the significance level of decreased predictability as various coupling coefficients are omitted. But because the data (daily precipitation and soil moisture) are distinctly non-Gaussian, we avoid using a VAR and instead express the daily precipitation events as a Markov model. We then test whether the probability of storm occurrence, conditioned on past information on precipitation, changes with information on soil moisture. Past information on precipitation is expressed both as the occurrence of previous day precipitation (to account for storm-scale persistence) and as a simple soil moisture-like precipitation-wetness index derived solely from precipitation (to account for seasonal-scale persistence). In this way only those fluctuations in moisture not attributable to past fluctuations in precipitation (e.g., those due to temperature) can influence the outcome of the test. The null hypothesis (no moisture influence) is evaluated by comparing observed changes in storm probability to Monte-Carlo simulated differences generated with unconditional occurrence probabilities. The null hypothesis is not rejected (p>0.5) suggesting that contrary to recently published results, insufficient evidence exists to support an influence of soil moisture on precipitation in Illinois.

The effects of the fungus Metarhizium anisopliae var. acridum on different stages of Lutzomyia longipalpis (Diptera: Psychodidae).

PubMed

Amóra, Sthenia Santos Albano; Bevilaqua, Claudia Maria Leal; Feijó, Francisco Marlon Carneiro; Pereira, Romeika Hermínia de Macedo Assunção; Alves, Nilza Dutra; Freire, Fúlvio Aurélio de Morais; Kamimura, Michel Toth; de Oliveira, Diana Magalhães; Luna-Alves Lima, Elza Aurea; Rocha, Marcos Fábio Gadelha

2010-03-01

The control of Visceral Leishmaniasis (VL) vector is often based on the application of chemical residual insecticide. However, this strategy has not been effective. The continuing search for an appropriate vector control may include the use of biological control. This study evaluates the effects of the fungus Metarhizium anisopliae var. acridum on Lutzomyia longipalpis. Five concentrations of the fungus were utilized, 1 x 10(4) to 1 x 10(8) conidia/ml, accompanied by controls. The unhatched eggs, larvae and dead adults previously exposed to fungi were sown to reisolate the fungi and analysis of parameters of growth. The fungus was subsequently identified by PCR and DNA sequencing. M. anisopliae var. acridum reduced egg hatching by 40%. The mortality of infected larvae was significant. The longevity of infected adults was lower than that of negative controls. The effects of fungal infection on the hatching of eggs laid by infected females were also significant. With respect to fungal growth parameters post-infection, only vegetative growth was not significantly higher than that of the fungi before infection. The revalidation of the identification of the reisolated fungus was confirmed post-passage only from adult insects. In terms of larvae mortality and the fecundity of infected females, the results were significant, proving that the main vector species of VL is susceptible to infection by this entomopathogenic fungus in the adult stage. Copyright 2009 Elsevier B.V. All rights reserved.

Dynamic RSA: Examining parasympathetic regulatory dynamics via vector-autoregressive modeling of time-varying RSA and heart period.

PubMed

Fisher, Aaron J; Reeves, Jonathan W; Chi, Cyrus

2016-07-01

Expanding on recently published methods, the current study presents an approach to estimating the dynamic, regulatory effect of the parasympathetic nervous system on heart period on a moment-to-moment basis. We estimated second-to-second variation in respiratory sinus arrhythmia (RSA) in order to estimate the contemporaneous and time-lagged relationships among RSA, interbeat interval (IBI), and respiration rate via vector autoregression. Moreover, we modeled these relationships at lags of 1 s to 10 s, in order to evaluate the optimal latency for estimating dynamic RSA effects. The IBI (t) on RSA (t-n) regression parameter was extracted from individual models as an operationalization of the regulatory effect of RSA on IBI-referred to as dynamic RSA (dRSA). Dynamic RSA positively correlated with standard averages of heart rate and negatively correlated with standard averages of RSA. We propose that dRSA reflects the active downregulation of heart period by the parasympathetic nervous system and thus represents a novel metric that provides incremental validity in the measurement of autonomic cardiac control-specifically, a method by which parasympathetic regulatory effects can be measured in process. © 2016 Society for Psychophysiological Research.

Sensor network based solar forecasting using a local vector autoregressive ridge framework

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

Xu, J.; Yoo, S.; Heiser, J.

2016-04-04

The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations duemore » to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.« less

Hybrid wavelet-support vector machine approach for modelling rainfall-runoff process.

PubMed

Komasi, Mehdi; Sharghi, Soroush

2016-01-01

Because of the importance of water resources management, the need for accurate modeling of the rainfall-runoff process has rapidly grown in the past decades. Recently, the support vector machine (SVM) approach has been used by hydrologists for rainfall-runoff modeling and the other fields of hydrology. Similar to the other artificial intelligence models, such as artificial neural network (ANN) and adaptive neural fuzzy inference system, the SVM model is based on the autoregressive properties. In this paper, the wavelet analysis was linked to the SVM model concept for modeling the rainfall-runoff process of Aghchai and Eel River watersheds. In this way, the main time series of two variables, rainfall and runoff, were decomposed to multiple frequent time series by wavelet theory; then, these time series were imposed as input data on the SVM model in order to predict the runoff discharge one day ahead. The obtained results show that the wavelet SVM model can predict both short- and long-term runoff discharges by considering the seasonality effects. Also, the proposed hybrid model is relatively more appropriate than classical autoregressive ones such as ANN and SVM because it uses the multi-scale time series of rainfall and runoff data in the modeling process.

Conventional and advanced time series estimation: application to the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database, 1993-2006.

PubMed

Moran, John L; Solomon, Patricia J

2011-02-01

Time series analysis has seen limited application in the biomedical Literature. The utility of conventional and advanced time series estimators was explored for intensive care unit (ICU) outcome series. Monthly mean time series, 1993-2006, for hospital mortality, severity-of-illness score (APACHE III), ventilation fraction and patient type (medical and surgical), were generated from the Australia and New Zealand Intensive Care Society adult patient database. Analyses encompassed geographical seasonal mortality patterns, series structural time changes, mortality series volatility using autoregressive moving average and Generalized Autoregressive Conditional Heteroscedasticity models in which predicted variances are updated adaptively, and bivariate and multivariate (vector error correction models) cointegrating relationships between series. The mortality series exhibited marked seasonality, declining mortality trend and substantial autocorrelation beyond 24 lags. Mortality increased in winter months (July-August); the medical series featured annual cycling, whereas the surgical demonstrated long and short (3-4 months) cycling. Series structural breaks were apparent in January 1995 and December 2002. The covariance stationary first-differenced mortality series was consistent with a seasonal autoregressive moving average process; the observed conditional-variance volatility (1993-1995) and residual Autoregressive Conditional Heteroscedasticity effects entailed a Generalized Autoregressive Conditional Heteroscedasticity model, preferred by information criterion and mean model forecast performance. Bivariate cointegration, indicating long-term equilibrium relationships, was established between mortality and severity-of-illness scores at the database level and for categories of ICUs. Multivariate cointegration was demonstrated for {log APACHE III score, log ICU length of stay, ICU mortality and ventilation fraction}. A system approach to understanding series time-dependence may be established using conventional and advanced econometric time series estimators. © 2010 Blackwell Publishing Ltd.

The relationship between carbon dioxide and agriculture in Ghana: a comparison of VECM and ARDL model.

PubMed

Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

2016-06-01

In this paper, the relationship between carbon dioxide and agriculture in Ghana was investigated by comparing a Vector Error Correction Model (VECM) and Autoregressive Distributed Lag (ARDL) Model. Ten study variables spanning from 1961 to 2012 were employed from the Food Agricultural Organization. Results from the study show that carbon dioxide emissions affect the percentage annual change of agricultural area, coarse grain production, cocoa bean production, fruit production, vegetable production, and the total livestock per hectare of the agricultural area. The vector error correction model and the autoregressive distributed lag model show evidence of a causal relationship between carbon dioxide emissions and agriculture; however, the relationship decreases periodically which may die over-time. All the endogenous variables except total primary vegetable production lead to carbon dioxide emissions, which may be due to poor agricultural practices to meet the growing food demand in Ghana. The autoregressive distributed lag bounds test shows evidence of a long-run equilibrium relationship between the percentage annual change of agricultural area, cocoa bean production, total livestock per hectare of agricultural area, total pulses production, total primary vegetable production, and carbon dioxide emissions. It is important to end hunger and ensure people have access to safe and nutritious food, especially the poor, orphans, pregnant women, and children under-5 years in order to reduce maternal and infant mortalities. Nevertheless, it is also important that the Government of Ghana institutes agricultural policies that focus on promoting a sustainable agriculture using environmental friendly agricultural practices. The study recommends an integration of climate change measures into Ghana's national strategies, policies and planning in order to strengthen the country's effort to achieving a sustainable environment.

Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba.

PubMed

Ortiz, Paulo L; Rivero, Alina; Linares, Yzenia; Pérez, Alina; Vázquez, Juan R

2015-04-01

Climate variability, the primary expression of climate change, is one of the most important environmental problems affecting human health, particularly vector-borne diseases. Despite research efforts worldwide, there are few studies addressing the use of information on climate variability for prevention and early warning of vector-borne infectious diseases. Show the utility of climate information for vector surveillance by developing spatial models using an entomological indicator and information on predicted climate variability in Cuba to provide early warning of danger of increased risk of dengue transmission. An ecological study was carried out using retrospective and prospective analyses of time series combined with spatial statistics. Several entomological and climatic indicators were considered using complex Bultó indices -1 and -2. Moran's I spatial autocorrelation coefficient specified for a matrix of neighbors with a radius of 20 km, was used to identify the spatial structure. Spatial structure simulation was based on simultaneous autoregressive and conditional autoregressive models; agreement between predicted and observed values for number of Aedes aegypti foci was determined by the concordance index Di and skill factor Bi. Spatial and temporal distributions of populations of Aedes aegypti were obtained. Models for describing, simulating and predicting spatial patterns of Aedes aegypti populations associated with climate variability patterns were put forward. The ranges of climate variability affecting Aedes aegypti populations were identified. Forecast maps were generated for the municipal level. Using the Bultó indices of climate variability, it is possible to construct spatial models for predicting increased Aedes aegypti populations in Cuba. At 20 x 20 km resolution, the models are able to provide warning of potential changes in vector populations in rainy and dry seasons and by month, thus demonstrating the usefulness of climate information for epidemiological surveillance.

Trans-dimensional inversion of microtremor array dispersion data with hierarchical autoregressive error models

NASA Astrophysics Data System (ADS)

Dettmer, Jan; Molnar, Sheri; Steininger, Gavin; Dosso, Stan E.; Cassidy, John F.

2012-02-01

This paper applies a general trans-dimensional Bayesian inference methodology and hierarchical autoregressive data-error models to the inversion of microtremor array dispersion data for shear wave velocity (vs) structure. This approach accounts for the limited knowledge of the optimal earth model parametrization (e.g. the number of layers in the vs profile) and of the data-error statistics in the resulting vs parameter uncertainty estimates. The assumed earth model parametrization influences estimates of parameter values and uncertainties due to different parametrizations leading to different ranges of data predictions. The support of the data for a particular model is often non-unique and several parametrizations may be supported. A trans-dimensional formulation accounts for this non-uniqueness by including a model-indexing parameter as an unknown so that groups of models (identified by the indexing parameter) are considered in the results. The earth model is parametrized in terms of a partition model with interfaces given over a depth-range of interest. In this work, the number of interfaces (layers) in the partition model represents the trans-dimensional model indexing. In addition, serial data-error correlations are addressed by augmenting the geophysical forward model with a hierarchical autoregressive error model that can account for a wide range of error processes with a small number of parameters. Hence, the limited knowledge about the true statistical distribution of data errors is also accounted for in the earth model parameter estimates, resulting in more realistic uncertainties and parameter values. Hierarchical autoregressive error models do not rely on point estimates of the model vector to estimate data-error statistics, and have no requirement for computing the inverse or determinant of a data-error covariance matrix. This approach is particularly useful for trans-dimensional inverse problems, as point estimates may not be representative of the state space that spans multiple subspaces of different dimensionalities. The order of the autoregressive process required to fit the data is determined here by posterior residual-sample examination and statistical tests. Inference for earth model parameters is carried out on the trans-dimensional posterior probability distribution by considering ensembles of parameter vectors. In particular, vs uncertainty estimates are obtained by marginalizing the trans-dimensional posterior distribution in terms of vs-profile marginal distributions. The methodology is applied to microtremor array dispersion data collected at two sites with significantly different geology in British Columbia, Canada. At both sites, results show excellent agreement with estimates from invasive measurements.

Corrected goodness-of-fit test in covariance structure analysis.

PubMed

Hayakawa, Kazuhiko

2018-05-17

Many previous studies report simulation evidence that the goodness-of-fit test in covariance structure analysis or structural equation modeling suffers from the overrejection problem when the number of manifest variables is large compared with the sample size. In this study, we demonstrate that one of the tests considered in Browne (1974) can address this long-standing problem. We also propose a simple modification of Satorra and Bentler's mean and variance adjusted test for non-normal data. A Monte Carlo simulation is carried out to investigate the performance of the corrected tests in the context of a confirmatory factor model, a panel autoregressive model, and a cross-lagged panel (panel vector autoregressive) model. The simulation results reveal that the corrected tests overcome the overrejection problem and outperform existing tests in most cases. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Comparison of six methods for the detection of causality in a bivariate time series

NASA Astrophysics Data System (ADS)

Krakovská, Anna; Jakubík, Jozef; Chvosteková, Martina; Coufal, David; Jajcay, Nikola; Paluš, Milan

2018-04-01

In this comparative study, six causality detection methods were compared, namely, the Granger vector autoregressive test, the extended Granger test, the kernel version of the Granger test, the conditional mutual information (transfer entropy), the evaluation of cross mappings between state spaces, and an assessment of predictability improvement due to the use of mixed predictions. Seven test data sets were analyzed: linear coupling of autoregressive models, a unidirectional connection of two Hénon systems, a unidirectional connection of chaotic systems of Rössler and Lorenz type and of two different Rössler systems, an example of bidirectionally connected two-species systems, a fishery model as an example of two correlated observables without a causal relationship, and an example of mediated causality. We tested not only 20 000 points long clean time series but also noisy and short variants of the data. The standard and the extended Granger tests worked only for the autoregressive models. The remaining methods were more successful with the more complex test examples, although they differed considerably in their capability to reveal the presence and the direction of coupling and to distinguish causality from mere correlation.

Identification and Characterization of the Spodoptera Su(var) 3-9 Histone H3K9 trimethyltransferase and Its Effect in AcMNPV Infection

PubMed Central

Li, Binbin; Li, Sisi; Yin, Juan; Zhong, Jiang

2013-01-01

Histone H3-lysine9 (H3K9) trimethyltransferase gene Su(var) 3-9 was cloned and identified in three Spodoptera insects, Spodoptera frugiperda ( S . frugiperda ), S . exigua and S . litura . Sequence analysis showed that Spodoptera Su(var) 3-9 is highly conserved evolutionarily. Su(var) 3-9 protein was found to be localized in the nucleus in Sf9 cells, and interact with histone H3, and the heterochromatin protein 1a (HP1a) and HP1b. A dose-dependent enzymatic activity was found at both 27 °C and 37 °C in vitro, with higher activity at 27 °C. Addition of specific inhibitor chaetocin resulted in decreased histone methylation level and host chromatin relaxation. In contrast, overexpression of Su(var) 3-9 caused increased histone methylation level and cellular genome compaction. In AcMNV-infected Sf9 cells, the transcription of Su(var) 3-9 increased at late time of infection, although the mRNA levels of most cellular genes decreased. Pre-treatment of Sf9 cells with chaetocin speeded up viral DNA replication, and increased the transcription level of a variety of virus genes, whereas in Sf9 cells pre-transformed with Su(var) 3-9 expression vector, viral DNA replication slow down slightly. These findings suggest that Su(var) 3-9 might participate in the viral genes expression an genome replication repression during AcMNPV infection. It provided a new insight for the understanding virus–host interaction mechanism. PMID:23894480

Data Assimilation of Lightning using 1D+3D/4D WRF Var Assimilation Schemes with Non-Linear Observation Operators

NASA Astrophysics Data System (ADS)

Navon, M. I.; Stefanescu, R.; Fuelberg, H. E.; Marchand, M.

2012-12-01

NASA's launch of the GOES-R Lightning Mapper (GLM) in 2015 will provide continuous, full disc, high resolution total lightning (IC + CG) data. The data will be available at a horizontal resolution of approximately 9 km. Compared to other types of data, the assimilation of lightning data into operational numerical models has received relatively little attention. Previous efforts of lightning assimilation mostly have employed nudging. This paper will describe the implementation of 1D+3D/4D Var assimilation schemes of existing ground-based WTLN (Worldwide Total Lightning Network) lightning observations using non-linear observation operators in the incremental WRFDA system. To mimic the expected output of GLM, the WTLN data were used to generate lightning super-observations characterized by flash rates/81 km2/20 min. A major difficulty associated with variational approaches is the complexity of the observation operator that defines the model equivalent of lightning. We use Convective Available Potential Energy (CAPE) as a proxy between lightning data and model variables. This operator is highly nonlinear. Marecal and Mahfouf (2003) have shown that nonlinearities can prevent direct assimilation of rainfall rates in the ECMWF 4D-VAR (using the incremental formulation proposed by Courtier et al. (1994)) from being successful. Using data from the 2011 Tuscaloosa, AL tornado outbreak, we have proved that the direct assimilation of lightning data into the WRF 3D/4D - Var systems is limited due to this incremental approach. Severe threshold limits must be imposed on the innovation vectors to obtain an improved analysis. We have implemented 1D+3D/4D Var schemes to assimilate lightning observations into the WRF model. Their use avoids innovation vector constrains from preventing the inclusion of a greater number of lightning observations Their use also minimizes the problem that nonlinearities in the moist convective scheme can introduce discontinuities in the cost function between inner and outer loops of the incremental 3-D/4-D VAR minimization. The first part of this paper will describe the methodology and performance analysis of the 1D-Var retrieval scheme that adjusts the WRF temperature profiles closer to an observed value as in Mahfouf et al. (2005). The second part will show the positive impact of these 1D-Var pseudo - temperature observations on both model 3D/4D-Var WRF analyses and short-range forecasts for three cases - the Tuscaloosa tornado outbreak (April 27, 2011) with intense but localized lightning, a second severe storm outbreak with more widespread but less intense lightning (June 27, 2011), and a northeaster containing much less lightning.

Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators

PubMed Central

Alwee, Razana; Hj Shamsuddin, Siti Mariyam; Sallehuddin, Roselina

2013-01-01

Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models. PMID:23766729

Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators.

PubMed

Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina

2013-01-01

Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.

At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study.

PubMed

Hamaker, E L; Asparouhov, T; Brose, A; Schmiedek, F; Muthén, B

2018-04-06

With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use composite scores of positive and negative affect and apply a multilevel vector autoregressive model to allow for individual differences in means, autoregressions, and cross-lagged effects. Then we extend the model to include random residual variances and covariance, and finally we investigate whether prior depression affects later depression scores through the random effects of the daily diary measures. We end with discussing several urgent-but mostly unresolved-issues in the area of dynamic multilevel modeling.

Detection and classification of subject-generated artifacts in EEG signals using autoregressive models.

PubMed

Lawhern, Vernon; Hairston, W David; McDowell, Kaleb; Westerfield, Marissa; Robbins, Kay

2012-07-15

We examine the problem of accurate detection and classification of artifacts in continuous EEG recordings. Manual identification of artifacts, by means of an expert or panel of experts, can be tedious, time-consuming and infeasible for large datasets. We use autoregressive (AR) models for feature extraction and characterization of EEG signals containing several kinds of subject-generated artifacts. AR model parameters are scale-invariant features that can be used to develop models of artifacts across a population. We use a support vector machine (SVM) classifier to discriminate among artifact conditions using the AR model parameters as features. Results indicate reliable classification among several different artifact conditions across subjects (approximately 94%). These results suggest that AR modeling can be a useful tool for discriminating among artifact signals both within and across individuals. Copyright © 2012 Elsevier B.V. All rights reserved.

Understanding the Role of Deterrence in Counterterrorism Security

DTIC Science & Technology

2009-11-01

30, No. 5, pp. 429–443. Enders, W., Sandler, T. (1993). “The Effectiveness of Anti-Terrorism Policies: Vector Autoregression Intervention Analysis ...occasional paper series . RAND occasional papers may include an informed perspective on a timely policy issue, a discussion of new research...United States safe? Are better means available for evaluating what may work or not and why? This series is designed to focus on a small set of

Investigating soil moisture feedbacks on precipitation with tests of Granger causality

NASA Astrophysics Data System (ADS)

Salvucci, Guido D.; Saleem, Jennifer A.; Kaufmann, Robert

Granger causality (GC) is used in the econometrics literature to identify the presence of one- and two-way coupling between terms in noisy multivariate dynamical systems. Here we test for the presence of GC to identify a soil moisture ( S) feedback on precipitation ( P) using data from Illinois. In this framework S is said to Granger cause P if F(P t|Ω t- Δt )≠F(P t|Ω t- Δt -S t- Δt ) where F denotes the conditional distribution of P, Ω t- Δt represents the set of all knowledge available at time t-Δ t, and Ω t- Δt -S t- Δt represents all knowledge except S. Critical for land-atmosphere interaction research is that Ω t- Δt includes all past information on P as well as S. Therefore that part of the relation between past soil moisture and current precipitation which results from precipitation autocorrelation and soil water balance will be accounted for and not attributed to causality. Tests for GC usually specify all relevant variables in a coupled vector autoregressive (VAR) model and then calculate the significance level of decreased predictability as various coupling coefficients are omitted. But because the data (daily precipitation and soil moisture) are distinctly non-Gaussian, we avoid using a VAR and instead express the daily precipitation events as a Markov model. We then test whether the probability of storm occurrence, conditioned on past information on precipitation, changes with information on soil moisture. Past information on precipitation is expressed both as the occurrence of previous day precipitation (to account for storm-scale persistence) and as a simple soil moisture-like precipitation-wetness index derived solely from precipitation (to account for seasonal-scale persistence). In this way only those fluctuations in moisture not attributable to past fluctuations in precipitation (e.g., those due to temperature) can influence the outcome of the test. The null hypothesis (no moisture influence) is evaluated by comparing observed changes in storm probability to Monte-Carlo simulated differences generated with unconditional occurrence probabilities. The null hypothesis is not rejected ( p>0.5) suggesting that contrary to recently published results, insufficient evidence exists to support an influence of soil moisture on precipitation in Illinois.

Promoting inclusive water governance and forecasting the structure of water consumption based on compositional data: A case study of Beijing.

PubMed

Wei, Yigang; Wang, Zhichao; Wang, Huiwen; Yao, Tang; Li, Yan

2018-09-01

Water is centrally important for agricultural security, environment, people's livelihoods, and socio-economic development, particularly in the face of extreme climate changes. Due to water shortages in many cities, the conflicts between various stakeholders and sectors over water use and allocation are becoming more common and intense. Effective inclusive governance of water use is critical for relieving water use conflicts. In addition, reliable forecasting of the structure of water usage among different sectors is a basic need for effective water governance planning. Although a large number of studies have attempted to forecast water use, little is known about the forecasted structure and trends of water use in the future. This paper aims to develop a forecasting model for the structure of water usage based on compositional data. Compositional data analysis is an effective approach for investigating the internal structure of a system. A host of data transformation methods and forecasting models were adopted and compared in order to derive the best-performing model. According to mean absolute percent error for compositional data (CoMAPE), a hyperspherical-transformation-based vector autoregression model for compositional data (VAR-DRHT) is the best-performing model. The proportions of the agricultural, industrial, domestic and environmental water will be 6.11%, 5.01%, 37.48% and 51.4% by 2020. Several recommendations for water inclusive development are provided to give a better account for the optimization of the water use structure, alleviation of water shortages, and improving stake holders' wellbeing. Overall, although we focus on groundwater, this study presents a powerful framework broadly applicable to resource management. Copyright © 2018 Elsevier B.V. All rights reserved.

A two-model hydrologic ensemble prediction of hydrograph: case study from the upper Nysa Klodzka river basin (SW Poland)

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Mizinski, Bartlomiej

2016-04-01

The HydroProg system has been elaborated in frame of the research project no. 2011/01/D/ST10/04171 of the National Science Centre of Poland and is steadily producing multimodel ensemble predictions of hydrograph in real time. Although there are six ensemble members available at present, the longest record of predictions and their statistics is available for two data-based models (uni- and multivariate autoregressive models). Thus, we consider 3-hour predictions of water levels, with lead times ranging from 15 to 180 minutes, computed every 15 minutes since August 2013 for the Nysa Klodzka basin (SW Poland) using the two approaches and their two-model ensemble. Since the launch of the HydroProg system there have been 12 high flow episodes, and the objective of this work is to present the performance of the two-model ensemble in the process of forecasting these events. For a sake of brevity, we limit our investigation to a single gauge located at the Nysa Klodzka river in the town of Klodzko, which is centrally located in the studied basin. We identified certain regular scenarios of how the models perform in predicting the high flows in Klodzko. At the initial phase of the high flow, well before the rising limb of hydrograph, the two-model ensemble is found to provide the most skilful prognoses of water levels. However, while forecasting the rising limb of hydrograph, either the two-model solution or the vector autoregressive model offers the best predictive performance. In addition, it is hypothesized that along with the development of the rising limb phase, the vector autoregression becomes the most skilful approach amongst the scrutinized ones. Our simple two-model exercise confirms that multimodel hydrologic ensemble predictions cannot be treated as universal solutions suitable for forecasting the entire high flow event, but their superior performance may hold only for certain phases of a high flow.

Mosquito larvicidal properties of Orthisiphon thymiflorus (Roth) Sleesen. (Family: Labiatae) against mosquito vectors, Anopheles stephensi, Culex quinquefasciatus and Aedes aegypti (Diptera: Culicidae)

USDA-ARS?s Scientific Manuscript database

Objective: To determine the larvicidal activity of hexane, chloroform, ethyl acetate, acetone, and methanol extracts of Orthosiphon thymiflorus leaves against Anopheles stephensi, Culex quinquefasciatus and Aedes aegypti. Methods: Larvicidal activity was determined in laboratory bioassays using var...

Potential for wind extraction from 4D-Var assimilation of aerosols and moisture

NASA Astrophysics Data System (ADS)

Zaplotnik, Žiga; Žagar, Nedjeljka

2017-04-01

We discuss the potential of the four-dimensional variational data assimilation (4D-Var) to retrieve the unobserved wind field from observations of atmospheric tracers and the mass field through internal model dynamics and the multivariate relationships in the background-error term for 4D-Var. The presence of non-linear moist dynamics makes the wind retrieval from tracers very difficult. On the other hand, it has been shown that moisture observations strongly influence both tropical and mid-latitude wind field in 4D-Var. We present an intermediate complexity model that describes nonlinear interactions between the wind, temperature, aerosols and moisture including their sinks and sources in the framework of the so-called first baroclinic mode atmosphere envisaged by A. Gill. Aerosol physical processes, which are included in the model, are the non-linear advection, diffusion and sources and sinks that exist as dry and wet deposition and diffusion. Precipitation is parametrized according to the Betts-Miller scheme. The control vector for 4D-Var includes aerosols, moisture and the three dynamical variables. The former is analysed univariately whereas wind field and mass field are analysed in a multivariate fashion taking into account quasi-geostrophic and unbalanced dynamics. The OSSE type of studies are performed for the tropical region to assess the ability of 4D-Var to extract wind-field information from the time series of observations of tracers as a function of the flow nonlinearity, the observations density and the length of the assimilation window (12 hours and 24 hours), in dry and moist environment. Results show that the 4D-Var assimilation of aerosols and temperature data is beneficial for the wind analysis with analysis errors strongly dependent on the moist processes and reliable background-error covariances.

Multispectral code excited linear prediction coding and its application in magnetic resonance images.

PubMed

Hu, J H; Wang, Y; Cahill, P T

1997-01-01

This paper reports a multispectral code excited linear prediction (MCELP) method for the compression of multispectral images. Different linear prediction models and adaptation schemes have been compared. The method that uses a forward adaptive autoregressive (AR) model has been proven to achieve a good compromise between performance, complexity, and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over nonoverlapping three-dimensional (3-D) macroblocks. Each macroblock is further divided into several 3-D micro-blocks, and the best excitation signal for each microblock is determined through an analysis-by-synthesis procedure. The MFCELP method has been applied to multispectral magnetic resonance (MR) images. To satisfy the high quality requirement for medical images, the error between the original image set and the synthesized one is further specified using a vector quantizer. This method has been applied to images from 26 clinical MR neuro studies (20 slices/study, three spectral bands/slice, 256x256 pixels/band, 12 b/pixel). The MFCELP method provides a significant visual improvement over the discrete cosine transform (DCT) based Joint Photographers Expert Group (JPEG) method, the wavelet transform based embedded zero-tree wavelet (EZW) coding method, and the vector tree (VT) coding method, as well as the multispectral segmented autoregressive moving average (MSARMA) method we developed previously.

Stochastic Parametrization for the Impact of Neglected Variability Patterns

NASA Astrophysics Data System (ADS)

Kaiser, Olga; Hien, Steffen; Achatz, Ulrich; Horenko, Illia

2017-04-01

An efficient description of the gravity wave variability and the related spontaneous emission processes requires an empirical stochastic closure for the impact of neglected variability patterns (subgridscales or SGS). In particular, we focus on the analysis of the IGW emission within a tangent linear model which requires a stochastic SGS parameterization for taking the self interaction of the ageostrophic flow components into account. For this purpose, we identify the best SGS model in terms of exactness and simplicity by deploying a wide range of different data-driven model classes, including standard stationary regression models, autoregression and artificial neuronal networks models - as well as the family of nonstationary models like FEM-BV-VARX model class (Finite Element based vector autoregressive time series analysis with bounded variation of the model parameters). The models are used to investigate the main characteristics of the underlying dynamics and to explore the significant spatial and temporal neighbourhood dependencies. The best SGS model in terms of exactness and simplicity is obtained for the nonstationary FEM-BV-VARX setting, determining only direct spatial and temporal neighbourhood as significant - and allowing to drastically reduce the number of informations that are required for the optimal SGS. Additionally, the models are characterized by sets of vector- and matrix-valued parameters that must be inferred from big data sets provided by simulations - making it a task that can not be solved without deploying high-performance computing facilities (HPC).

Papyracillic acid and its derivatives as biting deterrents against Aedes aegypti(Diptera: Culicidae): structure–activity relationships

USDA-ARS?s Scientific Manuscript database

Aedes aegypti L. is the major vector of the arboviruses responsible for dengue fever, one of the most devastating human diseases. Papyracillic acid, the main phytotoxin produced by Ascochyta agropyrina var. nana, was evaluated in a preliminary screening together with other fungal phytotoxins, cyclo...

Intra- and Interseasonal Autoregressive Prediction of Dengue Outbreaks Using Local Weather and Regional Climate for a Tropical Environment in Colombia

PubMed Central

Eastin, Matthew D.; Delmelle, Eric; Casas, Irene; Wexler, Joshua; Self, Cameron

2014-01-01

Dengue fever transmission results from complex interactions between the virus, human hosts, and mosquito vectors—all of which are influenced by environmental factors. Predictive models of dengue incidence rate, based on local weather and regional climate parameters, could benefit disease mitigation efforts. Time series of epidemiological and meteorological data for the urban environment of Cali, Colombia are analyzed from January of 2000 to December of 2011. Significant dengue outbreaks generally occur during warm-dry periods with extreme daily temperatures confined between 18°C and 32°C—the optimal range for mosquito survival and viral transmission. Two environment-based, multivariate, autoregressive forecast models are developed that allow dengue outbreaks to be anticipated from 2 weeks to 6 months in advance. These models have the potential to enhance existing dengue early warning systems, ultimately supporting public health decisions on the timing and scale of vector control efforts. PMID:24957546

Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

PubMed

Levine, Matthew E; Albers, David J; Hripcsak, George

2016-01-01

Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

Output-only modal parameter estimator of linear time-varying structural systems based on vector TAR model and least squares support vector machine

NASA Astrophysics Data System (ADS)

Zhou, Si-Da; Ma, Yuan-Chen; Liu, Li; Kang, Jie; Ma, Zhi-Sai; Yu, Lei

2018-01-01

Identification of time-varying modal parameters contributes to the structural health monitoring, fault detection, vibration control, etc. of the operational time-varying structural systems. However, it is a challenging task because there is not more information for the identification of the time-varying systems than that of the time-invariant systems. This paper presents a vector time-dependent autoregressive model and least squares support vector machine based modal parameter estimator for linear time-varying structural systems in case of output-only measurements. To reduce the computational cost, a Wendland's compactly supported radial basis function is used to achieve the sparsity of the Gram matrix. A Gamma-test-based non-parametric approach of selecting the regularization factor is adapted for the proposed estimator to replace the time-consuming n-fold cross validation. A series of numerical examples have illustrated the advantages of the proposed modal parameter estimator on the suppression of the overestimate and the short data. A laboratory experiment has further validated the proposed estimator.

Gaussian Process Autoregression for Simultaneous Proportional Multi-Modal Prosthetic Control With Natural Hand Kinematics.

PubMed

Xiloyannis, Michele; Gavriel, Constantinos; Thomik, Andreas A C; Faisal, A Aldo

2017-10-01

Matching the dexterity, versatility, and robustness of the human hand is still an unachieved goal in bionics, robotics, and neural engineering. A major limitation for hand prosthetics lies in the challenges of reliably decoding user intention from muscle signals when controlling complex robotic hands. Most of the commercially available prosthetic hands use muscle-related signals to decode a finite number of predefined motions and some offer proportional control of open/close movements of the whole hand. Here, in contrast, we aim to offer users flexible control of individual joints of their artificial hand. We propose a novel framework for decoding neural information that enables a user to independently control 11 joints of the hand in a continuous manner-much like we control our natural hands. Toward this end, we instructed six able-bodied subjects to perform everyday object manipulation tasks combining both dynamic, free movements (e.g., grasping) and isometric force tasks (e.g., squeezing). We recorded the electromyographic and mechanomyographic activities of five extrinsic muscles of the hand in the forearm, while simultaneously monitoring 11 joints of hand and fingers using a sensorized data glove that tracked the joints of the hand. Instead of learning just a direct mapping from current muscle activity to intended hand movement, we formulated a novel autoregressive approach that combines the context of previous hand movements with instantaneous muscle activity to predict future hand movements. Specifically, we evaluated a linear vector autoregressive moving average model with exogenous inputs and a novel Gaussian process ( ) autoregressive framework to learn the continuous mapping from hand joint dynamics and muscle activity to decode intended hand movement. Our approach achieves high levels of performance (RMSE of 8°/s and ). Crucially, we use a small set of sensors that allows us to control a larger set of independently actuated degrees of freedom of a hand. This novel undersensored control is enabled through the combination of nonlinear autoregressive continuous mapping between muscle activity and joint angles. The system evaluates the muscle signals in the context of previous natural hand movements. This enables us to resolve ambiguities in situations, where muscle signals alone cannot determine the correct action as we evaluate the muscle signals in their context of natural hand movements. autoregression is a particularly powerful approach which makes not only a prediction based on the context but also represents the associated uncertainty of its predictions, thus enabling the novel notion of risk-based control in neuroprosthetics. Our results suggest that autoregressive approaches with exogenous inputs lend themselves for natural, intuitive, and continuous control in neurotechnology, with the particular focus on prosthetic restoration of natural limb function, where high dexterity is required for complex movements.

Expression of amyloid-beta 1-40 and 1-42 peptides in Capsicum annum var. angulosum for oral immunization.

PubMed

Szabó, Beáta; Hori, Koichi; Nakajima, Akiko; Sasagawa, Noboru; Watanabe, Yuichiro; Ishiura, Shoichi

2004-08-01

Alzheimer's disease (AD), the leading cause of dementia in the elderly population, still remains without an effective treatment. The accumulation and deposition of the amyloid-beta peptide (Abeta) in the brain is thought to be a key event in the pathogenesis of AD. Recently, a novel exciting technology has been investigated to combat AD: new immunotherapeutic approaches have been described that are based on vaccination with the Abeta peptide itself, and this has been shown to induce functionally beneficial anti-Abeta antibody responses in different transgenic animal models of AD. Here we report the high level expression of GFP-Abeta1-40 and 1-42 peptides in Capsicum annum var. angulosum (green pepper) using a new tomato mosaic tobamovirus-based hybrid replication vector. After preinoculation of Nicotiana benthamiana plants with the in vitro transcript of the vector, the isolated virions were used to inoculate green pepper, which accumulated the GFPAbeta1-40 or 1-42 fusion proteins to a level of 100 microg/g of leaves 7 days after inoculation. These results make it possible to test whether oral immunization by feeding plant samples could stimulate antibody production against Abeta peptides.

Modified live infectious bursal disease virus (IBDV) vaccine delays infection of neonatal broiler chickens with variant IBDV compared to turkey herpesvirus (HVT)-IBDV vectored vaccine.

PubMed

Kurukulasuriya, Shanika; Ahmed, Khawaja Ashfaque; Ojkic, Davor; Gunawardana, Thushari; Goonewardene, Kalhari; Gupta, Ashish; Chow-Lockerbie, Betty; Popowich, Shelly; Willson, Philip; Tikoo, Suresh K; Gomis, Susantha

2017-02-07

Chickens are commonly processed around 35-45days of age in broiler chicken industry hence; diseases that occur at a young age are of paramount economic importance. Early age infection with infectious bursal disease virus (IBDV) results in long-lasting immunosuppression and profound economic losses. To our knowledge, this is the first study comparing the protection efficacy of modified live (MdLV) IBDV and herpesvirus turkey (HVT)-IBDV vaccines against early age variant IBDV (varIBDV) infection in chicks. Experiments were carried out in IBDV maternal antibody (MtAb) positive chicks (n=330), divided into 6 groups (n=50-60/group), namely Group 1 (saline), Group 2 (saline+varIBDV), Group 3 (HVT-IBDV), Group 4 (HVT-IBDV+varIBDV), Group 5 (MdLV) and Group 6 (MdLV+varIBDV). HVT-IBDV vaccination was given via the in ovo route to 18-day-old embryonated eggs. MdLV was administered via the subcutaneous route in day-old broilers. Group 2, Group 4 and Group 6 were orally challenged with varIBDV (SK-09, 3×10 3 EID 50 ) at day 6 post-hatch. IBDV seroconversion, bursal weight to body weight ratio (BBW) and bursal histopathology were assessed at 19 and 35days of age. Histopathological examination at day 19 revealed that varIBDV-SK09 challenge caused severe bursal atrophy and lower BBW in HVT-IBDV but not in MdLV vaccinated chicks. However by day 35, all challenged groups showed bursal atrophy and seroconversion. Interestingly, RT-qPCR analysis after varIBDV-SK09 challenge demonstrated an early (9days of age) and significantly high viral load (∼5744 folds) in HVT-IBDV vaccinated group vs unvaccinated challenged group (∼2.25 folds). Furthermore, flow cytometry analysis revealed inhibition of cytotoxic CD8 + T-cell response (CD44-downregulation) and decreased splenic lymphocytes counts in chicks after HVT-IBDV vaccination. Overall, our data suggest that MdLV delays varIBDV pathogenesis, whereas, HVT-IBDV vaccine is potentially immunosuppressive, which may increase the risk of early age varIBDV infection in broilers. Copyright © 2017 Elsevier Ltd. All rights reserved.

Measuring Treasury Bond Portfolio Risk and Portfolio Optimization with a Non-Gaussian Multivariate Model

NASA Astrophysics Data System (ADS)

Dong, Yijun

The research about measuring the risk of a bond portfolio and the portfolio optimization was relatively rare previously, because the risk factors of bond portfolios are not very volatile. However, this condition has changed recently. The 2008 financial crisis brought high volatility to the risk factors and the related bond securities, even if the highly rated U.S. treasury bonds. Moreover, the risk factors of bond portfolios show properties of fat-tailness and asymmetry like risk factors of equity portfolios. Therefore, we need to use advanced techniques to measure and manage risk of bond portfolios. In our paper, we first apply autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model with multivariate normal tempered stable (MNTS) distribution innovations to predict risk factors of U.S. treasury bonds and statistically demonstrate that MNTS distribution has the ability to capture the properties of risk factors based on the goodness-of-fit tests. Then based on empirical evidence, we find that the VaR and AVaR estimated by assuming normal tempered stable distribution are more realistic and reliable than those estimated by assuming normal distribution, especially for the financial crisis period. Finally, we use the mean-risk portfolio optimization to minimize portfolios' potential risks. The empirical study indicates that the optimized bond portfolios have better risk-adjusted performances than the benchmark portfolios for some periods. Moreover, the optimized bond portfolios obtained by assuming normal tempered stable distribution have improved performances in comparison to the optimized bond portfolios obtained by assuming normal distribution.

Reconstruction of late Holocene climate based on tree growth and mechanistic hierarchical models

USGS Publications Warehouse

Tipton, John; Hooten, Mevin B.; Pederson, Neil; Tingley, Martin; Bishop, Daniel

2016-01-01

Reconstruction of pre-instrumental, late Holocene climate is important for understanding how climate has changed in the past and how climate might change in the future. Statistical prediction of paleoclimate from tree ring widths is challenging because tree ring widths are a one-dimensional summary of annual growth that represents a multi-dimensional set of climatic and biotic influences. We develop a Bayesian hierarchical framework using a nonlinear, biologically motivated tree ring growth model to jointly reconstruct temperature and precipitation in the Hudson Valley, New York. Using a common growth function to describe the response of a tree to climate, we allow for species-specific parameterizations of the growth response. To enable predictive backcasts, we model the climate variables with a vector autoregressive process on an annual timescale coupled with a multivariate conditional autoregressive process that accounts for temporal correlation and cross-correlation between temperature and precipitation on a monthly scale. Our multi-scale temporal model allows for flexibility in the climate response through time at different temporal scales and predicts reasonable climate scenarios given tree ring width data.

A framework for developing a mimetic tensor artificial viscosity for Lagrangian hydrocodes on arbitrary polygonal and polyhedral meshes (u)

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

Lipnikov, Konstantin; Shashkov, Mikhail

2011-01-11

We construct a new mimetic tensor artificial viscosity on general polygonal and polyhedral meshes. The tensor artificial viscosity is based on a mimetic discretization of coordinate invariant operators, divergence of a tensor and gradient of a vector. The focus of this paper is on the symmetric form, div ({mu},{var_epsilon}(u)), of the tensor artificial viscosity where {var_epsilon}(u) is the symmetrized gradient of u and {mu}, is a tensor. The mimetic discretizations of this operator is derived for the case of a full tensor coefficient {mu}, that may reflect a shock direction. We demonstrate performance of the new viscosity for the Nohmore » implosion, Sedov explosion and Saltzman piston problems in both Cartesian and axisymmetric coordinate systems.« less

Evaluation of Animal and Plant Pathogens as Terrorism and Warfare Agents, Vectors and Pests

DTIC Science & Technology

2001-09-01

fever virus Bluetongue virus African horse sickness virus Nipah swine encephalitis virus Lumpy skin disease virus Camel pox virus Bacteria Bacillus...anthracis Bulkholderia (Pseudomonas) mallei Brucella spp. Mycoplasmas Contagious bovine (pleuropneum.) (M. mycoides var. mycoides type SC) (CBPP...virus Newcastle disease virus Rinderpest virus Pest des petits ruminants virus Bluetongue virus Teschen disease virus (Porcine enterovirus type 1) Rift

The impact of media campaigns on smoking cessation activity: a structural vector autoregression analysis.

PubMed

Langley, Tessa E; McNeill, Ann; Lewis, Sarah; Szatkowski, Lisa; Quinn, Casey

2012-11-01

To evaluate the effect of tobacco control media campaigns and pharmaceutical company-funded advertising for nicotine replacement therapy (NRT) on smoking cessation activity. Multiple time series analysis using structural vector autoregression, January 2002-May 2010. England and Wales. Tobacco control campaign data from the Central Office of Information; commercial NRT campaign data; data on calls to the National Health Service (NHS) stop smoking helpline from the Department of Health; point-of-sale data on over-the-counter (OTC) sales of NRT; and prescribing data from The Health Improvement Network (THIN), a database of UK primary care records. Monthly calls to the NHS stop smoking helpline and monthly rates of OTC sales and prescribing of NRT. A 1% increase in tobacco control television ratings (TVRs), a standard measure of advertising exposure, was associated with a statistically significant 0.085% increase in calls in the same month (P = 0.007), and no statistically significant effect in subsequent months. Tobacco control TVRs were not associated with OTC NRT sales or prescribed NRT. NRT advertising TVRs had a significant effect on NRT sales which became non-significant in the seasonally adjusted model, and no significant effect on prescribing or calls. Tobacco control campaigns appear to be more effective at triggering quitting behaviour than pharmaceutical company NRT campaigns. Any effect of such campaigns on quitting behaviour seems to be restricted to the month of the campaign, suggesting that such campaigns need to be sustained over time. © 2012 The Authors, Addiction © 2012 Society for the Study of Addiction.

Inference of Gene Regulatory Networks Incorporating Multi-Source Biological Knowledge via a State Space Model with L1 Regularization

PubMed Central

Hasegawa, Takanori; Yamaguchi, Rui; Nagasaki, Masao; Miyano, Satoru; Imoto, Seiya

2014-01-01

Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in the field of systems biology. Currently, there are two main approaches in GRN analysis using time-course observation data, namely an ordinary differential equation (ODE)-based approach and a statistical model-based approach. The ODE-based approach can generate complex dynamics of GRNs according to biologically validated nonlinear models. However, it cannot be applied to ten or more genes to simultaneously estimate system dynamics and regulatory relationships due to the computational difficulties. The statistical model-based approach uses highly abstract models to simply describe biological systems and to infer relationships among several hundreds of genes from the data. However, the high abstraction generates false regulations that are not permitted biologically. Thus, when dealing with several tens of genes of which the relationships are partially known, a method that can infer regulatory relationships based on a model with low abstraction and that can emulate the dynamics of ODE-based models while incorporating prior knowledge is urgently required. To accomplish this, we propose a method for inference of GRNs using a state space representation of a vector auto-regressive (VAR) model with L1 regularization. This method can estimate the dynamic behavior of genes based on linear time-series modeling constructed from an ODE-based model and can infer the regulatory structure among several tens of genes maximizing prediction ability for the observational data. Furthermore, the method is capable of incorporating various types of existing biological knowledge, e.g., drug kinetics and literature-recorded pathways. The effectiveness of the proposed method is shown through a comparison of simulation studies with several previous methods. For an application example, we evaluated mRNA expression profiles over time upon corticosteroid stimulation in rats, thus incorporating corticosteroid kinetics/dynamics, literature-recorded pathways and transcription factor (TF) information. PMID:25162401

[Farm animals as disease vectors of parasitic epizoonoses and zoophilic dermatophytes and their importance in dermatology].

PubMed

Beck, W

1999-09-01

Different pest arthropods and dermatophytes occurring primarily in farm animals may be transmissible to man and produce human dermatoses. The movement and cutaneous penetration habits of external parasites often cause crusted papules, severe itching and dermatitis or may damage their hosts by blood-sucking or by sensitizing them to their saliva. Furthermore different dermatophytes should be considered a possible cause of human skin lesions. Farm animals (cattle, pig, poultry, and rabbit) can transmit external parasites (ticks: Ixodes ricinus, Argas reflexus; fleas: Ceratophyllus gallinae, Spilopsyllus cuniculi, and mites: Sarcoptes scabiei var. bovis, Sarcoptes scabiei var. suis, Dermanyssus gallinae, Cheyletiella parasitovorax), and dermatophytes (Trichophyton sp., and Microsporum sp.). People who have close contact to infested farm animals are more often exposed to epizoonotic infections. Certain professions, such as farmers, and veterinarians, are especially vulnerable.

Stochastic-Constraints Method in Nonstationary Hot-Clutter Cancellation Part I: Fundamentals and Supervised Training Applications

DTIC Science & Technology

2003-04-01

any of the P interfering sources, and Hkt i (1) (P)] T is defined below. The P-variate vector = t kt , • t J consists of complex waveforms radiated by...line. More precisely, the (i, j ) t element of the matrix Hke is a complex 4-4 coefficient which is practically constant over the kth PRI, and is a...multivariate auto-regressive (AR) model of order n: Ykt + Z Bj Yk- j , t = tkt (25) j =l In the above equation, Bj are the M-variate matrices which are the

Kernel canonical-correlation Granger causality for multiple time series

NASA Astrophysics Data System (ADS)

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

2011-04-01

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

Evaluating Liquid and Granular Bacillus thuringiensis var. israelensis Broadcast Applications for Controlling Vectors of Dengue and Chikungunya Viruses in Artificial Containers and Tree Holes.

PubMed

Harwood, James F; Farooq, Muhammad; Turnwall, Brent T; Richardson, Alec G

2015-07-01

The principal vectors of chikungunya and dengue viruses typically oviposit in water-filled artificial and natural containers, including tree holes. Despite the risk these and similar tree hole-inhabiting mosquitoes present to global public health, surprisingly few studies have been conducted to determine an efficient method of applying larvicides specifically to tree holes. The Stihl SR 450, a backpack sprayer commonly utilized during military and civilian vector control operations, may be suitable for controlling larval tree-hole mosquitoes, as it is capable of delivering broadcast applications of granular and liquid dispersible formulations of Bacillus thuringiensis var. israelensis (Bti) to a large area relatively quickly. We compared the application effectiveness of two granular (AllPro Sustain MGB and VectoBac GR) and two liquid (Aquabac XT and VectoBac WDG) formulations of Bti in containers placed on bare ground, placed beneath vegetative cover, and hung 1.5 or 3 m above the ground to simulate tree holes. Aedes aegypti (L.) larval mortality and Bti droplet and granule density data (when appropriate) were recorded for each formulation. Overall, granular formulations of Bti resulted in higher mortality rates in the simulated tree-hole habitats, whereas applications of granular and liquid formulations resulted in similar levels of larval mortality in containers placed on the ground in the open and beneath vegetation. Published by Oxford University Press on behalf of Entomological Society of America 2015. This work is written by US Government employees and is in the public domain in the US.

Studies on black stain root disease in ponderosa pine. pp. 236-240. M. Garbelotto & P. Gonthier (Editors). Proceedings 12th International Conference on Root and Butt Rots of Forest Trees.

Treesearch

W. J. Otrosina; J. T. Kliejunas; S. S. Sung; S. Smith; D. R. Cluck

2008-01-01

Black stain root disease of ponderosa pine, caused by Lepfographium wageneri var. ponderosum (Harrington & Cobb) Harrington & Cobb, is increasing on many eastside pine stands in northeastern California. The disease is spread from tree to tree via root contacts and grafts but new infections are likely vectored by root...

Assessment of surface runoff depth changes in S\\varǎţel River basin, Romania using GIS techniques

NASA Astrophysics Data System (ADS)

Romulus, Costache; Iulia, Fontanine; Ema, Corodescu

2014-09-01

S\\varǎţel River basin, which is located in Curvature Subcarpahian area, has been facing an obvious increase in frequency of hydrological risk phenomena, associated with torrential events, during the last years. This trend is highly related to the increase in frequency of the extreme climatic phenomena and to the land use changes. The present study is aimed to highlight the spatial and quantitative changes occurred in surface runoff depth in S\\varǎţel catchment, between 1990-2006. This purpose was reached by estimating the surface runoff depth assignable to the average annual rainfall, by means of SCS-CN method, which was integrated into the GIS environment through the ArcCN-Runoff extension, for ArcGIS 10.1. In order to compute the surface runoff depth, by CN method, the land cover and the hydrological soil classes were introduced as vector (polygon data), while the curve number and the average annual rainfall were introduced as tables. After spatially modeling the surface runoff depth for the two years, the 1990 raster dataset was subtracted from the 2006 raster dataset, in order to highlight the changes in surface runoff depth.

Detection of tripping gait patterns in the elderly using autoregressive features and support vector machines.

PubMed

Lai, Daniel T H; Begg, Rezaul K; Taylor, Simon; Palaniswami, Marimuthu

2008-01-01

Elderly tripping falls cost billions annually in medical funds and result in high mortality rates often perpetrated by pulmonary embolism (internal bleeding) and infected fractures that do not heal well. In this paper, we propose an intelligent gait detection system (AR-SVM) for screening elderly individuals at risk of suffering tripping falls. The motivation of this system is to provide early detection of elderly gait reminiscent of tripping characteristics so that preventive measures could be administered. Our system is composed of two stages, a predictor model estimated by an autoregressive (AR) process and a support vector machine (SVM) classifier. The system input is a digital signal constructed from consecutive measurements of minimum toe clearance (MTC) representative of steady-state walking. The AR-SVM system was tested on 23 individuals (13 healthy and 10 having suffered at least one tripping fall in the past year) who each completed a minimum of 10 min of walking on a treadmill at a self-selected pace. In the first stage, a fourth order AR model required at least 64 MTC values to correctly detect all fallers and non-fallers. Detection was further improved to less than 1 min of walking when the model coefficients were used as input features to the SVM classifier. The system achieved a detection accuracy of 95.65% with the leave one out method using only 16 MTC samples, but was reduced to 69.57% when eight MTC samples were used. These results demonstrate a fast and efficient system requiring a small number of strides and only MTC measurements for accurate detection of tripping gait characteristics.

Fuzzy neural network technique for system state forecasting.

PubMed

Li, Dezhi; Wang, Wilson; Ismail, Fathy

2013-10-01

In many system state forecasting applications, the prediction is performed based on multiple datasets, each corresponding to a distinct system condition. The traditional methods dealing with multiple datasets (e.g., vector autoregressive moving average models and neural networks) have some shortcomings, such as limited modeling capability and opaque reasoning operations. To tackle these problems, a novel fuzzy neural network (FNN) is proposed in this paper to effectively extract information from multiple datasets, so as to improve forecasting accuracy. The proposed predictor consists of both autoregressive (AR) nodes modeling and nonlinear nodes modeling; AR models/nodes are used to capture the linear correlation of the datasets, and the nonlinear correlation of the datasets are modeled with nonlinear neuron nodes. A novel particle swarm technique [i.e., Laplace particle swarm (LPS) method] is proposed to facilitate parameters estimation of the predictor and improve modeling accuracy. The effectiveness of the developed FNN predictor and the associated LPS method is verified by a series of tests related to Mackey-Glass data forecast, exchange rate data prediction, and gear system prognosis. Test results show that the developed FNN predictor and the LPS method can capture the dynamics of multiple datasets effectively and track system characteristics accurately.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

PubMed

Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

PubMed Central

Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds. PMID:27382627

Non-linear models for the detection of impaired cerebral blood flow autoregulation.

PubMed

Chacón, Max; Jara, José Luis; Miranda, Rodrigo; Katsogridakis, Emmanuel; Panerai, Ronney B

2018-01-01

The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model's derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired.

Non-linear models for the detection of impaired cerebral blood flow autoregulation

PubMed Central

Miranda, Rodrigo; Katsogridakis, Emmanuel

2018-01-01

The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model’s derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired. PMID:29381724

Detection of shallow buried objects using an autoregressive model on the ground penetrating radar signal

NASA Astrophysics Data System (ADS)

Nabelek, Daniel P.; Ho, K. C.

2013-06-01

The detection of shallow buried low-metal content objects using ground penetrating radar (GPR) is a challenging task. This is because these targets are right underneath the ground and the ground bounce reflection interferes with their detections. They do not create distinctive hyperbolic signatures as required by most existing GPR detection algorithms due to their special geometric shapes and low metal content. This paper proposes the use of the Autoregressive (AR) modeling method for the detection of these targets. We fit an A-scan of the GPR data to an AR model. It is found that the fitting error will be small when such a target is present and large when it is absent. The ratio of the energy in an Ascan before and after AR model fitting is used as the confidence value for detection. We also apply AR model fitting over scans and utilize the fitting residual energies over several scans to form a feature vector for improving the detections. Using the data collected from a government test site, the proposed method can improve the detection of this kind of targets by 30% compared to the pre-screener, at a false alarm rate of 0.002/m2.

Estimating long-run equilibrium real exchange rates: short-lived shocks with long-lived impacts on Pakistan.

PubMed

Zardad, Asma; Mohsin, Asma; Zaman, Khalid

2013-12-01

The purpose of this study is to investigate the factors that affect real exchange rate volatility for Pakistan through the co-integration and error correction model over a 30-year time period, i.e. between 1980 and 2010. The study employed the autoregressive conditional heteroskedasticity (ARCH), generalized autoregressive conditional heteroskedasticity (GARCH) and Vector Error Correction model (VECM) to estimate the changes in the volatility of real exchange rate series, while an error correction model was used to determine the short-run dynamics of the system. The study is limited to a few variables i.e., productivity differential (i.e., real GDP per capita relative to main trading partner); terms of trade; trade openness and government expenditures in order to manage robust data. The result indicates that real effective exchange rate (REER) has been volatile around its equilibrium level; while, the speed of adjustment is relatively slow. VECM results confirm long run convergence of real exchange rate towards its equilibrium level. Results from ARCH and GARCH estimation shows that real shocks volatility persists, so that shocks die out rather slowly, and lasting misalignment seems to have occurred.

Predictability of Malaria Transmission Intensity in the Mpumalanga Province, South Africa, Using Land Surface Climatology and Autoregressive Analysis

NASA Technical Reports Server (NTRS)

Grass, David; Jasinski, Michael F.; Govere, John

2003-01-01

There has been increasing effort in recent years to employ satellite remotely sensed data to identify and map vector habitat and malaria transmission risk in data sparse environments. In the current investigation, available satellite and other land surface climatology data products are employed in short-term forecasting of infection rates in the Mpumalanga Province of South Africa, using a multivariate autoregressive approach. The climatology variables include precipitation, air temperature and other land surface states computed by the Off-line Land-Surface Global Assimilation System (OLGA) including soil moisture and surface evaporation. Satellite data products include the Normalized Difference Vegetation Index (NDVI) and other forcing data used in the Goddard Earth Observing System (GEOS-1) model. Predictions are compared to long- term monthly records of clinical and microscopic diagnoses. The approach addresses the high degree of short-term autocorrelation in the disease and weather time series. The resulting model is able to predict 11 of the 13 months that were classified as high risk during the validation period, indicating the utility of applying antecedent climatic variables to the prediction of malaria incidence for the Mpumalanga Province.

Carbon financial markets: A time-frequency analysis of CO2 prices

NASA Astrophysics Data System (ADS)

Sousa, Rita; Aguiar-Conraria, Luís; Soares, Maria Joana

2014-11-01

We characterize the interrelation of CO2 prices with energy prices (electricity, gas and coal), and with economic activity. Previous studies have relied on time-domain techniques, such as Vector Auto-Regressions. In this study, we use multivariate wavelet analysis, which operates in the time-frequency domain. Wavelet analysis provides convenient tools to distinguish relations at particular frequencies and at particular time horizons. Our empirical approach has the potential to identify relations getting stronger and then disappearing over specific time intervals and frequencies. We are able to examine the coherency of these variables and lead-lag relations at different frequencies for the time periods in focus.

Characteristics of the transmission of autoregressive sub-patterns in financial time series

NASA Astrophysics Data System (ADS)

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

2014-09-01

There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors.

Characteristics of the transmission of autoregressive sub-patterns in financial time series

PubMed Central

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

2014-01-01

There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors. PMID:25189200

A hybrid least squares support vector machines and GMDH approach for river flow forecasting

NASA Astrophysics Data System (ADS)

Samsudin, R.; Saad, P.; Shabri, A.

2010-06-01

This paper proposes a novel hybrid forecasting model, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM), known as GLSSVM. The GMDH is used to determine the useful input variables for LSSVM model and the LSSVM model which works as time series forecasting. In this study the application of GLSSVM for monthly river flow forecasting of Selangor and Bernam River are investigated. The results of the proposed GLSSVM approach are compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA) model, GMDH and LSSVM models using the long term observations of monthly river flow discharge. The standard statistical, the root mean square error (RMSE) and coefficient of correlation (R) are employed to evaluate the performance of various models developed. Experiment result indicates that the hybrid model was powerful tools to model discharge time series and can be applied successfully in complex hydrological modeling.

A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer.

PubMed

Khan, Adil Mehmood; Lee, Young-Koo; Lee, Sungyoung Y; Kim, Tae-Seong

2010-09-01

Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.

s-Ordered Exponential of Quadratic Forms Gained via IWSOP Technique

NASA Astrophysics Data System (ADS)

Bazrafkan, M. R.; Shähandeh, F.; Nahvifard, E.

2012-11-01

Using the generalized bar{s}-ordered Wigner operator, in which bar{s} is a vector over the field of complex numbers, the technique of integration within an s-ordered product of operators (IWSOP) has been extended to multimode case. We derive the bar{s}-ordered form of the widely applicable multimode exponential of quadratic form exp\\{sum_{i,j = 1}n ai^{dag}\\varLambda_{ij}{aj}\\} , each mode being in some particular order s i , applying this method.

Extended robust support vector machine based on financial risk minimization.

PubMed

Takeda, Akiko; Fujiwara, Shuhei; Kanamori, Takafumi

2014-11-01

Financial risk measures have been used recently in machine learning. For example, ν-support vector machine ν-SVM) minimizes the conditional value at risk (CVaR) of margin distribution. The measure is popular in finance because of the subadditivity property, but it is very sensitive to a few outliers in the tail of the distribution. We propose a new classification method, extended robust SVM (ER-SVM), which minimizes an intermediate risk measure between the CVaR and value at risk (VaR) by expecting that the resulting model becomes less sensitive than ν-SVM to outliers. We can regard ER-SVM as an extension of robust SVM, which uses a truncated hinge loss. Numerical experiments imply the ER-SVM's possibility of achieving a better prediction performance with proper parameter setting.

Modeling of Mean-VaR portfolio optimization by risk tolerance when the utility function is quadratic

NASA Astrophysics Data System (ADS)

Sukono, Sidi, Pramono; Bon, Abdul Talib bin; Supian, Sudradjat

2017-03-01

The problems of investing in financial assets are to choose a combination of weighting a portfolio can be maximized return expectations and minimizing the risk. This paper discusses the modeling of Mean-VaR portfolio optimization by risk tolerance, when square-shaped utility functions. It is assumed that the asset return has a certain distribution, and the risk of the portfolio is measured using the Value-at-Risk (VaR). So, the process of optimization of the portfolio is done based on the model of Mean-VaR portfolio optimization model for the Mean-VaR done using matrix algebra approach, and the Lagrange multiplier method, as well as Khun-Tucker. The results of the modeling portfolio optimization is in the form of a weighting vector equations depends on the vector mean return vector assets, identities, and matrix covariance between return of assets, as well as a factor in risk tolerance. As an illustration of numeric, analyzed five shares traded on the stock market in Indonesia. Based on analysis of five stocks return data gained the vector of weight composition and graphics of efficient surface of portfolio. Vector composition weighting weights and efficient surface charts can be used as a guide for investors in decisions to invest.

Acute larvicidal toxicity of five essential oils (Pinus nigra, Hyssopus officinalis, Satureja montana, Aloysia citrodora and Pelargonium graveolens) against the filariasis vector Culex quinquefasciatus: Synergistic and antagonistic effects.

PubMed

Benelli, Giovanni; Pavela, Roman; Canale, Angelo; Cianfaglione, Kevin; Ciaschetti, Giampiero; Conti, Fabio; Nicoletti, Marcello; Senthil-Nathan, Sengottayan; Mehlhorn, Heinz; Maggi, Filippo

2017-04-01

Mosquito vector control is facing a number of important and timely challenges, mainly due to the rapid development of pesticide resistance and environmental concerns. In this scenario, screening of botanical resources for their mosquitocidal activity may offer effective and eco-friendly tools against Culicidae vectors. Culex quinquefasciatus Say (Diptera: Culicidae) is a vector of lymphatic filariasis and of dangerous arboviral diseases, such as West Nile and St. Louis encephalitis. In this study, the chemical composition of five essential oils obtained from different plants, namely Pinus nigra J.F. Arnold var. italica (Pinaceae), Hyssopus officinalis L. subsp. aristatus (Lamiaceae), Satureja montana L. subsp. montana (Lamiaceae), Aloysia citriodora Palau (Verbenaceae) and Pelargonium graveolens L'Hér (Geraniaceae), was investigated by GC-MS analysis. Furthermore, it was evaluated their acute toxicity on larvae of C. quinquefasciatus. Then, the most effective oils were selected, in order to focus on the potential synergistic and antagonistic effects, testing them in binary mixtures on C. quinquefasciatus larvae. Results showed that the higher effectiveness was obtained by S. montana subsp. montana essential oil (LC 50 =25.6μL·L -1 ), followed by P. nigra var. italica (LC 50 =49.8μL·L -1 ) and A. citriodora (LC 50 =65.6μL·L -1 ), while the other essential oils showed LC 50 values higher than 90μL·L -1 . The larvicidal effectiveness can be enhanced by preparing simple binary mixtures of essential oils, such as S. montana+A. citriodora (ratio 1:1), which showed higher larvicidal toxicity (LC 50 =18.3μL·L -1 ). On the other hand, testing S. montana+P. nigra (1:1) an antagonistic effect was detected, leading to a LC 50 (72.5μL·L -1 ) higher than the LC 50 values calculated for the two oils tested separately. Overall, our results add useful knowledge to allow the employ of synergistic essential oil blends as effective, cheap and eco-friendly mosquito larvicides. Copyright © 2017 Elsevier B.V. All rights reserved.

[Species of Lutzomyia involved in an urban focus of visceral and cutaneous leishmaniasis].

PubMed

Cortés, Luis Alberto; Fernández, Jhon James

2008-09-01

A focus of leishmanias transmission was reported in the municipality of El Carmen de Bolívar in the province of Bolívar, Colombia, where both cutaneous and visceral leishmaniasis cases have occured. Vector identification, ecology and behavior of potential vector species have not been characterized in this region, however. Sand fly species of the genus Lutzomyia were identified, patterns of behavior were established, and their possible roles in leishmaniasis transmission were evaluated. CDC light traps were used in several different habitats; in addition, monthly collections were made with human bait as attraction inside houses as well as outdoor Shannon trap collections. The collection data were compared with independent variables including precipitation, temperature, relative humidity and wind velocity by means of a Pearson correlation matrix to estimate levels of association and to determine the influence of the climatic conditions on the density of adults of Lutzomyia evansi and L. gomezi in each of the habitats. Five species of Lutzomyia were captured: L. evansi, L. cayennensis cayennensis, L. gomezi, L. dubitansi, and L. walkeri. Lutzomyia evansi and L. gomezi presented a significant relationship in the abundance of adults indoors with respect to outdoor wind velocity. The Lutzomyia species captured showed an anthropophagic behavior with a constant activity between the 18:00 and 20:00 hrs. Lutzomyia evansi and L. gomezi are inversely proportional in relationship to wind velocity-when the wind diminishes, the activity of these species increases.

Parametric and nonparametric Granger causality testing: Linkages between international stock markets

NASA Astrophysics Data System (ADS)

De Gooijer, Jan G.; Sivarajasingham, Selliah

2008-04-01

This study investigates long-term linear and nonlinear causal linkages among eleven stock markets, six industrialized markets and five emerging markets of South-East Asia. We cover the period 1987-2006, taking into account the on-set of the Asian financial crisis of 1997. We first apply a test for the presence of general nonlinearity in vector time series. Substantial differences exist between the pre- and post-crisis period in terms of the total number of significant nonlinear relationships. We then examine both periods, using a new nonparametric test for Granger noncausality and the conventional parametric Granger noncausality test. One major finding is that the Asian stock markets have become more internationally integrated after the Asian financial crisis. An exception is the Sri Lankan market with almost no significant long-term linear and nonlinear causal linkages with other markets. To ensure that any causality is strictly nonlinear in nature, we also examine the nonlinear causal relationships of VAR filtered residuals and VAR filtered squared residuals for the post-crisis sample. We find quite a few remaining significant bi- and uni-directional causal nonlinear relationships in these series. Finally, after filtering the VAR-residuals with GARCH-BEKK models, we show that the nonparametric test statistics are substantially smaller in both magnitude and statistical significance than those before filtering. This indicates that nonlinear causality can, to a large extent, be explained by simple volatility effects.

Recent Progress on the Second Generation CMORPH: LEO-IR Based Precipitation Estimates and Cloud Motion Vector

NASA Astrophysics Data System (ADS)

Xie, Pingping; Joyce, Robert; Wu, Shaorong

2015-04-01

As reported at the EGU General Assembly of 2014, a prototype system was developed for the second generation CMORPH to produce global analyses of 30-min precipitation on a 0.05olat/lon grid over the entire globe from pole to pole through integration of information from satellite observations as well as numerical model simulations. The second generation CMORPH is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) as well as LEO platforms, and precipitation simulations from numerical global models. Key to the success of the 2nd generation CMORPH, among a couple of other elements, are the development of a LEO-IR based precipitation estimation to fill in the polar gaps and objectively analyzed cloud motion vectors to capture the cloud movements of various spatial scales over the entire globe. In this presentation, we report our recent work on the refinement for these two important algorithm components. The prototype algorithm for the LEO IR precipitation estimation is refined to achieve improved quantitative accuracy and consistency with PMW retrievals. AVHRR IR TBB data from all LEO satellites are first remapped to a 0.05olat/lon grid over the entire globe and in a 30-min interval. Temporally and spatially co-located data pairs of the LEO TBB and inter-calibrated combined satellite PMW retrievals (MWCOMB) are then collected to construct tables. Precipitation at a grid box is derived from the TBB through matching the PDF tables for the TBB and the MWCOMB. This procedure is implemented for different season, latitude band and underlying surface types to account for the variations in the cloud - precipitation relationship. At the meantime, a sub-system is developed to construct analyzed fields of cloud motion vectors from the GEO/LEO IR based precipitation estimates and the CFS Reanalysis (CFSR) precipitation fields. Motion vectors are first derived separately from the satellite IR based precipitation estimates and the CFSR precipitation fields. These individually derived motion vectors are then combined through a 2D-VAR technique to form an analyzed field of cloud motion vectors over the entire globe. Error function is experimented to best reflect the performance of the satellite IR based estimates and the CFSR in capturing the movements of precipitating cloud systems over different regions and for different seasons. Quantitative experiments are conducted to optimize the LEO IR based precipitation estimation technique and the 2D-VAR based motion vector analysis system. Detailed results will be reported at the EGU.

K π vector form factor, dispersive constraints and τ→ ν τ K π decays

NASA Astrophysics Data System (ADS)

Boito, Diogo R.; Escribano, Rafel; Jamin, Matthias

2009-02-01

Recent experimental data for the differential decay distribution of the decay τ -→ ν τ K S π - by the Belle collaboration are described by a theoretical model which is composed of the contributing vector and scalar form factors F {+/ K π }( s) and F {0/ K π }( s). Both form factors are constructed such that they fulfil constraints posed by analyticity and unitarity. A good description of the experimental measurement is achieved by incorporating two vector resonances and working with a three-times-subtracted dispersion relation in order to suppress higher-energy contributions. The resonance parameters of the charged K *(892) meson, defined as the pole of F {+/ K π }( s) in the complex s-plane, can be extracted, with the result M_{K^{*}}=892.0± 0.9 MeV and \\varGamma_{K^{*}}=46.2± 0.4 MeV . Finally, employing a three-times-subtracted dispersion relation allows one to determine the slope and curvature parameters λ'+=(24.7±0.8)×10-3 and λ″+=(12.0±0.2)×10-4 of the vector form factor F {+/ K π }( s) directly from the data.

An analysis of electricity price behavior when the market in California was dysfunctional

NASA Astrophysics Data System (ADS)

Lee, Yoo-Soo

The electricity market in California worked well for the first two years after restructuring, but in the summer of 2000 there were frequent high price spikes and then persistently high prices during the winter and the spring of 2001. This research develops econometric models to explain the behavior of the spot and forward prices for electricity and the relationship between them when the market in California was dysfunctional. The first results demonstrate that the high spot prices in the day-ahead market during the summer of 2000 were caused by changes in the bid behavior of buyers as well as by the offer behavior of sellers. After the Federal Energy Regulatory Commission (FERC) declared that these high spot prices were "unjust and unreasonable", the FERC approved the payment of refunds to customers in California but not in other areas within the Western Inter-Connection (WECC). However, the results of a Vector Auto-Regressive model (VAR) show that the high spot prices in California were transferred immediately to other states in the WECC and the spot prices at different trading hubs belong to a single market. After the intervention by FERC in December 2000, spot prices and forward prices of electricity were unusually high. Estimated distributed lag models, using both monthly and daily data, show that there were strong positive relationships between the price shocks for electricity and natural gas in the spot markets and the forward prices for electricity. Risk premiums in the forward prices for electricity were estimated and the results show that the price shocks for electricity after FERC's intervention were the primary cause of the high forward prices. The main conclusions for regulatory policy are (1) it is virtually impossible to contain the effects of a dysfunctional electricity market to a single region because other regions are linked through the electrical grid, and (2) it is essential to intervene immediately and effectively when the spot prices have been ruled by regulators to be unjust and unreasonable. The intervention by the FERC did not prevent persistently high spot and forward prices for customers throughout the WECC.

Riemannian multi-manifold modeling and clustering in brain networks

NASA Astrophysics Data System (ADS)

Slavakis, Konstantinos; Salsabilian, Shiva; Wack, David S.; Muldoon, Sarah F.; Baidoo-Williams, Henry E.; Vettel, Jean M.; Cieslak, Matthew; Grafton, Scott T.

2017-08-01

This paper introduces Riemannian multi-manifold modeling in the context of brain-network analytics: Brainnetwork time-series yield features which are modeled as points lying in or close to a union of a finite number of submanifolds within a known Riemannian manifold. Distinguishing disparate time series amounts thus to clustering multiple Riemannian submanifolds. To this end, two feature-generation schemes for brain-network time series are put forth. The first one is motivated by Granger-causality arguments and uses an auto-regressive moving average model to map low-rank linear vector subspaces, spanned by column vectors of appropriately defined observability matrices, to points into the Grassmann manifold. The second one utilizes (non-linear) dependencies among network nodes by introducing kernel-based partial correlations to generate points in the manifold of positivedefinite matrices. Based on recently developed research on clustering Riemannian submanifolds, an algorithm is provided for distinguishing time series based on their Riemannian-geometry properties. Numerical tests on time series, synthetically generated from real brain-network structural connectivity matrices, reveal that the proposed scheme outperforms classical and state-of-the-art techniques in clustering brain-network states/structures.

Comparison of causality analysis on simultaneously measured fMRI and NIRS signals during motor tasks.

PubMed

Anwar, Abdul Rauf; Muthalib, Makii; Perrey, Stephane; Galka, Andreas; Granert, Oliver; Wolff, Stephan; Deuschl, Guenther; Raethjen, Jan; Heute, Ulrich; Muthuraman, Muthuraman

2013-01-01

Brain activity can be measured using different modalities. Since most of the modalities tend to complement each other, it seems promising to measure them simultaneously. In to be presented research, the data recorded from Functional Magnetic Resonance Imaging (fMRI) and Near Infrared Spectroscopy (NIRS), simultaneously, are subjected to causality analysis using time-resolved partial directed coherence (tPDC). Time-resolved partial directed coherence uses the principle of state space modelling to estimate Multivariate Autoregressive (MVAR) coefficients. This method is useful to visualize both frequency and time dynamics of causality between the time series. Afterwards, causality results from different modalities are compared by estimating the Spearman correlation. In to be presented study, we used directionality vectors to analyze correlation, rather than actual signal vectors. Results show that causality analysis of the fMRI correlates more closely to causality results of oxy-NIRS as compared to deoxy-NIRS in case of a finger sequencing task. However, in case of simple finger tapping, no clear difference between oxy-fMRI and deoxy-fMRI correlation is identified.

Relationships among Energy Price Shocks, Stock Market, and the Macroeconomy: Evidence from China

PubMed Central

Cong, Rong-Gang; Shen, Shaochuan

2013-01-01

This paper investigates the interactive relationships among China energy price shocks, stock market, and the macroeconomy using multivariate vector autoregression. The results indicate that there is a long cointegration among them. A 1% rise in the energy price index can depress the stock market index by 0.54% and the industrial value-adding growth by 0.037%. Energy price shocks also cause inflation and have a 5-month lag effect on stock market, which may result in the stock market “underreacting.” The energy price can explain stock market fluctuations better than the interest rate over a longer time period. Consequently, investors should pay greater attention to the long-term effect of energy on the stock market. PMID:23690737

Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

DOE PAGES

Buitrago, Jaime; Asfour, Shihab

2017-01-01

Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input.more » Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.« less

Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

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

Buitrago, Jaime; Asfour, Shihab

Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input.more » Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.« less

Area-Wide Ground Applications of Bacillus thuringiensis var. israelensis for the Control of Aedes albopictus in Residential Neighborhoods: From Optimization to Operation

PubMed Central

Williams, Gregory M.; Faraji, Ary; Unlu, Isik; Healy, Sean P.; Farooq, Muhammad; Gaugler, Randy; Hamilton, George; Fonseca, Dina M.

2014-01-01

The increasing range of Aedes albopictus, the Asian tiger mosquito, in the USA and the threat of chikungunya and dengue outbreaks vectored by this species have necessitated novel approaches to control this peridomestic mosquito. Conventional methods such as adulticiding provide temporary relief, but fail to manage this pest on a sustained basis. We explored the use of cold aerosol foggers and misting machines for area-wide applications of Bacillus thuringiensis var. israelensis (VectoBac WDG) as a larvicide targeting Aedes albopictus. During 2010–2013 we performed initially open field trials and then 19 operational area-wide applications in urban and suburban residential areas in northeastern USA to test three truck-mounted sprayers at two application rates. Area-wide applications of WDG in open field conditions at 400 and 800 g/ha killed on average 87% of tested larvae. Once techniques were optimized in residential areas, applications with a Buffalo Turbine Mist Sprayer at a rate of 800 g/ha, the best combination, consistently provided over 90% mortality. Importantly, there was no significant decrease in efficacy with distance from the spray line even in blocks of row homes with trees and bushes in the backyards. Under laboratory conditions Bti deposition in bioassay cups during the operational trials resulted in over 6 weeks of residual control. Our results demonstrate that area-wide truck mounted applications of WDG can effectively suppress Ae. albopictus larvae and should be used in integrated mosquito management approaches to control this nuisance pest and disease vector. PMID:25329314

Area-wide ground applications of Bacillus thuringiensis var. israelensis for the control of Aedes albopictus in residential neighborhoods: from optimization to operation.

PubMed

Williams, Gregory M; Faraji, Ary; Unlu, Isik; Healy, Sean P; Farooq, Muhammad; Gaugler, Randy; Hamilton, George; Fonseca, Dina M

2014-01-01

The increasing range of Aedes albopictus, the Asian tiger mosquito, in the USA and the threat of chikungunya and dengue outbreaks vectored by this species have necessitated novel approaches to control this peridomestic mosquito. Conventional methods such as adulticiding provide temporary relief, but fail to manage this pest on a sustained basis. We explored the use of cold aerosol foggers and misting machines for area-wide applications of Bacillus thuringiensis var. israelensis (VectoBac WDG) as a larvicide targeting Aedes albopictus. During 2010-2013 we performed initially open field trials and then 19 operational area-wide applications in urban and suburban residential areas in northeastern USA to test three truck-mounted sprayers at two application rates. Area-wide applications of WDG in open field conditions at 400 and 800 g/ha killed on average 87% of tested larvae. Once techniques were optimized in residential areas, applications with a Buffalo Turbine Mist Sprayer at a rate of 800 g/ha, the best combination, consistently provided over 90% mortality. Importantly, there was no significant decrease in efficacy with distance from the spray line even in blocks of row homes with trees and bushes in the backyards. Under laboratory conditions Bti deposition in bioassay cups during the operational trials resulted in over 6 weeks of residual control. Our results demonstrate that area-wide truck mounted applications of WDG can effectively suppress Ae. albopictus larvae and should be used in integrated mosquito management approaches to control this nuisance pest and disease vector.

[New public health challenges in vector management: black flies in Murcia (Spain)].

PubMed

Sánchez-López, Pedro F; Ruiz-Arrondo, Ignacio; Kotter, Heiko; Pacheco Martínez, Francisco; Segovia Hernández, Manuel; Gómez Campoy, M Elisa

Historically, no black fly (Diptera: Simuliidae) nuisance has been reported in the Murcia Region. Back in September 2016 the Ojós City Council has contacted the Regional Public Health General Directorate for help regarding a local insect nuisance, most probably based on mosquitoes. After sampling with a BG-sentinel 2 trap, collecting adult specimens with an entomological aspirator, and collect larvae and pupae on submerged giant cane stalks at the river, it turned out that Simulium sergenti was the insect species causing the nuisance. This species is not considered particularly anthropophilic; therefore, a low vector risk for human health was considered. However, the high fly density impaired the life quality of the people at the village. A management plan was recommended, treating the river with Bacillus thuringiensis var israelensis. Copyright © 2017 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Incorporating measurement error in n = 1 psychological autoregressive modeling.

PubMed

Schuurman, Noémi K; Houtveen, Jan H; Hamaker, Ellen L

2015-01-01

Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model with a white noise term (AR+WN), and an autoregressive moving average (ARMA) model. In a simulation study we compare the parameter recovery performance of these models, and compare this performance for both a Bayesian and frequentist approach. We find that overall, the AR+WN model performs better. Furthermore, we find that for realistic (i.e., small) sample sizes, psychological research would benefit from a Bayesian approach in fitting these models. Finally, we illustrate the effect of disregarding measurement error in an AR(1) model by means of an empirical application on mood data in women. We find that, depending on the person, approximately 30-50% of the total variance was due to measurement error, and that disregarding this measurement error results in a substantial underestimation of the autoregressive parameters.

Is there a hybridization barrier between Gentiana lutea color morphs?

PubMed

Losada, María; Veiga, Tania; Guitián, Javier; Guitián, José; Guitián, Pablo; Sobral, Mar

2015-01-01

In Gentiana lutea two varieties are described: G. lutea var. aurantiaca with orange corolla colors and G. lutea var. lutea with yellow corolla colors. Both color varieties co-occur in NW Spain, and pollinators select flower color in this species. It is not known whether a hybridization barrier exists between these G. lutea color varieties. We aim to test the compatibility between flower color varieties in G. lutea and its dependence on pollen vectors. Within a sympatric population containing both flower color morphs, we analyzed differences in reproductive success (number, weight, viability and germinability of seeds) depending on fertilization treatments (autogamy and xenogamy within variety and among varieties). We found a 93% reduction in number of seeds and a 37% reduction in seed weight respectively of autogamy treatments compared to xenogamy crossings. Additionally, reproductive success is higher within color varieties than among varieties, due to a 45% seed viability reduction on hybrids from different varieties. Our results show that G. lutea reproductive success is strongly dependent on pollinators and that a partial hybridization barrier exists between G. lutea varieties.

Is there a hybridization barrier between Gentiana lutea color morphs?

PubMed Central

Losada, María; Veiga, Tania; Guitián, Javier; Guitián, José; Guitián, Pablo

2015-01-01

In Gentiana lutea two varieties are described: G. lutea var. aurantiaca with orange corolla colors and G. lutea var. lutea with yellow corolla colors. Both color varieties co-occur in NW Spain, and pollinators select flower color in this species. It is not known whether a hybridization barrier exists between these G. lutea color varieties. We aim to test the compatibility between flower color varieties in G. lutea and its dependence on pollen vectors. Within a sympatric population containing both flower color morphs, we analyzed differences in reproductive success (number, weight, viability and germinability of seeds) depending on fertilization treatments (autogamy and xenogamy within variety and among varieties). We found a 93% reduction in number of seeds and a 37% reduction in seed weight respectively of autogamy treatments compared to xenogamy crossings. Additionally, reproductive success is higher within color varieties than among varieties, due to a 45% seed viability reduction on hybrids from different varieties. Our results show that G. lutea reproductive success is strongly dependent on pollinators and that a partial hybridization barrier exists between G. lutea varieties. PMID:26528404

Get Over It! A Multilevel Threshold Autoregressive Model for State-Dependent Affect Regulation.

PubMed

De Haan-Rietdijk, Silvia; Gottman, John M; Bergeman, Cindy S; Hamaker, Ellen L

2016-03-01

Intensive longitudinal data provide rich information, which is best captured when specialized models are used in the analysis. One of these models is the multilevel autoregressive model, which psychologists have applied successfully to study affect regulation as well as alcohol use. A limitation of this model is that the autoregressive parameter is treated as a fixed, trait-like property of a person. We argue that the autoregressive parameter may be state-dependent, for example, if the strength of affect regulation depends on the intensity of affect experienced. To allow such intra-individual variation, we propose a multilevel threshold autoregressive model. Using simulations, we show that this model can be used to detect state-dependent regulation with adequate power and Type I error. The potential of the new modeling approach is illustrated with two empirical applications that extend the basic model to address additional substantive research questions.

Community-based biological control of malaria mosquitoes using Bacillus thuringiensis var. israelensis (Bti) in Rwanda: community awareness, acceptance and participation.

PubMed

Ingabire, Chantal Marie; Hakizimana, Emmanuel; Rulisa, Alexis; Kateera, Fredrick; Van Den Borne, Bart; Muvunyi, Claude Mambo; Mutesa, Leon; Van Vugt, Michelle; Koenraadt, Constantianus J M; Takken, Willem; Alaii, Jane

2017-10-03

Targeting the aquatic stages of malaria vectors via larval source management (LSM) in collaboration with local communities could accelerate progress towards malaria elimination when deployed in addition to existing vector control strategies. However, the precise role that communities can assume in implementing such an intervention has not been fully investigated. This study investigated community awareness, acceptance and participation in a study that incorporated the socio-economic and entomological impact of LSM using Bacillus thuringiensis var. israelensis (Bti) in eastern Rwanda, and identified challenges and recommendations for future scale-up. The implementation of the community-based LSM intervention took place in Ruhuha, Rwanda, from February to July 2015. The intervention included three arms: control, community-based (CB) and project-supervised (PS). Mixed methods were used to collect baseline and endline socio-economic data in January and October 2015. A high perceived safety and effectiveness of Bti was reported at the start of the intervention. Being aware of malaria symptoms and perceiving Bti as safe on other living organisms increased the likelihood of community participation through investment of labour time for Bti application. On the other hand, the likelihood for community participation was lower if respondents: (1) perceived rice farming as very profitable; (2) provided more money to the cooperative as a capital; and, (3) were already involved in rice farming for more than 6 years. After 6 months of implementation, an increase in knowledge and skills regarding Bti application was reported. The community perceived a reduction in mosquito density and nuisance biting on treated arms. Main operational, seasonal and geographical challenges included manual application of Bti, long working hours, and need for transportation for reaching the fields. Recommendations were made for future scale-up, including addressing above-mentioned concerns and government adoption of LSM as part of its vector control strategies. Community awareness and support for LSM increased following Bti application. A high effectiveness of Bti in terms of reduction of mosquito abundance and nuisance biting was perceived. The study confirmed the feasibility of community-based LSM interventions and served as evidence for future scale-up of Bti application and adoption into Rwandan malaria vector control strategies.

Robust Semi-Active Ride Control under Stochastic Excitation

DTIC Science & Technology

2014-01-01

broad classes of time-series models which are of practical importance; the Auto-Regressive (AR) models, the Integrated (I) models, and the Moving...Average (MA) models [12]. Combinations of these models result in autoregressive moving average (ARMA) and autoregressive integrated moving average...Down Up 4) Down Down These four cases can be written in compact form as: (20) Where is the Heaviside

Random Process Simulation for stochastic fatigue analysis. Ph.D. Thesis - Rice Univ., Houston, Tex.

NASA Technical Reports Server (NTRS)

Larsen, Curtis E.

1988-01-01

A simulation technique is described which directly synthesizes the extrema of a random process and is more efficient than the Gaussian simulation method. Such a technique is particularly useful in stochastic fatigue analysis because the required stress range moment E(R sup m), is a function only of the extrema of the random stress process. The family of autoregressive moving average (ARMA) models is reviewed and an autoregressive model is presented for modeling the extrema of any random process which has a unimodal power spectral density (psd). The proposed autoregressive technique is found to produce rainflow stress range moments which compare favorably with those computed by the Gaussian technique and to average 11.7 times faster than the Gaussian technique. The autoregressive technique is also adapted for processes having bimodal psd's. The adaptation involves using two autoregressive processes to simulate the extrema due to each mode and the superposition of these two extrema sequences. The proposed autoregressive superposition technique is 9 to 13 times faster than the Gaussian technique and produces comparable values for E(R sup m) for bimodal psd's having the frequency of one mode at least 2.5 times that of the other mode.

Incorporating measurement error in n = 1 psychological autoregressive modeling

PubMed Central

Schuurman, Noémi K.; Houtveen, Jan H.; Hamaker, Ellen L.

2015-01-01

Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model with a white noise term (AR+WN), and an autoregressive moving average (ARMA) model. In a simulation study we compare the parameter recovery performance of these models, and compare this performance for both a Bayesian and frequentist approach. We find that overall, the AR+WN model performs better. Furthermore, we find that for realistic (i.e., small) sample sizes, psychological research would benefit from a Bayesian approach in fitting these models. Finally, we illustrate the effect of disregarding measurement error in an AR(1) model by means of an empirical application on mood data in women. We find that, depending on the person, approximately 30–50% of the total variance was due to measurement error, and that disregarding this measurement error results in a substantial underestimation of the autoregressive parameters. PMID:26283988

Cutaneous Leishmaniasis and Sand Fly Fluctuations Are Associated with El Niño in Panamá

PubMed Central

Chaves, Luis Fernando; Calzada, José E.; Valderrama, Anayansí; Saldaña, Azael

2014-01-01

Background Cutaneous Leishmaniasis (CL) is a neglected tropical vector-borne disease. Sand fly vectors (SF) and Leishmania spp parasites are sensitive to changes in weather conditions, rendering disease transmission susceptible to changes in local and global scale climatic patterns. Nevertheless, it is unclear how SF abundance is impacted by El Niño Southern Oscillation (ENSO) and how these changes might relate to changes in CL transmission. Methodology and Findings We studied association patterns between monthly time series, from January 2000 to December 2010, of: CL cases, rainfall and temperature from Panamá, and an ENSO index. We employed autoregressive models and cross wavelet coherence, to quantify the seasonal and interannual impact of local climate and ENSO on CL dynamics. We employed Poisson Rate Generalized Linear Mixed Models to study SF abundance patterns across ENSO phases, seasons and eco-epidemiological settings, employing records from 640 night-trap sampling collections spanning 2000–2011. We found that ENSO, rainfall and temperature were associated with CL cycles at interannual scales, while seasonal patterns were mainly associated with rainfall and temperature. Sand fly (SF) vector abundance, on average, decreased during the hot and cold ENSO phases, when compared with the normal ENSO phase, yet variability in vector abundance was largest during the cold ENSO phase. Our results showed a three month lagged association between SF vector abundance and CL cases. Conclusion Association patterns of CL with ENSO and local climatic factors in Panamá indicate that interannual CL cycles might be driven by ENSO, while the CL seasonality was mainly associated with temperature and rainfall variability. CL cases and SF abundance were associated in a fashion suggesting that sudden extraordinary changes in vector abundance might increase the potential for CL epidemic outbreaks, given that CL epidemics occur during the cold ENSO phase, a time when SF abundance shows its highest fluctuations. PMID:25275503

Novel SHM method to locate damages in substructures based on VARX models

NASA Astrophysics Data System (ADS)

Ugalde, U.; Anduaga, J.; Martínez, F.; Iturrospe, A.

2015-07-01

A novel damage localization method is proposed, which is based on a substructuring approach and makes use of Vector Auto-Regressive with eXogenous input (VARX) models. The substructuring approach aims to divide the monitored structure into several multi-DOF isolated substructures. Later, each individual substructure is modelled as a VARX model, and the health of each substructure is determined analyzing the variation of the VARX model. The method allows to detect whether the isolated substructure is damaged, and besides allows to locate and quantify the damage within the substructure. It is not necessary to have a theoretical model of the structure and only the measured displacement data is required to estimate the isolated substructure's VARX model. The proposed method is validated by simulations of a two-dimensional lattice structure.

Primed for death: Law enforcement-citizen homicides, social media, and retaliatory violence.

PubMed

Bejan, Vladimir; Hickman, Matthew; Parkin, William S; Pozo, Veronica F

2018-01-01

We examine whether retaliatory violence exists between law enforcement and citizens while controlling for any social media contagion effect related to prior fatal encounters. Analyzed using a trivariate dynamic structural vector-autoregressive model, daily time-series data over a 21-month period captured the frequencies of police killed in the line of duty, police deadly use of force incidents, and social media coverage. The results support a significant retaliatory violence effect against minorities by police, yet there is no evidence of retaliatory violence against law enforcement officers by minorities. Also, social media coverage of the Black Lives Matter movement increases the risk of fatal victimization to both law enforcement officers and minorities. Possible explanations for these results are based in rational choice and terror management theories.

Causal Relationships among Technology Acquisition, Absorptive Capacity, and Innovation Performance: Evidence from the Pharmaceutical Industry.

PubMed

Jeon, Jieun; Hong, Suckchul; Ohm, Jay; Yang, Taeyong

2015-01-01

This paper discusses the importance of absorptive capacity in improving a firm's innovation performance. Specifically, we examine firm interaction with the knowledge and capabilities of outside organizations and the effect on the firm's bottom line. We use the impulse-response function of the vector auto-regressive model to gain insight into this relationship by estimating the time required for the effect of each activity level to reach outputs, the spillover effects. We apply this methodology to pharmaceutical firms, which we classify into two sub-groups--large firms and medium and small firms--based on sales. Our results show that the impact of an activity on any other activity is delayed by three years for large firms and by one to two years for small and medium firms.

Primed for death: Law enforcement-citizen homicides, social media, and retaliatory violence

PubMed Central

Bejan, Vladimir; Hickman, Matthew; Pozo, Veronica F.

2018-01-01

We examine whether retaliatory violence exists between law enforcement and citizens while controlling for any social media contagion effect related to prior fatal encounters. Analyzed using a trivariate dynamic structural vector-autoregressive model, daily time-series data over a 21-month period captured the frequencies of police killed in the line of duty, police deadly use of force incidents, and social media coverage. The results support a significant retaliatory violence effect against minorities by police, yet there is no evidence of retaliatory violence against law enforcement officers by minorities. Also, social media coverage of the Black Lives Matter movement increases the risk of fatal victimization to both law enforcement officers and minorities. Possible explanations for these results are based in rational choice and terror management theories. PMID:29320548

Chromosome numbers of populations of three varieties of Bidens pilosa in Taiwan.

PubMed

Huang, Ya-Lun; Kao, Wen-Yuan

2015-12-01

Hairy beggar-ticks (Bidens pilosa L.) is a common invasive plant in tropical and subtropical regions. The Flora of Taiwan listed three varieties of B. pilosa in Taiwan, var. minor, var. pilosa and var. radiata. Among the three varieties, var. radiata was the most recently, in 1970s, introduced into Taiwan. However, after its introduction into Taiwan, var. radiata has become dominant over the other two varieties and is considered a serious invasive plant in lowland of Taiwan. Our previous study showed that var. radiata is self-incompatible and the other two varieties are self-fertile. Could it be possible that different chromosome numbers contribute to the different breeding systems of these three varieties? In addition, the heterogeneities of traits of var. radiata were found higher than those of var. minor and var. pilosa. Is the phenomenon resulting from the hybridization between var. radiata with other varieties? We counted chromosome numbers of populations of these three varieties distributed in Taiwan and conducted hand pollination treatment between var. radiata (as pollen receiver) and var. minor or var. pilosa (as pollen donor) to provide answer for the aforementioned questions. No difference was found in chromosome numbers among populations of the same variety. Forty-eight chromosomes (2n = 48) were counted for var. radiata while 72 (2n = 72) chromosomes for var. minor and var. pilosa. Therefore, var. radiata is tetraploid and var. minor and var. pilosa are hexaploid. No successful hybridization was found between var. radiata and var. minor or between var. radiata and var. pilosa. This study provided the evidence that the invasive plant (B. pilosa var. radiata) has different chromosome numbers from the other two varieties and is unlikely to hybridize with the other two varieties.

The effect of case management and vector-control interventions on space-time patterns of malaria incidence in Uganda.

PubMed

Ssempiira, Julius; Kissa, John; Nambuusi, Betty; Kyozira, Carol; Rutazaana, Damian; Mukooyo, Eddie; Opigo, Jimmy; Makumbi, Fredrick; Kasasa, Simon; Vounatsou, Penelope

2018-04-12

Electronic reporting of routine health facility data in Uganda began with the adoption of the District Health Information Software System version 2 (DHIS2) in 2011. This has improved health facility reporting and overall data quality. In this study, the effects of case management with artemisinin-based combination therapy (ACT) and vector control interventions on space-time patterns of disease incidence were determined using DHIS2 data reported during 2013-2016. Bayesian spatio-temporal negative binomial models were fitted on district-aggregated monthly malaria cases, reported by two age groups, defined by a cut-off age of 5 years. The effects of interventions were adjusted for socio-economic and climatic factors. Spatial and temporal correlations were taken into account by assuming a conditional autoregressive and a first-order autoregressive AR(1) process on district and monthly specific random effects, respectively. Fourier trigonometric functions were incorporated in the models to take into account seasonal fluctuations in malaria transmission. The temporal variation in incidence was similar in both age groups and depicted a steady decline up to February 2014, followed by an increase from March 2015 onwards. The trends were characterized by a strong bi-annual seasonal pattern with two peaks during May-July and September-December. Average monthly incidence in children < 5 years declined from 74.7 cases (95% CI 72.4-77.1) in 2013 to 49.4 (95% CI 42.9-55.8) per 1000 in 2015 and followed by an increase in 2016 of up to 51.3 (95% CI 42.9-55.8). In individuals ≥ 5 years, a decline in incidence from 2013 to 2015 was followed by an increase in 2016. A 100% increase in insecticide-treated nets (ITN) coverage was associated with a decline in incidence by 44% (95% BCI 28-59%). Similarly, a 100% increase in ACT coverage reduces incidence by 28% (95% BCI 11-45%) and 25% (95% BCI 20-28%) in children < 5 years and individuals ≥ 5 years, respectively. The ITN effect was not statistically important in older individuals. The space-time patterns of malaria incidence in children < 5 are similar to those of parasitaemia risk predicted from the malaria indicator survey of 2014-15. The decline in malaria incidence highlights the effectiveness of vector-control interventions and case management with ACT in Uganda. This calls for optimizing and sustaining interventions to achieve universal coverage and curb reverses in malaria decline.

To center or not to center? Investigating inertia with a multilevel autoregressive model.

PubMed

Hamaker, Ellen L; Grasman, Raoul P P P

2014-01-01

Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion), cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship). This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction), cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model.

To center or not to center? Investigating inertia with a multilevel autoregressive model

PubMed Central

Hamaker, Ellen L.; Grasman, Raoul P. P. P.

2015-01-01

Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion), cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship). This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction), cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model. PMID:25688215

Biofilm monitoring using complex permittivity.

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

Altman, Susan Jeanne; McGrath, Lucas K.; Dolan, Patricia L.

2008-10-01

There is strong interest in the detection and monitoring of bio-fouling. Bio-fouling problems are common in numerous water treatments systems, medical and dental apparatus and food processing equipment. Current bio-fouling control protocols are time consuming and costly. New early detection techniques to monitor bio-forming contaminates are means to enhanced efficiency. Understanding the unique dielectric properties of biofilm development, colony forming bacteria and nutrient background will provide a basis to the effectiveness of controlling or preventing biofilm growth. Dielectric spectroscopy measurements provide values of complex permittivity, {var_epsilon}*, of biofilm formation by applying a weak alternating electric field at various frequencies. Themore » dielectric characteristic of the biofilm, {var_epsilon}{prime}, is the real component of {var_epsilon}* and measures the biofilm's unique ability to store energy. Graphically observed dependencies of {var_epsilon}{prime} to frequency indicate dielectric relaxation or dielectric dispersion behaviors that mark the particular stage of progression during the development of biofilms. In contrast, any frequency dependency of the imaginary component, {var_epsilon}{double_prime} the loss factor, is expressed as dielectric losses from the biofilm due to dipole relaxation. The tangent angle of these two component vectors is the ratio of the imaginary component to the real component, {var_epsilon}{double_prime}/{var_epsilon}{prime} and is referred to as the loss angle tangent (tan {delta}) or dielectric loss. Changes in tan {delta} are characteristic of changes in dielectric losses during various developmental stages of the films. Permittivity scans in the above figure are of biofilm growth from P. Fluorescens (10e7 CFU's at the start). Three trends are apparent from these scans, the first being a small drop in the imaginary permittivity over a 7 hours period, best seen in the Cole-Cole plot (a). The second trend is observed two hours after inoculation when the permittivity begins to increase slightly over the next 20 hours, best seen in the shift from 1000 Hz to 5000 Hz in tan {delta} at the high frequencies (c). Because of similar dielectric relaxation properties noted by the comparable size of the semicircles, plot (a), and the height of tan {delta}, plot (c), within the first 29 hours, cell activity levels did not appreciably change. The third trend is observed when the complex permittivity value drops by orders of magnitude between 29 hours and 37 hours, best seen in the log [E] plot (b), and in the drop of the dielectric loss, tan {delta}, to 0. This change in the dielectric properties in the bio environment is nearly independent of all frequencies (c) and dissimilar from the original condition when only bacteria and nutrient was present in the biofilm chambers. The semicircles in plot (a) for this period decreased below the resolution of the graph, implying a large difference in the dielectric behavior of the cells/biofilms consisting of low dielectric losses. We believe these large changes are related to the on-set of biofilms.« less

A Novel Signal Modeling Approach for Classification of Seizure and Seizure-Free EEG Signals.

PubMed

Gupta, Anubha; Singh, Pushpendra; Karlekar, Mandar

2018-05-01

This paper presents a signal modeling-based new methodology of automatic seizure detection in EEG signals. The proposed method consists of three stages. First, a multirate filterbank structure is proposed that is constructed using the basis vectors of discrete cosine transform. The proposed filterbank decomposes EEG signals into its respective brain rhythms: delta, theta, alpha, beta, and gamma. Second, these brain rhythms are statistically modeled with the class of self-similar Gaussian random processes, namely, fractional Brownian motion and fractional Gaussian noises. The statistics of these processes are modeled using a single parameter called the Hurst exponent. In the last stage, the value of Hurst exponent and autoregressive moving average parameters are used as features to design a binary support vector machine classifier to classify pre-ictal, inter-ictal (epileptic with seizure free interval), and ictal (seizure) EEG segments. The performance of the classifier is assessed via extensive analysis on two widely used data set and is observed to provide good accuracy on both the data set. Thus, this paper proposes a novel signal model for EEG data that best captures the attributes of these signals and hence, allows to boost the classification accuracy of seizure and seizure-free epochs.

How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information.

PubMed

Tuarob, Suppawong; Tucker, Conrad S; Kumara, Soundar; Giles, C Lee; Pincus, Aaron L; Conroy, David E; Ram, Nilam

2017-04-01

It is believed that anomalous mental states such as stress and anxiety not only cause suffering for the individuals, but also lead to tragedies in some extreme cases. The ability to predict the mental state of an individual at both current and future time periods could prove critical to healthcare practitioners. Currently, the practical way to predict an individual's mental state is through mental examinations that involve psychological experts performing the evaluations. However, such methods can be time and resource consuming, mitigating their broad applicability to a wide population. Furthermore, some individuals may also be unaware of their mental states or may feel uncomfortable to express themselves during the evaluations. Hence, their anomalous mental states could remain undetected for a prolonged period of time. The objective of this work is to demonstrate the ability of using advanced machine learning based approaches to generate mathematical models that predict current and future mental states of an individual. The problem of mental state prediction is transformed into the time series forecasting problem, where an individual is represented as a multivariate time series stream of monitored physical and behavioral attributes. A personalized mathematical model is then automatically generated to capture the dependencies among these attributes, which is used for prediction of mental states for each individual. In particular, we first illustrate the drawbacks of traditional multivariate time series forecasting methodologies such as vector autoregression. Then, we show that such issues could be mitigated by using machine learning regression techniques which are modified for capturing temporal dependencies in time series data. A case study using the data from 150 human participants illustrates that the proposed machine learning based forecasting methods are more suitable for high-dimensional psychological data than the traditional vector autoregressive model in terms of both magnitude of error and directional accuracy. These results not only present a successful usage of machine learning techniques in psychological studies, but also serve as a building block for multiple medical applications that could rely on an automated system to gauge individuals' mental states. Copyright © 2017 Elsevier Inc. All rights reserved.

A conditional Granger causality model approach for group analysis in functional MRI

PubMed Central

Zhou, Zhenyu; Wang, Xunheng; Klahr, Nelson J.; Liu, Wei; Arias, Diana; Liu, Hongzhi; von Deneen, Karen M.; Wen, Ying; Lu, Zuhong; Xu, Dongrong; Liu, Yijun

2011-01-01

Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed for identifying effective connectivity in the human brain with functional MR imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pairwise GCM has commonly been applied based on single voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of an fMRI data with GCM. To compare the effectiveness of our approach with traditional pairwise GCM models, we applied a well-established conditional GCM to pre-selected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis (ICA) of an fMRI dataset in the temporal domain. Datasets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM detected brain activation regions in the emotion related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state dataset, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network (DMN) that can be characterized as both afferent and efferent influences on the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive (MVAR) model can achieve greater accuracy in detecting network connectivity than the widely used pairwise GCM, and this group analysis methodology can be quite useful to extend the information obtainable in fMRI. PMID:21232892

Use of a Weather Generator for analysis of projections of future daily temperature and its validation with climate change indices

NASA Astrophysics Data System (ADS)

Di Piazza, A.; Cordano, E.; Eccel, E.

2012-04-01

The issue of climate change detection is considered a major challenge. In particular, high temporal resolution climate change scenarios are required in the evaluation of the effects of climate change on agricultural management (crop suitability, yields, risk assessment, etc.) energy production and water management. In this work, a "Weather Generator" technique was used for downscaling climate change scenarios for temperature. An R package (RMAWGEN, Cordano and Eccel, 2011 - available on http://cran.r-project.org) was developed aiming to generate synthetic daily weather conditions by using the theory of vectorial auto-regressive models (VAR). The VAR model was chosen for its ability in maintaining the temporal and spatial correlations among variables. In particular, observed time series of daily maximum and minimum temperature are transformed into "new" normally-distributed variable time series which are used to calibrate the parameters of a VAR model by using ordinary least square methods. Therefore the implemented algorithm, applied to monthly mean climatic values downscaled by Global Climate Model predictions, can generate several stochastic daily scenarios where the statistical consistency among series is saved. Further details are present in RMAWGEN documentation. An application is presented here by using a dataset with daily temperature time series recorded in 41 different sites of Trentino region for the period 1958-2010. Temperature time series were pre-processed to fill missing values (by a site-specific calibrated Inverse Distance Weighting algorithm, corrected with elevation) and to remove inhomogeneities. Several climatic indices were taken into account, useful for several impact assessment applications, and their time trends within the time series were analyzed. The indices go from the more classical ones, as annual mean temperatures, seasonal mean temperatures and their anomalies (from the reference period 1961-1990) to the climate change indices selected from the list recommended by the World Meteorological Organization Commission for Climatology (WMO-CCL) and the Research Programme on Climate Variability and Predictability (CLIVAR) project's Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI). Each index was applied to both observed (and processed) data and to synthetic time series produced by the Weather Generator, over the thirty year reference period 1981-2010, in order to validate the procedure. Climate projections were statistically downscaled for a selection of sites for the two 30-year periods 2021-2050 and 2071-2099 of the European project "Ensembles" multi-model output (scenario A1B). The use of several climatic indices strengthens the trend analysis of both the generated synthetic series and future climate projections.

Asymmetric conditional volatility in international stock markets

NASA Astrophysics Data System (ADS)

Ferreira, Nuno B.; Menezes, Rui; Mendes, Diana A.

2007-08-01

Recent studies show that a negative shock in stock prices will generate more volatility than a positive shock of similar magnitude. The aim of this paper is to appraise the hypothesis under which the conditional mean and the conditional variance of stock returns are asymmetric functions of past information. We compare the results for the Portuguese Stock Market Index PSI 20 with six other Stock Market Indices, namely the SP 500, FTSE 100, DAX 30, CAC 40, ASE 20, and IBEX 35. In order to assess asymmetric volatility we use autoregressive conditional heteroskedasticity specifications known as TARCH and EGARCH. We also test for asymmetry after controlling for the effect of macroeconomic factors on stock market returns using TAR and M-TAR specifications within a VAR framework. Our results show that the conditional variance is an asymmetric function of past innovations raising proportionately more during market declines, a phenomenon known as the leverage effect. However, when we control for the effect of changes in macroeconomic variables, we find no significant evidence of asymmetric behaviour of the stock market returns. There are some signs that the Portuguese Stock Market tends to show somewhat less market efficiency than other markets since the effect of the shocks appear to take a longer time to dissipate.

NEMOTAM: tangent and adjoint models for the ocean modelling platform NEMO

NASA Astrophysics Data System (ADS)

Vidard, A.; Bouttier, P.-A.; Vigilant, F.

2015-04-01

Tangent linear and adjoint models (TAMs) are efficient tools to analyse and to control dynamical systems such as NEMO. They can be involved in a large range of applications such as sensitivity analysis, parameter estimation or the computation of characteristic vectors. A TAM is also required by the 4D-Var algorithm, which is one of the major methods in data assimilation. This paper describes the development and the validation of the tangent linear and adjoint model for the NEMO ocean modelling platform (NEMOTAM). The diagnostic tools that are available alongside NEMOTAM are detailed and discussed, and several applications are also presented.

NEMOTAM: tangent and adjoint models for the ocean modelling platform NEMO

NASA Astrophysics Data System (ADS)

Vidard, A.; Bouttier, P.-A.; Vigilant, F.

2014-10-01

The tangent linear and adjoint model (TAM) are efficient tools to analyse and to control dynamical systems such as NEMO. They can be involved in a large range of applications such as sensitivity analysis, parameter estimation or the computation of characteristics vectors. TAM is also required by the 4-D-VAR algorithm which is one of the major method in Data Assimilation. This paper describes the development and the validation of the Tangent linear and Adjoint Model for the NEMO ocean modelling platform (NEMOTAM). The diagnostic tools that are available alongside NEMOTAM are detailed and discussed and several applications are also presented.

The dynamic relationship between Bursa Malaysia composite index and macroeconomic variables

NASA Astrophysics Data System (ADS)

Ismail, Mohd Tahir; Rose, Farid Zamani Che; Rahman, Rosmanjawati Abd.

2017-08-01

This study investigates and analyzes the long run and short run relationships between Bursa Malaysia Composite index (KLCI) and nine macroeconomic variables in a VAR/VECM framework. After regression analysis seven out the nine macroeconomic variables are chosen for further analysis. The use of Johansen-Juselius Cointegration and Vector Error Correction Model (VECM) technique indicate that there are long run relationships between the seven macroeconomic variables and KLCI. Meanwhile, Granger causality test shows that bidirectional relationship between KLCI and oil price. Furthermore, after 12 months the shock on KLCI are explained by innovations of the seven macroeconomic variables. This indicate the close relationship between macroeconomic variables and KLCI.

Impact of spatio-temporal scale of adjustment on variational assimilation of hydrologic and hydrometeorological data in operational distributed hydrologic models

NASA Astrophysics Data System (ADS)

Lee, H.; Seo, D.; McKee, P.; Corby, R.

2009-12-01

One of the large challenges in data assimilation (DA) into distributed hydrologic models is to reduce the large degrees of freedom involved in the inverse problem to avoid overfitting. To assess the sensitivity of the performance of DA to the dimensionality of the inverse problem, we design and carry out real-world experiments in which the control vector in variational DA (VAR) is solved at different scales in space and time, e.g., lumped, semi-distributed, and fully-distributed in space, and hourly, 6 hourly, etc., in time. The size of the control vector is related to the degrees of freedom in the inverse problem. For the assessment, we use the prototype 4-dimenational variational data assimilator (4DVAR) that assimilates streamflow, precipitation and potential evaporation data into the NWS Hydrology Laboratory’s Research Distributed Hydrologic Model (HL-RDHM). In this talk, we present the initial results for a number of basins in Oklahoma and Texas.

Transfer Entropy as a Log-Likelihood Ratio

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Bossomaier, Terry

2012-09-01

Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.

Transfer entropy as a log-likelihood ratio.

PubMed

Barnett, Lionel; Bossomaier, Terry

2012-09-28

Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.

Real Time Data Management for Estimating Probabilities of Incidents and Near Misses

NASA Astrophysics Data System (ADS)

Stanitsas, P. D.; Stephanedes, Y. J.

2011-08-01

Advances in real-time data collection, data storage and computational systems have led to development of algorithms for transport administrators and engineers that improve traffic safety and reduce cost of road operations. Despite these advances, problems in effectively integrating real-time data acquisition, processing, modelling and road-use strategies at complex intersections and motorways remain. These are related to increasing system performance in identification, analysis, detection and prediction of traffic state in real time. This research develops dynamic models to estimate the probability of road incidents, such as crashes and conflicts, and incident-prone conditions based on real-time data. The models support integration of anticipatory information and fee-based road use strategies in traveller information and management. Development includes macroscopic/microscopic probabilistic models, neural networks, and vector autoregressions tested via machine vision at EU and US sites.

Retrieving Tract Variables From Acoustics: A Comparison of Different Machine Learning Strategies.

PubMed

Mitra, Vikramjit; Nam, Hosung; Espy-Wilson, Carol Y; Saltzman, Elliot; Goldstein, Louis

2010-09-13

Many different studies have claimed that articulatory information can be used to improve the performance of automatic speech recognition systems. Unfortunately, such articulatory information is not readily available in typical speaker-listener situations. Consequently, such information has to be estimated from the acoustic signal in a process which is usually termed "speech-inversion." This study aims to propose and compare various machine learning strategies for speech inversion: Trajectory mixture density networks (TMDNs), feedforward artificial neural networks (FF-ANN), support vector regression (SVR), autoregressive artificial neural network (AR-ANN), and distal supervised learning (DSL). Further, using a database generated by the Haskins Laboratories speech production model, we test the claim that information regarding constrictions produced by the distinct organs of the vocal tract (vocal tract variables) is superior to flesh-point information (articulatory pellet trajectories) for the inversion process.

Comparison of ANN and SVM for classification of eye movements in EOG signals

NASA Astrophysics Data System (ADS)

Qi, Lim Jia; Alias, Norma

2018-03-01

Nowadays, electrooculogram is regarded as one of the most important biomedical signal in measuring and analyzing eye movement patterns. Thus, it is helpful in designing EOG-based Human Computer Interface (HCI). In this research, electrooculography (EOG) data was obtained from five volunteers. The (EOG) data was then preprocessed before feature extraction methods were employed to further reduce the dimensionality of data. Three feature extraction approaches were put forward, namely statistical parameters, autoregressive (AR) coefficients using Burg method, and power spectral density (PSD) using Yule-Walker method. These features would then become input to both artificial neural network (ANN) and support vector machine (SVM). The performance of the combination of different feature extraction methods and classifiers was presented and analyzed. It was found that statistical parameters + SVM achieved the highest classification accuracy of 69.75%.

Analysis of the Westland Data Set

NASA Technical Reports Server (NTRS)

Wen, Fang; Willett, Peter; Deb, Somnath

2001-01-01

The "Westland" set of empirical accelerometer helicopter data with seeded and labeled faults is analyzed with the aim of condition monitoring. The autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; and it has also been found that augmentation of these by harmonic and other parameters call improve classification significantly. Several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior oil training data and is thus able to quantify probability of error in all exact manner, such that features may be discarded or coarsened appropriately.

Causal Relationships among Technology Acquisition, Absorptive Capacity, and Innovation Performance: Evidence from the Pharmaceutical Industry

PubMed Central

Jeon, Jieun; Hong, Suckchul; Ohm, Jay; Yang, Taeyong

2015-01-01

This paper discusses the importance of absorptive capacity in improving a firm’s innovation performance. Specifically, we examine firm interaction with the knowledge and capabilities of outside organizations and the effect on the firm’s bottom line. We use the impulse-response function of the vector auto-regressive model to gain insight into this relationship by estimating the time required for the effect of each activity level to reach outputs, the spillover effects. We apply this methodology to pharmaceutical firms, which we classify into two sub-groups – large firms and medium and small firms – based on sales. Our results show that the impact of an activity on any other activity is delayed by three years for large firms and by one to two years for small and medium firms. PMID:26181440

Thermal signature identification system (TheSIS): a spread spectrum temperature cycling method

NASA Astrophysics Data System (ADS)

Merritt, Scott

2015-03-01

NASA GSFC's Thermal Signature Identification System (TheSIS) 1) measures the high order dynamic responses of optoelectronic components to direct sequence spread-spectrum temperature cycling, 2) estimates the parameters of multiple autoregressive moving average (ARMA) or other models the of the responses, 3) and selects the most appropriate model using the Akaike Information Criterion (AIC). Using the AIC-tested model and parameter vectors from TheSIS, one can 1) select high-performing components on a multivariate basis, i.e., with multivariate Figures of Merit (FOMs), 2) detect subtle reversible shifts in performance, and 3) investigate irreversible changes in component or subsystem performance, e.g. aging. We show examples of the TheSIS methodology for passive and active components and systems, e.g. fiber Bragg gratings (FBGs) and DFB lasers with coupled temperature control loops, respectively.

An Evaluation of Voluntary Varicella Vaccination Coverage in Zhejiang Province, East China.

PubMed

Hu, Yu; Chen, Yaping; Zhang, Bing; Li, Qian

2016-06-03

In 2014 a 2-doses varicella vaccine (VarV) schedule was recommended by the Zhejiang Provincial Center for Disease Control and Prevention. We aimed to assess the coverage of the 1st dose of VarV (VarV₁) and the 2nd dose of VarV (VarV₂) among children aged 2-6 years through the Zhejiang Provincial Immunization Information System (ZJIIS) and to explore the determinants associated with the VarV coverage. Children aged 2-6 years (born from 1 January 2009 to 31 December 2013) registered in ZJIIS were enrolled. Anonymized individual records of target children were extracted from the ZJIIS database on 1 January 2016, including their VarV and (measles-containing vaccine) MCV vaccination information. The VarV₁ and VarV₂ coverage rates were evaluated for each birth cohorts. The coverage of VarV also was estimated among strata defined by cities, gender and immigration status. We also evaluated the difference in coverage between VarV and MCV. A total of 3,028,222 children aged 2-6 years were enrolled. The coverage of VarV₁ ranged from 84.8% to 87.9% in the 2009-2013 birth cohorts, while the coverage of VarV₂ increased from 31.8% for the 2009 birth cohort to 48.7% for the 2011 birth cohort. Higher coverage rates for both VarV₁ and VarV₂ were observed among resident children in relevant birth cohorts. The coverage rates of VarV₁ and VarV₂ were lower than those for the 1st and 2nd dose of MCV, which were above 95%. The proportion of children who were vaccinated with VarV₁ at the recommended age increased from 34.6% for the 2009 birth cohort to 75.2% for the 2013 birth cohort, while the proportion of children who were vaccinated with VarV₂ at the recommended age increased from 19.7% for the 2009 birth cohort to 48.7% for the 2011 birth cohort. Our study showed a rapid increasing VarV₂ coverage of children, indicating a growing acceptance of the 2-doses VarV schedule among children's caregivers and physicians after the new recommendation released. We highlighted the necessity for a 2-doses VarV vaccination school-entry requirement to achieve the high coverage of >90% and to eliminate disparities in coverage among sub-populations. We also recommended continuous monitoring of the VarV coverage via ZJIIS over time.

The Global Modeling and Assimilation Office (GMAO) 4d-Var and its Adjoint-based Tools

NASA Technical Reports Server (NTRS)

Todling, Ricardo; Tremolet, Yannick

2008-01-01

The fifth generation of the Goddard Earth Observing System (GEOS-5) Data Assimilation System (DAS) is a 3d-var system that uses the Grid-point Statistical Interpolation (GSI) system developed in collaboration with NCEP, and a general circulation model developed at Goddard, that includes the finite-volume hydrodynamics of GEOS-4 wrapped in the Earth System Modeling Framework and physical packages tuned to provide a reliable hydrological cycle for the integration of the Modern Era Retrospective-analysis for Research and Applications (MERRA). This MERRA system is essentially complete and the next generation GEOS is under intense development. A prototype next generation system is now complete and has been producing preliminary results. This prototype system replaces the GSI-based Incremental Analysis Update procedure with a GSI-based 4d-var which uses the adjoint of the finite-volume hydrodynamics of GEOS-4 together with a vertical diffusing scheme for simplified physics. As part of this development we have kept the GEOS-5 IAU procedure as an option and have added the capability to experiment with a First Guess at the Appropriate Time (FGAT) procedure, thus allowing for at least three modes of running the data assimilation experiments. The prototype system is a large extension of GEOS-5 as it also includes various adjoint-based tools, namely, a forecast sensitivity tool, a singular vector tool, and an observation impact tool, that combines the model sensitivity tool with a GSI-based adjoint tool. These features bring the global data assimilation effort at Goddard up to date with technologies used in data assimilation systems at major meteorological centers elsewhere. Various aspects of the next generation GEOS will be discussed during the presentation at the Workshop, and preliminary results will illustrate the discussion.

Cytotaxonomic study of the Chilean endemic complex Alstroemeria magnifica Herb. (Alstroemeriaceae).

PubMed

Baeza, Carlos M; Finot, Víctor; Ruiz, Eduardo; Carrasco, Pedro; Novoa, Patricio; Rosas, Marcelo; Toro-Núñez, Oscar

2018-05-14

Alstroemeria L. (Alstroemeriaceae) represents one of the most diverse genera of vascular plants in Chile. It contains approximately 54 taxa, 40 of which are endemic. The "complex" Alstroemeria magnifica is endemic to Chile, and it comprises four varieties: A. magnifica var. magenta, A. magnifica var. magnifica, A. magnifica var. sierrae, and A. magnifica var. tofoensis. It is distributed from Coquimbo to the Valparaíso Region. We analyzed karyotypes of 10 populations along its natural distribution. All the populations presented an asymmetric karyotype, with 2n = 16 chromosomes but with three different karyotypic formulae. Alstroemeria magnifica var. magnifica and A. magnifica var. sierrae presented the same karyotypic fomula, and A. magnifica var. magenta, and A. magnifica var. tofoensis each had a different formula. The scatter plot among CVCL vs. MCA shows different groupings between populations of the four varieties. Based on the results, it is possible to consider raising Alstroemeria magnifica var. magenta to species level (A. magenta) and A. magnifica var. tofoensis to subspecies level (A. magnifica subsp. tofoensis); A. magnifica var. magnifica and A. magnifica var. sierrae should each remain as varieties. Nevertheless, these taxonomic changes should be considered tentative, as additional sources of evidence become available.

Adaptive spline autoregression threshold method in forecasting Mitsubishi car sales volume at PT Srikandi Diamond Motors

NASA Astrophysics Data System (ADS)

Susanti, D.; Hartini, E.; Permana, A.

2017-01-01

Sale and purchase of the growing competition between companies in Indonesian, make every company should have a proper planning in order to win the competition with other companies. One of the things that can be done to design the plan is to make car sales forecast for the next few periods, it’s required that the amount of inventory of cars that will be sold in proportion to the number of cars needed. While to get the correct forecasting, on of the methods that can be used is the method of Adaptive Spline Threshold Autoregression (ASTAR). Therefore, this time the discussion will focus on the use of Adaptive Spline Threshold Autoregression (ASTAR) method in forecasting the volume of car sales in PT.Srikandi Diamond Motors using time series data.In the discussion of this research, forecasting using the method of forecasting value Adaptive Spline Threshold Autoregression (ASTAR) produce approximately correct.

Classification of EEG signals using a genetic-based machine learning classifier.

PubMed

Skinner, B T; Nguyen, H T; Liu, D K

2007-01-01

This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices.

Tourism demand in the Algarve region: Evolution and forecast using SVARMA models

NASA Astrophysics Data System (ADS)

Lopes, Isabel Cristina; Soares, Filomena; Silva, Eliana Costa e.

2017-06-01

Tourism is one of the Portuguese economy's key sectors, and its relative weight has grown over recent years. The Algarve region is particularly focused on attracting foreign tourists and has built over the years a large offer of diversified hotel units. In this paper we present multivariate time series approach to forecast the number of overnight stays in hotel units (hotels, guesthouses or hostels, and tourist apartments) in Algarve. We adjust a seasonal vector autoregressive and moving averages model (SVARMA) to monthly data between 2006 and 2016. The forecast values were compared with the actual values of the overnight stays in Algarve in 2016 and led to a MAPE of 15.1% and RMSE= 53847.28. The MAPE for the Hotel series was merely 4.56%. These forecast values can be used by a hotel manager to predict their occupancy and to determine the best pricing policy.

Plausibility assessment of a 2-state self-paced mental task-based BCI using the no-control performance analysis.

PubMed

Faradji, Farhad; Ward, Rabab K; Birch, Gary E

2009-06-15

The feasibility of having a self-paced brain-computer interface (BCI) based on mental tasks is investigated. The EEG signals of four subjects performing five mental tasks each are used in the design of a 2-state self-paced BCI. The output of the BCI should only be activated when the subject performs a specific mental task and should remain inactive otherwise. For each subject and each task, the feature coefficient and the classifier that yield the best performance are selected, using the autoregressive coefficients as the features. The classifier with a zero false positive rate and the highest true positive rate is selected as the best classifier. The classifiers tested include: linear discriminant analysis, quadratic discriminant analysis, Mahalanobis discriminant analysis, support vector machine, and radial basis function neural network. The results show that: (1) some classifiers obtained the desired zero false positive rate; (2) the linear discriminant analysis classifier does not yield acceptable performance; (3) the quadratic discriminant analysis classifier outperforms the Mahalanobis discriminant analysis classifier and performs almost as well as the radial basis function neural network; and (4) the support vector machine classifier has the highest true positive rates but unfortunately has nonzero false positive rates in most cases.

Surgery

MedlinePlus

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Understanding Pneumonia

MedlinePlus

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Oxygen Therapy

MedlinePlus

... each(function(i){ var city = $(this).find('city').text(); var state = $(this).find('state').text(); var date = $(this).find('date').text(); if ((city != "") && (state != "")){ var citystate = ' | ' + city + ', ' + state; } else ...

Heterologous expression of Fusarium oxysporum tomatinase in Saccharomyces cerevisiae increases its resistance to saponins and improves ethanol production during the fermentation of Agave tequilana Weber var. azul and Agave salmiana must.

PubMed

Cira, Luis Alberto; González, Gloria Angélica; Torres, Juan Carlos; Pelayo, Carlos; Gutiérrez, Melesio; Ramírez, Jesús

2008-03-01

This paper describes the effect of the heterologous expression of tomatinase from Fusarium oxysporum f. sp lycopersici in Saccharomyces cerevisiae. The gene FoTom1 under the control of the S. cerevisiae phosphoglycerate kinase (PGK1) promoter was cloned into pYES2. S. cerevisiae strain Y45 was transformed with this vector and URA3 transformant strains were selected for resistance to alpha-tomatine. Two transformants were randomly selected for further study (designated Y45-1 and Y45-2). Control strain Y45 was inhibited at 50 muM alpha-tomatine, in contrast, transformants Y45-1 and Y45-2 did not show inhibition at 200 muM. Tomatinase activity was detected by HPLC monitoring tomatine disappearance and tomatidine appearance in the supernatants of culture medium. Maximum tomatinase activity was observed in the transformants after 6 h, remaining constant during the following 24 h. No tomatinase activity was detected in the parental strain. Moreover, the transformants were able to grow and produce ethanol in a mix of Agave tequilana Weber var. azul and Agave salmiana must, contrary to the Y45 strain which was unable to grow and ferment under these conditions.

Agrobacterium tumefaciens-mediated transformation of Narcissus tazzeta var. chinensis.

PubMed

Lu, Gang; Zou, Qingcheng; Guo, Deping; Zhuang, Xiaoying; Yu, Xiaolin; Xiang, Xun; Cao, Jiashu

2007-09-01

Phytoene synthase (PSY), as a key regulatory enzyme for carotene biosynthesis, plays an important role in regulating color formation in many species. In the present study, a protocol was developed for the transformation of Narcissus tazzeta var chinensis using Agrobacterium tumefaciens strain LBA4404 harboring a binary vector pCAMBIA1301 plasmid which contained an antisense phytoene synthase gene, a reporter beta-glucuronidase gene and a selectable marker hygromycin phosphotransferase gene. Effects of some factors on efficiency of transformation and regeneration were examined. Preculture of the explants for 6 days before inoculation enhanced the transient GUS expression. The addition of acetosyringone (AS) at 100 micromol l(-1) for inoculation and a period of 3 days co-cultivation yielded efficient transient GUS expression. Transformants were obtained through selection on MS medium containing 5 mg l(-1) 6-benzylaminopurine (BA), 0.1 mg l(-1)alpha-naphthalene acetic acid (NAA) and 40 mg l(-1) hygromycin. The transformation frequency was 1.24% based on PCR analysis of gus gene. One or two copies of transgene were demonstrated in different transformations by Southern blotting analyses. Northern blotting results confirmed that the transcription of the endogenous psy gene in transgenic plants was inhibited or silenced. The method reported here provides new opportunities for improvement of quality traits of Narcissus tazzeta via genetic transformation.

Cloning and expression of 130-kd mosquito-larvicidal delta-endotoxin gene of Bacillus thuringiensis var. Israelensis in Escherichia coli.

PubMed

Angsuthanasombat, C; Chungjatupornchai, W; Kertbundit, S; Luxananil, P; Settasatian, C; Wilairat, P; Panyim, S

1987-07-01

Five recombinant E. coli clones exhibiting toxicity to Aedes aegypti larvae were obtained from a library of 800 clones containing XbaI DNA fragments of 110 kb plasmid from B. thuringiensis var. israelensis. All the five clones (pMU 14/258/303/388/679) had the same 3.8-kb insert and encoded a major protein of 130 kDa which was highly toxic to A. aegypti larvae. Three clones (pMU 258/303/388) transcribed the 130 kD a gene in the same direction as that of lac Z promoter of pUC12 vector whereas the transcription of the other two (pMU 14/679) was in the opposite direction. A 1.9-kb fragment of the 3.8 kb insert coded for a protein of 65 kDa. Partial DNA sequence of the 3.8 kb insert, corresponding to the 5'-terminal of the 130 kDa gene, revealed a continuous reading frame, a Shine-Dalgarno sequence and a tentative 5'-regulatory region. These results demonstrated that the 3.8 kb insert is a minimal DNA fragment containing a regulatory region plus the coding sequence of the 130 kDa protein that is highly toxic to mosquito larvae.

Inhibition of initial adhesion of oral bacteria through a lectin from Bauhinia variegata L. var. variegata expressed in Escherichia coli.

PubMed

Klafke, G B; Borsuk, S; Gonçales, R A; Arruda, F V S; Carneiro, V A; Teixeira, E H; Coelho da Silva, A L; Cavada, B S; Dellagostin, O A; Pinto, L S

2013-11-01

The aim of the present work was to study the in vitro effect of native and recombinant Bauhinia variegata var. variegata lectins in inhibiting early adhesion of Streptococcus mutans, Streptococcus sanguis and Streptococcus sobrinus to experimentally acquired pellicle. Native lectin from B. variegata (BVL) was purified by affinity chromatography of extract of seeds. The recombinant lectin (rBVL-I) was expressed in E. coli strain BL21 (DE3) from a genomic clone encoding the mature B. variegata lectin gene using the vector pAE-bvlI. Recombinant protein deposited in inclusion bodies was solubilized and subsequently purified by affinity chromatography. The rBVL-I was compared to BVL for agglutination of erythrocytes and initial adherence of oral bacteria on a saliva-coated surface. The results revealed that rBVL-I acts similarly to BVL for agglutination of erythrocytes. Both lectins showed adhesion inhibition effect on Step. sanguis, Step. mutans and Step. sobrinus. We report, for the first time, the inhibition of early adhesion of oral bacteria by a recombinant lectin. Our results support the proposed biotechnological application of lectins in a strategy to reduce development of dental caries by inhibiting the initial adhesion and biofilm formation. © 2013 The Society for Applied Microbiology.

Beyond long memory in heart rate variability: An approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity

NASA Astrophysics Data System (ADS)

Leite, Argentina; Paula Rocha, Ana; Eduarda Silva, Maria

2013-06-01

Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. The ARFIMA-GARCH approach is applied to fifteen long term HRV series available at Physionet, leading to the discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation.

Forecasting coconut production in the Philippines with ARIMA model

NASA Astrophysics Data System (ADS)

Lim, Cristina Teresa

2015-02-01

The study aimed to depict the situation of the coconut industry in the Philippines for the future years applying Autoregressive Integrated Moving Average (ARIMA) method. Data on coconut production, one of the major industrial crops of the country, for the period of 1990 to 2012 were analyzed using time-series methods. Autocorrelation (ACF) and partial autocorrelation functions (PACF) were calculated for the data. Appropriate Box-Jenkins autoregressive moving average model was fitted. Validity of the model was tested using standard statistical techniques. The forecasting power of autoregressive moving average (ARMA) model was used to forecast coconut production for the eight leading years.

Living with Tuberculosis

MedlinePlus

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Learn About Sarcoidosis

MedlinePlus

... each(function(i){ var city = $(this).find('city').text(); var state = $(this).find('state').text(); var date = $(this).find('date').text(); if ((city != "") && (state != "")){ var citystate = ' | ' + city + ', ' + state; } else ...

How Lungs Work

MedlinePlus

... each(function(i){ var city = $(this).find('city').text(); var state = $(this).find('state').text(); var date = $(this).find('date').text(); if ((city != "") && (state != "")){ var citystate = ' | ' + city + ', ' + state; } else ...

Understand Your Medication

MedlinePlus

... each(function(i){ var city = $(this).find('city').text(); var state = $(this).find('state').text(); var date = $(this).find('date').text(); if ((city != "") && (state != "")){ var citystate = ' | ' + city + ', ' + state; } else ...

Learn About Silicosis

MedlinePlus

... each(function(i){ var city = $(this).find('city').text(); var state = $(this).find('state').text(); var date = $(this).find('date').text(); if ((city != "") && (state != "")){ var citystate = ' | ' + city + ', ' + state; } else ...

Learn About Tuberculosis

MedlinePlus

... each(function(i){ var city = $(this).find('city').text(); var state = $(this).find('state').text(); var date = $(this).find('date').text(); if ((city != "") && (state != "")){ var citystate = ' | ' + city + ', ' + state; } else ...

How to compare cross-lagged associations in a multilevel autoregressive model.

PubMed

Schuurman, Noémi K; Ferrer, Emilio; de Boer-Sonnenschein, Mieke; Hamaker, Ellen L

2016-06-01

By modeling variables over time it is possible to investigate the Granger-causal cross-lagged associations between variables. By comparing the standardized cross-lagged coefficients, the relative strength of these associations can be evaluated in order to determine important driving forces in the dynamic system. The aim of this study was twofold: first, to illustrate the added value of a multilevel multivariate autoregressive modeling approach for investigating these associations over more traditional techniques; and second, to discuss how the coefficients of the multilevel autoregressive model should be standardized for comparing the strength of the cross-lagged associations. The hierarchical structure of multilevel multivariate autoregressive models complicates standardization, because subject-based statistics or group-based statistics can be used to standardize the coefficients, and each method may result in different conclusions. We argue that in order to make a meaningful comparison of the strength of the cross-lagged associations, the coefficients should be standardized within persons. We further illustrate the bivariate multilevel autoregressive model and the standardization of the coefficients, and we show that disregarding individual differences in dynamics can prove misleading, by means of an empirical example on experienced competence and exhaustion in persons diagnosed with burnout. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Localization of a variational particle smoother

NASA Astrophysics Data System (ADS)

Morzfeld, M.; Hodyss, D.; Poterjoy, J.

2017-12-01

Given the success of 4D-variational methods (4D-Var) in numerical weather prediction,and recent efforts to merge ensemble Kalman filters with 4D-Var,we consider a method to merge particle methods and 4D-Var.This leads us to revisit variational particle smoothers (varPS).We study the collapse of varPS in high-dimensional problemsand show how it can be prevented by weight-localization.We test varPS on the Lorenz'96 model of dimensionsn=40, n=400, and n=2000.In our numerical experiments, weight localization prevents the collapse of the varPS,and we note that the varPS yields results comparable to ensemble formulations of 4D-variational methods,while it outperforms EnKF with tuned localization and inflation,and the localized standard particle filter.Additional numerical experiments suggest that using localized weights in varPS may not yield significant advantages over unweighted or linearizedsolutions in near-Gaussian problems.

Surface fatigue life of M50NiL and AISI 9310 spur gears and R C bars

NASA Technical Reports Server (NTRS)

Townsend, Dennis P.; Bamberger, Eric N.

1991-01-01

Spur gear endurance tests and rolling element surface fatigue tests were conducted to study vacuum induction melted, vacuum arc remelted (VIM-VAR) M50NiL steel for use as a gear steel in advanced aircraft applications, to determine its endurance characteristics, and to compare the results with those for standard VAR and VIM-VAR AISI 9310 gear material. Tests were conducted with spur gears and rolling contact bars manufactured from VIM-VAR M50NiL and VAR and VIM-VAR AISI 9310. The gear pitch diameter was 8.9 cm. Gear test conditions were an inlet oil temperature of 320 K, and outlet oil temperature of 350 K, a maximum Hertz stress of 1.71 GPa, and a speed of 10000 rpm. Bench rolling element fatigue tests were conducted at ambient temperatures with a bar speed of 12,500 rpm and a maximum Hertz stress of 4.83 GPa. The VIM-VAR M50NiL gears had a surface fatigue life that was 4.5 and 11.5 times that for VIM-VAR and VAR AISI 9310 gears, respectively. The surface fatigue life of the VIM-VAR M50NiL rolling contact bars was 13.2 and 21.6 times that for the VIM-VAR and VAR AISI 9310, respectively. The VIM-VAR M50NiL material was shown to have good resistance to fracture through a fatigue spall and superior fatigue life to both other gears.

What's in a Cigarette?

MedlinePlus

... each(function(i){ var city = $(this).find('city').text(); var state = $(this).find('state').text(); var date = $(this).find('date').text(); if ((city != "") && (state != "")){ var citystate = ' | ' + city + ', ' + state; } else ...

Managing Your COPD Medications

MedlinePlus

... each(function(i){ var city = $(this).find('city').text(); var state = $(this).find('state').text(); var date = $(this).find('date').text(); if ((city != "") && (state != "")){ var citystate = ' | ' + city + ', ' + state; } else ...

Facts about the Common Cold

MedlinePlus

... each(function(i){ var city = $(this).find('city').text(); var state = $(this).find('state').text(); var date = $(this).find('date').text(); if ((city != "") && (state != "")){ var citystate = ' | ' + city + ', ' + state; } else ...

Diagnosing and Treating Acute Bronchitis

MedlinePlus

... each(function(i){ var city = $(this).find('city').text(); var state = $(this).find('state').text(); var date = $(this).find('date').text(); if ((city != "") && (state != "")){ var citystate = ' | ' + city + ', ' + state; } else ...

Warning Signs of Lung Disease

MedlinePlus

... each(function(i){ var city = $(this).find('city').text(); var state = $(this).find('state').text(); var date = $(this).find('date').text(); if ((city != "") && (state != "")){ var citystate = ' | ' + city + ', ' + state; } else ...

Learn about Respiratory Syncytial Virus (RSV)

MedlinePlus

... each(function(i){ var city = $(this).find('city').text(); var state = $(this).find('state').text(); var date = $(this).find('date').text(); if ((city != "") && (state != "")){ var citystate = ' | ' + city + ', ' + state; } else ...

Symptoms, Diagnosis and Treatment of Pneumonia

MedlinePlus

... each(function(i){ var city = $(this).find('city').text(); var state = $(this).find('state').text(); var date = $(this).find('date').text(); if ((city != "") && (state != "")){ var citystate = ' | ' + city + ', ' + state; } else ...

75 FR 78932 - Federal Seed Act Regulations

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-17

....'', ``Broccoli-- Brassica oleracea L. var. botrytis L.'', ``Brussels sprouts--Brassica oleracea L. var. gemmifera...--Vicia faba L. var. faba'', ``Broccoli-- Brassica oleracea L. var. italica Plenck'', ``Brussels sprouts...

40 CFR 80.170 - Volumetric additive reconciliation (VAR), equipment calibration, and recordkeeping requirements.

Code of Federal Regulations, 2011 CFR

2011-07-01

..., the preceding twelve months of VAR formula records plus the preceding two months of VAR supporting... request, the preceding two months of VAR formula records and VAR supporting documentation. (3) Facilities... accurately and separately measured, either through the use of a separate storage tank, a separate meter, or...

40 CFR 80.170 - Volumetric additive reconciliation (VAR), equipment calibration, and recordkeeping requirements.

Code of Federal Regulations, 2014 CFR

2014-07-01

..., the preceding twelve months of VAR formula records plus the preceding two months of VAR supporting... request, the preceding two months of VAR formula records and VAR supporting documentation. (3) Facilities... accurately and separately measured, either through the use of a separate storage tank, a separate meter, or...

40 CFR 80.170 - Volumetric additive reconciliation (VAR), equipment calibration, and recordkeeping requirements.

Code of Federal Regulations, 2013 CFR

2013-07-01

..., the preceding twelve months of VAR formula records plus the preceding two months of VAR supporting... request, the preceding two months of VAR formula records and VAR supporting documentation. (3) Facilities... accurately and separately measured, either through the use of a separate storage tank, a separate meter, or...

40 CFR 80.170 - Volumetric additive reconciliation (VAR), equipment calibration, and recordkeeping requirements.

Code of Federal Regulations, 2010 CFR

2010-07-01

..., the preceding twelve months of VAR formula records plus the preceding two months of VAR supporting... request, the preceding two months of VAR formula records and VAR supporting documentation. (3) Facilities... accurately and separately measured, either through the use of a separate storage tank, a separate meter, or...

40 CFR 80.170 - Volumetric additive reconciliation (VAR), equipment calibration, and recordkeeping requirements.

Code of Federal Regulations, 2012 CFR

2012-07-01

..., the preceding twelve months of VAR formula records plus the preceding two months of VAR supporting... request, the preceding two months of VAR formula records and VAR supporting documentation. (3) Facilities... accurately and separately measured, either through the use of a separate storage tank, a separate meter, or...

Nontuberculous Mycobacterium Infections: Symptoms, Causes & Risk Factors

MedlinePlus

... each(function(i){ var city = $(this).find('city').text(); var state = $(this).find('state').text(); var date = $(this).find('date').text(); if ((city != "") && (state != "")){ var citystate = ' | ' + city + ', ' + state; } else ...

Murine Model for Preclinical Studies of Var2CSA-Mediated Pathology Associated with Malaria in Pregnancy

PubMed Central

Dechavanne, Sebastien; Sousa, Patrícia M.; Barateiro, André; Cunha, Sónia F.; Nunes-Silva, Sofia; Lima, Flávia A.; Murillo, Oscar; Marinho, Claudio R. F.; Gangnard, Stephane; Srivastava, Anand; Braks, Joanna A.; Janse, Chris J.; Gamain, Benoit; Penha-Gonçalves, Carlos

2016-01-01

Plasmodium falciparum infection during pregnancy leads to abortions, stillbirth, low birth weight, and maternal mortality. Infected erythrocytes (IEs) accumulate in the placenta by adhering to chondroitin sulfate A (CSA) via var2CSA protein exposed on the P. falciparum IE membrane. Plasmodium berghei IE infection in pregnant BALB/c mice is a model for severe placental malaria (PM). Here, we describe a transgenic P. berghei parasite expressing the full-length var2CSA extracellular region (domains DBL1X to DBL6ε) fused to a P. berghei exported protein (EMAP1) and characterize a var2CSA-based mouse model of PM. BALB/c mice were infected at midgestation with different doses of P. berghei-var2CSA (P. berghei-VAR) or P. berghei wild-type IEs. Infection with 104 P. berghei-VAR IEs induced a higher incidence of stillbirth and lower fetal weight than P. berghei. At doses of 105 and 106 IEs, P. berghei-VAR-infected mice showed increased maternal mortality during pregnancy and fetal loss, respectively. Parasite loads in infected placentas were similar between parasite lines despite differences in maternal outcomes. Fetal weight loss normalized for parasitemia was higher in P. berghei-VAR-infected mice than in P. berghei-infected mice. In vitro assays showed that higher numbers of P. berghei-VAR IEs than P. berghei IEs adhered to placental tissue. Immunization of mice with P. berghei-VAR elicited IgG antibodies reactive to DBL1-6 recombinant protein, indicating that the topology of immunogenic epitopes is maintained between DBL1-6–EMAP1 on P. berghei-VAR and recombinant DBL1-6 (recDBL1-6). Our data suggested that impairments in pregnancy caused by P. berghei-VAR infection were attributable to var2CSA expression. This model provides a tool for preclinical evaluation of protection against PM induced by approaches that target var2CSA. PMID:27045035

Assessment of the relationship of government spending on social assistance programs with Brazilian macroeconomic variables

NASA Astrophysics Data System (ADS)

de Senna, Viviane; Souza, Adriano Mendonça

2016-11-01

Since the 1988 Federal Constitution social assistance has become a duty of the State and a right to everyone, guaranteeing the population a dignified life. To ensure these rights federal government has created programs that can supply the main needs of people in extreme poverty. Among the programs that provide social assistance to the population, the best known are the ;Bolsa Família; Program - PBF and the Continuous Cash Benefit - Continuous Cash Benefit - BPC. This research's main purpose is to analyze the relationship between the main macroeconomic variables and the Federal government spending on social welfare policy in the period from January 2004 to August 2014. The used methodologies are the Vector auto regression model - VAR and Error Correction Vector - VEC. The conclusion, was that there is a meaningful relationship between macroeconomic variables and social assistance programs. This indicates that if the government takes a more abrupt resolution in changing the existing programs it will result in fluctuations in the main macroeconomic variables interfering with the stability of Brazilian domestic economy up to twelve months.

What is Multidrug and Extensively Drug Resistant TB?

MedlinePlus

... each(function(i){ var city = $(this).find('city').text(); var state = $(this).find('state').text(); var date = $(this).find('date').text(); if ((city != "") && (state != "")){ var citystate = ' | ' + city + ', ' + state; } else ...

Detoxification of Benzoxazolinone Allelochemicals from Wheat by Gaeumannomyces graminis var. tritici, G. graminis var. graminis, G. graminis var. avenae, and Fusarium culmorum

PubMed Central

Friebe, A.; Vilich, V.; Hennig, L.; Kluge, M.; Sicker, D.

1998-01-01

The ability of phytopathogenic fungi to overcome the chemical defense barriers of their host plants is of great importance for fungal pathogenicity. We studied the role of cyclic hydroxamic acids and their related benzoxazolinones in plant interactions with pathogenic fungi. We identified species-dependent differences in the abilities of Gaeumannomyces graminis var. tritici, Gaeumannomyces graminis var. graminis, Gaeumannomyces graminis var. avenae, and Fusarium culmorum to detoxify these allelochemicals of gramineous plants. The G. graminis var. graminis isolate degraded benzoxazolin-2(3H)-one (BOA) and 6-methoxy-benzoxazolin-2(3H)-one (MBOA) more efficiently than did G. graminis var. tritici and G. graminis var. avenae. F. culmorum degraded BOA but not MBOA. N-(2-Hydroxyphenyl)-malonamic acid and N-(2-hydroxy-4-methoxyphenyl)-malonamic acid were the primary G. graminis var. graminis and G. graminis var. tritici metabolites of BOA and MBOA, respectively, as well as of the related cyclic hydroxamic acids. 2-Amino-3H-phenoxazin-3-one was identified as an additional G. graminis var. tritici metabolite of BOA. No metabolite accumulation was detected for G. graminis var. avenae and F. culmorum by high-pressure liquid chromatography. The mycelial growth of the pathogenic fungi was inhibited more by BOA and MBOA than by their related fungal metabolites. The tolerance of Gaeumannomyces spp. for benzoxazolinone compounds is correlated with their detoxification ability. The ability of Gaeumannomyces isolates to cause root rot symptoms in wheat (cultivars Rektor and Astron) parallels their potential to degrade wheat allelochemicals to nontoxic compounds. PMID:9647804

Optimization of autoregressive, exogenous inputs-based typhoon inundation forecasting models using a multi-objective genetic algorithm

NASA Astrophysics Data System (ADS)

Ouyang, Huei-Tau

2017-07-01

Three types of model for forecasting inundation levels during typhoons were optimized: the linear autoregressive model with exogenous inputs (LARX), the nonlinear autoregressive model with exogenous inputs with wavelet function (NLARX-W) and the nonlinear autoregressive model with exogenous inputs with sigmoid function (NLARX-S). The forecast performance was evaluated by three indices: coefficient of efficiency, error in peak water level and relative time shift. Historical typhoon data were used to establish water-level forecasting models that satisfy all three objectives. A multi-objective genetic algorithm was employed to search for the Pareto-optimal model set that satisfies all three objectives and select the ideal models for the three indices. Findings showed that the optimized nonlinear models (NLARX-W and NLARX-S) outperformed the linear model (LARX). Among the nonlinear models, the optimized NLARX-W model achieved a more balanced performance on the three indices than the NLARX-S models and is recommended for inundation forecasting during typhoons.

Antigenic variation in malaria: in situ switching, relaxed and mutually exclusive transcription of var genes during intra-erythrocytic development in Plasmodium falciparum.

PubMed Central

Scherf, A; Hernandez-Rivas, R; Buffet, P; Bottius, E; Benatar, C; Pouvelle, B; Gysin, J; Lanzer, M

1998-01-01

Members of the Plasmodium falciparum var gene family encode clonally variant adhesins, which play an important role in the pathogenicity of tropical malaria. Here we employ a selective panning protocol to generate isogenic P.falciparum populations with defined adhesive phenotypes for CD36, ICAM-1 and CSA, expressing single and distinct var gene variants. This technique has established the framework for examining var gene expression, its regulation and switching. It was found that var gene switching occurs in situ. Ubiquitous transcription of all var gene variants appears to occur in early ring stages. However, var gene expression is tightly regulated in trophozoites and is exerted through a silencing mechanism. Transcriptional control is mutually exclusive in parasites that express defined adhesive phenotypes. In situ var gene switching is apparently mediated at the level of transcriptional initiation, as demonstrated by nuclear run-on analyses. Our results suggest that an epigenetic mechanism(s) is involved in var gene regulation. PMID:9736619

Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia.

PubMed

Ansari, Mozafar; Othman, Faridah; Abunama, Taher; El-Shafie, Ahmed

2018-04-01

The function of a sewage treatment plant is to treat the sewage to acceptable standards before being discharged into the receiving waters. To design and operate such plants, it is necessary to measure and predict the influent flow rate. In this research, the influent flow rate of a sewage treatment plant (STP) was modelled and predicted by autoregressive integrated moving average (ARIMA), nonlinear autoregressive network (NAR) and support vector machine (SVM) regression time series algorithms. To evaluate the models' accuracy, the root mean square error (RMSE) and coefficient of determination (R 2 ) were calculated as initial assessment measures, while relative error (RE), peak flow criterion (PFC) and low flow criterion (LFC) were calculated as final evaluation measures to demonstrate the detailed accuracy of the selected models. An integrated model was developed based on the individual models' prediction ability for low, average and peak flow. An initial assessment of the results showed that the ARIMA model was the least accurate and the NAR model was the most accurate. The RE results also prove that the SVM model's frequency of errors above 10% or below - 10% was greater than the NAR model's. The influent was also forecasted up to 44 weeks ahead by both models. The graphical results indicate that the NAR model made better predictions than the SVM model. The final evaluation of NAR and SVM demonstrated that SVM made better predictions at peak flow and NAR fit well for low and average inflow ranges. The integrated model developed includes the NAR model for low and average influent and the SVM model for peak inflow.

A comparison between EDA-EnVar and ETKF-EnVar data assimilation techniques using radar observations at convective scales through a case study of Hurricane Ike (2008)

NASA Astrophysics Data System (ADS)

Shen, Feifei; Xu, Dongmei; Xue, Ming; Min, Jinzhong

2017-07-01

This study examines the impacts of assimilating radar radial velocity (Vr) data for the simulation of hurricane Ike (2008) with two different ensemble generation techniques in the framework of the hybrid ensemble-variational (EnVar) data assimilation system of Weather Research and Forecasting model. For the generation of ensemble perturbations we apply two techniques, the ensemble transform Kalman filter (ETKF) and the ensemble of data assimilation (EDA). For the ETKF-EnVar, the forecast ensemble perturbations are updated by the ETKF, while for the EDA-EnVar, the hybrid is employed to update each ensemble member with perturbed observations. The ensemble mean is analyzed by the hybrid method with flow-dependent ensemble covariance for both EnVar. The sensitivity of analyses and forecasts to the two applied ensemble generation techniques is investigated in our current study. It is found that the EnVar system is rather stable with different ensemble update techniques in terms of its skill on improving the analyses and forecasts. The EDA-EnVar-based ensemble perturbations are likely to include slightly less organized spatial structures than those in ETKF-EnVar, and the perturbations of the latter are constructed more dynamically. Detailed diagnostics reveal that both of the EnVar schemes not only produce positive temperature increments around the hurricane center but also systematically adjust the hurricane location with the hurricane-specific error covariance. On average, the analysis and forecast from the ETKF-EnVar have slightly smaller errors than that from the EDA-EnVar in terms of track, intensity, and precipitation forecast. Moreover, ETKF-EnVar yields better forecasts when verified against conventional observations.

IkeNet: Social Network Analysis of E-mail Traffic in the Eisenhower Leadership Development Program

DTIC Science & Technology

2007-11-01

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Taxonomic study on Japanese Salvia (Lamiaceae): Phylogenetic position of S. akiensis, and polyphyletic nature of S. lutescens var. intermedia.

PubMed

Takano, Atsuko

2017-01-01

Both Salvia akiensis and S. lutescens (Lamiaceae) are endemic to Japan. Salvia akiensis was recently described in 2014 in the Chugoku (= SW Honshu) region, and each four varieties of S. lutescens distributed allopatrically. Among varieties in S. lutescens , var. intermedia show a disjunctive distribution in the Kanto (=E Honshu) and Kinki (= W Honshu) regions. Recent field studies of S. lutescens var. intermedia revealed several morphological differences between the Kanto and Kinki populations. Here, I evaluated these differences among Salvia lutescens var. intermedia and its allies with morphological analysis and molecular phylogenetic analyses of nuclear ribosomal DNA (internal and external transcribed spacer regions) and plastid DNA ( ycf1-rps15 spacer, rbcL , and trnL-F ) sequences. Both morphological analysis and molecular phylogenetic analyses showed that S. lutescens var. intermedia from the Kinki region and var. lutescens were closely related to each other. However, var. intermedia from the Kanto region exhibited an association with S. lutescens var. crenata and var. stolonifera, which also grew in eastern Japan, rather than var. intermedia in the Kinki region. These results indicated that S. lutescens var. intermedia is not a taxon with a disjunctive distribution, but a combination of two or more allopatric taxa. Present study also suggested that S. akiensis was most closely related to S. omerocalyx .

Arabidopsis VARIEGATED 3 encodes a chloroplast-targeted, zinc-finger protein required for chloroplast and palisade cell development.

PubMed

Naested, Henrik; Holm, Agnethe; Jenkins, Tom; Nielsen, H Bjørn; Harris, Cassandra A; Beale, Michael H; Andersen, Mathias; Mant, Alexandra; Scheller, Henrik; Camara, Bilal; Mattsson, Ole; Mundy, John

2004-09-15

The stable, recessive Arabidopsis variegated 3 (var3) mutant exhibits a variegated phenotype due to somatic areas lacking or containing developmentally retarded chloroplasts and greatly reduced numbers of palisade cells. The VAR3 gene, isolated by transposon tagging, encodes the 85.9 kDa VAR3 protein containing novel repeats and zinc fingers described as protein interaction domains. VAR3 interacts specifically in yeast and in vitro with NCED4, a putative polyene chain or carotenoid dioxygenase, and both VAR3 and NCED4 accumulate in the chloroplast stroma. Metabolic profiling demonstrates that pigment profiles are qualitatively similar in wild type and var3, although var3 accumulates lower levels of chlorophylls and carotenoids. These results indicate that VAR3 is a part of a protein complex required for normal chloroplast and palisade cell development.

Resistance to Southern Root-knot Nematode (Meloidogyne incognita) in Wild Watermelon (Citrullus lanatus var. citroides)

PubMed Central

Thies, Judy A.; Ariss, Jennifer J.; Kousik, Chandrasekar S.; Hassell, Richard L.; Levi, Amnon

2016-01-01

Southern root-knot nematode (RKN, Meloidogyne incognita) is a serious pest of cultivated watermelon (Citrullus lanatus var. lanatus) in southern regions of the United States and no resistance is known to exist in commercial watermelon cultivars. Wild watermelon relatives (Citrullus lanatus var. citroides) have been shown in greenhouse studies to possess varying degrees of resistance to RKN species. Experiments were conducted over 2 yr to assess resistance of southern RKN in C. lanatus var. citroides accessions from the U.S. Watermelon Plant Introduction Collection in an artificially infested field site at the U.S. Vegetable Laboratory in Charleston, SC. In the first study (2006), 19 accessions of C. lanatus var. citroides were compared with reference entries of Citrullus colocynthis and C. lanatus var. lanatus. Of the wild watermelon accessions, two entries exhibited significantly less galling than all other entries. Five of the best performing C. lanatus var. citroides accessions were evaluated with and without nematicide at the same field site in 2007. Citrullus lanatus var. citroides accessions performed better than C. lanatus var. lanatus and C. colocynthis. Overall, most entries of C. lanatus var. citroides performed similarly with and without nematicide treatment in regard to root galling, visible egg masses, vine vigor, and root mass. In both years of field evaluations, most C. lanatus var. citroides accessions showed lesser degrees of nematode reproduction and higher vigor and root mass than C. colocynthis and C. lanatus var. lanatus. The results of these two field evaluations suggest that wild watermelon populations may be useful sources of resistance to southern RKN. PMID:27168648

Resistance to Southern Root-knot Nematode (Meloidogyne incognita) in Wild Watermelon (Citrullus lanatus var. citroides).

PubMed

Thies, Judy A; Ariss, Jennifer J; Kousik, Chandrasekar S; Hassell, Richard L; Levi, Amnon

2016-03-01

Southern root-knot nematode (RKN, Meloidogyne incognita) is a serious pest of cultivated watermelon (Citrullus lanatus var. lanatus) in southern regions of the United States and no resistance is known to exist in commercial watermelon cultivars. Wild watermelon relatives (Citrullus lanatus var. citroides) have been shown in greenhouse studies to possess varying degrees of resistance to RKN species. Experiments were conducted over 2 yr to assess resistance of southern RKN in C. lanatus var. citroides accessions from the U.S. Watermelon Plant Introduction Collection in an artificially infested field site at the U.S. Vegetable Laboratory in Charleston, SC. In the first study (2006), 19 accessions of C. lanatus var. citroides were compared with reference entries of Citrullus colocynthis and C. lanatus var. lanatus. Of the wild watermelon accessions, two entries exhibited significantly less galling than all other entries. Five of the best performing C. lanatus var. citroides accessions were evaluated with and without nematicide at the same field site in 2007. Citrullus lanatus var. citroides accessions performed better than C. lanatus var. lanatus and C. colocynthis. Overall, most entries of C. lanatus var. citroides performed similarly with and without nematicide treatment in regard to root galling, visible egg masses, vine vigor, and root mass. In both years of field evaluations, most C. lanatus var. citroides accessions showed lesser degrees of nematode reproduction and higher vigor and root mass than C. colocynthis and C. lanatus var. lanatus. The results of these two field evaluations suggest that wild watermelon populations may be useful sources of resistance to southern RKN.

Using ClinVar as a Resource to Support Variant Interpretations

PubMed Central

Harrison, Steven M.; Riggs, Erin R.; Maglott, Donna R.; Lee, Jennifer M.; Azzariti, Danielle R.; Niehaus, Annie; Ramos, Erin M.; Martin, Christa L.; Landrum, Melissa J.; Rehm, Heidi L.

2016-01-01

ClinVar is a freely accessible, public archive of reports of the relationships among genomic variants and phenotypes. To facilitate evaluation of the clinical significance of each variant, ClinVar aggregates submissions of the same variant, displays supporting data from each submission, and determines if the submitted clinical interpretations are conflicting or concordant. The unit describes how to (1) identify sequence and structural variants of interest in ClinVar with by multiple searching approaches, including Variation Viewer and (2) understand the display of submissions to ClinVar and the evidence supporting each interpretation. By following this protocol, ClinVar users will be able to learn how to incorporate the wealth of resources and knowledge in ClinVar into variant curation and interpretation. PMID:27037489

Spatial Dynamics and Determinants of County-Level Education Expenditure in China

ERIC Educational Resources Information Center

Gu, Jiafeng

2012-01-01

In this paper, a multivariate spatial autoregressive model of local public education expenditure determination with autoregressive disturbance is developed and estimated. The existence of spatial interdependence is tested using Moran's I statistic and Lagrange multiplier test statistics for both the spatial error and spatial lag models. The full…

Spatial Autocorrelation And Autoregressive Models In Ecology

Treesearch

Jeremy W. Lichstein; Theodore R. Simons; Susan A. Shriner; Kathleen E. Franzreb

2003-01-01

Abstract. Recognition and analysis of spatial autocorrelation has defined a new paradigm in ecology. Attention to spatial pattern can lead to insights that would have been otherwise overlooked, while ignoring space may lead to false conclusions about ecological relationships. We used Gaussian spatial autoregressive models, fit with widely available...

Mathematical model with autoregressive process for electrocardiogram signals

NASA Astrophysics Data System (ADS)

Evaristo, Ronaldo M.; Batista, Antonio M.; Viana, Ricardo L.; Iarosz, Kelly C.; Szezech, José D., Jr.; Godoy, Moacir F. de

2018-04-01

The cardiovascular system is composed of the heart, blood and blood vessels. Regarding the heart, cardiac conditions are determined by the electrocardiogram, that is a noninvasive medical procedure. In this work, we propose autoregressive process in a mathematical model based on coupled differential equations in order to obtain the tachograms and the electrocardiogram signals of young adults with normal heartbeats. Our results are compared with experimental tachogram by means of Poincaré plot and dentrended fluctuation analysis. We verify that the results from the model with autoregressive process show good agreement with experimental measures from tachogram generated by electrical activity of the heartbeat. With the tachogram we build the electrocardiogram by means of coupled differential equations.

Volatility in GARCH Models of Business Tendency Index

NASA Astrophysics Data System (ADS)

Wahyuni, Dwi A. S.; Wage, Sutarman; Hartono, Ateng

2018-01-01

This paper aims to obtain a model of business tendency index by considering volatility factor. Volatility factor detected by ARCH (Autoregressive Conditional Heteroscedasticity). The ARCH checking was performed using the Lagrange multiplier test. The modeling is Generalized Autoregressive Conditional Heteroscedasticity (GARCH) are able to overcome volatility problems by incorporating past residual elements and residual variants.

Functional MRI and Multivariate Autoregressive Models

PubMed Central

Rogers, Baxter P.; Katwal, Santosh B.; Morgan, Victoria L.; Asplund, Christopher L.; Gore, John C.

2010-01-01

Connectivity refers to the relationships that exist between different regions of the brain. In the context of functional magnetic resonance imaging (fMRI), it implies a quantifiable relationship between hemodynamic signals from different regions. One aspect of this relationship is the existence of small timing differences in the signals in different regions. Delays of 100 ms or less may be measured with fMRI, and these may reflect important aspects of the manner in which brain circuits respond as well as the overall functional organization of the brain. The multivariate autoregressive time series model has features to recommend it for measuring these delays, and is straightforward to apply to hemodynamic data. In this review, we describe the current usage of the multivariate autoregressive model for fMRI, discuss the issues that arise when it is applied to hemodynamic time series, and consider several extensions. Connectivity measures like Granger causality that are based on the autoregressive model do not always reflect true neuronal connectivity; however, we conclude that careful experimental design could make this methodology quite useful in extending the information obtainable using fMRI. PMID:20444566

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico.

PubMed

Johansson, Michael A; Reich, Nicholas G; Hota, Aditi; Brownstein, John S; Santillana, Mauricio

2016-09-26

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model.

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

PubMed Central

Johansson, Michael A.; Reich, Nicholas G.; Hota, Aditi; Brownstein, John S.; Santillana, Mauricio

2016-01-01

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model. PMID:27665707

Positive Selection of Plasmodium falciparum Parasites With Multiple var2csa-Type PfEMP1 Genes During the Course of Infection in Pregnant Women

PubMed Central

Salanti, Ali; Lavstsen, Thomas; Nielsen, Morten A.; Theander, Thor G.; Leke, Rose G. F.; Lo, Yeung Y.; Bobbili, Naveen; Arnot, David E.; Taylor, Diane W.

2011-01-01

Placental malaria infections are caused by Plasmodium falciparum–infected red blood cells sequestering in the placenta by binding to chondroitin sulfate A, mediated by VAR2CSA, a variant of the PfEMP1 family of adhesion antigens. Recent studies have shown that many P. falciparum genomes have multiple genes coding for different VAR2CSA proteins, and parasites with >1 var2csa gene appear to be more common in pregnant women with placental malaria than in nonpregnant individuals. We present evidence that, in pregnant women, parasites containing multiple var2csa-type genes possess a selective advantage over parasites with a single var2csa gene. Accumulation of parasites with multiple copies of the var2csa gene during the course of pregnancy was also correlated with the development of antibodies involved in blocking VAR2CSA adhesion. The data suggest that multiplicity of var2csa-type genes enables P. falciparum parasites to persist for a longer period of time during placental infections, probably because of their greater capacity for antigenic variation and evasion of variant-specific immune responses. PMID:21592998

AFLP Phylogeny of 36 Erythroxylum species- genetic relationships among Erythroxylum species inferred by AFLP analysis

USDA-ARS?s Scientific Manuscript database

The plant genus Erythroxylum is known for four cultivated taxa, Erythroxylum coca var. coca (Ecc), Erythroxylum coca var. ipadu (Eci), Erythroxylum novogranatense var. novogranatense (Enn) and Erythroxylum novogranatense var. truxillense (Ent) that are cultivated primarily for the illicit extraction...

CRISPR/Cas9 Genome Editing Reveals That the Intron Is Not Essential for var2csa Gene Activation or Silencing in Plasmodium falciparum.

PubMed

Bryant, Jessica M; Regnault, Clément; Scheidig-Benatar, Christine; Baumgarten, Sebastian; Guizetti, Julien; Scherf, Artur

2017-07-11

Plasmodium falciparum relies on monoallelic expression of 1 of 60 var virulence genes for antigenic variation and host immune evasion. Each var gene contains a conserved intron which has been implicated in previous studies in both activation and repression of transcription via several epigenetic mechanisms, including interaction with the var promoter, production of long noncoding RNAs (lncRNAs), and localization to repressive perinuclear sites. However, functional studies have relied primarily on artificial expression constructs. Using the recently developed P. falciparum clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 system, we directly deleted the var2csa P. falciparum 3D7_1200600 (Pf3D7_1200600) endogenous intron, resulting in an intronless var gene in a natural, marker-free chromosomal context. Deletion of the var2csa intron resulted in an upregulation of transcription of the var2csa gene in ring-stage parasites and subsequent expression of the PfEMP1 protein in late-stage parasites. Intron deletion did not affect the normal temporal regulation and subsequent transcriptional silencing of the var gene in trophozoites but did result in increased rates of var gene switching in some mutant clones. Transcriptional repression of the intronless var2csa gene could be achieved via long-term culture or panning with the CD36 receptor, after which reactivation was possible with chondroitin sulfate A (CSA) panning. These data suggest that the var2csa intron is not required for silencing or activation in ring-stage parasites but point to a subtle role in regulation of switching within the var gene family. IMPORTANCE Plasmodium falciparum is the most virulent species of malaria parasite, causing high rates of morbidity and mortality in those infected. Chronic infection depends on an immune evasion mechanism termed antigenic variation, which in turn relies on monoallelic expression of 1 of ~60 var genes. Understanding antigenic variation and the transcriptional regulation of monoallelic expression is important for developing drugs and/or vaccines. The var gene family encodes the antigenic surface proteins that decorate infected erythrocytes. Until recently, studying the underlying genetic elements that regulate monoallelic expression in P. falciparum was difficult, and most studies relied on artificial systems such as episomal reporter genes. Our study was the first to use CRISPR/Cas9 genome editing for the functional study of an important, conserved genetic element of var genes-the intron-in an endogenous, episome-free manner. Our findings shed light on the role of the var gene intron in transcriptional regulation of monoallelic expression. Copyright © 2017 Bryant et al.

Kepler AutoRegressive Planet Search: Motivation & Methodology

NASA Astrophysics Data System (ADS)

Caceres, Gabriel; Feigelson, Eric; Jogesh Babu, G.; Bahamonde, Natalia; Bertin, Karine; Christen, Alejandra; Curé, Michel; Meza, Cristian

2015-08-01

The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Auto-Regressive Moving-Average (ARMA) models, Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), and related models are flexible, phenomenological methods used with great success to model stochastic temporal behaviors in many fields of study, particularly econometrics. Powerful statistical methods are implemented in the public statistical software environment R and its many packages. Modeling involves maximum likelihood fitting, model selection, and residual analysis. These techniques provide a useful framework to model stellar variability and are used in KARPS with the objective of reducing stellar noise to enhance opportunities to find as-yet-undiscovered planets. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; ARMA-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. We apply the procedures to simulated Kepler-like time series with known stellar and planetary signals to evaluate the effectiveness of the KARPS procedures. The ARMA-type modeling is effective at reducing stellar noise, but also reduces and transforms the transit signal into ingress/egress spikes. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. We also illustrate the efficient coding in R.

Systematics of Juniperus section Juniperus based on leaf essential oils and random amplified polymorphic DNAs (RAPDs).

PubMed

Adams

2000-07-01

The composition of the leaf essential oils of all the species of Juniperus in sect. Juniperus (=sect. Oxycedrus) are reported and compared (J. brevifolia, J. cedrus, J. communis, J. c. var. saxatilis, J. c. var. oblonga, J. formosana, J. oxycedrus, J. o. subsp. badia, J. o. subsp. macrocarpa, J. o. subsp. transtagana, J. rigida, J. r. subsp. conferta, J. sibirica, J. taxifolia and J. t. var. lutchuensis). In addition, DNA fingerprinting by RAPDs was utilized. Based on these data, several taxa remained at the same taxonomic level: J. brevifolia, J. cedrus, J. communis, J. c. var. saxatilis, J. formosana, J. oxycedrus, J. rigida, J. r. var. conferta, and J. taxifolia. However, several taxa exhibited considerable differentiation that warranted their recognition at the specific level: J. oblonga M.-Bieb. (=J. communis var. oblonga), J. badia H. Gay (=J. oxycedrus subsp. badia), J. macrocarpa Sibth. and Sm. (=J. oxycedrus subsp. macrocarpa), J. navicularis Gand. (=J. oxycedrus subsp. transtagana), J. sibirica Brugsd. (=J. communis var. saxatilis in part), and J. lutchuensis Koidz. (= J. taxifolia var. lutchuensis).

A Two-Dimensional Variational Analysis Method for NSCAT Ambiguity Removal: Methodology, Sensitivity, and Tuning

NASA Technical Reports Server (NTRS)

Hoffman, R. N.; Leidner, S. M.; Henderson, J. M.; Atlas, R.; Ardizzone, J. V.; Bloom, S. C.; Atlas, Robert (Technical Monitor)

2001-01-01

In this study, we apply a two-dimensional variational analysis method (2d-VAR) to select a wind solution from NASA Scatterometer (NSCAT) ambiguous winds. 2d-VAR determines a "best" gridded surface wind analysis by minimizing a cost function. The cost function measures the misfit to the observations, the background, and the filtering and dynamical constraints. The ambiguity closest in direction to the minimizing analysis is selected. 2d-VAR method, sensitivity and numerical behavior are described. 2d-VAR is compared to statistical interpolation (OI) by examining the response of both systems to a single ship observation and to a swath of unique scatterometer winds. 2d-VAR is used with both NSCAT ambiguities and NSCAT backscatter values. Results are roughly comparable. When the background field is poor, 2d-VAR ambiguity removal often selects low probability ambiguities. To avoid this behavior, an initial 2d-VAR analysis, using only the two most likely ambiguities, provides the first guess for an analysis using all the ambiguities or the backscatter data. 2d-VAR and median filter selected ambiguities usually agree. Both methods require horizontal consistency, so disagreements occur in clumps, or as linear features. In these cases, 2d-VAR ambiguities are often more meteorologically reasonable and more consistent with satellite imagery.

[Effect of Water Extracts from Rhizosphere Soil of Cultivated Astragalus membranaceus var. mongholicus on It's Seed Germination and Physiological Characteristics].

PubMed

Lang, Duo-yong; Fu, Xue-yan; Rong, Jia-wang; Zhang, Xin-hui

2015-01-01

To explore the relationship between continuous cropping obstacle and autotoxicity of Astragalus membranaceus var. mongholicus. Distilled water(CK), water extracts of rhizosphere soil(50, 100, 200 and 400 mg/mL) were applied to test their effect on early growth and physiological characteristics of Astragalus membranaceus var. mongholicus. The water extracts from rhizospher soil of cultivated Astragalus membranaceus var. mongholicus significantly increased seedling emergence rate, root length and vigor index of Astragalus membranaceus var. mongholicus seedling when at the concentration of 100 mg/mL or below, however,there was no significant effect at 200 mg/mL or higher. The water extracts from rhizosphere soil of cultivated Astragalus membranaceus var. mongholicus significantly reduced the SOD activity in Astragalus membranaceus var. mongholicus seedling at 400 mg/mL and POD activity at 200 mg/mL and 400 mg/mL,while significantly increased the MDA content. Water extracts from Astragalus membranaceus var. mongholicus rhizosphere soil significantly affected Astragalus membranaceus var. mongholicus germination and seedling growth in a concentration-dependent manner, generally, low concentrations increased the SOD and POD activity which improved seed germination and seedling growth, while high concentrations caused cell membrane damage of the seedling.

Effects of sex, parity, and sequence variation on seroreactivity to candidate pregnancy malaria vaccine antigens.

PubMed

Oleinikov, Andrew V; Rossnagle, Eddie; Francis, Susan; Mutabingwa, Theonest K; Fried, Michal; Duffy, Patrick E

2007-07-01

Plasmodium falciparum-infected erythrocytes adhere to chondroitin sulfate A (CSA) to sequester in the human placenta, and pregnancy malaria (PM) is associated with the development of disease in and the death of both mother and child. A PM vaccine appears to be feasible, because women become protected as they develop antibodies against placental infected erythrocytes (IEs). Two IE surface molecules, VAR1CSA and VAR2CSA, bind CSA in vitro and are potential vaccine candidates. We expressed all domains of VAR1CSA and VAR2CSA as mammalian cell surface proteins, using a novel approach that allows rapid purification, immobilization, and quantification of target antigen. For serum samples from East Africa, we measured reactivity to all domains, and we examined the effects of host sex and parity, as well as the effects of parasite antigenic variation. Serum samples obtained from multigravid women had a higher reactivity to all VAR2CSA domains than did those obtained from primigravid women or from men. Conversely, serum samples obtained from men had consistently higher reactivity to VAR1CSA domains than did those obtained from gravid women. Seroreactivity was strongly influenced by antigenic variation of VAR2CSA Duffy binding-like domains. Women acquire antibodies to VAR2CSA over successive pregnancies, but they lose reactivity to VAR1CSA. Serum reactivity to VAR2CSA is variant specific, and future studies should examine the degree to which functional antibodies, such as binding-inhibition antibodies, are variant specific.

Optimizing expression of the pregnancy malaria vaccine candidate, VAR2CSA in Pichia pastoris.

PubMed

Avril, Marion; Hathaway, Marianne J; Cartwright, Megan M; Gose, Severin O; Narum, David L; Smith, Joseph D

2009-06-29

VAR2CSA is the main candidate for a vaccine against pregnancy-associated malaria, but vaccine development is complicated by the large size and complex disulfide bonding pattern of the protein. Recent X-ray crystallographic information suggests that domain boundaries of VAR2CSA Duffy binding-like (DBL) domains may be larger than previously predicted and include two additional cysteine residues. This study investigated whether longer constructs would improve VAR2CSA recombinant protein secretion from Pichia pastoris and if domain boundaries were applicable across different VAR2CSA alleles. VAR2CSA sequences were bioinformatically analysed to identify the predicted C11 and C12 cysteine residues at the C-termini of DBL domains and revised N- and C-termimal domain boundaries were predicted in VAR2CSA. Multiple construct boundaries were systematically evaluated for protein secretion in P. pastoris and secreted proteins were tested as immunogens. From a total of 42 different VAR2CSA constructs, 15 proteins (36%) were secreted. Longer construct boundaries, including the predicted C11 and C12 cysteine residues, generally improved expression of poorly or non-secreted domains and permitted expression of all six VAR2CSA DBL domains. However, protein secretion was still highly empiric and affected by subtle differences in domain boundaries and allelic variation between VAR2CSA sequences. Eleven of the secreted proteins were used to immunize rabbits. Antibodies reacted with CSA-binding infected erythrocytes, indicating that P. pastoris recombinant proteins possessed native protein epitopes. These findings strengthen emerging data for a revision of DBL domain boundaries in var-encoded proteins and may facilitate pregnancy malaria vaccine development.

Optimizing expression of the pregnancy malaria vaccine candidate, VAR2CSA in Pichia pastoris

PubMed Central

Avril, Marion; Hathaway, Marianne J; Cartwright, Megan M; Gose, Severin O; Narum, David L; Smith, Joseph D

2009-01-01

Background VAR2CSA is the main candidate for a vaccine against pregnancy-associated malaria, but vaccine development is complicated by the large size and complex disulfide bonding pattern of the protein. Recent X-ray crystallographic information suggests that domain boundaries of VAR2CSA Duffy binding-like (DBL) domains may be larger than previously predicted and include two additional cysteine residues. This study investigated whether longer constructs would improve VAR2CSA recombinant protein secretion from Pichia pastoris and if domain boundaries were applicable across different VAR2CSA alleles. Methods VAR2CSA sequences were bioinformatically analysed to identify the predicted C11 and C12 cysteine residues at the C-termini of DBL domains and revised N- and C-termimal domain boundaries were predicted in VAR2CSA. Multiple construct boundaries were systematically evaluated for protein secretion in P. pastoris and secreted proteins were tested as immunogens. Results From a total of 42 different VAR2CSA constructs, 15 proteins (36%) were secreted. Longer construct boundaries, including the predicted C11 and C12 cysteine residues, generally improved expression of poorly or non-secreted domains and permitted expression of all six VAR2CSA DBL domains. However, protein secretion was still highly empiric and affected by subtle differences in domain boundaries and allelic variation between VAR2CSA sequences. Eleven of the secreted proteins were used to immunize rabbits. Antibodies reacted with CSA-binding infected erythrocytes, indicating that P. pastoris recombinant proteins possessed native protein epitopes. Conclusion These findings strengthen emerging data for a revision of DBL domain boundaries in var-encoded proteins and may facilitate pregnancy malaria vaccine development. PMID:19563628

Knowledge, Attitude and Practice of Pregnant Women towards Varicella and Their Children’s Varicella Vaccination: Evidence from Three Distrcits in Zhejiang Province, China

PubMed Central

Hu, Yu; Chen, Yaping; Wang, Ying; Liang, Hui

2017-01-01

Background: The objectives of this study were to examine the knowledge, attitudes and practice (KAP) towards varicella and varicella vaccine (VarV) vaccination among pregnant women in three distrcits in Zhejiang Province, China. Methods: From 1 January to 31 March 2014, pregnant women with ≥12 gestational weeks were recruited and received a self-administrated questionnaire. The first dose of VarV (VarV1) vaccination status of children from present pregnancy was extracted at 24 months of age from Zhejiang provincial immunization information system (ZJIIS). Three variables was defined as the main outcomes, which included: (1) knowing about both the availability of VarV and the number of doses required; (2) positive attitude towards the utility of varicella vaccination; (3) the vaccination coverage of VarV1, which meant the proportion of children having received the VarV1. Counts and proportions were used to describe the socio-demographic characteristics of study participants, and their relationship with study outcomes were tested using chi-square tests in univariate analysis and logistic regression in multivariable analysis. Results: A total of 629 pregnant women participated in this study. The majority of the participants (68.0%) answered correctly about the transmission route of varicella. The proportion of participants who heard about varicella vaccination was 76.5% and 66.8% knew that VarV was currently available. Only 13.5% of the participants answered correctly that the complete VarV series needed two doses. Age, immigration status, education level, household income, and number of children of the pregnant women were significant predictors of the KAP regarding the VarV vaccination. Conclusions: The current survey indicated that optimal KAP levels and coverage on VarV vaccination were observed in three districts of Zhejiang Province. Health education programs on varicella and VarV vaccination directed towards both pre-natal and post-natal women are needed, which will result in a better attitude on vaccination of VarV and in a high coverage of VarV. PMID:28946647

Diffusion of Lexical Change in Social Media

PubMed Central

Eisenstein, Jacob; O'Connor, Brendan; Smith, Noah A.; Xing, Eric P.

2014-01-01

Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity – especially with regard to race – plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified “netspeak” dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English. PMID:25409166

Vibration-based damage detection in a concrete beam under temperature variations using AR models and state-space approaches

NASA Astrophysics Data System (ADS)

Clément, A.; Laurens, S.

2011-07-01

The Structural Health Monitoring of civil structures subjected to ambient vibrations is very challenging. Indeed, the variations of environmental conditions and the difficulty to characterize the excitation make the damage detection a hard task. Auto-regressive (AR) models coefficients are often used as damage sensitive feature. The presented work proposes a comparison of the AR approach with a state-space feature formed by the Jacobian matrix of the dynamical process. Since the detection of damage can be formulated as a novelty detection problem, Mahalanobis distance is applied to track new points from an undamaged reference collection of feature vectors. Data from a concrete beam subjected to temperature variations and damaged by several static loading are analyzed. It is observed that the damage sensitive features are effectively sensitive to temperature variations. However, the use of the Mahalanobis distance makes possible the detection of cracking with both of them. Early damage (before cracking) is only revealed by the AR coefficients with a good sensibility.

Artificial bee colony algorithm for single-trial electroencephalogram analysis.

PubMed

Hsu, Wei-Yen; Hu, Ya-Ping

2015-04-01

In this study, we propose an analysis system combined with feature selection to further improve the classification accuracy of single-trial electroencephalogram (EEG) data. Acquiring event-related brain potential data from the sensorimotor cortices, the system comprises artifact and background noise removal, feature extraction, feature selection, and feature classification. First, the artifacts and background noise are removed automatically by means of independent component analysis and surface Laplacian filter, respectively. Several potential features, such as band power, autoregressive model, and coherence and phase-locking value, are then extracted for subsequent classification. Next, artificial bee colony (ABC) algorithm is used to select features from the aforementioned feature combination. Finally, selected subfeatures are classified by support vector machine. Comparing with and without artifact removal and feature selection, using a genetic algorithm on single-trial EEG data for 6 subjects, the results indicate that the proposed system is promising and suitable for brain-computer interface applications. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Shape classification of malignant lymphomas and leukemia by morphological watersheds and ARMA modeling

NASA Astrophysics Data System (ADS)

Celenk, Mehmet; Song, Yinglei; Ma, Limin; Zhou, Min

2003-05-01

A new algorithm that can be used to automatically recognize and classify malignant lymphomas and lukemia is proposed in this paper. The algorithm utilizes the morphological watershed to extract boundaries of cells from their grey-level images. It generates a sequence of Euclidean distances by selecting pixels in clockwise direction on the boundary of the cell and calculating the Euclidean distances of the selected pixels from the centroid of the cell. A feature vector associated with each cell is then obtained by applying the auto-regressive moving-average (ARMA) model to the generated sequence of Euclidean distances. The clustering measure J3=trace{inverse(Sw-1)Sm} involving the within (Sw) and mixed (Sm) class-scattering matrices is computed for both cell classes to provide an insight into the extent to which different cell classes in the training data are separated. Our test results suggest that the algorithm is highly accurate for the development of an interactive, computer-assisted diagnosis (CAD) tool.

Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data

PubMed Central

Young, Alistair A.; Li, Xiaosong

2014-01-01

Public health surveillance systems provide valuable data for reliable predication of future epidemic events. This paper describes a study that used nine types of infectious disease data collected through a national public health surveillance system in mainland China to evaluate and compare the performances of four time series methods, namely, two decomposition methods (regression and exponential smoothing), autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The data obtained from 2005 to 2011 and in 2012 were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The accuracy of the statistical models in forecasting future epidemic disease proved their effectiveness in epidemiological surveillance. Although the comparisons found that no single method is completely superior to the others, the present study indeed highlighted that the SVMs outperforms the ARIMA model and decomposition methods in most cases. PMID:24505382

State space model approach for forecasting the use of electrical energy (a case study on: PT. PLN (Persero) district of Kroya)

NASA Astrophysics Data System (ADS)

Kurniati, Devi; Hoyyi, Abdul; Widiharih, Tatik

2018-05-01

Time series data is a series of data taken or measured based on observations at the same time interval. Time series data analysis is used to perform data analysis considering the effect of time. The purpose of time series analysis is to know the characteristics and patterns of a data and predict a data value in some future period based on data in the past. One of the forecasting methods used for time series data is the state space model. This study discusses the modeling and forecasting of electric energy consumption using the state space model for univariate data. The modeling stage is began with optimal Autoregressive (AR) order selection, determination of state vector through canonical correlation analysis, estimation of parameter, and forecasting. The result of this research shows that modeling of electric energy consumption using state space model of order 4 with Mean Absolute Percentage Error (MAPE) value 3.655%, so the model is very good forecasting category.

Software sensors for biomass concentration in a SSC process using artificial neural networks and support vector machine.

PubMed

Acuña, Gonzalo; Ramirez, Cristian; Curilem, Millaray

2014-01-01

The lack of sensors for some relevant state variables in fermentation processes can be coped by developing appropriate software sensors. In this work, NARX-ANN, NARMAX-ANN, NARX-SVM and NARMAX-SVM models are compared when acting as software sensors of biomass concentration for a solid substrate cultivation (SSC) process. Results show that NARMAX-SVM outperforms the other models with an SMAPE index under 9 for a 20 % amplitude noise. In addition, NARMAX models perform better than NARX models under the same noise conditions because of their better predictive capabilities as they include prediction errors as inputs. In the case of perturbation of initial conditions of the autoregressive variable, NARX models exhibited better convergence capabilities. This work also confirms that a difficult to measure variable, like biomass concentration, can be estimated on-line from easy to measure variables like CO₂ and O₂ using an adequate software sensor based on computational intelligence techniques.

A tool box for operational mosquito larval control: preliminary results and early lessons from the Urban Malaria Control Programme in Dar es Salaam, Tanzania

PubMed Central

Fillinger, Ulrike; Kannady, Khadija; William, George; Vanek, Michael J; Dongus, Stefan; Nyika, Dickson; Geissbühler, Yvonne; Chaki, Prosper P; Govella, Nico J; Mathenge, Evan M; Singer, Burton H; Mshinda, Hassan; Lindsay, Steven W; Tanner, Marcel; Mtasiwa, Deo; de Castro, Marcia C; Killeen, Gerry F

2008-01-01

Background As the population of Africa rapidly urbanizes, large populations could be protected from malaria by controlling aquatic stages of mosquitoes if cost-effective and scalable implementation systems can be designed. Methods A recently initiated Urban Malaria Control Programme in Dar es Salaam delegates responsibility for routine mosquito control and surveillance to modestly-paid community members, known as Community-Owned Resource Persons (CORPs). New vector surveillance, larviciding and management systems were designed and evaluated in 15 city wards to allow timely collection, interpretation and reaction to entomologic monitoring data using practical procedures that rely on minimal technology. After one year of baseline data collection, operational larviciding with Bacillus thuringiensis var. israelensis commenced in March 2006 in three selected wards. Results The procedures and staff management systems described greatly improved standards of larval surveillance relative to that reported at the outset of this programme. In the first year of the programme, over 65,000 potential Anopheles habitats were surveyed by 90 CORPs on a weekly basis. Reaction times to vector surveillance at observations were one day, week and month at ward, municipal and city levels, respectively. One year of community-based larviciding reduced transmission by the primary malaria vector, Anopheles gambiae s.l., by 31% (95% C.I. = 21.6–37.6%; p = 0.04). Conclusion This novel management, monitoring and evaluation system for implementing routine larviciding of malaria vectors in African cities has shown considerable potential for sustained, rapidly responsive, data-driven and affordable application. Nevertheless, the true programmatic value of larviciding in urban Africa can only be established through longer-term programmes which are stably financed and allow the operational teams and management infrastructures to mature by learning from experience. PMID:18218148

Cryptococcosis

MedlinePlus

C. neoformans var. neoformans infection; C. neoformans var. gatti infection; C. neoformans var. grubii infection ... C. neoformans and C. gattii are the fungi that cause this disease. Infection with C. neoformans is ...

The Performance of Multilevel Growth Curve Models under an Autoregressive Moving Average Process

ERIC Educational Resources Information Center

Murphy, Daniel L.; Pituch, Keenan A.

2009-01-01

The authors examined the robustness of multilevel linear growth curve modeling to misspecification of an autoregressive moving average process. As previous research has shown (J. Ferron, R. Dailey, & Q. Yi, 2002; O. Kwok, S. G. West, & S. B. Green, 2007; S. Sivo, X. Fan, & L. Witta, 2005), estimates of the fixed effects were unbiased, and Type I…

Testing the Causal Links between School Climate, School Violence, and School Academic Performance: A Cross-Lagged Panel Autoregressive Model

ERIC Educational Resources Information Center

Benbenishty, Rami; Astor, Ron Avi; Roziner, Ilan; Wrabel, Stephani L.

2016-01-01

The present study explores the causal link between school climate, school violence, and a school's general academic performance over time using a school-level, cross-lagged panel autoregressive modeling design. We hypothesized that reductions in school violence and climate improvement would lead to schools' overall improved academic performance.…

Time to burn: Modeling wildland arson as an autoregressive crime function

Treesearch

Jeffrey P. Prestemon; David T. Butry

2005-01-01

Six Poisson autoregressive models of order p [PAR(p)] of daily wildland arson ignition counts are estimated for five locations in Florida (1994-2001). In addition, a fixed effects time-series Poisson model of annual arson counts is estimated for all Florida counties (1995-2001). PAR(p) model estimates reveal highly significant arson ignition autocorrelation, lasting up...

Theoretical results on fractionally integrated exponential generalized autoregressive conditional heteroskedastic processes

NASA Astrophysics Data System (ADS)

Lopes, Sílvia R. C.; Prass, Taiane S.

2014-05-01

Here we present a theoretical study on the main properties of Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroskedastic (FIEGARCH) processes. We analyze the conditions for the existence, the invertibility, the stationarity and the ergodicity of these processes. We prove that, if { is a FIEGARCH(p,d,q) process then, under mild conditions, { is an ARFIMA(q,d,0) with correlated innovations, that is, an autoregressive fractionally integrated moving average process. The convergence order for the polynomial coefficients that describes the volatility is presented and results related to the spectral representation and to the covariance structure of both processes { and { are discussed. Expressions for the kurtosis and the asymmetry measures for any stationary FIEGARCH(p,d,q) process are also derived. The h-step ahead forecast for the processes {, { and { are given with their respective mean square error of forecast. The work also presents a Monte Carlo simulation study showing how to generate, estimate and forecast based on six different FIEGARCH models. The forecasting performance of six models belonging to the class of autoregressive conditional heteroskedastic models (namely, ARCH-type models) and radial basis models is compared through an empirical application to Brazilian stock market exchange index.

The Drosophila Su(var)3-7 gene is required for oogenesis and female fertility, genetically interacts with piwi and aubergine, but impacts only weakly transposon silencing.

PubMed

Basquin, Denis; Spierer, Anne; Begeot, Flora; Koryakov, Dmitry E; Todeschini, Anne-Laure; Ronsseray, Stéphane; Vieira, Cristina; Spierer, Pierre; Delattre, Marion

2014-01-01

Heterochromatin is made of repetitive sequences, mainly transposable elements (TEs), the regulation of which is critical for genome stability. We have analyzed the role of the heterochromatin-associated Su(var)3-7 protein in Drosophila ovaries. We present evidences that Su(var)3-7 is required for correct oogenesis and female fertility. It accumulates in heterochromatic domains of ovarian germline and somatic cells nuclei, where it co-localizes with HP1. Homozygous mutant females display ovaries with frequent degenerating egg-chambers. Absence of Su(var)3-7 in embryos leads to defects in meiosis and first mitotic divisions due to chromatin fragmentation or chromosome loss, showing that Su(var)3-7 is required for genome integrity. Females homozygous for Su(var)3-7 mutations strongly impair repression of P-transposable element induced gonadal dysgenesis but have minor effects on other TEs. Su(var)3-7 mutations reduce piRNA cluster transcription and slightly impact ovarian piRNA production. However, this modest piRNA reduction does not correlate with transposon de-silencing, suggesting that the moderate effect of Su(var)3-7 on some TE repression is not linked to piRNA production. Strikingly, Su(var)3-7 genetically interacts with the piwi and aubergine genes, key components of the piRNA pathway, by strongly impacting female fertility without impairing transposon silencing. These results lead us to propose that the interaction between Su(var)3-7 and piwi or aubergine controls important developmental processes independently of transposon silencing.

Antibody levels to recombinant VAR2CSA domains vary with Plasmodium falciparum parasitaemia, gestational age, and gravidity, but do not predict pregnancy outcomes.

PubMed

Fried, Michal; Kurtis, Jonathan D; Swihart, Bruce; Morrison, Robert; Pond-Tor, Sunthorn; Barry, Amadou; Sidibe, Youssoufa; Keita, Sekouba; Mahamar, Almahamoudou; Andemel, Naissem; Attaher, Oumar; Dembele, Adama B; Cisse, Kadidia B; Diarra, Bacary S; Kanoute, Moussa B; Narum, David L; Dicko, Alassane; Duffy, Patrick E

2018-03-09

Maternal malaria is a tropical scourge associated with poor pregnancy outcomes. Women become resistant to Plasmodium falciparum pregnancy malaria as they acquire antibodies to the variant surface antigen VAR2CSA, a leading vaccine candidate. Because malaria infection may increase VAR2CSA antibody levels and thereby confound analyses of immune protection, gravidity-dependent changes in antibody levels during and after infection, and the effect of VAR2CSA antibodies on pregnancy outcomes were evaluated. Pregnant women enrolled in a longitudinal cohort study of mother-infant pairs in Ouelessebougou, Mali provided plasma samples at enrollment, gestational week 30-32, and delivery. Antibody levels to VAR2CSA domains were measured using a multiplex bead-based assay. Antibody levels to VAR2CSA were higher in multigravidae than primigravidae. Malaria infection was associated with increased antibody levels to VAR2CSA domains. In primigravidae but not in secundigravidae or multigravidae, antibodies levels sharply declined after an infection. A relationship between any VAR2CSA antibody specificity and protection from adverse pregnancy outcomes was not detected. During malaria infection, primigravidae acquire short-lived antibodies. The lack of an association between VAR2CSA domain antibody reactivity and improved pregnancy outcomes suggests that the recombinant proteins may not present native epitopes targeted by protective antibodies.

VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research

PubMed Central

Lai, Zhongwu; Markovets, Aleksandra; Ahdesmaki, Miika; Chapman, Brad; Hofmann, Oliver; McEwen, Robert; Johnson, Justin; Dougherty, Brian; Barrett, J. Carl; Dry, Jonathan R.

2016-01-01

Abstract Accurate variant calling in next generation sequencing (NGS) is critical to understand cancer genomes better. Here we present VarDict, a novel and versatile variant caller for both DNA- and RNA-sequencing data. VarDict simultaneously calls SNV, MNV, InDels, complex and structural variants, expanding the detected genetic driver landscape of tumors. It performs local realignments on the fly for more accurate allele frequency estimation. VarDict performance scales linearly to sequencing depth, enabling ultra-deep sequencing used to explore tumor evolution or detect tumor DNA circulating in blood. In addition, VarDict performs amplicon aware variant calling for polymerase chain reaction (PCR)-based targeted sequencing often used in diagnostic settings, and is able to detect PCR artifacts. Finally, VarDict also detects differences in somatic and loss of heterozygosity variants between paired samples. VarDict reprocessing of The Cancer Genome Atlas (TCGA) Lung Adenocarcinoma dataset called known driver mutations in KRAS, EGFR, BRAF, PIK3CA and MET in 16% more patients than previously published variant calls. We believe VarDict will greatly facilitate application of NGS in clinical cancer research. PMID:27060149

[Soluble and insoluble dietary fiber in cereals and legumes cultivated in Chile].

PubMed

Pak, N; Ayala, C; Vera, G; Pennacchiotti, I; Araya, H

1990-03-01

Insoluble, soluble and total dietary fiber (DF) were determined in 35 varieties of certified whole seeds (without processing) of cereals (rice, oat, rye, and wheat) and legumes (pea, cowpea, beans, chikpea, lentil and lupine). The enzymatic method of Asp, Johansson and Siljestrom was used, with modifications in relation to time of incubation with alpha amylase, filtration system and volumes of the filtrates. Results were expressed as g/100 g dry weight. Total DF for cereals showed a range from 10.1 (wheat var. Chasqui) to 22.2 (rice var Quella). Rye, var. Tetra Baer and oats var. Pony Baer presented the highest soluble fiber content (3.3 and 3.9, respectively). In legumes, total DF fluctuated between 12.7 (pea, var. yellow) and 36.6 (lupine, var. Multolupa). Bean, var. Pinto INIA and lupine var. Multolupa presented the highest soluble fiber values (5.8 for both). Based on the results of this research work, it might be concluded that great variation exists in regard to the amount of total soluble and insoluble DF in cereals and legumes, a fact which impedes generalization as to its content in each food item.

4D-Var Developement at GMAO

NASA Technical Reports Server (NTRS)

Pelc, Joanna S.; Todling, Ricardo; Akkraoui, Amal El

2014-01-01

The Global Modeling and Assimilation Offce (GMAO) is currently using an IAU-based 3D-Var data assimilation system. GMAO has been experimenting with a 3D-Var-hybrid version of its data assimilation system (DAS) for over a year now, which will soon become operational and it will rapidly progress toward a 4D-EnVar. Concurrently, the machinery to exercise traditional 4DVar is in place and it is desirable to have a comparison of the traditional 4D approach with the other available options, and evaluate their performance in the Goddard Earth Observing System (GEOS) DAS. This work will also explore the possibility for constructing a reduced order model (ROM) to make traditional 4D-Var computationally attractive for increasing model resolutions. Part of the research on ROM will be to search for a suitably acceptable space to carry on the corresponding reduction. This poster illustrates how the IAU-based 4D-Var assimilation compares with our currently used IAU-based 3D-Var.

Isolation of Cryptococcus neoformans var. gattii from Eucalyptus camaldulensis in India.

PubMed Central

Chakrabarti, A; Jatana, M; Kumar, P; Chatha, L; Kaushal, A; Padhye, A A

1997-01-01

Cryptococcus neoformans var. gattii has an ecological association with five Eucalyptus species: E. blakelyi, E. camaldulensis, E. gomphocephala, E. rudis, and E. tereticornis. After human infections due to C. neoformans var. gattii were diagnosed in the states of Punjab, Himachal Pradesh, and Karnataka, India, a study was undertaken to investigate the association of C. neoformans var. gattii with Indian eucalypts, especially in the state of Punjab. A total of 696 specimens collected from E. camaldulensis, E. citriodora and E. tereticornis (hybrid) trees were examined for the presence of C. neoformans var. gattii. Flowers from two trees of E. camaldulensis in the Chak Sarkar forest and one from the village of Periana near the Ferozepur area yielded five isolates of C. neoformans var. gattii. The origin of the trees could be traced to Australia, thus providing evidence that the distribution of E. camaldulensis correlated with the distribution of human cryptococcosis cases caused by C. neoformans var. gattii in northern India. PMID:9399553

Arundina graminifolia var. revoluta (Arethuseae, Orchidaceae) has fern-type rheophyte characteristics in the leaves.

PubMed

Yorifuji, Eri; Ishikawa, Naoko; Okada, Hiroshi; Tsukaya, Hirokazu

2015-03-01

Morphological and molecular variation between Arundina graminifolia var. graminifolia and the dwarf variety, A. graminifolia var. revoluta, was examined to assess the validity of their taxonomic characteristics and genetic background for identification. Morphological analysis in combination with field observations indicated that A. graminifolia var. revoluta is a rheophyte form of A. graminifolia characterized by narrow leaves, whereas the other morphological characteristics described for A. graminifolia var. revoluta, such as smaller flowers and short stems, were not always accompanied by the narrower leaf phenotype. Molecular analysis based on matK sequences indicated that only partial differentiation has occurred between A. graminifolia var. graminifolia and A. graminifolia var. revoluta. Therefore, we should consider the rheophyte form an ecotype rather than a variety. Anatomical observations of the leaves revealed that the rheophyte form of A. graminifolia possessed characteristics of the rheophytes of both ferns and angiosperms, such as narrower palisade tissue cells and thinner spongy tissue cells, as well as fewer cells in the leaf-width direction and fewer mesophyll cell layers.

Empirical analysis on future-cash arbitrage risk with portfolio VaR

NASA Astrophysics Data System (ADS)

Chen, Rongda; Li, Cong; Wang, Weijin; Wang, Ze

2014-03-01

This paper constructs the positive arbitrage position by alternating the spot index with Chinese Exchange Traded Fund (ETF) portfolio and estimating the arbitrage-free interval of futures with the latest trade data. Then, an improved Delta-normal method was used, which replaces the simple linear correlation coefficient with tail dependence correlation coefficient, to measure VaR (Value-at-risk) of the arbitrage position. Analysis of VaR implies that the risk of future-cash arbitrage is less than that of investing completely in either futures or spot market. Then according to the compositional VaR and the marginal VaR, we should increase the futures position and decrease the spot position appropriately to minimize the VaR, which can minimize risk subject to certain revenues.

Medium- and Long-term Prediction of LOD Change with the Leap-step Autoregressive Model

NASA Astrophysics Data System (ADS)

Liu, Q. B.; Wang, Q. J.; Lei, M. F.

2015-09-01

It is known that the accuracies of medium- and long-term prediction of changes of length of day (LOD) based on the combined least-square and autoregressive (LS+AR) decrease gradually. The leap-step autoregressive (LSAR) model is more accurate and stable in medium- and long-term prediction, therefore it is used to forecast the LOD changes in this work. Then the LOD series from EOP 08 C04 provided by IERS (International Earth Rotation and Reference Systems Service) is used to compare the effectiveness of the LSAR and traditional AR methods. The predicted series resulted from the two models show that the prediction accuracy with the LSAR model is better than that from AR model in medium- and long-term prediction.

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.

A var gene promoter implicated in severe malaria nucleates silencing and is regulated by 3’ untranslated region and intronic cis-elements

PubMed Central

Muhle, Rebecca A.; Adjalley, Sophie; Falkard, Brie; Nkrumah, Louis J.; Muhle, Michael E.; Fidock, David A.

2009-01-01

Questions surround the mechanism of mutually exclusive expression by which Plasmodium falciparum mediates activation and silencing of var genes. These encode PfEMP1 proteins, which function as cytoadherent and immunomodulatory molecules at the surface of parasitized erythrocytes. Current evidence suggests that promoter silencing by var introns might play a key role in var gene regulation. To evaluate the impact of cis-acting regulatory regions on var silencing, we generated P. falciparum lines in which luciferase was placed under the control of an UpsA var promoter. By utilizing the Bxb1 integrase system, these reporter cassettes were targeted to a genomic region that was not in apposition to var sub-telomeric domains. This eliminated possible effects from surrounding telomeric elements and removed the variability inherent in episomal systems. Studies with highly synchronized parasites revealed that the UpsA element possessed minimal activity in comparison with a heterologous (hrp3) promoter. This may well result from the integrated UpsA promoter being largely silenced by the neighboring cg6 promoter. Our analyses also revealed that the DownsA 3’ untranslated region further decreased the luciferase activity from both cassettes, whereas the var A intron repressed the UpsA promoter specifically. By applying multivariate analysis over the entire cell cycle, we confirmed the significance of these cis-elements and found the parasite stage to be the major factor regulating UpsA promoter activity. Additionally, we observed that the UpsA promoter was capable of nucleating reversible silencing that spread to a downstream promoter. We believe these studies are the first to analyze promoter activity of Group A var genes which have been implicated in severe malaria, and support the model that var introns can further suppress var expression. These data also suggest an important suppressive role for the DownsA terminator. Our findings imply the existence of multiple levels of var gene regulation in addition to intrinsic promoter-dependent silencing. PMID:19463825

Modeling, analysis, control and design application guidelines of Doubly Fed Induction Generator (DFIG) for wind power applications

NASA Astrophysics Data System (ADS)

Masaud, Tarek

Double Fed Induction Generators (DFIG) has been widely used for the past two decades in large wind farms. However, there are many open-ended problems yet to be solved before they can be implemented in some specific applications. This dissertation deals with the general analysis, modeling, control and applications of the DFIG for large wind farm applications. A detailed "d-q" model of DFIG along with other applications is simulated using the MATLAB/Simulink platform. The simulation results have been discussed in detail in both sub-synchronous and super-synchronous mode of operation. An improved vector control strategy based on the rotor flux oriented vector control has been proposed to control the active power output of the DFIG. The new vector control strategy is compared with the stator flux oriented vector control which is commonly used. It is observed that the new improved vector control method provides a better active power tracking accuracy compare with the stator flux oriented vector control. The behavior of the DFIG -based wind farm under the various grid disturbances is also studied in this dissertation. The implementation of the Flexible AC Transmission System devices (FACTS) to overcome the voltage stability issue for such applications is investigated. The study includes the implementation of both a static synchronous compensator (STATCOM), and the static VAR compensator (SVC) as dynamic reactive power compensators at the point of common coupling to support DFIG-based wind farm during disturbances. Integrating FACTS protect the grid connected DFIG-based wind farm from going offline during and after the disturbances. It is found that the both devices improve the transient performance and therefore helps the wind turbine generator system to remain in service during grid faults. A comparison between the performance of the two devices in terms of the amount of reactive power injected, time response and the application cost has been discussed in this dissertation. Finally, the integration of the battery energy storage system (BESS) into a grid connected DFIG- based wind turbine as a proposed solution to smooth out the output power during wind speed variations is also addressed.

Phytochemical characterization of several hawthorn (Crataegus spp.) species sampled from the Eastern Mediterranean region of Turkey.

PubMed

Calişkan, Oğuzhan; Gündüz, Kazim; Serçe, Sedat; Toplu, Celil; Kamiloğlu, Onder; Sengül, Memnune; Ercişli, Sezai

2012-01-01

We evaluated the total phenolic content, antioxidant capacity as well as antioxidant activity of five Crataegus species (A1, A2, Y1, Y2, Y4 accessions of Crataegus aronia var. aronia; B2, B3, B5, B6, B7, B9, Y5 accessions of C. aronia var. dentata; B10 accession of C. aronia var. minuta; Y3 accession of Crataegus orientalis var. orientalis and A3 accession of Crataegus monogyna subsp. azarella). Antioxidant activity and total phenolic content of fruits were determined by β-carotene bleaching and Folin-Ciocalteu assays. Antioxidant capacity was determined by using 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. C. monogyna subsp. azarella had the highest total phenol, antioxidant activity and antioxidant capacity of 55.2 mg gallic acid equivalents (GAE)/g dry weight (DW), 81.9% and 31.2%, respectively. C. aronia var. aronia was found to have the lowest total phenolic content (35.7 mg GAE/g DW). The antioxidant activities of fruit extracts increased in the order of C. orientalis var. orientalis < C. aronia var. minuta < C. aronia var. dentata < C. aronia var. aronia < C. monogyna subsp. azarella according to β-carotene/linoleic acid assay. In recent years, C. aronia var. dentata has gained importance as a commercial species in this region. B3 and B7 accessions had fruit weight more than 14 g, and considerable total phenol content, antioxidant activity and antioxidant capacity. This investigation shows the potential value of hawthorn fruit species as a good source of natural antioxidants and that consumption of hawthorn fruit or its products may contribute substantial amounts of antioxidants to the diet.

Phytochemical characterization of several hawthorn (Crataegus spp.) species sampled from the Eastern Mediterranean region of Turkey

PubMed Central

Çalişkan, Oğuzhan; Gündüz, Kazim; Serçe, Sedat; Toplu, Celil; Kamiloğlu, Önder; Şengül, Memnune; Ercişli, Sezai

2012-01-01

Background: We evaluated the total phenolic content, antioxidant capacity as well as antioxidant activity of five Crataegus species (A1, A2, Y1, Y2, Y4 accessions of Crataegus aronia var. aronia; B2, B3, B5, B6, B7, B9, Y5 accessions of C. aronia var. dentata; B10 accession of C. aronia var. minuta; Y3 accession of Crataegus orientalis var. orientalis and A3 accession of Crataegus monogyna subsp. azarella). Materials and Methods: Antioxidant activity and total phenolic content of fruits were determined by β-carotene bleaching and Folin–Ciocalteu assays. Antioxidant capacity was determined by using 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. Results: C. monogyna subsp. azarella had the highest total phenol, antioxidant activity and antioxidant capacity of 55.2 mg gallic acid equivalents (GAE)/g dry weight (DW), 81.9% and 31.2%, respectively. C. aronia var. aronia was found to have the lowest total phenolic content (35.7 mg GAE/g DW). The antioxidant activities of fruit extracts increased in the order of C. orientalis var. orientalis < C. aronia var. minuta < C. aronia var. dentata < C. aronia var. aronia < C. monogyna subsp. azarella according to β-carotene/linoleic acid assay. In recent years, C. aronia var. dentata has gained importance as a commercial species in this region. B3 and B7 accessions had fruit weight more than 14 g, and considerable total phenol content, antioxidant activity and antioxidant capacity. Conclusion: This investigation shows the potential value of hawthorn fruit species as a good source of natural antioxidants and that consumption of hawthorn fruit or its products may contribute substantial amounts of antioxidants to the diet. PMID:22438658

Texture classification using autoregressive filtering

NASA Technical Reports Server (NTRS)

Lawton, W. M.; Lee, M.

1984-01-01

A general theory of image texture models is proposed and its applicability to the problem of scene segmentation using texture classification is discussed. An algorithm, based on half-plane autoregressive filtering, which optimally utilizes second order statistics to discriminate between texture classes represented by arbitrary wide sense stationary random fields is described. Empirical results of applying this algorithm to natural and sysnthesized scenes are presented and future research is outlined.

Comparative Analysis of VaR Estimation of Double Long-Memory GARCH Models: Empirical Analysis of China's Stock Market

NASA Astrophysics Data System (ADS)

Cao, Guangxi; Guo, Jianping; Xu, Lin

GARCH models are widely used to model the volatility of financial assets and measure VaR. Based on the characteristics of long-memory and lepkurtosis and fat tail of stock market return series, we compared the ability of double long-memory GARCH models with skewed student-t-distribution to compute VaR, through the empirical analysis of Shanghai Composite Index (SHCI) and Shenzhen Component Index (SZCI). The results show that the ARFIMA-HYGARCH model performance better than others, and at less than or equal to 2.5 percent of the level of VaR, double long-memory GARCH models have stronger ability to evaluate in-sample VaRs in long position than in short position while there is a diametrically opposite conclusion for ability of out-of-sample VaR forecast.

Influence ofArtemisia princeps var.orientalis components on callus induction and growth.

PubMed

Kil, B S; Yun, K W; Lee, S Y

1992-08-01

An in vitro study was performed to determine the potential application of tissue culture in determining allelopathic potentialof Artemisia princeps var.Orientalis (wormwood). Aqueous extracts and volatile substances ofA. princeps var.Orientalis were tested to determine their effects on callus induction and growth of several tested species. Extracts of 5%A. princeps var.Orientalis caused some reduction in concentration, induction, and growth of callus, although they looked normal, whereas the expiants of most receptor plants did not develop callus at higher concentration. Lettuce andEclipta prostrata were the most sensitive species, andA. princeps var.Orientalis was affected by its own extracts. The growth of calluses in MS 121 medium treated with essential oil ofA. princeps var.Orientalis was inhibited, and the degree of inhibition was proportional to the concentration of the essential oil.

Semi-nonparametric VaR forecasts for hedge funds during the recent crisis

NASA Astrophysics Data System (ADS)

Del Brio, Esther B.; Mora-Valencia, Andrés; Perote, Javier

2014-05-01

The need to provide accurate value-at-risk (VaR) forecasting measures has triggered an important literature in econophysics. Although these accurate VaR models and methodologies are particularly demanded for hedge fund managers, there exist few articles specifically devoted to implement new techniques in hedge fund returns VaR forecasting. This article advances in these issues by comparing the performance of risk measures based on parametric distributions (the normal, Student’s t and skewed-t), semi-nonparametric (SNP) methodologies based on Gram-Charlier (GC) series and the extreme value theory (EVT) approach. Our results show that normal-, Student’s t- and Skewed t- based methodologies fail to forecast hedge fund VaR, whilst SNP and EVT approaches accurately success on it. We extend these results to the multivariate framework by providing an explicit formula for the GC copula and its density that encompasses the Gaussian copula and accounts for non-linear dependences. We show that the VaR obtained by the meta GC accurately captures portfolio risk and outperforms regulatory VaR estimates obtained through the meta Gaussian and Student’s t distributions.

Protective Antibodies against Placental Malaria and Poor Outcomes during Pregnancy, Benin

PubMed Central

Denoeud-Ndam, Lise; Doritchamou, Justin; Viwami, Firmine; Salanti, Ali; Nielsen, Morten A.; Fievet, Nadine; Massougbodji, Achille; Luty, Adrian J.F.; Deloron, Philippe

2015-01-01

Placental malaria is caused by Plasmodium falciparum–infected erythrocytes that bind to placental tissue. Binding is mediated by VAR2CSA, a parasite antigen coded by the var gene, which interacts with chondroitin sulfate A (CSA). Consequences include maternal anemia and fetal growth retardation. Antibody-mediated immunity to placental malaria is acquired during successive pregnancies, but the target of VAR2CSA-specific protective antibodies is unclear. We assessed VAR2CSA-specific antibodies in pregnant women and analyzed their relationships with protection against placental infection, preterm birth, and low birthweight. Antibody responses to the N-terminal region of VAR2CSA during early pregnancy were associated with reduced risks for infections and low birthweight. Among women infected during pregnancy, an increase in CSA binding inhibition was associated with reduced risks for placental infection, preterm birth, and low birthweight. These data suggest that antibodies against VAR2CSA N-terminal region mediate immunity to placental malaria and associated outcomes. Our results validate current vaccine development efforts with VAR2CSA N-terminal constructs. PMID:25898123

Fractal and chaotic laws on seismic dissipated energy in an energy system of engineering structures

NASA Astrophysics Data System (ADS)

Cui, Yu-Hong; Nie, Yong-An; Yan, Zong-Da; Wu, Guo-You

1998-09-01

Fractal and chaotic laws of engineering structures are discussed in this paper, it means that the intrinsic essences and laws on dynamic systems which are made from seismic dissipated energy intensity E d and intensity of seismic dissipated energy moment I e are analyzed. Based on the intrinsic characters of chaotic and fractal dynamic system of E d and I e, three kinds of approximate dynamic models are rebuilt one by one: index autoregressive model, threshold autoregressive model and local-approximate autoregressive model. The innate laws, essences and systematic error of evolutional behavior I e are explained over all, the short-term behavior predictability and long-term behavior probability of which are analyzed in the end. That may be valuable for earthquake-resistant theory and analysis method in practical engineering structures.

Improved L-BFGS diagonal preconditioners for a large-scale 4D-Var inversion system: application to CO2 flux constraints and analysis error calculation

NASA Astrophysics Data System (ADS)

Bousserez, Nicolas; Henze, Daven; Bowman, Kevin; Liu, Junjie; Jones, Dylan; Keller, Martin; Deng, Feng

2013-04-01

This work presents improved analysis error estimates for 4D-Var systems. From operational NWP models to top-down constraints on trace gas emissions, many of today's data assimilation and inversion systems in atmospheric science rely on variational approaches. This success is due to both the mathematical clarity of these formulations and the availability of computationally efficient minimization algorithms. However, unlike Kalman Filter-based algorithms, these methods do not provide an estimate of the analysis or forecast error covariance matrices, these error statistics being propagated only implicitly by the system. From both a practical (cycling assimilation) and scientific perspective, assessing uncertainties in the solution of the variational problem is critical. For large-scale linear systems, deterministic or randomization approaches can be considered based on the equivalence between the inverse Hessian of the cost function and the covariance matrix of analysis error. For perfectly quadratic systems, like incremental 4D-Var, Lanczos/Conjugate-Gradient algorithms have proven to be most efficient in generating low-rank approximations of the Hessian matrix during the minimization. For weakly non-linear systems though, the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS), a quasi-Newton descent algorithm, is usually considered the best method for the minimization. Suitable for large-scale optimization, this method allows one to generate an approximation to the inverse Hessian using the latest m vector/gradient pairs generated during the minimization, m depending upon the available core memory. At each iteration, an initial low-rank approximation to the inverse Hessian has to be provided, which is called preconditioning. The ability of the preconditioner to retain useful information from previous iterations largely determines the efficiency of the algorithm. Here we assess the performance of different preconditioners to estimate the inverse Hessian of a large-scale 4D-Var system. The impact of using the diagonal preconditioners proposed by Gilbert and Le Maréchal (1989) instead of the usual Oren-Spedicato scalar will be first presented. We will also introduce new hybrid methods that combine randomization estimates of the analysis error variance with L-BFGS diagonal updates to improve the inverse Hessian approximation. Results from these new algorithms will be evaluated against standard large ensemble Monte-Carlo simulations. The methods explored here are applied to the problem of inferring global atmospheric CO2 fluxes using remote sensing observations, and are intended to be integrated with the future NASA Carbon Monitoring System.

Comparative study on the chemical composition, antioxidant properties and hypoglycaemic activities of two Capsicum annuum L. cultivars (Acuminatum small and Cerasiferum).

PubMed

Tundis, Rosa; Loizzo, Monica R; Menichini, Federica; Bonesi, Marco; Conforti, Filomena; Statti, Giancarlo; De Luca, Damiano; de Cindio, Bruno; Menichini, Francesco

2011-09-01

The present study aimed to evaluate for the first time the phenols, flavonoids, carotenoids, capsaicin and dihydrocapsaicin content and the antioxidant and hypoglycemic properties of Capsicum annuum var. acuminatum small and C. annuum var. cerasiferum air-dried fruits. The ethanol extract of C. annuum var. acuminatum small, characterized by the major content of total poliphenols, flavonoids, carotenoids and capsaicinoids, showed the highest radical scavenging activity (IC(50) of 152.9 μg/ml). On the contrary, C. annuum var. cerasiferum showed a significant antioxidant activity evaluated by the β-carotene bleaching test (IC(50) of 3.1 μg/ml). The lipophilic fraction of both C. annuum var. acuminatum and C. annuum var. cerasiferum exhibited an interesting and selective inhibitory activity against α-amylase (IC(50) of 6.9 and 20.1 μg/ml, respectively).

Design of inhibitors of thymidylate kinase from Variola virus as new selective drugs against smallpox.

PubMed

Guimarães, Ana P; de Souza, Felipe R; Oliveira, Aline A; Gonçalves, Arlan S; de Alencastro, Ricardo B; Ramalho, Teodorico C; França, Tanos C C

2015-02-16

Recently we constructed a homology model of the enzyme thymidylate kinase from Variola virus (VarTMPK) and proposed it as a new target to the drug design against smallpox. In the present work, we used the antivirals cidofovir and acyclovir as reference compounds to choose eleven compounds as leads to the drug design of inhibitors for VarTMPK. Docking and molecular dynamics (MD) studies of the interactions of these compounds inside VarTMPK and human TMPK (HssTMPK) suggest that they compete for the binding region of the substrate and were used to propose the structures of ten new inhibitors for VarTMPK. Further docking and MD simulations of these compounds, inside VarTMPK and HssTMPK, suggest that nine among ten are potential selective inhibitors of VarTMPK. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application

NASA Astrophysics Data System (ADS)

Razavi, Saman; Gupta, Hoshin V.

2016-01-01

Based on the theoretical framework for sensitivity analysis called "Variogram Analysis of Response Surfaces" (VARS), developed in the companion paper, we develop and implement a practical "star-based" sampling strategy (called STAR-VARS), for the application of VARS to real-world problems. We also develop a bootstrap approach to provide confidence level estimates for the VARS sensitivity metrics and to evaluate the reliability of inferred factor rankings. The effectiveness, efficiency, and robustness of STAR-VARS are demonstrated via two real-data hydrological case studies (a 5-parameter conceptual rainfall-runoff model and a 45-parameter land surface scheme hydrology model), and a comparison with the "derivative-based" Morris and "variance-based" Sobol approaches are provided. Our results show that STAR-VARS provides reliable and stable assessments of "global" sensitivity across the full range of scales in the factor space, while being 1-2 orders of magnitude more efficient than the Morris or Sobol approaches.

Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation

NASA Astrophysics Data System (ADS)

Huang, Mengyun; Li, Wei; Liu, Zhangyun; Cheng, Linghao; Guan, Bai-Ou

2018-06-01

Auto-regressive (AR) spectral estimation technology is proposed to analyze the Brillouin scattering spectrum in Brillouin optical time-domain refelectometry. It shows that AR based method can reliably estimate the Brillouin frequency shift with an accuracy much better than fast Fourier transform (FFT) based methods provided the data length is not too short. It enables about 3 times improvement over FFT at a moderate spatial resolution.

Hedonic price models with omitted variables and measurement errors: a constrained autoregression-structural equation modeling approach with application to urban Indonesia

NASA Astrophysics Data System (ADS)

Suparman, Yusep; Folmer, Henk; Oud, Johan H. L.

2014-01-01

Omitted variables and measurement errors in explanatory variables frequently occur in hedonic price models. Ignoring these problems leads to biased estimators. In this paper, we develop a constrained autoregression-structural equation model (ASEM) to handle both types of problems. Standard panel data models to handle omitted variables bias are based on the assumption that the omitted variables are time-invariant. ASEM allows handling of both time-varying and time-invariant omitted variables by constrained autoregression. In the case of measurement error, standard approaches require additional external information which is usually difficult to obtain. ASEM exploits the fact that panel data are repeatedly measured which allows decomposing the variance of a variable into the true variance and the variance due to measurement error. We apply ASEM to estimate a hedonic housing model for urban Indonesia. To get insight into the consequences of measurement error and omitted variables, we compare the ASEM estimates with the outcomes of (1) a standard SEM, which does not account for omitted variables, (2) a constrained autoregression model, which does not account for measurement error, and (3) a fixed effects hedonic model, which ignores measurement error and time-varying omitted variables. The differences between the ASEM estimates and the outcomes of the three alternative approaches are substantial.

Sex differences in attenuation of nicotine reinstatement after individual and combined treatments of progesterone and varenicline.

PubMed

Swalve, Natashia; Smethells, John R; Carroll, Marilyn E

2016-07-15

Tobacco use is the largest cause of preventable mortality in the western world. Even after treatment, relapse rates for tobacco are high, and more effective pharmacological treatments are needed. Progesterone (PRO), a female hormone used in contraceptives, reduces stimulant use but its effects on tobacco addiction are unknown. Varenicline (VAR) is a commonly used medication that reduces tobacco use. The present study examined sex differences in the individual vs. combined effects of PRO and VAR on reinstatement of nicotine-seeking behavior in a rat model of relapse. Adult female and male Wistar rats self-administered nicotine (NIC, 0.03mg/kg/infusion) for 14days followed by 21days of extinction when no cues or drug were present. Rats were then divided into 4 treatment groups: control (VEH+SAL), PRO alone (PRO+SAL), VAR alone (VEH+VAR) and the combination (PRO+VAR). Reinstatement of nicotine-seeking behavior induced by priming injections of NIC or caffeine (CAF), presentation of cues (CUES), and the combination of drugs and cues (e.g. NIC+CUES, CAF+CUES) were tested after extinction. Male and female rats did not differ in self-administration of nicotine or extinction responding, and both showed elevated levels of responding to the CAF+CUES condition. However, males, but not females, reinstated active lever-pressing to the NIC+CUES condition, and that was attenuated by both VAR and VAR+PRO treatment. Thus, males were more sensitive to NIC+CUE-induced reinstatement than females, and VAR alone and VAR combined with PRO effectively reduced nicotine relapse. Copyright © 2016 Elsevier B.V. All rights reserved.

JIL-1 and Su(var)3-7 Interact Genetically and Counteract Each Other's Effect on Position-Effect Variegation in Drosophila

PubMed Central

Deng, Huai; Cai, Weili; Wang, Chao; Lerach, Stephanie; Delattre, Marion; Girton, Jack; Johansen, Jørgen; Johansen, Kristen M.

2010-01-01

The essential JIL-1 histone H3S10 kinase is a key regulator of chromatin structure that functions to maintain euchromatic domains while counteracting heterochromatization and gene silencing. In the absence of the JIL-1 kinase, two of the major heterochromatin markers H3K9me2 and HP1a spread in tandem to ectopic locations on the chromosome arms. Here we address the role of the third major heterochromatin component, the zinc-finger protein Su(var)3-7. We show that the lethality but not the chromosome morphology defects associated with the null JIL-1 phenotype to a large degree can be rescued by reducing the dose of the Su(var)3-7 gene and that Su(var)3-7 and JIL-1 loss-of-function mutations have an antagonistic and counterbalancing effect on position-effect variegation (PEV). Furthermore, we show that in the absence of JIL-1 kinase activity, Su(var)3-7 gets redistributed and upregulated on the chromosome arms. Reducing the dose of the Su(var)3-7 gene dramatically decreases this redistribution; however, the spreading of H3K9me2 to the chromosome arms was unaffected, strongly indicating that ectopic Su(var)3-9 activity is not a direct cause of lethality. These observations suggest a model where Su(var)3-7 functions as an effector downstream of Su(var)3-9 and H3K9 dimethylation in heterochromatic spreading and gene silencing that is normally counteracted by JIL-1 kinase activity. PMID:20457875

Burkitt lymphoma expresses oncofetal chondroitin sulfate without being a reservoir for placental malaria sequestration.

PubMed

Agerbaek, Mette Ø; Pereira, Marina A; Clausen, Thomas M; Pehrson, Caroline; Oo, Htoo Zarni; Spliid, Charlotte; Rich, Jamie R; Fung, Vincent; Nkrumah, Francis; Neequaye, Janet; Biggar, Robert J; Reynolds, Steven J; Tosato, Giovanna; Pullarkat, Sheeja T; Ayers, Leona W; Theander, Thor G; Daugaard, Mads; Bhatia, Kishor; Nielsen, Morten A; Mbulaiteye, Sam M; Salanti, Ali

2017-04-01

Burkitt lymphoma (BL) is a malignant disease, which is frequently found in areas with holoendemic Plasmodium falciparum malaria. We have previously found that the VAR2CSA protein is present on malaria-infected erythrocytes and facilitates a highly specific binding to the placenta. ofCS is absent in other non-malignant tissues and thus VAR2CSA generally facilitates parasite sequestration and accumulation in pregnant women. In this study, we show that the specific receptor for VAR2CSA, the oncofetal chondroitin sulfate (ofCS), is likewise present in BL tissue and cell lines. We therefore explored whether ofCS in BL could act as anchor site for VAR2CSA-expressing infected erythrocytes. In contrast to the placenta, we found no evidence of in vivo sequestering of infected erythrocytes in the BL tissue. Furthermore, we found VAR2CSA-specific antibody titers in children with endemic BL to be lower than in control children from the same malaria endemic region. The abundant presence of ofCS in BL tissue and the absence of ofCS in non-malignant tissue encouraged us to examine whether recombinant VAR2CSA could be used to target BL. We confirmed the binding of VAR2CSA to BL-derived cells and showed that a VAR2CSA drug conjugate efficiently killed the BL-derived cell lines in vitro. These results identify ofCS as a novel therapeutic BL target and highlight how VAR2CSA could be used as a tool for the discovery of novel approaches for directing BL therapy. © 2016 UICC.

Burkitt lymphoma express oncofetal Chondroitin Sulfate without being a reservoir for placental malaria sequestration

PubMed Central

Agerbæk, Mette Ø.; Pereira, Marina A.; Clausen, Thomas M.; Pehrson, Caroline; Oo, Htoo Zarni; Spliid, Charlotte; Rich, Jamie R.; Fung, Vincent; Nkrumah, Francis; Neequaye, Janet; Biggar, Robert J.; Reynolds, Steven J.; Tosato, Giovanna; Pullarkat, Sheeja T.; Ayers, Leona W.; Theander, Thor G.; Daugaard, Mads; Bhatia, Kishor; Nielsen, Morten A.; Mbulaiteye, Sam M.; Salanti, Ali

2016-01-01

Burkitt lymphoma (BL) is a malignant disease, which is frequently found in areas with holoendemic Plasmodium falciparum malaria. We have previously found that the VAR2CSA protein is present on malaria-infected erythrocytes and facilitates a highly specific binding to the placenta. OfCS is absent from other non-malignant tissues and thus VAR2CSA generally facilitates parasite sequestration and accumulation in pregnant women. In this study, we show that the specific receptor for VAR2CSA, the oncofetal chondroitin sulfate (ofCS), is likewise present in BL tissue and cell lines. We therefore explored whether ofCS in BL could act as anchor-site for VAR2CSA-expressing infected erythrocytes. In contrast to the placenta, we found no evidence of in vivo sequestering of infected erythrocytes in the BL tissue. Furthermore, we found VAR2CSA specific antibody titers in children with endemic BL to be lower than in control children from the same malaria endemic region. The abundant presence of ofCS in BL tissue and the absence of ofCS in non-malignant tissue, encouraged us to examine whether recombinant VAR2CSA could be used to target BL. We confirmed the binding of VAR2CSA to BL-derived cells and showed that a VAR2CSA drug conjugate efficiently killed the BL-derived cell lines in vitro. These results identify ofCS as a novel therapeutic BL target and highlight how VAR2CSA could be used as a tool for the discovery of novel approaches for directing BL therapy. PMID:27997697

A Novel Virus-Like Particle Based Vaccine Platform Displaying the Placental Malaria Antigen VAR2CSA.

PubMed

Thrane, Susan; Janitzek, Christoph M; Agerbæk, Mette Ø; Ditlev, Sisse B; Resende, Mafalda; Nielsen, Morten A; Theander, Thor G; Salanti, Ali; Sander, Adam F

2015-01-01

Placental malaria caused by Plasmodium falciparum is a major cause of mortality and severe morbidity. Clinical testing of a soluble protein-based vaccine containing the parasite ligand, VAR2CSA, has been initiated. VAR2CSA binds to the human receptor chondroitin sulphate A (CSA) and is responsible for sequestration of Plasmodium falciparum infected erythrocytes in the placenta. It is imperative that a vaccine against malaria in pregnancy, if administered to women before they become pregnant, can induce a strong and long lasting immune response. While most soluble protein-based vaccines have failed during clinical testing, virus-like particle (VLP) based vaccines (e.g., the licensed human papillomavirus vaccines) have demonstrated high efficacy, suggesting that the spatial assembly of the vaccine antigen is a critical parameter for inducing an optimal long-lasting protective immune response. We have developed a VLP vaccine display platform by identifying regions of the HPV16 L1 coat protein where a biotin acceptor site (AviTagTM) can be inserted without compromising VLP-assembly. Subsequent biotinylation of Avi-L1 VLPs allow us to anchor monovalent streptavidin (mSA)-fused proteins to the biotin, thereby obtaining a dense and repetitive VLP-display of the vaccine antigen. The mSA-VAR2CSA antigen was delivered on the Avi-L1 VLP platform and tested in C57BL/6 mice in comparison to two soluble protein-based vaccines consisting of naked VAR2CSA and mSA-VAR2CSA. The mSA-VAR2CSA Avi-L1 VLP and soluble mSA-VAR2CSA vaccines induced higher antibody titers than the soluble naked VAR2CSA vaccine after three immunizations. The VAR2CSA Avi-L1 VLP vaccine induced statistically significantly higher endpoint titres compared to the soluble mSA-VAR2CSA vaccine, after 1st and 2nd immunization; however, this difference was not statistically significant after 3rd immunization. Importantly, the VLP-VAR2CSA induced antibodies were functional in inhibiting the binding of parasites to CSA. This study demonstrates that the described Avi-L1 VLP-platform may serve as a versatile system for facilitating optimal VLP-display of large and complex vaccine antigens.

[Control of culicides by using Bacillus thuringiensis SH-14 var. israelensis in permanent breeding places of Fomento, province of Sancti Spiritus, Cuba].

PubMed

Cruz Pineda, Carlos Alberto; Montero Lago, Grisel; Navarro Ortega, Agustín; Morejón Martín, Pedro Lorenzo

2005-01-01

An ecological evaluation of retrospective and descriptive temporary trend was conducted from 1999 to 2000 .in 8 water bodies of Fomento, province of Sancti Spiritus. To evaluate the effectiveness and permanence of the biolarvicide, there were used data of systematic samplings and of control actions of the provincial surveillance and antivectorial fight programs taken from the records of each breeding place in the Municipal Unit of Higiene and Epidemiology of the locality. Doses of 10 ml of active ingredient per square meter were administered. It was attained the reduction and stabilization of the larval and adult indices in human primer of important species which are vectors of malaria, phyliriasis, and Western Nile's fever. The extension of the larval recovery range up to 3 weeks was proved.

Morphological analyses suggest a new taxonomic circumscription for Hymenaea courbaril L. (Leguminosae, Caesalpinioideae)

PubMed Central

Souza, Isys Mascarenhas; Funch, Ligia Silveira; de Queiroz, Luciano Paganucci

2014-01-01

Abstract Hymenaea is a genus of the Resin-producing Clade of the tribe Detarieae (Leguminosae: Caesalpinioideae) with 14 species. Hymenaea courbaril is the most widespread species of the genus, ranging from southern Mexico to southeastern Brazil. As currently circumscribed, Hymenaea courbaril is a polytypic species with six varieties: var. altissima, var. courbaril, var. longifolia, var. stilbocarpa, var. subsessilis, and var. villosa. These varieties are distinguishable mostly by traits related to leaflet shape and indumentation, and calyx indumentation. We carried out morphometric analyses of 14 quantitative (continuous) leaf characters in order to assess the taxonomy of Hymenaea courbaril under the Unified Species Concept framework. Cluster analysis used the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) based on Bray-Curtis dissimilarity matrices. Principal Component Analyses (PCA) were carried out based on the same morphometric matrix. Two sets of Analyses of Similarity and Non Parametric Multivariate Analysis of Variance were carried out to evaluate statistical support (1) for the major groups recovered using UPGMA and PCA, and (2) for the varieties. All analyses recovered three major groups coincident with (1) var. altissima, (2) var. longifolia, and (3) all other varieties. These results, together with geographical and habitat information, were taken as evidence of three separate metapopulation lineages recognized here as three distinct species. Nomenclatural adjustments, including reclassifying formerly misapplied types, are proposed. PMID:25009440

Morphological analyses suggest a new taxonomic circumscription for Hymenaea courbaril L. (Leguminosae, Caesalpinioideae).

PubMed

Souza, Isys Mascarenhas; Funch, Ligia Silveira; de Queiroz, Luciano Paganucci

2014-01-01

Hymenaea is a genus of the Resin-producing Clade of the tribe Detarieae (Leguminosae: Caesalpinioideae) with 14 species. Hymenaea courbaril is the most widespread species of the genus, ranging from southern Mexico to southeastern Brazil. As currently circumscribed, Hymenaea courbaril is a polytypic species with six varieties: var. altissima, var. courbaril, var. longifolia, var. stilbocarpa, var. subsessilis, and var. villosa. These varieties are distinguishable mostly by traits related to leaflet shape and indumentation, and calyx indumentation. We carried out morphometric analyses of 14 quantitative (continuous) leaf characters in order to assess the taxonomy of Hymenaea courbaril under the Unified Species Concept framework. Cluster analysis used the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) based on Bray-Curtis dissimilarity matrices. Principal Component Analyses (PCA) were carried out based on the same morphometric matrix. Two sets of Analyses of Similarity and Non Parametric Multivariate Analysis of Variance were carried out to evaluate statistical support (1) for the major groups recovered using UPGMA and PCA, and (2) for the varieties. All analyses recovered three major groups coincident with (1) var. altissima, (2) var. longifolia, and (3) all other varieties. These results, together with geographical and habitat information, were taken as evidence of three separate metapopulation lineages recognized here as three distinct species. Nomenclatural adjustments, including reclassifying formerly misapplied types, are proposed.

Oil and fatty acid contents in seed of Citrullus lanatus Schrad.

PubMed

Jarret, Robert L; Levy, Irvin J

2012-05-23

Intact seed of 475 genebank accessions of Citrullus ( C. lanatus var. lanatus and C. lanatus var. citroides) were analyzed for percent oil content using TD-NMR. Extracts from whole seed of 96 accessions of C. lanatus (30 var. citroides, 33 var. lanatus, and 33 egusi), C. colocynthis (n = 3), C. ecirrhosus (n = 1), C. rehmii (n = 1), and Benincasa fistulosa (n = 3) were also analyzed for their fatty acids content. Among the materials analyzed, seed oil content varied from 14.8 to 43.5%. Mean seed oil content in egusi types of C. lanatus was significantly higher (mean = 35.6%) than that of either var. lanatus (mean = 23.2%) or var. citroides (mean = 22.6%). Egusi types of C. lanatus had a significantly lower hull/kernel ratio when compared to other C. lanatus var. lanatus or C. lanatus var. citroides. The principal fatty acid in all C. lanatus materials examined was linoleic acid (43.6-73%). High levels of linoleic acid were also present in the materials of C. colocynthis (71%), C. ecirrhosus (62.7%), C. rehmii (75.8%), and B. fistulosa (73.2%), which were included for comparative purposes. Most all samples contained traces (<0.5%) of arachidonic acid. The data presented provide novel information on the range in oil content and variability in the concentrations of individual fatty acids present in a diverse array of C. lanatus, and its related species, germplasm.

The Emotion Recognition System Based on Autoregressive Model and Sequential Forward Feature Selection of Electroencephalogram Signals

PubMed Central

Hatamikia, Sepideh; Maghooli, Keivan; Nasrabadi, Ali Motie

2014-01-01

Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K-nearest neighbor (KNN) classifier using EEG signals during emotional audio-visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg's method) based on Levinson-Durbin's recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies–Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10-15% as compared to Davies–Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively. PMID:25298928

A hybrid approach to generating search subspaces in dynamically constrained 4-dimensional data assimilation

NASA Astrophysics Data System (ADS)

Yaremchuk, Max; Martin, Paul; Beattie, Christopher

2017-09-01

Development and maintenance of the linearized and adjoint code for advanced circulation models is a challenging issue, requiring a significant proportion of total effort in operational data assimilation (DA). The ensemble-based DA techniques provide a derivative-free alternative, which appears to be competitive with variational methods in many practical applications. This article proposes a hybrid scheme for generating the search subspaces in the adjoint-free 4-dimensional DA method (a4dVar) that does not use a predefined ensemble. The method resembles 4dVar in that the optimal solution is strongly constrained by model dynamics and search directions are supplied iteratively using information from the current and previous model trajectories generated in the process of optimization. In contrast to 4dVar, which produces a single search direction from exact gradient information, a4dVar employs an ensemble of directions to form a subspace in order to proceed. In the earlier versions of a4dVar, search subspaces were built using the leading EOFs of either the model trajectory or the projections of the model-data misfits onto the range of the background error covariance (BEC) matrix at the current iteration. In the present study, we blend both approaches and explore a hybrid scheme of ensemble generation in order to improve the performance and flexibility of the algorithm. In addition, we introduce balance constraints into the BEC structure and periodically augment the search ensemble with BEC eigenvectors to avoid repeating minimization over already explored subspaces. Performance of the proposed hybrid a4dVar (ha4dVar) method is compared with that of standard 4dVar in a realistic regional configuration assimilating real data into the Navy Coastal Ocean Model (NCOM). It is shown that the ha4dVar converges faster than a4dVar and can be potentially competitive with 4dvar both in terms of the required computational time and the forecast skill.

Development and characterization of 16 polymorphic microsatellite markers from Taiwan cow-tail fir, Keteleeria davidiana var. formosana (Pinaceae) and cross-species amplification in other Keteleeria taxa

PubMed Central

2014-01-01

Background Keteleeria davidiana var. formosana (Pinaceae), Taiwan cow-tail fir, is an endangered species listed on the IUCN Red List of Threatened Species and only two populations remain, both on the Taiwan Island. Sixteen polymorphic microsatellite loci were developed in an endangered and endemic gymnosperm species, Keteleeria davidiana var. formosana, and were tested in an additional 6 taxa, K. davidiana var. calcarea, K. davidiana var. chienpeii, K. evelyniana, K. fortunei, K. fortunei var. cyclolepis, and K. pubescens, to evaluate the genetic variation available for conservation management and to reconstruct the phylogeographic patterns of this ancient lineage. Findings Polymorphic primer sets were developed from K. davidiana var. formosana using the modified AFLP and magnetic bead enrichment method. The number of alleles ranged from 3 to 16, with the observed heterozygosity ranging from 0.28 to 1.00. All of the loci were found to be interspecifically amplifiable. Conclusions These polymorphic and transferable loci will be potentially useful for future studies that will focus on identifying distinct evolutionary units within species and establishing the phylogeographic patterns and the process of speciation among closely related species. PMID:24755442

Fluorescence Spectra of Individual Flowing Airborne Biological Particles Measured in Real Time

DTIC Science & Technology

2001-02-01

and fungal spores ( Aspergillus versicolor, ATCC 9577). B. subtilis var. niger (lyophilized cells) and E. herbicola were grown by streak- ing onto...Excitation Figure 7 shows fluorescence spectra of B. subtilis var. niger vegetative cells and fungal spores ( Aspergillus versicolor), both 5 µm in diameter...µm-diam clusters of B. subtilis var. niger spores, and B. subtilis var. niger vegetative cells ……………………………………… 10 5. Fluorescence spectra of starved

An unusual clinical presentation of tinea faciei caused by Trichophyton mentagrophytes var. erinacei.

PubMed

Lee, Deok-Woo; Yang, Ji-Hye; Choi, Seok-Joo; Won, Chong-Hyun; Chang, Sung-Eun; Lee, Mi-Woo; Choi, Jee-Ho; Moon, Kee-Chan; Kim, Mi-Na

2011-01-01

Trichophyton mentagrophytes var. erinacei, the natural host of which is the hedgehog, has been found to cause highly inflammatory and pruritic eruptions, including tinea manuum, tinea corporis, nail infection, kerion, scalp infection, and tinea barbae. To our knowledge, however, no reports have been made of tinea faciei caused by Trichophyton mentagrophytes var. erinacei in the English language literature. We provide here the case of tinea faciei caused by Trichophyton mentagrophytes var. erinacei. © 2011 Wiley Periodicals, Inc.

Modelling of volatility in monetary transmission mechanism

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

Dobešová, Anna; Klepáč, Václav; Kolman, Pavel

2015-03-10

The aim of this paper is to compare different approaches to modeling of volatility in monetary transmission mechanism. For this purpose we built time-varying parameter VAR (TVP-VAR) model with stochastic volatility and VAR-DCC-GARCH model with conditional variance. The data from three European countries are included in the analysis: the Czech Republic, Germany and Slovakia. Results show that VAR-DCC-GARCH system captures higher volatility of observed variables but main trends and detected breaks are generally identical in both approaches.

Modified Confidence Intervals for the Mean of an Autoregressive Process.

DTIC Science & Technology

1985-08-01

Validity of the method 45 3.6 Theorem 47 4 Derivation of corrections 48 Introduction 48 The zero order pivot 50 4.1 Algorithm 50 CONTENTS The first...of standard confidence intervals. There are several standard methods of setting confidence intervals in simulations, including the regener- ative... method , batch means, and time series methods . We-will focus-s on improved confidence intervals for the mean of an autoregressive process, and as such our

Autoregressive Methods for Spectral Estimation from Interferograms.

DTIC Science & Technology

1986-09-19

RL83 6?6 AUTOREGRESSIVE METHODS FOR SPECTRAL. ESTIMTION FROM / SPACE ENGINEERING E N RICHARDS ET AL. 19 SEPINEFRGAS.()UA TT NV GNCNE O C: 31SSF...was AUG1085 performed under subcontract to . Center for Space Engineering Utah State University Logan, UT 84322-4140 4 4 Scientific Report No. 17 AFGL...MONITORING ORGANIZATION Center for Space Engineering (iapplicable) Air Force Geophysics Laboratory e. AORESS (City. State and ZIP Code) 7b. AOORESS (City

Detecting P and S-wave of Mt. Rinjani seismic based on a locally stationary autoregressive (LSAR) model

NASA Astrophysics Data System (ADS)

Nurhaida, Subanar, Abdurakhman, Abadi, Agus Maman

2017-08-01

Seismic data is usually modelled using autoregressive processes. The aim of this paper is to find the arrival times of the seismic waves of Mt. Rinjani in Indonesia. Kitagawa algorithm's is used to detect the seismic P and S-wave. Householder transformation used in the algorithm made it effectively finding the number of change points and parameters of the autoregressive models. The results show that the use of Box-Cox transformation on the variable selection level makes the algorithm works well in detecting the change points. Furthermore, when the basic span of the subinterval is set 200 seconds and the maximum AR order is 20, there are 8 change points which occur at 1601, 2001, 7401, 7601,7801, 8001, 8201 and 9601. Finally, The P and S-wave arrival times are detected at time 1671 and 2045 respectively using a precise detection algorithm.

Parentage determination of Vanda Miss Joaquim (Orchidaceae) through two chloroplast genes rbcL and matK

PubMed Central

Khew, Gillian Su-Wen; Chia, Tet Fatt

2011-01-01

Background and aims The popular hybrid orchid Vanda Miss Joaquim was made Singapore's national flower in 1981. It was originally described in the Gardeners’ Chronicle in 1893, as a cross between Vanda hookeriana and Vanda teres. However, no record had been kept as to which parent contributed the pollen. This study was conducted using DNA barcoding techniques to determine the pod parent of V. Miss Joaquim, thereby inferring the pollen parent of the hybrid by exclusion. Methodology Two chloroplast genes, matK and rbcL, from five related taxa, V. hookeriana, V. teres var. alba, V. teres var. andersonii, V. teres var. aurorea and V. Miss Joaquim ‘Agnes’, were sequenced. The matK gene from herbarium specimens of V. teres and V. Miss Joaquim, both collected in 1893, was also sequenced. Principal results No sequence variation was found in the 600-bp region of rbcL sequenced. Sequence variation was found in the matK gene of V. hookeriana, V. teres var. alba, V. teres var. aurorea and V. Miss Joaquim ‘Agnes’. Complete sequence identity was established between V. teres var. andersonii and V. Miss Joaquim ‘Agnes’. The matK sequences obtained from the herbarium specimens of V. teres and V. Miss Joaquim were completely identical to the sequences obtained from the fresh samples of V. teres var. andersonii and V. Miss Joaquim ‘Agnes’. Conclusions The pod parent of V. Miss Joaquim ‘Agnes’ is V. teres var. andersonii and, by exclusion, the pollen parent is V. hookeriana. The herbarium and fresh samples of V. teres var. andersonii and V. Miss Joaquim share the same inferred maternity. The matK gene was more informative than rbcL and facilitated differentiation of varieties of V. teres. PMID:22476488

Transcriptome sequence analysis of an ornamental plant, Ananas comosus var. bracteatus, revealed the potential unigenes involved in terpenoid and phenylpropanoid biosynthesis.

PubMed

Ma, Jun; Kanakala, S; He, Yehua; Zhang, Junli; Zhong, Xiaolan

2015-01-01

Ananas comosus var. bracteatus (Red Pineapple) is an important ornamental plant for its colorful leaves and decorative red fruits. Because of its complex genome, it is difficult to understand the molecular mechanisms involved in the growth and development. Thus high-throughput transcriptome sequencing of Ananas comosus var. bracteatus is necessary to generate large quantities of transcript sequences for the purpose of gene discovery and functional genomic studies. The Ananas comosus var. bracteatus transcriptome was sequenced by the Illumina paired-end sequencing technology. We obtained a total of 23.5 million high quality sequencing reads, 1,555,808 contigs and 41,052 unigenes. In total 41,052 unigenes of Ananas comosus var. bracteatus, 23,275 unigenes were annotated in the NCBI non-redundant protein database and 23,134 unigenes were annotated in the Swiss-Port database. Out of these, 17,748 and 8,505 unigenes were assigned to gene ontology categories and clusters of orthologous groups, respectively. Functional annotation against Kyoto Encyclopedia of Genes and Genomes Pathway database identified 5,825 unigenes which were mapped to 117 pathways. The assembly predicted many unigenes that were previously unknown. The annotated unigenes were compared against pineapple, rice, maize, Arabidopsis, and sorghum. Unigenes that did not match any of those five sequence datasets are considered to be Ananas comosus var. bracteatus unique. We predicted unigenes encoding enzymes involved in terpenoid and phenylpropanoid biosynthesis. The sequence data provide the most comprehensive transcriptomic resource currently available for Ananas comosus var. bracteatus. To our knowledge; this is the first report on the de novo transcriptome sequencing of the Ananas comosus var. bracteatus. Unigenes obtained in this study, may help improve future gene expression, genetic and genomics studies in Ananas comosus var. bracteatus.

Transcriptome Sequence Analysis of an Ornamental Plant, Ananas comosus var. bracteatus, Revealed the Potential Unigenes Involved in Terpenoid and Phenylpropanoid Biosynthesis

PubMed Central

Ma, Jun; Kanakala, S.; He, Yehua; Zhang, Junli; Zhong, Xiaolan

2015-01-01

Background Ananas comosus var. bracteatus (Red Pineapple) is an important ornamental plant for its colorful leaves and decorative red fruits. Because of its complex genome, it is difficult to understand the molecular mechanisms involved in the growth and development. Thus high-throughput transcriptome sequencing of Ananas comosus var. bracteatus is necessary to generate large quantities of transcript sequences for the purpose of gene discovery and functional genomic studies. Results The Ananas comosus var. bracteatus transcriptome was sequenced by the Illumina paired-end sequencing technology. We obtained a total of 23.5 million high quality sequencing reads, 1,555,808 contigs and 41,052 unigenes. In total 41,052 unigenes of Ananas comosus var. bracteatus, 23,275 unigenes were annotated in the NCBI non-redundant protein database and 23,134 unigenes were annotated in the Swiss-Port database. Out of these, 17,748 and 8,505 unigenes were assigned to gene ontology categories and clusters of orthologous groups, respectively. Functional annotation against Kyoto Encyclopedia of Genes and Genomes Pathway database identified 5,825 unigenes which were mapped to 117 pathways. The assembly predicted many unigenes that were previously unknown. The annotated unigenes were compared against pineapple, rice, maize, Arabidopsis, and sorghum. Unigenes that did not match any of those five sequence datasets are considered to be Ananas comosus var. bracteatus unique. We predicted unigenes encoding enzymes involved in terpenoid and phenylpropanoid biosynthesis. Conclusion The sequence data provide the most comprehensive transcriptomic resource currently available for Ananas comosus var. bracteatus. To our knowledge; this is the first report on the de novo transcriptome sequencing of the Ananas comosus var. bracteatus. Unigenes obtained in this study, may help improve future gene expression, genetic and genomics studies in Ananas comosus var. bracteatus. PMID:25769053

Evaluation of immunogenicity and safety of VARIVAX™ New Seed Process (NSP) in children.

PubMed

Senders, Shelly D; Bundick, Nickoya D; Li, Jianing; Zecca, Carol; Helmond, Frans A

2018-02-01

Prior to availability of an effective vaccine, an estimated 4 million cases of varicella occurred annually in the United States, resulting in 10,000 hospitalizations and over 100 deaths. With the increased usage of a two-dose varicella vaccine (as recommended by the ACIP), approval of other VZV-containing products and the adoption of varicella vaccination in additional countries, the demand for VZV-containing vaccines has increased. This study (NCT02062502) evaluated the safety, tolerability, and immunogenicity of VARIVAX™ (VAR, varicella vaccine live) manufactured using a new seed manufacturing process (VAR NSP ) compared to the currently licensed VAR. Healthy children 12-23 months were randomized (1:1) into Group 1 (2 doses of VAR NSP given concomitantly with M-M-R™ II, ∼3 months apart) versus  Group 2 (2 doses of VAR given concomitantly with M-M-R™ II, ∼3 months apart).  Serum samples collected prior to vaccination on Day 1 and 6 weeks Postdose 1 were tested for antibody to VZV using a glycoprotein enzyme-linked immunosorbent assay (gpELISA).  Safety was assessed Days 1 to 42 following each vaccination. Six weeks Postdose 1, the response rate (percent of subjects with VZV antibody titer ≥5 gpELISA units/mL) of VAR NSP was non-inferior compared to VAR.  Vaccine-related adverse events (AEs) were comparable with the exception of measles-like rash, where a greater number of rashes were observed with VAR than VAR NSP .  The 2 vaccination groups were comparable with incidence rates of AEs, injection-site AEs, vaccine-related AEs, systemic AEs, and serious AEs. This new process is an important innovation for the extreme demand of sustaining sufficient supplies of varicella vaccine to protect our communities against diseases caused by VZV.

Time-series analysis of delta13C from tree rings. I. Time trends and autocorrelation.

PubMed

Monserud, R A; Marshall, J D

2001-09-01

Univariate time-series analyses were conducted on stable carbon isotope ratios obtained from tree-ring cellulose. We looked for the presence and structure of autocorrelation. Significant autocorrelation violates the statistical independence assumption and biases hypothesis tests. Its presence would indicate the existence of lagged physiological effects that persist for longer than the current year. We analyzed data from 28 trees (60-85 years old; mean = 73 years) of western white pine (Pinus monticola Dougl.), ponderosa pine (Pinus ponderosa Laws.), and Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. glauca) growing in northern Idaho. Material was obtained by the stem analysis method from rings laid down in the upper portion of the crown throughout each tree's life. The sampling protocol minimized variation caused by changing light regimes within each tree. Autoregressive moving average (ARMA) models were used to describe the autocorrelation structure over time. Three time series were analyzed for each tree: the stable carbon isotope ratio (delta(13)C); discrimination (delta); and the difference between ambient and internal CO(2) concentrations (c(a) - c(i)). The effect of converting from ring cellulose to whole-leaf tissue did not affect the analysis because it was almost completely removed by the detrending that precedes time-series analysis. A simple linear or quadratic model adequately described the time trend. The residuals from the trend had a constant mean and variance, thus ensuring stationarity, a requirement for autocorrelation analysis. The trend over time for c(a) - c(i) was particularly strong (R(2) = 0.29-0.84). Autoregressive moving average analyses of the residuals from these trends indicated that two-thirds of the individual tree series contained significant autocorrelation, whereas the remaining third were random (white noise) over time. We were unable to distinguish between individuals with and without significant autocorrelation beforehand. Significant ARMA models were all of low order, with either first- or second-order (i.e., lagged 1 or 2 years, respectively) models performing well. A simple autoregressive (AR(1)), model was the most common. The most useful generalization was that the same ARMA model holds for each of the three series (delta(13)C, delta, c(a) - c(i)) for an individual tree, if the time trend has been properly removed for each series. The mean series for the two pine species were described by first-order ARMA models (1-year lags), whereas the Douglas-fir mean series were described by second-order models (2-year lags) with negligible first-order effects. Apparently, the process of constructing a mean time series for a species preserves an underlying signal related to delta(13)C while canceling some of the random individual tree variation. Furthermore, the best model for the overall mean series (e.g., for a species) cannot be inferred from a consensus of the individual tree model forms, nor can its parameters be estimated reliably from the mean of the individual tree parameters. Because two-thirds of the individual tree time series contained significant autocorrelation, the normal assumption of a random structure over time is unwarranted, even after accounting for the time trend. The residuals of an appropriate ARMA model satisfy the independence assumption, and can be used to make hypothesis tests.

[Study on chemical constituents of volatile oil from rhizomes and leaves of Pileostegia viburnoides var. glabrescens by GC-MS].

PubMed

Zou, Ju-Ying; Chen, Sheng-Huang; Li, Qin-Wen; Chen, Han-Jun; Liu, Bei-Bei; Du, Fan

2012-04-01

To analyze the chemical constituents of volatile oil from the rhizomes and leaves of Pileostegia viburnoides var. glabrescens by GC-MS. The volatile oil was extracted from the rhizomes and leaves of Pileostegia viburnoides var. glabrescens by steam distillation. The constituents of volatile oil were identified by GC-MS technology. 37 compounds were identified from the oil of rhizomes. 36 compounds were identified from the oil of leaves. The rhizomes and leaves volatile oil had 18 compounds in common. This study is the first one to report the volatile components of Pileostegia viburnoides var. glabrescens. It can provide a scientific basis for rational use of the rhizomes and leaves of Pileostegia viburnoides var. glabrescens.

Characterization of 12 polymorphic microsatellite loci of Pityopsis graminifolia var. latifolia

USDA-ARS?s Scientific Manuscript database

Pityopsis graminifolia (Michx.) Small var. latifolia (Fern.) Semple is an herbaceous perennial that grows in close proximity to the federally endangered species P. ruthii (Small) Small. Twelve polymorphic microsatellite loci were identified from 87 samples of P. graminifolia var. latifolia and addit...

[Genetic variation of some varieties of common juniper Juniperus communis L. inferred from analysis of allozyme loci].

PubMed

Khantemirova, E V; Semerikov, V L

2010-05-01

Using the method of allozyme analysis, genetic variation, diversity, and population structure of Juniperus communis L. var. communis and J. communis L. var. saxatilis Pall. (= J. sibirica Burgsd. = J. nana Wild), growing on the territory of Russia, J. c. var. communis from Sweden, and J. c. var. depressa Pursh from Northern America (Alaska), was investigated. The total level of genetic variation of these varieties was found to be higher than the values obtained for the other conifers. The population samples of J. c. var. depressa from Alaska and J. c. var. saxatilis from Sakhalin were noticeably different from all other populations examined. Between the other samples, no substantial genetic differences were observed. These populations were characterized by weak interpopulation differentiation along with the absence of expressed geographical pattern of the allele frequency spatial distribution. The only exception was the procumbent form of common juniper from the high mountain populations of South and North Ural, which was somewhat different from the others.

Biological activities of organic extracts of four Aureobasidium pullulans varieties isolated from extreme marine and terrestrial habitats.

PubMed

Botić, Tanja; Kralj-Kunčič, Marjetka; Sepčić, Kristina; Batista, Urška; Zalar, Polona; Knez, Željko; Gunde-Cimerman, Nina

2014-01-01

We report on the screening for biological activities of organic extracts from seven strains that represent four varieties of the fungus Aureobasidium pullulans, that is A. pullulans var. melanogenum, A. pullulans var. pullulans, A. pullulans var. subglaciale and A. pullulans var. namibiae. We monitored haemolysis, cytotoxicity, antioxidant capacity and growth inhibition against three bacterial species. The haemolytic activity of A. pullulans var. pullulans EXF-150 strain was due to five different haemolytically active fractions. Extracts from all of the other varieties contained at least one haemolytically active fraction. Short-term exposure of cell lines to these haemolytically active organic extracts resulted in more than 95% cytotoxicity. Strong antioxidant capacity, corresponding to 163.88 μg ascorbic acid equivalent per gram of total solid, was measured in the organic extract of the strain EXF-3382, obtained from A. pullulans var. melanogenum, isolated from the deep sea. Organic extracts from selected varieties of A. pullulans exhibited weak antibacterial activities.

Identification of a Major Dimorphic Region in the Functionally Critical N-Terminal ID1 Domain of VAR2CSA

PubMed Central

Doritchamou, Justin; Sabbagh, Audrey; Jespersen, Jakob S.; Renard, Emmanuelle; Salanti, Ali; Nielsen, Morten A.; Deloron, Philippe; Tuikue Ndam, Nicaise

2015-01-01

The VAR2CSA protein of Plasmodium falciparum is transported to and expressed on the infected erythrocyte surface where it plays a key role in placental malaria (PM). It is the current leading candidate for a vaccine to prevent PM. However, the antigenic polymorphism integral to VAR2CSA poses a challenge for vaccine development. Based on detailed analysis of polymorphisms in the sequence of its ligand-binding N-terminal region, currently the main focus for vaccine development, we assessed var2csa from parasite isolates infecting pregnant women. The results reveal for the first time the presence of a major dimorphic region in the functionally critical N-terminal ID1 domain. Parasite isolates expressing VAR2CSA with particular motifs present within this domain are associated with gravidity- and parasite density-related effects. These observations are of particular interest in guiding efforts with respect to optimization of the VAR2CSA-based vaccines currently under development. PMID:26393516

Effects of seasonal variations on antioxidant activity of pink guava fruits

NASA Astrophysics Data System (ADS)

Ahmad, Haniza; Abdullah, Aminah

2014-09-01

This study aimed to evaluate the effects of seasonal variations during rainy and hot season on antioxidant activity of pink guava fruits in approximately one year duration specifically on November 2012, December 2012, January 2013, March 2013, April 2013, May 2013, July 2013, August 2013 and November 2013. Fruit samples (Sungkai and Semenyih variants) were collected from Sime Darby Beverages plantation located in Sitiawan. The fruits were samples for 9 times from Nov 2012 to Nov 2013 except Feb 2013, Jun 2013, Sept 2013 and Oct 2013. Fruits were peeled, seeded and blended into uniform puree. Samples were then extracted for its antioxidant activity determination using 50% acetone. Antioxidant activity was evaluated using total phenolic compounds (TPC) assay, ferric-reducing antioxidant power assay (FRAP) and 1,1-diphenyl1-2-picrylhydrazyl free radical-scavenging capacity (DPPH). Analysis was conducted using 96-well microplate spectrophotometer UV. The highest TPC result was Semenyih var recorded 2192.80 mg GAE/100g FW whilst Sungkai var 1595.98 mg GAE/100g FW both on July 2013 with rainfall was at the least (45mm) and the lowest for Sungkai var was 792.75 mg GAE/100g FW and 1032.41 mg GAE/100g FW for Semenyih var, both on Nov 2012 with 185mm rainfall. There were significant negative correlation between TPC and rainfall (mm) for both Semenyih var (r = - 0.699, p<0.005, r2 = 0.489) and Sungkai var (r = -0.72, p<0.05, r2 = 0.531). The highest FRAP result (mg TE/100g FW) was 1677.74 for Semenyih var (Aug 2013, rainfall = 160.5mm) and the highest FRAP for Sungkai var was 1104.60 (Jul 2013, rainfall = 45.0mm) whereas the lowest for Semenyih and Sungkai var was 1090.22 (Mar 2013, rainfall = 97.5mm) and 767.88 (Nov 2012, rainfall = 185.50) respectively. There was weak negative correlation between FRAP and rainfall(mm) for both Sungkai var (r = - 0.324, p<0.05, r2 = 0.105) and Semenyih var (r = - 0.362, p<0.05, r2 = 0.132). The highest DPPH for Semenyih var was 88.40% (Aug 2013, rainfall = 160.50mm) whilst Sungkai var was 79.71% (July 2013, rainfall = 45.0mm). There was no significant difference in correlation coefficient of DPPH and rainfall (mm). Meanwhile, there was significant correlation between TPC and FRAP (r = 0.794, p<0.05, r2 = 0.629), TPC and DPPH (r = 0.901,p<0.05, r2= 0.812) and FRAP and DPPH (r = 0.889, p<0.05, r2 = 0.792).

Four dimensional variational inversion of atmospheric chemical sources in WRFDA

NASA Astrophysics Data System (ADS)

Guerrette, J. J.

Atmospheric aerosols are known to affect health, weather, and climate, but their impacts on regional scales are uncertain due to heterogeneous source, transport, and transformation mechanisms. The Weather Research and Forecasting model with chemistry (WRF-Chem) can account for aerosol-meteorology feedbacks as it simultaneously integrates equations of dynamical and chemical processes. Here we develop and apply incremental four dimensional variational (4D-Var) data assimilation (DA) capabilities in WRF-Chem to constrain chemical emissions (WRFDA-Chem). We develop adjoint (ADM) and tangent linear (TLM) model descriptions of boundary layer mixing, emission, aging, dry deposition, and advection of black carbon (BC) aerosol. ADM and TLM model performance is verified against finite difference derivative approximations. A second order checkpointing scheme is used to reduce memory costs and enable simulations longer than six hours. We apply WRFDA-Chem to constraining anthropogenic and biomass burning sources of BC throughout California during the 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field campaign. Manual corrections to the prior emissions and subsequent inverse modeling reduce the spread in total emitted BC mass between two biomass burning inventories from a factor of x10 to only x2 across three days of measurements. We quantify posterior emission variance using an eigendecomposition of the cost function Hessian matrix. We also address the limited scalability of 4D-Var, which traditionally uses a sequential optimization algorithm (e.g., conjugate gradient) to approximate these Hessian eigenmodes. The Randomized Incremental Optimal Technique (RIOT) uses an ensemble of TLM and ADM instances to perform a Hessian singular value decomposition. While RIOT requires more ensemble members than Lanczos requires iterations to converge to a comparable posterior control vector, the wall-time of RIOT is x10 shorter since the ensemble is executed in parallel. This work demonstrates that RIOT improves the scalability of 4D-Var for high-dimensional nonlinear problems. Overall, WRFDA-Chem and RIOT provide a framework for air quality forecasting, campaign planning, and emissions constraint that can be used to refine our understanding of the interplay between atmospheric chemistry, meteorology, climate, and human health.

Anomalous Fluctuations in Autoregressive Models with Long-Term Memory

NASA Astrophysics Data System (ADS)

Sakaguchi, Hidetsugu; Honjo, Haruo

2015-10-01

An autoregressive model with a power-law type memory kernel is studied as a stochastic process that exhibits a self-affine-fractal-like behavior for a small time scale. We find numerically that the root-mean-square displacement Δ(m) for the time interval m increases with a power law as mα with α < 1/2 for small m but saturates at sufficiently large m. The exponent α changes with the power exponent of the memory kernel.

On the Stationarity of Multiple Autoregressive Approximants: Theory and Algorithms

DTIC Science & Technology

1976-08-01

a I (3.4) Hannan and Terrell (1972) consider problems of a similar nature. Efficient estimates A(1),... , A(p) , and i of A(1)... ,A(p) and...34Autoregressive model fitting for control, Ann . Inst. Statist. Math., 23, 163-180. Hannan, E. J. (1970), Multiple Time Series, New York, John Wiley...Hannan, E. J. and Terrell , R. D. (1972), "Time series regression with linear constraints, " International Economic Review, 13, 189-200. Masani, P

Forecasting Instability Indicators in the Horn of Africa

DTIC Science & Technology

2008-03-01

further than 2 (Makridakis, et al, 1983, 359). 2-32 Autoregressive Integrated Moving Average ( ARIMA ) Model . Similar to the ARMA model except for...stationary process. ARIMA models are described as ARIMA (p,d,q), where p is the order of the autoregressive process, d is the degree of the...differential process, and q is the order of the moving average process. The ARMA (1,1) model shown above is equivalent to an ARIMA (1,0,1) model . An ARIMA

Equilibrium Policy Proposals with Abstentions.

DTIC Science & Technology

1981-05-01

David M. Kreps. 262. ’Autoregressive Modelling and Money Income (ajusality Detection." by (heng lisiao. 263. "Measurement IError in a Dynamiic...34Autoregressive Modeling of"Canadian Money and Income Data," by Cheng Ilsjao. 277. "We Can’t Disagree IForever," by John 1). Geanakoplos and Heraklis...34*Optimal & Voluntary Income Distribution," by K. J. Arrow. 289. "’Asymptotic Values mif Mixed Gaime,.," by Abraham Neymnan. 290. "Tinie Series Modelling

Time series models on analysing mortality rates and acute childhood lymphoid leukaemia.

PubMed

Kis, Maria

2005-01-01

In this paper we demonstrate applying time series models on medical research. The Hungarian mortality rates were analysed by autoregressive integrated moving average models and seasonal time series models examined the data of acute childhood lymphoid leukaemia.The mortality data may be analysed by time series methods such as autoregressive integrated moving average (ARIMA) modelling. This method is demonstrated by two examples: analysis of the mortality rates of ischemic heart diseases and analysis of the mortality rates of cancer of digestive system. Mathematical expressions are given for the results of analysis. The relationships between time series of mortality rates were studied with ARIMA models. Calculations of confidence intervals for autoregressive parameters by tree methods: standard normal distribution as estimation and estimation of the White's theory and the continuous time case estimation. Analysing the confidence intervals of the first order autoregressive parameters we may conclude that the confidence intervals were much smaller than other estimations by applying the continuous time estimation model.We present a new approach to analysing the occurrence of acute childhood lymphoid leukaemia. We decompose time series into components. The periodicity of acute childhood lymphoid leukaemia in Hungary was examined using seasonal decomposition time series method. The cyclic trend of the dates of diagnosis revealed that a higher percent of the peaks fell within the winter months than in the other seasons. This proves the seasonal occurrence of the childhood leukaemia in Hungary.

EEG data reduction by means of autoregressive representation and discriminant analysis procedures.

PubMed

Blinowska, K J; Czerwosz, L T; Drabik, W; Franaszczuk, P J; Ekiert, H

1981-06-01

A program for automatic evaluation of EEG spectra, providing considerable reduction of data, was devised. Artefacts were eliminated in two steps: first, the longer duration eye movement artefacts were removed by a fast and simple 'moving integral' methods, then occasional spikes were identified by means of a detection function defined in the formalism of the autoregressive (AR) model. The evaluation of power spectra was performed by means of an FFT and autoregressive representation, which made possible the comparison of both methods. The spectra obtained by means of the AR model had much smaller statistical fluctuations and better resolution, enabling us to follow the time changes of the EEG pattern. Another advantage of the autoregressive approach was the parametric description of the signal. This last property appeared to be essential in distinguishing the changes in the EEG pattern. In a drug study the application of the coefficients of the AR model as input parameters in the discriminant analysis, instead of arbitrary chosen frequency bands, brought a significant improvement in distinguishing the effects of the medication. The favourable properties of the AR model are connected with the fact that the above approach fulfils the maximum entropy principle. This means that the method describes in a maximally consistent way the available information and is free from additional assumptions, which is not the case for the FFT estimate.

Prediction of global ionospheric VTEC maps using an adaptive autoregressive model

NASA Astrophysics Data System (ADS)

Wang, Cheng; Xin, Shaoming; Liu, Xiaolu; Shi, Chuang; Fan, Lei

2018-02-01

In this contribution, an adaptive autoregressive model is proposed and developed to predict global ionospheric vertical total electron content maps (VTEC). Specifically, the spherical harmonic (SH) coefficients are predicted based on the autoregressive model, and the order of the autoregressive model is determined adaptively using the F-test method. To test our method, final CODE and IGS global ionospheric map (GIM) products, as well as altimeter TEC data during low and mid-to-high solar activity period collected by JASON, are used to evaluate the precision of our forecasting products. Results indicate that the predicted products derived from the model proposed in this paper have good consistency with the final GIMs in low solar activity, where the annual mean of the root-mean-square value is approximately 1.5 TECU. However, the performance of predicted vertical TEC in periods of mid-to-high solar activity has less accuracy than that during low solar activity periods, especially in the equatorial ionization anomaly region and the Southern Hemisphere. Additionally, in comparison with forecasting products, the final IGS GIMs have the best consistency with altimeter TEC data. Future work is needed to investigate the performance of forecasting products using the proposed method in an operational environment, rather than using the SH coefficients from the final CODE products, to understand the real-time applicability of the method.

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

Distribution Management System Volt/VAR Evaluation | Grid Modernization |

Science.gov Websites

NREL Distribution Management System Volt/VAR Evaluation Distribution Management System Volt/VAR Evaluation This project involves building a prototype distribution management system testbed that links a GE Grid Solutions distribution management system to power hardware-in-the-loop testing. This setup is

Ultrastructural Effects of Bacillus thuringiensis var. san diego on Midgut Cells of the Cottonwood Leaf Beetle1

Treesearch

Leah S. Bauer; Stuart H. Pankratz

1992-01-01

Sequential observations of the ultrastructural effects of Bacillus thuringiensis var. san diego were made on midgut epithelial cells of the cottonwood leaf beetle, Chrysomela scripta F. Larvae imbibed a droplet of B. thuringiensis var. san diego containing endotoxin and live...

Antibacterial activity of Mediterranean Oyster mushrooms, species of genus Pleurotus (higher Basidiomycetes).

PubMed

Schillaci, Domenico; Arizza, Vincenzo; Gargano, Maria Letizia; Venturella, Giuseppe

2013-01-01

Extracts of the Mediterranean culinary-medicinal Oyster mushrooms Pleurotus eryngii var. eryngii, P. eryngii var. ferulae, P. eryngii var. elaeoselini, and P. nebrodensis were tested for their in vitro growth inhibitory activity against a group of bacterial reference strains of medical relevance: Staphylococcus aureus ATCC 25923, S. epidermidis RP62A, Pseudomonas aeruginosa ATCC 15442, and Escherichia coli ATCC10536. All of the Pleurotus species analyzed inhibited the tested microorganisms in varying degrees. The data included in this paper for P. nebrodensis and P. eryngii var. elaeoselinii are new reports.

Rolling Contact Fatigue Life and Spall Propagation Characteristics of AISI M50, M50 NiL, and AISI 52100. Part 3. Metallurgical Examination (Preprint)

DTIC Science & Technology

2009-10-01

bearing materials, namely AISI 52100, VIM-VAR M50 , and VIM-VAR M50 NiL steels . While...propagation characteristics of three bearing materials, namely AISI 52100, VIM-VAR M50 , and VIM-VAR M50 NiL steels . While there is substantial prior work...deformation from run in. Residual stress profiles for bearing 075B are shown in Figure 3. The bearing is made from M50 steel and the spall

Antifungal compounds from Zanthoxylum chiloperone var. angustifolium.

PubMed

Thouvenel, Céline; Gantier, Jean-Charles; Duret, Philippe; Fourneau, Christophe; Hocquemiller, Reynald; Ferreira, Maria-Elena; Rojas de Arias, Antonieta; Fournet, Alain

2003-06-01

An alkaloidal extract of the stem barks of Zanthoxylum chiloperone var. angustifolium exhibited antifungal activity against Candida albicans, Aspergillus fumigatus and Trichophyton mentagrophytes var. interdigitale using a TLC bioautographic method. Bioassay-guided fractionation of this extract resulted in the isolation of two active compounds identi fi ed as canthin-6-one and 5-methoxycanthin-6-one. Canthin-6-one exhibited a broad spectrum of activities against Aspergillus fumigatus, A. niger, A. terreus, Candida albicans, C. tropicalis, C. glabrata, Cryptococcus neoformans, Geotrichum candidum, Saccharomyces cerevisiae, Trichosporon beigelii, Trichosporon cutaneum and Trichophyton mentagrophytes var. interdigitale with MICs values between 5.3 and 46 micro mol/L. 5-methoxy-canthin-6-one was active against only Trichophyton mentagrophytes var. interdigitale with a MIC value of 12.3 micro mol/L. Copyright 2003 John Wiley & Sons, Ltd.

Differential recognition of terminal extracellular Plasmodium falciparum VAR2CSA domains by sera from multigravid, malaria-exposed Malian women.

PubMed

Travassos, Mark A; Coulibaly, Drissa; Bailey, Jason A; Niangaly, Amadou; Adams, Matthew; Nyunt, Myaing M; Ouattara, Amed; Lyke, Kirsten E; Laurens, Matthew B; Pablo, Jozelyn; Jasinskas, Algis; Nakajima, Rie; Berry, Andrea A; Takala-Harrison, Shannon; Kone, Abdoulaye K; Kouriba, Bourema; Rowe, J Alexandra; Doumbo, Ogobara K; Thera, Mahamadou A; Laufer, Miriam K; Felgner, Philip L; Plowe, Christopher V

2015-06-01

The Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) family mediates parasite sequestration in small capillaries through tissue-specific cytoadherence. The best characterized of these proteins is VAR2CSA, which is expressed on the surface of infected erythrocytes that bind to chondroitin sulfate in the placental matrix. Antibodies to VAR2CSA prevent placental cytoadherence and protect against placental malaria. The size and complexity of the VAR2CSA protein pose challenges for vaccine development, but smaller constitutive domains may be suitable for subunit vaccine development. A protein microarray was printed to include five overlapping fragments of the 3D7 VAR2CSA extracellular region. Malian women with a history of at least one pregnancy had antibody recognition of four of these fragments and had stronger reactivity against the two distal fragments than did nulliparous women, children, and men from Mali, suggesting that the C-terminal extracellular VAR2CSA domains are a potential focus of protective immunity. With carefully chosen sera from longitudinal studies of pregnant women, this approach has the potential to identify seroreactive VAR2CSA domains associated with protective immunity against pregnancy-associated malaria. © The American Society of Tropical Medicine and Hygiene.

Data assimilation and prognostic whole ice sheet modelling with the variationally derived, higher order, open source, and fully parallel ice sheet model VarGlaS

NASA Astrophysics Data System (ADS)

Brinkerhoff, D. J.; Johnson, J. V.

2013-07-01

We introduce a novel, higher order, finite element ice sheet model called VarGlaS (Variational Glacier Simulator), which is built on the finite element framework FEniCS. Contrary to standard procedure in ice sheet modelling, VarGlaS formulates ice sheet motion as the minimization of an energy functional, conferring advantages such as a consistent platform for making numerical approximations, a coherent relationship between motion and heat generation, and implicit boundary treatment. VarGlaS also solves the equations of enthalpy rather than temperature, avoiding the solution of a contact problem. Rather than include a lengthy model spin-up procedure, VarGlaS possesses an automated framework for model inversion. These capabilities are brought to bear on several benchmark problems in ice sheet modelling, as well as a 500 yr simulation of the Greenland ice sheet at high resolution. VarGlaS performs well in benchmarking experiments and, given a constant climate and a 100 yr relaxation period, predicts a mass evolution of the Greenland ice sheet that matches present-day observations of mass loss. VarGlaS predicts a thinning in the interior and thickening of the margins of the ice sheet.

Working alliance inventory applied to virtual and augmented reality (WAI-VAR): psychometrics and therapeutic outcomes

PubMed Central

Miragall, Marta; Baños, Rosa M.; Cebolla, Ausiàs; Botella, Cristina

2015-01-01

This study examines the psychometric properties of the Working Alliance Inventory-Short (WAI-S) adaptation to Virtual Reality (VR) and Augmented Reality (AR) therapies (WAI-VAR). The relationship between the therapeutic alliance (TA) with VR and AR and clinically significant change (CSC) is also explored. Seventy-five patients took part in this study (74.7% women, Mage = 34.41). Fear of flying and adjustment disorder patients received VR therapy, and cockroach phobia patients received AR therapy. Psychometric properties, CSC, one-way ANOVA, Spearman’s Correlations and Multiple Regression were calculated. The WAI-VAR showed a unidimensional structure, high internal consistency and adequate convergent validity. “Not changed” patients scored lower on the WAI-VAR than “improved” and “recovered” patients. Correlation between the WAI-VAR and CSC was moderate. The best fitting model for predicting CSC was a linear combination of the TA with therapist (WAI-S) and the TA with VR and AR (WAI-VAR), due to the latter variable slightly increased the percentage of variability accounted for in CSC. The WAI-VAR is the first validated instrument to measure the TA with VR and AR in research and clinical practice. This study reveals the importance of the quality of the TA with technologies in achieving positive outcomes in the therapy. PMID:26500589

Working alliance inventory applied to virtual and augmented reality (WAI-VAR): psychometrics and therapeutic outcomes.

PubMed

Miragall, Marta; Baños, Rosa M; Cebolla, Ausiàs; Botella, Cristina

2015-01-01

This study examines the psychometric properties of the Working Alliance Inventory-Short (WAI-S) adaptation to Virtual Reality (VR) and Augmented Reality (AR) therapies (WAI-VAR). The relationship between the therapeutic alliance (TA) with VR and AR and clinically significant change (CSC) is also explored. Seventy-five patients took part in this study (74.7% women, M age = 34.41). Fear of flying and adjustment disorder patients received VR therapy, and cockroach phobia patients received AR therapy. Psychometric properties, CSC, one-way ANOVA, Spearman's Correlations and Multiple Regression were calculated. The WAI-VAR showed a unidimensional structure, high internal consistency and adequate convergent validity. "Not changed" patients scored lower on the WAI-VAR than "improved" and "recovered" patients. Correlation between the WAI-VAR and CSC was moderate. The best fitting model for predicting CSC was a linear combination of the TA with therapist (WAI-S) and the TA with VR and AR (WAI-VAR), due to the latter variable slightly increased the percentage of variability accounted for in CSC. The WAI-VAR is the first validated instrument to measure the TA with VR and AR in research and clinical practice. This study reveals the importance of the quality of the TA with technologies in achieving positive outcomes in the therapy.

Evaluation of morpho-anatomical and chemical differences between varieties of the medicinal plant Casearia sylvestris Swartz.

PubMed

Claudino, Josiane C; Sacramento, Luis V S do; Koch, Ingrid; Santos, Helen A; Cavalheiro, Alberto J; Tininis, Aristeu G; Santos, André G dos

2013-01-01

Casearia sylvestris Swartz (Salicaceae) has been used in traditional medicine and its leaf extracts have been exhibited important pharmacological activities. The species presents morphological, chemical and genetic variation. Two varieties are considered due external morphological differences: C. sylvestris var. sylvestris and var. lingua. There are difficulties in definition of these varieties. The objective of this work is to evaluate chemical and morpho-anatomical differences between C. sylvestris varieties that can be applied in their distinction for pharmaceutical or botanical purposes. Transverse and paradermic sections of leaves were prepared for morpho-anatomical, histochemical and quantitative microscopy (stomatal and palisade index) analyses. Diterpene profiles of the specimens were obtained by HPLC-DAD and TLC. Morpho-anatomical analyses demonstrated significant differences between the varieties only in paradermic sections: var. sylvestris--polygonal epidermic cell walls and hypostomatic; var. lingua--rounded epidermic cell walls and amphistomatic. No differences were observed for stomatal index; palisade index was found 2.8 for var. lingua and 3.9 for var. sylvestris. Chromatographic analyses confirmed previous results demonstrating that diterpene profile in varieties differs, with predominance of these metabolites in var. sylvestris. In conclusion, this work indicates that chromatographic analysis besides morpho-anatomical analysis can be applied in distinction of C. sylvestris varieties.

In vitro antioxidant and anticancer effects of solvent fractions from Prunella vulgaris var. lilacina.

PubMed

Hwang, Yu-Jin; Lee, Eun-Ju; Kim, Haeng-Ran; Hwang, Kyung-A

2013-11-09

Recently, considerable attention has been focused on exploring the potential antioxidant properties of plant extracts or isolated products of plant origin. Prunella vulgaris var. lilacina is widely distributed in Korea, Japan, China, and Europe, and it continues to be used to treat inflammation, eye pain, headache, and dizziness. However, reports on the antioxidant activities of P. vulgaris var. lilacina are limited, particularly concerning the relationship between its phenolic content and antioxidant capacity. In this study, we investigated the antioxidant and anticancer activities of an ethanol extract from P. vulgaris var. lilacina and its fractions. Dried powder of P. vulgaris var. lilacina was extracted with ethanol, and the extract was fractionated to produce the hexane fraction, butanol fraction, chloroform fraction and residual water fraction. The phenolic content was assayed using the Folin-Ciocalteu colorimetric method. Subsequently, the antioxidant activities of the ethanol extract and its fractions were analyzed employing various antioxidant assay methods including DPPH, FRAP, ABTS, SOD activity and production of reactive oxygen species. Additionally, the extract and fractions were assayed for their ability to exert cytotoxic activities on various cancer cells using the MTT assay. We also investigated the expression of genes associated with apoptotic cell death by RT-PCR. The total phenolic contents of the ethanol extract and water fraction of P. vulgaris var. lilacina were 303.66 and 322.80 mg GAE/g dry weight (or fractions), respectively. The results showed that the ethanol extract and the water fraction of P. vulgaris var. lilacina had higher antioxidant content than other solvent fractions, similar to their total phenolic content. Anticancer activity was also tested using the HepG2, HT29, A549, MKN45 and HeLa cancer cell lines. The results clearly demonstrated that the P. vulgaris var. lilacina ethanol extract induced significant cytotoxic effects on the various cancer cell lines, and these effects were stronger than those induced by the P. vulgaris var. lilacina solvent fractions. We also investigated the expression of genes associated with apoptotic cell death. We confirmed that the P. vulgaris var. lilacina ethanol extract and water fraction significantly increased the expression of p53, Bax and Fas. These results suggest that the ethanol extract from P. vulgaris var. lilacina and its fractions could be applied as natural sources of antioxidants and anticancer activities in food and in the pharmaceutical industry.

Classification tree and minimum-volume ellipsoid analyses of the distribution of ponderosa pine in the western USA

USGS Publications Warehouse

Norris, Jodi R.; Jackson, Stephen T.; Betancourt, Julio L.

2006-01-01

Aim? Ponderosa pine (Pinus ponderosa Douglas ex Lawson & C. Lawson) is an economically and ecologically important conifer that has a wide geographic range in the western USA, but is mostly absent from the geographic centre of its distribution - the Great Basin and adjoining mountain ranges. Much of its modern range was achieved by migration of geographically distinct Sierra Nevada (P. ponderosa var. ponderosa) and Rocky Mountain (P. ponderosa var. scopulorum) varieties in the last 10,000 years. Previous research has confirmed genetic differences between the two varieties, and measurable genetic exchange occurs where their ranges now overlap in western Montana. A variety of approaches in bioclimatic modelling is required to explore the ecological differences between these varieties and their implications for historical biogeography and impending changes in western landscapes. Location? Western USA. Methods? We used a classification tree analysis and a minimum-volume ellipsoid as models to explain the broad patterns of distribution of ponderosa pine in modern environments using climatic and edaphic variables. Most biogeographical modelling assumes that the target group represents a single, ecologically uniform taxonomic population. Classification tree analysis does not require this assumption because it allows the creation of pathways that predict multiple positive and negative outcomes. Thus, classification tree analysis can be used to test the ecological uniformity of the species. In addition, a multidimensional ellipsoid was constructed to describe the niche of each variety of ponderosa pine, and distances from the niche were calculated and mapped on a 4-km grid for each ecological variable. Results? The resulting classification tree identified three dominant pathways predicting ponderosa pine presence. Two of these three pathways correspond roughly to the distribution of var. ponderosa, and the third pathway generally corresponds to the distribution of var. scopulorum. The classification tree and minimum-volume ellipsoid model show that both varieties have very similar temperature limitations, although var. ponderosa is more limited by the temperature extremes of the continental interior. The precipitation limitations of the two varieties are seasonally different, with var. ponderosa requiring significant winter moisture and var. scopulorum requiring significant summer moisture. Great Basin mountain ranges are too cold at higher elevations to support either variety of ponderosa pine, and at lower elevations are too dry in summer for var. scopulorum and too dry in winter for var. ponderosa. Main conclusions? The classification tree analysis indicates that var. ponderosa is ecologically as well as genetically distinct from var. scopulorum. Ecological differences may maintain genetic separation in spite of a limited zone of introgression between the two varieties in western Montana. Two hypotheses about past and future movements of ponderosa pine emerge from our analyses. The first hypothesis is that, during the last glacial period, colder and/or drier summers truncated most of the range of var. scopulorum in the central Rockies, but had less dramatic effects on the more maritime and winter-wet distribution of var. ponderosa. The second hypothesis is that, all other factors held constant, increasing summer temperatures in the future should produce changes in the distribution of var. scopulorum that are likely to involve range expansions in the central Rockies with the warming of mountain ranges currently too cold but sufficiently wet in summer for var. scopulorum. Finally, our results underscore the growing need to focus on genotypes in biogeographical modelling and ecological forecasting.

A new method for reconstruction of solar irradiance

NASA Astrophysics Data System (ADS)

Privalsky, Victor

2018-07-01

The purpose of this research is to show how time series should be reconstructed using an example with the data on total solar irradiation (TSI) of the Earth and on sunspot numbers (SSN) since 1749. The traditional approach through regression equation(s) is designed for time-invariant vectors of random variables and is not applicable to time series, which present random functions of time. The autoregressive reconstruction (ARR) method suggested here requires fitting a multivariate stochastic difference equation to the target/proxy time series. The reconstruction is done through the scalar equation for the target time series with the white noise term excluded. The time series approach is shown to provide a better reconstruction of TSI than the correlation/regression method. A reconstruction criterion is introduced which allows one to define in advance the achievable level of success in the reconstruction. The conclusion is that time series, including the total solar irradiance, cannot be reconstructed properly if the data are not treated as sample records of random processes and analyzed in both time and frequency domains.

Modeling the impact of transport energy consumption on CO2 emission in Pakistan: Evidence from ARDL approach.

PubMed

Danish; Baloch, Muhammad Awais; Suad, Shah

2018-04-01

The objective of this research is to examine the relationship between transport energy consumption, economic growth, and carbon dioxide emission (CO 2 ) from transport sector incorporating foreign direct investment and urbanization. This study is carried out in Pakistan by applying autoregressive distributive lag (ARDL) and vector error correction model (VECM) over 1990-2015. The empirical results indicate a strong significant impact of transport energy consumption on CO 2 emissions from the transportation sector. Furthermore, foreign direct investment also contributes to CO 2 emission. Interestingly, the impact of economic growth and urbanization on transport CO 2 emission is statistically insignificant. Overall, transport energy consumption and foreign direct investment are not environmentally friendly. The new empirical evidence from this study provides a complete picture of the determinants of emissions from the transport sector and these novel findings not only help to advance the existing literature but also can be of special interest to the country's policymakers. So, we urge that government needs to focus on promoting the energy efficient means of transportation to improve environmental quality with less adverse influence on economic growth.

The role of energy in economic growth.

PubMed

Stern, David I

2011-02-01

This paper reviews the mainstream, resource economics, and ecological economics models of growth. A possible synthesis of energy-based and mainstream models is presented. This shows that when energy is scarce it imposes a strong constraint on the growth of the economy; however, when energy is abundant, its effect on economic growth is much reduced. The industrial revolution released the constraints on economic growth by the development of new methods of using coal and the discovery of new fossil fuel resources. Time-series analysis shows that energy and GDP cointegrate, and energy use Granger causes GDP when capital and other production inputs are included in the vector autoregression model. However, various mechanisms can weaken the links between energy and growth. Energy used per unit of economic output has declined in developed and some developing countries, owing to both technological change and a shift from poorer quality fuels, such as coal, to the use of higher quality fuels, especially electricity. Substitution of other inputs for energy and sectoral shifts in economic activity play smaller roles. © 2011 New York Academy of Sciences.

Condition Monitoring for Helicopter Data. Appendix A

NASA Technical Reports Server (NTRS)

Wen, Fang; Willett, Peter; Deb, Somnath

2000-01-01

In this paper the classical "Westland" set of empirical accelerometer helicopter data is analyzed with the aim of condition monitoring for diagnostic purposes. The goal is to determine features for failure events from these data, via a proprietary signal processing toolbox, and to weigh these according to a variety of classification algorithms. As regards signal processing, it appears that the autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; it has also been found that augmentation of these by harmonic and other parameters can improve classification significantly. As regards classification, several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior on training data and is thus able to quantify probability of error in an exact manner, such that features may be discarded or coarsened appropriately.

Sign: large-scale gene network estimation environment for high performance computing.

PubMed

Tamada, Yoshinori; Shimamura, Teppei; Yamaguchi, Rui; Imoto, Seiya; Nagasaki, Masao; Miyano, Satoru

2011-01-01

Our research group is currently developing software for estimating large-scale gene networks from gene expression data. The software, called SiGN, is specifically designed for the Japanese flagship supercomputer "K computer" which is planned to achieve 10 petaflops in 2012, and other high performance computing environments including Human Genome Center (HGC) supercomputer system. SiGN is a collection of gene network estimation software with three different sub-programs: SiGN-BN, SiGN-SSM and SiGN-L1. In these three programs, five different models are available: static and dynamic nonparametric Bayesian networks, state space models, graphical Gaussian models, and vector autoregressive models. All these models require a huge amount of computational resources for estimating large-scale gene networks and therefore are designed to be able to exploit the speed of 10 petaflops. The software will be available freely for "K computer" and HGC supercomputer system users. The estimated networks can be viewed and analyzed by Cell Illustrator Online and SBiP (Systems Biology integrative Pipeline). The software project web site is available at http://sign.hgc.jp/ .

Non-Gaussian spatiotemporal simulation of multisite daily precipitation: downscaling framework

NASA Astrophysics Data System (ADS)

Ben Alaya, M. A.; Ouarda, T. B. M. J.; Chebana, F.

2018-01-01

Probabilistic regression approaches for downscaling daily precipitation are very useful. They provide the whole conditional distribution at each forecast step to better represent the temporal variability. The question addressed in this paper is: how to simulate spatiotemporal characteristics of multisite daily precipitation from probabilistic regression models? Recent publications point out the complexity of multisite properties of daily precipitation and highlight the need for using a non-Gaussian flexible tool. This work proposes a reasonable compromise between simplicity and flexibility avoiding model misspecification. A suitable nonparametric bootstrapping (NB) technique is adopted. A downscaling model which merges a vector generalized linear model (VGLM as a probabilistic regression tool) and the proposed bootstrapping technique is introduced to simulate realistic multisite precipitation series. The model is applied to data sets from the southern part of the province of Quebec, Canada. It is shown that the model is capable of reproducing both at-site properties and the spatial structure of daily precipitations. Results indicate the superiority of the proposed NB technique, over a multivariate autoregressive Gaussian framework (i.e. Gaussian copula).

Predicting groundwater level fluctuations with meteorological effect implications—A comparative study among soft computing techniques

NASA Astrophysics Data System (ADS)

Shiri, Jalal; Kisi, Ozgur; Yoon, Heesung; Lee, Kang-Kun; Hossein Nazemi, Amir

2013-07-01

The knowledge of groundwater table fluctuations is important in agricultural lands as well as in the studies related to groundwater utilization and management levels. This paper investigates the abilities of Gene Expression Programming (GEP), Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Support Vector Machine (SVM) techniques for groundwater level forecasting in following day up to 7-day prediction intervals. Several input combinations comprising water table level, rainfall and evapotranspiration values from Hongcheon Well station (South Korea), covering a period of eight years (2001-2008) were used to develop and test the applied models. The data from the first six years were used for developing (training) the applied models and the last two years data were reserved for testing. A comparison was also made between the forecasts provided by these models and the Auto-Regressive Moving Average (ARMA) technique. Based on the comparisons, it was found that the GEP models could be employed successfully in forecasting water table level fluctuations up to 7 days beyond data records.

Longitudinal relationship between economic development and occupational accidents in China.

PubMed

Song, Li; He, Xueqiu; Li, Chengwu

2011-01-01

The relativity between economic development and occupational accidents is a debated topic. Compared with the development courses of both economic development and occupational accidents in China during 1953-2008, this paper used statistic methods such as Granger causality test, cointegration test and impulse response function based on the vector autoregression model to investigate the relativity between economic development and occupational accidents in China from 1953 to 2008. Owing to fluctuation and growth scale characteristics of economic development, two dimensions including economic cycle and economic scale were divided. Results showed that there was no relationship between occupational accidents and economic scale during 1953-1978. Fatality rate per 10(5) workers was a conductive variable to gross domestic product per capita during 1979-2008. And economic cycle was an indicator to occupational accidents during 1979-2008. Variation of economic speed had important influence on occupational accidents in short term. Thus it is necessary to adjust Chinese occupational safety policy according to tempo variation of economic growth. Crown Copyright © 2010. Published by Elsevier Ltd. All rights reserved.

Phylogenetics and evolution of Su(var)3-9 SET genes in land plants: rapid diversification in structure and function.

PubMed

Zhu, Xinyu; Ma, Hong; Chen, Zhiduan

2011-03-09

Plants contain numerous Su(var)3-9 homologues (SUVH) and related (SUVR) genes, some of which await functional characterization. Although there have been studies on the evolution of plant Su(var)3-9 SET genes, a systematic evolutionary study including major land plant groups has not been reported. Large-scale phylogenetic and evolutionary analyses can help to elucidate the underlying molecular mechanisms and contribute to improve genome annotation. Putative orthologs of plant Su(var)3-9 SET protein sequences were retrieved from major representatives of land plants. A novel clustering that included most members analyzed, henceforth referred to as core Su(var)3-9 homologues and related (cSUVHR) gene clade, was identified as well as all orthologous groups previously identified. Our analysis showed that plant Su(var)3-9 SET proteins possessed a variety of domain organizations, and can be classified into five types and ten subtypes. Plant Su(var)3-9 SET genes also exhibit a wide range of gene structures among different paralogs within a family, even in the regions encoding conserved PreSET and SET domains. We also found that the majority of SUVH members were intronless and formed three subclades within the SUVH clade. A detailed phylogenetic analysis of the plant Su(var)3-9 SET genes was performed. A novel deep phylogenetic relationship including most plant Su(var)3-9 SET genes was identified. Additional domains such as SAR, ZnF_C2H2 and WIYLD were early integrated into primordial PreSET/SET/PostSET domain organization. At least three classes of gene structures had been formed before the divergence of Physcomitrella patens (moss) from other land plants. One or multiple retroposition events might have occurred among SUVH genes with the donor genes leading to the V-2 orthologous group. The structural differences among evolutionary groups of plant Su(var)3-9 SET genes with different functions were described, contributing to the design of further experimental studies.

Predation and fragmentation portrayed in the statistical structure of prey time series

PubMed Central

Hendrichsen, Ditte K; Topping, Chris J; Forchhammer, Mads C

2009-01-01

Background Statistical autoregressive analyses of direct and delayed density dependence are widespread in ecological research. The models suggest that changes in ecological factors affecting density dependence, like predation and landscape heterogeneity are directly portrayed in the first and second order autoregressive parameters, and the models are therefore used to decipher complex biological patterns. However, independent tests of model predictions are complicated by the inherent variability of natural populations, where differences in landscape structure, climate or species composition prevent controlled repeated analyses. To circumvent this problem, we applied second-order autoregressive time series analyses to data generated by a realistic agent-based computer model. The model simulated life history decisions of individual field voles under controlled variations in predator pressure and landscape fragmentation. Analyses were made on three levels: comparisons between predated and non-predated populations, between populations exposed to different types of predators and between populations experiencing different degrees of habitat fragmentation. Results The results are unambiguous: Changes in landscape fragmentation and the numerical response of predators are clearly portrayed in the statistical time series structure as predicted by the autoregressive model. Populations without predators displayed significantly stronger negative direct density dependence than did those exposed to predators, where direct density dependence was only moderately negative. The effects of predation versus no predation had an even stronger effect on the delayed density dependence of the simulated prey populations. In non-predated prey populations, the coefficients of delayed density dependence were distinctly positive, whereas they were negative in predated populations. Similarly, increasing the degree of fragmentation of optimal habitat available to the prey was accompanied with a shift in the delayed density dependence, from strongly negative to gradually becoming less negative. Conclusion We conclude that statistical second-order autoregressive time series analyses are capable of deciphering interactions within and across trophic levels and their effect on direct and delayed density dependence. PMID:19419539

Silence, Metaperformance, and Communication in Pedro Almodóvar's "Hable con ella"

ERIC Educational Resources Information Center

Fellie, Maria C.

2016-01-01

Many scenes in Pedro Almodóvar's "Hable con ella" (2002) include shots of metaperformances such as silent films, dances, television shows, concerts, and bullfights. Spectators often observe passive characters who are in turn observing. By presenting these performances within cinematic performance, Almodóvar highlights our role as viewers…

Water deficit induces swainsonine of some locoweed taxa, but with no swainsonine-growth trade-off

USDA-ARS?s Scientific Manuscript database

Locoweeds (Astragalus and Oxytropis spp.) contain swainsonine (SWA), an alkaloid toxic to vertebrates. In two common-garden experiments, we studied three A. mollissimus varieties that vary in SWA levels (barely detectable SWA var. ‘thompsonae’, intermediate SWA var. ‘bigelovii’, and high SWA var. ‘m...

40 CFR 180.1243 - Bacillus subtilis var. amyloliquefaciens strain FZB24; exemption from the requirement of a...

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 23 2010-07-01 2010-07-01 false Bacillus subtilis var... EXEMPTIONS FOR PESTICIDE CHEMICAL RESIDUES IN FOOD Exemptions From Tolerances § 180.1243 Bacillus subtilis... the requirement of a tolerance for residues of the Bacillus subtilis var. amyloliquefaciens strain...

Further elucidation of the taxonomic relationships and geographic distribution of Escobaria sneedii var. sneedii, E. sneedii var. leei, and E. guadalupensis (Cactaceae)

Treesearch

Marc A. Baker

2007-01-01

Individuals of E. sneedii var. sneedii were found to occur in greater abundance within the Guadalupe Mountains than was previously recorded. No additional populations morphologically intermediate between E. guadalupensis and E. sneedii were found. Taxonomic affiliation and geographic...

Residual Life and Strength Predictions and Life-Enhancement of Structures

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

Okada, H.; Atluri, S.N.

1998-09-01

In this paper, a method to quantitatively evaluate the T{sub {var_epsilon}}* integral directly from the measured near-tip displacement field for laboratory specimens made of metallic materials, is presented. This is the first time that such an attempt became a success. In order to develop the procedure, we carefully examine the nature of T{sub {var_epsilon}}* Hence, the nature of T{sub {var_epsilon}}* is further revealed. Following Okada and Atluri (1997), the relationship between energy balance statements for a cracked plate and the T{sub {var_epsilon}}* is discussed. It is concluded that T{sub {var_epsilon}}* quantifies the deformation energy dissipated near crack tip region [anmore » elongating strip of height e] per unit crack extension. In the evaluation of T{sub {var_epsilon}}* integral directly from measured displacement field, the use of deformation theory plasticity (J2-D theory) and the truncation of the near crack integral path on the experimental studies of Omori et el. (1995) are presented, and these show a good agreement with the results of finite element analysis.« less

A Life Study of Ausforged, Standard Forged and Standard Machined AISI M-50 Spur Gears

NASA Technical Reports Server (NTRS)

Townsend, D. P.; Bamberger, E. N.; Zaretsky, E. V.

1975-01-01

Tests were conducted at 350 K (170 F) with three groups of 8.9 cm (3.5 in.) pitch diameter spur gears made of vacuum induction melted (VIM) consumable-electrode vacuum-arc melted (VAR), AISI M-50 steel and one group of vacuum-arc remelted (VAR) AISI 9310 steel. The pitting fatigue life of the standard forged and ausforged gears was approximately five times that of the VAR AISI 9310 gears and ten times that of the bending fatigue life of the standard machined VIM-VAR AISI M-50 gears run under identical conditions. There was a slight decrease in the 10-percent life of the ausforged gears from that for the standard forged gears, but the difference is not statistically significant. The standard machined gears failed primarily by gear tooth fracture while the forged and ausforged VIM-VAR AISI M-50 and the VAR AISI 9310 gears failed primarily by surface pitting fatigue. The ausforged gears had a slightly greater tendency to fail by tooth fracture than the standard forged gears.

Processing on weak electric signals by the autoregressive model

NASA Astrophysics Data System (ADS)

Ding, Jinli; Zhao, Jiayin; Wang, Lanzhou; Li, Qiao

2008-10-01

A model of the autoregressive model of weak electric signals in two plants was set up for the first time. The result of the AR model to forecast 10 values of the weak electric signals is well. It will construct a standard set of the AR model coefficient of the plant electric signal and the environmental factor, and can be used as the preferences for the intelligent autocontrol system based on the adaptive characteristic of plants to achieve the energy saving on agricultural productions.

Fisher Consistency of AM-Estimates of the Autoregression Parameter Using Hard Rejection Filter Cleaners

DTIC Science & Technology

1987-02-04

U5tr,)! P(U 5-t Since U - F with F RS, we get (3.1). Case b: 0 S 5 k -a Now P([U~t]riM) = P(UZk-a) and P([ Ugt ]rM) = P(US-k-a) S P(US-(k-a)) which again...robustness for autoregressive processes." The Annals of Statistics, 12, 843-863. Mallows, C.L. (1980). "Some theory of nonlinear smoothen." The Annals of

Ape parasite origins of human malaria virulence genes

PubMed Central

Larremore, Daniel B.; Sundararaman, Sesh A.; Liu, Weimin; Proto, William R.; Clauset, Aaron; Loy, Dorothy E.; Speede, Sheri; Plenderleith, Lindsey J.; Sharp, Paul M.; Hahn, Beatrice H.; Rayner, Julian C.; Buckee, Caroline O.

2015-01-01

Antigens encoded by the var gene family are major virulence factors of the human malaria parasite Plasmodium falciparum, exhibiting enormous intra- and interstrain diversity. Here we use network analysis to show that var architecture and mosaicism are conserved at multiple levels across the Laverania subgenus, based on var-like sequences from eight single-species and three multi-species Plasmodium infections of wild-living or sanctuary African apes. Using select whole-genome amplification, we also find evidence of multi-domain var structure and synteny in Plasmodium gaboni, one of the ape Laverania species most distantly related to P. falciparum, as well as a new class of Duffy-binding-like domains. These findings indicate that the modular genetic architecture and sequence diversity underlying var-mediated host-parasite interactions evolved before the radiation of the Laverania subgenus, long before the emergence of P. falciparum. PMID:26456841

VAR2CSA domains expressed in Escherichia coli induce cross-reactive antibodies to native protein.

PubMed

Oleinikov, Andrew V; Francis, Susan E; Dorfman, Jeffrey R; Rossnagle, Eddie; Balcaitis, Stephanie; Getz, Tony; Avril, Marion; Gose, Severin; Smith, Joseph D; Fried, Michal; Duffy, Patrick E

2008-04-15

The variant surface antigen VAR2CSA is a pregnancy malaria vaccine candidate, but its size and polymorphism are obstacles to development. We expressed 3D7-type VAR2CSA domains in Escherichia coli as insoluble His-tagged proteins (Duffy binding-like [DBL] domains DBL1, DBL3, DBL4, and DBL5) that were denatured and refolded or as soluble glutathione S-transferase-tagged protein (DBL6). Anti-DBL5 antiserum cross-reacted with surface proteins of chondroitin sulfate A (CSA)-binding laboratory strains (3D7-CSA and FCR3-CSA) and a clinical pregnancy malaria isolate, whereas anti-DBL6 antiserum reacted only to 3D7 surface protein. This is the first report that E. coli-expressed VAR2CSA domains induce antibody to native VAR2CSA.

A Preliminary Examination of the Second Generation CMORPH Real-time Production

NASA Astrophysics Data System (ADS)

Joyce, R.; Xie, P.; Wu, S.

2017-12-01

The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05olat/lon grid over the entire globe, from pole-to-pole. The CMORPH2 is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and precipitation simulations from the NCEP operational global forecast system (GFS). Inputs from the various sources are first inter-calibrated to ensure quantitative consistencies in representing precipitation events of different intensities through PDF calibration against a common reference standard. The inter-calibrated PMW retrievals and IR-based precipitation estimates are then propagated from their respective observation times to the target analysis time along the motion vectors of the precipitating clouds. Motion vectors are first derived separately from the satellite IR based precipitation estimates and the GFS precipitation fields. These individually derived motion vectors are then combined through a 2D-VAR technique to form an analyzed field of cloud motion vectors over the entire globe. The propagated PMW and IR based precipitation estimates are finally integrated into a single field of global precipitation through the Kalman Filter framework. A set of procedures have been established to examine the performance of the CMORPH2 real-time production. CMORPH2 satellite precipitation estimates are compared against the CPC daily gauge analysis, Stage IV radar precipitation over the CONUS, and numerical model forecasts to discover potential shortcomings and quantify improvements against the first generation CMORPH. Special attention has been focused on the CMORPH behavior over high-latitude areas beyond the coverage of the first generation CMORPH. Detailed results will be reported at the AGU.

Triatoma maculata, the Vector of Trypanosoma cruzi, in Venezuela. Phenotypic and Genotypic Variability as Potential Indicator of Vector Displacement into the Domestic Habitat

PubMed Central

García-Alzate, Roberto; Lozano-Arias, Daisy; Reyes-Lugo, Rafael Matías; Morocoima, Antonio; Herrera, Leidi; Mendoza-León, Alexis

2014-01-01

Triatoma maculata is a wild vector of Trypanosoma cruzi, the causative agent of Chagas disease; its incursion in the domestic habitat is scant. In order to establish the possible domestic habitat of T. maculata, we evaluated wing variability and polymorphism of genotypic markers in subpopulations of T. maculata that live in different habitats in Venezuela. As markers, we used the mtCyt b gene, previously apply to evaluate population genetic structure in triatomine species, and the β-tubulin gene region, a marker employed to study genetic variability in Leishmania subgenera. Adults of T. maculata were captured in the period 2012–2013 at domestic, peridomestic (PD), and wild areas of towns in the Venezuelan states of Anzoátegui, Bolívar, Portuguesa, Monagas, Nueva Esparta, and Sucre. The phenotypic analysis was conducted through the determination of the isometric size and conformation of the left wing of each insect (492 individuals), using the MorphoJ program. Results reveal that insects of the domestic habitat showed significant reductions in wing size and variations in anatomical characteristics associated with flying, in relation to the PD and wild habitats. The largest variability was found in Anzoátegui and Monagas. The genotypic variability was assessed by in silico sequence comparison of the molecular markers and PCR-RFLP assays, demonstrating a marked polymorphism for the markers in insects of the domestic habitat in comparison with the other habitats. The highest polymorphism was found for the β-tubulin marker with enzymes BamHI and KpnI. Additionally, the infection rate by T. cruzi was higher in Monagas and Sucre (26.8 and 37.0%, respectively), while in domestic habitats the infestation rate was highest in Anzoátegui (22.3%). Results suggest domestic habitat colonization by T. maculata that in epidemiological terms, coupled with the presence in this habitat of nymphs of the vector, represents a high risk of transmission of Chagas disease. PMID:25325053

Development of highly regenerable callus lines and biolistic transformation of turf-type common bermudagrass [Cynodon dactylon (L.) Pers.].

PubMed

Li, L; Qu, R

2004-01-01

Common bermudagrass, Cynodon dactylon, is a widely used warm-season turf and forage species in the temperate and tropical regions of the world. Improvement of bermudagrass via biotechnology depends on improved tissue culture responses, especially in plant regeneration, and a successful scheme to introduce useful transgenes. When the concentration of 6-benzylaminopurine was adjusted in the culture medium, yellowish, compact calluses were observed from young inflorescence tissue culture of var. J1224. Nine long-term, highly regenerable callus lines (including a suspension-cultured line) were subsequently established, of which six were used for biolistic transformation. Five independent transgenic events, with four producing green plants, were obtained following hygromycin B selection from one callus line. Three transgenic events displayed resistance to the herbicide glufosinate, and one of these showed beta-glucuronidase activity since the co-transformation vector used in the experiments contained both the gusA and bar genes.

Cryptococcus neoformans var. grubii: Separate Varietal Status for Cryptococcus neoformans Serotype A Isolates

PubMed Central

Franzot, Sarah P.; Salkin, Ira F.; Casadevall, Arturo

1999-01-01

Cryptococcus neoformans var. neoformans presently includes isolates which have been determined by the immunologic reactivity of their capsular polysaccharides to be serotype A and those which have been determined to be serotype D. However, recent analyses of the URA5 sequences and DNA fingerprinting patterns suggest significant genetic differences between the two serotypes. Therefore, we propose to recognize these genotypic distinctions, as well as previously reported phenotypic differences, by restricting C. neoformans var. neoformans to isolates which are serotype D and describing a new variety, C. neoformans var. grubii, for serotype A isolates. PMID:9986871

Histopathological effects of cypermethrin and Bacillus thuringiensis var. israelensis on midgut of Chironomus calligraphus larvae (Diptera: Chironomidae).

PubMed

Lavarías, Sabrina; Arrighetti, Florencia; Siri, Augusto

2017-06-01

Pesticides are extensively used for the control of agricultural pests and disease vectors, but they also affect non-target organisms. Cypermethrin (CYP) is a synthetic pyrethroid used worldwide. Otherwise, bioinsecticides like Bacillus thuringiensis var. israelensis (Bti) have received great attention as an environmentally benign and desirable alternative. In order to evaluate the toxicity of those pesticides, Chironomus calligraphus was selected due to its high sensitivity to some toxicants. Third and fourth instars larvae were exposed to serial dilutions of CYP and Bti to determine LC 50 values. In order to evaluate the potentially histopathological alterations as biomarkers, after 96-h of exposure, live larvae were fixed for histological analysis of the mid region of digestive tract. The 96-h LC 50 values were 0.52 and 1.506μg/L for CYP and Bti, respectively. Midgut histological structure of the control group showed a single layer of cubical cells with microvilli in their apical surface and a big central nucleus. The midgut epithelium of larvae exposed to a low concentration of CYP (0.037μg/L) showed secretion activity and vacuolization while at high concentration (0.3μg/L) cells showed a greater disorganization and a more developed fat body. On the other hand, Bti caused progressive histological damage in this tissue. Chironomus calligraphus is sensitive to Bti and CYP toxicity like other Chironomus species. The histopathological alterations could be a valuable tool to assess toxicity mechanism of different pesticides. Copyright © 2017 Elsevier Inc. All rights reserved.

GMR-based eddy current probe for weld seam inspection and its non-scanning detection study

NASA Astrophysics Data System (ADS)

Gao, Peng; Wang, Chao; Li, Yang; Wang, Libin; Cong, Zheng; Zhi, Ya

2017-04-01

Eddy current testing is one of the most important non-destructive testing methods for welding defects detection. This paper presents the use of a probe consisting of 4 giant magneto-resistive (GMR) sensors to detect weld defects. Information from four measuring points above and on both sides of the weld seam is collected at the same time. By setting the GMR sensors' sensing axes perpendicular to the direction of the excitation magnetic field, the information collected mainly reflects the change in the eddy current which is caused by defects. Digital demodulation technology is applied to extract the real part and imaginary part of the GMR sensors' output signals. The variables containing directional information of the magnetic field are introduced. Based on the data from the four GMR (4-GMR) sensors' output signals, four values, Ran, Mean, Var and k are selected as the feature quantities for defect recognition. Experiments are carried out on weld seams with and without defects, and the detection outputs are given in this paper. The 4-GMR probe is also employed to investigate non-scanning weld defect detection and the four feature quantities (Ran, Mean, Var and k) are studied to evaluate weld quality. The non-scanning weld defect detection is presented. A support vector machine is used to classify and discriminate welds with and without defects. Experiments carried out show that through the method in this paper, the recognition rate is 92% for welds without defects and 90% for welds with defects, with an overall recognition rate of 90.9%, indicating that this method could effectively detect weld defects.

Expression of nattokinase in Escherichia coli and renaturation of its inclusion body.

PubMed

Ni, He; Guo, Peng-Cheng; Jiang, Wei-Ling; Fan, Xiao-Min; Luo, Xiang-Yu; Li, Hai-Hang

2016-08-10

Nattokinase is an important fibrinolytic enzyme with therapeutic applications for cardiovascular diseases. The full-length and mature nattokinase genes were cloned from Bacillus subtilis var. natto and expressed in pQE30 vector in Escherichia coli. The full-length gene expressed low nattokinase activity in the intracellular soluble and the medium fractions. The mature gene expressed low soluble nattokinase activity and large amount insoluble protein in inclusion bodies without enzyme activity. Large amount of refolding solutions (RSs) at different pH values were screening and RS-10 and RS-11 at pH 9 were selected to refold nattokinase inclusion bodies. The recombinant cells were lysed with 0.1mg/mL lysozyme and ultrasonic treatment. After centrifugation, the pellete was washed twice with 20mM Tris-HCl buffer (pH 7.5) containing 1% Triton X-100 to purify the inclusion bodies. The inclusion bodies were dissolved in water at pH 12.0 and refolded with RS-10. The refolded proteins showed 42.8IU/mg and 79.3IU/mg fibrinolytic activity by the traditional dilution method (20-fold dilution into RS-10) and the directly mixing the protein solution with equal volume RS-10, respectively, compared to the 52.0IU/mg of total water-soluble proteins from B. subtilis var. natto. This work demonstrated that the inclusion body of recombinant nattokinase expressed in E. coli could be simply refolded to the natural enzyme activity level by directly mixing the protein solution with equal volume refolding solution. Copyright © 2016 Elsevier B.V. All rights reserved.

A temporal study of Salmonella serovars in animals in Alberta between 1990 and 2001

PubMed Central

2005-01-01

Abstract Passive laboratory-based surveillance data from Alberta Agriculture Food and Rural Development were analyzed for common Salmonella serovars, prevalences, trends, and for the presence of temporal clusters. There were 1767 isolates between October 1990 and December 2001 comprising 63 different serovars, including 961 isolates from chickens, 418 from cattle, 108 from pigs, 102 from turkeys, and 178 from all other species combined. Salmonella Typhimurium, Heidelberg, Hadar, Kentucky, and Thompson were the 5 most frequently isolated serovars. Approximately 60% of the S. Typhimurium were isolated from cattle, whereas over 90% of the S. Heidelberg, Hadar, Kentucky, and Thompson were isolated from chickens. Salmonella Enteritidis was rarely isolated. There was an increasing trend in isolates from chickens, cattle, and pigs, and a decreasing trend in isolates from turkeys. Temporal clusters were observed in 11 of 15 serovars examined in chickens (S. Anatum, Heidelberg, Infantis, Kentucky, Mbandaka, Montevideo, Nienstedten, Oranienburg, Thompson, Typhimurium, and Typhimurium var. Copenhagen), 5 of 5 serovars in cattle (S. Dublin, Montevideo, Muenster, Typhimurium, and Typhimurium var. Copenhagen), and 1 of 3 serovars in pigs (S. Typhimurium). Short-duration clusters may imply point source infections, whereas long-duration clusters may indicate an increase in the prevalence of the serovar, farm-to-farm transmission, or a wide-spread common source. A higher concentration of clusters in the winter months may reflect greater confinement, reduced ventilation, stressors, or increased exposure to wildlife vectors that are sharing housing during the winter. Detection of large clusters of Salmonella may have public health implications in addition to animal health concerns. PMID:15971672

Feasibility of Stochastic Voltage/VAr Optimization Considering Renewable Energy Resources for Smart Grid

NASA Astrophysics Data System (ADS)

Momoh, James A.; Salkuti, Surender Reddy

2016-06-01

This paper proposes a stochastic optimization technique for solving the Voltage/VAr control problem including the load demand and Renewable Energy Resources (RERs) variation. The RERs often take along some inputs like stochastic behavior. One of the important challenges i. e., Voltage/VAr control is a prime source for handling power system complexity and reliability, hence it is the fundamental requirement for all the utility companies. There is a need for the robust and efficient Voltage/VAr optimization technique to meet the peak demand and reduction of system losses. The voltages beyond the limit may damage costly sub-station devices and equipments at consumer end as well. Especially, the RERs introduces more disturbances and some of the RERs are not even capable enough to meet the VAr demand. Therefore, there is a strong need for the Voltage/VAr control in RERs environment. This paper aims at the development of optimal scheme for Voltage/VAr control involving RERs. In this paper, Latin Hypercube Sampling (LHS) method is used to cover full range of variables by maximally satisfying the marginal distribution. Here, backward scenario reduction technique is used to reduce the number of scenarios effectively and maximally retain the fitting accuracy of samples. The developed optimization scheme is tested on IEEE 24 bus Reliability Test System (RTS) considering the load demand and RERs variation.

[Effects of different type urban forest plantations on soil fertility].

PubMed

Sun, Hui-zhen; Chen, Ming-yue; Cai, Chun-ju; Zhu, Ning

2009-12-01

Aimed to study the effects of different urban forest plantations on soil fertility, soil samples were collected from eight mono-cultured plantations (Larix gmelinii, Pinus sylvestris var. mongolica, Pinus tabulaeformis var. mukdensis, Phellodendron amurense, Juglans mandshurica, Fraxinus mandshurica, Betula platyphylla, and Quercus mongolica) and one mixed plantation (P. sylvestris var. mongolica + F. mandshurica + Picea koraiensis + P. amurense + B. platyphylla) established in Northeast Forestry University's Urban Forestry Demonstration Research Base in the 1950s, with two sites of neighboring farmland and abandoned farmland as the control. The soils in broadleaved forest plantations except Q. mongolica were near neutral, those in mixed plantation, L. gmelinii, P. sylvestris var. mongolica, and P. tabulaeformis var. mukdensis were slightly acidic, and that in Q. mongolica was acidic. The contents of soil organic matter, total N and P, available P and K, and hydrolysable N tended to decrease with soil depth. There existed significant differences in the chemical indices of the same soil layers among different plantations. The soil fertility was decreased in the order of F. mandshurica > P. amurense > mixed plantation > J. mandshurica > B. platyphylla > abandoned farmland > farmland > P. sylvestris var. mongolica > L. gmelinii > Q. mongolica > P. tabulaeformis var. mukdensis, suggesting that the soil fertility in broadleaved forest plantations except Q. mongolica and in mixed plantation increased, while that in needle-leaved forest plantations tended to decrease.

Why did bluetongue spread the way it did? Environmental factors influencing the velocity of bluetongue virus serotype 8 epizootic wave in France.

PubMed

Pioz, Maryline; Guis, Hélène; Crespin, Laurent; Gay, Emilie; Calavas, Didier; Durand, Benoît; Abrial, David; Ducrot, Christian

2012-01-01

Understanding where and how fast an infectious disease will spread during an epidemic is critical for its control. However, the task is a challenging one as numerous factors may interact and drive the spread of a disease, specifically when vector-borne diseases are involved. We advocate the use of simultaneous autoregressive models to identify environmental features that significantly impact the velocity of disease spread. We illustrate this approach by exploring several environmental factors influencing the velocity of bluetongue (BT) spread in France during the 2007-2008 epizootic wave to determine which ones were the most important drivers. We used velocities of BT spread estimated in 4,495 municipalities and tested sixteen covariates defining five thematic groups of related variables: elevation, meteorological-related variables, landscape-related variables, host availability, and vaccination. We found that ecological factors associated with vector abundance and activity (elevation and meteorological-related variables), as well as with host availability, were important drivers of the spread of the disease. Specifically, the disease spread more slowly in areas with high elevation and when heavy rainfall associated with extreme temperature events occurred one or two months prior to the first clinical case. Moreover, the density of dairy cattle was correlated negatively with the velocity of BT spread. These findings add substantially to our understanding of BT spread in a temperate climate. Finally, the approach presented in this paper can be used with other infectious diseases, and provides a powerful tool to identify environmental features driving the velocity of disease spread.

Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects.

PubMed

Dong, Ni; Huang, Helai; Zheng, Liang

2015-09-01

In zone-level crash prediction, accounting for spatial dependence has become an extensively studied topic. This study proposes Support Vector Machine (SVM) model to address complex, large and multi-dimensional spatial data in crash prediction. Correlation-based Feature Selector (CFS) was applied to evaluate candidate factors possibly related to zonal crash frequency in handling high-dimension spatial data. To demonstrate the proposed approaches and to compare them with the Bayesian spatial model with conditional autoregressive prior (i.e., CAR), a dataset in Hillsborough county of Florida was employed. The results showed that SVM models accounting for spatial proximity outperform the non-spatial model in terms of model fitting and predictive performance, which indicates the reasonableness of considering cross-zonal spatial correlations. The best model predictive capability, relatively, is associated with the model considering proximity of the centroid distance by choosing the RBF kernel and setting the 10% of the whole dataset as the testing data, which further exhibits SVM models' capacity for addressing comparatively complex spatial data in regional crash prediction modeling. Moreover, SVM models exhibit the better goodness-of-fit compared with CAR models when utilizing the whole dataset as the samples. A sensitivity analysis of the centroid-distance-based spatial SVM models was conducted to capture the impacts of explanatory variables on the mean predicted probabilities for crash occurrence. While the results conform to the coefficient estimation in the CAR models, which supports the employment of the SVM model as an alternative in regional safety modeling. Copyright © 2015 Elsevier Ltd. All rights reserved.

Why Did Bluetongue Spread the Way It Did? Environmental Factors Influencing the Velocity of Bluetongue Virus Serotype 8 Epizootic Wave in France

PubMed Central

Pioz, Maryline; Guis, Hélène; Crespin, Laurent; Gay, Emilie; Calavas, Didier; Durand, Benoît; Abrial, David; Ducrot, Christian

2012-01-01

Understanding where and how fast an infectious disease will spread during an epidemic is critical for its control. However, the task is a challenging one as numerous factors may interact and drive the spread of a disease, specifically when vector-borne diseases are involved. We advocate the use of simultaneous autoregressive models to identify environmental features that significantly impact the velocity of disease spread. We illustrate this approach by exploring several environmental factors influencing the velocity of bluetongue (BT) spread in France during the 2007–2008 epizootic wave to determine which ones were the most important drivers. We used velocities of BT spread estimated in 4,495 municipalities and tested sixteen covariates defining five thematic groups of related variables: elevation, meteorological-related variables, landscape-related variables, host availability, and vaccination. We found that ecological factors associated with vector abundance and activity (elevation and meteorological-related variables), as well as with host availability, were important drivers of the spread of the disease. Specifically, the disease spread more slowly in areas with high elevation and when heavy rainfall associated with extreme temperature events occurred one or two months prior to the first clinical case. Moreover, the density of dairy cattle was correlated negatively with the velocity of BT spread. These findings add substantially to our understanding of BT spread in a temperate climate. Finally, the approach presented in this paper can be used with other infectious diseases, and provides a powerful tool to identify environmental features driving the velocity of disease spread. PMID:22916249

40 CFR 80.157 - Volumetric additive reconciliation (“VAR”), equipment calibration, and recordkeeping requirements.

Code of Federal Regulations, 2014 CFR

2014-07-01

... preceding two months of VAR supporting documentation. (2) Except as provided in paragraph (g)(3) of this... immediately available to EPA, upon request, the preceding two months of VAR formula records and VAR supporting.... Detergent so used must be accurately and separately measured, either through the use of a separate storage...

40 CFR 80.157 - Volumetric additive reconciliation (“VAR”), equipment calibration, and recordkeeping requirements.

Code of Federal Regulations, 2012 CFR

2012-07-01

... preceding two months of VAR supporting documentation. (2) Except as provided in paragraph (g)(3) of this... immediately available to EPA, upon request, the preceding two months of VAR formula records and VAR supporting.... Detergent so used must be accurately and separately measured, either through the use of a separate storage...

40 CFR 80.157 - Volumetric additive reconciliation (“VAR”), equipment calibration, and recordkeeping requirements.

Code of Federal Regulations, 2013 CFR

2013-07-01

... preceding two months of VAR supporting documentation. (2) Except as provided in paragraph (g)(3) of this... immediately available to EPA, upon request, the preceding two months of VAR formula records and VAR supporting.... Detergent so used must be accurately and separately measured, either through the use of a separate storage...

40 CFR 80.157 - Volumetric additive reconciliation (“VAR”), equipment calibration, and recordkeeping requirements.

Code of Federal Regulations, 2011 CFR

2011-07-01

... preceding two months of VAR supporting documentation. (2) Except as provided in paragraph (g)(3) of this... immediately available to EPA, upon request, the preceding two months of VAR formula records and VAR supporting.... Detergent so used must be accurately and separately measured, either through the use of a separate storage...

40 CFR 80.157 - Volumetric additive reconciliation (“VAR”), equipment calibration, and recordkeeping requirements.

Code of Federal Regulations, 2010 CFR

2010-07-01

... preceding two months of VAR supporting documentation. (2) Except as provided in paragraph (g)(3) of this... immediately available to EPA, upon request, the preceding two months of VAR formula records and VAR supporting.... Detergent so used must be accurately and separately measured, either through the use of a separate storage...

Diversity, virulence and 2,4-diacetylphloroglucinol sensitivity of Gaeumannomyces graminis var. tritici isolates from Washington State

USDA-ARS?s Scientific Manuscript database

We determined whether isolates of the take-all pathogen Gaeumannomyces graminis var. tritici become less sensitive to 2,4-DAPG during wheat monoculture as a result of exposure to the antibiotic over multiple growing seasons. Over 177 isolates of G. graminis var. tritici were baited from roots of nat...

Ecological adaptations in Douglas-fir (Pseudotsuga menziesii var. glauca) populations. III. Central Idaho

Treesearch

Gerald E. Rehfeldt

1983-01-01

Rehfeldt, Gerald E. 1983. Ecological adaptations in Douglas-fir (Pseudotsuga menziesii var. glauca) populations. III. Central Idaho. Canadian Journal of Forest Research. 13: 626-632. Growth, phenology, and cold hardiness of seedlings from 74 populations of Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco) from central Idaho were compared in four...

Medium- and Long-term Prediction of LOD Change by the Leap-step Autoregressive Model

NASA Astrophysics Data System (ADS)

Wang, Qijie

2015-08-01

The accuracy of medium- and long-term prediction of length of day (LOD) change base on combined least-square and autoregressive (LS+AR) deteriorates gradually. Leap-step autoregressive (LSAR) model can significantly reduce the edge effect of the observation sequence. Especially, LSAR model greatly improves the resolution of signals’ low-frequency components. Therefore, it can improve the efficiency of prediction. In this work, LSAR is used to forecast the LOD change. The LOD series from EOP 08 C04 provided by IERS is modeled by both the LSAR and AR models. The results of the two models are analyzed and compared. When the prediction length is between 10-30 days, the accuracy improvement is less than 10%. When the prediction length amounts to above 30 day, the accuracy improved obviously, with the maximum being around 19%. The results show that the LSAR model has higher prediction accuracy and stability in medium- and long-term prediction.

Acceleration and Velocity Sensing from Measured Strain

NASA Technical Reports Server (NTRS)

Pak, Chan-Gi; Truax, Roger

2015-01-01

A simple approach for computing acceleration and velocity of a structure from the strain is proposed in this study. First, deflection and slope of the structure are computed from the strain using a two-step theory. Frequencies of the structure are computed from the time histories of strain using a parameter estimation technique together with an autoregressive moving average model. From deflection, slope, and frequencies of the structure, acceleration and velocity of the structure can be obtained using the proposed approach. Simple harmonic motion is assumed for the acceleration computations, and the central difference equation with a linear autoregressive model is used for the computations of velocity. A cantilevered rectangular wing model is used to validate the simple approach. Quality of the computed deflection, acceleration, and velocity values are independent of the number of fibers. The central difference equation with a linear autoregressive model proposed in this study follows the target response with reasonable accuracy. Therefore, the handicap of the backward difference equation, phase shift, is successfully overcome.

Sleep analysis for wearable devices applying autoregressive parametric models.

PubMed

Mendez, M O; Villantieri, O; Bianchi, A; Cerutti, S

2005-01-01

We applied time-variant and time-invariant parametric models in both healthy subjects and patients with sleep disorder recordings in order to assess the skills of those approaches to sleep disorders diagnosis in wearable devices. The recordings present the Obstructive Sleep Apnea (OSA) pathology which is characterized by fluctuations in the heart rate, bradycardia in apneonic phase and tachycardia at the recovery of ventilation. Data come from a web database in www.physionet.org. During OSA the spectral indexes obtained by time-variant lattice filters presented oscillations that correspond to the changes brady-tachycardia of the RR intervals and greater values than healthy ones. Multivariate autoregressive models showed an increment in very low frequency component (PVLF) at each apneic event. Also a rise in high frequency component (PHF) occurred over the breathing restore in the spectrum of both quadratic coherence and cross-spectrum in OSA. These autoregressive parametric approaches could help in the diagnosis of Sleep Disorder inside of the wearable devices.

Water balance models in one-month-ahead streamflow forecasting

USGS Publications Warehouse

Alley, William M.

1985-01-01

Techniques are tested that incorporate information from water balance models in making 1-month-ahead streamflow forecasts in New Jersey. The results are compared to those based on simple autoregressive time series models. The relative performance of the models is dependent on the month of the year in question. The water balance models are most useful for forecasts of April and May flows. For the stations in northern New Jersey, the April and May forecasts were made in order of decreasing reliability using the water-balance-based approaches, using the historical monthly means, and using simple autoregressive models. The water balance models were useful to a lesser extent for forecasts during the fall months. For the rest of the year the improvements in forecasts over those obtained using the simpler autoregressive models were either very small or the simpler models provided better forecasts. When using the water balance models, monthly corrections for bias are found to improve minimum mean-square-error forecasts as well as to improve estimates of the forecast conditional distributions.

[A novel method of multi-channel feature extraction combining multivariate autoregression and multiple-linear principal component analysis].

PubMed

Wang, Jinjia; Zhang, Yanna

2015-02-01

Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.

Phylogeography of Pinus subsection Australes in the Caribbean Basin

PubMed Central

Jardón-Barbolla, Lev; Delgado-Valerio, Patricia; Geada-López, Gretel; Vázquez-Lobo, Alejandra; Piñero, Daniel

2011-01-01

Background and Aims Four species of Pinus subsection Australes occur in the Caribbean Basin: P. caribaea, P. cubensis, P. maestrensis and P. occidentalis. This study analyses the phylogeography of these species to assess possible colonization events from Central America to the islands and subsequent population expansions during glacial periods driven by both drier climate and larger emerged land areas. Methods Allele size data were obtained for plastid microsatellites for 314 individuals from 24 populations, covering the distribution range of subsection Australes in the Caribbean Basin. Key Results In total, 113 plastid haplotypes were identified. The highest genetic diversity was found in populations of P. caribaea. Overall, Caribbean Basin populations fit the isolation by distance model. Significant phylogeographical structure was found (RST = 0·671 > permuted RST = 0·101; P < 0·0001). The haplotype network and a Bayesian analysis of population structure (BAPS) indicated different Central American origins for P. caribaea var. bahamensis and P. caribaea var. caribaea plastids, with Central America populations in northern and south-eastern groups. Sudden expansion times for BAPS clusters were close to three glacial maxima. Conclusions Central America contains ancestral plastid haplotypes. Population expansion has played a major role in the distribution of genetic diversity in P. caribaea var. hondurensis. Two colonization events gave rise to the P. caribaea var. bahamensis and P. caribaea var. caribaea lineages. Plastid variation in the eastern species (P. cubensis, P. maestrensis and P. occidentalis) evolved independently from that in P. caribaea var. caribaea. Incomplete lineage sorting between P. cubensis and P. maestrensis is apparent. Inferred expansion times for P. caribaea var. bahamensis and for the eastern lineages correspond to glacial maxima, whereas those for P. caribaea var. hondurensis correspond to the beginning of the temperature decrease that led to Marine Isotope Stage 8. PMID:21118838

Molecular dissection of placental malaria protein VAR2CSA interaction with a chemo-enzymatically synthesized chondroitin sulfate library.

PubMed

Sugiura, Nobuo; Clausen, Thomas Mandel; Shioiri, Tatsumasa; Gustavsson, Tobias; Watanabe, Hideto; Salanti, Ali

2016-12-01

Placental malaria, a serious infection caused by the parasite Plasmodium falciparum, is characterized by the selective accumulation of infected erythrocytes (IEs) in the placentas of the pregnant women. Placental adherence is mediated by the malarial VAR2CSA protein, which interacts with chondroitin sulfate (CS) proteoglycans present in the placental tissue. CS is a linear acidic polysaccharide composed of repeating disaccharide units of D-glucuronic acid and N-acetyl-D-galactosamine that are modified by sulfate groups at different positions. Previous reports have shown that placental-adhering IEs were associated with an unusually low sulfated form of chondroitin sulfate A (CSA) and that a partially sulfated dodecasaccharide is the minimal motif for the interaction. However, the fine molecular structure of this CS chain remains unclear. In this study, we have characterized the CS chain that interacts with a recombinant minimal CS-binding region of VAR2CSA (rVAR2) using a CS library of various defined lengths and sulfate compositions. The CS library was chemo-enzymatically synthesized with bacterial chondroitin polymerase and recombinant CS sulfotransferases. We found that C-4 sulfation of the N-acetyl-D-galactosamine residue is critical for supporting rVAR2 binding, whereas no other sulfate modifications showed effects. Interaction of rVAR2 with CS is highly correlated with the degree of C-4 sulfation and CS chain length. We confirmed that the minimum structure binding to rVAR2 is a tri-sulfated CSA dodecasaccharide, and found that a highly sulfated CSA eicosasaccharide is a more potent inhibitor of rVAR2 binding than the dodecasaccharides. These results suggest that CSA derivatives may potentially serve as targets in therapeutic strategies against placental malaria.

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

Boerwinkle, E.; Brown, S.; Patsch, W.

To quantify the effect of the apolipoprotein (apo) E polymorphism on the magnitude of postprandial lipemia, the authors have defined its role in determining the response to a single high-fat meal in a large sample of (N = 474) individuals taking part in the biethnic Atherosclerosis Risk in Communities Study. The profile of postprandial response in plasma was monitored over 8 h by triglyceride, triglyceride-rich lipoprotein (TGRL)-triglyceride, apo B-48/apo B-100 ratio, and retinyl palmitate concentrations, and the apo E polymorphism was determined by DNA amplification and digestion. The frequency of the apo E alleles and their effects on fasting lipidmore » levels in this sample with vitamin A was significantly different among apo E genotypes, with delayed clearance in individuals with an [var epsilon]2 allele, compared with [var epsilon]3/3 and [var epsilon]3/4 individuals. In the sample of 397 Caucasians, average retinyl palmitate response was 1,489 [mu]g/dl in [var epsilon]2/3 individuals, compared with 1,037 [mu]g/dl in [var epsilon]3/3 individuals and 1,108 [mu]g/dl in [var epsilon]3/4 individuals. The apo E polymorphism accounted for 7.1% of the interindividual variation in postprandial retinyl palmitate response, a contribution proportionally greater than its well-known effect on fasting LDL-cholesterol. However, despite this effect on postprandial retinyl palmitate, the profile of postprandial triglyceride response was not significantly different among apo E genotypes. The profile of postprandial response was consistent between the sample of Caucasians and a smaller sample of black subjects. While these data indicate that the removal of remnant particles from circulation is delayed in subjects with the [var epsilon]2/3 genotype, there is no reported evidence that the [var epsilon]2 allele predisposes to coronary artery disease (CAD). 82 refs., 6 figs., 4 tabs.« less

VARS-TOOL: A Comprehensive, Efficient, and Robust Sensitivity Analysis Toolbox

NASA Astrophysics Data System (ADS)

Razavi, S.; Sheikholeslami, R.; Haghnegahdar, A.; Esfahbod, B.

2016-12-01

VARS-TOOL is an advanced sensitivity and uncertainty analysis toolbox, applicable to the full range of computer simulation models, including Earth and Environmental Systems Models (EESMs). The toolbox was developed originally around VARS (Variogram Analysis of Response Surfaces), which is a general framework for Global Sensitivity Analysis (GSA) that utilizes the variogram/covariogram concept to characterize the full spectrum of sensitivity-related information, thereby providing a comprehensive set of "global" sensitivity metrics with minimal computational cost. VARS-TOOL is unique in that, with a single sample set (set of simulation model runs), it generates simultaneously three philosophically different families of global sensitivity metrics, including (1) variogram-based metrics called IVARS (Integrated Variogram Across a Range of Scales - VARS approach), (2) variance-based total-order effects (Sobol approach), and (3) derivative-based elementary effects (Morris approach). VARS-TOOL is also enabled with two novel features; the first one being a sequential sampling algorithm, called Progressive Latin Hypercube Sampling (PLHS), which allows progressively increasing the sample size for GSA while maintaining the required sample distributional properties. The second feature is a "grouping strategy" that adaptively groups the model parameters based on their sensitivity or functioning to maximize the reliability of GSA results. These features in conjunction with bootstrapping enable the user to monitor the stability, robustness, and convergence of GSA with the increase in sample size for any given case study. VARS-TOOL has been shown to achieve robust and stable results within 1-2 orders of magnitude smaller sample sizes (fewer model runs) than alternative tools. VARS-TOOL, available in MATLAB and Python, is under continuous development and new capabilities and features are forthcoming.

Bearing and gear steels for aerospace applications

NASA Technical Reports Server (NTRS)

Zaretsky, Erwin V.

1990-01-01

Research in metallurgy and processing for bearing and gear steels has resulted in improvements in rolling-element bearing and gear life for aerospace application by a factor of approximately 200 over that obtained in the early 1940's. The selection and specification of a bearing or gear steel is dependent on the integration of multiple metallurgical and physical variables. For most aerospace bearings, through-hardened VIM-VAR AISI M-50 steel is the material of preference. For gears, the preferential material is case-carburized VAR AISI 9310. However, the VAR processing for this material is being replaced by VIM-VAR processing. Since case-carburized VIM-VAR M-50NiL incorporates the desirable qualities of both the AISI M-50 and AISI 9310 materials, optimal life and reliability can be achieved in both bearings and gears with a single steel. Hence, this material offers the promise of a common steel for both bearings and gears for future aerospace applications.

Preventing the return of smallpox: molecular modeling studies on thymidylate kinase from Variola virus.

PubMed

Guimarães, Ana Paula; Ramalho, Teodorico Castro; França, Tanos Celmar Costa

2014-01-01

Smallpox was one of the most devastating diseases in the human history and still represents a serious menace today due to its potential use by bioterrorists. Considering this threat and the non-existence of effective chemotherapy, we propose the enzyme thymidylate kinase from Variola virus (VarTMPK) as a potential target to the drug design against smallpox. We first built a homology model for VarTMPK and performed molecular docking studies on it in order to investigate the interactions with inhibitors of Vaccinia virus TMPK (VacTMPK). Subsequently, molecular dynamics (MD) simulations of these compounds inside VarTMPK and human TMPK (HssTMPK) were carried out in order to select the most promising and selective compounds as leads for the design of potential VarTMPK inhibitors. Results of the docking and MD simulations corroborated to each other, suggesting selectivity towards VarTMPK and, also, a good correlation with the experimental data.

Reciprocal Associations between Negative Affect, Binge Eating, and Purging in the Natural Environment in Women with Bulimia Nervosa

PubMed Central

Lavender, Jason M.; Utzinger, Linsey M.; Cao, Li; Wonderlich, Stephen A.; Engel, Scott G.; Mitchell, James E.; Crosby, Ross D.

2016-01-01

Although negative affect (NA) has been identified as a common trigger for bulimic behaviors, findings regarding NA following such behaviors have been mixed. This study examined reciprocal associations between NA and bulimic behaviors using real-time, naturalistic data. Participants were 133 women with DSM-IV bulimia nervosa (BN) who completed a two-week ecological momentary assessment (EMA) protocol in which they recorded bulimic behaviors and provided multiple daily ratings of NA. A multilevel autoregressive cross-lagged analysis was conducted to examine concurrent, first-order autoregressive, and prospective associations between NA, binge eating, and purging across the day. Results revealed positive concurrent associations between all variables across all time points, as well as numerous autoregressive associations. For prospective associations, higher NA predicted subsequent bulimic symptoms at multiple time points; conversely, binge eating predicted lower NA at multiple time points, and purging predicted higher NA at one time point. Several autoregressive and prospective associations were also found between binge eating and purging. This study used a novel approach to examine NA in relation to bulimic symptoms, contributing to the existing literature by directly examining the magnitude of the associations, examining differences in the associations across the day, and controlling for other associations in testing each effect in the model. These findings may have relevance for understanding the etiology and/or maintenance of bulimic symptoms, as well as potentially informing psychological interventions for BN. PMID:26692122

Economic growth and CO2 emissions: an investigation with smooth transition autoregressive distributed lag models for the 1800-2014 period in the USA.

PubMed

Bildirici, Melike; Ersin, Özgür Ömer

2018-01-01

The study aims to combine the autoregressive distributed lag (ARDL) cointegration framework with smooth transition autoregressive (STAR)-type nonlinear econometric models for causal inference. Further, the proposed STAR distributed lag (STARDL) models offer new insights in terms of modeling nonlinearity in the long- and short-run relations between analyzed variables. The STARDL method allows modeling and testing nonlinearity in the short-run and long-run parameters or both in the short- and long-run relations. To this aim, the relation between CO 2 emissions and economic growth rates in the USA is investigated for the 1800-2014 period, which is one of the largest data sets available. The proposed hybrid models are the logistic, exponential, and second-order logistic smooth transition autoregressive distributed lag (LSTARDL, ESTARDL, and LSTAR2DL) models combine the STAR framework with nonlinear ARDL-type cointegration to augment the linear ARDL approach with smooth transitional nonlinearity. The proposed models provide a new approach to the relevant econometrics and environmental economics literature. Our results indicated the presence of asymmetric long-run and short-run relations between the analyzed variables that are from the GDP towards CO 2 emissions. By the use of newly proposed STARDL models, the results are in favor of important differences in terms of the response of CO 2 emissions in regimes 1 and 2 for the estimated LSTAR2DL and LSTARDL models.

Multifractality and autoregressive processes of dry spell lengths in Europe: an approach to their complexity and predictability

NASA Astrophysics Data System (ADS)

Lana, X.; Burgueño, A.; Serra, C.; Martínez, M. D.

2017-01-01

Dry spell lengths, DSL, defined as the number of consecutive days with daily rain amounts below a given threshold, may provide relevant information about drought regimes. Taking advantage of a daily pluviometric database covering a great extension of Europe, a detailed analysis of the multifractality of the dry spell regimes is achieved. At the same time, an autoregressive process is applied with the aim of predicting DSL. A set of parameters, namely Hurst exponent, H, estimated from multifractal spectrum, f( α), critical Hölder exponent, α 0, for which f( α) reaches its maximum value, spectral width, W, and spectral asymmetry, B, permits a first clustering of European rain gauges in terms of the complexity of their DSL series. This set of parameters also allows distinguishing between time series describing fine- or smooth-structure of the DSL regime by using the complexity index, CI. Results of previous monofractal analyses also permits establishing comparisons between smooth-structures, relatively low correlation dimensions, notable predictive instability and anti-persistence of DSL for European areas, sometimes submitted to long droughts. Relationships are also found between the CI and the mean absolute deviation, MAD, and the optimum autoregressive order, OAO, of an ARIMA( p, d,0) autoregressive process applied to the DSL series. The detailed analysis of the discrepancies between empiric and predicted DSL underlines the uncertainty over predictability of long DSL, particularly for the Mediterranean region.

Efficient Scores, Variance Decompositions and Monte Carlo Swindles.

DTIC Science & Technology

1984-08-28

to ;r Then a version .of Pythagoras ’ theorem gives the variance decomposition (6.1) varT var S var o(T-S) P P0 0 0 One way to see this is to note...complete sufficient statistics for (B, a) , and that the standard- ized residuals a(y - XB) 6 are ancillary. Basu’s sufficiency- ancillarity theorem

VarMod: modelling the functional effects of non-synonymous variants

PubMed Central

Pappalardo, Morena; Wass, Mark N.

2014-01-01

Unravelling the genotype–phenotype relationship in humans remains a challenging task in genomics studies. Recent advances in sequencing technologies mean there are now thousands of sequenced human genomes, revealing millions of single nucleotide variants (SNVs). For non-synonymous SNVs present in proteins the difficulties of the problem lie in first identifying those nsSNVs that result in a functional change in the protein among the many non-functional variants and in turn linking this functional change to phenotype. Here we present VarMod (Variant Modeller) a method that utilises both protein sequence and structural features to predict nsSNVs that alter protein function. VarMod develops recent observations that functional nsSNVs are enriched at protein–protein interfaces and protein–ligand binding sites and uses these characteristics to make predictions. In benchmarking on a set of nearly 3000 nsSNVs VarMod performance is comparable to an existing state of the art method. The VarMod web server provides extensive resources to investigate the sequence and structural features associated with the predictions including visualisation of protein models and complexes via an interactive JSmol molecular viewer. VarMod is available for use at http://www.wasslab.org/varmod. PMID:24906884

LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC.

PubMed

Allot, Alexis; Peng, Yifan; Wei, Chih-Hsuan; Lee, Kyubum; Phan, Lon; Lu, Zhiyong

2018-05-14

The identification and interpretation of genomic variants play a key role in the diagnosis of genetic diseases and related research. These tasks increasingly rely on accessing relevant manually curated information from domain databases (e.g. SwissProt or ClinVar). However, due to the sheer volume of medical literature and high cost of expert curation, curated variant information in existing databases are often incomplete and out-of-date. In addition, the same genetic variant can be mentioned in publications with various names (e.g. 'A146T' versus 'c.436G>A' versus 'rs121913527'). A search in PubMed using only one name usually cannot retrieve all relevant articles for the variant of interest. Hence, to help scientists, healthcare professionals, and database curators find the most up-to-date published variant research, we have developed LitVar for the search and retrieval of standardized variant information. In addition, LitVar uses advanced text mining techniques to compute and extract relationships between variants and other associated entities such as diseases and chemicals/drugs. LitVar is publicly available at https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/LitVar.

Changes in amino acid profile and metal content in seeds of Cicer arietinum L. (chickpea) grown under various fly-ash amendments.

PubMed

Gupta, D K; Tripathi, R D; Rai, U N; Dwivedi, S; Mishra, Seema; Srivastava, S; Inouhe, M

2006-11-01

Seeds of Cicer arietinum L. plants are edible and a valuable source of protein. Accumulation of toxic metals in the edible part of the plant, grown in fields close to fly-ash (FA) landfills, may pose a threat to human health. In the present study, the effects of FA and its amendments with different ameliorants viz., garden soil (GS), press mud (PM) and saw dust (SD), on total soluble protein contents, amino acid composition and metal accumulation in seeds were investigated in var. CSG-8962 and var. C-235 of C. arietinum. Plants accumulated adequate amounts of essential metals viz. Fe, Cu, Zn in seeds, while the toxic metals such as Cd and Cr were taken up in smaller quantities. The accumulation of Cr and Cd was less in var. C-235 than var. CSG-8962. Amendment of FA with PM enhanced the amount of soluble protein and amino acids in both varieties and was found to be superior among all tested ameliorants. Both quantitative and qualitative analysis of amino acids showed better response in var. C-235 as compared to var. CSG-8962. Thus var. C-235 seems to be suitable for cultivation in FA contaminated areas due to more accumulation of essential metals and less accumulation of toxic metals in seeds. Application of PM may further improve the growth of plants and nutritional quality of seeds.

Variogram Analysis of Response surfaces (VARS): A New Framework for Global Sensitivity Analysis of Earth and Environmental Systems Models

NASA Astrophysics Data System (ADS)

Razavi, S.; Gupta, H. V.

2015-12-01

Earth and environmental systems models (EESMs) are continually growing in complexity and dimensionality with continuous advances in understanding and computing power. Complexity and dimensionality are manifested by introducing many different factors in EESMs (i.e., model parameters, forcings, boundary conditions, etc.) to be identified. Sensitivity Analysis (SA) provides an essential means for characterizing the role and importance of such factors in producing the model responses. However, conventional approaches to SA suffer from (1) an ambiguous characterization of sensitivity, and (2) poor computational efficiency, particularly as the problem dimension grows. Here, we present a new and general sensitivity analysis framework (called VARS), based on an analogy to 'variogram analysis', that provides an intuitive and comprehensive characterization of sensitivity across the full spectrum of scales in the factor space. We prove, theoretically, that Morris (derivative-based) and Sobol (variance-based) methods and their extensions are limiting cases of VARS, and that their SA indices can be computed as by-products of the VARS framework. We also present a practical strategy for the application of VARS to real-world problems, called STAR-VARS, including a new sampling strategy, called "star-based sampling". Our results across several case studies show the STAR-VARS approach to provide reliable and stable assessments of "global" sensitivity across the full range of scales in the factor space, while being at least 1-2 orders of magnitude more efficient than the benchmark Morris and Sobol approaches.

Evidence for Globally Shared, Cross-Reacting Polymorphic Epitopes in the Pregnancy-Associated Malaria Vaccine Candidate VAR2CSA▿

PubMed Central

Avril, Marion; Kulasekara, Bridget R.; Gose, Severin O.; Rowe, Chris; Dahlbäck, Madeleine; Duffy, Patrick E.; Fried, Michal; Salanti, Ali; Misher, Lynda; Narum, David L.; Smith, Joseph D.

2008-01-01

Pregnancy-associated malaria (PAM) is characterized by the placental sequestration of Plasmodium falciparum-infected erythrocytes (IEs) with the ability to bind to chondroitin sulfate A (CSA). VAR2CSA is a leading candidate for a pregnancy malaria vaccine, but its large size (∼350 kDa) and extensive polymorphism may pose a challenge to vaccine development. In this study, rabbits were immunized with individual VAR2CSA Duffy binding-like (DBL) domains expressed in Pichia pastoris or var2csa plasmid DNA and sera were screened on different CSA-binding parasite lines. Rabbit antibodies to three recombinant proteins (DBL1, DBL3, and DBL6) and four plasmid DNAs (DBL1, DBL3, DBL5, and DBL6) reacted with homologous FCR3-CSA IEs. By comparison, antibodies to the DBL4 domain were unable to react with native VAR2CSA protein unless it was first partially proteolyzed with trypsin or chymotrypsin. To investigate the antigenic relationship of geographically diverse CSA-binding isolates, rabbit immune sera were screened on four heterologous CSA-binding lines from different continental origins. Antibodies did not target conserved epitopes exposed in all VAR2CSA alleles; however, antisera to several DBL domains cross-reacted on parasite isolates that had polymorphic loops in common with the homologous immunogen. This study demonstrates that VAR2CSA contains common polymorphic epitopes that are shared between geographically diverse CSA-binding lines. PMID:18250177

Evidence for globally shared, cross-reacting polymorphic epitopes in the pregnancy-associated malaria vaccine candidate VAR2CSA.

PubMed

Avril, Marion; Kulasekara, Bridget R; Gose, Severin O; Rowe, Chris; Dahlbäck, Madeleine; Duffy, Patrick E; Fried, Michal; Salanti, Ali; Misher, Lynda; Narum, David L; Smith, Joseph D

2008-04-01

Pregnancy-associated malaria (PAM) is characterized by the placental sequestration of Plasmodium falciparum-infected erythrocytes (IEs) with the ability to bind to chondroitin sulfate A (CSA). VAR2CSA is a leading candidate for a pregnancy malaria vaccine, but its large size ( approximately 350 kDa) and extensive polymorphism may pose a challenge to vaccine development. In this study, rabbits were immunized with individual VAR2CSA Duffy binding-like (DBL) domains expressed in Pichia pastoris or var2csa plasmid DNA and sera were screened on different CSA-binding parasite lines. Rabbit antibodies to three recombinant proteins (DBL1, DBL3, and DBL6) and four plasmid DNAs (DBL1, DBL3, DBL5, and DBL6) reacted with homologous FCR3-CSA IEs. By comparison, antibodies to the DBL4 domain were unable to react with native VAR2CSA protein unless it was first partially proteolyzed with trypsin or chymotrypsin. To investigate the antigenic relationship of geographically diverse CSA-binding isolates, rabbit immune sera were screened on four heterologous CSA-binding lines from different continental origins. Antibodies did not target conserved epitopes exposed in all VAR2CSA alleles; however, antisera to several DBL domains cross-reacted on parasite isolates that had polymorphic loops in common with the homologous immunogen. This study demonstrates that VAR2CSA contains common polymorphic epitopes that are shared between geographically diverse CSA-binding lines.

AR(p) -based detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Alvarez-Ramirez, J.; Rodriguez, E.

2018-07-01

Autoregressive models are commonly used for modeling time-series from nature, economics and finance. This work explored simple autoregressive AR(p) models to remove long-term trends in detrended fluctuation analysis (DFA). Crude oil prices and bitcoin exchange rate were considered, with the former corresponding to a mature market and the latter to an emergent market. Results showed that AR(p) -based DFA performs similar to traditional DFA. However, the former DFA provides information on stability of long-term trends, which is valuable for understanding and quantifying the dynamics of complex time series from financial systems.

Acquisition of Infrared Variable Angle Spectroscopic Ellipsometer (IR-VASE)

DTIC Science & Technology

2016-04-22

External Advisory Board Meeting in Rio Piedras, PR. March 2016 Quiñonez B.*, Castilla D., Almodóvar J.; “ Polysaccharide -based polyelectrolyte...April 2016 Quiñonez B.*, Castilla D., Almodóvar J.; “ Polysaccharide -based polyelectrolyte multilayers: Physicochemical characterization and in...2016 Quiñonez B.*, Castilla D., Almodóvar J.; “ Polysaccharide -based polyelectrolyte multilayers: Physicochemical characterization and in vitro

75 FR 807 - Pesticide Tolerance Crop Grouping Program II; Revision to General Tolerance Regulations

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-06

...) Pepper, nonbell, Capsicum Chinese Jacq., C. annuum L. var. annuum , C. frutescens L., C. baccatum L., C... muricatum Aiton 8-09B, 8-09C Pepper, bell, Capsicum annuum L. var. annuum, 8-09B Capsicum spp Pepper, nonbell, Capsicum chinese Jacq., C. 8-09B, 8-08C annuum L. var. annuum, C. frutescens L., C. baccatum L...

Comparative ozone responses of cutleaf coneflowers (Rudbeckia laciniata var. digitata, var. ampla) from Rocky Mountain and Great Smoky Mountains National Parks, USA.

PubMed

Neufeld, Howard S; Johnson, Jennifer; Kohut, Robert

2018-01-01

Cutleaf coneflower (Rudbeckia laciniata L. var. digitata) is native to Great Smoky Mountains National Park (GRSM) and an ozone bioindicator species. Variety ampla, whose ozone sensitivity is less well known, is native to Rocky Mountain National Park (ROMO). In the early 2000s, researchers found putative ozone symptoms on var. ampla and rhizomes were sent to Appalachian State University to verify that the symptoms were the result of ozone exposure. In 2011, potted plants were exposed to ambient ozone from May to August. These same plants were grown in open-top chambers (OTCs) in 2012 and 2013, and exposed to charcoal-filtered (CF), non-filtered (NF), elevated ozone (EO), NF+50ppb in 2012 for 47days and NF+30/NF+50ppb ozone in 2013 for 36 and 36days, respectively. Ozone symptoms similar to those found in ROMO (blue-black adaxial stippling) were reproduced both in ambient air and in the OTCs. Both varieties exhibited foliar injury in the OTCs in an exposure-dependent manner, verifying that symptoms resulted from ozone exposure. In two of the three study years, var. digitata appeared more sensitive than var. ampla. Exposure to EO caused reductions in ambient photosynthetic rate (A) and stomatal conductance (g s ) for both varieties. Light response curves indicated that ozone reduced A, g s , and the apparent quantum yield while it increased the light compensation point. In CF air, var. ampla had higher light saturated A (18.2±1.04 vs 11.6±0.37μmolm -2 s -1 ), higher light saturation (1833±166.7 vs 1108±141.7μmolm -2 s -1 ), and lower Ci/Ca ratio (0.67±0.01 vs 0.77±0.01) than var. digitata. Coneflowers in both Parks are adversely affected by exposure to ambient ozone and if ozone concentrations increase in the Rocky Mountains, greater amounts of injury on var. ampla can be expected. Copyright © 2017 Elsevier B.V. All rights reserved.

Species delimitation in northern European water scavenger beetles of the genus Hydrobius (Coleoptera, Hydrophilidae)

PubMed Central

Fossen, Erlend I.; Ekrem, Torbjørn; Nilsson, Anders N.; Bergsten, Johannes

2016-01-01

Abstract The chiefly Holarctic Hydrobius species complex (Coleoptera, Hydrophilidae) currently consists of Hydrobius arcticus Kuwert, 1890, and three morphological variants of Hydrobius fuscipes (Linnaeus, 1758): var. fuscipes, var. rottenbergii and var. subrotundus in northern Europe. Here molecular and morphological data are used to test the species boundaries in this species complex. Three gene segments (COI, H3 and ITS2) were sequenced and analyzed with Bayesian methods to infer phylogenetic relationships. The Generalized Mixed Yule Coalescent (GMYC) model and two versions of the Bayesian species delimitation method BPP, with or without an a priori defined guide tree (v2.2 & v3.0), were used to evaluate species limits. External and male genital characters of primarily Fennoscandian specimens were measured and statistically analyzed to test for significant differences in quantitative morphological characters. The four morphotypes formed separate genetic clusters on gene trees and were delimited as separate species by GMYC and by both versions of BPP, despite specimens of Hydrobius fuscipes var. fuscipes and Hydrobius fuscipes var. subrotundus being sympatric. Hydrobius arcticus and Hydrobius fuscipes var. rottenbergii could only be separated genetically with ITS2, and were delimited statistically with GMYC on ITS2 and with BPP on the combined data. In addition, six or seven potentially cryptic species of the Hydrobius fuscipes complex from regions outside northern Europe were delimited genetically. Although some overlap was found, the mean values of six male genital characters were significantly different between the morphotypes (p < 0.001). Morphological characters previously presumed to be diagnostic were less reliable to separate Hydrobius fuscipes var. fuscipes from Hydrobius fuscipes var. subrotundus, but characters in the literature for Hydrobius arcticus and Hydrobius fuscipes var. rottenbergii were diagnostic. Overall, morphological and molecular evidence strongly suggest that Hydrobius arcticus and the three morphological variants of Hydrobius fuscipes are separate species and Hydrobius rottenbergii Gerhardt, 1872, stat. n. and Hydrobius subrotundus Stephens, 1829, stat. n. are elevated to valid species. An identification key to northern European species of Hydrobius is provided. PMID:27081333

Essays on Commodity Prices and Macroeconomic Performance of Developing and Resources Rich Economies: Evidence from Kazakhstan

NASA Astrophysics Data System (ADS)

Bilgin, Ferhat I.

My dissertation consists of three essays in empirical macroeconomics. The objective of this research is to use rigorous time-series econometric analysis to investigate the impact of commodity prices on macroeconomic performance of a small, developing and resource-rich country, which is in the process of transition from a purely command and control economy to a market oriented one. Essay 1 studies the relationship between Kazakhstan's GDP, total government expenditure, real effective exchange rate and the world oil price. Specifically, I use the cointegrated vector autoregression (CVAR) and error correction modeling (ECM) approach to identify the long and short-run relations that may exist among these macroeconomic variables. I found a long-run relationship for Kazakhstan's GDP, which depends on government spending and the oil price positively, and on the real effective exchange rate negatively. In the short run, the growth rate of GDP depends on the growth rates of the oil price, investment and the magnitude of the deviation from the long-run equilibrium. Essay 2 studies the inflation process in Kazakhstan based on the analysis of price formation in the following sectors: monetary, external, labor and goods and services. The modeling is conducted from two different perspectives: the first is the monetary model of inflation framework and the second is the mark-up modeling framework. Encompassing test results show that the mark-up model performs better than the monetary model in explaining inflation in Kazakhstan. According to the mark-up inflation model, in the long run, the price level is positively related to unit labor costs, import prices and government administered prices as well the world oil prices. In the short run, the inflation is positively influenced by the previous quarter's inflation, the contemporaneous changes in the government administered prices, oil prices and by the changes of contemporaneous and lagged unit labor costs, and negatively affected by the previous quarter's mark-up. Essay 3 empirically examines the determinants of the trade balance for a small oil exporting country within the context of Kazakhstan. The dominant theory by Harberger-Lauren-Metzler (HML) predicts that positive terms of trade shocks will improve the trade balance in the short run, but will fade away in the long run. I estimate cointegrated vector autoregression (CVAR) and vector error correction model (VECM) to study the long-run and short-run impacts on the trade balance. The results suggest that, in the long run, an increase in the terms of trade has a positive effect on the trade balance, an increase in GDP and appreciation of the real effective exchange rate have negative effect on the trade balance. In the short run, the terms of trade has a direct positive impact on the trade balance, real income and real exchange rate. On the other hand, appreciation of the currency has a negative impact on the trade balance. The error correction term, which represents the deviation from the long- run equilibrium between the trade balance, real income, terms of trade and real exchange rate, has a negative effect on the growth rate of the trade balance. These results provide further evidence to the idea that, in the long run, the HML effect not only depends on the duration of the shock, but also depends on the structure of the economy.

[Chemical Constituents of Paris polyphylla var. chinensis Aerial Parts].

PubMed

Yin, Wei; Song, Zu-rong; Liu, Jin-qi; Zhang, Guo-sheng

2015-09-01

To study the chemical constituents of aerial parts of Paris polyphylla var. chinensis . Aerial parts of Paris polyphylla var. chinensis was extracted with 95% EtOH, and separated and purified by silica gel, RP 18 and Sephadex LH-20 col- umn chromatography. The structures were identified by spectroscopic analysis. A total of ten compounds were isolated and iden- tified as β-sitosterol (1) ergosta-7, 22-dien-3-one (2), β-ecdysone (3), kaempferol (4), daucosterol (5) luteolin (6) calonysterone (7), luteolin-7-O-glucoside (8), quercetin (9), and 3β, 5α, 9α-trihydroxyergosta-7, 22-dien-6-one (10). Compounds 2,6 and 10 are isolated from Paris polyphylla var. chinensis for the first time.

Essential oil compositions and anticholinesterase activities of two edible plants Tragopogon latifolius var. angustifolius and Lycopsis orientalis.

PubMed

Ertaş, Abdulselam; Gören, Ahmet C; Boğa, Mehmet; Yeşil, Yeter; Kolak, Ufuk

2014-01-01

This is the first report in the literature on essential oil compositions of Tragopogon latifolius var. angustifolius and Lycopsis orientalis which were analysed by using GC-FID and GC-MS techniques. The main constituents of T. latifolius var. angustifolius were identified as α-selinene (10.5%), 2,5-di-tert octyl-p-benzoquinone (9.5%) and valencene (7.0%); however, the main components of L. orientalis were identified as heptacosane (10.5%), τ-muurolene (9.6%) and tetratetracontane (9.4%). The essential oils of T. latifolius var. angustifolius and L. orientalis species exhibited moderate inhibitory activity against acetyl- and butyryl-cholinesterase enzymes at 200 μg/mL.

Definition of Linear Color Models in the RGB Vector Color Space to Detect Red Peaches in Orchard Images Taken under Natural Illumination

PubMed Central

Teixidó, Mercè; Font, Davinia; Pallejà, Tomàs; Tresanchez, Marcel; Nogués, Miquel; Palacín, Jordi

2012-01-01

This work proposes the detection of red peaches in orchard images based on the definition of different linear color models in the RGB vector color space. The classification and segmentation of the pixels of the image is then performed by comparing the color distance from each pixel to the different previously defined linear color models. The methodology proposed has been tested with images obtained in a real orchard under natural light. The peach variety in the orchard was the paraguayo (Prunus persica var. platycarpa) peach with red skin. The segmentation results showed that the area of the red peaches in the images was detected with an average error of 11.6%; 19.7% in the case of bright illumination; 8.2% in the case of low illumination; 8.6% for occlusion up to 33%; 12.2% in the case of occlusion between 34 and 66%; and 23% for occlusion above 66%. Finally, a methodology was proposed to estimate the diameter of the fruits based on an ellipsoidal fitting. A first diameter was obtained by using all the contour pixels and a second diameter was obtained by rejecting some pixels of the contour. This approach enables a rough estimate of the fruit occlusion percentage range by comparing the two diameter estimates. PMID:22969369

Definition of linear color models in the RGB vector color space to detect red peaches in orchard images taken under natural illumination.

PubMed

Teixidó, Mercè; Font, Davinia; Pallejà, Tomàs; Tresanchez, Marcel; Nogués, Miquel; Palacín, Jordi

2012-01-01

This work proposes the detection of red peaches in orchard images based on the definition of different linear color models in the RGB vector color space. The classification and segmentation of the pixels of the image is then performed by comparing the color distance from each pixel to the different previously defined linear color models. The methodology proposed has been tested with images obtained in a real orchard under natural light. The peach variety in the orchard was the paraguayo (Prunus persica var. platycarpa) peach with red skin. The segmentation results showed that the area of the red peaches in the images was detected with an average error of 11.6%; 19.7% in the case of bright illumination; 8.2% in the case of low illumination; 8.6% for occlusion up to 33%; 12.2% in the case of occlusion between 34 and 66%; and 23% for occlusion above 66%. Finally, a methodology was proposed to estimate the diameter of the fruits based on an ellipsoidal fitting. A first diameter was obtained by using all the contour pixels and a second diameter was obtained by rejecting some pixels of the contour. This approach enables a rough estimate of the fruit occlusion percentage range by comparing the two diameter estimates.

Complete Genome Sequences of Salmonella enterica Serovars Anatum and Anatum var. 15+, Isolated from Retail Ground Turkey

PubMed Central

Marasini, Daya; Abo-Shama, Usama H.

2016-01-01

The complete genome sequences of two isolates of Salmonella enterica serovars Anatum and Anatum var. 15+ revealed the presence of two plasmids of 112 kb and 3 kb in size in each. The chromosome of Salmonella Anatum (4.83 Mb) was slightly smaller than that of Salmonella Anatum var. 15+ (4.88 Mb). PMID:26798111

Population Genetic Structure and Phylogeography of Camellia flavida (Theaceae) Based on Chloroplast and Nuclear DNA Sequences

PubMed Central

Wei, Su-Juan; Lu, Yong-Bin; Ye, Quan-Qing; Tang, Shao-Qing

2017-01-01

Camellia flavida is an endangered species of yellow camellia growing in limestone mountains in southwest China. The current classification of C. flavida into two varieties, var. flavida and var. patens, is controversial. We conducted a genetic analysis of C. flavida to determine its taxonomic structure. A total of 188 individual plants from 20 populations across the entire distribution range in southwest China were analyzed using two DNA fragments: a chloroplast DNA fragment from the small single copy region and a single-copy nuclear gene called phenylalanine ammonia-lyase (PAL). Sequences from both chloroplast and nuclear DNA were highly diverse; with high levels of genetic differentiation and restricted gene flow. This result can be attributed to the high habitat heterogeneity in limestone karst, which isolates C. flavida populations from each other. Our nuclear DNA results demonstrate that there are three differentiated groups within C. flavida: var. flavida 1, var. flavida 2, and var. patens. These genetic groupings are consistent with the morphological characteristics of the plants. We suggest that the samples included in this study constitute three taxa and the var. flavida 2 group is the genuine C. flavida. The three groups should be recognized as three management units for conservation concerns. PMID:28579991

Phytoremediation potential of Pityrogramma calomelanos var. austroamericana and Pteris vittata L. grown at a highly variable arsenic contaminated site.

PubMed

Niazi, Nabeel Khan; Singh, Balwant; Van Zwieten, Lukas; Kachenko, Anthony George

2011-10-01

This study examined the phytoextraction potential of two arsenic (As) hyperaccumulators, Pteris vittata L. and Pityrogramma calomelanos var. austroamericana at a historical As-contaminated cattle dip site in northern New South Wales (NSW), Australia. Total As concentration in the surface soil (0-20 cm) showed a better spatial structure than phosphate-extractable As in the surface and sub-surface soil at this site. P. calomelanos var. austroamericana produced greater frond dry biomass (mean = 130 g plant(-1)) than P. vittata (mean = 81 g plant(-1)) after 10 months of growth. Arsenic concentration and uptake in fronds were also significantly higher in P. calomelanos var. austroamericana (means = 887 mg kg(-1) and 124 mg plant(-1)) than in P. vittata (means = 674 mg kg(-1) and 57 mg plant(-1)). Our results showed that under the field conditions and highly variable soil As at the site, P. calomelanos var. austroamericana performed better than P. vittata. We predict that P. calomelanos var. austroamericana would take approximately 100 years to reduce the total As to below 20 mg kg(-1) at the site compared to > or =200 years estimated for P. vittata. However, long-term data are required to confirm these observations under field conditions.

Extrafloral nectaries of four varieties of Chamaecrista ramosa (Vogel) H.S.Irwin & Barneby (Fabaceae): anatomy, chemical nature, mechanisms of nectar secretion, and elimination.

PubMed

da Silva Pereira, Priscila; de Almeida Gonçalves, Letícia; da Silva, Marcos José; Rezende, Maria Helena

2018-04-27

Considering the importance of extrafloral nectaries (EFNs) in Fabaceae, the objectives of this research were to analyze (1) the anatomical and histochemical characteristics of the EFNs of Chamaecrista ramosa var. ramosa, C. ramosa var. curvifoliola, C. ramosa var. parvifoliola, and C. ramosa var. lucida and (2) the ultrastructure of the EFNs of C. ramosa var. ramosa. Standard techniques in plant anatomy and transmission electron microscopy were used. The anatomical analyses confirmed the characteristics described for extrafloral nectaries, evidencing three well-defined regions: epidermis, nectariferous, and subnectariferous parenchymas. Carbohydrates, proteins, pectins/mucilages, and lipids were detected by histochemical analyzes in all varieties. The ultrastructure of the EFNs of C. ramosa var. ramosa allowed the observation of microchannels at the external periclinal cell walls of the epidermis covering the secretory region. The nectariferous and subnectariferous parenchyma cells have periplasmic spaces, large plastids containing starch grains and plastoglobules, mitochondria, developed endoplasmic reticulum, large vacuoles with electron-dense contents, and membrane residues may be associated with the vacuole, suggesting the occurrence of autophagic processes. The anatomical, histochemical, and ultrastructural patterns revealed characteristics that confirm the glands of C. ramosa as extrafloral nectaries and suggest the eccrine mechanism of secretion.

A single regulatory gene is sufficient to alter Vibrio aestuarianus pathogenicity in oysters.

PubMed

Goudenège, David; Travers, Marie Agnès; Lemire, Astrid; Petton, Bruno; Haffner, Philippe; Labreuche, Yannick; Tourbiez, Delphine; Mangenot, Sophie; Calteau, Alexandra; Mazel, Didier; Nicolas, Jean Louis; Jacq, Annick; Le roux, Frédérique

2015-11-01

Oyster diseases caused by pathogenic vibrios pose a major challenge to the sustainability of oyster farming. In France, since 2012 a disease affecting specifically adult oysters has been associated with the presence of Vibrio aestuarianus. Here, by combining genome comparison, phylogenetic analyses and high-throughput infections of strains isolated before or during the recent outbreaks, we show that virulent strains cluster into two V. aestuarianus lineages independently of the sampling dates. The bacterial lethal dose was not different between strains isolated before or after 2012. Hence, the emergence of a new highly virulent clonal strain is unlikely. Each lineage comprises nearly identical strains, the majority of them being virulent, suggesting that within these phylogenetically coherent virulent lineages a few strains have lost their pathogenicity. Comparative genomics allowed the identification of a single frameshift in a non-virulent strain. This mutation affects the varS gene that codes for a signal transduction histidine-protein kinase. Genetic analyses confirmed that varS is necessary for infection of oysters and for a secreted metalloprotease expression. For the first time in a Vibrio species, we show here that VarS is a key factor of pathogenicity. © 2014 Society for Applied Microbiology and John Wiley & Sons Ltd.

Experimental infection of European flat oyster Ostrea edulis with ostreid herpesvirus 1 microvar (OsHV-1μvar): Mortality, viral load and detection of viral transcripts by in situ hybridization.

PubMed

López Sanmartín, Monserrat; Power, Deborah M; de la Herrán, Roberto; Navas, José I; Batista, Frederico M

2016-06-02

Ostreid herpesvirus 1 (OsHV-1) infections have been reported in several bivalve species. Mortality of Pacific oyster Crassostrea gigas spat has increased considerably in Europe since 2008 linked to the spread of a variant of OsHV-1 called μvar. In the present study we demonstrated that O. edulis juveniles can be infected by OsHV-1μvar when administered as an intramuscular injection. Mortality in the oysters injected with OsHV-1μvar was first detected 4 days after injection and reached 25% mortality at day 10. Moreover, the high viral load observed and the detection of viral transcripts by in situ hybridization in several tissues of dying oysters suggested that OsHV-1μvar was the cause of mortality in the O. edulis juveniles. This is therefore the first study to provide evidence about the pathogenicity of OsHV-1μvar in a species that does not belong to the Crassostrea genus. Additionally, we present a novel method to detect OsHV-1 transcripts in infected individuals' using in situ hybridization. Copyright © 2016 Elsevier B.V. All rights reserved.

VarMod: modelling the functional effects of non-synonymous variants.

PubMed

Pappalardo, Morena; Wass, Mark N

2014-07-01

Unravelling the genotype-phenotype relationship in humans remains a challenging task in genomics studies. Recent advances in sequencing technologies mean there are now thousands of sequenced human genomes, revealing millions of single nucleotide variants (SNVs). For non-synonymous SNVs present in proteins the difficulties of the problem lie in first identifying those nsSNVs that result in a functional change in the protein among the many non-functional variants and in turn linking this functional change to phenotype. Here we present VarMod (Variant Modeller) a method that utilises both protein sequence and structural features to predict nsSNVs that alter protein function. VarMod develops recent observations that functional nsSNVs are enriched at protein-protein interfaces and protein-ligand binding sites and uses these characteristics to make predictions. In benchmarking on a set of nearly 3000 nsSNVs VarMod performance is comparable to an existing state of the art method. The VarMod web server provides extensive resources to investigate the sequence and structural features associated with the predictions including visualisation of protein models and complexes via an interactive JSmol molecular viewer. VarMod is available for use at http://www.wasslab.org/varmod. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Genomic resources and draft assemblies of the human and porcine varieties of scabies mites, Sarcoptes scabiei var. hominis and var. suis.

PubMed

Mofiz, Ehtesham; Holt, Deborah C; Seemann, Torsten; Currie, Bart J; Fischer, Katja; Papenfuss, Anthony T

2016-06-02

The scabies mite, Sarcoptes scabiei, is a parasitic arachnid and cause of the infectious skin disease scabies in humans and mange in other animal species. Scabies infections are a major health problem, particularly in remote Indigenous communities in Australia, where secondary group A streptococcal and Staphylococcus aureus infections of scabies sores are thought to drive the high rate of rheumatic heart disease and chronic kidney disease. We sequenced the genome of two samples of Sarcoptes scabiei var. hominis obtained from unrelated patients with crusted scabies located in different parts of northern Australia using the Illumina HiSeq. We also sequenced samples of Sarcoptes scabiei var. suis from a pig model. Because of the small size of the scabies mite, these data are derived from pools of thousands of mites and are metagenomic, including host and microbiome DNA. We performed cleaning and de novo assembly and present Sarcoptes scabiei var. hominis and var. suis draft reference genomes. We have constructed a preliminary annotation of this reference comprising 13,226 putative coding sequences based on sequence similarity to known proteins. We have developed extensive genomic resources for the scabies mite, including reference genomes and a preliminary annotation.

An efficient assisted history matching and uncertainty quantification workflow using Gaussian processes proxy models and variogram based sensitivity analysis: GP-VARS

NASA Astrophysics Data System (ADS)

Rana, Sachin; Ertekin, Turgay; King, Gregory R.

2018-05-01

Reservoir history matching is frequently viewed as an optimization problem which involves minimizing misfit between simulated and observed data. Many gradient and evolutionary strategy based optimization algorithms have been proposed to solve this problem which typically require a large number of numerical simulations to find feasible solutions. Therefore, a new methodology referred to as GP-VARS is proposed in this study which uses forward and inverse Gaussian processes (GP) based proxy models combined with a novel application of variogram analysis of response surface (VARS) based sensitivity analysis to efficiently solve high dimensional history matching problems. Empirical Bayes approach is proposed to optimally train GP proxy models for any given data. The history matching solutions are found via Bayesian optimization (BO) on forward GP models and via predictions of inverse GP model in an iterative manner. An uncertainty quantification method using MCMC sampling in conjunction with GP model is also presented to obtain a probabilistic estimate of reservoir properties and estimated ultimate recovery (EUR). An application of the proposed GP-VARS methodology on PUNQ-S3 reservoir is presented in which it is shown that GP-VARS provides history match solutions in approximately four times less numerical simulations as compared to the differential evolution (DE) algorithm. Furthermore, a comparison of uncertainty quantification results obtained by GP-VARS, EnKF and other previously published methods shows that the P50 estimate of oil EUR obtained by GP-VARS is in close agreement to the true values for the PUNQ-S3 reservoir.

Understanding mechanisms of rarity in pteridophytes: competition and climate change threaten the rare fern Asplenium scolopendrium var. americanum (Aspleniaceae).

PubMed

Testo, Weston L; Watkins, James E

2013-11-01

Understanding the ecology of rare species can inform aspects of conservation strategies; however, the mechanisms of rarity remain elusive for most pteridophytes, which possess independent and ecologically distinct gametophyte and sporophyte generations. To elucidate factors contributing to recent declines of the rare fern Asplenium scolopendrium var. americanum, we studied the ecology and ecophysiology of its gametophyte generation, focusing on responses to competition, temperature, and water stress. Gametophytes of A. scolopendrium var. americanum, its widespread European relative A. scolopendrium var. scolopendrium, and five co-occurring fern species were grown from spores. Gametophytes were grown at 20°C and 25°C, and germination rates, intra- and interspecific competition, desiccation tolerance, and sporophyte production were determined for all species. Gametophytes of A. scolopendrium var. americanum had the lowest rates of germination and sporophyte production among all species studied and exhibited the greatest sensitivity to interspecific competition, temperature increases, and desiccation. Mature gametophytes of A. scolopendrium var. americanum grown at 25°C were 84.6% smaller than those grown at 20°C, and only 1.5% produced sporophytes after 200 d in culture. Similar responses were not observed in other species studied. The recent declines and current status of populations of A. scolopendrium var. americanum are linked to its gametophyte's limited capacity to tolerate competition and physiological stress linked to climate change. This is the first study to develop a mechanistic understanding of rarity and decline in a fern and demonstrates the importance of considering the ecology of the gametophyte in plants with independent sporophyte and gametophyte generations.

Analysis of Single-cell Gene Transcription by RNA Fluorescent In Situ Hybridization (FISH)

PubMed Central

Ronander, Elena; Bengtsson, Dominique C.; Joergensen, Louise; Jensen, Anja T. R.; Arnot, David E.

2012-01-01

Adhesion of Plasmodium falciparum infected erythrocytes (IE) to human endothelial receptors during malaria infections is mediated by expression of PfEMP1 protein variants encoded by the var genes. The haploid P. falciparum genome harbors approximately 60 different var genes of which only one has been believed to be transcribed per cell at a time during the blood stage of the infection. How such mutually exclusive regulation of var gene transcription is achieved is unclear, as is the identification of individual var genes or sub-groups of var genes associated with different receptors and the consequence of differential binding on the clinical outcome of P. falciparum infections. Recently, the mutually exclusive transcription paradigm has been called into doubt by transcription assays based on individual P. falciparum transcript identification in single infected erythrocytic cells using RNA fluorescent in situ hybridization (FISH) analysis of var gene transcription by the parasite in individual nuclei of P. falciparum IE1. Here, we present a detailed protocol for carrying out the RNA-FISH methodology for analysis of var gene transcription in single-nuclei of P. falciparum infected human erythrocytes. The method is based on the use of digoxigenin- and biotin- labeled antisense RNA probes using the TSA Plus Fluorescence Palette System2 (Perkin Elmer), microscopic analyses and freshly selected P. falciparum IE. The in situ hybridization method can be used to monitor transcription and regulation of a variety of genes expressed during the different stages of the P. falciparum life cycle and is adaptable to other malaria parasite species and other organisms and cell types. PMID:23070076

Reciprocal associations between negative affect, binge eating, and purging in the natural environment in women with bulimia nervosa.

PubMed

Lavender, Jason M; Utzinger, Linsey M; Cao, Li; Wonderlich, Stephen A; Engel, Scott G; Mitchell, James E; Crosby, Ross D

2016-04-01

Although negative affect (NA) has been identified as a common trigger for bulimic behaviors, findings regarding NA following such behaviors have been mixed. This study examined reciprocal associations between NA and bulimic behaviors using real-time, naturalistic data. Participants were 133 women with bulimia nervosa (BN) according to the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders who completed a 2-week ecological momentary assessment protocol in which they recorded bulimic behaviors and provided multiple daily ratings of NA. A multilevel autoregressive cross-lagged analysis was conducted to examine concurrent, first-order autoregressive, and prospective associations between NA, binge eating, and purging across the day. Results revealed positive concurrent associations between all variables across all time points, as well as numerous autoregressive associations. For prospective associations, higher NA predicted subsequent bulimic symptoms at multiple time points; conversely, binge eating predicted lower NA at multiple time points, and purging predicted higher NA at 1 time point. Several autoregressive and prospective associations were also found between binge eating and purging. This study used a novel approach to examine NA in relation to bulimic symptoms, contributing to the existing literature by directly examining the magnitude of the associations, examining differences in the associations across the day, and controlling for other associations in testing each effect in the model. These findings may have relevance for understanding the etiology and/or maintenance of bulimic symptoms, as well as potentially informing psychological interventions for BN. (c) 2016 APA, all rights reserved).

Application of a new hybrid model with seasonal auto-regressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NARNN) in forecasting incidence cases of HFMD in Shenzhen, China.

PubMed

Yu, Lijing; Zhou, Lingling; Tan, Li; Jiang, Hongbo; Wang, Ying; Wei, Sheng; Nie, Shaofa

2014-01-01

Outbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic. In this paper, a hybrid model combining seasonal auto-regressive integrated moving average (ARIMA) model and nonlinear auto-regressive neural network (NARNN) is proposed to predict the expected incidence cases from December 2012 to May 2013, using the retrospective observations obtained from China Information System for Disease Control and Prevention from January 2008 to November 2012. The best-fitted hybrid model was combined with seasonal ARIMA [Formula: see text] and NARNN with 15 hidden units and 5 delays. The hybrid model makes the good forecasting performance and estimates the expected incidence cases from December 2012 to May 2013, which are respectively -965.03, -1879.58, 4138.26, 1858.17, 4061.86 and 6163.16 with an obviously increasing trend. The model proposed in this paper can predict the incidence trend of HFMD effectively, which could be helpful to policy makers. The usefulness of expected cases of HFMD perform not only in detecting outbreaks or providing probability statements, but also in providing decision makers with a probable trend of the variability of future observations that contains both historical and recent information.

Methodology for the AutoRegressive Planet Search (ARPS) Project

NASA Astrophysics Data System (ADS)

Feigelson, Eric; Caceres, Gabriel; ARPS Collaboration

2018-01-01

The detection of periodic signals of transiting exoplanets is often impeded by the presence of aperiodic photometric variations. This variability is intrinsic to the host star in space-based observations (typically arising from magnetic activity) and from observational conditions in ground-based observations. The most common statistical procedures to remove stellar variations are nonparametric, such as wavelet decomposition or Gaussian Processes regression. However, many stars display variability with autoregressive properties, wherein later flux values are correlated with previous ones. Providing the time series is evenly spaced, parametric autoregressive models can prove very effective. Here we present the methodology of the Autoregessive Planet Search (ARPS) project which uses Autoregressive Integrated Moving Average (ARIMA) models to treat a wide variety of stochastic short-memory processes, as well as nonstationarity. Additionally, we introduce a planet-search algorithm to detect periodic transits in the time-series residuals after application of ARIMA models. Our matched-filter algorithm, the Transit Comb Filter (TCF), replaces the traditional box-fitting step. We construct a periodogram based on the TCF to concentrate the signal of these periodic spikes. Various features of the original light curves, the ARIMA fits, the TCF periodograms, and folded light curves at peaks of the TCF periodogram can then be collected to provide constraints for planet detection. These features provide input into a multivariate classifier when a training set is available. The ARPS procedure has been applied NASA's Kepler mission observations of ~200,000 stars (Caceres, Dissertation Talk, this meeting) and will be applied in the future to other datasets.

Evaluation of two health education interventions to improve the varicella vaccination: a randomized controlled trial from a province in the east China.

PubMed

Hu, Yu; Li, Qian; Chen, Yaping

2018-01-16

We evaluated the effect of two Elaboration Likelihood Model (ELM)-based health educational interventions on varicella vaccine (VarV) vaccination among pregnant women in a province in the east China. A prospective randomized controlled trial was conducted among 200 pregnant women with ≥12 gestation weeks to test two interventions, including a messaging video and a messaging booklet. The participants were randomly assigned into the control group, the video group or the booklet group. The VarV coverage at 12 and 24 months old was compared among the children of the three groups and relative risks (RRs) were calculated, by using the coverage of the control group as reference. The timeliness of VarV was also assessed. Furthermore, differences in the effects on the knowledge and attitude of VarV vaccination between the two interventions was evaluated. The VarV coverage of their children by 24 months of age was 86.4%, 76.1% and 56.7% for the video group, the booklet group and the control group, respectively. The relative risks (RRs) for the coverage of VarV at 24 months of age were 4.8 (95% CI: 2.06-11.3) for the video group and 2.4 (95% CI: 1.2-5.1) for the booklet group. The means of delays were 57.3 days in the video group, 76.9 days in the booklet group, and 100.6 days in the control group. The proportion of women who intended to vaccinate their children with VarV was higher in the video group than the booklet group (93.9% vs. 82.1%, p < 0.05). Our findings indicated that perinatal health education through booklet or video could improve the coverage and schedule adherence for children's VarV vaccination.

Colour-scent associations in a tropical orchid: three colours but two odours.

PubMed

Delle-Vedove, Roxane; Juillet, Nicolas; Bessière, Jean-Marie; Grison, Claude; Barthes, Nicolas; Pailler, Thierry; Dormont, Laurent; Schatz, Bertrand

2011-06-01

Colour and scent are the major pollinator attractants to flowers, and their production may be linked by shared biosynthetic pathways. Species with polymorphic floral traits are particularly relevant to study the joint evolution of floral traits. We used in this study the tropical orchid Calanthe sylvatica from Réunion Island. Three distinct colour varieties are observed, presenting lilac, white or purple flowers, and named respectively C. sylvaticavar.lilacina (hereafter referred as var. lilacina), C. sylvaticavar. alba (var. alba) and C. sylvatica var. purpurea (var. purpurea). We investigated the composition of the floral scent produced by these colour varieties using the non-invasive SPME technique in the wild. Scent emissions are dominated by aromatic compounds. Nevertheless, the presence of the terpenoid (E)-4,8-dimethylnona-1,3,7-triène (DMNT) is diagnostic of var. purpurea, with the volatile organic compounds (VOC) produced by some individuals containing up to 60% of DMNT. We evidence specific colour-scent associations in C. sylvatica, with two distinct scent profiles in the three colour varieties: the lilacina-like profile containing no or very little DMNT (<2%) and the purpurea-like profile containing DMNT (>2%). Calanthe sylvatica var. alba individuals group with one or the other scent profile independently of their population of origin. We suggest that white-flowered individuals have evolved at least twice, once from var. lilacina and at least once from var. purpurea after the colonisation of la Réunion. White-flowered individuals may have been favoured by the particular pollinator fauna characterising the island. These flowering varieties of C. sylvatica, which display three colours but two scents profiles prove that colour is not always a good indicator of odour and that colour-scent associations may be complex, depending on pollination ecology of the populations concerned. Copyright © 2011 Elsevier Ltd. All rights reserved.

Optimal HRF and smoothing parameters for fMRI time series within an autoregressive modeling framework.

PubMed

Galka, Andreas; Siniatchkin, Michael; Stephani, Ulrich; Groening, Kristina; Wolff, Stephan; Bosch-Bayard, Jorge; Ozaki, Tohru

2010-12-01

The analysis of time series obtained by functional magnetic resonance imaging (fMRI) may be approached by fitting predictive parametric models, such as nearest-neighbor autoregressive models with exogeneous input (NNARX). As a part of the modeling procedure, it is possible to apply instantaneous linear transformations to the data. Spatial smoothing, a common preprocessing step, may be interpreted as such a transformation. The autoregressive parameters may be constrained, such that they provide a response behavior that corresponds to the canonical haemodynamic response function (HRF). We present an algorithm for estimating the parameters of the linear transformations and of the HRF within a rigorous maximum-likelihood framework. Using this approach, an optimal amount of both the spatial smoothing and the HRF can be estimated simultaneously for a given fMRI data set. An example from a motor-task experiment is discussed. It is found that, for this data set, weak, but non-zero, spatial smoothing is optimal. Furthermore, it is demonstrated that activated regions can be estimated within the maximum-likelihood framework.

Spectral Analysis of Ultrasound Radiofrequency Backscatter for the Detection of Intercostal Blood Vessels.

PubMed

Klingensmith, Jon D; Haggard, Asher; Fedewa, Russell J; Qiang, Beidi; Cummings, Kenneth; DeGrande, Sean; Vince, D Geoffrey; Elsharkawy, Hesham

2018-04-19

Spectral analysis of ultrasound radiofrequency backscatter has the potential to identify intercostal blood vessels during ultrasound-guided placement of paravertebral nerve blocks and intercostal nerve blocks. Autoregressive models were used for spectral estimation, and bandwidth, autoregressive order and region-of-interest size were evaluated. Eight spectral parameters were calculated and used to create random forests. An autoregressive order of 10, bandwidth of 6 dB and region-of-interest size of 1.0 mm resulted in the minimum out-of-bag error. An additional random forest, using these chosen values, was created from 70% of the data and evaluated independently from the remaining 30% of data. The random forest achieved a predictive accuracy of 92% and Youden's index of 0.85. These results suggest that spectral analysis of ultrasound radiofrequency backscatter has the potential to identify intercostal blood vessels. (jokling@siue.edu) © 2018 World Federation for Ultrasound in Medicine and Biology. Copyright © 2018 World Federation for Ultrasound in Medicine and Biology. Published by Elsevier Inc. All rights reserved.

(Re)evaluating the Implications of the Autoregressive Latent Trajectory Model Through Likelihood Ratio Tests of Its Initial Conditions.

PubMed

Ou, Lu; Chow, Sy-Miin; Ji, Linying; Molenaar, Peter C M

2017-01-01

The autoregressive latent trajectory (ALT) model synthesizes the autoregressive model and the latent growth curve model. The ALT model is flexible enough to produce a variety of discrepant model-implied change trajectories. While some researchers consider this a virtue, others have cautioned that this may confound interpretations of the model's parameters. In this article, we show that some-but not all-of these interpretational difficulties may be clarified mathematically and tested explicitly via likelihood ratio tests (LRTs) imposed on the initial conditions of the model. We show analytically the nested relations among three variants of the ALT model and the constraints needed to establish equivalences. A Monte Carlo simulation study indicated that LRTs, particularly when used in combination with information criterion measures, can allow researchers to test targeted hypotheses about the functional forms of the change process under study. We further demonstrate when and how such tests may justifiably be used to facilitate our understanding of the underlying process of change using a subsample (N = 3,995) of longitudinal family income data from the National Longitudinal Survey of Youth.

Kepler AutoRegressive Planet Search (KARPS)

NASA Astrophysics Data System (ADS)

Caceres, Gabriel

2018-01-01

One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The Kepler AutoRegressive Planet Search (KARPS) project implements statistical methodology associated with autoregressive processes (in particular, ARIMA and ARFIMA) to model stellar lightcurves in order to improve exoplanet transit detection. We also develop a novel Transit Comb Filter (TCF) applied to the AR residuals which provides a periodogram analogous to the standard Box-fitting Least Squares (BLS) periodogram. We train a random forest classifier on known Kepler Objects of Interest (KOIs) using select features from different stages of this analysis, and then use ROC curves to define and calibrate the criteria to recover the KOI planet candidates with high fidelity. These statistical methods are detailed in a contributed poster (Feigelson et al., this meeting).These procedures are applied to the full DR25 dataset of NASA’s Kepler mission. Using the classification criteria, a vast majority of known KOIs are recovered and dozens of new KARPS Candidate Planets (KCPs) discovered, including ultra-short period exoplanets. The KCPs will be briefly presented and discussed.

Time series modelling of increased soil temperature anomalies during long period

NASA Astrophysics Data System (ADS)

Shirvani, Amin; Moradi, Farzad; Moosavi, Ali Akbar

2015-10-01

Soil temperature just beneath the soil surface is highly dynamic and has a direct impact on plant seed germination and is probably the most distinct and recognisable factor governing emergence. Autoregressive integrated moving average as a stochastic model was developed to predict the weekly soil temperature anomalies at 10 cm depth, one of the most important soil parameters. The weekly soil temperature anomalies for the periods of January1986-December 2011 and January 2012-December 2013 were taken into consideration to construct and test autoregressive integrated moving average models. The proposed model autoregressive integrated moving average (2,1,1) had a minimum value of Akaike information criterion and its estimated coefficients were different from zero at 5% significance level. The prediction of the weekly soil temperature anomalies during the test period using this proposed model indicated a high correlation coefficient between the observed and predicted data - that was 0.99 for lead time 1 week. Linear trend analysis indicated that the soil temperature anomalies warmed up significantly by 1.8°C during the period of 1986-2011.

Monthly streamflow forecasting with auto-regressive integrated moving average

NASA Astrophysics Data System (ADS)

Nasir, Najah; Samsudin, Ruhaidah; Shabri, Ani

2017-09-01

Forecasting of streamflow is one of the many ways that can contribute to better decision making for water resource management. The auto-regressive integrated moving average (ARIMA) model was selected in this research for monthly streamflow forecasting with enhancement made by pre-processing the data using singular spectrum analysis (SSA). This study also proposed an extension of the SSA technique to include a step where clustering was performed on the eigenvector pairs before reconstruction of the time series. The monthly streamflow data of Sungai Muda at Jeniang, Sungai Muda at Jambatan Syed Omar and Sungai Ketil at Kuala Pegang was gathered from the Department of Irrigation and Drainage Malaysia. A ratio of 9:1 was used to divide the data into training and testing sets. The ARIMA, SSA-ARIMA and Clustered SSA-ARIMA models were all developed in R software. Results from the proposed model are then compared to a conventional auto-regressive integrated moving average model using the root-mean-square error and mean absolute error values. It was found that the proposed model can outperform the conventional model.

Pectic polysaccharides extracted from Rauvolfia verticillata (Lour.) Baill. var. hainanensis Tsiang increase LκB-α expression and ameliorate ulcerative colitis.

PubMed

Miao, X P; Sun, X N; Wei, H; Liu, Z J; Cui, L J; Deng, T Z

2015-02-01

The therapeutic potential of pectic polysaccharides extracted from Rauvolfia verticillata (Lour.) Baill. var. hainanensis Tsiang in ulcerative colitis were investigated. This study showed that pectic polysaccharides extracted from Rauvolfia verticillata (Lour.) Baill. var. hainanensis Tsiang ameliorated ulcerative colitis and were proposed to exhibit anti-inflammatory effects via increased expression of IκB-α proteins and suppressing NF-αB translocation.

Immunogenicity and safety of measles-mumps-rubella and varicella vaccines coadministered with a fourth dose of Haemophilus influenzae type b and Neisseria meningitidis serogroups C and Y-tetanus toxoid conjugate vaccine in toddlers: a pooled analysis of randomized trials.

PubMed

Bryant, Kristina; McVernon, Jodie; Marchant, Colin; Nolan, Terry; Marshall, Gary; Richmond, Peter; Marshall, Helen; Nissen, Michael; Lambert, Stephen; Aris, Emmanuel; Mesaros, Narcisa; Miller, Jacqueline

2012-08-01

A pooled analysis was conducted of 1257 toddlers who received a fourth dose of Haemophilus influenzae type b-Neisseria meningitidis serogroups C and Y-tetanus toxoid conjugate vaccine (HibMenCY-TT) or Hib conjugate vaccine (Hib polysaccharide conjugated to N. meningitidis outer membrane protein) coadministered with measles-mumps-rubella (MMR) and varicella (VAR) vaccines (NCT00134719/NCT00289783). Noninferiority of immunological responses to MMR and VAR was demonstrated between groups and incidences of MMR- and VAR-specific solicited symptoms were similar, indicating that HibMenCY-TT can be coadministered with MMR and VAR.

New family of pectinase genes PGU1b-PGU3b of the pectinolytic yeast Saccharomyces bayanus var. uvarum.

PubMed

Naumov, G I; Shalamitskiy, M Yu; Naumova, E S

2016-03-01

Using yeast genome databases and literature data, we have conducted a phylogenetic analysis of pectinase PGU genes from Saccharomyces strains assigned to the biological species S. arboricola, S. bayanus (var. uvarum), S. cariocanus, S. cerevisiae, S. kudriavzevii, S. mikatae, S. paradoxus, and hybrid taxon S. pastorianus (syn. S. carlsbergensis). Single PGU genes were observed in all Saccharomyces species, except S. bayanus. The superfamily of divergent PGU genes has been documented in S. bayanus var. uvarum for the first time. Chromosomal localization of new PGU1b, PGU2b, and PGU3b genes in the yeast S. bayanus var. uvarum has been determined by molecular karyotyping and Southern hybridization.

New Approach To Hour-By-Hour Weather Forecast

NASA Astrophysics Data System (ADS)

Liao, Q. Q.; Wang, B.

2017-12-01

Fine hourly forecast in single station weather forecast is required in many human production and life application situations. Most previous MOS (Model Output Statistics) which used a linear regression model are hard to solve nonlinear natures of the weather prediction and forecast accuracy has not been sufficient at high temporal resolution. This study is to predict the future meteorological elements including temperature, precipitation, relative humidity and wind speed in a local region over a relatively short period of time at hourly level. By means of hour-to-hour NWP (Numeral Weather Prediction)meteorological field from Forcastio (https://darksky.net/dev/docs/forecast) and real-time instrumental observation including 29 stations in Yunnan and 3 stations in Tianjin of China from June to October 2016, predictions are made of the 24-hour hour-by-hour ahead. This study presents an ensemble approach to combine the information of instrumental observation itself and NWP. Use autoregressive-moving-average (ARMA) model to predict future values of the observation time series. Put newest NWP products into the equations derived from the multiple linear regression MOS technique. Handle residual series of MOS outputs with autoregressive (AR) model for the linear property presented in time series. Due to the complexity of non-linear property of atmospheric flow, support vector machine (SVM) is also introduced . Therefore basic data quality control and cross validation makes it able to optimize the model function parameters , and do 24 hours ahead residual reduction with AR/SVM model. Results show that AR model technique is better than corresponding multi-variant MOS regression method especially at the early 4 hours when the predictor is temperature. MOS-AR combined model which is comparable to MOS-SVM model outperform than MOS. Both of their root mean square error and correlation coefficients for 2 m temperature are reduced to 1.6 degree Celsius and 0.91 respectively. The forecast accuracy of 24- hour forecast deviation no more than 2 degree Celsius is 78.75 % for MOS-AR model and 81.23 % for AR model.

Computational problems in autoregressive moving average (ARMA) models

NASA Technical Reports Server (NTRS)

Agarwal, G. C.; Goodarzi, S. M.; Oneill, W. D.; Gottlieb, G. L.

1981-01-01

The choice of the sampling interval and the selection of the order of the model in time series analysis are considered. Band limited (up to 15 Hz) random torque perturbations are applied to the human ankle joint. The applied torque input, the angular rotation output, and the electromyographic activity using surface electrodes from the extensor and flexor muscles of the ankle joint are recorded. Autoregressive moving average models are developed. A parameter constraining technique is applied to develop more reliable models. The asymptotic behavior of the system must be taken into account during parameter optimization to develop predictive models.

Ecology of West Nile virus across four European countries: empirical modelling of the Culex pipiens abundance dynamics as a function of weather.

PubMed

Groen, Thomas A; L'Ambert, Gregory; Bellini, Romeo; Chaskopoulou, Alexandra; Petric, Dusan; Zgomba, Marija; Marrama, Laurence; Bicout, Dominique J

2017-10-26

Culex pipiens is the major vector of West Nile virus in Europe, and is causing frequent outbreaks throughout the southern part of the continent. Proper empirical modelling of the population dynamics of this species can help in understanding West Nile virus epidemiology, optimizing vector surveillance and mosquito control efforts. But modelling results may differ from place to place. In this study we look at which type of models and weather variables can be consistently used across different locations. Weekly mosquito trap collections from eight functional units located in France, Greece, Italy and Serbia for several years were combined. Additionally, rainfall, relative humidity and temperature were recorded. Correlations between lagged weather conditions and Cx. pipiens dynamics were analysed. Also seasonal autoregressive integrated moving-average (SARIMA) models were fitted to describe the temporal dynamics of Cx. pipiens and to check whether the weather variables could improve these models. Correlations were strongest between mean temperatures at short time lags, followed by relative humidity, most likely due to collinearity. Precipitation alone had weak correlations and inconsistent patterns across sites. SARIMA models could also make reasonable predictions, especially when longer time series of Cx. pipiens observations are available. Average temperature was a consistently good predictor across sites. When only short time series (~ < 4 years) of observations are available, average temperature can therefore be used to model Cx. pipiens dynamics. When longer time series (~ > 4 years) are available, SARIMAs can provide better statistical descriptions of Cx. pipiens dynamics, without the need for further weather variables. This suggests that density dependence is also an important determinant of Cx. pipiens dynamics.

Varenicline impairs extinction and enhances reinstatement across repeated cycles of nicotine self-administration in rats.

PubMed

Macnamara, Claire L; Holmes, Nathan M; Westbrook, R Fred; Clemens, Kelly J

2016-06-01

Varenicline is a partial nicotine receptor agonist widely prescribed as a smoking cessation medication. Repeated (or long-term) use of varenicline has been proposed as a treatment option for tobacco addiction. However the effect of repeated varenicline use on motivation for nicotine is unknown. Here the intravenous nicotine self-administration paradigm in rats was used to model the consequences of varenicline treatment across repeated cycles of administration, extinction and reinstatement. Rats acquired nicotine self-administration across 20 days before undergoing 6 days of extinction, where each extinction session was preceded by a single injection of varenicline or saline. This was followed by a single varenicline-free nicotine-primed reinstatement test. All rats then reacquired nicotine self-administration for 10 days followed by a second cycle of extinction. Across this period, rats either received a second cycle of varenicline (VAR-VAR) or saline (SAL-SAL), or the alternative treatment (SAL-VAR, VAR-SAL), followed by a final reinstatement test. Treatment with varenicline increased responding across the first cycle of extinction, but did not affect responding in the reinstatement test. Across the second cycle, varenicline again increased responding across extinction, and critically, rats treated with varenicline across cycle 1 and saline across cycle 2 (Group VAR-SAL) exhibited more reinstatement than rats in any other group. The effect of VAR on nicotine seeking was not due to its effects on locomotor activity. Instead, the results suggest that a history of VAR can increase vulnerability to reinstatement/relapse when its treatment is discontinued. The possible mechanisms of this increased vulnerability are discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Unique Chromosomal Rearrangement in the Cryptococcus neoformans var. grubii Type Strain Enhances Key Phenotypes Associated with Virulence

PubMed Central

Morrow, Carl A.; Lee, I. Russel; Chow, Eve W. L.; Ormerod, Kate L.; Goldinger, Anita; Byrnes, Edmond J.; Nielsen, Kirsten; Heitman, Joseph; Schirra, Horst Joachim; Fraser, James A.

2012-01-01

ABSTRACT The accumulation of genomic structural variation between closely related populations over time can lead to reproductive isolation and speciation. The fungal pathogen Cryptococcus is thought to have recently diversified, forming a species complex containing members with distinct morphologies, distributions, and pathologies of infection. We have investigated structural changes in genomic architecture such as inversions and translocations that distinguish the most pathogenic variety, Cryptococcus neoformans var. grubii, from the less clinically prevalent Cryptococcus neoformans var. neoformans and Cryptococcus gattii. Synteny analysis between the genomes of the three Cryptococcus species/varieties (strains H99, JEC21, and R265) reveals that C. neoformans var. grubii possesses surprisingly few unique genomic rearrangements. All but one are relatively small and are shared by all molecular subtypes of C. neoformans var. grubii. In contrast, the large translocation peculiar to the C. neoformans var. grubii type strain is found in all tested subcultures from multiple laboratories, suggesting that it has possessed this rearrangement since its isolation from a human clinical sample. Furthermore, we find that the translocation directly disrupts two genes. The first of these encodes a novel protein involved in metabolism of glucose at human body temperature and affects intracellular levels of trehalose. The second encodes a homeodomain-containing transcription factor that modulates melanin production. Both mutations would be predicted to increase pathogenicity; however, when recreated in an alternate genetic background, these mutations do not affect virulence in animal models. The type strain of C. neoformans var. grubii in which the majority of molecular studies have been performed is therefore atypical for carbon metabolism and key virulence attributes. PMID:22375073

β-Galactomannan and Saccharomyces cerevisiae var. boulardii modulate the immune response against Salmonella enterica serovar Typhimurium in porcine intestinal epithelial and dendritic cells.

PubMed

Badia, Roger; Brufau, M Teresa; Guerrero-Zamora, Ana Maria; Lizardo, Rosil; Dobrescu, Irina; Martin-Venegas, Raquel; Ferrer, Ruth; Salmon, Henri; Martínez, Paz; Brufau, Joaquim

2012-03-01

Salmonella enterica serovar Typhimurium is a facultative intracellular pathogen that causes inflammation, necrosis, and diarrhea in pigs, as well as being an important source of food-borne diseases in humans. Probiotics and prebiotics are promising alternatives to antibiotics to control and prevent intestinal infections. The present work investigated a recently developed β-galactomannan (βGM) prebiotic compared to the proven probiotic Saccharomyces cerevisiae var. boulardii on porcine ileum intestinal epithelial cells (IECs) of the IPI-2I line and monocyte-derived dendritic cells (DCs) cocultured in vitro with Salmonella. We observed that both S. cerevisiae var. boulardii and βGM inhibited the association of Salmonella with IECs in vitro. Our data indicated that βGM has a higher ability than S. cerevisiae var. boulardii to inhibit Salmonella-induced proinflammatory mRNA (cytokines tumor necrosis factor alpha [TNF-α], interleukin-1α [IL-1α], IL-6, and granulocyte-macrophage colony-stimulating factor [GM-CSF] and chemokines CCL2, CCL20, and CXCL8) and at protein levels (IL-6 and CXCL8). Additionally, βGM and S. cerevisiae var. boulardii induced some effects on DCs that were not observed on IECs: βGM and S. cerevisiae var. boulardii showed slight upregulation of mRNA for TNF-α, GM-CSF, and CCR7 receptor on porcine monocyte-derived dendritic cells (DCs). Indeed, the addition of βGM or S. cerevisiae var. boulardii on DCs cocultured with Salmonella showed higher gene expression (mRNA) for TNF-α, GM-CSF, and CXCL8 compared to that of the control with Salmonella. In conclusion, the addition of βGM inhibits Salmonella-induced proinflammatory profiles in IECs but may promote DC activation, although associated molecular mechanisms remain to be elucidated.

β-Galactomannan and Saccharomyces cerevisiae var. boulardii Modulate the Immune Response against Salmonella enterica Serovar Typhimurium in Porcine Intestinal Epithelial and Dendritic Cells

PubMed Central

Brufau, M. Teresa; Guerrero-Zamora, Ana Maria; Lizardo, Rosil; Dobrescu, Irina; Martin-Venegas, Raquel; Ferrer, Ruth; Salmon, Henri; Martínez, Paz

2012-01-01

Salmonella enterica serovar Typhimurium is a facultative intracellular pathogen that causes inflammation, necrosis, and diarrhea in pigs, as well as being an important source of food-borne diseases in humans. Probiotics and prebiotics are promising alternatives to antibiotics to control and prevent intestinal infections. The present work investigated a recently developed β-galactomannan (βGM) prebiotic compared to the proven probiotic Saccharomyces cerevisiae var. boulardii on porcine ileum intestinal epithelial cells (IECs) of the IPI-2I line and monocyte-derived dendritic cells (DCs) cocultured in vitro with Salmonella. We observed that both S. cerevisiae var. boulardii and βGM inhibited the association of Salmonella with IECs in vitro. Our data indicated that βGM has a higher ability than S. cerevisiae var. boulardii to inhibit Salmonella-induced proinflammatory mRNA (cytokines tumor necrosis factor alpha [TNF-α], interleukin-1α [IL-1α], IL-6, and granulocyte-macrophage colony-stimulating factor [GM-CSF] and chemokines CCL2, CCL20, and CXCL8) and at protein levels (IL-6 and CXCL8). Additionally, βGM and S. cerevisiae var. boulardii induced some effects on DCs that were not observed on IECs: βGM and S. cerevisiae var. boulardii showed slight upregulation of mRNA for TNF-α, GM-CSF, and CCR7 receptor on porcine monocyte-derived dendritic cells (DCs). Indeed, the addition of βGM or S. cerevisiae var. boulardii on DCs cocultured with Salmonella showed higher gene expression (mRNA) for TNF-α, GM-CSF, and CXCL8 compared to that of the control with Salmonella. In conclusion, the addition of βGM inhibits Salmonella-induced proinflammatory profiles in IECs but may promote DC activation, although associated molecular mechanisms remain to be elucidated. PMID:22301691

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

Hang, Bo; Guliaev, Anton B.; Chenna, Ahmed

1,N{sup 6}-Ethanoadenine (EA) is an exocyclic adduct formed from DNA reaction with the antitumor agent, 1,3-bis(2-chloroethyl)-1-nitrosourea (BCNU). To understand the role of this adduct in the mechanism of mutagenicity or carcinogenicity by BCNU, an oligonucleotide with a site-specific EA was synthesized using phosphoramidite chemistry. We now report the in vitro miscoding properties of EA in translesion DNA synthesis catalyzed by mammalian DNA polymerases (pols) {alpha}, {beta}, {eta} and {iota}. These data were also compared with those obtained for the structurally related exocyclic adduct, 1,N{sup 6}-ethenoadenine ({var_epsilon}A). Using a primer extension assay, both pols {alpha} and {beta} were primarily blocked bymore » EA or {var_epsilon}A with very minor extension. Pol {eta} a member of the Y family of polymerases, was capable of catalyzing a significant amount of bypass across both adducts. Pol {eta} incorporated all four nucleotides opposite EA and {var_epsilon}A, but with differential preferences and mainly in an error-prone manner. Human pol {iota}, a paralog of human pol {eta}, was blocked by both adducts with a very small amount of synthesis past {var_epsilon}A. It incorporated C and, to a much lesser extent, T, opposite either adduct. In addition, the presence of an A adduct, e.g. {var_epsilon}A, could affect the specificity of pol {iota} toward the template T immediately 3 feet to the adduct. In conclusion, the four polymerases assayed on templates containing an EA or {var_epsilon}A showed differential bypass capacity and nucleotide incorporation specificity, with the two adducts not completely identical in influencing these properties. Although there was a measurable extent of error-free nucleotide incorporation, all these polymerases primarily misincorporated opposite EA, indicating that the adduct, similar to {var_epsilon}A, is a miscoding lesion.« less

Renewable energy consumption and economic growth in nine OECD countries: bounds test approach and causality analysis.

PubMed

Hung-Pin, Lin

2014-01-01

The purpose of this paper is to investigate the short-run and long-run causality between renewable energy (RE) consumption and economic growth (EG) in nine OECD countries from the period between 1982 and 2011. To examine the linkage, this paper uses the autoregressive distributed lag (ARDL) bounds testing approach of cointegration test and vector error-correction models to test the causal relationship between variables. The co-integration and causal relationships are found in five countries-United States of America (USA), Japan, Germany, Italy, and United Kingdom (UK). The overall results indicate that (1) a short-run unidirectional causality runs from EG to RE in Italy and UK; (2) long-run unidirectional causalities run from RE to EG for Germany, Italy, and UK; (3) a long-run unidirectional causality runs from EG to RE in USA, and Japan; (4) both long-run and strong unidirectional causalities run from RE to EG for Germany and UK; and (5) Finally, both long-run and strong unidirectional causalities run from EG to RE in only USA. Further evidence reveals that policies for renewable energy conservation may have no impact on economic growth in France, Denmark, Portugal, and Spain.

Does Specification Matter? Experiments with Simple Multiregional Probabilistic Population Projections

PubMed Central

Raymer, James; Abel, Guy J.; Rogers, Andrei

2012-01-01

Population projection models that introduce uncertainty are a growing subset of projection models in general. In this paper, we focus on the importance of decisions made with regard to the model specifications adopted. We compare the forecasts and prediction intervals associated with four simple regional population projection models: an overall growth rate model, a component model with net migration, a component model with in-migration and out-migration rates, and a multiregional model with destination-specific out-migration rates. Vector autoregressive models are used to forecast future rates of growth, birth, death, net migration, in-migration and out-migration, and destination-specific out-migration for the North, Midlands and South regions in England. They are also used to forecast different international migration measures. The base data represent a time series of annual data provided by the Office for National Statistics from 1976 to 2008. The results illustrate how both the forecasted subpopulation totals and the corresponding prediction intervals differ for the multiregional model in comparison to other simpler models, as well as for different assumptions about international migration. The paper ends end with a discussion of our results and possible directions for future research. PMID:23236221

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

NASA Astrophysics Data System (ADS)

Omenzetter, Piotr; de Lautour, Oliver R.

2010-04-01

Developed for studying long, periodic records of various measured quantities, time series analysis methods are inherently suited and offer interesting possibilities for Structural Health Monitoring (SHM) applications. However, their use in SHM can still be regarded as an emerging application and deserves more studies. In this research, Autoregressive (AR) models were used to fit experimental acceleration time histories from two experimental structural systems, a 3- storey bookshelf-type laboratory structure and the ASCE Phase II SHM Benchmark Structure, in healthy and several damaged states. The coefficients of the AR models were chosen as damage sensitive features. Preliminary visual inspection of the large, multidimensional sets of AR coefficients to check the presence of clusters corresponding to different damage severities was achieved using Sammon mapping - an efficient nonlinear data compression technique. Systematic classification of damage into states based on the analysis of the AR coefficients was achieved using two supervised classification techniques: Nearest Neighbor Classification (NNC) and Learning Vector Quantization (LVQ), and one unsupervised technique: Self-organizing Maps (SOM). This paper discusses the performance of AR coefficients as damage sensitive features and compares the efficiency of the three classification techniques using experimental data.

Testing competing forms of the Milankovitch hypothesis: A multivariate approach

NASA Astrophysics Data System (ADS)

Kaufmann, Robert K.; Juselius, Katarina

2016-02-01

We test competing forms of the Milankovitch hypothesis by estimating the coefficients and diagnostic statistics for a cointegrated vector autoregressive model that includes 10 climate variables and four exogenous variables for solar insolation. The estimates are consistent with the physical mechanisms postulated to drive glacial cycles. They show that the climate variables are driven partly by solar insolation, determining the timing and magnitude of glaciations and terminations, and partly by internal feedback dynamics, pushing the climate variables away from equilibrium. We argue that the latter is consistent with a weak form of the Milankovitch hypothesis and that it should be restated as follows: internal climate dynamics impose perturbations on glacial cycles that are driven by solar insolation. Our results show that these perturbations are likely caused by slow adjustment between land ice volume and solar insolation. The estimated adjustment dynamics show that solar insolation affects an array of climate variables other than ice volume, each at a unique rate. This implies that previous efforts to test the strong form of the Milankovitch hypothesis by examining the relationship between solar insolation and a single climate variable are likely to suffer from omitted variable bias.

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

NASA Astrophysics Data System (ADS)

Wang, Wen-Chuan; Chau, Kwok-Wing; Cheng, Chun-Tian; Qiu, Lin

2009-08-01

SummaryDeveloping a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation ( R), Nash-Sutcliffe efficiency coefficient ( E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.

Renewable Energy Consumption and Economic Growth in Nine OECD Countries: Bounds Test Approach and Causality Analysis

PubMed Central

Hung-Pin, Lin

2014-01-01

The purpose of this paper is to investigate the short-run and long-run causality between renewable energy (RE) consumption and economic growth (EG) in nine OECD countries from the period between 1982 and 2011. To examine the linkage, this paper uses the autoregressive distributed lag (ARDL) bounds testing approach of cointegration test and vector error-correction models to test the causal relationship between variables. The co-integration and causal relationships are found in five countries—United States of America (USA), Japan, Germany, Italy, and United Kingdom (UK). The overall results indicate that (1) a short-run unidirectional causality runs from EG to RE in Italy and UK; (2) long-run unidirectional causalities run from RE to EG for Germany, Italy, and UK; (3) a long-run unidirectional causality runs from EG to RE in USA, and Japan; (4) both long-run and strong unidirectional causalities run from RE to EG for Germany and UK; and (5) Finally, both long-run and strong unidirectional causalities run from EG to RE in only USA. Further evidence reveals that policies for renewable energy conservation may have no impact on economic growth in France, Denmark, Portugal, and Spain. PMID:24558343

Reconstruction of Complex Directional Networks with Group Lasso Nonlinear Conditional Granger Causality.

PubMed

Yang, Guanxue; Wang, Lin; Wang, Xiaofan

2017-06-07

Reconstruction of networks underlying complex systems is one of the most crucial problems in many areas of engineering and science. In this paper, rather than identifying parameters of complex systems governed by pre-defined models or taking some polynomial and rational functions as a prior information for subsequent model selection, we put forward a general framework for nonlinear causal network reconstruction from time-series with limited observations. With obtaining multi-source datasets based on the data-fusion strategy, we propose a novel method to handle nonlinearity and directionality of complex networked systems, namely group lasso nonlinear conditional granger causality. Specially, our method can exploit different sets of radial basis functions to approximate the nonlinear interactions between each pair of nodes and integrate sparsity into grouped variables selection. The performance characteristic of our approach is firstly assessed with two types of simulated datasets from nonlinear vector autoregressive model and nonlinear dynamic models, and then verified based on the benchmark datasets from DREAM3 Challenge4. Effects of data size and noise intensity are also discussed. All of the results demonstrate that the proposed method performs better in terms of higher area under precision-recall curve.

A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia.

PubMed

Aboagye-Sarfo, Patrick; Mai, Qun; Sanfilippo, Frank M; Preen, David B; Stewart, Louise M; Fatovich, Daniel M

2015-10-01

To develop multivariate vector-ARMA (VARMA) forecast models for predicting emergency department (ED) demand in Western Australia (WA) and compare them to the benchmark univariate autoregressive moving average (ARMA) and Winters' models. Seven-year monthly WA state-wide public hospital ED presentation data from 2006/07 to 2012/13 were modelled. Graphical and VARMA modelling methods were used for descriptive analysis and model fitting. The VARMA models were compared to the benchmark univariate ARMA and Winters' models to determine their accuracy to predict ED demand. The best models were evaluated by using error correction methods for accuracy. Descriptive analysis of all the dependent variables showed an increasing pattern of ED use with seasonal trends over time. The VARMA models provided a more precise and accurate forecast with smaller confidence intervals and better measures of accuracy in predicting ED demand in WA than the ARMA and Winters' method. VARMA models are a reliable forecasting method to predict ED demand for strategic planning and resource allocation. While the ARMA models are a closely competing alternative, they under-estimated future ED demand. Copyright © 2015 Elsevier Inc. All rights reserved.

Analyzing brain networks with PCA and conditional Granger causality.

PubMed

Zhou, Zhenyu; Chen, Yonghong; Ding, Mingzhou; Wright, Paul; Lu, Zuhong; Liu, Yijun

2009-07-01

Identifying directional influences in anatomical and functional circuits presents one of the greatest challenges for understanding neural computations in the brain. Granger causality mapping (GCM) derived from vector autoregressive models of data has been employed for this purpose, revealing complex temporal and spatial dynamics underlying cognitive processes. However, the traditional GCM methods are computationally expensive, as signals from thousands of voxels within selected regions of interest (ROIs) are individually processed, and being based on pairwise Granger causality, they lack the ability to distinguish direct from indirect connectivity among brain regions. In this work a new algorithm called PCA based conditional GCM is proposed to overcome these problems. The algorithm implements the following two procedures: (i) dimensionality reduction in ROIs of interest with principle component analysis (PCA), and (ii) estimation of the direct causal influences in local brain networks, using conditional Granger causality. Our results show that the proposed method achieves greater accuracy in detecting network connectivity than the commonly used pairwise Granger causality method. Furthermore, the use of PCA components in conjunction with conditional GCM greatly reduces the computational cost relative to the use of individual voxel time series. Copyright 2009 Wiley-Liss, Inc

Turning points in nonlinear business cycle theories, financial crisis and the 2007-2008 downturn.

PubMed

Dore, Mohammed H I; Singh, Ragiv G

2009-10-01

This paper reviews three nonlinear dynamical business cycle theories of which only one (The Goodwin model) reflects the stylized facts of observed business cycles and has a plausible turning point mechanism. The paper then examines the US (and now global) financial crisis of 2008 and the accompanying downturn in the US. The paper argues that a skewed income distribution could not sustain effective demand and that over the 2001-2006 expansion demand was maintained through massive amounts of credit, with more than 50 percent of sales in the US being maintained through credit. A vector autoregression model confirms the crucial role played by credit. However legislative changes that dismantled the restrictions placed on the financial sector after the crash of 1929 and the consequent structural changes in the financial sector after 1980 enabled the growth of new debt instruments and credit. But overexpansion of credit when profits and house prices were declining in 2005/06 led to a nonlinear shift due to a new realization of the poor quality of some of this debt, namely mortgage backed securities. Bankruptcies, followed by retrenchment at the banks, then led to the bursting of the credit bubble, with the possibility of a severe recession.

Evidence for the role of the Atlantic multidecadal oscillation and the ocean heat uptake in hiatus prediction

NASA Astrophysics Data System (ADS)

Pasini, Antonello; Triacca, Umberto; Attanasio, Alessandro

2017-08-01

The recent hiatus in global temperature at the surface has been analysed by several studies, mainly using global climate models. The common accepted picture is that since the late 1990s, the increase in anthropogenic radiative forcings has been counterbalanced by other factors, e.g., a decrease in natural forcings, augmented ocean heat storage and negative phases of ocean-atmosphere-coupled oscillation patterns. Here, simple vector autoregressive models are used for forecasting the temperature hiatus in the period 2001-2014. This gives new insight into the problem of understanding the ocean contribution (in terms of heat uptake and atmosphere-ocean-coupled oscillations) to the appearance of this recent hiatus. In particular, considering data about the ocean heat content until a depth of 700 m and the Atlantic multidecadal oscillation is necessary for correctly forecasting the hiatus, so catching both trend and interannual variability. Our models also show that the ocean heat uptake is substantially driven by the natural component of the total radiative forcing at a decadal time scale, confining the importance of the anthropogenic influences to a longer range warming of the ocean.

Surface Fatigue Tests Of M50NiL Gears And Bars

NASA Technical Reports Server (NTRS)

Townsend, Dennis P.; Bamberger, Eric N.

1994-01-01

Report presents results of tests of steels for use in gears and bearings of advanced aircraft. Spur-gear endurance tests and rolling-element surface fatigue tests performed on gear and bar specimens of M50NiL steel processed by vacuum induction melting and vacuum arc remelting (VIM-VAR). Compares results of tests with similar tests of specimens of VIM-VAR AISI 9310 steel and of AISI 9310 steel subjected to VAR only.

A NEW SPECIES OF Bolivar Zaldívar-Riverón et Rodríguez-Jiménez (BRACONIDAE, DORYCTINAE) FROM BRAZIL, WITH NEW RECORDS OF THE AMAZONIAN B. ecuadorensis Zaldívar-Riverón et López-Estrada.

PubMed

Nunes, Juliano Fiorelini; Penteado-Dias, Angelica Maria; Souza-Gadelha, Sian De; Zaldívar-Riverón, Alejandro

2016-05-06

A new species of the doryctine genus Bolivar (Braconidae), B. brasiliensis sp. nov., is described from the Atlantic coastal region in Brazil. New records and taxonomic notes of the Amazonian B. ecuadorensis Zaldívar-Riverón et López-Estrada are also provided.

M-X Environmental Technical Report. Environmental Characteristics of Alternative Designated Deployment Areas, Protected Species.

DTIC Science & Technology

1980-12-22

RARE PLANTS LEGEND NUMBER SPECIES I Agave utabensis var. ebossspina 74 D. asperella var. ziorns, 147 Mentzela. leswophyll. 3 Angelica scabrida 75 D...cliffs Agave utahensis var. *boriapina Arctomecon merriami I Arenari a a tenomeres Gill. ripleyi Species known from bajadas of limestone mountains, with...hunters. Illegal collection of rare species of cacti or Agave . 3824-1 :Rare plants may he affected In the sa"e manner as native vegetation. See ETR

Use of Magnetic Bead Resin and Automated Liquid Handler Extraction Methods to Robotically Isolate Nucleic Acids of Biological Agent Simulates

DTIC Science & Technology

2003-09-01

concentration, and Bacillus subtilis var. niger spores were detectable at 10,000 CFU/ml. When combined with bead beating, these spores were consistently...Bioloeical Aaent Simulants. Cell suspensions of Bacillus subtilis var. niger spores (BG spores ) and Erwinia herbicola vegetative cells were prepared for...use as biological simulants. BG spores were prepared by inoculating 1 g spores of Bacillus subtilis var. niger (Merck & Co., Inc., Whitehouse Station

Antidepressant-like Effect of Kaempferol and Quercitirin, Isolated from Opuntia ficus-indica var. saboten

PubMed Central

Park, Soo-Hyun; Sim, Yun-Beom; Han, Pyung-Lim; Lee, Jin-Koo

2010-01-01

Opuntia ficus-indica var. saboten. is widely cultivated in Jeju Island (South Korea) for use in manufacture of health foods. This study described antidepressant effect of two flavonoids (kaempferol and quercitrin) isolated from the Opuntia ficus-indica var. saboten. The expression of the hypothalamic POMC mRNA or plasma β-endorphin levels were increased by extract of Opuntia ficus-indica var. saboten or its flavoniods administered orally. In addition, antidepressant activity was studied using tail suspension test (TST), forced swimming test (FST) and rota-rod test in chronically restraint immobilization stress group in mice. After restraint stress (2 hrs/day for 14 days), animals were kept in cage for 14 days without any further stress, bet with drugs. Mice were fed with a diet supplemented for 14 days and during the behavioral test period with kaempferol or quercitrin (30 mg/kg/day). POMC mRNA or plasma β-endorphin level was increased by extract of Opuntia ficus-indica var. saboten and its flavoniods. In addition, immobility time in TST and FST was significantly reduced by kaempferol or quercitrin. In rota-rod test, the time of permanence was maintained to the semblance of control group in turning at 15 rpm. Our results suggest that two flavonoids (kaempferol and quercitrin) isolated from the Opuntia ficus-indica var. saboten. show a potent antidepressant effect. PMID:22110339

Antidepressant-like Effect of Kaempferol and Quercitirin, Isolated from Opuntia ficus-indica var. saboten.

PubMed

Park, Soo-Hyun; Sim, Yun-Beom; Han, Pyung-Lim; Lee, Jin-Koo; Suh, Hong-Won

2010-06-01

Opuntia ficus-indica var. saboten. is widely cultivated in Jeju Island (South Korea) for use in manufacture of health foods. This study described antidepressant effect of two flavonoids (kaempferol and quercitrin) isolated from the Opuntia ficus-indica var. saboten. The expression of the hypothalamic POMC mRNA or plasma β-endorphin levels were increased by extract of Opuntia ficus-indica var. saboten or its flavoniods administered orally. In addition, antidepressant activity was studied using tail suspension test (TST), forced swimming test (FST) and rota-rod test in chronically restraint immobilization stress group in mice. After restraint stress (2 hrs/day for 14 days), animals were kept in cage for 14 days without any further stress, bet with drugs. Mice were fed with a diet supplemented for 14 days and during the behavioral test period with kaempferol or quercitrin (30 mg/kg/day). POMC mRNA or plasma β-endorphin level was increased by extract of Opuntia ficus-indica var. saboten and its flavoniods. In addition, immobility time in TST and FST was significantly reduced by kaempferol or quercitrin. In rota-rod test, the time of permanence was maintained to the semblance of control group in turning at 15 rpm. Our results suggest that two flavonoids (kaempferol and quercitrin) isolated from the Opuntia ficus-indica var. saboten. show a potent antidepressant effect.

Recurrence risk model for esophageal cancer after radical surgery.

PubMed

Lu, Jincheng; Tao, Hua; Song, Dan; Chen, Cheng

2013-10-01

The aim of the present study was to construct a risk assessment model which was tested by disease-free survival (DFS) of esophageal cancer after radical surgery. A total of 164 consecutive esophageal cancer patients who had undergone radical surgery between January 2005 and December 2006 were retrospectively analyzed. The cutpoint of value at risk (VaR) was inferred by stem-and-leaf plot, as well as by independent-samples t-test for recurrence-free time, further confirmed by crosstab chi-square test, univariate analysis and Cox regression analysis for DFS. The cutpoint of VaR was 0.3 on the basis of our model. The rate of recurrence was 30.3% (30/99) and 52.3% (34/65) in VaR <0.3 and VaR ≥0.3 (chi-square test, (χ) (2) =7.984, P=0.005), respectively. The 1-, 3-, and 5-year DFS of esophageal cancer after radical surgery was 70.4%, 48.7%, and 45.3%, respectively in VaR ≥0.3, whereas 91.5%, 75.8%, and 67.3%, respectively in VaR <0.3 (Log-rank test, (χ) (2) =9.59, P=0.0020), and further confirmed by Cox regression analysis [hazard ratio =2.10, 95% confidence interval (CI): 1.2649-3.4751; P=0.0041]. The model could be applied for integrated assessment of recurrence risk after radical surgery for esophageal cancer.

Recurrence risk model for esophageal cancer after radical surgery

PubMed Central

Tao, Hua; Song, Dan; Chen, Cheng

2013-01-01

Objective The aim of the present study was to construct a risk assessment model which was tested by disease-free survival (DFS) of esophageal cancer after radical surgery. Methods A total of 164 consecutive esophageal cancer patients who had undergone radical surgery between January 2005 and December 2006 were retrospectively analyzed. The cutpoint of value at risk (VaR) was inferred by stem-and-leaf plot, as well as by independent-samples t-test for recurrence-free time, further confirmed by crosstab chi-square test, univariate analysis and Cox regression analysis for DFS. Results The cutpoint of VaR was 0.3 on the basis of our model. The rate of recurrence was 30.3% (30/99) and 52.3% (34/65) in VaR <0.3 and VaR ≥0.3 (chi-square test, χ2 =7.984, P=0.005), respectively. The 1-, 3-, and 5-year DFS of esophageal cancer after radical surgery was 70.4%, 48.7%, and 45.3%, respectively in VaR ≥0.3, whereas 91.5%, 75.8%, and 67.3%, respectively in VaR <0.3 (Log-rank test, χ2 =9.59, P=0.0020), and further confirmed by Cox regression analysis [hazard ratio =2.10, 95% confidence interval (CI): 1.2649-3.4751; P=0.0041]. Conclusions The model could be applied for integrated assessment of recurrence risk after radical surgery for esophageal cancer. PMID:24255579

Functional Antibodies against VAR2CSA in Nonpregnant Populations from Colombia Exposed to Plasmodium falciparum and Plasmodium vivax

PubMed Central

Doritchamou, Justin; Arango, Eliana M.; Cabrera, Ana; Arroyo, Maria Isabel; Kain, Kevin C.; Ndam, Nicaise Tuikue; Maestre, Amanda

2014-01-01

In pregnancy, parity-dependent immunity is observed in response to placental infection with Plasmodium falciparum. Antibodies recognize the surface antigen, VAR2CSA, expressed on infected red blood cells and inhibit cytoadherence to the placental tissue. In most settings of malaria endemicity, antibodies against VAR2CSA are predominantly observed in multigravid women and infrequently in men, children, and nulligravid women. However, in Colombia, we detected antibodies against multiple constructs of VAR2CSA among men and children with acute P. falciparum and Plasmodium vivax infection. The majority of men and children (>60%) had high levels of IgGs against three recombinant domains of VAR2CSA: DBL5ε, DBL3X, and ID1-ID2. Surprisingly, these antibodies were observed only in pregnant women, men, and children exposed either to P. falciparum or to P. vivax. Moreover, the anti-VAR2CSA antibodies are of high avidity and efficiently inhibit adherence of infected red blood cells to chondroitin sulfate A in vitro, suggesting that they are specific and functional. These unexpected results suggest that there may be genotypic or phenotypic differences in the parasites of this region or in the host response to either P. falciparum or P. vivax infection outside pregnancy. These findings may hold significant clinical relevance to the pathophysiology and outcome of malaria infections in this region. PMID:24686068

Forecasting VaR and ES of stock index portfolio: A Vine copula method

NASA Astrophysics Data System (ADS)

Zhang, Bangzheng; Wei, Yu; Yu, Jiang; Lai, Xiaodong; Peng, Zhenfeng

2014-12-01

Risk measurement has both theoretical and practical significance in risk management. Using daily sample of 10 international stock indices, firstly this paper models the internal structures among different stock markets with C-Vine, D-Vine and R-Vine copula models. Secondly, the Value-at-Risk (VaR) and Expected Shortfall (ES) of the international stock markets portfolio are forecasted using Monte Carlo method based on the estimated dependence of different Vine copulas. Finally, the accuracy of VaR and ES measurements obtained from different statistical models are evaluated by UC, IND, CC and Posterior analysis. The empirical results show that the VaR forecasts at the quantile levels of 0.9, 0.95, 0.975 and 0.99 with three kinds of Vine copula models are sufficiently accurate. Several traditional methods, such as historical simulation, mean-variance and DCC-GARCH models, fail to pass the CC backtesting. The Vine copula methods can accurately forecast the ES of the portfolio on the base of VaR measurement, and D-Vine copula model is superior to other Vine copulas.

Genetic relationships among some hawthorn (Crataegus spp.) species and genotypes.

PubMed

Yilmaz, Kadir Ugurtan; Yanar, Makbule; Ercisli, Sezai; Sahiner, Hatice; Taskin, Tuncer; Zengin, Yasar

2010-10-01

The genus Crataegus is well distributed in Turkey as a wild plant, with numerous, inherently variable species and genotypes. RAPD markers were used to study 17 hawthorn genotypes belonging to Crataegus monogyna ssp. monogyna Jacq (2 genotypes), C. monogyna ssp. azarella Jacq (1), Crataegus pontica K.Koch (3), Crataegus orientalis var. orientalis Pallas Ex Bieb (3), Crataegus pseudoheterophylla Pojark (1), Crataegus aronia var. dentata Browicz (1), C. aronia var. aronia Browicz (4), and Crateagus x bornmuelleri Zabel (2). The 10 RAPD primers produced 72 polymorphic bands (88% polymorphism). A dendrogram based on Jaccard's index included four major groups and one outgroup according to taxa. The lowest genetic variability was observed within C. aronia var. aronia genotypes. The study demonstrated that RAPD analysis is efficient for genotyping wild-grown hawthorns.

Kepler AutoRegressive Planet Search

NASA Astrophysics Data System (ADS)

Feigelson, Eric

NASA's Kepler mission is the source of more exoplanets than any other instrument, but the discovery depends on complex statistical analysis procedures embedded in the Kepler pipeline. A particular challenge is mitigating irregular stellar variability without loss of sensitivity to faint periodic planetary transits. This proposal presents a two-stage alternative analysis procedure. First, parametric autoregressive ARFIMA models, commonly used in econometrics, remove most of the stellar variations. Second, a novel matched filter is used to create a periodogram from which transit-like periodicities are identified. This analysis procedure, the Kepler AutoRegressive Planet Search (KARPS), is confirming most of the Kepler Objects of Interest and is expected to identify additional planetary candidates. The proposed research will complete application of the KARPS methodology to the prime Kepler mission light curves of 200,000: stars, and compare the results with Kepler Objects of Interest obtained with the Kepler pipeline. We will then conduct a variety of astronomical studies based on the KARPS results. Important subsamples will be extracted including Habitable Zone planets, hot super-Earths, grazing-transit hot Jupiters, and multi-planet systems. Groundbased spectroscopy of poorly studied candidates will be performed to better characterize the host stars. Studies of stellar variability will then be pursued based on KARPS analysis. The autocorrelation function and nonstationarity measures will be used to identify spotted stars at different stages of autoregressive modeling. Periodic variables with folded light curves inconsistent with planetary transits will be identified; they may be eclipsing or mutually-illuminating binary star systems. Classification of stellar variables with KARPS-derived statistical properties will be attempted. KARPS procedures will then be applied to archived K2 data to identify planetary transits and characterize stellar variability.

Nonrandom variability in respiratory cycle parameters of humans during stage 2 sleep.

PubMed

Modarreszadeh, M; Bruce, E N; Gothe, B

1990-08-01

We analyzed breath-to-breath inspiratory time (TI), expiratory time (TE), inspiratory volume (VI), and minute ventilation (Vm) from 11 normal subjects during stage 2 sleep. The analysis consisted of 1) fitting first- and second-order autoregressive models (AR1 and AR2) and 2) obtaining the power spectra of the data by fast-Fourier transform. For the AR2 model, the only coefficients that were statistically different from zero were the average alpha 1 (a1) for TI, VI, and Vm (a1 = 0.19, 0.29, and 0.15, respectively). However, the power spectra of all parameters often exhibited peaks at low frequency (less than 0.2 cycles/breath) and/or at high frequency (greater than 0.2 cycles/breath), indicative of periodic oscillations. After accounting for the corrupting effects of added oscillations on the a1 estimates, we conclude that 1) breath-to-breath fluctuations of VI, and to a lesser extent TI and Vm, exhibit a first-order autoregressive structure such that fluctuations of each breath are positively correlated with those of immediately preceding breaths and 2) the correlated components of variability in TE are mostly due to discrete high- and/or low-frequency oscillations with no underlying autoregressive structure. We propose that the autoregressive structure of VI, TI, and Vm during spontaneous breathing in stage 2 sleep may reflect either a central neural mechanism or the effects of noise in respiratory chemical feedback loops; the presence of low-frequency oscillations, seen more often in Vm, suggests possible instability in the chemical feedback loops. Mechanisms of high-frequency periodicities, seen more often in TE, are unknown.

Autoregressive Processes in Homogenization of GNSS Tropospheric Data

NASA Astrophysics Data System (ADS)

Klos, A.; Bogusz, J.; Teferle, F. N.; Bock, O.; Pottiaux, E.; Van Malderen, R.

2016-12-01

Offsets due to changes in hardware equipment or any other artificial event are all a subject of a task of homogenization of tropospheric data estimated within a processing of Global Navigation Satellite System (GNSS) observables. This task is aimed at identifying exact epochs of offsets and estimate their magnitudes since they may artificially under- or over-estimate trend and its uncertainty delivered from tropospheric data and used in climate studies. In this research, we analysed a common data set of differences of Integrated Water Vapour (IWV) from GPS and ERA-Interim (1995-2010) provided for a homogenization group working within ES1206 COST Action GNSS4SWEC. We analysed daily IWV records of GPS and ERA-Interim in terms of trend, seasonal terms and noise model with Maximum Likelihood Estimation in Hector software. We found that this data has a character of autoregressive process (AR). Basing on this analysis, we performed Monte Carlo simulations of 25 years long data with two different noise types: white as well as combination of white and autoregressive and also added few strictly defined offsets. This synthetic data set of exactly the same character as IWV from GPS and ERA-Interim was then subjected to a task of manual and automatic/statistical homogenization. We made blind tests and detected possible epochs of offsets manually. We found that simulated offsets were easily detected in series with white noise, no influence of seasonal signal was noticed. The autoregressive series were much more problematic when offsets had to be determined. We found few epochs, for which no offset was simulated. This was mainly due to strong autocorrelation of data, which brings an artificial trend within. Due to regime-like behaviour of AR it is difficult for statistical methods to properly detect epochs of offsets, which was previously reported by climatologists.

Advanced Micro Grid Energy Management Coupled with Integrated Volt/VAR Control for Improved Energy Efficiency, Energy Security, and Power Quality at DoD Installations

DTIC Science & Technology

2016-10-28

assumptions. List of Assumptions: Price of electrical energy : $0.07/kWh flat rate for energy at the base Price of peak power: $15/MW peak power...EW-201147) Advanced Micro-Grid Energy Management Coupled with Integrated Volt/VAR Control for Improved Energy Efficiency, Energy Security, and...12-C-0002 5b. GRANT NUMBER Advanced Micro-Grid Energy Management Coupled with Integrated Volt/VAR Control for Improved Energy Efficiency, Energy

Four new synonyms and a new combination in Parnassia (Celastraceae).

PubMed

Shu, Yumin; Zhang, Zhixiang

2017-01-01

Parnassia yunnanensis had been previously described based on mixed specimens containing materials partially belonging to Parnassia cacuminum , which makes the application of Parnassia yunnanensis ambiguous. Therefore, we lectotypified Parnassia yunnanensis and meanwhile synonymized Parnassia lanceolata var. oblongipetala under it. Parnassia yunnanensis var. longistipitata was found more similar to Parnassia cacuminum rather than Parnassia yunnanensis , thus a new combination, Parnassia cacuminum var. longistipitata comb. nov. was proposed. Furthermore, other three names ( Parnassia vevusta , Parnassia degeensis and Parnassia kangdingensis ) were reduced to synonyms of Parnassia cacuminum too.

Selected aspects of modelling monetary transmission mechanism by BVAR model

NASA Astrophysics Data System (ADS)

Vaněk, Tomáš; Dobešová, Anna; Hampel, David

2013-10-01

In this paper we use the BVAR model with the specifically defined prior to evaluate data including high-lag dependencies. The results are compared to both restricted and common VAR model. The data depicts the monetary transmission mechanism in the Czech Republic and Slovakia from January 2002 to February 2013. The results point to the inadequacy of the common VAR model. The restricted VAR model and the BVAR model appear to be similar in the sense of impulse responses.

Fungal growth and the presence of sterigmatocystin in hard cheese.

PubMed

Northolt, M D; van Egmond, H P; Soentoro, P; Deijll, E

1980-01-01

Molds isolated from visibly molded cheeses in shops, households, and warehouses have been identified. Mold flora of cheeses in shops and households consisted mainly of Penicillium verrucosum var. cyclopium. On cheeses ripening in warehouses, Penicillium verrucosum var. cyclopium, Aspergillus versicolor, Aspergillus repens, and Enicillium verrucosum var. verrucosum were the dominant mold species. Cheese ripening in warehouses and molded with A. versicolor were examined for sterigmatocystin. Nine of 39 cheese samples contained sterigmatocystin in the surface layer in concentrations ranging from 5 to 600 micrograms/kg.

The past, present, and future of the U.S. electric power sector: Examining regulatory changes using multivariate time series approaches

NASA Astrophysics Data System (ADS)

Binder, Kyle Edwin

The U.S. energy sector has undergone continuous change in the regulatory, technological, and market environments. These developments show no signs of slowing. Accordingly, it is imperative that energy market regulators and participants develop a strong comprehension of market dynamics and the potential implications of their actions. This dissertation contributes to a better understanding of the past, present, and future of U.S. energy market dynamics and interactions with policy. Advancements in multivariate time series analysis are employed in three related studies of the electric power sector. Overall, results suggest that regulatory changes have had and will continue to have important implications for the electric power sector. The sector, however, has exhibited adaptability to past regulatory changes and is projected to remain resilient in the future. Tests for constancy of the long run parameters in a vector error correction model are applied to determine whether relationships among coal inventories in the electric power sector, input prices, output prices, and opportunity costs have remained constant over the past 38 years. Two periods of instability are found, the first following railroad deregulation in the U.S. and the second corresponding to a number of major regulatory changes in the electric power and natural gas sectors. Relationships among Renewable Energy Credit prices, electricity prices, and natural gas prices are estimated using a vector error correction model. Results suggest that Renewable Energy Credit prices do not completely behave as previously theorized in the literature. Potential reasons for the divergence between theory and empirical evidence are the relative immaturity of current markets and continuous institutional intervention. Potential impacts of future CO2 emissions reductions under the Clean Power Plan on economic and energy sector activity are estimated. Conditional forecasts based on an outlined path for CO2 emissions are developed from a factor-augmented vector autoregressive model for a large dataset. Unconditional and conditional forecasts are compared for U.S. industrial production, real personal income, and estimated factors. Results suggest that economic growth will be slower under the Clean Power Plan than it would otherwise; however, CO2 emissions reductions and economic growth can be achieved simultaneously.

The COOH-terminal peptide of platelet factor-4 variant (CXCL4L1/PF-4var47-70) strongly inhibits angiogenesis and suppresses B16 melanoma growth in vivo.

PubMed

Vandercappellen, Jo; Liekens, Sandra; Bronckaers, Annelies; Noppen, Samuel; Ronsse, Isabelle; Dillen, Chris; Belleri, Mirella; Mitola, Stefania; Proost, Paul; Presta, Marco; Struyf, Sofie; Van Damme, Jo

2010-03-01

Chemokines influence tumor growth directly or indirectly via both angiogenesis and tumor-leukocyte interactions. Platelet factor-4 (CXCL4/PF-4), which is released from alpha-granules of activated platelets, is the first described angiostatic chemokine. Recently, it was found that the variant of CXCL4/PF-4 (CXCL4L1/PF-4var) could exert a more pronounced angiostatic and antitumoral effect than CXCL4/PF-4. However, the molecular mechanisms of the angiostatic activities of the PF-4 forms remain partially elusive. Here, we studied the biological properties of the chemically synthesized COOH-terminal peptides of CXCL4/PF-4 (CXCL4/PF-4(47-70)) and CXCL4L1/PF-4var (CXCL4L1/PF-4var(47-70)). Both PF-4 peptides lacked monocyte and lymphocyte chemotactic activity but equally well inhibited (25 nmol/L) endothelial cell motility and proliferation in the presence of a single stimulus (i.e., exogenous recombinant fibroblast growth factor-2). In contrast, when assayed in more complex angiogenesis test systems characterized by the presence of multiple mediators, including in vitro wound-healing (2.5 nmol/L versus 12.5 nmol/L), Matrigel (60 nmol/L versus 300 nmol/L), and chorioallantoic membrane assays, CXCL4L1/PF-4var(47-70) was found to be significantly (5-fold) more angiostatic than CXCL4/PF-4(47-70). In addition, low (7 microg total) doses of intratumoral CXCL4L1/PF-4var(47-70) inhibited B16 melanoma growth in mice more extensively than CXCL4/PF-4(47-70). This antitumoral activity was predominantly mediated through inhibition of angiogenesis (without affecting blood vessel stability) and induction of apoptosis, as evidenced by immunohistochemical and fluorescent staining of B16 tumor tissue. In conclusion, CXCL4L1/PF-4var(47-70) is a potent antitumoral and antiangiogenic peptide. These results may represent the basis for the design of CXCL4L1/PF-4var COOH-terminal-derived peptidomimetic anticancer drugs.