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Sample records for nonparametric methods cart

  1. Using and comparing two nonparametric methods (CART and RF and SPOT-HRG satellite data to predictive tree diversity distribution

    Directory of Open Access Journals (Sweden)

    SIAVASH KALBI

    2014-05-01

    Full Text Available Kalbi S, Fallah A, Hojjati SM. 2014. Using and comparing two nonparametric methods (CART and RF and SPOT-HRG satellite data to predictive tree diversity distribution. Nusantara Bioscience 6: 57-62. The prediction of spatial distributions of tree species by means of survey data has recently been used for conservation planning. Numerous methods have been developed for building species habitat suitability models. The present study was carried out to find the possible proper relationships between tree species diversity indices and SPOT-HRG reflectance values in Hyrcanian forests, North of Iran. Two different modeling techniques, Classification and Regression Trees (CART and Random Forest (RF, were fitted to the data in order to find the most successfully model. Simpson, Shannon diversity and the reciprocal of Simpson indices were used for estimating tree diversity. After collecting terrestrial information on trees in the 100 samples, the tree diversity indices were calculated in each plot. RF with determinate coefficient and RMSE from 56.3 to 63.9 and RMSE from 0.15 to 0.84 has better results than CART algorithms with determinate coefficient 42.3 to 63.3 and RMSE from 0.188 to 0.88. Overall the results showed that the SPOT-HRG satellite data and nonparametric regression could be useful for estimating tree diversity in Hyrcanian forests, North of Iran.

  2. Applied nonparametric statistical methods

    CERN Document Server

    Sprent, Peter

    2007-01-01

    While preserving the clear, accessible style of previous editions, Applied Nonparametric Statistical Methods, Fourth Edition reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets. Reorganized and with additional material, this edition begins with a brief summary of some relevant general statistical concepts and an introduction to basic ideas of nonparametric or distribution-free methods. Designed experiments, including those with factorial treatment structures, are now the focus of an entire chapter. The text also e

  3. Why preferring parametric forecasting to nonparametric methods?

    Science.gov (United States)

    Jabot, Franck

    2015-05-07

    A recent series of papers by Charles T. Perretti and collaborators have shown that nonparametric forecasting methods can outperform parametric methods in noisy nonlinear systems. Such a situation can arise because of two main reasons: the instability of parametric inference procedures in chaotic systems which can lead to biased parameter estimates, and the discrepancy between the real system dynamics and the modeled one, a problem that Perretti and collaborators call "the true model myth". Should ecologists go on using the demanding parametric machinery when trying to forecast the dynamics of complex ecosystems? Or should they rely on the elegant nonparametric approach that appears so promising? It will be here argued that ecological forecasting based on parametric models presents two key comparative advantages over nonparametric approaches. First, the likelihood of parametric forecasting failure can be diagnosed thanks to simple Bayesian model checking procedures. Second, when parametric forecasting is diagnosed to be reliable, forecasting uncertainty can be estimated on virtual data generated with the fitted to data parametric model. In contrast, nonparametric techniques provide forecasts with unknown reliability. This argumentation is illustrated with the simple theta-logistic model that was previously used by Perretti and collaborators to make their point. It should convince ecologists to stick to standard parametric approaches, until methods have been developed to assess the reliability of nonparametric forecasting. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. portfolio optimization based on nonparametric estimation methods

    Directory of Open Access Journals (Sweden)

    mahsa ghandehari

    2017-03-01

    Full Text Available One of the major issues investors are facing with in capital markets is decision making about select an appropriate stock exchange for investing and selecting an optimal portfolio. This process is done through the risk and expected return assessment. On the other hand in portfolio selection problem if the assets expected returns are normally distributed, variance and standard deviation are used as a risk measure. But, the expected returns on assets are not necessarily normal and sometimes have dramatic differences from normal distribution. This paper with the introduction of conditional value at risk ( CVaR, as a measure of risk in a nonparametric framework, for a given expected return, offers the optimal portfolio and this method is compared with the linear programming method. The data used in this study consists of monthly returns of 15 companies selected from the top 50 companies in Tehran Stock Exchange during the winter of 1392 which is considered from April of 1388 to June of 1393. The results of this study show the superiority of nonparametric method over the linear programming method and the nonparametric method is much faster than the linear programming method.

  5. Application of Nonparametric Methods in Studying Energy ...

    African Journals Online (AJOL)

    Consumer behaviour towards different forms of energy varies over time. The variance can be so large that the quality of the estimation functional relationship between the response variable and its associated explanatory variables is seriously affected. To attenuate this, kernel smoothing a nonparametric regression ...

  6. Application of nonparametric statistic method for DNBR limit calculation

    International Nuclear Information System (INIS)

    Dong Bo; Kuang Bo; Zhu Xuenong

    2013-01-01

    Background: Nonparametric statistical method is a kind of statistical inference method not depending on a certain distribution; it calculates the tolerance limits under certain probability level and confidence through sampling methods. The DNBR margin is one important parameter of NPP design, which presents the safety level of NPP. Purpose and Methods: This paper uses nonparametric statistical method basing on Wilks formula and VIPER-01 subchannel analysis code to calculate the DNBR design limits (DL) of 300 MW NPP (Nuclear Power Plant) during the complete loss of flow accident, simultaneously compared with the DL of DNBR through means of ITDP to get certain DNBR margin. Results: The results indicate that this method can gain 2.96% DNBR margin more than that obtained by ITDP methodology. Conclusions: Because of the reduction of the conservation during analysis process, the nonparametric statistical method can provide greater DNBR margin and the increase of DNBR margin is benefited for the upgrading of core refuel scheme. (authors)

  7. Comparing parametric and nonparametric regression methods for panel data

    DEFF Research Database (Denmark)

    Czekaj, Tomasz Gerard; Henningsen, Arne

    We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb-Douglas and......We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb......-Douglas and Translog, are unsuitable for analysing the optimal firm size. We show that the Translog functional form implies an implausible linear relationship between the (logarithmic) firm size and the elasticity of scale, where the slope is artificially related to the substitutability between the inputs....... The practical applicability of the parametric and non-parametric regression methods is scrutinised and compared by an empirical example: we analyse the production technology and investigate the optimal size of Polish crop farms based on a firm-level balanced panel data set. A nonparametric specification test...

  8. Non-parametric versus parametric methods in environmental sciences

    Directory of Open Access Journals (Sweden)

    Muhammad Riaz

    2017-06-01

    Full Text Available This current report intends to highlight the importance of considering background assumptions required for the analysis of real datasets in different disciplines. We will provide comparative discussion of parametric methods (that depends on distributional assumptions (like normality relative to non-parametric methods (that are free from many distributional assumptions. We have chosen a real dataset from environmental sciences (one of the application areas. The findings may be extended to the other disciplines following the same spirit.

  9. International Conference on Robust Rank-Based and Nonparametric Methods

    CERN Document Server

    McKean, Joseph

    2016-01-01

    The contributors to this volume include many of the distinguished researchers in this area. Many of these scholars have collaborated with Joseph McKean to develop underlying theory for these methods, obtain small sample corrections, and develop efficient algorithms for their computation. The papers cover the scope of the area, including robust nonparametric rank-based procedures through Bayesian and big data rank-based analyses. Areas of application include biostatistics and spatial areas. Over the last 30 years, robust rank-based and nonparametric methods have developed considerably. These procedures generalize traditional Wilcoxon-type methods for one- and two-sample location problems. Research into these procedures has culminated in complete analyses for many of the models used in practice including linear, generalized linear, mixed, and nonlinear models. Settings are both multivariate and univariate. With the development of R packages in these areas, computation of these procedures is easily shared with r...

  10. Using non-parametric methods in econometric production analysis

    DEFF Research Database (Denmark)

    Czekaj, Tomasz Gerard; Henningsen, Arne

    2012-01-01

    by investigating the relationship between the elasticity of scale and the farm size. We use a balanced panel data set of 371~specialised crop farms for the years 2004-2007. A non-parametric specification test shows that neither the Cobb-Douglas function nor the Translog function are consistent with the "true......Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify a functional form of the production function of which the Cobb...... parameter estimates, but also in biased measures which are derived from the parameters, such as elasticities. Therefore, we propose to use non-parametric econometric methods. First, these can be applied to verify the functional form used in parametric production analysis. Second, they can be directly used...

  11. Using non-parametric methods in econometric production analysis

    DEFF Research Database (Denmark)

    Czekaj, Tomasz Gerard; Henningsen, Arne

    Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify the functional form of the production function. Most often, the Cobb...... results—including measures that are of interest of applied economists, such as elasticities. Therefore, we propose to use nonparametric econometric methods. First, they can be applied to verify the functional form used in parametric estimations of production functions. Second, they can be directly used...

  12. Digital spectral analysis parametric, non-parametric and advanced methods

    CERN Document Server

    Castanié, Francis

    2013-01-01

    Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature.The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models.An entire chapter is devoted to the non-parametric methods most widely used in industry.High resolution methods a

  13. Speaker Linking and Applications using Non-Parametric Hashing Methods

    Science.gov (United States)

    2016-09-08

    Speaker Linking and Applications using Non-Parametric Hashing Methods† Douglas Sturim and William M. Campbell MIT Lincoln Laboratory, Lexington, MA...to prove this by extending the proof of [8] to non-parametric hashing. 6. References [1] W. M. Campbell, D. E. Sturim, and D. A. Reynolds , “Support vec...EPFL-CONF-192414, 2012. [13] S. H. Shum, W. M. Campbell, and D. A. Reynolds , “Large-scale community detection on speaker content graphs,” in

  14. Non-parametric and least squares Langley plot methods

    Science.gov (United States)

    Kiedron, P. W.; Michalsky, J. J.

    2016-01-01

    Langley plots are used to calibrate sun radiometers primarily for the measurement of the aerosol component of the atmosphere that attenuates (scatters and absorbs) incoming direct solar radiation. In principle, the calibration of a sun radiometer is a straightforward application of the Bouguer-Lambert-Beer law V = V0e-τ ṡ m, where a plot of ln(V) voltage vs. m air mass yields a straight line with intercept ln(V0). This ln(V0) subsequently can be used to solve for τ for any measurement of V and calculation of m. This calibration works well on some high mountain sites, but the application of the Langley plot calibration technique is more complicated at other, more interesting, locales. This paper is concerned with ferreting out calibrations at difficult sites and examining and comparing a number of conventional and non-conventional methods for obtaining successful Langley plots. The 11 techniques discussed indicate that both least squares and various non-parametric techniques produce satisfactory calibrations with no significant differences among them when the time series of ln(V0)'s are smoothed and interpolated with median and mean moving window filters.

  15. Analysis of small sample size studies using nonparametric bootstrap test with pooled resampling method.

    Science.gov (United States)

    Dwivedi, Alok Kumar; Mallawaarachchi, Indika; Alvarado, Luis A

    2017-06-30

    Experimental studies in biomedical research frequently pose analytical problems related to small sample size. In such studies, there are conflicting findings regarding the choice of parametric and nonparametric analysis, especially with non-normal data. In such instances, some methodologists questioned the validity of parametric tests and suggested nonparametric tests. In contrast, other methodologists found nonparametric tests to be too conservative and less powerful and thus preferred using parametric tests. Some researchers have recommended using a bootstrap test; however, this method also has small sample size limitation. We used a pooled method in nonparametric bootstrap test that may overcome the problem related with small samples in hypothesis testing. The present study compared nonparametric bootstrap test with pooled resampling method corresponding to parametric, nonparametric, and permutation tests through extensive simulations under various conditions and using real data examples. The nonparametric pooled bootstrap t-test provided equal or greater power for comparing two means as compared with unpaired t-test, Welch t-test, Wilcoxon rank sum test, and permutation test while maintaining type I error probability for any conditions except for Cauchy and extreme variable lognormal distributions. In such cases, we suggest using an exact Wilcoxon rank sum test. Nonparametric bootstrap paired t-test also provided better performance than other alternatives. Nonparametric bootstrap test provided benefit over exact Kruskal-Wallis test. We suggest using nonparametric bootstrap test with pooled resampling method for comparing paired or unpaired means and for validating the one way analysis of variance test results for non-normal data in small sample size studies. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  16. Nonparametric Change Point Diagnosis Method of Concrete Dam Crack Behavior Abnormality

    Directory of Open Access Journals (Sweden)

    Zhanchao Li

    2013-01-01

    Full Text Available The study on diagnosis method of concrete crack behavior abnormality has always been a hot spot and difficulty in the safety monitoring field of hydraulic structure. Based on the performance of concrete dam crack behavior abnormality in parametric statistical model and nonparametric statistical model, the internal relation between concrete dam crack behavior abnormality and statistical change point theory is deeply analyzed from the model structure instability of parametric statistical model and change of sequence distribution law of nonparametric statistical model. On this basis, through the reduction of change point problem, the establishment of basic nonparametric change point model, and asymptotic analysis on test method of basic change point problem, the nonparametric change point diagnosis method of concrete dam crack behavior abnormality is created in consideration of the situation that in practice concrete dam crack behavior may have more abnormality points. And the nonparametric change point diagnosis method of concrete dam crack behavior abnormality is used in the actual project, demonstrating the effectiveness and scientific reasonableness of the method established. Meanwhile, the nonparametric change point diagnosis method of concrete dam crack behavior abnormality has a complete theoretical basis and strong practicality with a broad application prospect in actual project.

  17. A new method of joint nonparametric estimation of probability density and its support

    OpenAIRE

    Moriyama, Taku

    2017-01-01

    In this paper we propose a new method of joint nonparametric estimation of probability density and its support. As is well known, nonparametric kernel density estimator has "boundary bias problem" when the support of the population density is not the whole real line. To avoid the unknown boundary effects, our estimator detects the boundary, and eliminates the boundary-bias of the estimator simultaneously. Moreover, we refer an extension to a simple multivariate case, and propose an improved e...

  18. A Least Squares Method for Variance Estimation in Heteroscedastic Nonparametric Regression

    Directory of Open Access Journals (Sweden)

    Yuejin Zhou

    2014-01-01

    Full Text Available Interest in variance estimation in nonparametric regression has grown greatly in the past several decades. Among the existing methods, the least squares estimator in Tong and Wang (2005 is shown to have nice statistical properties and is also easy to implement. Nevertheless, their method only applies to regression models with homoscedastic errors. In this paper, we propose two least squares estimators for the error variance in heteroscedastic nonparametric regression: the intercept estimator and the slope estimator. Both estimators are shown to be consistent and their asymptotic properties are investigated. Finally, we demonstrate through simulation studies that the proposed estimators perform better than the existing competitor in various settings.

  19. Addiction Severity Index Recent and Lifetime Summary Indexes Based on Nonparametric Item Response Theory Methods

    Science.gov (United States)

    Alterman, Arthur I.; Cacciola, John S.; Habing, Brian; Lynch, Kevin G.

    2007-01-01

    Baseline Addiction Severity Index (5th ed.; ASI-5) data of 2,142 substance abuse patients were analyzed with two nonparametric item response theory (NIRT) methods: Mokken scaling and conditional covariance techniques. Nine reliable and dimensionally homogeneous Recent Problem indexes emerged in the ASI-5's seven areas, including two each in the…

  20. Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison

    NARCIS (Netherlands)

    Verrelst, Jochem; Rivera, Juan Pablo; Veroustraete, Frank; Muñoz-Marí, Jordi; Clevers, J.G.P.W.; Camps-Valls, Gustau; Moreno, José

    2015-01-01

    Given the forthcoming availability of Sentinel-2 (S2) images, this paper provides a systematic comparison of retrieval accuracy and processing speed of a multitude of parametric, non-parametric and physically-based retrieval methods using simulated S2 data. An experimental field dataset (SPARC),

  1. Nonparametric methods for drought severity estimation at ungauged sites

    Science.gov (United States)

    Sadri, S.; Burn, D. H.

    2012-12-01

    The objective in frequency analysis is, given extreme events such as drought severity or duration, to estimate the relationship between that event and the associated return periods at a catchment. Neural networks and other artificial intelligence approaches in function estimation and regression analysis are relatively new techniques in engineering, providing an attractive alternative to traditional statistical models. There are, however, few applications of neural networks and support vector machines in the area of severity quantile estimation for drought frequency analysis. In this paper, we compare three methods for this task: multiple linear regression, radial basis function neural networks, and least squares support vector regression (LS-SVR). The area selected for this study includes 32 catchments in the Canadian Prairies. From each catchment drought severities are extracted and fitted to a Pearson type III distribution, which act as observed values. For each method-duration pair, we use a jackknife algorithm to produce estimated values at each site. The results from these three approaches are compared and analyzed, and it is found that LS-SVR provides the best quantile estimates and extrapolating capacity.

  2. Parametric and nonparametric statistical methods for genomic selection of traits with additive and epistatic genetic architectures.

    Science.gov (United States)

    Howard, Réka; Carriquiry, Alicia L; Beavis, William D

    2014-04-11

    Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods including least squares regression, ridge regression, Bayesian ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian LASSO, best linear unbiased prediction (BLUP), Bayes A, Bayes B, Bayes C, and Bayes Cπ. We also review nonparametric methods including Nadaraya-Watson estimator, reproducing kernel Hilbert space, support vector machine regression, and neural networks. We assess the relative merits of these 14 methods in terms of accuracy and mean squared error (MSE) using simulated genetic architectures consisting of completely additive or two-way epistatic interactions in an F2 population derived from crosses of inbred lines. Each simulated genetic architecture explained either 30% or 70% of the phenotypic variability. The greatest impact on estimates of accuracy and MSE was due to genetic architecture. Parametric methods were unable to predict phenotypic values when the underlying genetic architecture was based entirely on epistasis. Parametric methods were slightly better than nonparametric methods for additive genetic architectures. Distinctions among parametric methods for additive genetic architectures were incremental. Heritability, i.e., proportion of phenotypic variability, had the second greatest impact on estimates of accuracy and MSE. Copyright © 2014 Howard et al.

  3. A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale.

    Science.gov (United States)

    Mircioiu, Constantin; Atkinson, Jeffrey

    2017-05-10

    A trenchant and passionate dispute over the use of parametric versus non-parametric methods for the analysis of Likert scale ordinal data has raged for the past eight decades. The answer is not a simple "yes" or "no" but is related to hypotheses, objectives, risks, and paradigms. In this paper, we took a pragmatic approach. We applied both types of methods to the analysis of actual Likert data on responses from different professional subgroups of European pharmacists regarding competencies for practice. Results obtained show that with "large" (>15) numbers of responses and similar (but clearly not normal) distributions from different subgroups, parametric and non-parametric analyses give in almost all cases the same significant or non-significant results for inter-subgroup comparisons. Parametric methods were more discriminant in the cases of non-similar conclusions. Considering that the largest differences in opinions occurred in the upper part of the 4-point Likert scale (ranks 3 "very important" and 4 "essential"), a "score analysis" based on this part of the data was undertaken. This transformation of the ordinal Likert data into binary scores produced a graphical representation that was visually easier to understand as differences were accentuated. In conclusion, in this case of Likert ordinal data with high response rates, restraining the analysis to non-parametric methods leads to a loss of information. The addition of parametric methods, graphical analysis, analysis of subsets, and transformation of data leads to more in-depth analyses.

  4. A computationally efficient method for nonparametric modeling of neural spiking activity with point processes.

    Science.gov (United States)

    Coleman, Todd P; Sarma, Sridevi S

    2010-08-01

    Point-process models have been shown to be useful in characterizing neural spiking activity as a function of extrinsic and intrinsic factors. Most point-process models of neural activity are parametric, as they are often efficiently computable. However, if the actual point process does not lie in the assumed parametric class of functions, misleading inferences can arise. Nonparametric methods are attractive due to fewer assumptions, but computation in general grows with the size of the data. We propose a computationally efficient method for nonparametric maximum likelihood estimation when the conditional intensity function, which characterizes the point process in its entirety, is assumed to be a Lipschitz continuous function but otherwise arbitrary. We show that by exploiting much structure, the problem becomes efficiently solvable. We next demonstrate a model selection procedure to estimate the Lipshitz parameter from data, akin to the minimum description length principle and demonstrate consistency of our estimator under appropriate assumptions. Finally, we illustrate the effectiveness of our method with simulated neural spiking data, goldfish retinal ganglion neural data, and activity recorded in CA1 hippocampal neurons from an awake behaving rat. For the simulated data set, our method uncovers a more compact representation of the conditional intensity function when it exists. For the goldfish and rat neural data sets, we show that our nonparametric method gives a superior absolute goodness-of-fit measure used for point processes than the most common parametric and splines-based approaches.

  5. Hadron Energy Reconstruction for ATLAS Barrel Combined Calorimeter Using Non-Parametrical Method

    CERN Document Server

    Kulchitskii, Yu A

    2000-01-01

    Hadron energy reconstruction for the ATLAS barrel prototype combined calorimeter in the framework of the non-parametrical method is discussed. The non-parametrical method utilizes only the known e/h ratios and the electron calibration constants and does not require the determination of any parameters by a minimization technique. Thus, this technique lends itself to fast energy reconstruction in a first level trigger. The reconstructed mean values of the hadron energies are within \\pm1% of the true values and the fractional energy resolution is [(58\\pm 3)%{\\sqrt{GeV}}/\\sqrt{E}+(2.5\\pm0.3)%]\\bigoplus(1.7\\pm0.2) GeV/E. The value of the e/h ratio obtained for the electromagnetic compartment of the combined calorimeter is 1.74\\pm0.04. Results of a study of the longitudinal hadronic shower development are also presented.

  6. The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis

    DEFF Research Database (Denmark)

    Czekaj, Tomasz Gerard

    This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...... and hence, also in biased measures, which are derived from the estimated parameters. This, in turn, can result in incorrect economic conclusions and recommendations for managers, politicians and decision makers in general. This PhD thesis focuses on a nonparametric econometric approach that can be used...... kernel methods are well-suited to econometric production analysis and can outperform traditional parametric methods. Although the empirical focus of this thesis is on the application of nonparametric kernel regression in applied production analysis, the findings are also applicable to econometric...

  7. Multiple imputation methods for nonparametric inference on cumulative incidence with missing cause of failure

    Science.gov (United States)

    Lee, Minjung; Dignam, James J.; Han, Junhee

    2014-01-01

    We propose a nonparametric approach for cumulative incidence estimation when causes of failure are unknown or missing for some subjects. Under the missing at random assumption, we estimate the cumulative incidence function using multiple imputation methods. We develop asymptotic theory for the cumulative incidence estimators obtained from multiple imputation methods. We also discuss how to construct confidence intervals for the cumulative incidence function and perform a test for comparing the cumulative incidence functions in two samples with missing cause of failure. Through simulation studies, we show that the proposed methods perform well. The methods are illustrated with data from a randomized clinical trial in early stage breast cancer. PMID:25043107

  8. Tremor Detection Using Parametric and Non-Parametric Spectral Estimation Methods: A Comparison with Clinical Assessment.

    Science.gov (United States)

    Martinez Manzanera, Octavio; Elting, Jan Willem; van der Hoeven, Johannes H; Maurits, Natasha M

    2016-01-01

    In the clinic, tremor is diagnosed during a time-limited process in which patients are observed and the characteristics of tremor are visually assessed. For some tremor disorders, a more detailed analysis of these characteristics is needed. Accelerometry and electromyography can be used to obtain a better insight into tremor. Typically, routine clinical assessment of accelerometry and electromyography data involves visual inspection by clinicians and occasionally computational analysis to obtain objective characteristics of tremor. However, for some tremor disorders these characteristics may be different during daily activity. This variability in presentation between the clinic and daily life makes a differential diagnosis more difficult. A long-term recording of tremor by accelerometry and/or electromyography in the home environment could help to give a better insight into the tremor disorder. However, an evaluation of such recordings using routine clinical standards would take too much time. We evaluated a range of techniques that automatically detect tremor segments in accelerometer data, as accelerometer data is more easily obtained in the home environment than electromyography data. Time can be saved if clinicians only have to evaluate the tremor characteristics of segments that have been automatically detected in longer daily activity recordings. We tested four non-parametric methods and five parametric methods on clinical accelerometer data from 14 patients with different tremor disorders. The consensus between two clinicians regarding the presence or absence of tremor on 3943 segments of accelerometer data was employed as reference. The nine methods were tested against this reference to identify their optimal parameters. Non-parametric methods generally performed better than parametric methods on our dataset when optimal parameters were used. However, one parametric method, employing the high frequency content of the tremor bandwidth under consideration

  9. CAR-T Cells: A Systematic Review and Mixed Methods Analysis of the Clinical Trial Landscape.

    Science.gov (United States)

    Pettitt, David; Arshad, Zeeshaan; Smith, James; Stanic, Tijana; Holländer, Georg; Brindley, David

    2018-02-07

    CAR-T cells are a promising new therapy that offer significant advantages compared with conventional immunotherapies. This systematic review and clinical trial landscape identifies and critiques published CAR-T cell clinical trials and examines the critical factors required to enable CAR-T cells to become a standard therapy. A review of the literature was conducted to identify suitable studies from the MEDLINE and Ovid bibliographic databases. The literature and database searches identified 20 studies for inclusion. The average number of participants per clinical trial examined was 11 patients. All studies included in this systematic review investigated CAR-T cells and were prospective, uncontrolled clinical studies. Leukemia is the most common cancer subtype and accounts for 57.4% (n = 120) of disease indications. The majority of studies used an autologous cell source (85%, n = 17) rather than an allogeneic cell source. Translational challenges encompass technical considerations relating to CAR-T cell development, manufacturing practicability, clinical trial approaches, CAR-T cell quality and persistence, and patient management. Copyright © 2017 The American Society of Gene and Cell Therapy. Published by Elsevier Inc. All rights reserved.

  10. A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale

    OpenAIRE

    Mircioiu, Constantin; Atkinson, Jeffrey

    2017-01-01

    A trenchant and passionate dispute over the use of parametric versus non-parametric methods for the analysis of Likert scale ordinal data has raged for the past eight decades. The answer is not a simple “yes” or “no” but is related to hypotheses, objectives, risks, and paradigms. In this paper, we took a pragmatic approach. We applied both types of methods to the analysis of actual Likert data on responses from different professional subgroups of European pharmacists regarding competencies fo...

  11. Impulse response identification with deterministic inputs using non-parametric methods

    International Nuclear Information System (INIS)

    Bhargava, U.K.; Kashyap, R.L.; Goodman, D.M.

    1985-01-01

    This paper addresses the problem of impulse response identification using non-parametric methods. Although the techniques developed herein apply to the truncated, untruncated, and the circulant models, we focus on the truncated model which is useful in certain applications. Two methods of impulse response identification will be presented. The first is based on the minimization of the C/sub L/ Statistic, which is an estimate of the mean-square prediction error; the second is a Bayesian approach. For both of these methods, we consider the effects of using both the identity matrix and the Laplacian matrix as weights on the energy in the impulse response. In addition, we present a method for estimating the effective length of the impulse response. Estimating the length is particularly important in the truncated case. Finally, we develop a method for estimating the noise variance at the output. Often, prior information on the noise variance is not available, and a good estimate is crucial to the success of estimating the impulse response with a nonparametric technique

  12. Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model

    Directory of Open Access Journals (Sweden)

    Isaac Mugume

    2016-01-01

    Full Text Available Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE, the mean absolute error (MAE, mean error (ME, skewness, and the bias easy estimate (BES and nonparametric (the sign test, STM methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.

  13. A web application for evaluating Phase I methods using a non-parametric optimal benchmark.

    Science.gov (United States)

    Wages, Nolan A; Varhegyi, Nikole

    2017-10-01

    In evaluating the performance of Phase I dose-finding designs, simulation studies are typically conducted to assess how often a method correctly selects the true maximum tolerated dose under a set of assumed dose-toxicity curves. A necessary component of the evaluation process is to have some concept for how well a design can possibly perform. The notion of an upper bound on the accuracy of maximum tolerated dose selection is often omitted from the simulation study, and the aim of this work is to provide researchers with accessible software to quickly evaluate the operating characteristics of Phase I methods using a benchmark. The non-parametric optimal benchmark is a useful theoretical tool for simulations that can serve as an upper limit for the accuracy of maximum tolerated dose identification based on a binary toxicity endpoint. It offers researchers a sense of the plausibility of a Phase I method's operating characteristics in simulation. We have developed an R shiny web application for simulating the benchmark. The web application has the ability to quickly provide simulation results for the benchmark and requires no programming knowledge. The application is free to access and use on any device with an Internet browser. The application provides the percentage of correct selection of the maximum tolerated dose and an accuracy index, operating characteristics typically used in evaluating the accuracy of dose-finding designs. We hope this software will facilitate the use of the non-parametric optimal benchmark as an evaluation tool in dose-finding simulation.

  14. Hadron energy reconstruction for the ATLAS calorimetry in the framework of the nonparametrical method

    CERN Document Server

    Akhmadaliev, S Z; Ambrosini, G; Amorim, A; Anderson, K; Andrieux, M L; Aubert, Bernard; Augé, E; Badaud, F; Baisin, L; Barreiro, F; Battistoni, G; Bazan, A; Bazizi, K; Belymam, A; Benchekroun, D; Berglund, S R; Berset, J C; Blanchot, G; Bogush, A A; Bohm, C; Boldea, V; Bonivento, W; Bosman, M; Bouhemaid, N; Breton, D; Brette, P; Bromberg, C; Budagov, Yu A; Burdin, S V; Calôba, L P; Camarena, F; Camin, D V; Canton, B; Caprini, M; Carvalho, J; Casado, M P; Castillo, M V; Cavalli, D; Cavalli-Sforza, M; Cavasinni, V; Chadelas, R; Chalifour, M; Chekhtman, A; Chevalley, J L; Chirikov-Zorin, I E; Chlachidze, G; Citterio, M; Cleland, W E; Clément, C; Cobal, M; Cogswell, F; Colas, Jacques; Collot, J; Cologna, S; Constantinescu, S; Costa, G; Costanzo, D; Crouau, M; Daudon, F; David, J; David, M; Davidek, T; Dawson, J; De, K; de La Taille, C; Del Peso, J; Del Prete, T; de Saintignon, P; Di Girolamo, B; Dinkespiler, B; Dita, S; Dodd, J; Dolejsi, J; Dolezal, Z; Downing, R; Dugne, J J; Dzahini, D; Efthymiopoulos, I; Errede, D; Errede, S; Evans, H; Eynard, G; Fassi, F; Fassnacht, P; Ferrari, A; Ferrer, A; Flaminio, Vincenzo; Fournier, D; Fumagalli, G; Gallas, E; Gaspar, M; Giakoumopoulou, V; Gianotti, F; Gildemeister, O; Giokaris, N; Glagolev, V; Glebov, V Yu; Gomes, A; González, V; González de la Hoz, S; Grabskii, V; Graugès-Pous, E; Grenier, P; Hakopian, H H; Haney, M; Hébrard, C; Henriques, A; Hervás, L; Higón, E; Holmgren, Sven Olof; Hostachy, J Y; Hoummada, A; Huston, J; Imbault, D; Ivanyushenkov, Yu M; Jézéquel, S; Johansson, E K; Jon-And, K; Jones, R; Juste, A; Kakurin, S; Karyukhin, A N; Khokhlov, Yu A; Khubua, J I; Klioukhine, V I; Kolachev, G M; Kopikov, S V; Kostrikov, M E; Kozlov, V; Krivkova, P; Kukhtin, V V; Kulagin, M; Kulchitskii, Yu A; Kuzmin, M V; Labarga, L; Laborie, G; Lacour, D; Laforge, B; Lami, S; Lapin, V; Le Dortz, O; Lefebvre, M; Le Flour, T; Leitner, R; Leltchouk, M; Li, J; Liablin, M V; Linossier, O; Lissauer, D; Lobkowicz, F; Lokajícek, M; Lomakin, Yu F; López-Amengual, J M; Lund-Jensen, B; Maio, A; Makowiecki, D S; Malyukov, S N; Mandelli, L; Mansoulié, B; Mapelli, Livio P; Marin, C P; Marrocchesi, P S; Marroquim, F; Martin, P; Maslennikov, A L; Massol, N; Mataix, L; Mazzanti, M; Mazzoni, E; Merritt, F S; Michel, B; Miller, R; Minashvili, I A; Miralles, L; Mnatzakanian, E A; Monnier, E; Montarou, G; Mornacchi, Giuseppe; Moynot, M; Muanza, G S; Nayman, P; Némécek, S; Nessi, Marzio; Nicoleau, S; Niculescu, M; Noppe, J M; Onofre, A; Pallin, D; Pantea, D; Paoletti, R; Park, I C; Parrour, G; Parsons, J; Pereira, A; Perini, L; Perlas, J A; Perrodo, P; Pilcher, J E; Pinhão, J; Plothow-Besch, Hartmute; Poggioli, Luc; Poirot, S; Price, L; Protopopov, Yu; Proudfoot, J; Puzo, P; Radeka, V; Rahm, David Charles; Reinmuth, G; Renzoni, G; Rescia, S; Resconi, S; Richards, R; Richer, J P; Roda, C; Rodier, S; Roldán, J; Romance, J B; Romanov, V; Romero, P; Rossel, F; Rusakovitch, N A; Sala, P; Sanchis, E; Sanders, H; Santoni, C; Santos, J; Sauvage, D; Sauvage, G; Sawyer, L; Says, L P; Schaffer, A C; Schwemling, P; Schwindling, J; Seguin-Moreau, N; Seidl, W; Seixas, J M; Selldén, B; Seman, M; Semenov, A; Serin, L; Shaldaev, E; Shochet, M J; Sidorov, V; Silva, J; Simaitis, V J; Simion, S; Sissakian, A N; Snopkov, R; Söderqvist, J; Solodkov, A A; Soloviev, A; Soloviev, I V; Sonderegger, P; Soustruznik, K; Spanó, F; Spiwoks, R; Stanek, R; Starchenko, E A; Stavina, P; Stephens, R; Suk, M; Surkov, A; Sykora, I; Takai, H; Tang, F; Tardell, S; Tartarelli, F; Tas, P; Teiger, J; Thaler, J; Thion, J; Tikhonov, Yu A; Tisserant, S; Tokar, S; Topilin, N D; Trka, Z; Turcotte, M; Valkár, S; Varanda, M J; Vartapetian, A H; Vazeille, F; Vichou, I; Vinogradov, V; Vorozhtsov, S B; Vuillemin, V; White, A; Wielers, M; Wingerter-Seez, I; Wolters, H; Yamdagni, N; Yosef, C; Zaitsev, A; Zitoun, R; Zolnierowski, Y

    2002-01-01

    This paper discusses hadron energy reconstruction for the ATLAS barrel prototype combined calorimeter (consisting of a lead-liquid argon electromagnetic part and an iron-scintillator hadronic part) in the framework of the nonparametrical method. The nonparametrical method utilizes only the known e/h ratios and the electron calibration constants and does not require the determination of any parameters by a minimization technique. Thus, this technique lends itself to an easy use in a first level trigger. The reconstructed mean values of the hadron energies are within +or-1% of the true values and the fractional energy resolution is [(58+or-3)%/ square root E+(2.5+or-0.3)%](+)(1.7+or-0.2)/E. The value of the e/h ratio obtained for the electromagnetic compartment of the combined calorimeter is 1.74+or-0.04 and agrees with the prediction that e/h >1.66 for this electromagnetic calorimeter. Results of a study of the longitudinal hadronic shower development are also presented. The data have been taken in the H8 beam...

  15. The application of non-parametric statistical method for an ALARA implementation

    International Nuclear Information System (INIS)

    Cho, Young Ho; Herr, Young Hoi

    2003-01-01

    The cost-effective reduction of Occupational Radiation Dose (ORD) at a nuclear power plant could not be achieved without going through an extensive analysis of accumulated ORD data of existing plants. Through the data analysis, it is required to identify what are the jobs of repetitive high ORD at the nuclear power plant. In this study, Percentile Rank Sum Method (PRSM) is proposed to identify repetitive high ORD jobs, which is based on non-parametric statistical theory. As a case study, the method is applied to ORD data of maintenance and repair jobs at Kori units 3 and 4 that are pressurized water reactors with 950 MWe capacity and have been operated since 1986 and 1987, respectively in Korea. The results was verified and validated, and PRSM has been demonstrated to be an efficient method of analyzing the data

  16. Assessing non-inferiority with time-to-event data via the method of non-parametric covariance.

    Science.gov (United States)

    Zhang, Xinji; Xu, Jinfang; He, Jia

    2013-06-01

    Non-parametric methods have been well recognised as useful tools for time-to-event (survival) data analysis because they provide valid statistical inference with few assumptions. Tangen and Koch have proposed the use of the method of non-parametric covariance for time-to-event data in a traditional superiority setting. In this article, we extended their method to assess non-inferiority of two treatments. To evaluate this non-parametric method versus the classical semi-parametric Cox proportional hazards regression model, simulations in terms of the Type 1 error rate and power were performed and compared. The results showed that the two methods were generally comparable regarding the Type 1 error rate when adjustment for the covariates correlated with the survival time was made. In the non-inferiority setting, the covariate-adjusted non-parametric analysis was shown to always increase power. However, this was not necessarily the case for the adjusted Cox model where results were inconsistent to those seen in the superiority setting. For illustration, an application of the proposed non-parametric method to a trial involving pemetrexed, a recently approved drug for first-line treatment of non-small cell lung cancer, is included.

  17. Nonparametric Methods in Astronomy: Think, Regress, Observe—Pick Any Three

    Science.gov (United States)

    Steinhardt, Charles L.; Jermyn, Adam S.

    2018-02-01

    Telescopes are much more expensive than astronomers, so it is essential to minimize required sample sizes by using the most data-efficient statistical methods possible. However, the most commonly used model-independent techniques for finding the relationship between two variables in astronomy are flawed. In the worst case they can lead without warning to subtly yet catastrophically wrong results, and even in the best case they require more data than necessary. Unfortunately, there is no single best technique for nonparametric regression. Instead, we provide a guide for how astronomers can choose the best method for their specific problem and provide a python library with both wrappers for the most useful existing algorithms and implementations of two new algorithms developed here.

  18. Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering

    KAUST Repository

    Xu, Zhiqiang

    2017-02-16

    Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.

  19. A Nonparametric, Multiple Imputation-Based Method for the Retrospective Integration of Data Sets

    Science.gov (United States)

    Carrig, Madeline M.; Manrique-Vallier, Daniel; Ranby, Krista W.; Reiter, Jerome P.; Hoyle, Rick H.

    2015-01-01

    Complex research questions often cannot be addressed adequately with a single data set. One sensible alternative to the high cost and effort associated with the creation of large new data sets is to combine existing data sets containing variables related to the constructs of interest. The goal of the present research was to develop a flexible, broadly applicable approach to the integration of disparate data sets that is based on nonparametric multiple imputation and the collection of data from a convenient, de novo calibration sample. We demonstrate proof of concept for the approach by integrating three existing data sets containing items related to the extent of problematic alcohol use and associations with deviant peers. We discuss both necessary conditions for the approach to work well and potential strengths and weaknesses of the method compared to other data set integration approaches. PMID:26257437

  20. Nonparametric identification of nonlinear dynamic systems using a synchronisation-based method

    Science.gov (United States)

    Kenderi, Gábor; Fidlin, Alexander

    2014-12-01

    The present study proposes an identification method for highly nonlinear mechanical systems that does not require a priori knowledge of the underlying nonlinearities to reconstruct arbitrary restoring force surfaces between degrees of freedom. This approach is based on the master-slave synchronisation between a dynamic model of the system as the slave and the real system as the master using measurements of the latter. As the model synchronises to the measurements, it becomes an observer of the real system. The optimal observer algorithm in a least-squares sense is given by the Kalman filter. Using the well-known state augmentation technique, the Kalman filter can be turned into a dual state and parameter estimator to identify parameters of a priori characterised nonlinearities. The paper proposes an extension of this technique towards nonparametric identification. A general system model is introduced by describing the restoring forces as bilateral spring-dampers with time-variant coefficients, which are estimated as augmented states. The estimation procedure is followed by an a posteriori statistical analysis to reconstruct noise-free restoring force characteristics using the estimated states and their estimated variances. Observability is provided using only one measured mechanical quantity per degree of freedom, which makes this approach less demanding in the number of necessary measurement signals compared with truly nonparametric solutions, which typically require displacement, velocity and acceleration signals. Additionally, due to the statistical rigour of the procedure, it successfully addresses signals corrupted by significant measurement noise. In the present paper, the method is described in detail, which is followed by numerical examples of one degree of freedom (1DoF) and 2DoF mechanical systems with strong nonlinearities of vibro-impact type to demonstrate the effectiveness of the proposed technique.

  1. Two non-parametric methods for derivation of constraints from radiotherapy dose–histogram data

    International Nuclear Information System (INIS)

    Ebert, M A; Kennedy, A; Joseph, D J; Gulliford, S L; Buettner, F; Foo, K; Haworth, A; Denham, J W

    2014-01-01

    Dose constraints based on histograms provide a convenient and widely-used method for informing and guiding radiotherapy treatment planning. Methods of derivation of such constraints are often poorly described. Two non-parametric methods for derivation of constraints are described and investigated in the context of determination of dose-specific cut-points—values of the free parameter (e.g., percentage volume of the irradiated organ) which best reflect resulting changes in complication incidence. A method based on receiver operating characteristic (ROC) analysis and one based on a maximally-selected standardized rank sum are described and compared using rectal toxicity data from a prostate radiotherapy trial. Multiple test corrections are applied using a free step-down resampling algorithm, which accounts for the large number of tests undertaken to search for optimal cut-points and the inherent correlation between dose–histogram points. Both methods provide consistent significant cut-point values, with the rank sum method displaying some sensitivity to the underlying data. The ROC method is simple to implement and can utilize a complication atlas, though an advantage of the rank sum method is the ability to incorporate all complication grades without the need for grade dichotomization. (note)

  2. Two non-parametric methods for derivation of constraints from radiotherapy dose-histogram data

    Science.gov (United States)

    Ebert, M. A.; Gulliford, S. L.; Buettner, F.; Foo, K.; Haworth, A.; Kennedy, A.; Joseph, D. J.; Denham, J. W.

    2014-07-01

    Dose constraints based on histograms provide a convenient and widely-used method for informing and guiding radiotherapy treatment planning. Methods of derivation of such constraints are often poorly described. Two non-parametric methods for derivation of constraints are described and investigated in the context of determination of dose-specific cut-points—values of the free parameter (e.g., percentage volume of the irradiated organ) which best reflect resulting changes in complication incidence. A method based on receiver operating characteristic (ROC) analysis and one based on a maximally-selected standardized rank sum are described and compared using rectal toxicity data from a prostate radiotherapy trial. Multiple test corrections are applied using a free step-down resampling algorithm, which accounts for the large number of tests undertaken to search for optimal cut-points and the inherent correlation between dose-histogram points. Both methods provide consistent significant cut-point values, with the rank sum method displaying some sensitivity to the underlying data. The ROC method is simple to implement and can utilize a complication atlas, though an advantage of the rank sum method is the ability to incorporate all complication grades without the need for grade dichotomization.

  3. Controlling errors in unidosis carts

    Directory of Open Access Journals (Sweden)

    Inmaculada Díaz Fernández

    2010-01-01

    Full Text Available Objective: To identify errors in the unidosis system carts. Method: For two months, the Pharmacy Service controlled medication either returned or missing from the unidosis carts both in the pharmacy and in the wards. Results: Uncorrected unidosis carts show a 0.9% of medication errors (264 versus 0.6% (154 which appeared in unidosis carts previously revised. In carts not revised, the error is 70.83% and mainly caused when setting up unidosis carts. The rest are due to a lack of stock or unavailability (21.6%, errors in the transcription of medical orders (6.81% or that the boxes had not been emptied previously (0.76%. The errors found in the units correspond to errors in the transcription of the treatment (3.46%, non-receipt of the unidosis copy (23.14%, the patient did not take the medication (14.36%or was discharged without medication (12.77%, was not provided by nurses (14.09%, was withdrawn from the stocks of the unit (14.62%, and errors of the pharmacy service (17.56% . Conclusions: It is concluded the need to redress unidosis carts and a computerized prescription system to avoid errors in transcription.Discussion: A high percentage of medication errors is caused by human error. If unidosis carts are overlooked before sent to hospitalization units, the error diminishes to 0.3%.

  4. Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison

    Science.gov (United States)

    Verrelst, Jochem; Rivera, Juan Pablo; Veroustraete, Frank; Muñoz-Marí, Jordi; Clevers, Jan G. P. W.; Camps-Valls, Gustau; Moreno, José

    2015-10-01

    Given the forthcoming availability of Sentinel-2 (S2) images, this paper provides a systematic comparison of retrieval accuracy and processing speed of a multitude of parametric, non-parametric and physically-based retrieval methods using simulated S2 data. An experimental field dataset (SPARC), collected at the agricultural site of Barrax (Spain), was used to evaluate different retrieval methods on their ability to estimate leaf area index (LAI). With regard to parametric methods, all possible band combinations for several two-band and three-band index formulations and a linear regression fitting function have been evaluated. From a set of over ten thousand indices evaluated, the best performing one was an optimized three-band combination according to (ρ560 -ρ1610 -ρ2190) / (ρ560 +ρ1610 +ρ2190) with a 10-fold cross-validation RCV2 of 0.82 (RMSECV : 0.62). This family of methods excel for their fast processing speed, e.g., 0.05 s to calibrate and validate the regression function, and 3.8 s to map a simulated S2 image. With regard to non-parametric methods, 11 machine learning regression algorithms (MLRAs) have been evaluated. This methodological family has the advantage of making use of the full optical spectrum as well as flexible, nonlinear fitting. Particularly kernel-based MLRAs lead to excellent results, with variational heteroscedastic (VH) Gaussian Processes regression (GPR) as the best performing method, with a RCV2 of 0.90 (RMSECV : 0.44). Additionally, the model is trained and validated relatively fast (1.70 s) and the processed image (taking 73.88 s) includes associated uncertainty estimates. More challenging is the inversion of a PROSAIL based radiative transfer model (RTM). After the generation of a look-up table (LUT), a multitude of cost functions and regularization options were evaluated. The best performing cost function is Pearson's χ -square. It led to a R2 of 0.74 (RMSE: 0.80) against the validation dataset. While its validation went fast

  5. A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support.

    Science.gov (United States)

    Connolly, Brian; Cohen, K Bretonnel; Santel, Daniel; Bayram, Ulya; Pestian, John

    2017-08-07

    Probabilistic assessments of clinical care are essential for quality care. Yet, machine learning, which supports this care process has been limited to categorical results. To maximize its usefulness, it is important to find novel approaches that calibrate the ML output with a likelihood scale. Current state-of-the-art calibration methods are generally accurate and applicable to many ML models, but improved granularity and accuracy of such methods would increase the information available for clinical decision making. This novel non-parametric Bayesian approach is demonstrated on a variety of data sets, including simulated classifier outputs, biomedical data sets from the University of California, Irvine (UCI) Machine Learning Repository, and a clinical data set built to determine suicide risk from the language of emergency department patients. The method is first demonstrated on support-vector machine (SVM) models, which generally produce well-behaved, well understood scores. The method produces calibrations that are comparable to the state-of-the-art Bayesian Binning in Quantiles (BBQ) method when the SVM models are able to effectively separate cases and controls. However, as the SVM models' ability to discriminate classes decreases, our approach yields more granular and dynamic calibrated probabilities comparing to the BBQ method. Improvements in granularity and range are even more dramatic when the discrimination between the classes is artificially degraded by replacing the SVM model with an ad hoc k-means classifier. The method allows both clinicians and patients to have a more nuanced view of the output of an ML model, allowing better decision making. The method is demonstrated on simulated data, various biomedical data sets and a clinical data set, to which diverse ML methods are applied. Trivially extending the method to (non-ML) clinical scores is also discussed.

  6. Nonparametric Comparison of Two Dynamic Parameter Setting Methods in a Meta-Heuristic Approach

    Directory of Open Access Journals (Sweden)

    Seyhun HEPDOGAN

    2007-10-01

    Full Text Available Meta-heuristics are commonly used to solve combinatorial problems in practice. Many approaches provide very good quality solutions in a short amount of computational time; however most meta-heuristics use parameters to tune the performance of the meta-heuristic for particular problems and the selection of these parameters before solving the problem can require much time. This paper investigates the problem of setting parameters using a typical meta-heuristic called Meta-RaPS (Metaheuristic for Randomized Priority Search.. Meta-RaPS is a promising meta-heuristic optimization method that has been applied to different types of combinatorial optimization problems and achieved very good performance compared to other meta-heuristic techniques. To solve a combinatorial problem, Meta-RaPS uses two well-defined stages at each iteration: construction and local search. After a number of iterations, the best solution is reported. Meta-RaPS performance depends on the fine tuning of two main parameters, priority percentage and restriction percentage, which are used during the construction stage. This paper presents two different dynamic parameter setting methods for Meta-RaPS. These dynamic parameter setting approaches tune the parameters while a solution is being found. To compare these two approaches, nonparametric statistic approaches are utilized since the solutions are not normally distributed. Results from both these dynamic parameter setting methods are reported.

  7. Multi Criteria Credit Rating Model for Small Enterprise Using a Nonparametric Method

    Directory of Open Access Journals (Sweden)

    Guotai Chi

    2017-10-01

    Full Text Available A small enterprise’s credit rating is employed to measure its probability of defaulting on a debt, but, for small enterprises, financial data are insufficient or even unreliable. Thus, building a multi criteria credit rating model based on the qualitative and quantitative criteria is of importance to finance small enterprises’ activities. Till now, there has not been a multicriteria credit risk model based on the rank sum test and entropy weighting method. In this paper, we try to fill this gap by offering three innovative contributions. First, the rank sum test shows significant differences in the average ranks associated with index data for the default and entire sample, ensuring that an index makes an effective differentiation between the default and non-default sample. Second, the rating equation’s capacity is tested to identify the potential defaults by verifying a clear difference between the average ranks of samples with default ratings (i.e., not index values and the entire sample. Third, in our nonparametric test, the rank sum test is used with rank correlation analysis made to screen for indices, thereby avoiding the assumption of normality associated with more common credit rating methods.

  8. The impact of type of manual medication cart filling method on the frequency of medication administration errors : A prospective before and after study

    NARCIS (Netherlands)

    Schimmel, Anneliene M.; Becker, Matthijs L.; van den Bout, Tilly; Taxis, Katja; van den Bemt, Patricia M. L. A.

    Background: The medication cart can be filled using an automated system or a manual method and when using a manual method the medication can be arranged either by round time or by medication name. For the manual methods, it is hypothesized that the latter method would result in a lower frequency of

  9. On the use of permutation in and the performance of a class of nonparametric methods to detect differential gene expression.

    Science.gov (United States)

    Pan, Wei

    2003-07-22

    Recently a class of nonparametric statistical methods, including the empirical Bayes (EB) method, the significance analysis of microarray (SAM) method and the mixture model method (MMM), have been proposed to detect differential gene expression for replicated microarray experiments conducted under two conditions. All the methods depend on constructing a test statistic Z and a so-called null statistic z. The null statistic z is used to provide some reference distribution for Z such that statistical inference can be accomplished. A common way of constructing z is to apply Z to randomly permuted data. Here we point our that the distribution of z may not approximate the null distribution of Z well, leading to possibly too conservative inference. This observation may apply to other permutation-based nonparametric methods. We propose a new method of constructing a null statistic that aims to estimate the null distribution of a test statistic directly. Using simulated data and real data, we assess and compare the performance of the existing method and our new method when applied in EB, SAM and MMM. Some interesting findings on operating characteristics of EB, SAM and MMM are also reported. Finally, by combining the idea of SAM and MMM, we outline a simple nonparametric method based on the direct use of a test statistic and a null statistic.

  10. Assessment of water quality trends in the Minnesota River using non-parametric and parametric methods

    Science.gov (United States)

    Johnson, H.O.; Gupta, S.C.; Vecchia, A.V.; Zvomuya, F.

    2009-01-01

    Excessive loading of sediment and nutrients to rivers is a major problem in many parts of the United States. In this study, we tested the non-parametric Seasonal Kendall (SEAKEN) trend model and the parametric USGS Quality of Water trend program (QWTREND) to quantify trends in water quality of the Minnesota River at Fort Snelling from 1976 to 2003. Both methods indicated decreasing trends in flow-adjusted concentrations of total suspended solids (TSS), total phosphorus (TP), and orthophosphorus (OP) and a generally increasing trend in flow-adjusted nitrate plus nitrite-nitrogen (NO3-N) concentration. The SEAKEN results were strongly influenced by the length of the record as well as extreme years (dry or wet) earlier in the record. The QWTREND results, though influenced somewhat by the same factors, were more stable. The magnitudes of trends between the two methods were somewhat different and appeared to be associated with conceptual differences between the flow-adjustment processes used and with data processing methods. The decreasing trends in TSS, TP, and OP concentrations are likely related to conservation measures implemented in the basin. However, dilution effects from wet climate or additional tile drainage cannot be ruled out. The increasing trend in NO3-N concentrations was likely due to increased drainage in the basin. Since the Minnesota River is the main source of sediments to the Mississippi River, this study also addressed the rapid filling of Lake Pepin on the Mississippi River and found the likely cause to be increased flow due to recent wet climate in the region. Copyright ?? 2009 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.

  11. The impact of type of manual medication cart filling method on the frequency of medication administration errors: a prospective before and after study.

    Science.gov (United States)

    Schimmel, Anneliene M; Becker, Matthijs L; van den Bout, Tilly; Taxis, Katja; van den Bemt, Patricia M L A

    2011-07-01

    The medication cart can be filled using an automated system or a manual method and when using a manual method the medication can be arranged either by round time or by medication name. For the manual methods, it is hypothesized that the latter method would result in a lower frequency of medication administration errors because nurses are forced to read the medication labels, but evidence for this hypothesis is lacking. The aim of this study was to compare the frequency of medication administration errors of two different manual medication cart filling methods, namely arranging medication by round time or by medication name. A prospective, observational study with a before-after design. Eighty-six patients who stayed on an orthopaedic ward in one university medical centre in the Netherlands were included. Disguised observation was used to detect medication administration errors. The medication cart filling method in usual care was to fill the cart with medication arranged by round time. The intervention was the implementation of the second medication cart filling method, where the medication cart was filled by arranging medicines by their names. The primary outcome was the frequency of medication administrations with one or more error(s) after the intervention compared with before the intervention. The secondary outcome was the frequency of subtypes of medication administration errors. After the intervention 170 of 740 (23.0%) medication administrations with one or more medication administration error(s) were observed compared to 114 of 589 (19.4%) before the intervention (odds ratio 1.24 [95% confidence interval 0.95-1.62]). The distribution of subtypes of medication administration errors before and after the intervention was statistically significantly different (p<0.001). Analysis of subtypes revealed more omissions and wrong time errors after the intervention than before the intervention. Unauthorized medication errors were detected more frequently before the

  12. Non-parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods

    DEFF Research Database (Denmark)

    Høg, Esben

    In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean-reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...

  13. Non-Parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods

    DEFF Research Database (Denmark)

    Høg, Esben

    2003-01-01

    In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean--reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...

  14. Trends in three decades of HIV/AIDS epidemic in Thailand by nonparametric backcalculation method.

    Science.gov (United States)

    Punyacharoensin, Narat; Viwatwongkasem, Chukiat

    2009-06-01

    To reconstruct the past HIV incidence and prevalence in Thailand from 1980 to 2008 and predict the country's AIDS incidence from 2009 to 2011. Nonparametric backcalculation was adopted utilizing 100 quarterly observed new AIDS counts excluding pediatric cases. The accuracy of data was enhanced through a series of data adjustments using the weight method to account for several surveillance reporting issues. The mixture of time-dependent distributions allowed the effects of age at seroconversion and antiretroviral therapy to be incorporated simultaneously. Sensitivity analyses were conducted to assess model variations that were subject to major uncertainties. Future AIDS incidence was projected for various predetermined HIV incidence patterns. HIV incidence in Thailand reached its peak in 1992 with approximately 115,000 cases. A steep decline thereafter discontinued in 1997 and was followed by another strike of 42,000 cases in 1999. The second surge, which happened concurrently with the major economic crisis, brought on 60,000 new infections. As of December 2008, more than 1 million individuals had been infected and around 430,000 adults were living with HIV corresponding to a prevalence rate of 1.2%. The incidence rate had become less than 0.1% since 2002. The backcalculated estimates were dominated by postulated median AIDS progression time and adjustments to surveillance data. Our analysis indicated that, thus far, the 1990s was the most severe era of HIV/AIDS epidemic in Thailand with two HIV incidence peaks. A drop in new infections led to a decrease in recent AIDS incidence, and this tendency is likely to remain unchanged until 2011, if not further.

  15. Statistic Non-Parametric Methods of Measurement and Interpretation of Existing Statistic Connections within Seaside Hydro Tourism

    OpenAIRE

    Mirela Secară

    2008-01-01

    Tourism represents an important field of economic and social life in our country, and the main sector of the economy of Constanta County is the balneary touristic capitalization of Romanian seaside. In order to statistically analyze hydro tourism on Romanian seaside, we have applied non-parametric methods of measuring and interpretation of existing statistic connections within seaside hydro tourism. Major objective of this research is represented by hydro tourism re-establishment on Romanian ...

  16. Parametric and non-parametric approach for sensory RATA (Rate-All-That-Apply) method of ledre profile attributes

    Science.gov (United States)

    Hastuti, S.; Harijono; Murtini, E. S.; Fibrianto, K.

    2018-03-01

    This current study is aimed to investigate the use of parametric and non-parametric approach for sensory RATA (Rate-All-That-Apply) method. Ledre as Bojonegoro unique local food product was used as point of interest, in which 319 panelists were involved in the study. The result showed that ledre is characterized as easy-crushed texture, sticky in mouth, stingy sensation and easy to swallow. It has also strong banana flavour with brown in colour. Compared to eggroll and semprong, ledre has more variances in terms of taste as well the roll length. As RATA questionnaire is designed to collect categorical data, non-parametric approach is the common statistical procedure. However, similar results were also obtained as parametric approach, regardless the fact of non-normal distributed data. Thus, it suggests that parametric approach can be applicable for consumer study with large number of respondents, even though it may not satisfy the assumption of ANOVA (Analysis of Variances).

  17. Parametric and non-parametric species delimitation methods result in the recognition of two new Neotropical woody bamboo species.

    Science.gov (United States)

    Ruiz-Sanchez, Eduardo

    2015-12-01

    The Neotropical woody bamboo genus Otatea is one of five genera in the subtribe Guaduinae. Of the eight described Otatea species, seven are endemic to Mexico and one is also distributed in Central and South America. Otatea acuminata has the widest geographical distribution of the eight species, and two of its recently collected populations do not match the known species morphologically. Parametric and non-parametric methods were used to delimit the species in Otatea using five chloroplast markers, one nuclear marker, and morphological characters. The parametric coalescent method and the non-parametric analysis supported the recognition of two distinct evolutionary lineages. Molecular clock estimates were used to estimate divergence times in Otatea. The results for divergence time in Otatea estimated the origin of the speciation events from the Late Miocene to Late Pleistocene. The species delimitation analyses (parametric and non-parametric) identified that the two populations of O. acuminata from Chiapas and Hidalgo are from two separate evolutionary lineages and these new species have morphological characters that separate them from O. acuminata s.s. The geological activity of the Trans-Mexican Volcanic Belt and the Isthmus of Tehuantepec may have isolated populations and limited the gene flow between Otatea species, driving speciation. Based on the results found here, I describe Otatea rzedowskiorum and Otatea victoriae as two new species, morphologically different from O. acuminata. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Bayesian nonparametric data analysis

    CERN Document Server

    Müller, Peter; Jara, Alejandro; Hanson, Tim

    2015-01-01

    This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.

  19. Non-parametric order statistics method applied to uncertainty propagation in fuel rod calculations

    International Nuclear Information System (INIS)

    Arimescu, V.E.; Heins, L.

    2001-01-01

    Advances in modeling fuel rod behavior and accumulations of adequate experimental data have made possible the introduction of quantitative methods to estimate the uncertainty of predictions made with best-estimate fuel rod codes. The uncertainty range of the input variables is characterized by a truncated distribution which is typically a normal, lognormal, or uniform distribution. While the distribution for fabrication parameters is defined to cover the design or fabrication tolerances, the distribution of modeling parameters is inferred from the experimental database consisting of separate effects tests and global tests. The final step of the methodology uses a Monte Carlo type of random sampling of all relevant input variables and performs best-estimate code calculations to propagate these uncertainties in order to evaluate the uncertainty range of outputs of interest for design analysis, such as internal rod pressure and fuel centerline temperature. The statistical method underlying this Monte Carlo sampling is non-parametric order statistics, which is perfectly suited to evaluate quantiles of populations with unknown distribution. The application of this method is straightforward in the case of one single fuel rod, when a 95/95 statement is applicable: 'with a probability of 95% and confidence level of 95% the values of output of interest are below a certain value'. Therefore, the 0.95-quantile is estimated for the distribution of all possible values of one fuel rod with a statistical confidence of 95%. On the other hand, a more elaborate procedure is required if all the fuel rods in the core are being analyzed. In this case, the aim is to evaluate the following global statement: with 95% confidence level, the expected number of fuel rods which are not exceeding a certain value is all the fuel rods in the core except only a few fuel rods. In both cases, the thresholds determined by the analysis should be below the safety acceptable design limit. An indirect

  20. PROFILE CONTROL CHARTS BASED ON NONPARAMETRIC L-1 REGRESSION METHODS1

    Science.gov (United States)

    Wei, Ying; Zhao, Zhibiao; Lin, Dennis K. J.

    2012-01-01

    Classical statistical process control often relies on univariate characteristics. In many contemporary applications, however, the quality of products must be characterized by some functional relation between a response variable and its explanatory variables. Monitoring such functional profiles has been a rapidly growing field due to increasing demands. This paper develops a novel nonparametric L-1 location-scale model to screen the shapes of profiles. The model is built on three basic elements: location shifts, local shape distortions, and overall shape deviations, which are quantified by three individual metrics. The proposed approach is applied to the previously analyzed vertical density profile data, leading to some interesting insights. PMID:23539524

  1. A nonparametric mean-variance smoothing method to assess Arabidopsis cold stress transcriptional regulator CBF2 overexpression microarray data.

    Science.gov (United States)

    Hu, Pingsha; Maiti, Tapabrata

    2011-01-01

    Microarray is a powerful tool for genome-wide gene expression analysis. In microarray expression data, often mean and variance have certain relationships. We present a non-parametric mean-variance smoothing method (NPMVS) to analyze differentially expressed genes. In this method, a nonlinear smoothing curve is fitted to estimate the relationship between mean and variance. Inference is then made upon shrinkage estimation of posterior means assuming variances are known. Different methods have been applied to simulated datasets, in which a variety of mean and variance relationships were imposed. The simulation study showed that NPMVS outperformed the other two popular shrinkage estimation methods in some mean-variance relationships; and NPMVS was competitive with the two methods in other relationships. A real biological dataset, in which a cold stress transcription factor gene, CBF2, was overexpressed, has also been analyzed with the three methods. Gene ontology and cis-element analysis showed that NPMVS identified more cold and stress responsive genes than the other two methods did. The good performance of NPMVS is mainly due to its shrinkage estimation for both means and variances. In addition, NPMVS exploits a non-parametric regression between mean and variance, instead of assuming a specific parametric relationship between mean and variance. The source code written in R is available from the authors on request.

  2. Nonparametric method for genomics-based prediction of performance of quantitative traits involving epistasis in plant breeding.

    Directory of Open Access Journals (Sweden)

    Xiaochun Sun

    Full Text Available Genomic selection (GS procedures have proven useful in estimating breeding value and predicting phenotype with genome-wide molecular marker information. However, issues of high dimensionality, multicollinearity, and the inability to deal effectively with epistasis can jeopardize accuracy and predictive ability. We, therefore, propose a new nonparametric method, pRKHS, which combines the features of supervised principal component analysis (SPCA and reproducing kernel Hilbert spaces (RKHS regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Instead of assigning a specific relationship to represent the underlying epistasis, the method maps genotype to phenotype in a nonparametric way, thus requiring fewer genetic assumptions. SPCA decreases the number of markers needed for prediction by filtering out low-signal markers with the optimal marker set determined by cross-validation. Principal components are computed from reduced marker matrix (called supervised principal components, SPC and included in the smoothing spline ANOVA model as independent variables to fit the data. The new method was evaluated in comparison with current popular methods for practicing GS, specifically RR-BLUP, BayesA, BayesB, as well as a newer method by Crossa et al., RKHS-M, using both simulated and real data. Results demonstrate that pRKHS generally delivers greater predictive ability, particularly when epistasis impacts trait expression. Beyond prediction, the new method also facilitates inferences about the extent to which epistasis influences trait expression.

  3. Nonparametric method for genomics-based prediction of performance of quantitative traits involving epistasis in plant breeding.

    Science.gov (United States)

    Sun, Xiaochun; Ma, Ping; Mumm, Rita H

    2012-01-01

    Genomic selection (GS) procedures have proven useful in estimating breeding value and predicting phenotype with genome-wide molecular marker information. However, issues of high dimensionality, multicollinearity, and the inability to deal effectively with epistasis can jeopardize accuracy and predictive ability. We, therefore, propose a new nonparametric method, pRKHS, which combines the features of supervised principal component analysis (SPCA) and reproducing kernel Hilbert spaces (RKHS) regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Instead of assigning a specific relationship to represent the underlying epistasis, the method maps genotype to phenotype in a nonparametric way, thus requiring fewer genetic assumptions. SPCA decreases the number of markers needed for prediction by filtering out low-signal markers with the optimal marker set determined by cross-validation. Principal components are computed from reduced marker matrix (called supervised principal components, SPC) and included in the smoothing spline ANOVA model as independent variables to fit the data. The new method was evaluated in comparison with current popular methods for practicing GS, specifically RR-BLUP, BayesA, BayesB, as well as a newer method by Crossa et al., RKHS-M, using both simulated and real data. Results demonstrate that pRKHS generally delivers greater predictive ability, particularly when epistasis impacts trait expression. Beyond prediction, the new method also facilitates inferences about the extent to which epistasis influences trait expression.

  4. Theory of nonparametric tests

    CERN Document Server

    Dickhaus, Thorsten

    2018-01-01

    This textbook provides a self-contained presentation of the main concepts and methods of nonparametric statistical testing, with a particular focus on the theoretical foundations of goodness-of-fit tests, rank tests, resampling tests, and projection tests. The substitution principle is employed as a unified approach to the nonparametric test problems discussed. In addition to mathematical theory, it also includes numerous examples and computer implementations. The book is intended for advanced undergraduate, graduate, and postdoc students as well as young researchers. Readers should be familiar with the basic concepts of mathematical statistics typically covered in introductory statistics courses.

  5. A nonparametric efficiency analysis of bus subsystem in Belgrade city, using DEA methods

    Directory of Open Access Journals (Sweden)

    Ćiraković Lazar S.

    2014-01-01

    Full Text Available One of the most important principles in the company is the principle of efficiency which consists in achieving the highest possible economic effects with minimum economic investment. In this paper, we present a nonparametric efficiency analysis, bus subsystems of public transport passengers Belgrade City, a sample of five small, three medium and two large companies. It was shown that the overall bus transport system has a low level of overall efficiency, only 0.66%. It was found that the economies of scale has no significant impact on the level of efficiency when it comes to classification that was generated for this study. Small and medium companies are in a worse situation than large companies when it comes to economies of scale. These results should indicate the necessity of the transport system and bus companies organizational and technological redesign.

  6. A CART extention using Quadratic Decision Borders

    DEFF Research Database (Denmark)

    Hartelius, Karsten

    1999-01-01

    In this article we put forward an extention to the hierarchical CART classification method which uses quadratic decision borders. The original CART applies univariate splits on individual variables as well as splits on combinations of variables to recursively partition the feature-space into subs......In this article we put forward an extention to the hierarchical CART classification method which uses quadratic decision borders. The original CART applies univariate splits on individual variables as well as splits on combinations of variables to recursively partition the feature......-space into subsets which are successively more class-homogeneous. Guided by the fact that class-distributions in feature-space are very often hyper-elliptical shaped, we give an extension to the original CART which also uses quadratic shaped decision borders which can be modelled by a mean-vector and a dispersion...

  7. A comparison of parametric and nonparametric methods for normalising cDNA microarray data.

    Science.gov (United States)

    Khondoker, Mizanur R; Glasbey, Chris A; Worton, Bruce J

    2007-12-01

    Normalisation is an essential first step in the analysis of most cDNA microarray data, to correct for effects arising from imperfections in the technology. Loess smoothing is commonly used to correct for trends in log-ratio data. However, parametric models, such as the additive plus multiplicative variance model, have been preferred for scale normalisation, though the variance structure of microarray data may be of a more complex nature than can be accommodated by a parametric model. We propose a new nonparametric approach that incorporates location and scale normalisation simultaneously using a Generalised Additive Model for Location, Scale and Shape (GAMLSS, Rigby and Stasinopoulos, 2005, Applied Statistics, 54, 507-554). We compare its performance in inferring differential expression with Huber et al.'s (2002, Bioinformatics, 18, 96-104) arsinh variance stabilising transformation (AVST) using real and simulated data. We show GAMLSS to be as powerful as AVST when the parametric model is correct, and more powerful when the model is wrong. (c) 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

  8. Evaluating Left-Censored Data Through Substitution, Parametric, Semi-parametric, and Nonparametric Methods: A Simulation Study.

    Science.gov (United States)

    Tekindal, Mustafa Agah; Erdoğan, Beyza Doğanay; Yavuz, Yasemin

    2017-06-01

    In this study, an attempt was made to determine the degrees of bias in particular sampling sizes and methods. The aim of the study was to determine deviations from the median, the mean, and the standard deviation (SD) in different sample sizes and at different censoring rates for log-normal, exponential, and Weibull distributions in the case of full and censored data sampling. Thus, the concept of "censoring" and censoring types was handled in the first place. Then substitution, parametric (MLE), nonparametric (KM), and semi-parametric (ROS) methods were introduced for the evaluation of left-censored observations. Within the scope of the present study, the data were produced uncensored based on the different parameters of each distribution. Then the datasets were left-censored at the ratios of 5, 25, 45, and 65 %. The censored data were estimated through substitution (LOD and LOD/[Formula: see text]), parametric (MLE), semi-parametric (ROS), and nonparametric (KM) methods. In addition, evaluation was made by increasing the sample size from 20 to 300 by tens. Performance comparison was made between the uncensored dataset and the censored dataset on the basis of deviations from the median, the mean, and the SD. The results of simulation studies show that LOD/[Formula: see text] and ROS methods give better results than other methods in deviation from the mean in different sample sizes and at different censoring rates, while ROS gives better results than other methods in deviation from the median in almost all sample sizes and at almost all censoring rates.

  9. Nonparametric modal regression

    OpenAIRE

    Chen, Yen-Chi; Genovese, Christopher R.; Tibshirani, Ryan J.; Wasserman, Larry

    2016-01-01

    Modal regression estimates the local modes of the distribution of $Y$ given $X=x$, instead of the mean, as in the usual regression sense, and can hence reveal important structure missed by usual regression methods. We study a simple nonparametric method for modal regression, based on a kernel density estimate (KDE) of the joint distribution of $Y$ and $X$. We derive asymptotic error bounds for this method, and propose techniques for constructing confidence sets and prediction sets. The latter...

  10. Spectral analyses of the KTB sonic and density logs using robust nonparametric methods

    Science.gov (United States)

    Jones, Alan G.; Holliger, Klaus

    1997-08-01

    We use robust techniques to estimate power spectra, coherences and transfer functions of the German Continental Deep Drilling Program (KTB) sonic and density logs and lithologically defined subsets thereof. Our results confirm the overall 1/wavelength-decay of the power spectra inferred by parametric analyses, but provide superior resolution and nonparametric estimates of errors and statistical significance. We demonstrate the absence of any statistically meaningful coherence between the velocity logs from the main and pilot holes, suggesting a spatially quasi-isotropic upper crustal velocity structure. Also, there is little coherence between the physical and caliper logs, indicating that disturbances introduced by breakouts and uneven relief of the borehole wall mostly contribute to the uncorrelated portions of the velocity logs. Coherence between the gamma and physical logs is weak to absent, indicating that the observed velocity and density fluctuations are dominated by the physical state of the rocks rather than by their petrological composition. Attempts to derive Poisson's ratio, and its variation with wavelength, from the relationship between the shear and compressional velocity logs met with limited success, but imply that caution should be exercised when comparing Poisson's ratio derived from laboratory studies on samples representative of a region to crustal-scale seismic determinations. Our preferred interpretation is that fluctuations in the physical logs in the intermediate wavelength range (˜10-150 m) are dominated by cracks and their level of fluid saturation. At larger wavelengths (>50-150 m) the effects of the petrology becomes more significant as shown by changes in slope of the power spectra and the emerging coherence between the Vp and the gamma logs.

  11. High-Throughput Flow Cytometric Method for the Simultaneous Measurement of CAR-T Cell Characterization and Cytotoxicity against Solid Tumor Cell Lines.

    Science.gov (United States)

    Martinez, Emily M; Klebanoff, Samuel D; Secrest, Stephanie; Romain, Gabrielle; Haile, Samuel T; Emtage, Peter C R; Gilbert, Amy E

    2018-04-01

    High-throughput flow cytometry is an attractive platform for the analysis of adoptive cellular therapies such as chimeric antigen receptor T cell therapy (CAR-T) because it allows for the concurrent measurement of T cell-dependent cellular cytotoxicity (TDCC) and the functional characterization of engineered T cells with respect to percentage of CAR transduction, T cell phenotype, and measurement of T cell function such as activation in a single assay. The use of adherent tumor cell lines can be challenging in these flow-based assays. Here, we present the development of a high-throughput flow-based assay to measure TDCC for a CAR-T construct co-cultured with multiple adherent tumor cell lines. We describe optimal assay conditions (such as adherent cell dissociation techniques to minimize impact on cell viability) that result in robust cytotoxicity assays. In addition, we report on the concurrent use of T cell transduction and activation antibody panels (CD25) that provide further dissection of engineered T cell function. In conclusion, we present the development of a high-throughput flow cytometry method allowing for in vitro interrogation of solid tumor, targeting CAR-T cell-mediated cytotoxicity, CAR transduction, and engineered T cell characterization in a single assay.

  12. Bayesian nonparametric hierarchical modeling.

    Science.gov (United States)

    Dunson, David B

    2009-04-01

    In biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects. Parametric assumptions on latent variable distributions can be challenging to check and are typically unwarranted, given available prior knowledge. This article reviews some recent developments in Bayesian nonparametric methods motivated by complex, multivariate and functional data collected in biomedical studies. The author provides a brief review of flexible parametric approaches relying on finite mixtures and latent class modeling. Dirichlet process mixture models are motivated by the need to generalize these approaches to avoid assuming a fixed finite number of classes. Focusing on an epidemiology application, the author illustrates the practical utility and potential of nonparametric Bayes methods.

  13. Characterization of the Two CART Genes (CART1 and CART2 in Chickens (Gallus gallus.

    Directory of Open Access Journals (Sweden)

    Guoqing Cai

    Full Text Available Cocaine- and amphetamine-regulated transcript (CART peptide is implicated in the control of avian energy balance, however, the structure and expression of CART gene(s remains largely unknown in birds. Here, we cloned and characterized two CART genes (named cCART1 and cCART2 in chickens. The cloned cCART1 is predicted to generate two bioactive peptides, cCART1(42-89 and cCART1(49-89, which share high amino acid sequence identity (94-98% with their mammalian counterparts, while the novel cCART2 may produce a bioactive peptide cCART2(51-91 with 59% identity to cCART1. Interestingly, quantitative RT-PCR revealed that cCART1 is predominantly expressed in the anterior pituitary and less abundantly in the hypothalamus. In accordance with this finding, cCART1 peptide was easily detected in the anterior pituitary by Western blot, and its secretion from chick pituitaries incubated in vitro was enhanced by ionomycin and forskolin treatment, indicating that cCART1 is a novel peptide hormone produced by the anterior pituitary. Moreover, cCART1 mRNA expression in both the pituitary and hypothalamus is down-regulated by 48-h fasting, suggesting its expression is affected by energy status. Unlike cCART1, cCART2 is only weakly expressed in most tissues examined by RT-PCR, implying a less significant role of cCART2 in chickens. As in chickens, 2 or more CART genes, likely generated by gene and genome duplication event(s, were also identified in other non-mammalian vertebrate species including coelacanth. Collectively, the identification and characterization of CART genes in birds helps to uncover the roles of CART peptide(s in vertebrates and provides clues to the evolutionary history of vertebrate CART genes.

  14. Estimation and Testing Based on Data Subject to Measurement Errors: From Parametric to Non-Parametric Likelihood Methods

    Science.gov (United States)

    Vexler, Albert; Tsai, Wan-Min; Malinovsky, Yaakov

    2013-01-01

    Measurement error problems can cause bias or inconsistency of statistical inferences. When investigators are unable to obtain correct measurements of biological assays, special techniques to quantify measurement errors (ME) need to be applied. The sampling based on repeated measurements is a common strategy to allow for ME. This method has been well-addressed in the literature under parametric assumptions. The approach with repeated measures data may not be applicable when the replications are complicated due to cost and/or time concerns. Pooling designs have been proposed as cost-efficient sampling procedures that can assist to provide correct statistical operations based on data subject to ME. We demonstrate that a mixture of both pooled and unpooled data (a hybrid pooled-unpooled design) can support very efficient estimation and testing in the presence of ME. Nonparametric techniques have not been well investigated to analyze repeated measures data or pooled data subject to ME. We propose and examine both the parametric and empirical likelihood methodologies for data subject to ME. We conclude that the likelihood methods based on the hybrid samples are very efficient and powerful. The results of an extensive Monte Carlo study support our conclusions. Real data examples demonstrate the efficiency of the proposed methods in practice. PMID:21805485

  15. Non-parametric method for separating domestic hot water heating spikes and space heating

    DEFF Research Database (Denmark)

    Bacher, Peder; de Saint-Aubain, Philip Anton; Christiansen, Lasse Engbo

    2016-01-01

    In this paper a method for separating spikes from a noisy data series, where the data change and evolve over time, is presented. The method is applied on measurements of the total heat load for a single family house. It relies on the fact that the domestic hot water heating is a process generatin...

  16. Comparison of non-parametric methods for ungrouping coarsely aggregated data

    DEFF Research Database (Denmark)

    Rizzi, Silvia; Thinggaard, Mikael; Engholm, Gerda

    2016-01-01

    composite link model performs the best. Conclusion We give an overview and test different methods to estimate detailed distributions from grouped count data. Health researchers can benefit from these versatile methods, which are ready for use in the statistical software R. We recommend using the penalized...

  17. A non-parametric method for correction of global radiation observations

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Perers, Bengt

    2013-01-01

    the observed and the calculated radiation in order to find systematic deviations between them. The method is applied to correct global radiation observations from a climate station located at a district heating plant in Denmark. The results are compared to observations recorded at the Danish Technical...

  18. A Non-parametric Method for Calculating Conditional Stressed Value at Risk

    Directory of Open Access Journals (Sweden)

    Kohei Marumo

    2017-01-01

    Full Text Available We consider the Value at Risk (VaR of a portfolio under stressed conditions. In practice, the stressed VaR (sVaR is commonly calculated using the data set that includes the stressed period. It tells us how much the risk amount increases if we use the stressed data set. In this paper, we consider the VaR under stress scenarios. Technically, this can be done by deriving the distribution of profit or loss conditioned on the value of risk factors. We use two methods; the one that uses the linear model and the one that uses the Hermite expansion discussed by Marumo and Wolff (2013, 2016. Numerical examples shows that the method using the Hermite expansion is capable of capturing the non-linear effects such as correlation collapse and volatility clustering, which are often observed in the markets.

  19. Nonparametric statistical inference

    CERN Document Server

    Gibbons, Jean Dickinson

    2014-01-01

    Thoroughly revised and reorganized, the fourth edition presents in-depth coverage of the theory and methods of the most widely used nonparametric procedures in statistical analysis and offers example applications appropriate for all areas of the social, behavioral, and life sciences. The book presents new material on the quantiles, the calculation of exact and simulated power, multiple comparisons, additional goodness-of-fit tests, methods of analysis of count data, and modern computer applications using MINITAB, SAS, and STATXACT. It includes tabular guides for simplified applications of tests and finding P values and confidence interval estimates.

  20. Classification of multispectral image data by the Binary Diamond neural network and by nonparametric, pixel-by-pixel methods

    Science.gov (United States)

    Salu, Yehuda; Tilton, James

    1993-01-01

    The classification of multispectral image data obtained from satellites has become an important tool for generating ground cover maps. This study deals with the application of nonparametric pixel-by-pixel classification methods in the classification of pixels, based on their multispectral data. A new neural network, the Binary Diamond, is introduced, and its performance is compared with a nearest neighbor algorithm and a back-propagation network. The Binary Diamond is a multilayer, feed-forward neural network, which learns from examples in unsupervised, 'one-shot' mode. It recruits its neurons according to the actual training set, as it learns. The comparisons of the algorithms were done by using a realistic data base, consisting of approximately 90,000 Landsat 4 Thematic Mapper pixels. The Binary Diamond and the nearest neighbor performances were close, with some advantages to the Binary Diamond. The performance of the back-propagation network lagged behind. An efficient nearest neighbor algorithm, the binned nearest neighbor, is described. Ways for improving the performances, such as merging categories, and analyzing nonboundary pixels, are addressed and evaluated.

  1. Jeux de cartes

    Directory of Open Access Journals (Sweden)

    Pierre GENTELLE

    1986-09-01

    Full Text Available Dans la grande tradition de la science-fiction et des lieux imaginaires traduits ici en «jeux» de cartes, l'auteur bouleverse quelques localisations au prix de mouvements tectoniques imprévus et en prévoit quelques conséquences.

  2. Jeux de cartes

    Directory of Open Access Journals (Sweden)

    Priscilla DE ROO

    1988-03-01

    Full Text Available Le dessinateur Cabu illustre par la carte deux étapes récentes de l'État de la France: les rapports entre eux (État et institutions et nous (les Français et leur territoire avant et après la cohabitation.

  3. Nonparametric statistics for social and behavioral sciences

    CERN Document Server

    Kraska-MIller, M

    2013-01-01

    Introduction to Research in Social and Behavioral SciencesBasic Principles of ResearchPlanning for ResearchTypes of Research Designs Sampling ProceduresValidity and Reliability of Measurement InstrumentsSteps of the Research Process Introduction to Nonparametric StatisticsData AnalysisOverview of Nonparametric Statistics and Parametric Statistics Overview of Parametric Statistics Overview of Nonparametric StatisticsImportance of Nonparametric MethodsMeasurement InstrumentsAnalysis of Data to Determine Association and Agreement Pearson Chi-Square Test of Association and IndependenceContingency

  4. Decision support using nonparametric statistics

    CERN Document Server

    Beatty, Warren

    2018-01-01

    This concise volume covers nonparametric statistics topics that most are most likely to be seen and used from a practical decision support perspective. While many degree programs require a course in parametric statistics, these methods are often inadequate for real-world decision making in business environments. Much of the data collected today by business executives (for example, customer satisfaction opinions) requires nonparametric statistics for valid analysis, and this book provides the reader with a set of tools that can be used to validly analyze all data, regardless of type. Through numerous examples and exercises, this book explains why nonparametric statistics will lead to better decisions and how they are used to reach a decision, with a wide array of business applications. Online resources include exercise data, spreadsheets, and solutions.

  5. Nonparametric statistical inference

    CERN Document Server

    Gibbons, Jean Dickinson

    2010-01-01

    Overall, this remains a very fine book suitable for a graduate-level course in nonparametric statistics. I recommend it for all people interested in learning the basic ideas of nonparametric statistical inference.-Eugenia Stoimenova, Journal of Applied Statistics, June 2012… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. … a very well-written and organized book on nonparametric statistics, especially useful and recommended for teachers and graduate students.-Biometrics, 67, September 2011This excellently presente

  6. Nonparametric estimation of ultrasound pulses

    DEFF Research Database (Denmark)

    Jensen, Jørgen Arendt; Leeman, Sidney

    1994-01-01

    An algorithm for nonparametric estimation of 1D ultrasound pulses in echo sequences from human tissues is derived. The technique is a variation of the homomorphic filtering technique using the real cepstrum, and the underlying basis of the method is explained. The algorithm exploits a priori...

  7. Testing discontinuities in nonparametric regression

    KAUST Repository

    Dai, Wenlin

    2017-01-19

    In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100

  8. Nonparametric combinatorial sequence models.

    Science.gov (United States)

    Wauthier, Fabian L; Jordan, Michael I; Jojic, Nebojsa

    2011-11-01

    This work considers biological sequences that exhibit combinatorial structures in their composition: groups of positions of the aligned sequences are "linked" and covary as one unit across sequences. If multiple such groups exist, complex interactions can emerge between them. Sequences of this kind arise frequently in biology but methodologies for analyzing them are still being developed. This article presents a nonparametric prior on sequences which allows combinatorial structures to emerge and which induces a posterior distribution over factorized sequence representations. We carry out experiments on three biological sequence families which indicate that combinatorial structures are indeed present and that combinatorial sequence models can more succinctly describe them than simpler mixture models. We conclude with an application to MHC binding prediction which highlights the utility of the posterior distribution over sequence representations induced by the prior. By integrating out the posterior, our method compares favorably to leading binding predictors.

  9. Tables des cartes, graphiques, tableaux

    OpenAIRE

    2015-01-01

    Carte 1. Nombre d’indigents secourus pour 1 000 habitants dans les départements français en 1847 65 Carte 2. Taux de mendicité dans les départements français en 1847 66 Carte 3. Taux de personnes recensées en Bretagne Sud au milieu du xixe siècle sous les rubriques « mendiants, vagabonds, bohémiens » ou « sans profession » 70 Carte 4. Taux d’indigence dans les Côtes-du-Nord en 1854 71 Carte 5. Taux d’indigence en Ille-et-Vilaine en 1890 72 Carte 6. Taux de personnes inscrites sur les listes d...

  10. On Rigorous Drought Assessment Using Daily Time Scale: Non-Stationary Frequency Analyses, Revisited Concepts, and a New Method to Yield Non-Parametric Indices

    Directory of Open Access Journals (Sweden)

    Charles Onyutha

    2017-10-01

    Full Text Available Some of the problems in drought assessments are that: analyses tend to focus on coarse temporal scales, many of the methods yield skewed indices, a few terminologies are ambiguously used, and analyses comprise an implicit assumption that the observations come from a stationary process. To solve these problems, this paper introduces non-stationary frequency analyses of quantiles. How to use non-parametric rescaling to obtain robust indices that are not (or minimally skewed is also introduced. To avoid ambiguity, some concepts on, e.g., incidence, extremity, etc., were revisited through shift from monthly to daily time scale. Demonstrations on the introduced methods were made using daily flow and precipitation insufficiency (precipitation minus potential evapotranspiration from the Blue Nile basin in Africa. Results show that, when a significant trend exists in extreme events, stationarity-based quantiles can be far different from those when non-stationarity is considered. The introduced non-parametric indices were found to closely agree with the well-known standardized precipitation evapotranspiration indices in many aspects but skewness. Apart from revisiting some concepts, the advantages of the use of fine instead of coarse time scales in drought assessment were given. The links for obtaining freely downloadable tools on how to implement the introduced methods were provided.

  11. On Cooper's Nonparametric Test.

    Science.gov (United States)

    Schmeidler, James

    1978-01-01

    The basic assumption of Cooper's nonparametric test for trend (EJ 125 069) is questioned. It is contended that the proper assumption alters the distribution of the statistic and reduces its usefulness. (JKS)

  12. Nonparametric Econometrics: The np Package

    Directory of Open Access Journals (Sweden)

    Tristen Hayfield

    2008-07-01

    Full Text Available We describe the R np package via a series of applications that may be of interest to applied econometricians. The np package implements a variety of nonparametric and semiparametric kernel-based estimators that are popular among econometricians. There are also procedures for nonparametric tests of significance and consistent model specification tests for parametric mean regression models and parametric quantile regression models, among others. The np package focuses on kernel methods appropriate for the mix of continuous, discrete, and categorical data often found in applied settings. Data-driven methods of bandwidth selection are emphasized throughout, though we caution the user that data-driven bandwidth selection methods can be computationally demanding.

  13. Fan Cart: The Next Generation

    Science.gov (United States)

    Lamore, Brian

    2016-01-01

    For years the fan cart has provided physics students with an excellent resource for exploring fundamental mechanics concepts such as acceleration, Newton's laws, impulse, momentum, work-energy, and energy conversions. "The Physics Teacher" has even seen some excellent do-it-yourself (DIY) fan carts and activities. If you are interested…

  14. Nonparametric Inference for Periodic Sequences

    KAUST Repository

    Sun, Ying

    2012-02-01

    This article proposes a nonparametric method for estimating the period and values of a periodic sequence when the data are evenly spaced in time. The period is estimated by a "leave-out-one-cycle" version of cross-validation (CV) and complements the periodogram, a widely used tool for period estimation. The CV method is computationally simple and implicitly penalizes multiples of the smallest period, leading to a "virtually" consistent estimator of integer periods. This estimator is investigated both theoretically and by simulation.We also propose a nonparametric test of the null hypothesis that the data have constantmean against the alternative that the sequence of means is periodic. Finally, our methodology is demonstrated on three well-known time series: the sunspots and lynx trapping data, and the El Niño series of sea surface temperatures. © 2012 American Statistical Association and the American Society for Quality.

  15. FMIT alignment cart

    International Nuclear Information System (INIS)

    Potter, R.C.; Dauelsberg, L.B.; Clark, D.C.; Grieggs, R.J.

    1981-01-01

    The Fusion Materials Irradiation Test (FMIT) Facility alignment cart must perform several functions. It must serve as a fixture to receive the drift-tube girder assembly when it is removed from the linac tank. It must transport the girder assembly from the linac vault to the area where alignment or disassembly is to take place. It must serve as a disassembly fixture to hold the girder while individual drift tubes are removed for repair. It must align the drift tube bores in a straight line parallel to the girder, using an optical system. These functions must be performed without violating any clearances found within the building. The bore tubes of the drift tubes will be irradiated, and shielding will be included in the system for easier maintenance

  16. A la Carte Community

    DEFF Research Database (Denmark)

    Gundelach, Peter; Brincker, Benedikte

    2010-01-01

    and shows that there are high levels of virtual as well as face-to-face interaction among the members. The participants feel that they belong to the community and many also feel that they are recognised as part of the community. However, the members do not share common values neither in relation to software......The exchange of open source software is a phenomenon that is becoming in- creasingly significant to IT users. This article presents the results of a study of the TYPO3 community, a community related to an open source CMS software. The article explores the community, identity and values of TYPO3...... pro- duction nor generally. Instead, they stress that you are free to choose your own values. Against this background, the authors introduce the notion of an ‘a la carte community', i.e. a community where individuals pick and choose their degree of participation and integra- tion into the community...

  17. A la Carte Community

    DEFF Research Database (Denmark)

    Gundelach, Peter; Brincker, Benedikte

    2010-01-01

    The exchange of open source software is a phenomenon that is becoming in- creasingly significant to IT users. This article presents the results of a study of the TYPO3 community, a community related to an open source CMS software. The article explores the community, identity and values of TYPO3...... and shows that there are high levels of virtual as well as face-to-face interaction among the members. The participants feel that they belong to the community and many also feel that they are recognised as part of the community. However, the members do not share common values neither in relation to software...... pro- duction nor generally. Instead, they stress that you are free to choose your own values. Against this background, the authors introduce the notion of an ‘a la carte community', i.e. a community where individuals pick and choose their degree of participation and integra- tion into the community...

  18. Nonparametric Transfer Function Models

    Science.gov (United States)

    Liu, Jun M.; Chen, Rong; Yao, Qiwei

    2009-01-01

    In this paper a class of nonparametric transfer function models is proposed to model nonlinear relationships between ‘input’ and ‘output’ time series. The transfer function is smooth with unknown functional forms, and the noise is assumed to be a stationary autoregressive-moving average (ARMA) process. The nonparametric transfer function is estimated jointly with the ARMA parameters. By modeling the correlation in the noise, the transfer function can be estimated more efficiently. The parsimonious ARMA structure improves the estimation efficiency in finite samples. The asymptotic properties of the estimators are investigated. The finite-sample properties are illustrated through simulations and one empirical example. PMID:20628584

  19. On nonparametric hazard estimation.

    Science.gov (United States)

    Hobbs, Brian P

    The Nelson-Aalen estimator provides the basis for the ubiquitous Kaplan-Meier estimator, and therefore is an essential tool for nonparametric survival analysis. This article reviews martingale theory and its role in demonstrating that the Nelson-Aalen estimator is uniformly consistent for estimating the cumulative hazard function for right-censored continuous time-to-failure data.

  20. Engineering CAR-T cells.

    Science.gov (United States)

    Zhang, Cheng; Liu, Jun; Zhong, Jiang F; Zhang, Xi

    2017-01-01

    Chimeric antigen receptor redirected T cells (CAR-T cells) have achieved inspiring outcomes in patients with B cell malignancies, and are now being investigated in other hematologic malignancies and solid tumors. CAR-T cells are generated by the T cells from patients' or donors' blood. After the T cells are expanded and genetically modified, they are reinfused into the patients. However, many challenges still need to be resolved in order for this technology to gain widespread adoption. In this review, we first discuss the structure and evolution of chimeric antigen receptors. We then report on the tools used for production of CAR-T cells. Finally, we address the challenges posed by CAR-T cells.

  1. Benchmark of the non-parametric Bayesian deconvolution method implemented in the SINBAD code for X/γ rays spectra processing

    Energy Technology Data Exchange (ETDEWEB)

    Rohée, E. [CEA, LIST, Laboratoire Capteurs et Architectures Electroniques, F-91191 Gif-sur-Yvette (France); Coulon, R., E-mail: romain.coulon@cea.fr [CEA, LIST, Laboratoire Capteurs et Architectures Electroniques, F-91191 Gif-sur-Yvette (France); Carrel, F. [CEA, LIST, Laboratoire Capteurs et Architectures Electroniques, F-91191 Gif-sur-Yvette (France); Dautremer, T.; Barat, E.; Montagu, T. [CEA, LIST, Laboratoire de Modélisation et Simulation des Systèmes, F-91191 Gif-sur-Yvette (France); Normand, S. [CEA, DAM, Le Ponant, DPN/STXN, F-75015 Paris (France); Jammes, C. [CEA, DEN, Cadarache, DER/SPEx/LDCI, F-13108 Saint-Paul-lez-Durance (France)

    2016-11-11

    Radionuclide identification and quantification are a serious concern for many applications as for in situ monitoring at nuclear facilities, laboratory analysis, special nuclear materials detection, environmental monitoring, and waste measurements. High resolution gamma-ray spectrometry based on high purity germanium diode detectors is the best solution available for isotopic identification. Over the last decades, methods have been developed to improve gamma spectra analysis. However, some difficulties remain in the analysis when full energy peaks are folded together with high ratio between their amplitudes, and when the Compton background is much larger compared to the signal of a single peak. In this context, this study deals with the comparison between a conventional analysis based on “iterative peak fitting deconvolution” method and a “nonparametric Bayesian deconvolution” approach developed by the CEA LIST and implemented into the SINBAD code. The iterative peak fit deconvolution is used in this study as a reference method largely validated by industrial standards to unfold complex spectra from HPGe detectors. Complex cases of spectra are studied from IAEA benchmark protocol tests and with measured spectra. The SINBAD code shows promising deconvolution capabilities compared to the conventional method without any expert parameter fine tuning.

  2. Benchmark of the non-parametric Bayesian deconvolution method implemented in the SINBAD code for X/γ rays spectra processing

    International Nuclear Information System (INIS)

    Rohée, E.; Coulon, R.; Carrel, F.; Dautremer, T.; Barat, E.; Montagu, T.; Normand, S.; Jammes, C.

    2016-01-01

    Radionuclide identification and quantification are a serious concern for many applications as for in situ monitoring at nuclear facilities, laboratory analysis, special nuclear materials detection, environmental monitoring, and waste measurements. High resolution gamma-ray spectrometry based on high purity germanium diode detectors is the best solution available for isotopic identification. Over the last decades, methods have been developed to improve gamma spectra analysis. However, some difficulties remain in the analysis when full energy peaks are folded together with high ratio between their amplitudes, and when the Compton background is much larger compared to the signal of a single peak. In this context, this study deals with the comparison between a conventional analysis based on “iterative peak fitting deconvolution” method and a “nonparametric Bayesian deconvolution” approach developed by the CEA LIST and implemented into the SINBAD code. The iterative peak fit deconvolution is used in this study as a reference method largely validated by industrial standards to unfold complex spectra from HPGe detectors. Complex cases of spectra are studied from IAEA benchmark protocol tests and with measured spectra. The SINBAD code shows promising deconvolution capabilities compared to the conventional method without any expert parameter fine tuning.

  3. Nonparametric identification of copula structures

    KAUST Repository

    Li, Bo

    2013-06-01

    We propose a unified framework for testing a variety of assumptions commonly made about the structure of copulas, including symmetry, radial symmetry, joint symmetry, associativity and Archimedeanity, and max-stability. Our test is nonparametric and based on the asymptotic distribution of the empirical copula process.We perform simulation experiments to evaluate our test and conclude that our method is reliable and powerful for assessing common assumptions on the structure of copulas, particularly when the sample size is moderately large. We illustrate our testing approach on two datasets. © 2013 American Statistical Association.

  4. A comparison of selected parametric and non-parametric imputation methods for estimating forest biomass and basal area

    Science.gov (United States)

    Donald Gagliasso; Susan Hummel; Hailemariam. Temesgen

    2014-01-01

    Various methods have been used to estimate the amount of above ground forest biomass across landscapes and to create biomass maps for specific stands or pixels across ownership or project areas. Without an accurate estimation method, land managers might end up with incorrect biomass estimate maps, which could lead them to make poorer decisions in their future...

  5. Visualization and Comparison of Single and Combined Parametric and Nonparametric Discriminant Methods for Leukemia Type Recognition Based on Gene Expression

    Directory of Open Access Journals (Sweden)

    Ćwiklińska-Jurkowska Małgorzata M.

    2015-12-01

    Full Text Available A gene expression data set, containing 3051 genes and 38 tumor mRNA training samples, from a leukemia microarray study, was used for differentiation between ALL and AML groups of leukemia. In this paper, single and combined discriminant methods were applied on the basis of the selected few most discriminative variables according to Wilks’ lambda or the leave-one-out error of first nearest neighbor classifier. For the linear, quadratic, regularized, uncorrelated discrimination, kernel, nearest neighbor and naive Bayesian classifiers, two-dimensional graphs of the boundaries and discriminant functions for diagnostics are presented. Cross-validation and leave-one-out errors were used as measures of classifier performance to support diagnosis coming from this genomic data set. A small number of best discriminating genes, from two to ten, was sufficient to build discriminant methods of good performance. Especially useful were nearest neighbor methods. The results presented herein were comparable with outcomes obtained by other authors for larger numbers of applied genes. The linear, quadratic, uncorrelated Bayesian and regularized discrimination methods were subjected to bagging or boosting in order to assess the accuracy of the fusion. A conclusion drawn from the analysis was that resampling ensembles were not beneficial for two-dimensional discrimination.

  6. Recent Advances and Trends in Nonparametric Statistics

    CERN Document Server

    Akritas, MG

    2003-01-01

    The advent of high-speed, affordable computers in the last two decades has given a new boost to the nonparametric way of thinking. Classical nonparametric procedures, such as function smoothing, suddenly lost their abstract flavour as they became practically implementable. In addition, many previously unthinkable possibilities became mainstream; prime examples include the bootstrap and resampling methods, wavelets and nonlinear smoothers, graphical methods, data mining, bioinformatics, as well as the more recent algorithmic approaches such as bagging and boosting. This volume is a collection o

  7. A note on Nonparametric Confidence Interval for a Shift Parameter ...

    African Journals Online (AJOL)

    In this article an application of a kernel based nonparametric approach in constructing a large sample nonparametric confidence interval for a shift parameter is considered. The method is illustrated using the Cauchy distribution as a location model. The kernel-based method is found to have a shorter interval for the shift ...

  8. Structural analysis of the CDF transporter cart

    International Nuclear Information System (INIS)

    Leininger, M.

    1982-01-01

    The transporter cart serves as a dolly to move the large toroids (539 tons) and the Roman arches (600 tons) which are part of the central detector. ANSYS has been used to compute deflections and stresses in this cart

  9. Nonparametric statistics with applications to science and engineering

    CERN Document Server

    Kvam, Paul H

    2007-01-01

    A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provide...

  10. Influence of pH, soil type and soil organic matter content on soil-to-plant transfer of radiocesium and -strontium as analyzed by a nonparametric method

    International Nuclear Information System (INIS)

    Bergeijk, K.E. van; Noordijk, H.; Lembrechts, J.; Frissel, M.J.

    1992-01-01

    A new nonparametric method, called the relative comparison method, is presented to evaluate the effect of some soil parameters on transfer of Cs or Sr from soil to edible plant parts. A large number of transfer factors is used for 134 Cs, 137 Cs, 85 Sr, 89 Sr and 90 Sr. Transfer of Cs increased with increasing organic matter content. Compared to the transfer of Cs in soils with an organic matter content of less than 5%, the effect was approximately a factor of 2-5 to 20% and increased up to a factor of 10 in soils with an organic matter content of more than 50%. In contrast, transfer of Sr decreased with increasing soil organic matter content. Relative to the transfer of Sr in soils with an organic matter content of less than 5% the decrease was by about a factor of 1.5 at 6-10% organic matter up to a factor of 10 in soils with an organic matter content of more than 50%. In the range pH 3.9-8.4, transfer of Cs was not affected by soil pH. On average, transfer of Sr decreased by a factor of 1.7 when soil pH increased from pH 4.5 to 7.4. Transfer of Cs in clay or loam was lower than in sand, by five or three times, respectively. For Sr the transfer in clay or loam was about 1.4 times lower than in sand. These results may be used as correction factors when evaluating soil-to-plant transfer factors of Cs or Sr. (author)

  11. Nonparametric confidence intervals for monotone functions

    NARCIS (Netherlands)

    Groeneboom, P.; Jongbloed, G.

    2015-01-01

    We study nonparametric isotonic confidence intervals for monotone functions. In [Ann. Statist. 29 (2001) 1699–1731], pointwise confidence intervals, based on likelihood ratio tests using the restricted and unrestricted MLE in the current status model, are introduced. We extend the method to the

  12. Nonparametric tests for censored data

    CERN Document Server

    Bagdonavicus, Vilijandas; Nikulin, Mikhail

    2013-01-01

    This book concerns testing hypotheses in non-parametric models. Generalizations of many non-parametric tests to the case of censored and truncated data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most statistical software is highlighted and discussed.

  13. Mokken scale analysis of mental health and well-being questionnaire item responses: a non-parametric IRT method in empirical research for applied health researchers

    Directory of Open Access Journals (Sweden)

    Stochl Jan

    2012-06-01

    Full Text Available Abstract Background Mokken scaling techniques are a useful tool for researchers who wish to construct unidimensional tests or use questionnaires that comprise multiple binary or polytomous items. The stochastic cumulative scaling model offered by this approach is ideally suited when the intention is to score an underlying latent trait by simple addition of the item response values. In our experience, the Mokken model appears to be less well-known than for example the (related Rasch model, but is seeing increasing use in contemporary clinical research and public health. Mokken's method is a generalisation of Guttman scaling that can assist in the determination of the dimensionality of tests or scales, and enables consideration of reliability, without reliance on Cronbach's alpha. This paper provides a practical guide to the application and interpretation of this non-parametric item response theory method in empirical research with health and well-being questionnaires. Methods Scalability of data from 1 a cross-sectional health survey (the Scottish Health Education Population Survey and 2 a general population birth cohort study (the National Child Development Study illustrate the method and modeling steps for dichotomous and polytomous items respectively. The questionnaire data analyzed comprise responses to the 12 item General Health Questionnaire, under the binary recoding recommended for screening applications, and the ordinal/polytomous responses to the Warwick-Edinburgh Mental Well-being Scale. Results and conclusions After an initial analysis example in which we select items by phrasing (six positive versus six negatively worded items we show that all items from the 12-item General Health Questionnaire (GHQ-12 – when binary scored – were scalable according to the double monotonicity model, in two short scales comprising six items each (Bech’s “well-being” and “distress” clinical scales. An illustration of ordinal item analysis

  14. Mokken scale analysis of mental health and well-being questionnaire item responses: a non-parametric IRT method in empirical research for applied health researchers.

    Science.gov (United States)

    Stochl, Jan; Jones, Peter B; Croudace, Tim J

    2012-06-11

    Mokken scaling techniques are a useful tool for researchers who wish to construct unidimensional tests or use questionnaires that comprise multiple binary or polytomous items. The stochastic cumulative scaling model offered by this approach is ideally suited when the intention is to score an underlying latent trait by simple addition of the item response values. In our experience, the Mokken model appears to be less well-known than for example the (related) Rasch model, but is seeing increasing use in contemporary clinical research and public health. Mokken's method is a generalisation of Guttman scaling that can assist in the determination of the dimensionality of tests or scales, and enables consideration of reliability, without reliance on Cronbach's alpha. This paper provides a practical guide to the application and interpretation of this non-parametric item response theory method in empirical research with health and well-being questionnaires. Scalability of data from 1) a cross-sectional health survey (the Scottish Health Education Population Survey) and 2) a general population birth cohort study (the National Child Development Study) illustrate the method and modeling steps for dichotomous and polytomous items respectively. The questionnaire data analyzed comprise responses to the 12 item General Health Questionnaire, under the binary recoding recommended for screening applications, and the ordinal/polytomous responses to the Warwick-Edinburgh Mental Well-being Scale. After an initial analysis example in which we select items by phrasing (six positive versus six negatively worded items) we show that all items from the 12-item General Health Questionnaire (GHQ-12)--when binary scored--were scalable according to the double monotonicity model, in two short scales comprising six items each (Bech's "well-being" and "distress" clinical scales). An illustration of ordinal item analysis confirmed that all 14 positively worded items of the Warwick-Edinburgh Mental

  15. Sex-specific reference intervals of hematologic and biochemical analytes in Sprague-Dawley rats using the nonparametric rank percentile method.

    Science.gov (United States)

    He, Qili; Su, Guoming; Liu, Keliang; Zhang, Fangcheng; Jiang, Yong; Gao, Jun; Liu, Lida; Jiang, Zhongren; Jin, Minwu; Xie, Huiping

    2017-01-01

    Hematologic and biochemical analytes of Sprague-Dawley rats are commonly used to determine effects that were induced by treatment and to evaluate organ dysfunction in toxicological safety assessments, but reference intervals have not been well established for these analytes. Reference intervals as presently defined for these analytes in Sprague-Dawley rats have not used internationally recommended statistical method nor stratified by sex. Thus, we aimed to establish sex-specific reference intervals for hematologic and biochemical parameters in Sprague-Dawley rats according to Clinical and Laboratory Standards Institute C28-A3 and American Society for Veterinary Clinical Pathology guideline. Hematology and biochemistry blood samples were collected from 500 healthy Sprague-Dawley rats (250 males and 250 females) in the control groups. We measured 24 hematologic analytes with the Sysmex XT-2100i analyzer, 9 biochemical analytes with the Olympus AU400 analyzer. We then determined statistically relevant sex partitions and calculated reference intervals, including corresponding 90% confidence intervals, using nonparametric rank percentile method. We observed that most hematologic and biochemical analytes of Sprague-Dawley rats were significantly influenced by sex. Males had higher hemoglobin, hematocrit, red blood cell count, red cell distribution width, mean corpuscular volume, mean corpuscular hemoglobin, white blood cell count, neutrophils, lymphocytes, monocytes, percentage of neutrophils, percentage of monocytes, alanine aminotransferase, aspartate aminotransferase, and triglycerides compared to females. Females had higher mean corpuscular hemoglobin concentration, plateletcrit, platelet count, eosinophils, percentage of lymphocytes, percentage of eosinophils, creatinine, glucose, total cholesterol and urea compared to males. Sex partition was required for most hematologic and biochemical analytes in Sprague-Dawley rats. We established sex-specific reference

  16. Non-parametric approach to the study of phenotypic stability.

    Science.gov (United States)

    Ferreira, D F; Fernandes, S B; Bruzi, A T; Ramalho, M A P

    2016-02-19

    The aim of this study was to undertake the theoretical derivations of non-parametric methods, which use linear regressions based on rank order, for stability analyses. These methods were extension different parametric methods used for stability analyses and the result was compared with a standard non-parametric method. Intensive computational methods (e.g., bootstrap and permutation) were applied, and data from the plant-breeding program of the Biology Department of UFLA (Minas Gerais, Brazil) were used to illustrate and compare the tests. The non-parametric stability methods were effective for the evaluation of phenotypic stability. In the presence of variance heterogeneity, the non-parametric methods exhibited greater power of discrimination when determining the phenotypic stability of genotypes.

  17. Evaluation of Nigerian hospital meal carts

    Science.gov (United States)

    Ayodeji, Sesan P.; Adeyeri, Michael K.; Omoniyi, Olaoluwa

    2015-03-01

    Hospital meal carts are used to deliver meals, drugs and some other materials to patients in the hospital environment. These carts which are moved manually by operators, the health workers, mostly do not comply with ergonomics guidelines and physical requirements of the equipment users in terms of anthropometry data of the region thus increasing the risk of musculoskeletal disorder among the meal cart users. This study carried out ergonomic evaluation of the available meal carts in some western Nigeria hospitals. A well-structured questionnaire has two major segments: Operational survey and biomechanical survey, which were administered to the health workers using hospital meal carts in some hospitals in southwestern Nigeria, and physical assessment, which was undertaken to collect data for the ergonomic evaluation. The responses from the questionnaires show that some areas on the existing hospital meal carts are of concern to the users which need to be improved upon.

  18. Stochastic Non-Parametric Frontier Analysis

    OpenAIRE

    Mohammad Rahmani; Gholamreza Jahanshahloo

    2014-01-01

    In this paper we develop an approach that synthesizes the best features of the two main methods in the estimation of production eciency. Specically, our approach rst allows for statistical noise, similar to Stochastic frontier analysis , and second, it allows modeling multiple-inputs-multiple-outputs technologies without imposing parametric assumptions on production relationship, similar to what is done in non-parametric methods. The methodology is based on the theory of local maximum likelih...

  19. Bayesian Nonparametric Longitudinal Data Analysis.

    Science.gov (United States)

    Quintana, Fernando A; Johnson, Wesley O; Waetjen, Elaine; Gold, Ellen

    2016-01-01

    Practical Bayesian nonparametric methods have been developed across a wide variety of contexts. Here, we develop a novel statistical model that generalizes standard mixed models for longitudinal data that include flexible mean functions as well as combined compound symmetry (CS) and autoregressive (AR) covariance structures. AR structure is often specified through the use of a Gaussian process (GP) with covariance functions that allow longitudinal data to be more correlated if they are observed closer in time than if they are observed farther apart. We allow for AR structure by considering a broader class of models that incorporates a Dirichlet Process Mixture (DPM) over the covariance parameters of the GP. We are able to take advantage of modern Bayesian statistical methods in making full predictive inferences and about characteristics of longitudinal profiles and their differences across covariate combinations. We also take advantage of the generality of our model, which provides for estimation of a variety of covariance structures. We observe that models that fail to incorporate CS or AR structure can result in very poor estimation of a covariance or correlation matrix. In our illustration using hormone data observed on women through the menopausal transition, biology dictates the use of a generalized family of sigmoid functions as a model for time trends across subpopulation categories.

  20. Nonparametric Bayes Modeling of Multivariate Categorical Data.

    Science.gov (United States)

    Dunson, David B; Xing, Chuanhua

    2012-01-01

    Modeling of multivariate unordered categorical (nominal) data is a challenging problem, particularly in high dimensions and cases in which one wishes to avoid strong assumptions about the dependence structure. Commonly used approaches rely on the incorporation of latent Gaussian random variables or parametric latent class models. The goal of this article is to develop a nonparametric Bayes approach, which defines a prior with full support on the space of distributions for multiple unordered categorical variables. This support condition ensures that we are not restricting the dependence structure a priori. We show this can be accomplished through a Dirichlet process mixture of product multinomial distributions, which is also a convenient form for posterior computation. Methods for nonparametric testing of violations of independence are proposed, and the methods are applied to model positional dependence within transcription factor binding motifs.

  1. A nonparametric mixture model for cure rate estimation.

    Science.gov (United States)

    Peng, Y; Dear, K B

    2000-03-01

    Nonparametric methods have attracted less attention than their parametric counterparts for cure rate analysis. In this paper, we study a general nonparametric mixture model. The proportional hazards assumption is employed in modeling the effect of covariates on the failure time of patients who are not cured. The EM algorithm, the marginal likelihood approach, and multiple imputations are employed to estimate parameters of interest in the model. This model extends models and improves estimation methods proposed by other researchers. It also extends Cox's proportional hazards regression model by allowing a proportion of event-free patients and investigating covariate effects on that proportion. The model and its estimation method are investigated by simulations. An application to breast cancer data, including comparisons with previous analyses using a parametric model and an existing nonparametric model by other researchers, confirms the conclusions from the parametric model but not those from the existing nonparametric model.

  2. Shopping cart injuries, entrapment, and childhood fatality.

    Science.gov (United States)

    Jensen, Lisbeth; Charlwood, Cheryl; Byard, Roger W

    2008-09-01

    Shopping carts may be associated with a variety of injuries, particularly in toddlers and young children. These usually relate to falls from carts or to tip-overs. Injuries that are sustained include hematomas/contusions, abrasions, lacerations, fractures, and fingertip amputations. Fatal episodes are uncommon and are usually due to blunt craniocerebral trauma from falls. A case involving a 19-month-old girl is reported who became entrapped when she inserted her head through the side frame of a cart that had been removed from a supermarket and left at her home address. Death was caused by neck compression. Although rare, the potential for lethal entrapment during unsupervised play means that the presence of stray shopping carts at private residences and in public places, including playgrounds and parks, is of concern. Strategies, such as coin deposits, should be encouraged to assist in the return of such carts to supermarkets.

  3. Changes in RANKL during the first two years after cART initiation in HIV-infected cART naïve adults

    DEFF Research Database (Denmark)

    Mathiesen, Inger Hee Mabuza; Salem, Mohammad; Gerstoft, Jan

    2017-01-01

    BACKGROUND: By assessing the changes in concentration of soluble receptor activator of nuclear factor κ B ligand (RANKL) and osteoprotegrin (OPG) after initiation of combination antiretroviral therapy (cART) in treatment-naïve HIV-infected patients we aimed to evaluate whether the initial...... accelerated bone loss could be mediated by increased soluble RANKL (sRANKL) levels associated with CD4+ T cell recovery. METHODS: We used multiplex immunoassays to determine sRANKL and OPG concentrations in plasma from 48 HIV patients at baseline and 12, 24, 48 and 96 weeks after cART initiation. RESULTS...... and changes in sRANKL. CONCLUSION: In this study there was no indication that the accelerated bone loss after cART initiation was mediated by early changes in sRANKL due to CD4+ T cell recovery. Future studies should focus on the initial weeks after initiation of cART. TRIAL REGISTRATION: Clinical...

  4. Statistical methods for ranking data

    CERN Document Server

    Alvo, Mayer

    2014-01-01

    This book introduces advanced undergraduate, graduate students and practitioners to statistical methods for ranking data. An important aspect of nonparametric statistics is oriented towards the use of ranking data. Rank correlation is defined through the notion of distance functions and the notion of compatibility is introduced to deal with incomplete data. Ranking data are also modeled using a variety of modern tools such as CART, MCMC, EM algorithm and factor analysis. This book deals with statistical methods used for analyzing such data and provides a novel and unifying approach for hypotheses testing. The techniques described in the book are illustrated with examples and the statistical software is provided on the authors’ website.

  5. Nonparametric Mixture Models for Supervised Image Parcellation.

    Science.gov (United States)

    Sabuncu, Mert R; Yeo, B T Thomas; Van Leemput, Koen; Fischl, Bruce; Golland, Polina

    2009-09-01

    We present a nonparametric, probabilistic mixture model for the supervised parcellation of images. The proposed model yields segmentation algorithms conceptually similar to the recently developed label fusion methods, which register a new image with each training image separately. Segmentation is achieved via the fusion of transferred manual labels. We show that in our framework various settings of a model parameter yield algorithms that use image intensity information differently in determining the weight of a training subject during fusion. One particular setting computes a single, global weight per training subject, whereas another setting uses locally varying weights when fusing the training data. The proposed nonparametric parcellation approach capitalizes on recently developed fast and robust pairwise image alignment tools. The use of multiple registrations allows the algorithm to be robust to occasional registration failures. We report experiments on 39 volumetric brain MRI scans with expert manual labels for the white matter, cerebral cortex, ventricles and subcortical structures. The results demonstrate that the proposed nonparametric segmentation framework yields significantly better segmentation than state-of-the-art algorithms.

  6. Network structure exploration via Bayesian nonparametric models

    International Nuclear Information System (INIS)

    Chen, Y; Wang, X L; Xiang, X; Tang, B Z; Bu, J Z

    2015-01-01

    Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance. (paper)

  7. Non-Parametric Estimation of Correlation Functions

    DEFF Research Database (Denmark)

    Brincker, Rune; Rytter, Anders; Krenk, Steen

    In this paper three methods of non-parametric correlation function estimation are reviewed and evaluated: the direct method, estimation by the Fast Fourier Transform and finally estimation by the Random Decrement technique. The basic ideas of the techniques are reviewed, sources of bias are point...... out, and methods to prevent bias are presented. The techniques are evaluated by comparing their speed and accuracy on the simple case of estimating auto-correlation functions for the response of a single degree-of-freedom system loaded with white noise....

  8. Glaucoma Monitoring in a Clinical Setting Glaucoma Progression Analysis vs Nonparametric Progression Analysis in the Groningen Longitudinal Glaucoma Study

    NARCIS (Netherlands)

    Wesselink, Christiaan; Heeg, Govert P.; Jansonius, Nomdo M.

    Objective: To compare prospectively 2 perimetric progression detection algorithms for glaucoma, the Early Manifest Glaucoma Trial algorithm (glaucoma progression analysis [GPA]) and a nonparametric algorithm applied to the mean deviation (MD) (nonparametric progression analysis [NPA]). Methods:

  9. Getting started with OpenCart module development

    CERN Document Server

    Nepali, Rupak

    2013-01-01

    Written as a step-by-step guide, Getting Started with OpenCart Module Development will teach you all you need to know about OpenCart, from custom extensions to module development.This book is for developers who want to develop OpenCart extensions and for those who want to learn more about the code workflow of OpenCart. Basic knowledge of OpenCart would be an added advantage.

  10. Nonparametric predictive inference in reliability

    International Nuclear Information System (INIS)

    Coolen, F.P.A.; Coolen-Schrijner, P.; Yan, K.J.

    2002-01-01

    We introduce a recently developed statistical approach, called nonparametric predictive inference (NPI), to reliability. Bounds for the survival function for a future observation are presented. We illustrate how NPI can deal with right-censored data, and discuss aspects of competing risks. We present possible applications of NPI for Bernoulli data, and we briefly outline applications of NPI for replacement decisions. The emphasis is on introduction and illustration of NPI in reliability contexts, detailed mathematical justifications are presented elsewhere

  11. Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia.

    Science.gov (United States)

    Henrard, S; Speybroeck, N; Hermans, C

    2015-11-01

    Haemophilia is a rare genetic haemorrhagic disease characterized by partial or complete deficiency of coagulation factor VIII, for haemophilia A, or IX, for haemophilia B. As in any other medical research domain, the field of haemophilia research is increasingly concerned with finding factors associated with binary or continuous outcomes through multivariable models. Traditional models include multiple logistic regressions, for binary outcomes, and multiple linear regressions for continuous outcomes. Yet these regression models are at times difficult to implement, especially for non-statisticians, and can be difficult to interpret. The present paper sought to didactically explain how, why, and when to use classification and regression tree (CART) analysis for haemophilia research. The CART method is non-parametric and non-linear, based on the repeated partitioning of a sample into subgroups based on a certain criterion. Breiman developed this method in 1984. Classification trees (CTs) are used to analyse categorical outcomes and regression trees (RTs) to analyse continuous ones. The CART methodology has become increasingly popular in the medical field, yet only a few examples of studies using this methodology specifically in haemophilia have to date been published. Two examples using CART analysis and previously published in this field are didactically explained in details. There is increasing interest in using CART analysis in the health domain, primarily due to its ease of implementation, use, and interpretation, thus facilitating medical decision-making. This method should be promoted for analysing continuous or categorical outcomes in haemophilia, when applicable. © 2015 John Wiley & Sons Ltd.

  12. Graph embedded nonparametric mutual information for supervised dimensionality reduction.

    Science.gov (United States)

    Bouzas, Dimitrios; Arvanitopoulos, Nikolaos; Tefas, Anastasios

    2015-05-01

    In this paper, we propose a novel algorithm for dimensionality reduction that uses as a criterion the mutual information (MI) between the transformed data and their corresponding class labels. The MI is a powerful criterion that can be used as a proxy to the Bayes error rate. Furthermore, recent quadratic nonparametric implementations of MI are computationally efficient and do not require any prior assumptions about the class densities. We show that the quadratic nonparametric MI can be formulated as a kernel objective in the graph embedding framework. Moreover, we propose its linear equivalent as a novel linear dimensionality reduction algorithm. The derived methods are compared against the state-of-the-art dimensionality reduction algorithms with various classifiers and on various benchmark and real-life datasets. The experimental results show that nonparametric MI as an optimization objective for dimensionality reduction gives comparable and in most of the cases better results compared with other dimensionality reduction methods.

  13. Nonparametric Mixture of Regression Models.

    Science.gov (United States)

    Huang, Mian; Li, Runze; Wang, Shaoli

    2013-07-01

    Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.

  14. A ¤nonparametric dynamic additive regression model for longitudinal data

    DEFF Research Database (Denmark)

    Martinussen, T.; Scheike, T. H.

    2000-01-01

    dynamic linear models, estimating equations, least squares, longitudinal data, nonparametric methods, partly conditional mean models, time-varying-coefficient models......dynamic linear models, estimating equations, least squares, longitudinal data, nonparametric methods, partly conditional mean models, time-varying-coefficient models...

  15. GLYCAN-DIRECTED CAR-T CELLS.

    Science.gov (United States)

    Steentoft, Catharina; Migliorini, Denis; King, Tiffany R; Mandel, Ulla; June, Carl H; Posey, Avery D

    2018-01-23

    Cancer immunotherapy is rapidly advancing in the treatment of a variety of hematopoietic cancers, including pediatric acute lymphoblastic leukemia and diffuse large B cell lymphoma, with chimeric antigen receptor (CAR)-T cells. CARs are genetically encoded artificial T cell receptors that combine the antigen specificity of an antibody with the machinery of T cell activation. However, implementation of CAR technology in the treatment of solid tumors has been progressing much slower. Solid tumors are characterized by a number of challenges that need to be overcome, including cellular heterogeneity, immunosuppressive tumor microenvironment (TME), and, in particular, few known cancer-specific targets. Post-translational modifications that differentially occur in malignant cells generate valid cell surface, cancer-specific targets for CAR-T cells. We previously demonstrated that CAR-T cells targeting an aberrant O-glycosylation of MUC1, a common cancer marker associated with changes in cell adhesion, tumor growth, and poor prognosis, could control malignant growth in mouse models. Here, we discuss the field of glycan-directed CAR-T cells and review the different classes of antibodies specific for glycan-targeting, including the generation of high affinity O-glycopeptide antibodies. Finally, we discuss historic and recently investigated glycan targets for CAR-T cells and provide our perspective on how targeting the tumor glycoproteome and/or glycome will improve CAR-T immunotherapy. © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  16. CART - a Scandinavian approach to computer aided radiation therapy

    International Nuclear Information System (INIS)

    Walstam, R.

    1987-01-01

    The CART project, a program of computer-aided radiation therapy developed as a joint venture by Scandinavian countries is described. The history is outlined and the individual areas of the CART program are listed. (L.O.). 3 refs

  17. Clinical trials of CAR-T cells in China

    Directory of Open Access Journals (Sweden)

    Bingshan Liu

    2017-10-01

    Full Text Available Abstract Novel immunotherapeutic agents targeting tumor-site microenvironment are revolutionizing cancer therapy. Chimeric antigen receptor (CAR-engineered T cells are widely studied for cancer immunotherapy. CD19-specific CAR-T cells, tisagenlecleucel, have been recently approved for clinical application. Ongoing clinical trials are testing CAR designs directed at novel targets involved in hematological and solid malignancies. In addition to trials of single-target CAR-T cells, simultaneous and sequential CAR-T cells are being studied for clinical applications. Multi-target CAR-engineered T cells are also entering clinical trials. T cell receptor-engineered CAR-T and universal CAR-T cells represent new frontiers in CAR-T cell development. In this study, we analyzed the characteristics of CAR constructs and registered clinical trials of CAR-T cells in China and provided a quick glimpse of the landscape of CAR-T studies in China.

  18. Clinical trials of CAR-T cells in China.

    Science.gov (United States)

    Liu, Bingshan; Song, Yongping; Liu, Delong

    2017-10-23

    Novel immunotherapeutic agents targeting tumor-site microenvironment are revolutionizing cancer therapy. Chimeric antigen receptor (CAR)-engineered T cells are widely studied for cancer immunotherapy. CD19-specific CAR-T cells, tisagenlecleucel, have been recently approved for clinical application. Ongoing clinical trials are testing CAR designs directed at novel targets involved in hematological and solid malignancies. In addition to trials of single-target CAR-T cells, simultaneous and sequential CAR-T cells are being studied for clinical applications. Multi-target CAR-engineered T cells are also entering clinical trials. T cell receptor-engineered CAR-T and universal CAR-T cells represent new frontiers in CAR-T cell development. In this study, we analyzed the characteristics of CAR constructs and registered clinical trials of CAR-T cells in China and provided a quick glimpse of the landscape of CAR-T studies in China.

  19. Investigation on the Expansion of Urban Construction Land Use Based on the CART-CA Model

    Directory of Open Access Journals (Sweden)

    Yongxiang Yao

    2017-05-01

    Full Text Available Change in urban construction land use is an important factor when studying urban expansion. Many scholars have combined cellular automata (CA with data mining algorithms to perform relevant simulation studies. However, the parameters for rule extraction are difficult to determine and the rules are simplex, and together, these factors tend to introduce excessive fitting problems and low modeling accuracy. In this paper, we propose a method to extract the transformation rules for a CA model based on the Classification and Regression Tree (CART. In this method, CART is used to extract the transformation rules for the CA. This method first adopts the CART decision tree using the bootstrap algorithm to mine the rules from the urban land use while considering the factors that impact the geographic spatial variables in the CART regression procedure. The weights of individual impact factors are calculated to generate a logistic regression function that reflects the change in urban construction land use. Finally, a CA model is constructed to simulate and predict urban construction land expansion. The urban area of Xinyang City in China is used as an example for this experimental research. After removing the spatial invariant region, the overall simulation accuracy is 81.38% and the kappa coefficient is 0.73. The results indicate that by using the CART decision tree to train the impact factor weights and extract the rules, it can effectively increase the simulation accuracy of the CA model. From convenience and accuracy perspectives for rule extraction, the structure of the CART decision tree is clear, and it is very suitable for obtaining the cellular rules. The CART-CA model has a relatively high simulation accuracy in modeling urban construction land use expansion, it provides reliable results, and is suitable for use as a scientific reference for urban construction land use expansion.

  20. A contingency table approach to nonparametric testing

    CERN Document Server

    Rayner, JCW

    2000-01-01

    Most texts on nonparametric techniques concentrate on location and linear-linear (correlation) tests, with less emphasis on dispersion effects and linear-quadratic tests. Tests for higher moment effects are virtually ignored. Using a fresh approach, A Contingency Table Approach to Nonparametric Testing unifies and extends the popular, standard tests by linking them to tests based on models for data that can be presented in contingency tables.This approach unifies popular nonparametric statistical inference and makes the traditional, most commonly performed nonparametric analyses much more comp

  1. Panel data specifications in nonparametric kernel regression

    DEFF Research Database (Denmark)

    Czekaj, Tomasz Gerard; Henningsen, Arne

    We discuss nonparametric regression models for panel data. A fully nonparametric panel data specification that uses the time variable and the individual identifier as additional (categorical) explanatory variables is considered to be the most suitable. We use this estimator and conventional...... parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...... found the estimates of the fully nonparametric panel data model to be more reliable....

  2. Stochastic Non-Parametric Frontier Analysis

    Directory of Open Access Journals (Sweden)

    Mohammad Rahmani

    2014-05-01

    Full Text Available In this paper we develop an approach that synthesizes the best features of the two main methods in the estimation of production eciency. Specically, our approach rst allows for statistical noise, similar to Stochastic frontier analysis , and second, it allows modeling multiple-inputs-multiple-outputs technologies without imposing parametric assumptions on production relationship, similar to what is done in non-parametric methods. The methodology is based on the theory of local maximum likelihood estimation and extends recent works of Kumbhakar et al. We will use local-spherical coordinate system to transform multi-input multi-output data to more exible system which we can use in our approach. We also illustrate the performance of our approach with simulated example

  3. Nonparametric dark energy reconstruction from supernova data.

    Science.gov (United States)

    Holsclaw, Tracy; Alam, Ujjaini; Sansó, Bruno; Lee, Herbert; Heitmann, Katrin; Habib, Salman; Higdon, David

    2010-12-10

    Understanding the origin of the accelerated expansion of the Universe poses one of the greatest challenges in physics today. Lacking a compelling fundamental theory to test, observational efforts are targeted at a better characterization of the underlying cause. If a new form of mass-energy, dark energy, is driving the acceleration, the redshift evolution of the equation of state parameter w(z) will hold essential clues as to its origin. To best exploit data from observations it is necessary to develop a robust and accurate reconstruction approach, with controlled errors, for w(z). We introduce a new, nonparametric method for solving the associated statistical inverse problem based on Gaussian process modeling and Markov chain Monte Carlo sampling. Applying this method to recent supernova measurements, we reconstruct the continuous history of w out to redshift z=1.5.

  4. Decompounding random sums: A nonparametric approach

    DEFF Research Database (Denmark)

    Hansen, Martin Bøgsted; Pitts, Susan M.

    Observations from sums of random variables with a random number of summands, known as random, compound or stopped sums arise within many areas of engineering and science. Quite often it is desirable to infer properties of the distribution of the terms in the random sum. In the present paper we...... review a number of applications and consider the nonlinear inverse problem of inferring the cumulative distribution function of the components in the random sum. We review the existing literature on non-parametric approaches to the problem. The models amenable to the analysis are generalized considerably...... and the properties of the proposed estimators are studied. Bootstrap methods are suggested to provide confidence bounds. Finally a number of algorithms are suggested to make the methods operational and tested on simulated data. In particular we show how Panjer recursion in general can be inverted for the Panjer...

  5. Nonparametric Bayesian inference in biostatistics

    CERN Document Server

    Müller, Peter

    2015-01-01

    As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...

  6. A general approach to posterior contraction in nonparametric inverse problems

    NARCIS (Netherlands)

    Knapik, Bartek; Salomond, Jean Bernard

    In this paper, we propose a general method to derive an upper bound for the contraction rate of the posterior distribution for nonparametric inverse problems. We present a general theorem that allows us to derive contraction rates for the parameter of interest from contraction rates of the related

  7. Nonparametric inference of network structure and dynamics

    Science.gov (United States)

    Peixoto, Tiago P.

    The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among

  8. Nonparametric e-Mixture Estimation.

    Science.gov (United States)

    Takano, Ken; Hino, Hideitsu; Akaho, Shotaro; Murata, Noboru

    2016-12-01

    This study considers the common situation in data analysis when there are few observations of the distribution of interest or the target distribution, while abundant observations are available from auxiliary distributions. In this situation, it is natural to compensate for the lack of data from the target distribution by using data sets from these auxiliary distributions-in other words, approximating the target distribution in a subspace spanned by a set of auxiliary distributions. Mixture modeling is one of the simplest ways to integrate information from the target and auxiliary distributions in order to express the target distribution as accurately as possible. There are two typical mixtures in the context of information geometry: the [Formula: see text]- and [Formula: see text]-mixtures. The [Formula: see text]-mixture is applied in a variety of research fields because of the presence of the well-known expectation-maximazation algorithm for parameter estimation, whereas the [Formula: see text]-mixture is rarely used because of its difficulty of estimation, particularly for nonparametric models. The [Formula: see text]-mixture, however, is a well-tempered distribution that satisfies the principle of maximum entropy. To model a target distribution with scarce observations accurately, this letter proposes a novel framework for a nonparametric modeling of the [Formula: see text]-mixture and a geometrically inspired estimation algorithm. As numerical examples of the proposed framework, a transfer learning setup is considered. The experimental results show that this framework works well for three types of synthetic data sets, as well as an EEG real-world data set.

  9. A Bayesian Nonparametric Approach to Factor Analysis

    DEFF Research Database (Denmark)

    Piatek, Rémi; Papaspiliopoulos, Omiros

    2018-01-01

    This paper introduces a new approach for the inference of non-Gaussian factor models based on Bayesian nonparametric methods. It relaxes the usual normality assumption on the latent factors, widely used in practice, which is too restrictive in many settings. Our approach, on the contrary, does...... not impose any particular assumptions on the shape of the distribution of the factors, but still secures the basic requirements for the identification of the model. We design a new sampling scheme based on marginal data augmentation for the inference of mixtures of normals with location and scale...... restrictions. This approach is augmented by the use of a retrospective sampler, to allow for the inference of a constrained Dirichlet process mixture model for the distribution of the latent factors. We carry out a simulation study to illustrate the methodology and demonstrate its benefits. Our sampler is very...

  10. Nonparametric estimation of location and scale parameters

    KAUST Repository

    Potgieter, C.J.

    2012-12-01

    Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal assumptions regarding the form of the distribution functions of X and Y. We discuss an approach to the estimation problem that is based on asymptotic likelihood considerations. Our results enable us to provide a methodology that can be implemented easily and which yields estimators that are often near optimal when compared to fully parametric methods. We evaluate the performance of the estimators in a series of Monte Carlo simulations. © 2012 Elsevier B.V. All rights reserved.

  11. Decision Boundary Feature Extraction for Nonparametric Classification

    Science.gov (United States)

    Lee, Chulhee; Landgrebe, David A.

    1993-01-01

    Feature extraction has long been an important topic in pattern recognition. Although many authors have studied feature extraction for parametric classifiers, relatively few feature extraction algorithms are available for nonparametric classifiers. A new feature extraction algorithm based on decision boundaries for nonparametric classifiers is proposed. It is noted that feature extraction for pattern recognition is equivalent to retaining 'discriminantly informative features' and a discriminantly informative feature is related to the decision boundary. Since nonparametric classifiers do not define decision boundaries in analytic form, the decision boundary and normal vectors must be estimated numerically. A procedure to extract discriminantly informative features based on a decision boundary for non-parametric classification is proposed. Experiments show that the proposed algorithm finds effective features for the nonparametric classifier with Parzen density estimation.

  12. Hypothalamic CART is a new anorectic peptide regulated by leptin.

    Science.gov (United States)

    Kristensen, P; Judge, M E; Thim, L; Ribel, U; Christjansen, K N; Wulff, B S; Clausen, J T; Jensen, P B; Madsen, O D; Vrang, N; Larsen, P J; Hastrup, S

    1998-05-07

    The mammalian hypothalamus strongly influences ingestive behaviour through several different signalling molecules and receptor systems. Here we show that CART (cocaine- and amphetamine-regulated transcript), a brain-located peptide, is a satiety factor and is closely associated with the actions of two important regulators of food intake, leptin and neuropeptide Y. Food-deprived animals show a pronounced decrease in expression of CART messenger RNA in the arcuate nucleus. In animal models of obesity with disrupted leptin signalling, CART mRNA is almost absent from the arcuate nucleus. Peripheral administration of leptin to obese mice stimulates CART mRNA expression. When injected intracerebroventricularly into rats, recombinant CART peptide inhibits both normal and starvation-induced feeding, and completely blocks the feeding response induced by neuropeptide Y. An antiserum against CART increases feeding in normal rats, indicating that CART may be an endogenous inhibitor of food intake in normal animals.

  13. Parallelization of a three-dimensional whole core transport code DeCART

    Energy Technology Data Exchange (ETDEWEB)

    Jin Young, Cho; Han Gyu, Joo; Ha Yong, Kim; Moon-Hee, Chang [Korea Atomic Energy Research Institute, Yuseong-gu, Daejon (Korea, Republic of)

    2003-07-01

    Parallelization of the DeCART (deterministic core analysis based on ray tracing) code is presented that reduces the computational burden of the tremendous computing time and memory required in three-dimensional whole core transport calculations. The parallelization employs the concept of MPI grouping and the MPI/OpenMP mixed scheme as well. Since most of the computing time and memory are used in MOC (method of characteristics) and the multi-group CMFD (coarse mesh finite difference) calculation in DeCART, variables and subroutines related to these two modules are the primary targets for parallelization. Specifically, the ray tracing module was parallelized using a planar domain decomposition scheme and an angular domain decomposition scheme. The parallel performance of the DeCART code is evaluated by solving a rodded variation of the C5G7MOX three dimensional benchmark problem and a simplified three-dimensional SMART PWR core problem. In C5G7MOX problem with 24 CPUs, a speedup of maximum 21 is obtained on an IBM Regatta machine and 22 on a LINUX Cluster in the MOC kernel, which indicates good parallel performance of the DeCART code. In the simplified SMART problem, the memory requirement of about 11 GBytes in the single processor cases reduces to 940 Mbytes with 24 processors, which means that the DeCART code can now solve large core problems with affordable LINUX clusters. (authors)

  14. Effect of once-daily FDC treatment era on initiation of cART

    Directory of Open Access Journals (Sweden)

    David M Mosen

    2010-02-01

    Full Text Available David M Mosen1, Michael Horberg2, Douglas Roblin3, Christina M Gullion1, Richard Meenan1, Wendy Leyden2, Weiming Hu11Center for Health Research, Kaiser Permanente Northwest, Portland, OR, USA; 2Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA; 3Center for Health Research, Kaiser Permanente Georgia, Atlanta, GA, USAObjectives: Combination antiretroviral therapy (cART is associated with increased survival among HIV-infected persons. Yet, no research to date has examined whether introduction of once-daily fixed-dosed combinations (FDC affects the likelihood of cART initiation. We aimed to determine whether implementation of once-daily FDC regimens was associated with changes to cART initiation. We also identified clinical, treatment regimen, and provider characteristics possibly associated with cART initiation. Study design: Retrospective observational analysis.Methods: We queried electronic medical records between July 1999–June 2006 to identify incident cases of detectable HIV infection in antiretroviral-naïve adults. Cox regression with time-dependent covariates was used to examine the effects of once-daily FDC era, clinical, provider, and treatment regimen characteristics on cART initiation.Results: Once-daily FDC availability did not change the likelihood of cART initiation, but other characteristics were associated with an increased likelihood: AIDS diagnosis, above-median daily pill consumption, and 16+ yrs of physician HIV experience. Decreased likelihood of cART initiation was associated with CD4 201–350 cells/µL, HIV RNA < 100,000 copies/mL, and with CD4 > 350 cells/µL (any HIV RNA level, compared to CD4 ≤ 200 cells/µL.Conclusion: Availability of once-daily FDC-based regimens did not affect likelihood of cART initiation. Patient clinical characteristics appear to be more important predictors of cART initiation.Keywords: ARV treatment, once-daily FDC therapies, ARV-naïve

  15. Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

    Science.gov (United States)

    Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F

    2013-04-01

    In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.

  16. Nonparametric Bayesian Modeling of Complex Networks

    DEFF Research Database (Denmark)

    Schmidt, Mikkel Nørgaard; Mørup, Morten

    2013-01-01

    an infinite mixture model as running example, we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model?s fit and predictive performance. We explain how advanced nonparametric models......Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...

  17. Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering

    Directory of Open Access Journals (Sweden)

    Xin Tian

    2017-06-01

    Full Text Available We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster’s sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments.

  18. Nonparametric functional mapping of quantitative trait loci.

    Science.gov (United States)

    Yang, Jie; Wu, Rongling; Casella, George

    2009-03-01

    Functional mapping is a useful tool for mapping quantitative trait loci (QTL) that control dynamic traits. It incorporates mathematical aspects of biological processes into the mixture model-based likelihood setting for QTL mapping, thus increasing the power of QTL detection and the precision of parameter estimation. However, in many situations there is no obvious functional form and, in such cases, this strategy will not be optimal. Here we propose to use nonparametric function estimation, typically implemented with B-splines, to estimate the underlying functional form of phenotypic trajectories, and then construct a nonparametric test to find evidence of existing QTL. Using the representation of a nonparametric regression as a mixed model, the final test statistic is a likelihood ratio test. We consider two types of genetic maps: dense maps and general maps, and the power of nonparametric functional mapping is investigated through simulation studies and demonstrated by examples.

  19. Car sharing à la carte

    CERN Document Server

    Anaïs Schaeffer

    2012-01-01

    Do you want to make your commute to CERN easier, while saving money at the same time? Would you prefer not to spend a quarter of an hour crawling round the CERN car parks looking for a space? If so, read on: this article might well be of great interest to you.   We would like to draw your attention to a well established, albeit sadly under-used, method of transport: car sharing. To promote car-sharing, the GS Department has stepped in to call on the services of the Swiss firm Green Monkeys which specialises in this user-friendly and intelligent transport scheme. The company’s slogan is:  “Car-sharing as you want, when you want and as much as you want”. The principle is very straightforward. To use this car-sharing facility, you simply complete your free online registration with Green Monkeys, providing the following details: your journey, departure time, arrival time and days of the week, and indicating whether you are a passenger or driver or both. &a...

  20. Fire behavior of e-tablets stored in aircraft galley carts.

    Science.gov (United States)

    2015-04-01

    The use of electronic-tablets (e-tablets) as replacements for conventional in-flight entertainment systems has gained popularity : among airlines globally. Innovative methods of storing and charging e-tablets in galley carts have been suggested or ar...

  1. Comparing nonparametric Bayesian tree priors for clonal reconstruction of tumors.

    Science.gov (United States)

    Deshwar, Amit G; Vembu, Shankar; Morris, Quaid

    2015-01-01

    Statistical machine learning methods, especially nonparametric Bayesian methods, have become increasingly popular to infer clonal population structure of tumors. Here we describe the treeCRP, an extension of the Chinese restaurant process (CRP), a popular construction used in nonparametric mixture models, to infer the phylogeny and genotype of major subclonal lineages represented in the population of cancer cells. We also propose new split-merge updates tailored to the subclonal reconstruction problem that improve the mixing time of Markov chains. In comparisons with the tree-structured stick breaking prior used in PhyloSub, we demonstrate superior mixing and running time using the treeCRP with our new split-merge procedures. We also show that given the same number of samples, TSSB and treeCRP have similar ability to recover the subclonal structure of a tumor…

  2. Nonparametric Regression Estimation for Multivariate Null Recurrent Processes

    Directory of Open Access Journals (Sweden)

    Biqing Cai

    2015-04-01

    Full Text Available This paper discusses nonparametric kernel regression with the regressor being a \\(d\\-dimensional \\(\\beta\\-null recurrent process in presence of conditional heteroscedasticity. We show that the mean function estimator is consistent with convergence rate \\(\\sqrt{n(Th^{d}}\\, where \\(n(T\\ is the number of regenerations for a \\(\\beta\\-null recurrent process and the limiting distribution (with proper normalization is normal. Furthermore, we show that the two-step estimator for the volatility function is consistent. The finite sample performance of the estimate is quite reasonable when the leave-one-out cross validation method is used for bandwidth selection. We apply the proposed method to study the relationship of Federal funds rate with 3-month and 5-year T-bill rates and discover the existence of nonlinearity of the relationship. Furthermore, the in-sample and out-of-sample performance of the nonparametric model is far better than the linear model.

  3. La carte scolaire et son assouplissement

    OpenAIRE

    Merle, Pierre

    2014-01-01

    Cet article a pour objet l'étude de la politique d’assouplissement de la carte scolaire mise en œuvre à partir de la rentrée scolaire 2007. Cette politique poursuit officiellement deux objectifs : apporter une plus grande liberté de choix de l’établissement aux parents ; favoriser la mixité sociale. L’étude de cette politique repose, dans un premier temps, sur l’analyse de la réalisation formelle des objectifs poursuivis (notamment la comparaison des anciens et nouveaux critères de dérogation...

  4. Using CART to segment road images

    Science.gov (United States)

    Davies, Bob; Lienhart, Rainer

    2006-01-01

    The 2005 DARPA Grand Challenge is a 132 mile race through the desert with autonomous robotic vehicles. Lasers mounted on the car roof provide a map of the road up to 20 meters ahead of the car but the car needs to see further in order to go fast enough to win the race. Computer vision can extend that map of the road ahead but desert road is notoriously similar to the surrounding desert. The CART algorithm (Classification and Regression Trees) provided a machine learning boost to find road while at the same time measuring when that road could not be distinguished from surrounding desert.

  5. Assessing the HIV Care Continuum in Latin America: progress in clinical retention, cART use and viral suppression

    Science.gov (United States)

    Rebeiro, Peter F; Cesar, Carina; Shepherd, Bryan E; De Boni, Raquel B; Cortés, Claudia P; Rodriguez, Fernanda; Belaunzarán-Zamudio, Pablo; Pape, Jean W; Padgett, Denis; Hoces, Daniel; McGowan, Catherine C; Cahn, Pedro

    2016-01-01

    Introduction We assessed trends in HIV Care Continuum outcomes associated with delayed disease progression and reduced transmission within a large Latin American cohort over a decade: clinical retention, combination antiretroviral therapy (cART) use and viral suppression (VS). Methods Adults from Caribbean, Central and South America network for HIV epidemiology clinical cohorts in seven countries contributed data between 2003 and 2012. Retention was defined as two or more HIV care visits annually, >90 days apart. cART was defined as prescription of three or more antiretroviral agents annually. VS was defined as HIV-1 RNA <200 copies/mL at last measurement annually. cART and VS denominators were subjects with at least one visit annually. Multivariable modified Poisson regression was used to assess temporal trends and examine associations between age, sex, HIV transmission mode, cohort, calendar year and time in care. Results Among 18,799 individuals in retention analyses, 14,380 in cART analyses and 13,330 in VS analyses, differences existed between those meeting indicator definitions versus those not by most characteristics. Retention, cART and VS significantly improved from 2003 to 2012 (63 to 77%, 74 to 91% and 53 to 82%, respectively; p<0.05, each). Female sex (risk ratio (RR)=0.97 vs. males) and injection drug use as HIV transmission mode (RR=0.83 vs. male sexual contact with males (MSM)) were significantly associated with lower retention, but unrelated with cART or VS. MSM (RR=0.96) significantly decreased the probability of cART compared with heterosexual transmission. Conclusions HIV Care Continuum outcomes improved over time in Latin America, though disparities for vulnerable groups remain. Efforts must be made to increase retention, cART and VS, while engaging in additional research to sustain progress in these settings. PMID:27065108

  6. AN EFFECTIVE TECHNIQUE OF MULTIPLE IMPUTATION IN NONPARAMETRIC QUANTILE REGRESSION

    OpenAIRE

    Yanan Hu; Qianqian Zhu; Maozai Tian

    2014-01-01

    In this study, we consider the nonparametric quantile regression model with the covariates Missing at Random (MAR). Multiple imputation is becoming an increasingly popular approach for analyzing missing data, which combined with quantile regression is not well-developed. We propose an effective and accurate two-stage multiple imputation method for the model based on the quantile regression, which consists of initial imputation in the first stage and multiple imputation in the second stage. Th...

  7. Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model.

    Science.gov (United States)

    Seera, Manjeevan; Lim, Chee Peng; Ishak, Dahaman; Singh, Harapajan

    2012-01-01

    In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

  8. Nonparametric Estimation of Item and Respondent Locations from Unfolding-type Items.

    Science.gov (United States)

    Johnson, Matthew S

    2006-06-01

    Unlike their monotone counterparts, nonparametric unfolding response models, which assume the item response function is unimodal, have seen little attention in the psychometric literature. This paper studies the nonparametric behavior of unfolding models by building on the work of Post (1992). The paper provides rigorous justification for a class of nonparametric estimators of respondents' latent attitudes by proving that the estimators consistently rank order the respondents. The paper also suggests an algorithm for the rank ordering of items along the attitudes scale. Finally, the methods are evaluated using simulated data.

  9. Single versus mixture Weibull distributions for nonparametric satellite reliability

    International Nuclear Information System (INIS)

    Castet, Jean-Francois; Saleh, Joseph H.

    2010-01-01

    Long recognized as a critical design attribute for space systems, satellite reliability has not yet received the proper attention as limited on-orbit failure data and statistical analyses can be found in the technical literature. To fill this gap, we recently conducted a nonparametric analysis of satellite reliability for 1584 Earth-orbiting satellites launched between January 1990 and October 2008. In this paper, we provide an advanced parametric fit, based on mixture of Weibull distributions, and compare it with the single Weibull distribution model obtained with the Maximum Likelihood Estimation (MLE) method. We demonstrate that both parametric fits are good approximations of the nonparametric satellite reliability, but that the mixture Weibull distribution provides significant accuracy in capturing all the failure trends in the failure data, as evidenced by the analysis of the residuals and their quasi-normal dispersion.

  10. A Rapid Cell Expansion Process for Production of Engineered Autologous CAR-T Cell Therapies.

    Science.gov (United States)

    Lu, Tangying Lily; Pugach, Omar; Somerville, Robert; Rosenberg, Steven A; Kochenderfer, James N; Better, Marc; Feldman, Steven A

    2016-12-01

    The treatment of B-cell malignancies by adoptive cell transfer (ACT) of anti-CD19 chimeric antigen receptor T cells (CD19 CAR-T) has proven to be a highly successful therapeutic modality in several clinical trials. 1-6 The anti-CD19 CAR-T cell production method used to support initial trials relied on numerous manual, open process steps, human serum, and 10 days of cell culture to achieve a clinical dose. 7 This approach limited the ability to support large multicenter clinical trials, as well as scale up for commercial cell production. Therefore, studies were completed to streamline and optimize the original National Cancer Institute production process by removing human serum from the process in order to minimize the risk of viral contamination, moving process steps from an open system to functionally closed system operations in order to minimize the risk of microbial contamination, and standardizing additional process steps in order to maximize process consistency. This study reports a procedure for generating CD19 CAR-T cells in 6 days, using a functionally closed manufacturing process and defined, serum-free medium. This method is able to produce CD19 CAR-T cells that are phenotypically and functionally indistinguishable from cells produced for clinical trials by the previously described production process.

  11. The Retarding Force on a Fan-Cart Reversing Direction

    Science.gov (United States)

    Aurora, Tarlok S.; Brunner, Bernard J.

    2011-01-01

    In introductory physics, students learn that an object tossed upward has a constant downward acceleration while going up, at the highest point and while falling down. To demonstrate this concept, a self-propelled fan cart system is used on a frictionless track. A quick push is given to the fan cart and it is allowed to move away on a track under…

  12. Reduced CSF CART in dementia with Lewy bodies

    DEFF Research Database (Denmark)

    Schultz, Kristofer; Wiehager, Sara; Nilsson, Karin

    2009-01-01

    , DLB patients displayed hypothalamic atrophy whereas this region was not affected in AD patients. Cocaine and amphetamine regulated transcript (CART) is a neuropeptide expressed selectively in neurons in the hypothalamus. Here, we found that CSF CART levels were significantly reduced by 30% in DLB...

  13. CART in the Regulation of Appetite and Energy Homeostasis

    Directory of Open Access Journals (Sweden)

    Jackie eLau

    2014-10-01

    Full Text Available The cocaine- and amphetamine-regulated transcript (CART has been the subject of significant interest for over a decade. Work to decipher the detailed mechanism of CART function has been hampered by the lack of specific pharmacological tools like antagonists and the absence of a specific CART receptor(s. However, extensive research has been devoted to elucidate the role of the CART peptide and it is now evident that CART is a key neurotransmitter and hormone involved in the regulation of diverse biological processes, including food intake, maintenance of body weight, reward and addiction, stress response, psychostimulant effects and endocrine functions1,2. In this review, we focus on knowledge gained on CART’s role in controlling appetite and energy homeostasis, and also address certain species differences between rodents and humans.

  14. Establishing guidelines for CAR-T cells: challenges and considerations.

    Science.gov (United States)

    Wang, Wei; Qin, Di-Yuan; Zhang, Bing-Lan; Wei, Wei; Wang, Yong-Sheng; Wei, Yu-Quan

    2016-04-01

    T cells, genetically modified by chimeric antigen receptors (CAR-T), are endowed with specificity to a desired antigen and are cytotoxic to cells expressing the targeted antigen. CAR-T-based cancer immunotherapy is a promising therapy for curing hematological malignancy, such as acute lymphoid leukemia, and is promising for extending their efficacy to defeat solid tumors. To date, dozens of different CAR-T cells have been evaluated in clinical trials to treat tumors; this necessitates the establishment of guidelines for the production and application of CAR-T cells. However, it is challenging to standardize CAR-T cancer therapy because it involves a combination of gene therapy and cell therapy. In this review, we compare the existing guidelines for CAR-T cells and discuss the challenges and considerations for establishing guidance for CAR-T-based cancer immunotherapy.

  15. Nonparametric correlation models for portfolio allocation

    DEFF Research Database (Denmark)

    Aslanidis, Nektarios; Casas, Isabel

    2013-01-01

    This article proposes time-varying nonparametric and semiparametric estimators of the conditional cross-correlation matrix in the context of portfolio allocation. Simulations results show that the nonparametric and semiparametric models are best in DGPs with substantial variability or structural...... breaks in correlations. Only when correlations are constant does the parametric DCC model deliver the best outcome. The methodologies are illustrated by evaluating two interesting portfolios. The first portfolio consists of the equity sector SPDRs and the S&P 500, while the second one contains major...

  16. Involvement of CART in estradiol-induced anorexia.

    Science.gov (United States)

    Dandekar, Manoj P; Nakhate, Kartik T; Kokare, Dadasaheb M; Subhedar, Nishikant K

    2012-01-18

    Since estradiol exercises inhibitory effect on food intake, we wanted to find out if this influence of estradiol is mediated by cocaine- and amphetamine-regulated transcript peptide (CART), a well established anorectic agent in the brain. Ovariectomized (OVX) rats, replaced with estradiol to produce estrous-phase like conditions, showed a significant decrease in food intake as compared with that in OVX controls. Intracerebroventricular (icv) administration of CART (0.5-1 μg/rat) to OVX rats, resulted in a dose-dependent reduction in the food intake. The lower dose (0.25 μg) had no effect, and was considered subeffective. In estradiol replaced OVX rats, CART at subeffective dose, further reduced food intake. However, CART failed to reduce food intake in estradiol replaced OVX rats pretreated with anti-estrogenic agent tamoxifen (3 mg/kg, subcutaneous). Administration of CART antibody (1:500 dilution/rat, i.c.v.) significantly attenuated estradiol-induced anorexia in the OVX rats. While estradiol replacement significantly increased CART-immunoreactivity in the cells/fibers of paraventricular nucleus (PVN) of OVX rats, fibers in the anteroventral periventricular nucleus (AVPV), and cells/fibers in the arcuate nucleus (ARC) showed considerable reduction. These changes were attenuated following concurrent injection of tamoxifen to the estradiol replaced OVX rats. However, CART-immunoreactive cells/fibers in the periventricular area did not respond to any of the treatments. We suggest that estradiol treatment might influence the hypothalamic CART system in a site specific manner. While increased CART activity in the PVN might produce anorexia, reduction of CART in ARC and AVPV might represent a compensatory homeostatic response. Copyright © 2011 Elsevier Inc. All rights reserved.

  17. Evaluation of the New York City Green Carts program

    Directory of Open Access Journals (Sweden)

    Shannon M Farley

    2015-12-01

    Full Text Available Access to fresh fruits and vegetables is a concern, particularly among low-income populations. Mobile vending is one strategy to expand produce availability and access to increase consumption. In 2008, New York City launched a mobile vending initiative, Green Carts. We report on the evaluation. Three waves of cross-sectional observational surveys of produce availability, variety, and quality were conducted during the summers of 2008, 2009, and 2011 in a stratified random sample of stores and carts comparing establishments in Green Cart neighborhoods (n = 13 with comparison neighborhoods (n = 3. Bivariate analyses for availability, variety, and quality comparing Green Cart and comparison neighborhoods were presented across years, and logistic and negative binomial regressions were used to test whether fruit and vegetable availability, variety, and quality increased in Green Cart compared with comparison neighborhoods, adjusting for clustering and neighborhood demographics. Establishments selling fruits and vegetables in Green Cart neighborhoods increased between 2008 and 2011 (50% to 69%, p <0.0001; there was no comparable increase in comparison neighborhoods. Establishments selling more than 10 fruits and vegetables types increased from 31% to 38% (p = 0.0414 in Green Cart neighborhoods; there was no change in comparison neighborhoods. Produce quality was high among comparison establishments, with 95% and 94% meeting the quality threshold in 2008 and 2011, while declining in Green Cart neighborhood establishments from 96% to 88% (p < 0.0001. Sustained produce availability was found in Green Cart neighborhoods between 2008–2011. Green Carts are one strategy contributing to improving produce access among New Yorkers.

  18. Network reconstruction using nonparametric additive ODE models.

    Science.gov (United States)

    Henderson, James; Michailidis, George

    2014-01-01

    Network representations of biological systems are widespread and reconstructing unknown networks from data is a focal problem for computational biologists. For example, the series of biochemical reactions in a metabolic pathway can be represented as a network, with nodes corresponding to metabolites and edges linking reactants to products. In a different context, regulatory relationships among genes are commonly represented as directed networks with edges pointing from influential genes to their targets. Reconstructing such networks from data is a challenging problem receiving much attention in the literature. There is a particular need for approaches tailored to time-series data and not reliant on direct intervention experiments, as the former are often more readily available. In this paper, we introduce an approach to reconstructing directed networks based on dynamic systems models. Our approach generalizes commonly used ODE models based on linear or nonlinear dynamics by extending the functional class for the functions involved from parametric to nonparametric models. Concomitantly we limit the complexity by imposing an additive structure on the estimated slope functions. Thus the submodel associated with each node is a sum of univariate functions. These univariate component functions form the basis for a novel coupling metric that we define in order to quantify the strength of proposed relationships and hence rank potential edges. We show the utility of the method by reconstructing networks using simulated data from computational models for the glycolytic pathway of Lactocaccus Lactis and a gene network regulating the pluripotency of mouse embryonic stem cells. For purposes of comparison, we also assess reconstruction performance using gene networks from the DREAM challenges. We compare our method to those that similarly rely on dynamic systems models and use the results to attempt to disentangle the distinct roles of linearity, sparsity, and derivative

  19. Nonparametric estimation in models for unobservable heterogeneity

    OpenAIRE

    Hohmann, Daniel

    2014-01-01

    Nonparametric models which allow for data with unobservable heterogeneity are studied. The first publication introduces new estimators and their asymptotic properties for conditional mixture models. The second publication considers estimation of a function from noisy observations of its Radon transform in a Gaussian white noise model.

  20. How Are Teachers Teaching? A Nonparametric Approach

    Science.gov (United States)

    De Witte, Kristof; Van Klaveren, Chris

    2014-01-01

    This paper examines which configuration of teaching activities maximizes student performance. For this purpose a nonparametric efficiency model is formulated that accounts for (1) self-selection of students and teachers in better schools and (2) complementary teaching activities. The analysis distinguishes both individual teaching (i.e., a…

  1. How are teachers teaching? A nonparametric approach

    NARCIS (Netherlands)

    De Witte, Kristof; Van Klaveren, Chris

    This paper examines which configuration of teaching activities maximizes student performance. For this purpose a nonparametric efficiency model is formulated that accounts for (1) self-selection of students and teachers in better schools and (2) complementary teaching activities. The analysis

  2. Relationship between antiretrovirals used as part of a cART regimen and CD4 count increases in patients with suppressed viremia

    DEFF Research Database (Denmark)

    Mocroft, A; Phillips, A; Ledergerber, B

    2006-01-01

    BACKGROUND: It is unknown if the CD4 cell count response differs according to antiretroviral drugs used in combination antiretroviral therapy (cART) in patients with maximal virological suppression [viral load (VL) ... consecutive measurements with VL used. METHODS: Generalized linear models, accounting for multiple measurements within patients, were used to compare CD4 cell count changes after adjustment for antiretrovirals, time...... from starting cART, age, CD4 at first VL treatment, and change in CD4 cell count since starting cART. RESULTS: We studied 28418 instances of VL

  3. Evaluation of Nonparametric Probabilistic Forecasts of Wind Power

    DEFF Research Database (Denmark)

    Pinson, Pierre; Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg, orlov 31.07.2008

    Predictions of wind power production for horizons up to 48-72 hour ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind power predictions are not only provided with point predictions, which are estimates of the most...... likely outcome for each look-ahead time, but also with uncertainty estimates given by probabilistic forecasts. In order to avoid assumptions on the shape of predictive distributions, these probabilistic predictions are produced from nonparametric methods, and then take the form of a single or a set...

  4. El cartón, arquitectura y paisje

    OpenAIRE

    Montes López, Bartolomé

    2016-01-01

    “El Cartón, paisaje y arquitectura” es un taller que tiene como principal objetivo la asimilación de conocimientos prácticos acerca del cartón reciclado para la construcción de maquetas descriptivas y está dividido en dos partes. En la primera mitad, teórica, enumeraremos y ahondaremos en los requisitos necesarios para conformar nuestro propio taller. En la segunda parte abordaremos entre todos la construcción de una maqueta aplicando los conocimientos previos. “El Cartón, paisaje y arqu...

  5. Prediksi Kerawanan Wilayah Terhadap Tindak Pencurian Sepeda Motor Menggunakan Metode (SARIMA Dan CART

    Directory of Open Access Journals (Sweden)

    Pradita Eko Prasetyo Utomo

    2017-07-01

    Full Text Available Motor vehicle theft is a crime that is most common in Indonesia. Growth of vehicle motorcycle significant in each year accompanied by the increasing theft of motorcycles in each year, we need a system that is able to forecast the development and the theft of the motorcycle. This research proposes the development of forecasting models vulnerability criminal offense of theft of motorcycles with ARIMA forecasting method. This method not only forecast from variable of theft but also residents, vehicles and unemployment. The study also determined the classification level of vulnerability to the crime of theft of a motorcycle using a method based on the Decision Tree CART ARIMA forecasting method. Forecasting time series data with ARIMA method performed by each of the variables to produce the best ARIMA forecasting model which varies based on the data pattern of each of those variables. The results of classification by CART method to get the value of accuracy of 92% for the city of Yogyakarta and 85% for DIY. Based on the above, the results of ARIMA forecasting and classification CART can be used in determining the level of vulnerability to the crime of theft of motorcycles.

  6. Injerto columelar extendido angulado. Método para prevenir la rotación cefálica y lateral de los injertos de cartílago en la punta nasal Angulated extended collumelar graft. A method to prevent the cephalic and lateral rotation of the cartilage graft in the nasal tip

    OpenAIRE

    Y. Castro Govea; A. Fuente del Campo; H. Chacón Martínez; S. Pérez Porras; O. Vázquez Costilla; G. Mendoza Adam; O. De la Garza Pineda; A. Salazar Lozano

    2011-01-01

    El paciente mestizo generalmente posee una nariz pequeña, de base ancha, con fosas nasales redondas y dorso convexo. Los cartílagos alares son débiles, delgados y cortos, proporcionando un soporte estructural deficiente y pobre definición de la punta nasal. Los injertos de cartílago de la punta nasal se usan frecuentemente para corregir esta condición; sin embargo un problema común es la rotación cefálica, caudal y lateral de estos cartílagos. Empleamos un injerto columelar extendido angulado...

  7. CAR-T Cell Therapies From the Transfusion Medicine Perspective.

    Science.gov (United States)

    Fesnak, Andrew; Lin, ChieYu; Siegel, Don L; Maus, Marcela V

    2016-07-01

    The use of chimeric antigen receptor (CAR)-T cell therapy for the treatment of hematologic malignancies has generated significant excitement over the last several years. From a transfusion medicine perspective, the implementation of CAR-T therapy as a potential mainstay treatment for not only hematologic but also solid-organ malignancies represents a significant opportunity for growth and expansion. In this review, we will describe the rationale for the development of genetically redirected T cells as a cancer therapeutic, the different elements that are required to engineer these cells, as well as an overview of the process by which patient cells are harvested and processed to create and subsequently validate CAR-T cells. Finally, we will briefly describe some of the toxicities and clinical efficacy of CAR-T cells in the setting of patients with advanced malignancy. Copyright © 2016 Elsevier Inc. All rights reserved.

  8. La Carte de Localisation Probable des Avalanches (CPLA

    Directory of Open Access Journals (Sweden)

    Gilles BORREL

    1994-12-01

    Full Text Available La Carte de Localisation Probable des Avalanches (CPLA indique l’enveloppe des limites extrêmes connues atteintes par les avalanches, ainsi que les travaux de protection associés. Il s’agit d’un document informatif et non d’une carte de risque. Depuis 1990, les données thématiques sont numérisées.

  9. Clinical trials of CAR-T cells in China

    OpenAIRE

    Bingshan Liu; Yongping Song; Delong Liu

    2017-01-01

    Abstract Novel immunotherapeutic agents targeting tumor-site microenvironment are revolutionizing cancer therapy. Chimeric antigen receptor (CAR)-engineered T cells are widely studied for cancer immunotherapy. CD19-specific CAR-T cells, tisagenlecleucel, have been recently approved for clinical application. Ongoing clinical trials are testing CAR designs directed at novel targets involved in hematological and solid malignancies. In addition to trials of single-target CAR-T cells, simultaneous...

  10. Adaptive Confidence Bands for Nonparametric Regression Functions.

    Science.gov (United States)

    Cai, T Tony; Low, Mark; Ma, Zongming

    2014-01-01

    A new formulation for the construction of adaptive confidence bands in non-parametric function estimation problems is proposed. Confidence bands are constructed which have size that adapts to the smoothness of the function while guaranteeing that both the relative excess mass of the function lying outside the band and the measure of the set of points where the function lies outside the band are small. It is shown that the bands adapt over a maximum range of Lipschitz classes. The adaptive confidence band can be easily implemented in standard statistical software with wavelet support. Numerical performance of the procedure is investigated using both simulated and real datasets. The numerical results agree well with the theoretical analysis. The procedure can be easily modified and used for other nonparametric function estimation models.

  11. Nonparametric and semiparametric dynamic additive regression models

    DEFF Research Database (Denmark)

    Scheike, Thomas Harder; Martinussen, Torben

    Dynamic additive regression models provide a flexible class of models for analysis of longitudinal data. The approach suggested in this work is suited for measurements obtained at random time points and aims at estimating time-varying effects. Both fully nonparametric and semiparametric models can...... in special cases. We investigate the finite sample properties of the estimators and conclude that the asymptotic results are valid for even samll samples....

  12. Potential Antidepressant Role of Neurotransmitter CART: Implications for Mental Disorders

    Directory of Open Access Journals (Sweden)

    Peizhong Mao

    2011-01-01

    Full Text Available Depression is one of the most prevalent and debilitating public health concerns. Although no single cause of depression has been identified, it appears that interaction among genetic, epigenetic, biochemical, environmental, and psychosocial factors may explain its etiology. Further, only a fraction of depressed patients show full remission while using current antidepressants. Therefore, identifying common pathways of the disorder and using that knowledge to develop more effective pharmacological treatments are two primary targets of research in this field. Brain-enriched neurotransmitter CART (cocaine- and amphetamine-regulated transcript has multiple functions related to emotions. It is a potential neurotrophic factor and is involved in the regulation of hypothalamic-pituitary-adrenal axis and stress response as well as in energy homeostasis. CART is also highly expressed in limbic system, which is considered to have an important role in regulating mood. Notably, adolescents carrying a missense mutation in the CART gene exhibit increased depression and anxiety. Hence, CART peptide may be a novel promising antidepressant agent. In this paper, we summarize recent progress in depression and CART. In particular, we emphasize a new antidepressant function for CART.

  13. Robustifying Bayesian nonparametric mixtures for count data.

    Science.gov (United States)

    Canale, Antonio; Prünster, Igor

    2017-03-01

    Our motivating application stems from surveys of natural populations and is characterized by large spatial heterogeneity in the counts, which makes parametric approaches to modeling local animal abundance too restrictive. We adopt a Bayesian nonparametric approach based on mixture models and innovate with respect to popular Dirichlet process mixture of Poisson kernels by increasing the model flexibility at the level both of the kernel and the nonparametric mixing measure. This allows to derive accurate and robust estimates of the distribution of local animal abundance and of the corresponding clusters. The application and a simulation study for different scenarios yield also some general methodological implications. Adding flexibility solely at the level of the mixing measure does not improve inferences, since its impact is severely limited by the rigidity of the Poisson kernel with considerable consequences in terms of bias. However, once a kernel more flexible than the Poisson is chosen, inferences can be robustified by choosing a prior more general than the Dirichlet process. Therefore, to improve the performance of Bayesian nonparametric mixtures for count data one has to enrich the model simultaneously at both levels, the kernel and the mixing measure. © 2016, The International Biometric Society.

  14. Comparison of single and boosted protease inhibitor versus nonnucleoside reverse transcriptase inhibitor-containing cART regimens in antiretroviral-naïve patients starting cART after January 1, 2000

    DEFF Research Database (Denmark)

    Mocroft, A; Horban, A; Clumeck, N

    2006-01-01

    increase) response in antiretroviral-naïve patients starting either a single protease inhibitor (PI; n = 183), a ritonavir-boosted PI regimen (n = 197), or a nonnucleoside reverse transcriptase inhibitor (NNRTI)-based cART regimen (n = 447) after January 1, 2000, and the odds of lack of virologic...... or immunologic response at 3 years after starting cART. METHOD: Cox proportional hazards models and logistic regression. RESULTS: After adjustment, compared to patients taking an NNRTI-regimen, patients taking a single-PI regimen were significantly less likely to achieve a viral load (VL)

  15. A Bayesian nonparametric estimation of distributions and quantiles

    International Nuclear Information System (INIS)

    Poern, K.

    1988-11-01

    The report describes a Bayesian, nonparametric method for the estimation of a distribution function and its quantiles. The method, presupposing random sampling, is nonparametric, so the user has to specify a prior distribution on a space of distributions (and not on a parameter space). In the current application, where the method is used to estimate the uncertainty of a parametric calculational model, the Dirichlet prior distribution is to a large extent determined by the first batch of Monte Carlo-realizations. In this case the results of the estimation technique is very similar to the conventional empirical distribution function. The resulting posterior distribution is also Dirichlet, and thus facilitates the determination of probability (confidence) intervals at any given point in the space of interest. Another advantage is that also the posterior distribution of a specified quantitle can be derived and utilized to determine a probability interval for that quantile. The method was devised for use in the PROPER code package for uncertainty and sensitivity analysis. (orig.)

  16. Pendekatan Cart untuk Mendapatkan Faktor yang Mempengaruhi Terjangkitnya Penyakit Demam Tifoid di Aceh Utara

    Directory of Open Access Journals (Sweden)

    Dina Yuanita

    2010-05-01

    research conducted to find factors that influence the outbreak of typhoid fever in NAD. research using the CART Method. The results of the analysis indicate that the main factor causing typhoid fever was drinking water reservoirs. The other factors are waste water reservoirs, the physical quality of drinking water, a habit washing hands with soap before eating, the bowel, the dump, gender, socioeconomic status, habits of washing hands with soap after defecation and health education.

  17. Introduction to nonparametric statistics for the biological sciences using R

    CERN Document Server

    MacFarland, Thomas W

    2016-01-01

    This book contains a rich set of tools for nonparametric analyses, and the purpose of this supplemental text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: To introduce when nonparametric approaches to data analysis are appropriate To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses a...

  18. Aux cartes citoyens. La démocratie par les cartes

    Directory of Open Access Journals (Sweden)

    Anne Chapuis

    2000-11-01

    Full Text Available Les cartes, images puissantes rendant visible et compréhensibles des informations nombreuses et complexes, ont une vocation naturelle à servir de support aux débats sur l'aménagement du territoire et aux actions de développement, et en cela être un des outils privilégiés de la démocratie. Plusieurs expériences au sein de collectivités territoriales en France et au sein d'organismes de développement en Inde nous ont démontré que l'utilisation de cartes et matrices pour l'aide à la décision est le plus souvent jugée dangereuse. Bien construites elles sont révélatrices des déséquilibres spatiaux, des enjeux de pouvoir et bien entendu de la corruption. Nous présentons quelques unes de ces expériences avec en arrière plan les enjeux de la démocratie et de la Transparence tant vantées et si peu pratiquées.

  19. Nonparametric Bayesian density estimation on manifolds with applications to planar shapes.

    Science.gov (United States)

    Bhattacharya, Abhishek; Dunson, David B

    2010-12-01

    Statistical analysis on landmark-based shape spaces has diverse applications in morphometrics, medical diagnostics, machine vision and other areas. These shape spaces are non-Euclidean quotient manifolds. To conduct nonparametric inferences, one may define notions of centre and spread on this manifold and work with their estimates. However, it is useful to consider full likelihood-based methods, which allow nonparametric estimation of the probability density. This article proposes a broad class of mixture models constructed using suitable kernels on a general compact metric space and then on the planar shape space in particular. Following a Bayesian approach with a nonparametric prior on the mixing distribution, conditions are obtained under which the Kullback-Leibler property holds, implying large support and weak posterior consistency. Gibbs sampling methods are developed for posterior computation, and the methods are applied to problems in density estimation and classification with shape-based predictors. Simulation studies show improved estimation performance relative to existing approaches.

  20. CART neurons in the arcuate nucleus and lateral hypothalamic area exert differential controls on energy homeostasis.

    Science.gov (United States)

    Lau, Jackie; Farzi, Aitak; Qi, Yue; Heilbronn, Regine; Mietzsch, Mario; Shi, Yan-Chuan; Herzog, Herbert

    2018-01-01

    The cocaine- and amphetamine-regulated transcript (CART) codes for a pivotal neuropeptide important in the control of appetite and energy homeostasis. However, limited understanding exists for the defined effector sites underlying CART function, as discrepant effects of central CART administration have been reported. By combining Cart-cre knock-in mice with a Cart adeno-associated viral vector designed using the flip-excision switch (AAV-FLEX) technology, specific reintroduction or overexpression of CART selectively in CART neurons in the arcuate nucleus (Arc) and lateral hypothalamic area (LHA), respectively, was achieved. The effects on energy homeostasis control were investigated. Here we show that CART neuron-specific reintroduction of CART into the Arc and LHA leads to distinct effects on energy homeostasis control. Specifically, CART reintroduction into the Arc of otherwise CART-deficient Cart cre/cre mice markedly decreased fat mass and body weight, whereas CART reintroduction into the LHA caused significant fat mass gain and lean mass loss, but overall unaltered body weight. The reduced adiposity in Arc CART ;Cart cre/cre mice was associated with an increase in both energy expenditure and physical activity, along with significantly decreased Npy mRNA levels in the Arc but with no change in food consumption. Distinctively, the elevated fat mass in LHA CART ;Cart cre/cre mice was accompanied by diminished insulin responsiveness and glucose tolerance, greater spontaneous food intake, and reduced energy expenditure, which is consistent with the observed decrease of brown adipose tissue temperature. This is also in line with significantly reduced tyrosine hydroxylase (Th) and notably increased corticotropin-releasing hormone (Crh) mRNA expressions in the paraventricular nucleus (PVN). Taken together, these results identify catabolic and anabolic effects of CART in the Arc and LHA, respectively, demonstrating for the first time the distinct and region

  1. Single-cell multiplexed cytokine profiling of CD19 CAR-T cells reveals a diverse landscape of polyfunctional antigen-specific response.

    Science.gov (United States)

    Xue, Qiong; Bettini, Emily; Paczkowski, Patrick; Ng, Colin; Kaiser, Alaina; McConnell, Timothy; Kodrasi, Olja; Quigley, Máire F; Heath, James; Fan, Rong; Mackay, Sean; Dudley, Mark E; Kassim, Sadik H; Zhou, Jing

    2017-11-21

    It remains challenging to characterize the functional attributes of chimeric antigen receptor (CAR)-engineered T cell product targeting CD19 related to potency and immunotoxicity ex vivo, despite promising in vivo efficacy in patients with B cell malignancies. We employed a single-cell, 16-plex cytokine microfluidics device and new analysis techniques to evaluate the functional profile of CD19 CAR-T cells upon antigen-specific stimulation. CAR-T cells were manufactured from human PBMCs transfected with the lentivirus encoding the CD19-BB-z transgene and expanded with anti-CD3/anti-CD28 coated beads. The enriched CAR-T cells were stimulated with anti-CAR or control IgG beads, stained with anti-CD4 RPE and anti-CD8 Alexa Fluor 647 antibodies, and incubated for 16 h in a single-cell barcode chip (SCBC). Each SCBC contains ~12,000 microchambers, covered with a glass slide that was pre-patterned with a complete copy of a 16-plex antibody array. Protein secretions from single CAR-T cells were captured and subsequently analyzed using proprietary software and new visualization methods. We demonstrate a new method for single-cell profiling of CD19 CAR-T pre-infusion products prepared from 4 healthy donors. CAR-T single cells exhibited a marked heterogeneity of cytokine secretions and polyfunctional (2+ cytokine) subsets specific to anti-CAR bead stimulation. The breadth of responses includes anti-tumor effector (Granzyme B, IFN-γ, MIP-1α, TNF-α), stimulatory (GM-CSF, IL-2, IL-8), regulatory (IL-4, IL-13, IL-22), and inflammatory (IL-6, IL-17A) functions. Furthermore, we developed two new bioinformatics tools for more effective polyfunctional subset visualization and comparison between donors. Single-cell, multiplexed, proteomic profiling of CD19 CAR-T product reveals a diverse landscape of immune effector response of CD19 CAR-T cells to antigen-specific challenge, providing a new platform for capturing CAR-T product data for correlative analysis. Additionally, such high

  2. Paralleled comparison of vectors for the generation of CAR-T cells.

    Science.gov (United States)

    Qin, Di-Yuan; Huang, Yong; Li, Dan; Wang, Yong-Sheng; Wang, Wei; Wei, Yu-Quan

    2016-09-01

    T-lymphocytes genetically engineered with the chimeric antigen receptor (CAR-T) have shown great therapeutic potential in cancer treatment. A variety of preclinical researches and clinical trials of CAR-T therapy have been carried out to lay the foundation for future clinical application. In these researches, several gene-transfer methods were used to deliver CARs or other genes into T-lymphocytes, equipping CAR-modified T cells with a property of recognizing and attacking antigen-expressing tumor cells in a major histocompatibility complex-independent manner. Here, we summarize the gene-transfer vectors commonly used in the generation of CAR-T cell, including retrovirus vectors, lentivirus vectors, the transposon/transposase system, the plasmid-based system, and the messenger RNA electroporation system. The following aspects were compared in parallel: efficiency of gene transfer, the integration methods in the modified T cells, foreground of scale-up production, and application and development in clinical trials. These aspects should be taken into account to generate the optimal CAR-gene vector that may be suitable for future clinical application.

  3. Bayesian nonparametric dictionary learning for compressed sensing MRI.

    Science.gov (United States)

    Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping

    2014-12-01

    We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.

  4. Bioprocess iterative batch-to-batch optimization based on hybrid parametric/nonparametric models.

    Science.gov (United States)

    Teixeira, Ana P; Clemente, João J; Cunha, António E; Carrondo, Manuel J T; Oliveira, Rui

    2006-01-01

    This paper presents a novel method for iterative batch-to-batch dynamic optimization of bioprocesses. The relationship between process performance and control inputs is established by means of hybrid grey-box models combining parametric and nonparametric structures. The bioreactor dynamics are defined by material balance equations, whereas the cell population subsystem is represented by an adjustable mixture of nonparametric and parametric models. Thus optimizations are possible without detailed mechanistic knowledge concerning the biological system. A clustering technique is used to supervise the reliability of the nonparametric subsystem during the optimization. Whenever the nonparametric outputs are unreliable, the objective function is penalized. The technique was evaluated with three simulation case studies. The overall results suggest that the convergence to the optimal process performance may be achieved after a small number of batches. The model unreliability risk constraint along with sampling scheduling are crucial to minimize the experimental effort required to attain a given process performance. In general terms, it may be concluded that the proposed method broadens the application of the hybrid parametric/nonparametric modeling technique to "newer" processes with higher potential for optimization.

  5. Non-parametric correlative uncertainty quantification and sensitivity analysis: Application to a Langmuir bimolecular adsorption model

    Directory of Open Access Journals (Sweden)

    Jinchao Feng

    2018-03-01

    Full Text Available We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data. The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.

  6. Non-parametric correlative uncertainty quantification and sensitivity analysis: Application to a Langmuir bimolecular adsorption model

    Science.gov (United States)

    Feng, Jinchao; Lansford, Joshua; Mironenko, Alexander; Pourkargar, Davood Babaei; Vlachos, Dionisios G.; Katsoulakis, Markos A.

    2018-03-01

    We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data). The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.

  7. Multivariate nonparametric regression and visualization with R and applications to finance

    CERN Document Server

    Klemelä, Jussi

    2014-01-01

    A modern approach to statistical learning and its applications through visualization methods With a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generatingmechanisms, the book begins with an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for learning samples and functio

  8. A Nonparametric Test for Seasonal Unit Roots

    OpenAIRE

    Kunst, Robert M.

    2009-01-01

    Abstract: We consider a nonparametric test for the null of seasonal unit roots in quarterly time series that builds on the RUR (records unit root) test by Aparicio, Escribano, and Sipols. We find that the test concept is more promising than a formalization of visual aids such as plots by quarter. In order to cope with the sensitivity of the original RUR test to autocorrelation under its null of a unit root, we suggest an augmentation step by autoregression. We present some evidence on the siz...

  9. CAR-T therapy for leukemia: progress and challenges.

    Science.gov (United States)

    Wang, Xin; Xiao, Qing; Wang, Zhe; Feng, Wen-Li

    2017-04-01

    Despite the rapid development of therapeutic strategies, leukemia remains a type of difficult-to-treat hematopoietic malignancy that necessitates introduction of more effective treatment options to improve life expectancy and quality of patients. Genetic engineering in adoptively transferred T cells to express antigen-specific chimeric antigen receptors (CARs) has proved highly powerful and efficacious in inducing sustained responses in patients with refractory malignancies, as exemplified by the success of CD19-targeting CAR-T treatment in patients with relapsed acute lymphoblastic leukemia. Recent strategies, including manipulating intracellular activating domains and transducing viral vectors, have resulted in better designed and optimized CAR-T cells. This is further facilitated by the rapid identification of an accumulating number of potential leukemic antigens that may serve as therapeutic targets for CAR-T cells. This review will provide a comprehensive background and scrutinize recent important breakthrough studies on anti-leukemia CAR-T cells, with focus on recently identified antigens for CAR-T therapy design and approaches to overcome critical challenges. Copyright © 2016 Elsevier Inc. All rights reserved.

  10. Registro de los cartógrafos medievales activos en el puerto de Mallorca

    Directory of Open Access Journals (Sweden)

    Llompart, Gabriel

    1997-12-01

    Full Text Available In medieval times, with the opening of the Atlantic trading routes at the beginning of the 14th century, the port of the Ciutat de Mallorques became important as the base of a gathering of both cartographers and copiers of maps. Today these maps are dispersed in museums throughout the world. Until the present day, these early map makers were known only through their works. Presented here is the first register of the "maestros" of navigational charts who worked in the "port of Mallorca". The documentation is taken from local notarial and administrative sources, all of which help the better clarification of their names, birthplaces, their Sitz im Leben and their methods of production, which were later surpassed and improved by the advent of the modern printing press.[fr] Le port de Ciutat de Mallorques fût très important pour l'histoire de la cartographie médiévale, parce-qu'un certain nombre de dessinateurs et copistes de cartes s'établirent là au commencement du XIV siècle, en raison de l'ouverture de la route atlantique. Maintenant, ces cartes se trouvent dispersées en diferents musées par tout le monde. Les auteurs de ces cartes étaient connus et datés jusqu'aujourd'hui à travers ses ouvrages. Dans cet article nous est donné un premier registre des maîtres de cartes de naviguer que travaillèrent au port de Majorque, provenant de sources locales, notariales et administratives, que nous permettent d'eclircir leurs noms, leur date de naissance leur Sitz im Leben et leurs méthodes de travail, peu après débordés et dépassés par la presse moderne.

  11. Using Classification and Regression Trees (CART) to Identify Prescribing Thresholds for Cardiovascular Disease.

    Science.gov (United States)

    Schilling, Chris; Mortimer, Duncan; Dalziel, Kim; Heeley, Emma; Chalmers, John; Clarke, Philip

    2016-02-01

    Many guidelines for clinical decisions are hierarchical and nonlinear. Evaluating if these guidelines are used in practice requires methods that can identify such structures and thresholds. Classification and regression trees (CART) were used to analyse prescribing patterns of Australian general practitioners (GPs) for the primary prevention of cardiovascular disease (CVD). Our aim was to identify if GPs use absolute risk (AR) guidelines in favour of individual risk factors to inform their prescribing decisions of lipid-lowering medications. We employed administrative prescribing information that is linked to patient-level data from a clinical assessment and patient survey (the AusHeart Study), and assessed prescribing of lipid-lowering medications over a 12-month period for patients (n = 1903) who were not using such medications prior to recruitment. CART models were developed to explain prescribing practice. Out-of-sample performance was evaluated using receiver operating characteristic (ROC) curves, and optimised via pruning. We found that individual risk factors (low-density lipoprotein, diabetes, triglycerides and a history of CVD), GP-estimated rather than Framingham AR, and sociodemographic factors (household income, education) were the predominant drivers of GP prescribing. However, sociodemographic factors and some individual risk factors (triglycerides and CVD history) only become relevant for patients with a particular profile of other risk factors. The ROC area under the curve was 0.63 (95% confidence interval [CI] 0.60-0.64). There is little evidence that AR guidelines recommended by the National Heart Foundation and National Vascular Disease Prevention Alliance, or conditional individual risk eligibility guidelines from the Pharmaceutical Benefits Scheme, are adopted in prescribing practice. The hierarchy of conditional relationships between risk factors and socioeconomic factors identified by CART provides new insights into prescribing decisions

  12. Shopper marketing nutrition interventions: Social norms on grocery carts increase produce spending without increasing shopper budgets☆

    Science.gov (United States)

    Payne, Collin R.; Niculescu, Mihai; Just, David R.; Kelly, Michael P.

    2015-01-01

    Objectives We assessed the efficacy of an easy-to-implement shopper marketing nutrition intervention in a pilot and two additional studies to increase produce demand without decreasing store profitability or increasing shopper budgets. Methods We created grocery cart placards that detailed the number of produce items purchased (i.e., descriptive norm) at particular stores (i.e., provincial norm). The effect of these placards on produce spending was assessed across 971,706 individual person grocery store transactions aggregated by day. The pilot study designated a baseline period (in both control and intervention store) followed by installation of grocery cart placards (in the intervention store) for two weeks. The pilot study was conducted in Texas in 2012. In two additional stores, we designated baseline periods followed by 28 days of the same grocery cart placard intervention as in the pilot. Additional interventions were conducted in New Mexico in 2013. Results The pilot study resulted in a significant difference between average produce spending per day per person across treatment periods (i.e., intervention versus same time period in control) (16%) and the difference between average produce spending per day per person across stores in the control periods (4%); Furthermore, the same intervention in two additional stores resulted in significant produce spending increases of 12.4% and 7.5% per day per person respectively. In all stores, total spending did not change. Conclusions Descriptive and provincial social norm messages (i.e., on grocery cart placards) may be an overlooked tool to increase produce demand without decreasing store profitability and increasing shopper budgets. PMID:26844084

  13. An application of CART algorithm in genetics: IGFs and cGH polymorphisms in Japanese quail

    Science.gov (United States)

    Kaplan, Selçuk

    2017-04-01

    The avian insulin-like growth factor-1 (IGFs) and avian growth hormone (cGH) genes are the most important genes that can affect bird performance traits because of its important function in growth and metabolism. Understanding the molecular genetic basis of variation in growth-related traits is of importance for continued improvement and increased rates of genetic gain. The objective of the present study was to identify polymorphisms of cGH and IGFs genes in Japanese quail using conventional least square method (LSM) and CART algorithm. Therefore, this study was aimed to demonstrate at determining the polymorphisms of two genes related growth characteristics via CART algorithm. A simulated data set was generated to analyze by adhering the results of some poultry genetic studies which it includes live weights at 5 weeks of age, 3 alleles and 6 genotypes of cGH and 2 alleles and 3 genotypes of IGFs. As a result, it has been determined that the CART algorithm has some advantages as for that LSM.

  14. A comparative study of non-parametric models for identification of ...

    African Journals Online (AJOL)

    However, the frequency response method using random binary signals was good for unpredicted white noise characteristics and considered the best method for non-parametric system identifica-tion. The autoregressive external input (ARX) model was very useful for system identification, but on applicati-on, few input ...

  15. Les voix/voies de la carte

    Directory of Open Access Journals (Sweden)

    Louise Bénat-Tachot

    2012-01-01

    Full Text Available La confection du padrón real par les experts de la Casa de la Contratación de Séville dans la première moitié du XVIe siècle, a été un enjeu majeur non seulement pour établir la nouvelle configuration globale du monde mais aussi pour légitimer les entreprises d’expansion des nations ibériques en affirmant la maîtrise de la navigation. Dans quelle mesure la rédaction des premières chroniques des Indes est-elle liée à cette activité cartographique d’état qui lui est contemporaine ? Les liens épistémologiques, rhétoriques et politiques qui lient ces deux productions seront étudiés à partir de la carte universelle de Diego de Ribeiro et de deux chroniques : celle de Gonzalo Fernández de Oviedo et celle de Francisco López de Gómara.La confección del padrón real por los expertos de la Casa de la Contratación de Sevilla durante la primera mitad del siglo XVI, fue de mayor trascendencia no sólo por cartografiar la nueva configuración del mundo sino también porque legitimaba la empresa de expansión de las naciones ibéricas así como para afirmaba su dominio de la navegación. ¿ En qué medida se vincula la redacción de las primeras crónicas de Indias con esta actividad de estado cartográfica contemporánea ? Los vínculos epistemológicos, retóricos y políticos que unen estas dos producciones serán analizados a partir de la carta universal de Diego de Ribeiro y de las dos crónicas primitivas de Indias : la de Gonzalo Fernández de Oviedo y la de Francisco López de Gómara.

  16. Nonparametric Analyses of Log-Periodic Precursors to Financial Crashes

    Science.gov (United States)

    Zhou, Wei-Xing; Sornette, Didier

    We apply two nonparametric methods to further test the hypothesis that log-periodicity characterizes the detrended price trajectory of large financial indices prior to financial crashes or strong corrections. The term "parametric" refers here to the use of the log-periodic power law formula to fit the data; in contrast, "nonparametric" refers to the use of general tools such as Fourier transform, and in the present case the Hilbert transform and the so-called (H, q)-analysis. The analysis using the (H, q)-derivative is applied to seven time series ending with the October 1987 crash, the October 1997 correction and the April 2000 crash of the Dow Jones Industrial Average (DJIA), the Standard & Poor 500 and Nasdaq indices. The Hilbert transform is applied to two detrended price time series in terms of the ln(tc-t) variable, where tc is the time of the crash. Taking all results together, we find strong evidence for a universal fundamental log-frequency f=1.02±0.05 corresponding to the scaling ratio λ=2.67±0.12. These values are in very good agreement with those obtained in earlier works with different parametric techniques. This note is extracted from a long unpublished report with 58 figures available at , which extensively describes the evidence we have accumulated on these seven time series, in particular by presenting all relevant details so that the reader can judge for himself or herself the validity and robustness of the results.

  17. IL-7 and CCL19 expression in CAR-T cells improves immune cell infiltration and CAR-T cell survival in the tumor.

    Science.gov (United States)

    Adachi, Keishi; Kano, Yosuke; Nagai, Tomohiko; Okuyama, Namiko; Sakoda, Yukimi; Tamada, Koji

    2018-04-01

    Infiltration, accumulation, and survival of chimeric antigen receptor T (CAR-T) cells in solid tumors is crucial for tumor clearance. We engineered CAR-T cells to express interleukin (IL)-7 and CCL19 (7 × 19 CAR-T cells), as these factors are essential for the maintenance of T-cell zones in lymphoid organs. In mice, 7 × 19 CAR-T cells achieved complete regression of pre-established solid tumors and prolonged mouse survival, with superior anti-tumor activity compared to conventional CAR-T cells. Histopathological analyses showed increased infiltration of dendritic cells (DC) and T cells into tumor tissues following 7 × 19 CAR-T cell therapy. Depletion of recipient T cells before 7 × 19 CAR-T cell administration dampened the therapeutic effects of 7 × 19 CAR-T cell treatment, suggesting that CAR-T cells and recipient immune cells collaborated to exert anti-tumor activity. Following treatment of mice with 7 × 19 CAR-T cells, both recipient conventional T cells and administered CAR-T cells generated memory responses against tumors.

  18. Nonparametric Facial Feature Localization Using Segment-Based Eigenfeatures

    Directory of Open Access Journals (Sweden)

    Hyun-Chul Choi

    2016-01-01

    Full Text Available We present a nonparametric facial feature localization method using relative directional information between regularly sampled image segments and facial feature points. Instead of using any iterative parameter optimization technique or search algorithm, our method finds the location of facial feature points by using a weighted concentration of the directional vectors originating from the image segments pointing to the expected facial feature positions. Each directional vector is calculated by linear combination of eigendirectional vectors which are obtained by a principal component analysis of training facial segments in feature space of histogram of oriented gradient (HOG. Our method finds facial feature points very fast and accurately, since it utilizes statistical reasoning from all the training data without need to extract local patterns at the estimated positions of facial features, any iterative parameter optimization algorithm, and any search algorithm. In addition, we can reduce the storage size for the trained model by controlling the energy preserving level of HOG pattern space.

  19. Indoor Positioning Using Nonparametric Belief Propagation Based on Spanning Trees

    Directory of Open Access Journals (Sweden)

    Savic Vladimir

    2010-01-01

    Full Text Available Nonparametric belief propagation (NBP is one of the best-known methods for cooperative localization in sensor networks. It is capable of providing information about location estimation with appropriate uncertainty and to accommodate non-Gaussian distance measurement errors. However, the accuracy of NBP is questionable in loopy networks. Therefore, in this paper, we propose a novel approach, NBP based on spanning trees (NBP-ST created by breadth first search (BFS method. In addition, we propose a reliable indoor model based on obtained measurements in our lab. According to our simulation results, NBP-ST performs better than NBP in terms of accuracy and communication cost in the networks with high connectivity (i.e., highly loopy networks. Furthermore, the computational and communication costs are nearly constant with respect to the transmission radius. However, the drawbacks of proposed method are a little bit higher computational cost and poor performance in low-connected networks.

  20. Nonparametric estimation of stochastic differential equations with sparse Gaussian processes.

    Science.gov (United States)

    García, Constantino A; Otero, Abraham; Félix, Paulo; Presedo, Jesús; Márquez, David G

    2017-08-01

    The application of stochastic differential equations (SDEs) to the analysis of temporal data has attracted increasing attention, due to their ability to describe complex dynamics with physically interpretable equations. In this paper, we introduce a nonparametric method for estimating the drift and diffusion terms of SDEs from a densely observed discrete time series. The use of Gaussian processes as priors permits working directly in a function-space view and thus the inference takes place directly in this space. To cope with the computational complexity that requires the use of Gaussian processes, a sparse Gaussian process approximation is provided. This approximation permits the efficient computation of predictions for the drift and diffusion terms by using a distribution over a small subset of pseudosamples. The proposed method has been validated using both simulated data and real data from economy and paleoclimatology. The application of the method to real data demonstrates its ability to capture the behavior of complex systems.

  1. Bayesian Nonparametric Clustering for Positive Definite Matrices.

    Science.gov (United States)

    Cherian, Anoop; Morellas, Vassilios; Papanikolopoulos, Nikolaos

    2016-05-01

    Symmetric Positive Definite (SPD) matrices emerge as data descriptors in several applications of computer vision such as object tracking, texture recognition, and diffusion tensor imaging. Clustering these data matrices forms an integral part of these applications, for which soft-clustering algorithms (K-Means, expectation maximization, etc.) are generally used. As is well-known, these algorithms need the number of clusters to be specified, which is difficult when the dataset scales. To address this issue, we resort to the classical nonparametric Bayesian framework by modeling the data as a mixture model using the Dirichlet process (DP) prior. Since these matrices do not conform to the Euclidean geometry, rather belongs to a curved Riemannian manifold,existing DP models cannot be directly applied. Thus, in this paper, we propose a novel DP mixture model framework for SPD matrices. Using the log-determinant divergence as the underlying dissimilarity measure to compare these matrices, and further using the connection between this measure and the Wishart distribution, we derive a novel DPM model based on the Wishart-Inverse-Wishart conjugate pair. We apply this model to several applications in computer vision. Our experiments demonstrate that our model is scalable to the dataset size and at the same time achieves superior accuracy compared to several state-of-the-art parametric and nonparametric clustering algorithms.

  2. Improving Odometric Accuracy for an Autonomous Electric Cart

    Directory of Open Access Journals (Sweden)

    Jonay Toledo

    2018-01-01

    Full Text Available In this paper, a study of the odometric system for the autonomous cart Verdino, which is an electric vehicle based on a golf cart, is presented. A mathematical model of the odometric system is derived from cart movement equations, and is used to compute the vehicle position and orientation. The inputs of the system are the odometry encoders, and the model uses the wheels diameter and distance between wheels as parameters. With this model, a least square minimization is made in order to get the nominal best parameters. This model is updated, including a real time wheel diameter measurement improving the accuracy of the results. A neural network model is used in order to learn the odometric model from data. Tests are made using this neural network in several configurations and the results are compared to the mathematical model, showing that the neural network can outperform the first proposed model.

  3. Improving Odometric Accuracy for an Autonomous Electric Cart.

    Science.gov (United States)

    Toledo, Jonay; Piñeiro, Jose D; Arnay, Rafael; Acosta, Daniel; Acosta, Leopoldo

    2018-01-12

    In this paper, a study of the odometric system for the autonomous cart Verdino, which is an electric vehicle based on a golf cart, is presented. A mathematical model of the odometric system is derived from cart movement equations, and is used to compute the vehicle position and orientation. The inputs of the system are the odometry encoders, and the model uses the wheels diameter and distance between wheels as parameters. With this model, a least square minimization is made in order to get the nominal best parameters. This model is updated, including a real time wheel diameter measurement improving the accuracy of the results. A neural network model is used in order to learn the odometric model from data. Tests are made using this neural network in several configurations and the results are compared to the mathematical model, showing that the neural network can outperform the first proposed model.

  4. Simulation and Test of a Fuel Cell Hybrid Golf Cart

    Directory of Open Access Journals (Sweden)

    Jingming Liang

    2014-01-01

    Full Text Available This paper establishes the simulation model of fuel cell hybrid golf cart (FCHGC, which applies the non-GUI mode of the Advanced Vehicle Simulator (ADVISOR and the genetic algorithm (GA to optimize it. Simulation of the objective function is composed of fuel consumption and vehicle dynamic performance; the variables are the fuel cell stack power sizes and the battery numbers. By means of simulation, the optimal parameters of vehicle power unit, fuel cell stack, and battery pack are worked out. On this basis, GUI mode of ADVISOR is used to select the rated power of vehicle motor. In line with simulation parameters, an electrical golf cart is refitted by adding a 2 kW hydrogen air proton exchange membrane fuel cell (PEMFC stack system and test the FCHGC. The result shows that the simulation data is effective but it needs improving compared with that of the real cart test.

  5. New development in CAR-T cell therapy

    Directory of Open Access Journals (Sweden)

    Zhenguang Wang

    2017-02-01

    Full Text Available Abstract Chimeric antigen receptor (CAR-engineered T cells (CAR-T cells have yielded unprecedented efficacy in B cell malignancies, most remarkably in anti-CD19 CAR-T cells for B cell acute lymphoblastic leukemia (B-ALL with up to a 90% complete remission rate. However, tumor antigen escape has emerged as a main challenge for the long-term disease control of this promising immunotherapy in B cell malignancies. In addition, this success has encountered significant hurdles in translation to solid tumors, and the safety of the on-target/off-tumor recognition of normal tissues is one of the main reasons. In this mini-review, we characterize some of the mechanisms for antigen loss relapse and new strategies to address this issue. In addition, we discuss some novel CAR designs that are being considered to enhance the safety of CAR-T cell therapy in solid tumors.

  6. New development in CAR-T cell therapy.

    Science.gov (United States)

    Wang, Zhenguang; Wu, Zhiqiang; Liu, Yang; Han, Weidong

    2017-02-21

    Chimeric antigen receptor (CAR)-engineered T cells (CAR-T cells) have yielded unprecedented efficacy in B cell malignancies, most remarkably in anti-CD19 CAR-T cells for B cell acute lymphoblastic leukemia (B-ALL) with up to a 90% complete remission rate. However, tumor antigen escape has emerged as a main challenge for the long-term disease control of this promising immunotherapy in B cell malignancies. In addition, this success has encountered significant hurdles in translation to solid tumors, and the safety of the on-target/off-tumor recognition of normal tissues is one of the main reasons. In this mini-review, we characterize some of the mechanisms for antigen loss relapse and new strategies to address this issue. In addition, we discuss some novel CAR designs that are being considered to enhance the safety of CAR-T cell therapy in solid tumors.

  7. Decision boundary feature selection for non-parametric classifier

    Science.gov (United States)

    Lee, Chulhee; Landgrebe, David A.

    1991-01-01

    Feature selection has been one of the most important topics in pattern recognition. Although many authors have studied feature selection for parametric classifiers, few algorithms are available for feature selection for nonparametric classifiers. In this paper we propose a new feature selection algorithm based on decision boundaries for nonparametric classifiers. We first note that feature selection for pattern recognition is equivalent to retaining 'discriminantly informative features', and a discriminantly informative feature is related to the decision boundary. A procedure to extract discriminantly informative features based on a decision boundary for nonparametric classification is proposed. Experiments show that the proposed algorithm finds effective features for the nonparametric classifier with Parzen density estimation.

  8. 2º Concurso de ideas en cartón

    OpenAIRE

    Álvaro Tordesillas, Antonio; Galván Desvaux, Noelia; Alonso Rodríguez, Marta

    2015-01-01

    Se trata de diseñar una CARPA o CUBIERTA móvil de cartón que se ubicará en los espacios interiores de la Escuela de Arquitectura y servirá para cubrir espacios con diferentes usos. informativos, expositivos, stands feriales, etc. Será fácilmente montable, desmontable y transportable y estará basada en pliegues autoportantes en cartójn, sin pegamento. Urbanismo y Representación de la Arquitectura

  9. Generative Temporal Modelling of Neuroimaging - Decomposition and Nonparametric Testing

    DEFF Research Database (Denmark)

    Hald, Ditte Høvenhoff

    The goal of this thesis is to explore two improvements for functional magnetic resonance imaging (fMRI) analysis; namely our proposed decomposition method and an extension to the non-parametric testing framework. Analysis of fMRI allows researchers to investigate the functional processes...... of the brain, and provides insight into neuronal coupling during mental processes or tasks. The decomposition method is a Gaussian process-based independent components analysis (GPICA), which incorporates a temporal dependency in the sources. A hierarchical model specification is used, featuring both...... instantaneous and convolutive mixing, and the inferred temporal patterns. Spatial maps are seen to capture smooth and localized stimuli-related components, and often identifiable noise components. The implementation is freely available as a GUI/SPM plugin, and we recommend using GPICA as an additional tool when...

  10. MatchIt: Nonparametric Preprocessing for Parametric Causal Inference

    Directory of Open Access Journals (Sweden)

    Daniel Ho

    2011-08-01

    Full Text Available MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007 for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing data with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. MatchIt is an R program, and also works seamlessly with Zelig.

  11. Fruit Carts: A Domain and Corpus for Research in Dialogue Systems and Psycholinguistics.

    Science.gov (United States)

    Aist, Gregory; Campana, Ellen; Allen, James; Swift, Mary; Tanenhaus, Michael K

    2012-09-01

    We describe a novel domain, Fruit Carts, aimed at eliciting human language production for the twin purposes of (a) dialogue system research and development and (b) psycholinguistic research. Fruit Carts contains five tasks: choosing a cart, placing it on a map, painting the cart, rotating the cart, and filling the cart with fruit. Fruit Carts has been used for research in psycholinguistics and in dialogue systems. Based on these experiences, we discuss how well the Fruit Carts domain meets four desired features: unscripted, context-constrained, controllable difficulty, and separability into semi-independent subdialogues. We describe the domain in sufficient detail to allow others to replicate it; researchers interested in using the corpora themselves are encouraged to contact the authors directly.

  12. On Parametric (and Non-Parametric Variation

    Directory of Open Access Journals (Sweden)

    Neil Smith

    2009-11-01

    Full Text Available This article raises the issue of the correct characterization of ‘Parametric Variation’ in syntax and phonology. After specifying their theoretical commitments, the authors outline the relevant parts of the Principles–and–Parameters framework, and draw a three-way distinction among Universal Principles, Parameters, and Accidents. The core of the contribution then consists of an attempt to provide identity criteria for parametric, as opposed to non-parametric, variation. Parametric choices must be antecedently known, and it is suggested that they must also satisfy seven individually necessary and jointly sufficient criteria. These are that they be cognitively represented, systematic, dependent on the input, deterministic, discrete, mutually exclusive, and irreversible.

  13. Nonparametric predictive pairwise comparison with competing risks

    International Nuclear Information System (INIS)

    Coolen-Maturi, Tahani

    2014-01-01

    In reliability, failure data often correspond to competing risks, where several failure modes can cause a unit to fail. This paper presents nonparametric predictive inference (NPI) for pairwise comparison with competing risks data, assuming that the failure modes are independent. These failure modes could be the same or different among the two groups, and these can be both observed and unobserved failure modes. NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. The focus is on the lower and upper probabilities for the event that the lifetime of a future unit from one group, say Y, is greater than the lifetime of a future unit from the second group, say X. The paper also shows how the two groups can be compared based on particular failure mode(s), and the comparison of the two groups when some of the competing risks are combined is discussed

  14. Overview of the West Valley Vitrification Facility transfer cart control system

    International Nuclear Information System (INIS)

    Bradley, E.C.; Rupple, F.R.

    1993-01-01

    Oak Ridge National Laboratory (ORNL) has designed the control system for the West Valley Demonstration Project Vitrification Facility transfer cart. The transfer cart will transfer canisters of vitrified high-level waste remotely within the Vitrification Facility. The control system will operate the cart under battery power by wireless control. The equipment includes cart mounted control electronics, battery charger, control pendants, engineer's console, and facility antennas

  15. Assessing pupil and school performance by non-parametric and parametric techniques

    NARCIS (Netherlands)

    de Witte, K.; Thanassoulis, E.; Simpson, G.; Battisti, G.; Charlesworth-May, A.

    2010-01-01

    This paper discusses the use of the non-parametric free disposal hull (FDH) and the parametric multi-level model (MLM) as alternative methods for measuring pupil and school attainment where hierarchical structured data are available. Using robust FDH estimates, we show how to decompose the overall

  16. Nonparametric estimation of the stationary M/G/1 workload distribution function

    DEFF Research Database (Denmark)

    Hansen, Martin Bøgsted

    2005-01-01

    In this paper it is demonstrated how a nonparametric estimator of the stationary workload distribution function of the M/G/1-queue can be obtained by systematic sampling the workload process. Weak convergence results and bootstrap methods for empirical distribution functions for stationary associ...

  17. Consistent computation of the age of water parcels using CART

    Science.gov (United States)

    Mercier, Ch.; Delhez, E. J. M.

    The Constituent-oriented Age and Residence time Theory (CART) provides a flexible and efficient framework to diagnose the dynamics of marine systems. Beside the equation for the concentration of appropriate (real or artificial) tracers, the method requires the resolution of differential problems for the so-called age concentration of each of these tracers. Thanks to its Eulerian formulation as an advection/diffusion problem with source terms, the method is easily implemented in existing models. However, some numerical artifacts should be avoided in order to produce physically meaningful results leading to a better understanding of the system under study. In this paper, we address two such issues that are related to the degree of implicitness of the different terms and to the advection scheme. To enforce the consistency between the discrete equations for the concentration of a tracer and for its age concentration, the degree of implicitness must be identical in the source/sink terms of the two equations. However, the ageing term should be computed in a completely explicit (respectively implicit) way if the discretization of the source/sink terms is implicit in time (respectively explicit). A specific attention should also be paid to the advection schemes for the concentration and the age concentration. The raw application of Total Variation Diminishing (TVD) scheme for both equations can lead to the occurrence of artificial local extreme values and spatial oscillations of the age field. While the TVD behavior of the discrete age field cannot be guaranteed, appropriate modifications of the flux/slope limiters used in the TVD schemes can be implemented to enforce a maximum principle that prevents the occurrence of age values outside the physically acceptable range.

  18. An application in identifying high-risk populations in alternative tobacco product use utilizing logistic regression and CART: a heuristic comparison.

    Science.gov (United States)

    Lei, Yang; Nollen, Nikki; Ahluwahlia, Jasjit S; Yu, Qing; Mayo, Matthew S

    2015-04-09

    rate was 0.342 for the training sample and 0.346 for the validation sample. The CART model was easier to interpret and discovered target populations that possess clinical significance. This study suggests that the non-parametric CART model is parsimonious, potentially easier to interpret, and provides additional information in identifying the subgroups at high risk of ATP use among cigarette smokers.

  19. Smart Shopping Carts: How Real-Time Feedback Influences Spending

    NARCIS (Netherlands)

    Ittersum, van K.; Wansink, B.; Pennings, J.M.E.; Sheehan, D.

    2013-01-01

    Although interest in smart shopping carts is increasing, both retailers and consumer groups have concerns about how real-time spending feedback will influence shopping behavior. Building on budgeting and spending theories, the authors conduct three lab and grocery store experiments that robustly

  20. Smart shopping carts : How real-time feedback influences spending

    NARCIS (Netherlands)

    van Ittersum, Koert; Wansink, B.; Pennings, J.M.E.; Sheehan, D.

    2013-01-01

    Although interest in smart shopping carts is increasing, both retailers and consumer groups have concerns about how real-time spending feedback will influence shopping behavior. Building on budgeting and spending theories, the authors conduct three lab and grocery store experiments that robustly

  1. Carte : Liens entre recherche et politiques dans le cadre du ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Le programme ACCA appuie la recherche action participative servant à éclairer les processus d'élaboration des politiques au moyen de données scientifiques probantes sur la vulnérabilité de certaines populations face aux changements climatiques et les mesures d'adaptation à mettre en oeuvre. La présente carte illustre ...

  2. Nonparametric modeling of dynamic functional connectivity in fmri data

    DEFF Research Database (Denmark)

    Nielsen, Søren Føns Vind; Madsen, Kristoffer H.; Røge, Rasmus

    2015-01-01

    dynamic changes. The existing approaches modeling dynamic connectivity have primarily been based on time-windowing the data and k-means clustering. We propose a nonparametric generative model for dynamic FC in fMRI that does not rely on specifying window lengths and number of dynamic states. Rooted......Dynamic functional connectivity (FC) has in recent years become a topic of interest in the neuroimaging community. Several models and methods exist for both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), and the results point towards the conclusion that FC exhibits...... in Bayesian statistical modeling we use the predictive likelihood to investigate if the model can discriminate between a motor task and rest both within and across subjects. We further investigate what drives dynamic states using the model on the entire data collated across subjects and task/rest. We find...

  3. Debt and growth: A non-parametric approach

    Science.gov (United States)

    Brida, Juan Gabriel; Gómez, David Matesanz; Seijas, Maria Nela

    2017-11-01

    In this study, we explore the dynamic relationship between public debt and economic growth by using a non-parametric approach based on data symbolization and clustering methods. The study uses annual data of general government consolidated gross debt-to-GDP ratio and gross domestic product for sixteen countries between 1977 and 2015. Using symbolic sequences, we introduce a notion of distance between the dynamical paths of different countries. Then, a Minimal Spanning Tree and a Hierarchical Tree are constructed from time series to help detecting the existence of groups of countries sharing similar economic performance. The main finding of the study appears for the period 2008-2016 when several countries surpassed the 90% debt-to-GDP threshold. During this period, three groups (clubs) of countries are obtained: high, mid and low indebted countries, suggesting that the employed debt-to-GDP threshold drives economic dynamics for the selected countries.

  4. A Structural Labor Supply Model with Nonparametric Preferences

    NARCIS (Netherlands)

    van Soest, A.H.O.; Das, J.W.M.; Gong, X.

    2000-01-01

    Nonparametric techniques are usually seen as a statistic device for data description and exploration, and not as a tool for estimating models with a richer economic structure, which are often required for policy analysis.This paper presents an example where nonparametric flexibility can be attained

  5. Simple nonparametric checks for model data fit in CAT

    NARCIS (Netherlands)

    Meijer, R.R.

    2005-01-01

    In this paper, the usefulness of several nonparametric checks is discussed in a computerized adaptive testing (CAT) context. Although there is no tradition of nonparametric scalability in CAT, it can be argued that scalability checks can be useful to investigate, for example, the quality of item

  6. Testing the race model inequality : a nonparametric approach

    NARCIS (Netherlands)

    Maris, G.K.J.; Maris, E.

    2003-01-01

    This paper introduces a nonparametric procedure for testing the race model explanation of the redundant signals effect. The null hypothesis is the race model inequality derived from the race model by Miller (Cognitive Psychol. 14 (1982) 247). The construction of a nonparametric test is made possible

  7. Nonparametric analysis of blocked ordered categories data: some examples revisited

    Directory of Open Access Journals (Sweden)

    O. Thas

    2006-08-01

    Full Text Available Nonparametric analysis for general block designs can be given by using the Cochran-Mantel-Haenszel (CMH statistics. We demonstrate this with four examples and note that several well-known nonparametric statistics are special cases of CMH statistics.

  8. Experiment Design for Nonparametric Models Based On Minimizing Bayes Risk: Application to Voriconazole1

    Science.gov (United States)

    Bayard, David S.; Neely, Michael

    2016-01-01

    An experimental design approach is presented for individualized therapy in the special case where the prior information is specified by a nonparametric (NP) population model. Here, a nonparametric model refers to a discrete probability model characterized by a finite set of support points and their associated weights. An important question arises as to how to best design experiments for this type of model. Many experimental design methods are based on Fisher Information or other approaches originally developed for parametric models. While such approaches have been used with some success across various applications, it is interesting to note that they largely fail to address the fundamentally discrete nature of the nonparametric model. Specifically, the problem of identifying an individual from a nonparametric prior is more naturally treated as a problem of classification, i.e., to find a support point that best matches the patient’s behavior. This paper studies the discrete nature of the NP experiment design problem from a classification point of view. Several new insights are provided including the use of Bayes Risk as an information measure, and new alternative methods for experiment design. One particular method, denoted as MMopt (Multiple-Model Optimal), will be examined in detail and shown to require minimal computation while having distinct advantages compared to existing approaches. Several simulated examples, including a case study involving oral voriconazole in children, are given to demonstrate the usefulness of MMopt in pharmacokinetics applications. PMID:27909942

  9. 2nd Conference of the International Society for Nonparametric Statistics

    CERN Document Server

    Manteiga, Wenceslao; Romo, Juan

    2016-01-01

    This volume collects selected, peer-reviewed contributions from the 2nd Conference of the International Society for Nonparametric Statistics (ISNPS), held in Cádiz (Spain) between June 11–16 2014, and sponsored by the American Statistical Association, the Institute of Mathematical Statistics, the Bernoulli Society for Mathematical Statistics and Probability, the Journal of Nonparametric Statistics and Universidad Carlos III de Madrid. The 15 articles are a representative sample of the 336 contributed papers presented at the conference. They cover topics such as high-dimensional data modelling, inference for stochastic processes and for dependent data, nonparametric and goodness-of-fit testing, nonparametric curve estimation, object-oriented data analysis, and semiparametric inference. The aim of the ISNPS 2014 conference was to bring together recent advances and trends in several areas of nonparametric statistics in order to facilitate the exchange of research ideas, promote collaboration among researchers...

  10. Decision tree methods: applications for classification and prediction.

    Science.gov (United States)

    Song, Yan-Yan; Lu, Ying

    2015-04-25

    Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure.

  11. Le “Carte Villarosa”. Sei fascicoli di carte vichiane non rilegate(Ms XIX, 42. Fascicoli I-VI.

    Directory of Open Access Journals (Sweden)

    Giambattista Vico

    2016-12-01

    Full Text Available Digital edition of the so-called “Carte Villarosa”, an essential collection of Vico's manuscripts preserved at the National Library of Naples "V. Emanuele III". Edition by the ISPF-CNR Center for Digital Humanities. Foreword by Manuela Sanna. Edited by Leonardo Pica Ciamarra, Roberto Evangelista, Assunta Sansone, Roberta Visone, Ruggero Cerino.

  12. Le “Carte Villarosa”. Sei fascicoli di carte vichiane non rilegate (Ms XIX, 42. Nota editoriale e indici.

    Directory of Open Access Journals (Sweden)

    ISPF-CNR Center for Digital Humanities

    2016-12-01

    Full Text Available Editorial Note and Indexes for the digital edition of the so-called “Carte Villarosa”, an essential collection of Vico's manuscripts preserved at the National Library of Naples "V. Emanuele III". Edition by the ISPF-CNR Center for Digital Humanities. Foreword by Manuela Sanna. Edited by Leonardo Pica Ciamarra, Roberto Evangelista, Assunta Sansone, Roberta Visone, Ruggero Cerino.

  13. Testing for constant nonparametric effects in general semiparametric regression models with interactions

    KAUST Repository

    Wei, Jiawei

    2011-07-01

    We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work was originally motivated by a unique testing problem in genetic epidemiology (Chatterjee, et al., 2006) that involved a typical generalized linear model but with an additional term reminiscent of the Tukey one-degree-of-freedom formulation, and their interest was in testing for main effects of the genetic variables, while gaining statistical power by allowing for a possible interaction between genes and the environment. Later work (Maity, et al., 2009) involved the possibility of modeling the environmental variable nonparametrically, but they focused on whether there was a parametric main effect for the genetic variables. In this paper, we consider the complementary problem, where the interest is in testing for the main effect of the nonparametrically modeled environmental variable. We derive a generalized likelihood ratio test for this hypothesis, show how to implement it, and provide evidence that our method can improve statistical power when compared to standard partially linear models with main effects only. We use the method for the primary purpose of analyzing data from a case-control study of colorectal adenoma.

  14. Nonparametric estimation of the stationary M/G/1 workload distribution function

    DEFF Research Database (Denmark)

    Hansen, Martin Bøgsted

    In this paper it is demonstrated how a nonparametric estimator of the stationary workload distribution function of the M/G/1-queue can be obtained by systematic sampling the workload process. Weak convergence results and bootstrap methods for empirical distribution functions for stationary...... associated sequences are used to derive asymptotic results and bootstrap methods for inference about the workload distribution function. The potential of the method is illustrated by a simulation study of the M/D/1 model....

  15. Exploring the social determinants of mental health service use using intersectionality theory and CART analysis.

    Science.gov (United States)

    Cairney, John; Veldhuizen, Scott; Vigod, Simone; Streiner, David L; Wade, Terrance J; Kurdyak, Paul

    2014-02-01

    Fewer than half of individuals with a mental disorder seek formal care in a given year. Much research has been conducted on the factors that influence service use in this population, but the methods generally used cannot easily identify the complex interactions that are thought to exist. In this paper, we examine predictors of subsequent service use among respondents to a population health survey who met criteria for a past-year mood, anxiety or substance-related disorder. To determine service use, we use an administrative database including all physician consultations in the period of interest. To identify predictors, we use classification tree (CART) analysis, a data mining technique with the ability to identify unsuspected interactions. We compare results to those from logistic regression models. We identify 1213 individuals with past-year disorder. In the year after the survey, 24% (n=312) of these had a mental health-related physician consultation. Logistic regression revealed that age, sex and marital status predicted service use. CART analysis yielded a set of rules based on age, sex, marital status and income adequacy, with marital status playing a role among men and by income adequacy important among women. CART analysis proved moderately effective overall, with agreement of 60%, sensitivity of 82% and specificity of 53%. Results highlight the potential of data-mining techniques to uncover complex interactions, and offer support to the view that the intersection of multiple statuses influence health and behaviour in ways that are difficult to identify with conventional statistics. The disadvantages of these methods are also discussed.

  16. Modeling and Simulation of a Simple Cart with Low-Impact Casters

    Directory of Open Access Journals (Sweden)

    Tagami Masaharu

    2016-01-01

    Full Text Available Low-impact casters are of great importance in realizing low-crash and anti-vibration carts for a range of purposes. Here the principle of center of percussion is efficiently applied to a caster in order to suppress the transfer of crash forces to the cart from the caster. Excellent performance of this force suppression has been confirmed previously by both simulation and experiments in a single-caster cart. However, carts in real-life applications usually require four sets of casters. In this context, a mathematical model of a cart with four sets of low-impact casters was formulated. In this paper, the modeling a four-caster cart, and simulations for the cart passing over a bump are discussed.

  17. Testing of the West Valley Vitrification Facility transfer cart control system

    International Nuclear Information System (INIS)

    Halliwell, J.W.; Bradley, E.C.

    1995-01-01

    Oak Ridge National Laboratory (ORNL) has designed and tested the control system for the West Valley Demonstration Project Vitrification Facility transfer cart. The transfer cart will transfer canisters of vitrified high-level waste remotely within the Vitrification Facility. The control system operates the cart under battery power by wireless control. The equipment includes cart-mounted control electronics, battery charger, control pendants, engineer's console, and facility antennas. Testing was performed in several phases of development: (1) prototype equipment was built and tested during design, (2) board-level testing was then performed at ORNL during fabrication, and (3) system-level testing was then performed by ORNL at the fabrication subcontractor's facility for the completed cart system. These tests verified (1) the performance of the cart relative to design requirements and (2) operation of various built-in cart features. The final phase of testing is planned to be conducted during installation at the West Valley Vitrification Facility

  18. Non-parametric Tuning of PID Controllers A Modified Relay-Feedback-Test Approach

    CERN Document Server

    Boiko, Igor

    2013-01-01

    The relay feedback test (RFT) has become a popular and efficient  tool used in process identification and automatic controller tuning. Non-parametric Tuning of PID Controllers couples new modifications of classical RFT with application-specific optimal tuning rules to form a non-parametric method of test-and-tuning. Test and tuning are coordinated through a set of common parameters so that a PID controller can obtain the desired gain or phase margins in a system exactly, even with unknown process dynamics. The concept of process-specific optimal tuning rules in the nonparametric setup, with corresponding tuning rules for flow, level pressure, and temperature control loops is presented in the text.   Common problems of tuning accuracy based on parametric and non-parametric approaches are addressed. In addition, the text treats the parametric approach to tuning based on the modified RFT approach and the exact model of oscillations in the system under test using the locus of a perturbedrelay system (LPRS) meth...

  19. La carte bancaire, l’individu sur les comptes

    OpenAIRE

    Yernaux, Sébastien

    2017-01-01

    « A moins de vivre comme Diogène ou comme Siméon stylite, l’homme contemporain est maintenant un individu perpétuellement repérable. Avec la carte bancaire et les paiements dont elle est la première fonction, on peut connaître vos habitudes alimentaires, vos goûts en matière de loisirs et, pour peu que vous régliez vos péages d’autoroutes avec ce petit rectangle de plastique, la nature de vos déplacements ». La carte bancaire est probablement l’un des moyens de paiements les plus utilisés auj...

  20. A School Experiment in Kinematics: Shooting from a Ballistic Cart

    Science.gov (United States)

    Kranjc, T.; Razpet, N.

    2011-10-01

    Many physics textbooks start with kinematics. In the lab, students observe the motions, describe and make predictions, and get acquainted with basic kinematics quantities and their meaning. Then they can perform calculations and compare the results with experimental findings. In this paper we describe an experiment that is not often done, but is interesting and attractive to students—the ballistic cart, i.e., the shooting of a ball from a cart moving along a slope. For that, one has to be familiar with one-dimensional uniform motion and one-dimensional motion with constant acceleration, as well as curvilinear motion that is a combination of such motions.1,2 The experimental results confirm theoretical predictions.

  1. Méthodologie de Conception de Cartes Rapides Workshop

    CERN Document Server

    2003-01-01

    - Problématique de développement de carte rapide pour l'expérience LHCb(transparents) - Solution basée sur le flot Cadence PSD14.2 de conception de circuits imprimés(CI) rapides (transparents) - Nouveau partage des taches entre l'ingénieur concepteur et le professionnel développeur de CI (transparents) - Démonstration du flot à partir d'un exemple concret pour l'expérience LHCb (Démo 1h min) - Conclusion : Essentiellement pour lancer une discussion sur l'impact de ce nouveau flot de conception Cadence sur la méthode de développement de cartes à l'IN2P3.

  2. Sur l'utilité de la carte en proportions

    Directory of Open Access Journals (Sweden)

    Isabelle DEBEER

    1996-03-01

    Full Text Available Seule la carte en proportions révèle des répartitions de masses. Cette règle élémentaire de cartographie est rappelée ici par un exemple opérationnel: la répartition spatiale des logements chauffés à l'énergie solaire en Belgique.

  3. Nonparametric Bayes Classification and Hypothesis Testing on Manifolds

    Science.gov (United States)

    Bhattacharya, Abhishek; Dunson, David

    2012-01-01

    Our first focus is prediction of a categorical response variable using features that lie on a general manifold. For example, the manifold may correspond to the surface of a hypersphere. We propose a general kernel mixture model for the joint distribution of the response and predictors, with the kernel expressed in product form and dependence induced through the unknown mixing measure. We provide simple sufficient conditions for large support and weak and strong posterior consistency in estimating both the joint distribution of the response and predictors and the conditional distribution of the response. Focusing on a Dirichlet process prior for the mixing measure, these conditions hold using von Mises-Fisher kernels when the manifold is the unit hypersphere. In this case, Bayesian methods are developed for efficient posterior computation using slice sampling. Next we develop Bayesian nonparametric methods for testing whether there is a difference in distributions between groups of observations on the manifold having unknown densities. We prove consistency of the Bayes factor and develop efficient computational methods for its calculation. The proposed classification and testing methods are evaluated using simulation examples and applied to spherical data applications. PMID:22754028

  4. Probability Machines: Consistent Probability Estimation Using Nonparametric Learning Machines

    Science.gov (United States)

    Malley, J. D.; Kruppa, J.; Dasgupta, A.; Malley, K. G.; Ziegler, A.

    2011-01-01

    Summary Background Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. Objectives The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Methods Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Results Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Conclusions Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications. PMID:21915433

  5. Multi-sample nonparametric treatments comparison in medical ...

    African Journals Online (AJOL)

    Multi-sample nonparametric treatments comparison in medical follow-up study with unequal observation processes through simulation and bladder tumour case study. P. L. Tan, N.A. Ibrahim, M.B. Adam, J. Arasan ...

  6. Weak Disposability in Nonparametric Production Analysis with Undesirable Outputs

    NARCIS (Netherlands)

    Kuosmanen, T.K.

    2005-01-01

    Environmental Economics and Natural Resources Group at Wageningen University in The Netherlands Weak disposability of outputs means that firms can abate harmful emissions by decreasing the activity level. Modeling weak disposability in nonparametric production analysis has caused some confusion.

  7. Nonparametric Bayesian drift estimation for multidimensional stochastic differential equations

    NARCIS (Netherlands)

    Gugushvili, S.; Spreij, P.

    2014-01-01

    We consider nonparametric Bayesian estimation of the drift coefficient of a multidimensional stochastic differential equation from discrete-time observations on the solution of this equation. Under suitable regularity conditions, we establish posterior consistency in this context.

  8. Regional CAR-T cell infusions for peritoneal carcinomatosis are superior to systemic delivery.

    Science.gov (United States)

    Katz, S C; Point, G R; Cunetta, M; Thorn, M; Guha, P; Espat, N J; Boutros, C; Hanna, N; Junghans, R P

    2016-05-01

    Metastatic spread of colorectal cancer (CRC) to the peritoneal cavity is common and difficult to treat, with many patients dying from malignant bowel obstruction. Chimeric antigen receptor T cell (CAR-T) immunotherapy has shown great promise, and we previously reported murine and phase I clinical studies on regional intrahepatic CAR-T infusion for CRC liver metastases. We are now studying intraperitoneal (IP) delivery of CAR-Ts for peritoneal carcinomatosis. Regional IP infusion of CAR-T resulted in superior protection against carcinoembryonic antigen (CEA+) peritoneal tumors, when compared with systemically infused CAR-Ts. IP CAR-Ts also provided prolonged protection against IP tumor re-challenges and demonstrated an increase in effector memory phenotype over time. IP CAR-Ts provided protection against tumor growth at distant subcutaneous (SC) sites in association with increases in serum IFNγ levels. Given the challenges posed by immunoinhibitory pathways in solid tumors, we combined IP CAR-T treatment with suppressor cell targeting. High frequencies of myeloid-derived suppressor cells (MDSC) and regulatory T cells (Treg) were found within the IP tumors, with MDSC expressing high levels of immunosuppressive PD-L1. Combinatorial IP CAR-T treatment with depleting antibodies against MDSC and Treg further improved efficacy against peritoneal metastases. Our data support further development of combinatorial IP CAR-T immunotherapy for peritoneal malignancies.

  9. CryoCart Restoration and Vacuum Pipe Construction

    Science.gov (United States)

    Chaidez, Mariana

    2016-01-01

    first completed at the component level. During this process, the igniter of the main engine and the RCS thrusters will be tested under a vacuum. To complete the testing of the components, the test setup first needed to be finalized. The CryoCart is being used to feed the propellants to the test article. The CryoCart is a movable test set-up that was developed in 2009 to provide a mobile platform for testing oxygen/methane systems with hot-fire capability up to 100 lbf. The CryoCart consists of three different systems: Oxygen, Methane, and liquid Nitrogen. The Oxygen and Methane systems are placed into two different carts while the liquid nitrogen system is mainly located in the methane cart. Over the years, the CryoCart has been utilized for different projects and has undergone deterioration. For this reason, a new phase has been developed to rebuild it to working conditions once again. During my internship, I was aiding in the construction and restoration of the CryoCart. In the initial stages of the process, I updated the fluid and electrical schematics for the oxygen, methane, and test article systems. The original CryoCart consisted of an electrical panel that utilized electromechanical relays and a terminal to drive the igniter power and signal, as well as the main fuel and oxygen valves. This electrical panel connected to the CryoCart through various wire harnesses that could be found exiting from the CryoCart. First, it was determined how these harnesses connected to the electromechanical relays so that they worked correctly. Once the electrical system was understood, an alternative for the electromechanical relays and the Molex connectors used throughout the system was sought since these components can often prove to be unreliable. Solid State relays and MIL connectors were purchased to serve as replacements. Upon arrival of the parts, crimping and wiring was completed to install the new solid state relays and MIL connectors. During the replacement of the relays

  10. On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests

    Directory of Open Access Journals (Sweden)

    Aaditya Ramdas

    2017-01-01

    Full Text Available Nonparametric two-sample or homogeneity testing is a decision theoretic problem that involves identifying differences between two random variables without making parametric assumptions about their underlying distributions. The literature is old and rich, with a wide variety of statistics having being designed and analyzed, both for the unidimensional and the multivariate setting. Inthisshortsurvey,wefocusonteststatisticsthatinvolvetheWassersteindistance. Usingan entropic smoothing of the Wasserstein distance, we connect these to very different tests including multivariate methods involving energy statistics and kernel based maximum mean discrepancy and univariate methods like the Kolmogorov–Smirnov test, probability or quantile (PP/QQ plots and receiver operating characteristic or ordinal dominance (ROC/ODC curves. Some observations are implicit in the literature, while others seem to have not been noticed thus far. Given nonparametric two-sample testing’s classical and continued importance, we aim to provide useful connections for theorists and practitioners familiar with one subset of methods but not others.

  11. Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza.

    Science.gov (United States)

    Cybis, Gabriela B; Sinsheimer, Janet S; Bedford, Trevor; Rambaut, Andrew; Lemey, Philippe; Suchard, Marc A

    2018-01-30

    Influenza is responsible for up to 500,000 deaths every year, and antigenic variability represents much of its epidemiological burden. To visualize antigenic differences across many viral strains, antigenic cartography methods use multidimensional scaling on binding assay data to map influenza antigenicity onto a low-dimensional space. Analysis of such assay data ideally leads to natural clustering of influenza strains of similar antigenicity that correlate with sequence evolution. To understand the dynamics of these antigenic groups, we present a framework that jointly models genetic and antigenic evolution by combining multidimensional scaling of binding assay data, Bayesian phylogenetic machinery and nonparametric clustering methods. We propose a phylogenetic Chinese restaurant process that extends the current process to incorporate the phylogenetic dependency structure between strains in the modeling of antigenic clusters. With this method, we are able to use the genetic information to better understand the evolution of antigenicity throughout epidemics, as shown in applications of this model to H1N1 influenza. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  12. Probability machines: consistent probability estimation using nonparametric learning machines.

    Science.gov (United States)

    Malley, J D; Kruppa, J; Dasgupta, A; Malley, K G; Ziegler, A

    2012-01-01

    Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications.

  13. Nonparametric predictive inference for combining diagnostic tests with parametric copula

    Science.gov (United States)

    Muhammad, Noryanti; Coolen, F. P. A.; Coolen-Maturi, T.

    2017-09-01

    Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine and health care. The Receiver Operating Characteristic (ROC) curve is a popular statistical tool for describing the performance of diagnostic tests. The area under the ROC curve (AUC) is often used as a measure of the overall performance of the diagnostic test. In this paper, we interest in developing strategies for combining test results in order to increase the diagnostic accuracy. We introduce nonparametric predictive inference (NPI) for combining two diagnostic test results with considering dependence structure using parametric copula. NPI is a frequentist statistical framework for inference on a future observation based on past data observations. NPI uses lower and upper probabilities to quantify uncertainty and is based on only a few modelling assumptions. While copula is a well-known statistical concept for modelling dependence of random variables. A copula is a joint distribution function whose marginals are all uniformly distributed and it can be used to model the dependence separately from the marginal distributions. In this research, we estimate the copula density using a parametric method which is maximum likelihood estimator (MLE). We investigate the performance of this proposed method via data sets from the literature and discuss results to show how our method performs for different family of copulas. Finally, we briefly outline related challenges and opportunities for future research.

  14. Robust Discriminant Analysis Based on Nonparametric Maximum Entropy

    Science.gov (United States)

    He, Ran; Hu, Bao-Gang; Yuan, Xiao-Tong

    In this paper, we propose a Robust Discriminant Analysis based on maximum entropy (MaxEnt) criterion (MaxEnt-RDA), which is derived from a nonparametric estimate of Renyi’s quadratic entropy. MaxEnt-RDA uses entropy as both objective and constraints; thus the structural information of classes is preserved while information loss is minimized. It is a natural extension of LDA from Gaussian assumption to any distribution assumption. Like LDA, the optimal solution of MaxEnt-RDA can also be solved by an eigen-decomposition method, where feature extraction is achieved by designing two Parzen probability matrices that characterize the within-class variation and the between-class variation respectively. Furthermore, MaxEnt-RDA makes use of high order statistics (entropy) to estimate the probability matrix so that it is robust to outliers. Experiments on toy problem , UCI datasets and face datasets demonstrate the effectiveness of the proposed method with comparison to other state-of-the-art methods.

  15. Multinomial Response Models, for Modeling and Determining Important Factors in Different Contraceptive Methods in Women

    Directory of Open Access Journals (Sweden)

    E Haji Nejad

    2001-06-01

    Full Text Available Difference aspects of multinomial statistical modelings and its classifications has been studied so far. In these type of problems Y is the qualitative random variable with T possible states which are considered as classifications. The goal is prediction of Y based on a random Vector X ? IR^m. Many methods for analyzing these problems were considered. One of the modern and general method of classification is Classification and Regression Trees (CART. Another method is recursive partitioning techniques which has a strange relationship with nonparametric regression. Classical discriminant analysis is a standard method for analyzing these type of data. Flexible discriminant analysis method which is a combination of nonparametric regression and discriminant analysis and classification using spline that includes least square regression and additive cubic splines. Neural network is an advanced statistical method for analyzing these types of data. In this paper properties of multinomial logistics regression were investigated and this method was used for modeling effective factors in selecting contraceptive methods in Ghom province for married women age 15-49. The response variable has a tetranomial distibution. The levels of this variable are: nothing, pills, traditional and a collection of other contraceptive methods. A collection of significant independent variables were: place, age of women, education, history of pregnancy and family size. Menstruation age and age at marriage were not statistically significant.

  16. A Car Transportation System in Cooperation by Multiple Mobile Robots for Each Wheel: iCART II

    Science.gov (United States)

    Kashiwazaki, Koshi; Yonezawa, Naoaki; Kosuge, Kazuhiro; Sugahara, Yusuke; Hirata, Yasuhisa; Endo, Mitsuru; Kanbayashi, Takashi; Shinozuka, Hiroyuki; Suzuki, Koki; Ono, Yuki

    The authors proposed a car transportation system, iCART (intelligent Cooperative Autonomous Robot Transporters), for automation of mechanical parking systems by two mobile robots. However, it was difficult to downsize the mobile robot because the length of it requires at least the wheelbase of a car. This paper proposes a new car transportation system, iCART II (iCART - type II), based on “a-robot-for-a-wheel” concept. A prototype system, MRWheel (a Mobile Robot for a Wheel), is designed and downsized less than half the conventional robot. First, a method for lifting up a wheel by MRWheel is described. In general, it is very difficult for mobile robots such as MRWheel to move to desired positions without motion errors caused by slipping, etc. Therefore, we propose a follower's motion error estimation algorithm based on the internal force applied to each follower by extending a conventional leader-follower type decentralized control algorithm for cooperative object transportation. The proposed algorithm enables followers to estimate their motion errors and enables the robots to transport a car to a desired position. In addition, we analyze and prove the stability and convergence of the resultant system with the proposed algorithm. In order to extract only the internal force from the force applied to each robot, we also propose a model-based external force compensation method. Finally, proposed methods are applied to the car transportation system, the experimental results confirm their validity.

  17. Non-parametric Bayesian networks: Improving theory and reviewing applications

    International Nuclear Information System (INIS)

    Hanea, Anca; Morales Napoles, Oswaldo; Ababei, Dan

    2015-01-01

    Applications in various domains often lead to high dimensional dependence modelling. A Bayesian network (BN) is a probabilistic graphical model that provides an elegant way of expressing the joint distribution of a large number of interrelated variables. BNs have been successfully used to represent uncertain knowledge in a variety of fields. The majority of applications use discrete BNs, i.e. BNs whose nodes represent discrete variables. Integrating continuous variables in BNs is an area fraught with difficulty. Several methods that handle discrete-continuous BNs have been proposed in the literature. This paper concentrates only on one method called non-parametric BNs (NPBNs). NPBNs were introduced in 2004 and they have been or are currently being used in at least twelve professional applications. This paper provides a short introduction to NPBNs, a couple of theoretical advances, and an overview of applications. The aim of the paper is twofold: one is to present the latest improvements of the theory underlying NPBNs, and the other is to complement the existing overviews of BNs applications with the NPNBs applications. The latter opens the opportunity to discuss some difficulties that applications pose to the theoretical framework and in this way offers some NPBN modelling guidance to practitioners. - Highlights: • The paper gives an overview of the current NPBNs methodology. • We extend the NPBN methodology by relaxing the conditions of one of its fundamental theorems. • We propose improvements of the data mining algorithm for the NPBNs. • We review the professional applications of the NPBNs.

  18. A boundary-layer cloud study using Southern Great Plains Cloud and radiation testbed (CART) data

    Energy Technology Data Exchange (ETDEWEB)

    Albrecht, B.; Mace, G.; Dong, X.; Syrett, W. [Pennsylvania State Univ., University Park, PA (United States)] [and others

    1996-04-01

    Boundary layer clouds-stratus and fairweather cumulus - are closely coupled involves the radiative impact of the clouds on the surface energy budget and the strong dependence of cloud formation and maintenance on the turbulent fluxes of heat and moisture in the boundary layer. The continuous data collection at the Southern Great Plains (SGP) Cloud and Radiation Testbed (CART) site provides a unique opportunity to study components of the coupling processes associated with boundary layer clouds and to provide descriptions of cloud and boundary layer structure that can be used to test parameterizations used in climate models. But before the CART data can be used for process studies and parameterization testing, it is necessary to evaluate and validate data and to develop techniques for effectively combining the data to provide meaningful descriptions of cloud and boundary layer characteristics. In this study we use measurements made during an intensive observing period we consider a case where low-level stratus were observed at the site for about 18 hours. This case is being used to examine the temporal evolution of cloud base, cloud top, cloud liquid water content, surface radiative fluxes, and boundary layer structure. A method for inferring cloud microphysics from these parameters is currently being evaluated.

  19. Analyse des sous-performances energetiques d'eoliennes en utilisant des cartes de controle EWMA =

    Science.gov (United States)

    Lepvrier, Romain

    L'exploitation de l'energie eolienne est en forte croissance depuis plus de deux decennies. Beaucoup de centrales eoliennes ont ete construites durant cette periode. La tendance aujourd'hui est de surveiller les centrales eoliennes existantes pour obtenir la meilleure performance possible en assurant des conditions d'exploitation optimales et une grande disponibilite de l'equipement. Les recentes avancees technologiques n'ayant pas pu etre implantees a l'epoque ou ces centrales ont ete construites, par consequent, il faut trouver des outils d'analyses pour detecter, le plus precocement possible, les sous-performances des eoliennes et d'anticiper la correction les problemes d'une maniere preventive. En premiere partie, ce memoire presente la construction des courbes de puissance de reference pour un fonctionnement normal des machines. De l'obtention des donnees, provenant directement des eoliennes in-situ, a la courbe de puissance finale permettant l'eventuelle detection de sous-performances d'une maniere robuste et commode, tout le processus est decrit etape par etape. Dans une deuxieme partie, une carte de controle EWMA a ete developpee. A l'aide d'une methode graphique suivie d'un algorithme, elle permet de detecter les faibles sousperformances graduelles qui surviennent au cours du temps sur une eolienne. Il a ete montre que la carte developpee est capable de detecter un niveau de baisse de performance de 1 % par rapport a la performance normale de l'eolienne.

  20. A Bayesian nonparametric approach to reconstruction and prediction of random dynamical systems.

    Science.gov (United States)

    Merkatas, Christos; Kaloudis, Konstantinos; Hatjispyros, Spyridon J

    2017-06-01

    We propose a Bayesian nonparametric mixture model for the reconstruction and prediction from observed time series data, of discretized stochastic dynamical systems, based on Markov Chain Monte Carlo methods. Our results can be used by researchers in physical modeling interested in a fast and accurate estimation of low dimensional stochastic models when the size of the observed time series is small and the noise process (perhaps) is non-Gaussian. The inference procedure is demonstrated specifically in the case of polynomial maps of an arbitrary degree and when a Geometric Stick Breaking mixture process prior over the space of densities, is applied to the additive errors. Our method is parsimonious compared to Bayesian nonparametric techniques based on Dirichlet process mixtures, flexible and general. Simulations based on synthetic time series are presented.

  1. Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection

    Science.gov (United States)

    Kumar, Sricharan; Srivistava, Ashok N.

    2012-01-01

    Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap methods is proposed. Bootstrap methods allow for a non-parametric approach to computing prediction intervals with no specific assumptions about the sampling distribution of the noise or the data. The asymptotic fidelity of the proposed prediction intervals is theoretically proved. Subsequently, the validity of the bootstrap based prediction intervals is illustrated via simulations. Finally, the bootstrap prediction intervals are applied to the problem of anomaly detection on aviation data.

  2. Using nonparametrics to specify a model to measure the value of travel time

    DEFF Research Database (Denmark)

    Fosgerau, Mogens

    2007-01-01

    Using a range of nonparametric methods, the paper examines the specification of a model to evaluate the willingness-to-pay (WTP) for travel time changes from binomial choice data from a simple time-cost trading experiment. The analysis favours a model with random WTP as the only source of randomn......Using a range of nonparametric methods, the paper examines the specification of a model to evaluate the willingness-to-pay (WTP) for travel time changes from binomial choice data from a simple time-cost trading experiment. The analysis favours a model with random WTP as the only source...... unobserved heterogeneity. This formulation is useful for parametric modelling. The index indicates that the WTP varies systematically with income and other individual characteristics. The WTP varies also with the time difference presented in the experiment which is in contradiction of standard utility theory....

  3. A Bayesian nonparametric approach to reconstruction and prediction of random dynamical systems

    Science.gov (United States)

    Merkatas, Christos; Kaloudis, Konstantinos; Hatjispyros, Spyridon J.

    2017-06-01

    We propose a Bayesian nonparametric mixture model for the reconstruction and prediction from observed time series data, of discretized stochastic dynamical systems, based on Markov Chain Monte Carlo methods. Our results can be used by researchers in physical modeling interested in a fast and accurate estimation of low dimensional stochastic models when the size of the observed time series is small and the noise process (perhaps) is non-Gaussian. The inference procedure is demonstrated specifically in the case of polynomial maps of an arbitrary degree and when a Geometric Stick Breaking mixture process prior over the space of densities, is applied to the additive errors. Our method is parsimonious compared to Bayesian nonparametric techniques based on Dirichlet process mixtures, flexible and general. Simulations based on synthetic time series are presented.

  4. The PASCO Wireless Smart Cart: A Game Changer in the Undergraduate Physics Laboratory

    Science.gov (United States)

    Shakur, Asif; Connor, Rainor

    2018-03-01

    With the introduction of the Wireless Smart Cart by PASCO scientific in April 2016, we expect a paradigm shift in undergraduate physics laboratory instruction. We have evaluated the feasibility of using the smart cart by carrying out experiments that are usually performed using traditional PASCO equipment. The simplicity, convenience, and cost-saving achieved by replacing a plethora of traditional laboratory sensors, wires, and equipment clutter with the smart cart are reported here.

  5. Differential expression of CART in feeding and reward circuits in binge eating rat model.

    Science.gov (United States)

    Bharne, Ashish P; Borkar, Chandrashekhar D; Subhedar, Nishikant K; Kokare, Dadasaheb M

    2015-09-15

    Binge eating (BE) disrupts feeding and subverts reward mechanism. Since cocaine- and amphetamine-regulated transcript peptide (CART) mediates satiety as well as reward, its role in BE justifies investigation. To induce BE, rats were provided restricted access to high fat sweet palatable diet (HFSPD) for a period of 4 weeks. Immunoreactivity profile of the CART elements, and accompanying neuroplastic changes were studied in satiety- and reward-regulating brain nuclei. Further, we investigated the effects of CART, CART-antibody or rimonabant on the intake of normal chow or HFSPD. Rats fed on HFSPD showed development of BE-like phenotype as reflected by significant consumption of HFSPD in short time frame, suggestive of dysregulated satiety mechanisms. At the mid-point during BE, CART-immunoreactivity was significantly increased in hypothalamic arcuate (ARC), lateral (LH), nucleus accumbens shell (AcbSh) and paraventricular nucleus of thalamus (PVT). However, for next 22-h post-binge time-period, the animals showed no interest in food, and low CART expression. Pre-binge treatment with rimonabant, a drug recommended for the treatment of BE, produced anorexia, increased CART expression in ARC and LH, but not in AcbSh and PVT. Higher dose of CART was required to produce anorexia in binged rats. While neuronal tracing studies confirmed CART fiber connectivity from ARC and LH to AcbSh, increase in CART and synaptophysin immunostaining in this pathway in BE rats suggested strengthening of the CART connectivity. We conclude that CART bearing ARC-LH-PVT-AcbSh reward circuit may override the satiety signaling in ARC-PVN pathway in BE rats. Copyright © 2015 Elsevier B.V. All rights reserved.

  6. Myeloid Conditioning with c-kit-Targeted CAR-T Cells Enables Donor Stem Cell Engraftment.

    Science.gov (United States)

    Arai, Yasuyuki; Choi, Uimook; Corsino, Cristina I; Koontz, Sherry M; Tajima, Masaki; Sweeney, Colin L; Black, Mary A; Feldman, Steven A; Dinauer, Mary C; Malech, Harry L

    2018-03-10

    We report a novel approach to bone marrow (BM) conditioning using c-kit-targeted chimeric antigen receptor T (c-kit CAR-T) cells in mice. Previous reports using anti-c-kit or anti-CD45 antibody linked to a toxin such as saporin have been promising. We developed a distinctly different approach using c-kit CAR-T cells. Initial studies demonstrated in vitro killing of hematopoietic stem cells by c-kit CAR-T cells but poor expansion in vivo and poor migration of CAR-T cells into BM. Pre-treatment of recipient mice with low-dose cyclophosphamide (125 mg/kg) together with CXCR4 transduction in the CAR-T cells enhanced trafficking to and expansion in BM (CAR-T cells were used in the Thy1.2-recipient mice, anti-Thy1.1 antibody could be used to deplete CAR-T cells in vivo before donor BM transplant. This achieved 20%-40% multilineage engraftment. We applied this conditioning to achieve an average of 28% correction of chronic granulomatous disease mice by wild-type BM transplant. Our findings provide a proof of concept that c-kit CAR-T cells can achieve effective BM conditioning without chemo-/radiotherapy. Our work also demonstrates that co-expression of a trafficking receptor can enhance targeting of CAR-T cells to a designated tissue. Published by Elsevier Inc.

  7. Characterisation of CART-containing neurons and cells in the porcine pancreas, gastro-intestinal tract, adrenal and thyroid glands

    Directory of Open Access Journals (Sweden)

    Gunnarsdóttir Anna

    2007-07-01

    Full Text Available Abstract Background The peptide CART is widely expressed in central and peripheral neurons, as well as in endocrine cells. Known peripheral sites of expression include the gastrointestinal (GI tract, the pancreas, and the adrenal glands. In rodent pancreas CART is expressed both in islet endocrine cells and in nerve fibers, some of which innervate the islets. Recent data show that CART is a regulator of islet hormone secretion, and that CART null mutant mice have islet dysfunction. CART also effects GI motility, mainly via central routes. In addition, CART participates in the regulation of the hypothalamus-pituitary-adrenal-axis. We investigated CART expression in porcine pancreas, GI-tract, adrenal glands, and thyroid gland using immunocytochemistry. Results CART immunoreactive (IR nerve cell bodies and fibers were numerous in pancreatic and enteric ganglia. The majority of these were also VIP IR. The finding of intrinsic CART containing neurons indicates that pancreatic and GI CART IR nerve fibers have an intrinsic origin. No CART IR endocrine cells were detected in the pancreas or in the GI tract. The adrenal medulla harboured numerous CART IR endocrine cells, most of which were adrenaline producing. In addition CART IR fibers were frequently seen in the adrenal cortex and capsule. The capsule also contained CART IR nerve cell bodies. The majority of the adrenal CART IR neuronal elements were also VIP IR. CART IR was also seen in a substantial proportion of the C-cells in the thyroid gland. The majority of these cells were also somatostatin IR, and/or 5-HT IR, and/or VIP IR. Conclusion CART is a major neuropeptide in intrinsic neurons of the porcine GI-tract and pancreas, a major constituent of adrenaline producing adrenomedullary cells, and a novel peptide of the thyroid C-cells. CART is suggested to be a regulatory peptide in the porcine pancreas, GI-tract, adrenal gland and thyroid.

  8. Bayesian nonparametric meta-analysis using Polya tree mixture models.

    Science.gov (United States)

    Branscum, Adam J; Hanson, Timothy E

    2008-09-01

    Summary. A common goal in meta-analysis is estimation of a single effect measure using data from several studies that are each designed to address the same scientific inquiry. Because studies are typically conducted in geographically disperse locations, recent developments in the statistical analysis of meta-analytic data involve the use of random effects models that account for study-to-study variability attributable to differences in environments, demographics, genetics, and other sources that lead to heterogeneity in populations. Stemming from asymptotic theory, study-specific summary statistics are modeled according to normal distributions with means representing latent true effect measures. A parametric approach subsequently models these latent measures using a normal distribution, which is strictly a convenient modeling assumption absent of theoretical justification. To eliminate the influence of overly restrictive parametric models on inferences, we consider a broader class of random effects distributions. We develop a novel hierarchical Bayesian nonparametric Polya tree mixture (PTM) model. We present methodology for testing the PTM versus a normal random effects model. These methods provide researchers a straightforward approach for conducting a sensitivity analysis of the normality assumption for random effects. An application involving meta-analysis of epidemiologic studies designed to characterize the association between alcohol consumption and breast cancer is presented, which together with results from simulated data highlight the performance of PTMs in the presence of nonnormality of effect measures in the source population.

  9. On Consistent Nonparametric Statistical Tests of Symmetry Hypotheses

    Directory of Open Access Journals (Sweden)

    Jean-François Quessy

    2016-05-01

    Full Text Available Being able to formally test for symmetry hypotheses is an important topic in many fields, including environmental and physical sciences. In this paper, one concentrates on a large family of nonparametric tests of symmetry based on Cramér–von Mises statistics computed from empirical distribution and characteristic functions. These tests possess the highly desirable property of being universally consistent in the sense that they detect any kind of departure from symmetry as the sample size becomes large. The asymptotic behaviour of these test statistics under symmetry is deduced from the theory of first-order degenerate V-statistics. The issue of computing valid p-values is tackled using the multiplier bootstrap method suitably adapted to V-statistics, yielding elegant, easy-to-compute and quick procedures for testing symmetry. A special focus is put on tests of univariate symmetry, bivariate exchangeability and reflected symmetry; a simulation study indicates the good sampling properties of these tests. Finally, a framework for testing general symmetry hypotheses is introduced.

  10. The Utility of Nonparametric Transformations for Imputation of Survey Data

    Directory of Open Access Journals (Sweden)

    Robbins Michael W.

    2014-12-01

    Full Text Available Missing values present a prevalent problem in the analysis of establishment survey data. Multivariate imputation algorithms (which are used to fill in missing observations tend to have the common limitation that imputations for continuous variables are sampled from Gaussian distributions. This limitation is addressed here through the use of robust marginal transformations. Specifically, kernel-density and empirical distribution-type transformations are discussed and are shown to have favorable properties when used for imputation of complex survey data. Although such techniques have wide applicability (i.e., they may be easily applied in conjunction with a wide array of imputation techniques, the proposed methodology is applied here with an algorithm for imputation in the USDA’s Agricultural Resource Management Survey. Data analysis and simulation results are used to illustrate the specific advantages of the robust methods when compared to the fully parametric techniques and to other relevant techniques such as predictive mean matching. To summarize, transformations based upon parametric densities are shown to distort several data characteristics in circumstances where the parametric model is ill fit; however, no circumstances are found in which the transformations based upon parametric models outperform the nonparametric transformations. As a result, the transformation based upon the empirical distribution (which is the most computationally efficient is recommended over the other transformation procedures in practice.

  11. A local non-parametric model for trade sign inference

    Science.gov (United States)

    Blazejewski, Adam; Coggins, Richard

    2005-03-01

    We investigate a regularity in market order submission strategies for 12 stocks with large market capitalization on the Australian Stock Exchange. The regularity is evidenced by a predictable relationship between the trade sign (trade initiator), size of the trade, and the contents of the limit order book before the trade. We demonstrate this predictability by developing an empirical inference model to classify trades into buyer-initiated and seller-initiated. The model employs a local non-parametric method, k-nearest neighbor, which in the past was used successfully for chaotic time series prediction. The k-nearest neighbor with three predictor variables achieves an average out-of-sample classification accuracy of 71.40%, compared to 63.32% for the linear logistic regression with seven predictor variables. The result suggests that a non-linear approach may produce a more parsimonious trade sign inference model with a higher out-of-sample classification accuracy. Furthermore, for most of our stocks the observed regularity in market order submissions seems to have a memory of at least 30 trading days.

  12. DPpackage: Bayesian Semi- and Nonparametric Modeling in R

    Directory of Open Access Journals (Sweden)

    Alejandro Jara

    2011-04-01

    Full Text Available Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison and for eliciting the precision parameter of the Dirichlet process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code.

  13. 1er Concurso de ideas en cartón

    OpenAIRE

    Álvaro Tordesillas, Antonio; Galván Desvaux, Noelia; Alonso Rodríguez, Marta

    2014-01-01

    Innovación Educativa Diseñar una mesa/mueble/expositor de cartón que: - sirva de expositor para maquetas y otros trabajos docentes de la Escuela Técnica Superior de Arquitectura de Valladolid, necesidad real actualmente planteada y urgente. - pueda entenderse también como familia de expositores con versiones en diferentes tamaños. Los expositores que resulten ganadores, y respondan adecuadamente a las necesidades planteadas, se ejecutarán y colocarán en los espacios públicos de ...

  14. The use of a cutting balloon in contemporary reverse controlled antegrade and retrograde subintimal tracking (reverse CART) technique.

    Science.gov (United States)

    Nakabayashi, Keisuke; Okada, Hisayuki; Oka, Toshiaki

    2017-07-01

    The key concept of reverse controlled antegrade and retrograde tracking (CART) technique is retrograde puncture with a tapered wire to an antegrade balloon (contemporary reverse CART) or new connections between the antegrade and retrograde subintimal space (classical reverse CART). In our case, a 75-year-old man with severe chronic total occlusion of the right coronary artery, reverse CART with conventional balloons could not be accomplished. Externalization wiring was completed by contemporary reverse CART using a cutting balloon as an antegrade balloon to improve the fenestration force of the retrograde guidewire. Thus, the use of a cutting balloon for contemporary reverse CART might be promising.

  15. Bayesian nonparametric estimation of continuous monotone functions with applications to dose-response analysis.

    Science.gov (United States)

    Bornkamp, Björn; Ickstadt, Katja

    2009-03-01

    In this article, we consider monotone nonparametric regression in a Bayesian framework. The monotone function is modeled as a mixture of shifted and scaled parametric probability distribution functions, and a general random probability measure is assumed as the prior for the mixing distribution. We investigate the choice of the underlying parametric distribution function and find that the two-sided power distribution function is well suited both from a computational and mathematical point of view. The model is motivated by traditional nonlinear models for dose-response analysis, and provides possibilities to elicitate informative prior distributions on different aspects of the curve. The method is compared with other recent approaches to monotone nonparametric regression in a simulation study and is illustrated on a data set from dose-response analysis.

  16. Promotion time cure rate model with nonparametric form of covariate effects.

    Science.gov (United States)

    Chen, Tianlei; Du, Pang

    2018-05-10

    Survival data with a cured portion are commonly seen in clinical trials. Motivated from a biological interpretation of cancer metastasis, promotion time cure model is a popular alternative to the mixture cure rate model for analyzing such data. The existing promotion cure models all assume a restrictive parametric form of covariate effects, which can be incorrectly specified especially at the exploratory stage. In this paper, we propose a nonparametric approach to modeling the covariate effects under the framework of promotion time cure model. The covariate effect function is estimated by smoothing splines via the optimization of a penalized profile likelihood. Point-wise interval estimates are also derived from the Bayesian interpretation of the penalized profile likelihood. Asymptotic convergence rates are established for the proposed estimates. Simulations show excellent performance of the proposed nonparametric method, which is then applied to a melanoma study. Copyright © 2018 John Wiley & Sons, Ltd.

  17. Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures.

    Science.gov (United States)

    Filippi, Sarah; Holmes, Chris C; Nieto-Barajas, Luis E

    2016-11-16

    In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM) models for detecting pairwise dependence between random variables while accounting for uncertainty in the form of the underlying distributions. A key criteria is that the procedures should scale to large data sets. In this regard we find that the formal calculation of the Bayes factor for a dependent-vs.-independent DPM joint probability measure is not feasible computationally. To address this we present Bayesian diagnostic measures for characterising evidence against a "null model" of pairwise independence. In simulation studies, as well as for a real data analysis, we show that our approach provides a useful tool for the exploratory nonparametric Bayesian analysis of large multivariate data sets.

  18. A multitemporal and non-parametric approach for assessing the impacts of drought on vegetation greenness

    DEFF Research Database (Denmark)

    Carrao, Hugo; Sepulcre, Guadalupe; Horion, Stéphanie Marie Anne F

    2013-01-01

    This study evaluates the relationship between the frequency and duration of meteorological droughts and the subsequent temporal changes on the quantity of actively photosynthesizing biomass (greenness) estimated from satellite imagery on rainfed croplands in Latin America. An innovative non-parametric...... and non-supervised approach, based on the Fisher-Jenks optimal classification algorithm, is used to identify multi-scale meteorological droughts on the basis of empirical cumulative distributions of 1, 3, 6, and 12-monthly precipitation totals. As input data for the classifier, we use the gridded GPCC...... for the period between 1998 and 2010. The time-series analysis of vegetation greenness is performed during the growing season with a non-parametric method, namely the seasonal Relative Greenness (RG) of spatially accumulated fAPAR. The Global Land Cover map of 2000 and the GlobCover maps of 2005/2006 and 2009...

  19. Geostatistical radar-raingauge combination with nonparametric correlograms: methodological considerations and application in Switzerland

    Directory of Open Access Journals (Sweden)

    R. Schiemann

    2011-05-01

    Full Text Available Modelling spatial covariance is an essential part of all geostatistical methods. Traditionally, parametric semivariogram models are fit from available data. More recently, it has been suggested to use nonparametric correlograms obtained from spatially complete data fields. Here, both estimation techniques are compared. Nonparametric correlograms are shown to have a substantial negative bias. Nonetheless, when combined with the sample variance of the spatial field under consideration, they yield an estimate of the semivariogram that is unbiased for small lag distances. This justifies the use of this estimation technique in geostatistical applications.

    Various formulations of geostatistical combination (Kriging methods are used here for the construction of hourly precipitation grids for Switzerland based on data from a sparse realtime network of raingauges and from a spatially complete radar composite. Two variants of Ordinary Kriging (OK are used to interpolate the sparse gauge observations. In both OK variants, the radar data are only used to determine the semivariogram model. One variant relies on a traditional parametric semivariogram estimate, whereas the other variant uses the nonparametric correlogram. The variants are tested for three cases and the impact of the semivariogram model on the Kriging prediction is illustrated. For the three test cases, the method using nonparametric correlograms performs equally well or better than the traditional method, and at the same time offers great practical advantages.

    Furthermore, two variants of Kriging with external drift (KED are tested, both of which use the radar data to estimate nonparametric correlograms, and as the external drift variable. The first KED variant has been used previously for geostatistical radar-raingauge merging in Catalonia (Spain. The second variant is newly proposed here and is an extension of the first. Both variants are evaluated for the three test cases

  20. On the Choice of Difference Sequence in a Unified Framework for Variance Estimation in Nonparametric Regression

    KAUST Repository

    Dai, Wenlin

    2017-09-01

    Difference-based methods do not require estimating the mean function in nonparametric regression and are therefore popular in practice. In this paper, we propose a unified framework for variance estimation that combines the linear regression method with the higher-order difference estimators systematically. The unified framework has greatly enriched the existing literature on variance estimation that includes most existing estimators as special cases. More importantly, the unified framework has also provided a smart way to solve the challenging difference sequence selection problem that remains a long-standing controversial issue in nonparametric regression for several decades. Using both theory and simulations, we recommend to use the ordinary difference sequence in the unified framework, no matter if the sample size is small or if the signal-to-noise ratio is large. Finally, to cater for the demands of the application, we have developed a unified R package, named VarED, that integrates the existing difference-based estimators and the unified estimators in nonparametric regression and have made it freely available in the R statistical program http://cran.r-project.org/web/packages/.

  1. Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times.

    Science.gov (United States)

    Xu, Yanxun; Müller, Peter; Wahed, Abdus S; Thall, Peter F

    2016-01-01

    We analyze a dataset arising from a clinical trial involving multi-stage chemotherapy regimes for acute leukemia. The trial design was a 2 × 2 factorial for frontline therapies only. Motivated by the idea that subsequent salvage treatments affect survival time, we model therapy as a dynamic treatment regime (DTR), that is, an alternating sequence of adaptive treatments or other actions and transition times between disease states. These sequences may vary substantially between patients, depending on how the regime plays out. To evaluate the regimes, mean overall survival time is expressed as a weighted average of the means of all possible sums of successive transitions times. We assume a Bayesian nonparametric survival regression model for each transition time, with a dependent Dirichlet process prior and Gaussian process base measure (DDP-GP). Posterior simulation is implemented by Markov chain Monte Carlo (MCMC) sampling. We provide general guidelines for constructing a prior using empirical Bayes methods. The proposed approach is compared with inverse probability of treatment weighting, including a doubly robust augmented version of this approach, for both single-stage and multi-stage regimes with treatment assignment depending on baseline covariates. The simulations show that the proposed nonparametric Bayesian approach can substantially improve inference compared to existing methods. An R program for implementing the DDP-GP-based Bayesian nonparametric analysis is freely available at https://www.ma.utexas.edu/users/yxu/.

  2. DeCART v1.2 User's Manual

    International Nuclear Information System (INIS)

    Cho, J. Y.; Kim, K. S.; Kim, H. Y.; Lee, C. C.; Zee, S. Q; Joo, H. G.

    2007-07-01

    DeCART (Deterministic Core Analysis based on Ray Tracing) is a whole core neutron transport code capable of direct subpin level flux calculation at power generating conditions. It does not require a priori homogenization nor group condensation needed in conventional reactor physics calculations. The depletion and transient calculation capabilities are also available. This manual serves as a self-sufficient guide to use the code. First of all, the various features of the code are explained which encompass various modeling options as well as the basic calculation functionalities. The instructions for running the code are also given with a description of the output files generated. Next, the underlying concepts and principles of preparing a DeCART model for a problem under consideration are presented. Each part of the input needed to specify the geometry, material composition, thermal operating condition, program execution control parameters are explained with examples. The descriptions of all the input cards are then followed. Finally, various sample model inputs ranging from a simple 2D pin cell to a realistic 3D core problem, steady-state to transient problems, and from rectangular to hexagonal core problems are presented

  3. Development of Electric Cart with Function of Maintaining/Improving Exercise Ability—Part I: Design of the Electric Cart System—

    Science.gov (United States)

    Ohyama, Yasuhiro; She, Jin-Hua; Kobayashi, Hiroyuki; Naemura, Kiyoshi

    This paper explains the development of a three-wheeled electric cart that not only is a means of transportation, but also provides the driver with a way of getting some physical exercise. Based on an investigation of the physiological decline accompanying aging, pedaling was chosen to implement the function of maintaining or improving physical strength; and an ergonomically designed pedal unit was mounted on a cart. An interface board that handles inputs and outputs was assembled to simplify the design of the system. Finally, a simple bilateral master-slave control system was built to test the cart. Experimental results on a fabricated cart demonstrate the effectiveness of pedaling and the usability of the system structure.

  4. Schoolchildren's Consumption of Competitive Foods and Beverages, Excluding a la Carte

    Science.gov (United States)

    Kakarala, Madhuri; Keast, Debra R.; Hoerr, Sharon

    2010-01-01

    Background: Competitive foods/beverages are those in school vending machines, school stores, snack bars, special sales, and items sold a la carte in the school cafeteria that compete with United States Department of Agriculture (USDA) meal program offerings. Grouping a la carte items with less nutritious items allowed in less regulated venues may…

  5. Smooth semi-nonparametric (SNP) estimation of the cumulative incidence function.

    Science.gov (United States)

    Duc, Anh Nguyen; Wolbers, Marcel

    2017-08-15

    This paper presents a novel approach to estimation of the cumulative incidence function in the presence of competing risks. The underlying statistical model is specified via a mixture factorization of the joint distribution of the event type and the time to the event. The time to event distributions conditional on the event type are modeled using smooth semi-nonparametric densities. One strength of this approach is that it can handle arbitrary censoring and truncation while relying on mild parametric assumptions. A stepwise forward algorithm for model estimation and adaptive selection of smooth semi-nonparametric polynomial degrees is presented, implemented in the statistical software R, evaluated in a sequence of simulation studies, and applied to data from a clinical trial in cryptococcal meningitis. The simulations demonstrate that the proposed method frequently outperforms both parametric and nonparametric alternatives. They also support the use of 'ad hoc' asymptotic inference to derive confidence intervals. An extension to regression modeling is also presented, and its potential and challenges are discussed. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

  6. Joint nonparametric correction estimator for excess relative risk regression in survival analysis with exposure measurement error.

    Science.gov (United States)

    Wang, Ching-Yun; Cullings, Harry; Song, Xiao; Kopecky, Kenneth J

    2017-11-01

    Observational epidemiological studies often confront the problem of estimating exposure-disease relationships when the exposure is not measured exactly. In the paper, we investigate exposure measurement error in excess relative risk regression, which is a widely used model in radiation exposure effect research. In the study cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies a generalized version of the classical additive measurement error model, but it may or may not have repeated measurements. In addition, an instrumental variable is available for individuals in a subset of the whole cohort. We develop a nonparametric correction (NPC) estimator using data from the subcohort, and further propose a joint nonparametric correction (JNPC) estimator using all observed data to adjust for exposure measurement error. An optimal linear combination estimator of JNPC and NPC is further developed. The proposed estimators are nonparametric, which are consistent without imposing a covariate or error distribution, and are robust to heteroscedastic errors. Finite sample performance is examined via a simulation study. We apply the developed methods to data from the Radiation Effects Research Foundation, in which chromosome aberration is used to adjust for the effects of radiation dose measurement error on the estimation of radiation dose responses.

  7. Application of a nonparametric approach to analyze delta-pCO2 data from the Southern Ocean

    CSIR Research Space (South Africa)

    Pretorius, WB

    2011-11-01

    Full Text Available In this paper the authors discuss the application of a classical nonparametric inference approach to analyse delta-pCO2 measurements from the Southern Ocean, which is a novel method to analysing data in this area, as well as comparing results...

  8. AZ-101 Mixer Pump Demonstration Data Acquisition System and Gamma Cart Data Acquisition Control System Software Configuration Management Plan

    International Nuclear Information System (INIS)

    WHITE, D.A.

    1999-01-01

    This Software Configuration Management Plan (SCMP) provides the instructions for change control of the AZ1101 Mixer Pump Demonstration Data Acquisition System (DAS) and the Sludge Mobilization Cart (Gamma Cart) Data Acquisition and Control System (DACS)

  9. CRISPR-Cas9 mediated LAG-3 disruption in CAR-T cells.

    Science.gov (United States)

    Zhang, Yongping; Zhang, Xingying; Cheng, Chen; Mu, Wei; Liu, Xiaojuan; Li, Na; Wei, Xiaofei; Liu, Xiang; Xia, Changqing; Wang, Haoyi

    2017-12-01

    T cells engineered with chimeric antigen receptor (CAR) have been successfully applied to treat advanced refractory B cell malignancy. However, many challenges remain in extending its application toward the treatment of solid tumors. The immunosuppressive nature of tumor microenvironment is considered one of the key factors limiting CAR-T efficacy. One negative regulator of Tcell activity is lymphocyte activation gene-3 (LAG-3). We successfully generated LAG-3 knockout Tand CAR-T cells with high efficiency using CRISPR-Cas9 mediated gene editing and found that the viability and immune phenotype were not dramatically changed during in vitro culture. LAG-3 knockout CAR-T cells displayed robust antigen-specific antitumor activity in cell culture and in murine xenograft model, which is comparable to standard CAR-T cells. Our study demonstrates an efficient approach to silence immune checkpoint in CAR-T cells via gene editing.

  10. [Current Status and Challenges of CAR-T Immunotherapy in Hematologic Malignancies -Review].

    Science.gov (United States)

    Cheng, Xin; Wang, Ya-Jie; Feng, Shuai; Wu, Ya-Yun; Yang, Tong-Hua; Lai, Xun

    2018-04-01

    The chimeric antigen receptor (CAR) T cell therapy has gradually became a new trend in the treatment of refractory and relapsed hematologic malignancies by developing for 30 years. With the exciting development of genetic engineering, CAR-T technology has subjected to 4 generations of innovation. Structure of CAR-T started from a single signal molecule to 2 or more than 2 co-stimulatory molecules, and then coding the CAR gene or promoter. CAR-T can specifically recognize tumor antigens, and does not be restricted by major histocompatibility complex (MHC), thus making a breakthrough in clinical treatment. In this review, the history, structure and mechanism of action of CAR-T, as well as the current status and challenges of CAR-T immunotherapy in acute lymphoblastic leukemia, acute myeloid leukemia, chronic myeloid leukemia and multiple myeloma are summarized.

  11. Insights into cytokine release syndrome and neurotoxicity after CD19-specific CAR-T cell therapy.

    Science.gov (United States)

    Gauthier, Jordan; Turtle, Cameron J

    2018-04-03

    T-cells engineered to express CD19-specific chimeric antigen receptors (CD19 CAR-T cells) can achieve high response rates in patients with refractory/relapsed (R/R) CD19+ hematologic malignancies. Nonetheless, the efficacy of CD19-specific CAR-T cell therapy can be offset by significant toxicities, such as cytokine release syndrome (CRS) and neurotoxicity. In this report of our presentation at the 2018 Second French International Symposium on CAR-T cells (CAR-T day), we describe the clinical presentations of CRS and neurotoxicity in a cohort of 133 adults treated with CD19 CAR-T cells at the Fred Hutchinson Cancer Research Center, and provide insights into the mechanisms contributing to these toxicities. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

  12. Parametric vs. Nonparametric Regression Modelling within Clinical Decision Support

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan; Zvárová, Jana

    2017-01-01

    Roč. 5, č. 1 (2017), s. 21-27 ISSN 1805-8698 R&D Projects: GA ČR GA17-01251S Institutional support: RVO:67985807 Keywords : decision support systems * decision rules * statistical analysis * nonparametric regression Subject RIV: IN - Informatics, Computer Science OBOR OECD: Statistics and probability

  13. Nonparametric Item Response Function Estimates with the EM Algorithm.

    Science.gov (United States)

    Rossi, Natasha; Wang, Xiaohui; Ramsay, James O.

    2002-01-01

    Combined several developments in statistics and item response theory to develop a procedure for analysis of dichotomously scored test data. This version of nonparametric item response analysis, as illustrated through simulation and with data from other studies, marginalizes the role of the ability parameter theta. (SLD)

  14. Nonparametric model assisted model calibrated estimation in two ...

    African Journals Online (AJOL)

    Nonparametric model assisted model calibrated estimation in two stage survey sampling. RO Otieno, PN Mwita, PN Kihara. Abstract. No Abstract > East African Journal of Statistics Vol. 1 (3) 2007: pp.261-281. Full Text: EMAIL FULL TEXT EMAIL FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL TEXT.

  15. Nonparametric estimation of the maximum of conditional hazard ...

    African Journals Online (AJOL)

    The maximum of the conditional hazard function is a parameter of great importance in statistics, in particular in seismicity studies, because it constitutes the maximum risk of occurrence of an earthquake in a given interval of time. Using the kernel nonparametric estimates based on convolution kernel techniques of the rst ...

  16. Nonparametric modeling of dynamic functional connectivity in fmri data

    DEFF Research Database (Denmark)

    Nielsen, Søren Føns Vind; Madsen, Kristoffer H; Røge, Rasmus

    2015-01-01

    dynamic changes. The existing approaches modeling dynamic connectivity have primarily been based on time-windowing the data and k-means clustering. We propose a nonparametric generative model for dynamic FC in fMRI that does not rely on specifying window lengths and number of dynamic states. Rooted...

  17. Use of parametric and non-parametric survival analysis techniques ...

    African Journals Online (AJOL)

    This paper presents parametric and non-parametric survival analysis procedures that can be used to compare acaricides. The effectiveness of Delta Tick Pour On and Delta Tick Spray in knocking down tsetse flies were determined. The two formulations were supplied by Chemplex. The comparison was based on data ...

  18. Estimation of Stochastic Volatility Models by Nonparametric Filtering

    DEFF Research Database (Denmark)

    Kanaya, Shin; Kristensen, Dennis

    2016-01-01

    /estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise. The resulting estimators of the stochastic volatility model will carry additional biases...

  19. Non-Parametric Analysis of Rating Transition and Default Data

    DEFF Research Database (Denmark)

    Fledelius, Peter; Lando, David; Perch Nielsen, Jens

    2004-01-01

    We demonstrate the use of non-parametric intensity estimation - including construction of pointwise confidence sets - for analyzing rating transition data. We find that transition intensities away from the class studied here for illustration strongly depend on the direction of the previous move b...

  20. Nonparametric tests for data in randomised blocks with Ordered alternatives

    OpenAIRE

    Rayner, J. C. W.; Best, D. J.

    1999-01-01

    For randomized block designs, nonparametric treatment comparisons are usually made using the Friedman test for complete designs, and the Durbin test for incomplete designs; see, for example, Conover (1998). This permits assessment of only the mean rankings. Such comparisons are here extended to permit assessments of bivariate effects such as the linear by linear effect and the quadratic by linear, or umbrella effect.

  1. Panel data nonparametric estimation of production risk and risk preferences

    DEFF Research Database (Denmark)

    Czekaj, Tomasz Gerard; Henningsen, Arne

    We apply nonparametric panel data kernel regression to investigate production risk, out-put price uncertainty, and risk attitudes of Polish dairy farms based on a firm-level unbalanced panel data set that covers the period 2004–2010. We compare different model specifications and different...

  2. Nonparametric Item Response Curve Estimation with Correction for Measurement Error

    Science.gov (United States)

    Guo, Hongwen; Sinharay, Sandip

    2011-01-01

    Nonparametric or kernel regression estimation of item response curves (IRCs) is often used in item analysis in testing programs. These estimates are biased when the observed scores are used as the regressor because the observed scores are contaminated by measurement error. Accuracy of this estimation is a concern theoretically and operationally.…

  3. Nonparametric, Coupled ,Bayesian ,Dictionary ,and Classifier Learning for Hyperspectral Classification.

    Science.gov (United States)

    Akhtar, Naveed; Mian, Ajmal

    2017-10-03

    We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictionary and the classifier. These processes are coupled under the same sets of Bernoulli distributions. In our approach, these distributions signify the frequency of the dictionary atom usage in representing class-specific training spectra, which also makes the dictionary discriminative. Due to the coupling between the dictionary and the classifier, the popularity of the atoms for representing different classes gets encoded into the classifier. This helps in predicting the class labels of test spectra that are first represented over the dictionary by solving a simultaneous sparse optimization problem. The labels of the spectra are predicted by feeding the resulting representations to the classifier. Our approach exploits the nonparametric Bayesian framework to automatically infer the dictionary size--the key parameter in discriminative dictionary learning. Moreover, it also has the desirable property of adaptively learning the association between the dictionary atoms and the class labels by itself. We use Gibbs sampling to infer the posterior probability distributions over the dictionary and the classifier under the proposed model, for which, we derive analytical expressions. To establish the effectiveness of our approach, we test it on benchmark hyperspectral images. The classification performance is compared with the state-of-the-art dictionary learning-based classification methods.

  4. Non-parametric estimation of low-concentration benzene metabolism.

    Science.gov (United States)

    Cox, Louis A; Schnatter, A Robert; Boogaard, Peter J; Banton, Marcy; Ketelslegers, Hans B

    2017-12-25

    Two apparently contradictory findings in the literature on low-dose human metabolism of benzene are as follows. First, metabolism is approximately linear at low concentrations, e.g., below 10 ppm. This is consistent with decades of quantitative modeling of benzene pharmacokinetics and dose-dependent metabolism. Second, measured benzene exposure and metabolite concentrations for occupationally exposed benzene workers in Tianjin, China show that dose-specific metabolism (DSM) ratios of metabolite concentrations per ppm of benzene in air decrease steadily with benzene concentration, with the steepest decreases below 3 ppm. This has been interpreted as indicating that metabolism at low concentrations of benzene is highly nonlinear. We reexamine the data using non-parametric methods. Our main conclusion is that both findings are correct; they are not contradictory. Low-concentration metabolism can be linear, with metabolite concentrations proportional to benzene concentrations in air, and yet DSM ratios can still decrease with benzene concentrations. This is because a ratio of random variables can be negatively correlated with its own denominator even if the mean of the numerator is proportional to the denominator. Interpreting DSM ratios that decrease with air benzene concentrations as evidence of nonlinear metabolism is therefore unwarranted when plots of metabolite concentrations against benzene ppm in air show approximately straight-line relationships between them, as in the Tianjin data. Thus, an apparent contradiction that has fueled heated discussions in the recent literature can be resolved by recognizing that highly nonlinear, decreasing DSM ratios are consistent with linear metabolism. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  5. Purification and characterisation of a new hypothalamic satiety peptide, cocaine and amphetamine regulated transcript (CART), produced in yeast.

    Science.gov (United States)

    Thim, L; Nielsen, P F; Judge, M E; Andersen, A S; Diers, I; Egel-Mitani, M; Hastrup, S

    1998-05-29

    Cocaine and amphetamine regulated transcript (CART) is a newly discovered hypothalamic peptide with a potent appetite suppressing activity following intracerebroventricular administration. When the mature rat CART sequence encoding CART(1-102) was inserted in the yeast expression plasmid three CART peptides could be purified from the fermentation broth reflecting processing at dibasic sequences. None of these corresponded to the naturally occurring CART(55-102). In order to obtain CART(55-102) the precursor Glu-Glu-Ile-Asp-CART(55-102) has been produced and CART(55-102) was generated by digestion of the precursor with dipeptidylaminopeptidase-1. All four generated CART peptides have been characterised by N-terminal amino acid sequencing and mass spectrometry. The CART peptides contain six cysteine residues and using the yeast expressed CART(62-102) the disulphide bond configuration was found to be I-III, II-V and IV-VI. When the four CART peptides were intracerebroventricularly injected in fasted mice (0.1 to 2.0 microg) they all produced a dose dependent inhibition of food intake.

  6. Association of Cocaine- and Amphetamine-Regulated Transcript (CART) Messenger RNA Level, Food Intake, and Growth in Channel Catfish

    Science.gov (United States)

    Cocaine-and Amphetamine-Regulated Transcript (CART) is a potent hypothalamic anorectic peptide in mammals and fish. We hypothesized that increased food intake is associated with changes in expression of CART mRNA within the brain of channel catfish. Objectives were to clone the CART gene, examine ...

  7. Validation of Nonparametric Two-Sample Bootstrap in ROC Analysis on Large Datasets.

    Science.gov (United States)

    Wu, Jin Chu; Martin, Alvin F; Kacker, Raghu N

    The nonparametric two-sample bootstrap is applied to computing uncertainties of measures in ROC analysis on large datasets in areas such as biometrics, speaker recognition, etc., when the analytical method cannot be used. Its validation was studied by computing the SE of the area under ROC curve using the well-established analytical Mann-Whitney-statistic method and also using the bootstrap. The analytical result is unique. The bootstrap results are expressed as a probability distribution due to its stochastic nature. The comparisons were carried out using relative errors and hypothesis testing. They match very well. This validation provides a sound foundation for such computations.

  8. A sequential nonparametric pattern classification algorithm based on the Wald SPRT. [Sequential Probability Ratio Test

    Science.gov (United States)

    Poage, J. L.

    1975-01-01

    A sequential nonparametric pattern classification procedure is presented. The method presented is an estimated version of the Wald sequential probability ratio test (SPRT). This method utilizes density function estimates, and the density estimate used is discussed, including a proof of convergence in probability of the estimate to the true density function. The classification procedure proposed makes use of the theory of order statistics, and estimates of the probabilities of misclassification are given. The procedure was tested on discriminating between two classes of Gaussian samples and on discriminating between two kinds of electroencephalogram (EEG) responses.

  9. Nonparametric model reconstruction for stochastic differential equations from discretely observed time-series data.

    Science.gov (United States)

    Ohkubo, Jun

    2011-12-01

    A scheme is developed for estimating state-dependent drift and diffusion coefficients in a stochastic differential equation from time-series data. The scheme does not require to specify parametric forms for the drift and diffusion coefficients in advance. In order to perform the nonparametric estimation, a maximum likelihood method is combined with a concept based on a kernel density estimation. In order to deal with discrete observation or sparsity of the time-series data, a local linearization method is employed, which enables a fast estimation.

  10. A simulation study of nonparametric total deviation index as a measure of agreement based on quantile regression.

    Science.gov (United States)

    Lin, Lawrence; Pan, Yi; Hedayat, A S; Barnhart, Huiman X; Haber, Michael

    2016-01-01

    Total deviation index (TDI) captures a prespecified quantile of the absolute deviation of paired observations from raters, observers, methods, assays, instruments, etc. We compare the performance of TDI using nonparametric quantile regression to the TDI assuming normality (Lin, 2000). This simulation study considers three distributions: normal, Poisson, and uniform at quantile levels of 0.8 and 0.9 for cases with and without contamination. Study endpoints include the bias of TDI estimates (compared with their respective theoretical values), standard error of TDI estimates (compared with their true simulated standard errors), and test size (compared with 0.05), and power. Nonparametric TDI using quantile regression, although it slightly underestimates and delivers slightly less power for data without contamination, works satisfactorily under all simulated cases even for moderate (say, ≥40) sample sizes. The performance of the TDI based on a quantile of 0.8 is in general superior to that of 0.9. The performances of nonparametric and parametric TDI methods are compared with a real data example. Nonparametric TDI can be very useful when the underlying distribution on the difference is not normal, especially when it has a heavy tail.

  11. Different Subsets of T Cells, Memory, Effector Functions, and CAR-T Immunotherapy

    Directory of Open Access Journals (Sweden)

    Vita Golubovskaya

    2016-03-01

    Full Text Available This review is focused on different subsets of T cells: CD4 and CD8, memory and effector functions, and their role in CAR-T therapy––a cellular adoptive immunotherapy with T cells expressing chimeric antigen receptor. The CAR-T cells recognize tumor antigens and induce cytotoxic activities against tumor cells. Recently, differences in T cell functions and the role of memory and effector T cells were shown to be important in CAR-T cell immunotherapy. The CD4+ subsets (Th1, Th2, Th9, Th17, Th22, Treg, and Tfh and CD8+ memory and effector subsets differ in extra-cellular (CD25, CD45RO, CD45RA, CCR-7, L-Selectin [CD62L], etc.; intracellular markers (FOXP3; epigenetic and genetic programs; and metabolic pathways (catabolic or anabolic; and these differences can be modulated to improve CAR-T therapy. In addition, CD4+ Treg cells suppress the efficacy of CAR-T cell therapy, and different approaches to overcome this suppression are discussed in this review. Thus, next-generation CAR-T immunotherapy can be improved, based on our knowledge of T cell subsets functions, differentiation, proliferation, and signaling pathways to generate more active CAR-T cells against tumors.

  12. Different Subsets of T Cells, Memory, Effector Functions, and CAR-T Immunotherapy.

    Science.gov (United States)

    Golubovskaya, Vita; Wu, Lijun

    2016-03-15

    This review is focused on different subsets of T cells: CD4 and CD8, memory and effector functions, and their role in CAR-T therapy--a cellular adoptive immunotherapy with T cells expressing chimeric antigen receptor. The CAR-T cells recognize tumor antigens and induce cytotoxic activities against tumor cells. Recently, differences in T cell functions and the role of memory and effector T cells were shown to be important in CAR-T cell immunotherapy. The CD4⁺ subsets (Th1, Th2, Th9, Th17, Th22, Treg, and Tfh) and CD8⁺ memory and effector subsets differ in extra-cellular (CD25, CD45RO, CD45RA, CCR-7, L-Selectin [CD62L], etc.); intracellular markers (FOXP3); epigenetic and genetic programs; and metabolic pathways (catabolic or anabolic); and these differences can be modulated to improve CAR-T therapy. In addition, CD4⁺ Treg cells suppress the efficacy of CAR-T cell therapy, and different approaches to overcome this suppression are discussed in this review. Thus, next-generation CAR-T immunotherapy can be improved, based on our knowledge of T cell subsets functions, differentiation, proliferation, and signaling pathways to generate more active CAR-T cells against tumors.

  13. New analogs of the CART peptide with anorexigenic potency: the importance of individual disulfide bridges.

    Science.gov (United States)

    Blechová, Miroslava; Nagelová, Veronika; Záková, Lenka; Demianová, Zuzana; Zelezná, Blanka; Maletínská, Lenka

    2013-01-01

    The CART (cocaine- and amphetamine-regulated transcript) peptide is an anorexigenic neuropeptide that acts in the hypothalamus. The receptor and the mechanism of action of this peptide are still unknown. In our previous study, we showed that the CART peptide binds specifically to PC12 rat pheochromocytoma cells in both the native and differentiated into neuronal phenotype. Two biologically active forms, CART(55-102) and CART(61-102), with equal biological activity, contain three disulfide bridges. To clarify the importance of each of these disulfide bridges in maintaining the biological activity of CART(61-102), an Ala scan at particular S-S bridges forming cysteines was performed, and analogs with only one or two disulfide bridges were synthesized. In this study, a stabilized CART(61-102) analog with norleucine instead of methionine at position 67 was also prepared and was found to bind to PC12 cells with an anorexigenic potency similar to that of CART(61-102). The binding study revealed that out of all analogs tested, [Ala(68,86)]CART(61-102), which contains two disulfide bridges (positions 74-94 and 88-101), preserved a high affinity to both native PC12 cells and those that had been differentiated into neurons. In food intake and behavioral tests with mice after intracerebroventricular administration, this analog showed strong and long-lasting anorexigenic potency. Therefore, the disulfide bridge between cysteines 68 and 86 in CART(61-102) can be omitted without a loss of biological activity, but the preservation of two other disulfide bridges and the full-length peptide are essential for biological activity. Copyright © 2012 Elsevier Inc. All rights reserved.

  14. Quantitative evaluation of CART-containing cells in urinary bladder of rats with renovascular hypertension

    Directory of Open Access Journals (Sweden)

    I. Janiuk

    2015-04-01

    Full Text Available Recent biological advances make it possible to discover new peptides associated with hypertension. The cocaine- and amphetamine-regulated transcript (CART is a known factor in appetite and feeding behaviour. Various lines of evidence suggest that this peptide participates not only in control of feeding behaviour but also in the regulation of the cardiovascular and sympathetic systems and blood pressure. The role of CART in blood pressure regulation led us to undertake a study aimed at analysing quantitative changes in CART-containing cells in urinary bladders (UB of rats with renovascular hypertension. We used the Goldblatt model of arterial hypertension (two-kidney, one clip to evaluate quantitative changes. This model provides researchers with a commonly used tool to analyse the renin-angiotensin system of blood pressure control and, eventually, to develop drugs for the treatment of chronic hypertension. The study was performed on sections of urinary bladders of rats after 3-, 14-, 28-, 42 and 91 days from hypertension induction. Immunohistochemical identification of CART cells was performed on paraffin for the UBs of all the study animals. CART was detected in the endocrine cells, especially numerous in the submucosa and muscularis layers, with a few found in the transitional epithelium and only occasionally in serosa. Hypertension significantly increased the number of CART-positive cells in the rat UBs. After 3 and 42 days following the procedure, statistically significantly higher numbers of CART-positive cells were identified in comparison with the control animals. The differences between the hypertensive rats and the control animals concerned not only the number density of CART-immunoreactive cells but also their localization. After a 6-week period, each of the rats subjected to the renal artery clipping procedure developed stable hypertension. CART appeared in numerous transitional epithelium cells. As this study provides novel findings

  15. Parametric and Non-Parametric System Modelling

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg

    1999-01-01

    considered. It is shown that adaptive estimation in conditional parametric models can be performed by combining the well known methods of local polynomial regression and recursive least squares with exponential forgetting. The approach used for estimation in conditional parametric models also highlights how...... of a linear model are estimated as functions of some explanatory variable(s). Also, software for handling the estimation is presented. The software runs under S-PLUS and R and contains also a number of tools useful when doing model diagnostics or interpreting the results. Adaptive estimation is also...... networks is included. In this paper, neural networks are used for predicting the electricity production of a wind farm. The results are compared with results obtained using an adaptively estimated ARX-model. Finally, two papers on stochastic differential equations are included. In the first paper, among...

  16. Nonparametric additive regression for repeatedly measured data

    KAUST Repository

    Carroll, R. J.

    2009-05-20

    We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. © 2009 Biometrika Trust.

  17. Nonparametric Collective Spectral Density Estimation and Clustering

    KAUST Repository

    Maadooliat, Mehdi

    2017-04-12

    In this paper, we develop a method for the simultaneous estimation of spectral density functions (SDFs) for a collection of stationary time series that share some common features. Due to the similarities among the SDFs, the log-SDF can be represented using a common set of basis functions. The basis shared by the collection of the log-SDFs is estimated as a low-dimensional manifold of a large space spanned by a pre-specified rich basis. A collective estimation approach pools information and borrows strength across the SDFs to achieve better estimation efficiency. Also, each estimated spectral density has a concise representation using the coefficients of the basis expansion, and these coefficients can be used for visualization, clustering, and classification purposes. The Whittle pseudo-maximum likelihood approach is used to fit the model and an alternating blockwise Newton-type algorithm is developed for the computation. A web-based shiny App found at

  18. Comparison of Rank Analysis of Covariance and Nonparametric Randomized Blocks Analysis.

    Science.gov (United States)

    Porter, Andrew C.; McSweeney, Maryellen

    The relative power of three possible experimental designs under the condition that data is to be analyzed by nonparametric techniques; the comparison of the power of each nonparametric technique to its parametric analogue; and the comparison of relative powers using nonparametric and parametric techniques are discussed. The three nonparametric…

  19. Combined parametric-nonparametric identification of block-oriented systems

    CERN Document Server

    Mzyk, Grzegorz

    2014-01-01

    This book considers a problem of block-oriented nonlinear dynamic system identification in the presence of random disturbances. This class of systems includes various interconnections of linear dynamic blocks and static nonlinear elements, e.g., Hammerstein system, Wiener system, Wiener-Hammerstein ("sandwich") system and additive NARMAX systems with feedback. Interconnecting signals are not accessible for measurement. The combined parametric-nonparametric algorithms, proposed in the book, can be selected dependently on the prior knowledge of the system and signals. Most of them are based on the decomposition of the complex system identification task into simpler local sub-problems by using non-parametric (kernel or orthogonal) regression estimation. In the parametric stage, the generalized least squares or the instrumental variables technique is commonly applied to cope with correlated excitations. Limit properties of the algorithms have been shown analytically and illustrated in simple experiments.

  20. Nonparametric instrumental regression with non-convex constraints

    Science.gov (United States)

    Grasmair, M.; Scherzer, O.; Vanhems, A.

    2013-03-01

    This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition.

  1. Nonparametric instrumental regression with non-convex constraints

    International Nuclear Information System (INIS)

    Grasmair, M; Scherzer, O; Vanhems, A

    2013-01-01

    This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition. (paper)

  2. Non-parametric system identification from non-linear stochastic response

    DEFF Research Database (Denmark)

    Rüdinger, Finn; Krenk, Steen

    2001-01-01

    An estimation method is proposed for identification of non-linear stiffness and damping of single-degree-of-freedom systems under stationary white noise excitation. Non-parametric estimates of the stiffness and damping along with an estimate of the white noise intensity are obtained by suitable...... processing of records of the stochastic response. The stiffness estimation is based on a local iterative procedure, which compares the elastic energy at mean-level crossings with the kinetic energy at the extremes. The damping estimation is based on a generic expression for the probability density...

  3. CATDAT - A program for parametric and nonparametric categorical data analysis user's manual, Version 1.0

    International Nuclear Information System (INIS)

    Peterson, James R.; Haas, Timothy C.; Lee, Danny C.

    2000-01-01

    Natural resource professionals are increasingly required to develop rigorous statistical models that relate environmental data to categorical responses data. Recent advances in the statistical and computing sciences have led to the development of sophisticated methods for parametric and nonparametric analysis of data with categorical responses. The statistical software package CATDAT was designed to make some of these relatively new and powerful techniques available to scientists. The CATDAT statistical package includes 4 analytical techniques: generalized logit modeling; binary classification tree; extended K-nearest neighbor classification; and modular neural network

  4. Nonparametric Identification of Dynamic Games with Discrete and Continuous Choices

    OpenAIRE

    Jason R. Blevins

    2010-01-01

    This paper shows that the payoff functions in a class of dynamic games of incomplete information are nonparametrically identified under standard assumptions currently used in applied work. Models of this kind are prevalent in empirical industrial organization where, for example, firms in oligopolistic industries make discrete entry and exit decisions followed by continuous investment or pricing decisions. We also provide results for single-agent models, a leading special case which is commonl...

  5. Nonparametric Bayesian models through probit stick-breaking processes.

    Science.gov (United States)

    Rodríguez, Abel; Dunson, David B

    2011-03-01

    We describe a novel class of Bayesian nonparametric priors based on stick-breaking constructions where the weights of the process are constructed as probit transformations of normal random variables. We show that these priors are extremely flexible, allowing us to generate a great variety of models while preserving computational simplicity. Particular emphasis is placed on the construction of rich temporal and spatial processes, which are applied to two problems in finance and ecology.

  6. Nonparametric Estimation of Information-Based Measures of Statistical Dispersion

    Czech Academy of Sciences Publication Activity Database

    Košťál, Lubomír; Pokora, Ondřej

    2012-01-01

    Roč. 14, č. 7 (2012), s. 1221-1233 ISSN 1099-4300 R&D Projects: GA ČR(CZ) GAP103/11/0282; GA ČR(CZ) GBP304/12/G069; GA ČR(CZ) GPP103/12/ P558 Institutional support: RVO:67985823 Keywords : statistical dispersion * entropy * Fisher information * nonparametric density estimation * neuronal activity Subject RIV: FH - Neurology Impact factor: 1.347, year: 2012

  7. A Novel Nonparametric Distance Estimator for Densities with Error Bounds

    Directory of Open Access Journals (Sweden)

    Alexandre R.F. Carvalho

    2013-05-01

    Full Text Available The use of a metric to assess distance between probability densities is an important practical problem. In this work, a particular metric induced by an α-divergence is studied. The Hellinger metric can be interpreted as a particular case within the framework of generalized Tsallis divergences and entropies. The nonparametric Parzen’s density estimator emerges as a natural candidate to estimate the underlying probability density function, since it may account for data from different groups, or experiments with distinct instrumental precisions, i.e., non-independent and identically distributed (non-i.i.d. data. However, the information theoretic derived metric of the nonparametric Parzen’s density estimator displays infinite variance, limiting the direct use of resampling estimators. Based on measure theory, we present a change of measure to build a finite variance density allowing the use of resampling estimators. In order to counteract the poor scaling with dimension, we propose a new nonparametric two-stage robust resampling estimator of Hellinger’s metric error bounds for heterocedastic data. The approach presents very promising results allowing the use of different covariances for different clusters with impact on the distance evaluation.

  8. Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models.

    Science.gov (United States)

    Zeng, Ping; Zhou, Xiang

    2017-09-06

    Using genotype data to perform accurate genetic prediction of complex traits can facilitate genomic selection in animal and plant breeding programs, and can aid in the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling all genetic variants together via polygenic methods. Here, we develop such a polygenic method, which we refer to as the latent Dirichlet process regression model. Dirichlet process regression is non-parametric in nature, relies on the Dirichlet process to flexibly and adaptively model the effect size distribution, and thus enjoys robust prediction performance across a broad spectrum of genetic architectures. We compare Dirichlet process regression with several commonly used prediction methods with simulations. We further apply Dirichlet process regression to predict gene expressions, to conduct PrediXcan based gene set test, to perform genomic selection of four traits in two species, and to predict eight complex traits in a human cohort.Genetic prediction of complex traits with polygenic architecture has wide application from animal breeding to disease prevention. Here, Zeng and Zhou develop a non-parametric genetic prediction method based on latent Dirichlet Process regression models.

  9. Nonparametric estimation for censored mixture data with application to the Cooperative Huntington's Observational Research Trial.

    Science.gov (United States)

    Wang, Yuanjia; Garcia, Tanya P; Ma, Yanyuan

    2012-01-01

    This work presents methods for estimating genotype-specific distributions from genetic epidemiology studies where the event times are subject to right censoring, the genotypes are not directly observed, and the data arise from a mixture of scientifically meaningful subpopulations. Examples of such studies include kin-cohort studies and quantitative trait locus (QTL) studies. Current methods for analyzing censored mixture data include two types of nonparametric maximum likelihood estimators (NPMLEs) which do not make parametric assumptions on the genotype-specific density functions. Although both NPMLEs are commonly used, we show that one is inefficient and the other inconsistent. To overcome these deficiencies, we propose three classes of consistent nonparametric estimators which do not assume parametric density models and are easy to implement. They are based on the inverse probability weighting (IPW), augmented IPW (AIPW), and nonparametric imputation (IMP). The AIPW achieves the efficiency bound without additional modeling assumptions. Extensive simulation experiments demonstrate satisfactory performance of these estimators even when the data are heavily censored. We apply these estimators to the Cooperative Huntington's Observational Research Trial (COHORT), and provide age-specific estimates of the effect of mutation in the Huntington gene on mortality using a sample of family members. The close approximation of the estimated non-carrier survival rates to that of the U.S. population indicates small ascertainment bias in the COHORT family sample. Our analyses underscore an elevated risk of death in Huntington gene mutation carriers compared to non-carriers for a wide age range, and suggest that the mutation equally affects survival rates in both genders. The estimated survival rates are useful in genetic counseling for providing guidelines on interpreting the risk of death associated with a positive genetic testing, and in facilitating future subjects at risk

  10. Comparative analysis of automotive paints by laser induced breakdown spectroscopy and nonparametric permutation tests

    International Nuclear Information System (INIS)

    McIntee, Erin; Viglino, Emilie; Rinke, Caitlin; Kumor, Stephanie; Ni Liqiang; Sigman, Michael E.

    2010-01-01

    Laser-induced breakdown spectroscopy (LIBS) has been investigated for the discrimination of automobile paint samples. Paint samples from automobiles of different makes, models, and years were collected and separated into sets based on the color, presence or absence of effect pigments and the number of paint layers. Twelve LIBS spectra were obtained for each paint sample, each an average of a five single shot 'drill down' spectra from consecutive laser ablations in the same spot on the sample. Analyses by a nonparametric permutation test and a parametric Wald test were performed to determine the extent of discrimination within each set of paint samples. The discrimination power and Type I error were assessed for each data analysis method. Conversion of the spectral intensity to a log-scale (base 10) resulted in a higher overall discrimination power while observing the same significance level. Working on the log-scale, the nonparametric permutation tests gave an overall 89.83% discrimination power with a size of Type I error being 4.44% at the nominal significance level of 5%. White paint samples, as a group, were the most difficult to differentiate with the power being only 86.56% followed by 95.83% for black paint samples. Parametric analysis of the data set produced lower discrimination (85.17%) with 3.33% Type I errors, which is not recommended for both theoretical and practical considerations. The nonparametric testing method is applicable across many analytical comparisons, with the specific application described here being the pairwise comparison of automotive paint samples.

  11. Single-case effect size calculation: comparing regression and non-parametric approaches across previously published reading intervention data sets.

    Science.gov (United States)

    Ross, Sarah G; Begeny, John C

    2014-08-01

    Growing from demands for accountability and research-based practice in the field of education, there is recent focus on developing standards for the implementation and analysis of single-case designs. Effect size methods for single-case designs provide a useful way to discuss treatment magnitude in the context of individual intervention. Although a standard effect size methodology does not yet exist within single-case research, panel experts recently recommended pairing regression and non-parametric approaches when analyzing effect size data. This study compared two single-case effect size methods: the regression-based, Allison-MT method and the newer, non-parametric, Tau-U method. Using previously published research that measured the Words read Correct per Minute (WCPM) variable, these two methods were examined by comparing differences in overall effect size scores and rankings of intervention effect. Results indicated that the regression method produced significantly larger effect sizes than the non-parametric method, but the rankings of the effect size scores had a strong, positive relation. Implications of these findings for research and practice are discussed. Copyright © 2014 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

  12. Parallelization characteristics of a three-dimensional whole-core code DeCART

    International Nuclear Information System (INIS)

    Cho, J. Y.; Joo, H.K.; Kim, H. Y.; Lee, J. C.; Jang, M. H.

    2003-01-01

    Neutron transport calculation for three-dimensional amount of computing time but also huge memory. Therefore, whole-core codes such as DeCART need both also parallel computation and distributed memory capabilities. This paper is to implement such parallel capabilities based on MPI grouping and memory distribution on the DeCART code, and then to evaluate the performance by solving the C5G7 three-dimensional benchmark and a simplified three-dimensional SMART core problem. In C5G7 problem with 24 CPUs, a speedup of maximum 22 is obtained on IBM regatta machine and 21 on a LINUX cluster for the MOC kernel, which indicates good parallel performance of the DeCART code. The simplified SMART problem which need about 11 GBytes memory with one processors requires about 940 MBytes, which means that the DeCART code can now solve large core problems on affordable LINUX clusters

  13. Shopper marketing nutrition interventions: Social norms on grocery carts increase produce spending without increasing shopper budgets

    Directory of Open Access Journals (Sweden)

    Collin R. Payne

    2015-01-01

    Conclusions: Descriptive and provincial social norm messages (i.e., on grocery cart placards may be an overlooked tool to increase produce demand without decreasing store profitability and increasing shopper budgets.

  14. An adaptive distance measure for use with nonparametric models

    International Nuclear Information System (INIS)

    Garvey, D. R.; Hines, J. W.

    2006-01-01

    Distance measures perform a critical task in nonparametric, locally weighted regression. Locally weighted regression (LWR) models are a form of 'lazy learning' which construct a local model 'on the fly' by comparing a query vector to historical, exemplar vectors according to a three step process. First, the distance of the query vector to each of the exemplar vectors is calculated. Next, these distances are passed to a kernel function, which converts the distances to similarities or weights. Finally, the model output or response is calculated by performing locally weighted polynomial regression. To date, traditional distance measures, such as the Euclidean, weighted Euclidean, and L1-norm have been used as the first step in the prediction process. Since these measures do not take into consideration sensor failures and drift, they are inherently ill-suited for application to 'real world' systems. This paper describes one such LWR model, namely auto associative kernel regression (AAKR), and describes a new, Adaptive Euclidean distance measure that can be used to dynamically compensate for faulty sensor inputs. In this new distance measure, the query observations that lie outside of the training range (i.e. outside the minimum and maximum input exemplars) are dropped from the distance calculation. This allows for the distance calculation to be robust to sensor drifts and failures, in addition to providing a method for managing inputs that exceed the training range. In this paper, AAKR models using the standard and Adaptive Euclidean distance are developed and compared for the pressure system of an operating nuclear power plant. It is shown that using the standard Euclidean distance for data with failed inputs, significant errors in the AAKR predictions can result. By using the Adaptive Euclidean distance it is shown that high fidelity predictions are possible, in spite of the input failure. In fact, it is shown that with the Adaptive Euclidean distance prediction

  15. Nonparametric estimation of age-specific reference percentile curves with radial smoothing.

    Science.gov (United States)

    Wan, Xiaohai; Qu, Yongming; Huang, Yao; Zhang, Xiao; Song, Hanping; Jiang, Honghua

    2012-01-01

    Reference percentile curves represent the covariate-dependent distribution of a quantitative measurement and are often used to summarize and monitor dynamic processes such as human growth. We propose a new nonparametric method based on a radial smoothing (RS) technique to estimate age-specific reference percentile curves assuming the underlying distribution is relatively close to normal. We compared the RS method with both the LMS and the generalized additive models for location, scale and shape (GAMLSS) methods using simulated data and found that our method has smaller estimation error than the two existing methods. We also applied the new method to analyze height growth data from children being followed in a clinical observational study of growth hormone treatment, and compared the growth curves between those with growth disorders and the general population. Copyright © 2011 Elsevier Inc. All rights reserved.

  16. Reducing the false positive rate in the non-parametric analysis of molecular coevolution

    Directory of Open Access Journals (Sweden)

    O'Dea Shirley

    2008-04-01

    Full Text Available Abstract Background The strength of selective constraints operating on amino acid sites of proteins has a multifactorial nature. In fact, amino acid sites within proteins coevolve due to their functional and/or structural relationships. Different methods have been developed that attempt to account for the evolutionary dependencies between amino acid sites. Researchers have invested a significant effort to increase the sensitivity of such methods. However, the difficulty in disentangling functional co-dependencies from historical covariation has fuelled the scepticism over their power to detect biologically meaningful results. In addition, the biological parameters connecting linear sequence evolution to structure evolution remain elusive. For these reasons, most of the evolutionary studies aimed at identifying functional dependencies among protein domains have focused on the structural properties of proteins rather than on the information extracted from linear multiple sequence alignments (MSA. Non-parametric methods to detect coevolution have been reported to be especially susceptible to produce false positive results based on the properties of MSAs. However, no formal statistical analysis has been performed to definitively test the differential effects of these properties on the sensitivity of such methods. Results Here we test the effect that variations on the MSA properties have over the sensitivity of non-parametric methods to detect coevolution. We test the effect that the size of the MSA (number of sequences, mean pairwise amino acid distance per site and the strength of the coevolution signal have on the ability of non-parametric methods to detect coevolution. Our results indicate that all three factors have significant effects on the accuracy of non-parametric methods. Further, introducing statistical filters improves the sensitivity and increases the statistical power of the methods to detect functional coevolution. Statistical

  17. Arquitecturas efímeras con cartón: el caso de la village en cartón de Guy Rottier

    OpenAIRE

    Sánchez Campos, Roberto

    2016-01-01

    En este trabajo se plantea un proceso de investigación sobre la arquitectura con cartón, que va desde el estudio del material y la arquitectura construida, hasta la profundización en las ideas del arquitecto Guy Rottier sobre estas arquitecturas, plasmadas en su totalidad en el proyecto de la village en carton. El trabajo se ha desarrollado en 5 fases: En la primera fase se pretende demostrar el interés del cartón como material para la arquitectura y las nuevas posibilidades que abre su us...

  18. Le commentaire de cartes et la «nouvelle géographie»

    Directory of Open Access Journals (Sweden)

    Alain REYNAUD

    1987-06-01

    Full Text Available Les cartes topographiques intéressent le public et attirent l'attention des penseurs, car elles constituent un support aussi bien pour le raisonnement et le rêve que pour l'action. Au même moment, le commentaire de cartes est remis en question parmi les géographes; mais il peut garder sa place à la condition d'affûter ses outils conceptuels et de diversifier ses thèmes.

  19. Phase I Escalating-Dose Trial of CAR-T Therapy Targeting CEA+ Metastatic Colorectal Cancers.

    Science.gov (United States)

    Zhang, Chengcheng; Wang, Zhe; Yang, Zhi; Wang, Meiling; Li, Shiqi; Li, Yunyan; Zhang, Rui; Xiong, Zhouxing; Wei, Zhihao; Shen, Junjie; Luo, Yongli; Zhang, Qianzhen; Liu, Limei; Qin, Hong; Liu, Wei; Wu, Feng; Chen, Wei; Pan, Feng; Zhang, Xianquan; Bie, Ping; Liang, Houjie; Pecher, Gabriele; Qian, Cheng

    2017-05-03

    Chimeric antigen receptor T (CAR-T) cells have shown promising efficacy in treatment of hematological malignancies, but its applications in solid tumors need further exploration. In this study, we investigated CAR-T therapy targeting carcino-embryonic antigen (CEA)-positive colorectal cancer (CRC) patients with metastases to evaluate its safety and efficacy. Five escalating dose levels (DLs) (1 × 10 5 to 1 × 10 8 /CAR + /kg cells) of CAR-T were applied in 10 CRC patients. Our data showed that severe adverse events related to CAR-T therapy were not observed. Of the 10 patients, 7 patients who experienced progressive disease (PD) in previous treatments had stable disease after CAR-T therapy. Two patients remained with stable disease for more than 30 weeks, and two patients showed tumor shrinkage by positron emission tomography (PET)/computed tomography (CT) and MRI analysis, respectively. Decline of serum CEA level was apparent in most patients even in long-term observation. Furthermore, we observed persistence of CAR-T cells in peripheral blood of patients receiving high doses of CAR-T therapy. Importantly, we observed CAR-T cell proliferation especially in patients after a second CAR-T therapy. Taken together, we demonstrated that CEA CAR-T cell therapy was well tolerated in CEA + CRC patients even in high doses, and some efficacy was observed in most of the treated patients. Copyright © 2017 The American Society of Gene and Cell Therapy. Published by Elsevier Inc. All rights reserved.

  20. Motion sickness in ancient China: Seasickness and cart-sickness.

    Science.gov (United States)

    Brandt, Thomas; Bauer, Matthias; Benson, Judy; Huppert, Doreen

    2016-07-19

    To find and analyze descriptions of motion sickness in Chinese historical sources. Databases and dictionaries were searched for various terms for seasickness and travel sickness, which were then entered into databases of full texts allowing selection of relevant passages from about the third to the 19th century ad. Already in 300 ad the Chinese differentiated cart-sickness, particularly experienced by persons from the arid north of China, from a ship-illness experienced by persons from the south, where rivers were important for transportation and travel. In the Middle Ages, a third form of motion sickness was called litter-influence experienced by persons transported in a bed suspended between 2 long poles. The ancient Chinese recognized the particular susceptibility of children to motion sickness. Therapeutic recommendations include drinking the urine of young boys, swallowing white sand-syrup, collecting water drops from a bamboo stick, or hiding some earth from the middle of the kitchen hearth under the hair. The Chinese medical classics distinguished several forms of travel sickness, all of which had their own written characters. The pathophysiologic mechanism was explained by the medicine of correspondences, which was based on malfunctions within the body, its invasion by external pathogens like wind, or the deficit or surfeit of certain bodily substances such as the life force Qi. The concept of motion as the trigger of sickness initially appeared in a chapter on warding off the influence of demons and corpses, e.g., ancient magic and beliefs. © 2016 American Academy of Neurology.

  1. To Love—To Live: Barrow and Cart

    Directory of Open Access Journals (Sweden)

    Lisa McDonald

    2013-02-01

    Full Text Available From the residue of meaning, an ensemble of shadows. From the glint of souvenir, pliable impressions. In this paper, we work a poetics of encounter, of being, keeping, homage, of paying homage to fragility, to object and to interspecies—ways are found to engage motion from within and around co-extensive bodies. With the consolation of images, we follow the terse rhythms of routine and street where dwelling is a case of affective dissent. Zones of departure appear through testimony as well as chance, taking their own form. A footfall brings us as observers into quiet spaces which refuse self-estrangement as we travel by way of an unquiet ground. Breath, respiration, aspiration. Precipitation. Sculptures of mist are also the language of lives, of kinship between object, footfall and air. A language of brackets, questions, ellipses. There may be a man, a dog, a barrow. There may be a woman, a cart. Air. How shall this image be made?

  2. Intermediate transport in Southeast Asia. [Carts, cycles, mini-buses

    Energy Technology Data Exchange (ETDEWEB)

    Meier, A.K.

    1977-06-01

    Traffic flows through the streets of Southeast Asian countries even though they are used for almost all aspects of human and animal existence. The carts, bicycles, tricycles, and motorcycles, motorized three-wheelers, mini-buses are the so-called intermediate-transport vehicles. It is upon this group of vehicles that a culture--constrained by its own unique economic, environmental, and technological factors--exerts its influence most directly toward the solution of the transport problem. Transportation fills more service roles in Southeast Asian cities than in Western cities. Communication facilities such as telephones and postal services are notoriously unreliable. The personal encounter is all important in social and business interactions in Southeast Asia. Each of the transport modes is examined in view of design and use in a number of specific cultural settings for the countries in Southeast Asia. Present use of intermediate transport in developed countries is discussed briefly, and its further development predicted--pointing out the health and conservation advantages. (MCW)

  3. A semi-nonparametric mixture model for selecting functionally consistent proteins.

    Science.gov (United States)

    Yu, Lianbo; Doerge, Rw

    2010-09-28

    High-throughput technologies have led to a new era of proteomics. Although protein microarray experiments are becoming more common place there are a variety of experimental and statistical issues that have yet to be addressed, and that will carry over to new high-throughput technologies unless they are investigated. One of the largest of these challenges is the selection of functionally consistent proteins. We present a novel semi-nonparametric mixture model for classifying proteins as consistent or inconsistent while controlling the false discovery rate and the false non-discovery rate. The performance of the proposed approach is compared to current methods via simulation under a variety of experimental conditions. We provide a statistical method for selecting functionally consistent proteins in the context of protein microarray experiments, but the proposed semi-nonparametric mixture model method can certainly be generalized to solve other mixture data problems. The main advantage of this approach is that it provides the posterior probability of consistency for each protein.

  4. Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification.

    Science.gov (United States)

    Fan, Jianqing; Feng, Yang; Jiang, Jiancheng; Tong, Xin

    We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.

  5. Histamine Recycling Is Mediated by CarT, a Carcinine Transporter in Drosophila Photoreceptors

    Science.gov (United States)

    Xu, Ying; An, Futing; Borycz, Jolanta A.; Borycz, Janusz; Meinertzhagen, Ian A.; Wang, Tao

    2015-01-01

    Histamine is an important chemical messenger that regulates multiple physiological processes in both vertebrate and invertebrate animals. Even so, how glial cells and neurons recycle histamine remains to be elucidated. Drosophila photoreceptor neurons use histamine as a neurotransmitter, and the released histamine is recycled through neighboring glia, where it is conjugated to β-alanine to form carcinine. However, how carcinine is then returned to the photoreceptor remains unclear. In an mRNA-seq screen for photoreceptor cell-enriched transporters, we identified CG9317, an SLC22 transporter family protein, and named it CarT (Carcinine Transporter). S2 cells that express CarT are able to take up carcinine in vitro. In the compound eye, CarT is exclusively localized to photoreceptor terminals. Null mutations of cart alter the content of histamine and its metabolites. Moreover, null cart mutants are defective in photoreceptor synaptic transmission and lack phototaxis. These findings reveal that CarT is required for histamine recycling at histaminergic photoreceptors and provide evidence for a CarT-dependent neurotransmitter trafficking pathway between glial cells and photoreceptor terminals. PMID:26713872

  6. Analysis of sequence variability in the CART gene in relation to obesity in a Caucasian population

    Directory of Open Access Journals (Sweden)

    Hercberg Serge

    2005-04-01

    Full Text Available Abstract Background Cocaine and amphetamine regulated transcript (CART is an anorectic neuropeptide located principally in hypothalamus. CART has been shown to be involved in control of feeding behavior, but a direct relationship with obesity has not been established. The aim of this study was to evaluate the effect of polymorphisms within the CART gene with regards to a possible association with obesity in a Caucasian population. Results Screening of the entire gene as well as a 3.7 kb region of 5' upstream sequence revealed 31 SNPs and 3 rare variants ; 14 of which were subsequently genotyped in 292 French morbidly obese subjects and 368 controls. Haplotype analysis suggested an association with obesity which was found to be mainly due to SNP-3608T>C (rs7379701 (p = 0.009. Genotyping additional cases and controls also of European Caucasian origin supported further this possible association between the CART SNP -3608T>C T allele and obesity (global p-value = 0.0005. Functional studies also suggested that the SNP -3608T>C could modulate nuclear protein binding. Conclusion CART SNP -3608T>C may possibly contribute to the genetic risk for obesity in the Caucasian population. However confirmation of the importance of the role of the CART gene in energy homeostasis and obesity will require investigation and replication in further populations.

  7. Evaluation of solar-assisted, electric and gas golf carts, Bathurst Glen golf course, Richmond Hill, Ontario

    International Nuclear Information System (INIS)

    2010-08-01

    Municipalities try to limit air pollution resulting from the use of small gasoline engines. Indeed, these engines participate in the smog and greenhouse gas (GHG) emissions and they present operating costs more important than electric equivalents. The potential positive impacts of the use of electric or solar electric golf carts instead of gasoline carts are analyzed through a study that compares two solar-assisted electric golf carts, two standard electric golf carts and two gas-powered golf carts. The energy use and related Co2 emissions, the dependability, and the relative costs were evaluated and Golfer preference was also considered thanks to a feedback survey. The comparison between the solar-assisted and the standard electric carts was made on the basis of electricity measures at three points: alternating current (AC) electricity taken from the grid, direct current (DC) electricity flowing into and out of the batteries, and DC electricity generated by the solar panels. The data collected during this study suggested that other factors associated with cart condition or driver behaviours can be more important than the solar panels in determining overall energy consumption. Choosing an area with full sun exposure to install the solar panel and connecting directly to the grid would also maximize generation potential. The comparison of performance between electric carts and gas carts showed the most considerable positive findings. Indeed, fuel costs and emissions are significantly lower in the case of the electric carts, which also present a better fuel efficiency. Switching the 20 percent of gas-powered carts counted within a 100 km radius of Toronto with electric carts could be comparable to removing 155 mid-sized gasoline cars of the road. The electric golf carts present many important financial and environmental benefits when compared to gas carts. The performance is marginally enhanced with the use of solar panels on electric carts and the date collected from

  8. Demographic and financial characteristics of school districts with low and high à la Carte sales in rural Kansas Public Schools.

    Science.gov (United States)

    Nollen, Nicole L; Kimminau, Kim S; Nazir, Niaman

    2011-06-01

    Reducing à la carte items in schools-foods and beverages sold outside the reimbursable meals program-can have important implications for childhood obesity. However, schools are reluctant to reduce à la carte offerings because of the impact these changes could have on revenue. Some foodservice programs operate with limited à la carte sales, but little is known about these programs. This secondary data analysis compared rural and urban/suburban school districts with low and high à la carte sales. Foodservice financial records (2007-2008) were obtained from the Kansas State Department of Education for all public K-12 school districts (n=302). χ² and t tests were used to examine the independent association of variables to à la carte sales. A multivariate model was then constructed of the factors most strongly associated with low à la carte sales. In rural districts with low à la carte sales, lunch prices and participation were higher, lunch costs and à la carte quality were lower, and fewer free/reduced price lunches were served compared to rural districts with high à la carte sales. Lunch price (odds ratio=1.2; 95% confidence interval, 1.1 to 1.4) and free/reduced price lunch participation (odds ratio=3.0; 95% confidence interval, 1.0 to 9.8) remained in the multivariate model predicting low à la carte sales. No differences were found between urban/suburban districts with low and high à la carte sales. Findings highlight important factors to maintaining low à la carte sales. Schools should consider raising lunch prices and increasing meal participation rates as two potential strategies for reducing the sale of à la carte items without compromising foodservice revenue. Copyright © 2011 American Dietetic Association. Published by Elsevier Inc. All rights reserved.

  9. Expression of Cocaine and Amphetamine Regulated Transcript (CART) in the Porcine Intramural Neurons of Stomach in the Course of Experimentally Induced Diabetes Mellitus.

    Science.gov (United States)

    Bulc, Michał; Gonkowski, Sławomir; Całka, Jarosław

    2015-11-01

    In the present study, the effect of streptozotocin-induced diabetes on the cocaine- and amphetamine-regulated transcript-like immunoreactive (CART-LI) enteric nervous structures was investigated within the porcine stomach. To induce diabetes, the pigs were administered intravenously streptozotocin at a dose of 150 mg/kg of body weight. A significant decrease of the number of CART-LI perikarya was observed in the myenteric plexus of the gastric antrum, corpus, and pylorus in the experimental group. In contrast, submucous plexus was devoid of CART-positive neuronal cells both in control and experimental animals. In the control group, the highest densities of CART-LI nerve fibers were observed in the circular muscle layer of antrum and slightly less nerve fibers were present in the muscle layer of corpus and pylorus. In turn, submucous layer of all studied stomach regions revealed relatively smaller number of CART-positive nerve fibers. Diabetes caused statistically significant decrease in the expression of CART-LI nerve fibers only in the antrum circular muscle layer. Also, no changes in the CART-like immunoreactivity in the intraganglionic nerve fibers were observed. The obtained results suggest that acute hyperglycemia produced significant reduction of the CART expression in enteric perikarya throughout entire stomach as well as decrease of density the CART-LI fibers in circular muscle layer of the antrum. Additionally, we suggest that CART might be involved in the regulation of stomach function especially in the gastric motility.

  10. Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements

    KAUST Repository

    Ryu, Duchwan

    2010-09-28

    We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves. © 2010, The International Biometric Society.

  11. Effect of the 1973 oil price embargo; A non-parametric analysis

    Energy Technology Data Exchange (ETDEWEB)

    Goel, R.K.; Morey, M.J. (Illinois State Univ., Normal, IL (United States). Dept. of Economics)

    1993-01-01

    This paper focuses on the effect of the oil shock of 1973 on US gasoline demand by examining the price elasticities of demand before and after the 1973 embargo. Price elasticities provide useful input to the development of public policy dealing with taxation and pollution control. The extensive data used include state level observations for nearly three decades spanning 1952-80. We apply non-parametric regression methods that are more appropriate to our investigation than traditional parametric techniques. Unlike standard regression techniques, non-parametric methods neither assume a functional form for the demand relation nor restrict the distribution of the dependent variable. Our results show that the mean price elasticity of gasoline demand for the USA was - 0.243 for 1952-73 and the corresponding number for 1973-80 was - 0.576, statistically different at the 5% level of significance. The relatively higher price elasticity in the post-embargo period is consistent with the hypothesis that consumers sought substitutes and restricted their consumption in response to prices as well as social responsibility. The policy implications of these results are also discussed. (author)

  12. Non-parametric kernel density estimation of species sensitivity distributions in developing water quality criteria of metals.

    Science.gov (United States)

    Wang, Ying; Wu, Fengchang; Giesy, John P; Feng, Chenglian; Liu, Yuedan; Qin, Ning; Zhao, Yujie

    2015-09-01

    Due to use of different parametric models for establishing species sensitivity distributions (SSDs), comparison of water quality criteria (WQC) for metals of the same group or period in the periodic table is uncertain and results can be biased. To address this inadequacy, a new probabilistic model, based on non-parametric kernel density estimation was developed and optimal bandwidths and testing methods are proposed. Zinc (Zn), cadmium (Cd), and mercury (Hg) of group IIB of the periodic table are widespread in aquatic environments, mostly at small concentrations, but can exert detrimental effects on aquatic life and human health. With these metals as target compounds, the non-parametric kernel density estimation method and several conventional parametric density estimation methods were used to derive acute WQC of metals for protection of aquatic species in China that were compared and contrasted with WQC for other jurisdictions. HC5 values for protection of different types of species were derived for three metals by use of non-parametric kernel density estimation. The newly developed probabilistic model was superior to conventional parametric density estimations for constructing SSDs and for deriving WQC for these metals. HC5 values for the three metals were inversely proportional to atomic number, which means that the heavier atoms were more potent toxicants. The proposed method provides a novel alternative approach for developing SSDs that could have wide application prospects in deriving WQC and use in assessment of risks to ecosystems.

  13. Does less frequent routine monitoring of patients on a stable, fully suppressed cART regimen lead to an increased risk of treatment failure?

    DEFF Research Database (Denmark)

    Reekie, Joanne; Mocroft, Amanda; Sambatakou, Helen

    2008-01-01

    SIDA study who maintained a stable and fully suppressed cART regimen for 1 year were included in the analysis. METHODS: Risk of treatment failure, defined by viral rebound, fall in CD4 cell count, development of new AIDS-defining illness, serious opportunistic infection or death, in the 12 months following...... interval (CI) 0.1-0.5], 2.2% (95% CI 1.6-2.8) and 6.0% (95% CI 5.0-7.0) risk of treatment failure, respectively. Patients who spent more than 80% of their time on cART with fully suppressed viraemia prior to baseline had a 38% reduced risk of treatment failure, hazard ratio 0.62 (95% CI 0.42-0.90, P = 0...

  14. CART (cocaine- and amphetamine-regulated transcript) peptide specific binding sites in PC12 cells have characteristics of CART peptide receptors

    Czech Academy of Sciences Publication Activity Database

    Nagelová, Veronika; Pirnik, Z.; Železná, Blanka; Maletínská, Lenka

    2014-01-01

    Roč. 1547, Feb 14 (2014), s. 16-24 ISSN 0006-8993 R&D Projects: GA ČR GAP303/10/1368 Institutional support: RVO:61388963 Keywords : CART peptide * PC12 cell * differentiation * binding * signaling * c-Jun Subject RIV: CE - Biochemistry Impact factor: 2.843, year: 2014

  15. Changes in RANKL during the first two years after cART initiation in HIV-infected cART naïve adults

    DEFF Research Database (Denmark)

    Mathiesen, Inger Hee; Salem, Mohammad; Gerstoft, Jan

    2017-01-01

    Background: By assessing the changes in concentration of soluble receptor activator of nuclear factor Κ B ligand (RANKL) and osteoprotegrin (OPG) after initiation of combination antiretroviral therapy (cART) in treatment-naïve HIV-infected patients we aimed to evaluate whether the initial acceler...

  16. Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information

    Science.gov (United States)

    2017-01-01

    Gene regulatory networks (GRNs) play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are used to capture the nonlinear relationships flexibly and penalties are used to avoid overfitting. Further, we incorporate the topology information into the Bayesian method as a prior. We present the application of our method on DREAM3 and DREAM4 datasets and two real biological datasets. The results show that our method performs better than existing methods and the topology information prior can improve the result. PMID:28133490

  17. Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information

    Directory of Open Access Journals (Sweden)

    Yue Fan

    2017-01-01

    Full Text Available Gene regulatory networks (GRNs play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are used to capture the nonlinear relationships flexibly and penalties are used to avoid overfitting. Further, we incorporate the topology information into the Bayesian method as a prior. We present the application of our method on DREAM3 and DREAM4 datasets and two real biological datasets. The results show that our method performs better than existing methods and the topology information prior can improve the result.

  18. A Nonparametric Bayesian Approach For Emission Tomography Reconstruction

    International Nuclear Information System (INIS)

    Barat, Eric; Dautremer, Thomas

    2007-01-01

    We introduce a PET reconstruction algorithm following a nonparametric Bayesian (NPB) approach. In contrast with Expectation Maximization (EM), the proposed technique does not rely on any space discretization. Namely, the activity distribution--normalized emission intensity of the spatial poisson process--is considered as a spatial probability density and observations are the projections of random emissions whose distribution has to be estimated. This approach is nonparametric in the sense that the quantity of interest belongs to the set of probability measures on R k (for reconstruction in k-dimensions) and it is Bayesian in the sense that we define a prior directly on this spatial measure. In this context, we propose to model the nonparametric probability density as an infinite mixture of multivariate normal distributions. As a prior for this mixture we consider a Dirichlet Process Mixture (DPM) with a Normal-Inverse Wishart (NIW) model as base distribution of the Dirichlet Process. As in EM-family reconstruction, we use a data augmentation scheme where the set of hidden variables are the emission locations for each observed line of response in the continuous object space. Thanks to the data augmentation, we propose a Markov Chain Monte Carlo (MCMC) algorithm (Gibbs sampler) which is able to generate draws from the posterior distribution of the spatial intensity. A difference with EM is that one step of the Gibbs sampler corresponds to the generation of emission locations while only the expected number of emissions per pixel/voxel is used in EM. Another key difference is that the estimated spatial intensity is a continuous function such that there is no need to compute a projection matrix. Finally, draws from the intensity posterior distribution allow the estimation of posterior functionnals like the variance or confidence intervals. Results are presented for simulated data based on a 2D brain phantom and compared to Bayesian MAP-EM

  19. CAR-T cells and allogeneic hematopoietic stem cell transplantation for relapsed/refractory B-cell acute lymphoblastic leukemia.

    Science.gov (United States)

    Liu, Jun; Zhang, Xi; Zhong, Jiang F; Zhang, Cheng

    2017-10-01

    Relapsed/refractory acute lymphoblastic leukemia (ALL) has a low remission rate after chemotherapy, a high relapse rate and poor long-term survival even when allogeneic hematopoietic stem cell transplantation (allo-HSCT) is performed. Chimeric antigen receptors redirected T cells (CAR-T cells) can enhance disease remission with a favorable outcome for relapsed/refractory ALL, though some cases quickly relapsed after CAR-T cell treatment. Thus, treatment with CAR-T cells followed by allo-HSCT may be the best way to treat relapsed/refractory ALL. In this review, we first discuss the different types of CAR-T cells. We then discuss the treatment of relapsed/refractory ALL using only CAR-T cells. Finally, we discuss the use of CAR-T cells, followed by allo-HSCT, for the treatment of relapsed/refractory ALL.

  20. A Bayesian nonparametric approach to causal inference on quantiles.

    Science.gov (United States)

    Xu, Dandan; Daniels, Michael J; Winterstein, Almut G

    2018-02-25

    We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approach to answer an important clinical question involving acute kidney injury using electronic health records. © 2018, The International Biometric Society.

  1. Nonparametric Cointegration Analysis of Fractional Systems With Unknown Integration Orders

    DEFF Research Database (Denmark)

    Nielsen, Morten Ørregaard

    2009-01-01

    In this paper a nonparametric variance ratio testing approach is proposed for determining the number of cointegrating relations in fractionally integrated systems. The test statistic is easily calculated without prior knowledge of the integration order of the data, the strength of the cointegrating....... The asymptotic distribution theory for the proposed test is non-standard but easily tabulated. Monte Carlo simulations demonstrate excellent finite sample properties, even rivaling those of well-specified parametric tests. The proposed methodology is applied to the term structure of interest rates, where...

  2. Nonparametric statistics a step-by-step approach

    CERN Document Server

    Corder, Gregory W

    2014-01-01

    "…a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory.  It also deserves a place in libraries of all institutions where introductory statistics courses are taught."" -CHOICE This Second Edition presents a practical and understandable approach that enhances and expands the statistical toolset for readers. This book includes: New coverage of the sign test and the Kolmogorov-Smirnov two-sample test in an effort to offer a logical and natural progression to statistical powerSPSS® (Version 21) software and updated screen ca

  3. Categorical and nonparametric data analysis choosing the best statistical technique

    CERN Document Server

    Nussbaum, E Michael

    2014-01-01

    Featuring in-depth coverage of categorical and nonparametric statistics, this book provides a conceptual framework for choosing the most appropriate type of test in various research scenarios. Class tested at the University of Nevada, the book's clear explanations of the underlying assumptions, computer simulations, and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of the techniques. The underlying assumptions of each test and the factors that impact validity and statistical power are reviewed so readers can explain

  4. Nonparametric likelihood based estimation of linear filters for point processes

    DEFF Research Database (Denmark)

    Hansen, Niels Richard

    2015-01-01

    We consider models for multivariate point processes where the intensity is given nonparametrically in terms of functions in a reproducing kernel Hilbert space. The likelihood function involves a time integral and is consequently not given in terms of a finite number of kernel evaluations. The main...... the implementation relies crucially on the use of sparse matrices. As an illustration we consider neuron network modeling, and we use this example to investigate how the computational costs of the approximations depend on the resolution of the time discretization. The implementation is available in the R package...

  5. 241-AZ-101 Mixer Pump Demonstration Test Gamma Cart Acceptance Test Procedure and Quality Test Plan (ATP and QTP)

    International Nuclear Information System (INIS)

    WHITE, D.A.

    2000-01-01

    Shop Test of the Gamma Cart System to be used in the AZ-101 Mixer Pump Demonstration Test. Tests hardware and software. This procedure involves testing the Instrumentation involved with the Gamma Cart System, local and remote, including: depth indicators, speed controls, interface to data acquisition software and the raising and lowering functions. This Procedure will be performed twice, once for each Gamma Cart System. This procedure does not test the accuracy of the data acquisition software

  6. Nonparametric Identification of Glucose-Insulin Process in IDDM Patient with Multi-meal Disturbance

    Science.gov (United States)

    Bhattacharjee, A.; Sutradhar, A.

    2012-12-01

    Modern close loop control for blood glucose level in a diabetic patient necessarily uses an explicit model of the process. A fixed parameter full order or reduced order model does not characterize the inter-patient and intra-patient parameter variability. This paper deals with a frequency domain nonparametric identification of the nonlinear glucose-insulin process in an insulin dependent diabetes mellitus patient that captures the process dynamics in presence of uncertainties and parameter variations. An online frequency domain kernel estimation method has been proposed that uses the input-output data from the 19th order first principle model of the patient in intravenous route. Volterra equations up to second order kernels with extended input vector for a Hammerstein model are solved online by adaptive recursive least square (ARLS) algorithm. The frequency domain kernels are estimated using the harmonic excitation input data sequence from the virtual patient model. A short filter memory length of M = 2 was found sufficient to yield acceptable accuracy with lesser computation time. The nonparametric models are useful for closed loop control, where the frequency domain kernels can be directly used as the transfer function. The validation results show good fit both in frequency and time domain responses with nominal patient as well as with parameter variations.

  7. Bayesian nonparametric inference on quantile residual life function: Application to breast cancer data.

    Science.gov (United States)

    Park, Taeyoung; Jeong, Jong-Hyeon; Lee, Jae Won

    2012-08-15

    There is often an interest in estimating a residual life function as a summary measure of survival data. For ease in presentation of the potential therapeutic effect of a new drug, investigators may summarize survival data in terms of the remaining life years of patients. Under heavy right censoring, however, some reasonably high quantiles (e.g., median) of a residual lifetime distribution cannot be always estimated via a popular nonparametric approach on the basis of the Kaplan-Meier estimator. To overcome the difficulties in dealing with heavily censored survival data, this paper develops a Bayesian nonparametric approach that takes advantage of a fully model-based but highly flexible probabilistic framework. We use a Dirichlet process mixture of Weibull distributions to avoid strong parametric assumptions on the unknown failure time distribution, making it possible to estimate any quantile residual life function under heavy censoring. Posterior computation through Markov chain Monte Carlo is straightforward and efficient because of conjugacy properties and partial collapse. We illustrate the proposed methods by using both simulated data and heavily censored survival data from a recent breast cancer clinical trial conducted by the National Surgical Adjuvant Breast and Bowel Project. Copyright © 2012 John Wiley & Sons, Ltd.

  8. Bayesian Nonparametric Measurement of Factor Betas and Clustering with Application to Hedge Fund Returns

    Directory of Open Access Journals (Sweden)

    Urbi Garay

    2016-03-01

    Full Text Available We define a dynamic and self-adjusting mixture of Gaussian Graphical Models to cluster financial returns, and provide a new method for extraction of nonparametric estimates of dynamic alphas (excess return and betas (to a choice set of explanatory factors in a multivariate setting. This approach, as well as the outputs, has a dynamic, nonstationary and nonparametric form, which circumvents the problem of model risk and parametric assumptions that the Kalman filter and other widely used approaches rely on. The by-product of clusters, used for shrinkage and information borrowing, can be of use to determine relationships around specific events. This approach exhibits a smaller Root Mean Squared Error than traditionally used benchmarks in financial settings, which we illustrate through simulation. As an illustration, we use hedge fund index data, and find that our estimated alphas are, on average, 0.13% per month higher (1.6% per year than alphas estimated through Ordinary Least Squares. The approach exhibits fast adaptation to abrupt changes in the parameters, as seen in our estimated alphas and betas, which exhibit high volatility, especially in periods which can be identified as times of stressful market events, a reflection of the dynamic positioning of hedge fund portfolio managers.

  9. Combined non-parametric and parametric approach for identification of time-variant systems

    Science.gov (United States)

    Dziedziech, Kajetan; Czop, Piotr; Staszewski, Wieslaw J.; Uhl, Tadeusz

    2018-03-01

    Identification of systems, structures and machines with variable physical parameters is a challenging task especially when time-varying vibration modes are involved. The paper proposes a new combined, two-step - i.e. non-parametric and parametric - modelling approach in order to determine time-varying vibration modes based on input-output measurements. Single-degree-of-freedom (SDOF) vibration modes from multi-degree-of-freedom (MDOF) non-parametric system representation are extracted in the first step with the use of time-frequency wavelet-based filters. The second step involves time-varying parametric representation of extracted modes with the use of recursive linear autoregressive-moving-average with exogenous inputs (ARMAX) models. The combined approach is demonstrated using system identification analysis based on the experimental mass-varying MDOF frame-like structure subjected to random excitation. The results show that the proposed combined method correctly captures the dynamics of the analysed structure, using minimum a priori information on the model.

  10. Non-parametric transformation for data correlation and integration: From theory to practice

    Energy Technology Data Exchange (ETDEWEB)

    Datta-Gupta, A.; Xue, Guoping; Lee, Sang Heon [Texas A& M Univ., College Station, TX (United States)

    1997-08-01

    The purpose of this paper is two-fold. First, we introduce the use of non-parametric transformations for correlating petrophysical data during reservoir characterization. Such transformations are completely data driven and do not require a priori functional relationship between response and predictor variables which is the case with traditional multiple regression. The transformations are very general, computationally efficient and can easily handle mixed data types for example, continuous variables such as porosity, permeability and categorical variables such as rock type, lithofacies. The power of the non-parametric transformation techniques for data correlation has been illustrated through synthetic and field examples. Second, we utilize these transformations to propose a two-stage approach for data integration during heterogeneity characterization. The principal advantages of our approach over traditional cokriging or cosimulation methods are: (1) it does not require a linear relationship between primary and secondary data, (2) it exploits the secondary information to its fullest potential by maximizing the correlation between the primary and secondary data, (3) it can be easily applied to cases where several types of secondary or soft data are involved, and (4) it significantly reduces variance function calculations and thus, greatly facilitates non-Gaussian cosimulation. We demonstrate the data integration procedure using synthetic and field examples. The field example involves estimation of pore-footage distribution using well data and multiple seismic attributes.

  11. Distributed Nonparametric and Semiparametric Regression on SPARK for Big Data Forecasting

    Directory of Open Access Journals (Sweden)

    Jelena Fiosina

    2017-01-01

    Full Text Available Forecasting in big datasets is a common but complicated task, which cannot be executed using the well-known parametric linear regression. However, nonparametric and semiparametric methods, which enable forecasting by building nonlinear data models, are computationally intensive and lack sufficient scalability to cope with big datasets to extract successful results in a reasonable time. We present distributed parallel versions of some nonparametric and semiparametric regression models. We used MapReduce paradigm and describe the algorithms in terms of SPARK data structures to parallelize the calculations. The forecasting accuracy of the proposed algorithms is compared with the linear regression model, which is the only forecasting model currently having parallel distributed realization within the SPARK framework to address big data problems. The advantages of the parallelization of the algorithm are also provided. We validate our models conducting various numerical experiments: evaluating the goodness of fit, analyzing how increasing dataset size influences time consumption, and analyzing time consumption by varying the degree of parallelism (number of workers in the distributed realization.

  12. Nonparametric analysis of the time structure of seismicity in a geographic region

    Directory of Open Access Journals (Sweden)

    A. Quintela-del-Río

    2002-06-01

    Full Text Available As an alternative to traditional parametric approaches, we suggest nonparametric methods for analyzing temporal data on earthquake occurrences. In particular, the kernel method for estimating the hazard function and the intensity function are presented. One novelty of our approaches is that we take into account the possible dependence of the data to estimate the distribution of time intervals between earthquakes, which has not been considered in most statistics studies on seismicity. Kernel estimation of hazard function has been used to study the occurrence process of cluster centers (main shocks. Kernel intensity estimation, on the other hand, has helped to describe the occurrence process of cluster members (aftershocks. Similar studies in two geographic areas of Spain (Granada and Galicia have been carried out to illustrate the estimation methods suggested.

  13. 1st Conference of the International Society for Nonparametric Statistics

    CERN Document Server

    Lahiri, S; Politis, Dimitris

    2014-01-01

    This volume is composed of peer-reviewed papers that have developed from the First Conference of the International Society for NonParametric Statistics (ISNPS). This inaugural conference took place in Chalkidiki, Greece, June 15-19, 2012. It was organized with the co-sponsorship of the IMS, the ISI, and other organizations. M.G. Akritas, S.N. Lahiri, and D.N. Politis are the first executive committee members of ISNPS, and the editors of this volume. ISNPS has a distinguished Advisory Committee that includes Professors R.Beran, P.Bickel, R. Carroll, D. Cook, P. Hall, R. Johnson, B. Lindsay, E. Parzen, P. Robinson, M. Rosenblatt, G. Roussas, T. SubbaRao, and G. Wahba. The Charting Committee of ISNPS consists of more than 50 prominent researchers from all over the world.   The chapters in this volume bring forth recent advances and trends in several areas of nonparametric statistics. In this way, the volume facilitates the exchange of research ideas, promotes collaboration among researchers from all over the wo...

  14. Using Mathematica to build Non-parametric Statistical Tables

    Directory of Open Access Journals (Sweden)

    Gloria Perez Sainz de Rozas

    2003-01-01

    Full Text Available In this paper, I present computational procedures to obtian statistical tables. The tables of the asymptotic distribution and the exact distribution of Kolmogorov-Smirnov statistic Dn for one population, the table of the distribution of the runs R, the table of the distribution of Wilcoxon signed-rank statistic W+ and the table of the distribution of Mann-Whitney statistic Ux using Mathematica, Version 3.9 under Window98. I think that it is an interesting cuestion because many statistical packages give the asymptotic significance level in the statistical tests and with these porcedures one can easily calculate the exact significance levels and the left-tail and right-tail probabilities with non-parametric distributions. I have used mathematica to make these calculations because one can use symbolic language to solve recursion relations. It's very easy to generate the format of the tables, and it's possible to obtain any table of the mentioned non-parametric distributions with any precision, not only with the standard parameters more used in Statistics, and without transcription mistakes. Furthermore, using similar procedures, we can generate tables for the following distribution functions: Binomial, Poisson, Hypergeometric, Normal, x2 Chi-Square, T-Student, F-Snedecor, Geometric, Gamma and Beta.

  15. Otolaryngology Consult Carts: Maximizing Patient Care, Surgeon Efficiency, and Cost Containment.

    Science.gov (United States)

    Royer, Mark C; Royer, Allison K

    2015-11-01

    The objective of this study was to develop an otolaryngology consult cart system to ensure prompt delivery to the bedside of all the unique equipment and medications required for emergent and urgent otolaryngology consults. An otolaryngology practice responsible for emergency room and hospital consult coverage sought to create a cart containing all equipment, medications, and supplies for otolaryngology consults. Meetings with hospital administration and emergency room, nursing, pharmacy, central processing, and operating room staff were held to develop a system for the emergent delivery of the cart to the needed location, sterilization and restocking of equipment between uses, and appropriate billing of supplies. Two months were required from conception to implementation. All equipment was purchased new, including flexible scopes and headlights. The cart is sterilized, restocked, and maintained by central processing after each use. The equipment is available to handle all airway emergencies as well as all common otolaryngology consults and is delivered bedside in less than 5 minutes. The development of a self-contained otolaryngology consult cart requires coordination with a wide variety of hospital departments. This system, while requiring initial monetary and time investment, has resulted in improved patient care, cost containment, and surgeon convenience. © The Author(s) 2015.

  16. CAR-T cell therapy in gastrointestinal tumors and hepatic carcinoma: From bench to bedside.

    Science.gov (United States)

    Zhang, Qi; Zhang, Zimu; Peng, Meiyu; Fu, Shuyu; Xue, Zhenyi; Zhang, Rongxin

    2016-01-01

    The chimeric antigen receptor (CAR) is a genetically engineered receptor that combines a scFv domain, which specifically recognizes the tumor-specific antigen, with T cell activation domains. CAR-T cell therapies have demonstrated tremendous efficacy against hematologic malignancies in many clinical trials. Recent studies have extended these efforts to the treatment of solid tumors. However, the outcomes of CAR-T cell therapy for solid tumors are not as remarkable as the outcomes have been for hematologic malignancies. A series of hurdles has arisen with respect to CAR-T cell-based immunotherapy, which needs to be overcome to target solid tumors. The major challenge for CAR-T cell therapy in solid tumors is the selection of the appropriate specific antigen to demarcate the tumor from normal tissue. In this review, we discuss the application of CAR-T cells to gastrointestinal and hepatic carcinomas in preclinical and clinical research. Furthermore, we analyze the usefulness of several specific markers in the study of gastrointestinal tumors and hepatic carcinoma.

  17. Cancer Immunotherapy Using CAR-T Cells: From the Research Bench to the Assembly Line.

    Science.gov (United States)

    Gomes-Silva, Diogo; Ramos, Carlos A

    2018-02-01

    The focus of cancer treatment has recently shifted toward targeted therapies, including immunotherapy, which allow better individualization of care and are hoped to increase the probability of success for patients. Specifically, T cells genetically modified to express chimeric antigen receptors (CARs; CAR-T cells) have generated exciting results. Recent clinical successes with this cutting-edge therapy have helped to push CAR-T cells toward approval for wider use. However, several limitations need to be addressed before the widespread use of CAR-T cells as a standard treatment. Here, a succinct background on adoptive T-cell therapy (ATCT)is given. A brief overview of the structure of CARs, how they are introduced into T cells, and how CAR-T cell expansion and selection is achieved in vitro is then presented. Some of the challenges in CAR design are discussed, as well as the difficulties that arise in large-scale CAR-T cell manufacture that will need to be addressed to achieve successful commercialization of this type of cell therapy. Finally, developments already on the horizon are discussed. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. The development of CAR design for tumor CAR-T cell therapy.

    Science.gov (United States)

    Xu, Dandan; Jin, Guoliang; Chai, Dafei; Zhou, Xiaowan; Gu, Weiyu; Chong, Yanyun; Song, Jingyuan; Zheng, Junnian

    2018-03-02

    In recent years, the chimeric antigen receptor modified T cells (Chimeric antigen receptor T cells, CAR-T) immunotherapy has developed rapidly, which has been considered the most promising therapy. Efforts to enhance the efficacy of CAR-based anti-tumor therapy have been made, such as the improvement of structures of CAR-T cells, including the development of extracellular antigen recognition receptors, intracellular co-stimulatory molecules and the combination application of CARs and synthetic small molecules. In addition, effects on the function of the CAR-T cells that the space distance between the antigen binding domains and tumor targets and the length of the spacer domains have are also being investigated. Given the fast-moving nature of this field, it is necessary to make a summary of the development of CAR-T cells. In this review, we mainly focus on the present design strategies of CAR-T cells with the hope that they can provide insights to increase the anti-tumor efficacy and safety.

  19. CD47-CAR-T Cells Effectively Kill Target Cancer Cells and Block Pancreatic Tumor Growth.

    Science.gov (United States)

    Golubovskaya, Vita; Berahovich, Robert; Zhou, Hua; Xu, Shirley; Harto, Hizkia; Li, Le; Chao, Cheng-Chi; Mao, Mike Ming; Wu, Lijun

    2017-10-21

    CD47 is a glycoprotein of the immunoglobulin superfamily that is often overexpressed in different types of hematological and solid cancer tumors and plays important role in blocking phagocytosis, increased tumor survival, metastasis and angiogenesis. In the present report, we designed CAR (chimeric antigen receptor)-T cells that bind CD47 antigen. We used ScFv (single chain variable fragment) from mouse CD47 antibody to generate CD47-CAR-T cells for targeting different cancer cell lines. CD47-CAR-T cells effectively killed ovarian, pancreatic and other cancer cells and produced high level of cytokines that correlated with expression of CD47 antigen. In addition, CD47-CAR-T cells significantly blocked BxPC3 pancreatic xenograft tumor growth after intratumoral injection into NSG mice. Moreover, we humanized mouse CD47 ScFv and showed that it effectively bound CD47 antigen. The humanized CD47-CAR-T cells also specifically killed ovarian, pancreatic, and cervical cancer cell lines and produced IL-2 that correlated with expression of CD47. Thus, CD47-CAR-T cells can be used as a novel cellular therapeutic agent for treating different types of cancer.

  20. Bacterial Contamination and Disinfection Status of Laryngoscopes Stored in Emergency Crash Carts

    Directory of Open Access Journals (Sweden)

    Jae Hyung Choi

    2017-05-01

    Full Text Available Objectives To identify bacterial contamination rates of laryngoscope blades and handles stored in emergency crash carts by hospital and area according to the frequency of intubation attempts. Methods One hundred forty-eight handles and 71 blades deemed ready for patient use from two tertiary hospitals were sampled with sterile swabs using a standardized rolling technique. Samples were considered negative (not contaminated if no colonies were present on the blood agar plate after an 18-hour incubation period. Samples were stratified by hospital and according to the frequency of intubation attempts (10 attempts per year using the χ2-test and Fisher exact test. Results One or more species of bacteria were isolated from 4 (5.6% handle tops, 20 (28.2% handles with knurled surfaces, and 27 (18.2% blades. No significant differences were found in microbial contamination levels on the handle tops and blades between the two hospitals and two areas according to the frequency of intubation attempts. However, significant differences were found between the two hospitals and two areas in the level of microbial contamination on the handles with knurled surfaces (p<0.05. Conclusions Protocols and policies must be reviewed to standardize procedures to clean and disinfect laryngoscope blades and handles; handles should be re-designed to eliminate points of contact with the blade; and single-use, one-piece laryngoscopes should be introduced.

  1. Sequential Vacc-4x and romidepsin during combination antiretroviral therapy (cART)

    DEFF Research Database (Denmark)

    Tapia, G; Højen, J F; Ökvist, M

    2017-01-01

    OBJECTIVES: The REDUC clinical study Part B investigated Vacc-4x/rhuGM-CSF therapeutic vaccination prior to HIV latency reversal using romidepsin. The main finding was a statistically significant reduction from baseline in viral reservoir measurements. Here we evaluated HIV-specific functional T-...... by therapeutic vaccination, CD8+ T-cell proliferation represents a valuable means to monitor functional immune responses as part of the path towards functional HIV cure.......OBJECTIVES: The REDUC clinical study Part B investigated Vacc-4x/rhuGM-CSF therapeutic vaccination prior to HIV latency reversal using romidepsin. The main finding was a statistically significant reduction from baseline in viral reservoir measurements. Here we evaluated HIV-specific functional T......-cell responses following Vacc-4x/rhuGM-CSF immunotherapy in relation to virological outcomes on the HIV reservoir. METHODS: This study, conducted in Aarhus, Denmark, enrolled participants (n = 20) with CD4>500 cells/mm(3) on cART. Six Vacc-4x (1.2 mg) intradermal immunizations using rhuGM-CSF (60 μg) as adjuvant...

  2. THORACIC DISC HERNIATION: SURGICAL DECOMPRESSION BY POSTERIOR APPROACH A LA CARTE

    Directory of Open Access Journals (Sweden)

    MURILO TAVARES DAHER

    Full Text Available ABSTRACT Objectives: To present the clinical and radiographic results of patients with thoracic disc herniation treated by the posterior approach, according to location and type of hernia (à la carte. Methods: We evaluated thirteen patients (14 hernias treated by the posterior approach. Eight (61.5% patients were male and the mean age was 53 years (34-81. Clinical evaluation was performed by the Frankel and JOA modified scales. All the patients underwent the posterior approach, which was performed by facetectomy, transpedicular approach, transpedicular + partial body resection, costotransversectomy or costotransversectomy + reconstruction with CAGE. Results: The mean follow-up was 2 years and 6 months (11-77 months. Of the 14 operated hernias, six (43% were lateral, 2 (14% paramedian, and 6 (43% central. Seven were soft (50% and seven were calcified. The transfacet approach was carried out in 5 cases (36%, transpedicular in 1 case (7%, transpedicular + partial body resection in 4 (29%, costotransversectomy in 3 (21%, and costotransversectomy + CAGE in one case (7%. The majority of patients with lateral hernia (5/6 were subjected to transfacet decompression and in cases of central and paramedian hernias, all patients underwent decompression, which is more extensive. Conclusions: The posterior approach is safe and effective, and the best approach must be chosen based on location and type of the herniation and the surgeon's experience.

  3. Development of a Childhood Attachment and Relational Trauma Screen (CARTS: a relational-socioecological framework for surveying attachment security and childhood trauma history

    Directory of Open Access Journals (Sweden)

    Paul A. Frewen

    2013-04-01

    Full Text Available Background : Current psychometric measures of childhood trauma history generally fail to assess the relational-socioecological context within which childhood maltreatment occurs, including the relationship of abusers to abused persons, the emotional availability of caregivers, and the respondent's own thoughts, feelings, and actions in response to maltreatment. Objective : To evaluate a computerized approach to measuring the relational-socioecological context within which childhood maltreatment occurs. Method : The psychometric properties of a Childhood Attachment and Relational Trauma Screen (CARTS were evaluated as a retrospective survey of childhood maltreatment history designed to be appropriate for completion by adults. Participants were undergraduates (n=222, an internet sample (n=123, and psychiatric outpatients (n=30. Results : The internal reliability, convergent, and concurrent validity of the CARTS were supported across samples. Paired differences in means and correlations between rated item-descriptiveness to self, mothers, and fathers also accorded with findings of prior attachment and maltreatment research, illustrating the utility of assessing the occurrence and effects of maltreatment within a relational-socioecological framework. Conclusions : Results preliminarily support a new survey methodology for assessing childhood maltreatment within a relational-socioecological framework. Further psychometric evaluation of the CARTS is warranted.

  4. A BAYESIAN NONPARAMETRIC MIXTURE MODEL FOR SELECTING GENES AND GENE SUBNETWORKS.

    Science.gov (United States)

    Zhao, Yize; Kang, Jian; Yu, Tianwei

    2014-06-01

    It is very challenging to select informative features from tens of thousands of measured features in high-throughput data analysis. Recently, several parametric/regression models have been developed utilizing the gene network information to select genes or pathways strongly associated with a clinical/biological outcome. Alternatively, in this paper, we propose a nonparametric Bayesian model for gene selection incorporating network information. In addition to identifying genes that have a strong association with a clinical outcome, our model can select genes with particular expressional behavior, in which case the regression models are not directly applicable. We show that our proposed model is equivalent to an infinity mixture model for which we develop a posterior computation algorithm based on Markov chain Monte Carlo (MCMC) methods. We also propose two fast computing algorithms that approximate the posterior simulation with good accuracy but relatively low computational cost. We illustrate our methods on simulation studies and the analysis of Spellman yeast cell cycle microarray data.

  5. Bicoid signal extraction with a selection of parametric and nonparametric signal processing techniques.

    Science.gov (United States)

    Ghodsi, Zara; Silva, Emmanuel Sirimal; Hassani, Hossein

    2015-06-01

    The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraction in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average. Copyright © 2015 The Authors. Production and hosting by Elsevier Ltd.. All rights reserved.

  6. The use of ZIP and CART to model cryptosporidiosis in relation to climatic variables.

    Science.gov (United States)

    Hu, Wenbiao; Mengersen, Kerrie; Fu, Shiu-Yun; Tong, Shilu

    2010-07-01

    This research assesses the potential impact of weekly weather variability on the incidence of cryptosporidiosis disease using time series zero-inflated Poisson (ZIP) and classification and regression tree (CART) models. Data on weather variables, notified cryptosporidiosis cases and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Both time series ZIP and CART models show a clear association between weather variables (maximum temperature, relative humidity, rainfall and wind speed) and cryptosporidiosis disease. The time series CART models indicated that, when weekly maximum temperature exceeded 31 degrees C and relative humidity was less than 63%, the relative risk of cryptosporidiosis rose by 13.64 (expected morbidity: 39.4; 95% confidence interval: 30.9-47.9). These findings may have applications as a decision support tool in planning disease control and risk-management programs for cryptosporidiosis disease.

  7. Golf cart prototype development and navigation simulation using ROS and Gazebo

    Directory of Open Access Journals (Sweden)

    Shimchik Ilya

    2016-01-01

    Full Text Available This paper presents our approach to development of an autonomous golf cart, which will navigate in inaccessible by regular vehicles private areas. For this purpose, we have built a virtual golf course terrain and golf cart model in Gazebo, selected and modernized ROS-based packages in order to use them with Ackermann steering vehicle simulation. To verify our simulation and algorithms, we navigated the golf cart model from one golf hole to another within a virtual 3D golf course. For the real world algorithms’ verification, we developed a small-size vehicle prototype based on Traxxas radio-controlled car model, which is equipped with an on-board controller and sensors. The autonomous navigation of Traxxas-based vehicle prototype has been tested in indoor environment, where it utilized sensory data about environment and vehicle states, and performed localization, optimal trajectory computation and dynamic obstacles’ recognition with adjusting the route in real time.

  8. Le commentaire de cartes, la géographie et les concours

    Directory of Open Access Journals (Sweden)

    Alain REYNAUD

    1988-09-01

    Full Text Available ace aux détracteurs de la carte topographique, il faut répéter qu'elle garde tout son intérêt mais être également conscient qu'elle n'est qu'un type de document géographique parmi beaucoup d'autres. Les critiques actuelles trouvent un aliment dans la place du commentaire de cartes aux concours, en fait aux agrégations qui excluent tout autre type de document géographique. Paradoxalement, le poids du commentaire de cartes aux agrégations a été sensiblement renforcé depuis 1968, alors que se sont multipliés dans le même temps les types de documents géographiques. Cette contradiction explique les tensions actuelles et certaines réactions excessives de part et d'autre.

  9. Hurdles of CAR-T cell-based cancer immunotherapy directed against solid tumors.

    Science.gov (United States)

    Zhang, Bing-Lan; Qin, Di-Yuan; Mo, Ze-Ming; Li, Yi; Wei, Wei; Wang, Yong-Sheng; Wang, Wei; Wei, Yu-Quan

    2016-04-01

    Recent reports on the impressive efficacy of chimeric antigen receptor (CAR)-modified T cells against hematologic malignancies have inspired oncologists to extend these efforts for the treatment of solid tumors. Clinical trials of CAR-T-based cancer immunotherapy for solid tumors showed that the efficacies are not as remarkable as in the case of hematologic malignancies. There are several challenges that researchers must face when treating solid cancers with CAR-T cells, these include choosing an ideal target, promoting efficient trafficking and infiltration, overcoming the immunosuppressive microenvironment, and avoiding associated toxicity. In this review, we discuss the obstacles imposed by solid tumors on CAR-T cell-based immunotherapy and strategies adopted to improve the therapeutic potential of this approach. Continued investigations are necessary to improve therapeutic outcomes and decrease the adverse effects of CAR-T cell therapy in patients with solid malignancies in the future.

  10. New Approaches in CAR-T Cell Immunotherapy for Breast Cancer.

    Science.gov (United States)

    Wang, Jinghua; Zhou, Penghui

    2017-01-01

    Despite significant advances in surgery, chemotherapy, radiotherapy, endocrine therapy, and molecular-targeted therapy, breast cancer remains the leading cause of death from malignant tumors among women. Immunotherapy has recently become a critical component of breast cancer treatment with encouraging activity and mild safety profiles. CAR-T therapy using genetically modifying T cells with chimeric antigen receptors (CAR) is the most commonly used approach to generate tumor-specific T cells. It has shown good curative effect for a variety of malignant diseases, especially for hematological malignancies. In this review, we briefly introduce the history and the present state of CAR research. Then we discuss the barriers of solid tumors for CARs application and possible strategies to improve therapeutic response with a focus on breast cancer. At last, we outlook the future directions of CAR-T therapy including managing toxicities and developing universal CAR-T cells.

  11. Chimeric-antigen receptor T (CAR-T) cell therapy for solid tumors: challenges and opportunities.

    Science.gov (United States)

    Xia, An-Liang; Wang, Xiao-Chen; Lu, Yi-Jun; Lu, Xiao-Jie; Sun, Beicheng

    2017-10-27

    Chimeric antigen receptor (CAR)-engineered T cells (CAR-T cells) have been shown to have unprecedented efficacy in B cell malignancies, most notably in B cell acute lymphoblastic leukemia (B-ALL) with up to a 90% complete remission rate using anti-CD19 CAR-T cells. However, CAR T-cell therapy for solid tumors currently is faced with numerous challenges such as physical barriers, the immunosuppressive tumor microenvironment and the specificity and safety. The clinical results in solid tumors have been much less encouraging, with multiple cases of toxicity and a lack of therapeutic response. In this review, we will discuss the current stats and challenges of CAR-T cell therapy for solid tumors, and propose possibl e solutions and future perspectives.

  12. Testing and Estimating Shape-Constrained Nonparametric Density and Regression in the Presence of Measurement Error

    KAUST Repository

    Carroll, Raymond J.

    2011-03-01

    In many applications we can expect that, or are interested to know if, a density function or a regression curve satisfies some specific shape constraints. For example, when the explanatory variable, X, represents the value taken by a treatment or dosage, the conditional mean of the response, Y , is often anticipated to be a monotone function of X. Indeed, if this regression mean is not monotone (in the appropriate direction) then the medical or commercial value of the treatment is likely to be significantly curtailed, at least for values of X that lie beyond the point at which monotonicity fails. In the case of a density, common shape constraints include log-concavity and unimodality. If we can correctly guess the shape of a curve, then nonparametric estimators can be improved by taking this information into account. Addressing such problems requires a method for testing the hypothesis that the curve of interest satisfies a shape constraint, and, if the conclusion of the test is positive, a technique for estimating the curve subject to the constraint. Nonparametric methodology for solving these problems already exists, but only in cases where the covariates are observed precisely. However in many problems, data can only be observed with measurement errors, and the methods employed in the error-free case typically do not carry over to this error context. In this paper we develop a novel approach to hypothesis testing and function estimation under shape constraints, which is valid in the context of measurement errors. Our method is based on tilting an estimator of the density or the regression mean until it satisfies the shape constraint, and we take as our test statistic the distance through which it is tilted. Bootstrap methods are used to calibrate the test. The constrained curve estimators that we develop are also based on tilting, and in that context our work has points of contact with methodology in the error-free case.

  13. Improving salt marsh digital elevation model accuracy with full-waveform lidar and nonparametric predictive modeling

    Science.gov (United States)

    Rogers, Jeffrey N.; Parrish, Christopher E.; Ward, Larry G.; Burdick, David M.

    2018-03-01

    Salt marsh vegetation tends to increase vertical uncertainty in light detection and ranging (lidar) derived elevation data, often causing the data to become ineffective for analysis of topographic features governing tidal inundation or vegetation zonation. Previous attempts at improving lidar data collected in salt marsh environments range from simply computing and subtracting the global elevation bias to more complex methods such as computing vegetation-specific, constant correction factors. The vegetation specific corrections can be used along with an existing habitat map to apply separate corrections to different areas within a study site. It is hypothesized here that correcting salt marsh lidar data by applying location-specific, point-by-point corrections, which are computed from lidar waveform-derived features, tidal-datum based elevation, distance from shoreline and other lidar digital elevation model based variables, using nonparametric regression will produce better results. The methods were developed and tested using full-waveform lidar and ground truth for three marshes in Cape Cod, Massachusetts, U.S.A. Five different model algorithms for nonparametric regression were evaluated, with TreeNet's stochastic gradient boosting algorithm consistently producing better regression and classification results. Additionally, models were constructed to predict the vegetative zone (high marsh and low marsh). The predictive modeling methods used in this study estimated ground elevation with a mean bias of 0.00 m and a standard deviation of 0.07 m (0.07 m root mean square error). These methods appear very promising for correction of salt marsh lidar data and, importantly, do not require an existing habitat map, biomass measurements, or image based remote sensing data such as multi/hyperspectral imagery.

  14. Aproximación a la metodología basada en árboles de decisión (CART: Mortalidad hospitalaria del infarto agudo de miocardio Approach to the methodology of classification and regression trees

    Directory of Open Access Journals (Sweden)

    Javier Trujillano

    2008-02-01

    Full Text Available Objetivo: : Realizar una aproximación a la metodología de árboles de decisión tipo CART (Classification and Regression Trees desarrollando un modelo para calcular la probabilidad de muerte hospitalaria en infarto agudo de miocardio (IAM. Método: Se utiliza el conjunto mínimo básico de datos al alta hospitalaria (CMBD de Andalucía, Cataluña, Madrid y País Vasco de los años 2001 y 2002, que incluye los casos con IAM como diagnóstico principal. Los 33.203 pacientes se dividen aleatoriamente (70 y 30 % en grupo de desarrollo (GD = 23.277 y grupo de validación (GV = 9.926. Como CART se utiliza un modelo inductivo basado en el algoritmo de Breiman, con análisis de sensibilidad mediante el índice de Gini y sistema de validación cruzada. Se compara con un modelo de regresión logística (RL y una red neuronal artificial (RNA (multilayer perceptron. Los modelos desarrollados se contrastan en el GV y sus propiedades se comparan con el área bajo la curva ROC (ABC (intervalo de confianza del 95%. Resultados: En el GD el CART con ABC = 0,85 (0,86-0,88, RL 0,87 (0,86-0,88 y RNA 0,85 (0,85-0,86. En el GV el CART con ABC = 0,85 (0,85-0,88, RL 0,86 (0,85-0,88 y RNA 0,84 (0,83-0,86. Conclusiones: Los 3 modelos obtienen resultados similares en su capacidad de discriminación. El modelo CART ofrece como ventaja su simplicidad de uso y de interpretación, ya que las reglas de decisión que generan pueden aplicarse sin necesidad de procesos matemáticos.Objective: To provide an overview of decision trees based on CART (Classification and Regression Trees methodology. As an example, we developed a CART model intended to estimate the probability of intrahospital death from acute myocardial infarction (AMI. Method: We employed the minimum data set (MDS of Andalusia, Catalonia, Madrid and the Basque Country (2001-2002, which included 33,203 patients with a diagnosis of AMI. The 33,203 patients were randomly divided (70% and 30% into the development (DS

  15. Incorporation of Immune Checkpoint Blockade into Chimeric Antigen Receptor T Cells (CAR-Ts: Combination or Built-In CAR-T

    Directory of Open Access Journals (Sweden)

    Dok Hyun Yoon

    2018-01-01

    Full Text Available Chimeric antigen receptor (CAR T cell therapy represents the first U.S. Food and Drug Administration approved gene therapy and these engineered cells function with unprecedented efficacy in the treatment of refractory CD19 positive hematologic malignancies. CAR translation to solid tumors is also being actively investigated; however, efficacy to date has been variable due to tumor-evolved mechanisms that inhibit local immune cell activity. To bolster the potency of CAR-T cells, modulation of the immunosuppressive tumor microenvironment with immune-checkpoint blockade is a promising strategy. The impact of this approach on hematological malignancies is in its infancy, and in this review we discuss CAR-T cells and their synergy with immune-checkpoint blockade.

  16. Incorporation of Immune Checkpoint Blockade into Chimeric Antigen Receptor T Cells (CAR-Ts): Combination or Built-In CAR-T.

    Science.gov (United States)

    Yoon, Dok Hyun; Osborn, Mark J; Tolar, Jakub; Kim, Chong Jai

    2018-01-24

    Chimeric antigen receptor (CAR) T cell therapy represents the first U.S. Food and Drug Administration approved gene therapy and these engineered cells function with unprecedented efficacy in the treatment of refractory CD19 positive hematologic malignancies. CAR translation to solid tumors is also being actively investigated; however, efficacy to date has been variable due to tumor-evolved mechanisms that inhibit local immune cell activity. To bolster the potency of CAR-T cells, modulation of the immunosuppressive tumor microenvironment with immune-checkpoint blockade is a promising strategy. The impact of this approach on hematological malignancies is in its infancy, and in this review we discuss CAR-T cells and their synergy with immune-checkpoint blockade.

  17. Tratamiento de los defectos del cartílago articular en la rodilla

    OpenAIRE

    Reverté Vinaixa, María Mercedes

    2015-01-01

    Las lesiones condrales y osteocondrales en la rodilla aparecen en gente joven, activa y con gran expectativa de vida. Estás suponen un gran problema, puesto que este una vez destruido, ya no se repara. Debido a la escasa capacidad intrínseca de curación del cartílago, sus lesiones tienden a causar morbilidad en forma de dolor articular y disfunción. Existen diferentes técnicas quirúrgicas que mejoran la sintomatología y la historia natural de las lesiones condrales. Las lesiones del cartílago...

  18. Hidrogeles biodegradables para la regeneración de cartílago articular

    OpenAIRE

    CANTÓ LORAS, PETRA

    2015-01-01

    [CAT] El cartílag articular és un teixit connectiu molt especialitzat present a les articulacions, que actua com amortidor suportant enormes carregues y proporcionant una superfície de lliscament de baixa fricció. Les patologies del cartílag articular suposen la pèrdua de l’estructura i de la funció del teixit i són una de les principals causes de discapacitat en persones grans. Es tracta d’un teixit avascular que presenta una capacitat d’autoreparació limitada. S’han desenvolupat nombrosos p...

  19. CAR-T cells: the long and winding road to solid tumors.

    Science.gov (United States)

    D'Aloia, Maria Michela; Zizzari, Ilaria Grazia; Sacchetti, Benedetto; Pierelli, Luca; Alimandi, Maurizio

    2018-02-15

    Adoptive cell therapy of solid tumors with reprogrammed T cells can be considered the "next generation" of cancer hallmarks. CAR-T cells fail to be as effective as in liquid tumors for the inability to reach and survive in the microenvironment surrounding the neoplastic foci. The intricate net of cross-interactions occurring between tumor components, stromal and immune cells leads to an ineffective anergic status favoring the evasion from the host's defenses. Our goal is hereby to trace the road imposed by solid tumors to CAR-T cells, highlighting pitfalls and strategies to be developed and refined to possibly overcome these hurdles.

  20. Prior processes and their applications nonparametric Bayesian estimation

    CERN Document Server

    Phadia, Eswar G

    2016-01-01

    This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the past four decades for dealing with Bayesian approach to solving selected nonparametric inference problems. This revised edition has been substantially expanded to reflect the current interest in this area. After an overview of different prior processes, it examines the now pre-eminent Dirichlet process and its variants including hierarchical processes, then addresses new processes such as dependent Dirichlet, local Dirichlet, time-varying and spatial processes, all of which exploit the countable mixture representation of the Dirichlet process. It subsequently discusses various neutral to right type processes, including gamma and extended gamma, beta and beta-Stacy processes, and then describes the Chinese Restaurant, Indian Buffet and infinite gamma-Poisson processes, which prove to be very useful in areas such as machine learning, information retrieval and featural modeling. Tailfree and P...

  1. Nonparametric Statistical Structuring of Knowledge Systems Using Binary Feature Matches

    DEFF Research Database (Denmark)

    Mørup, Morten; Kano Glückstad, Fumiko; Herlau, Tue

    2014-01-01

    Structuring knowledge systems with binary features is often based on imposing a similarity measure and clustering objects according to this similarity. Unfortunately, such analyses can be heavily influenced by the choice of similarity measure. Furthermore, it is unclear at which level clusters have...... statistical support and how this approach generalizes to the structuring and alignment of knowledge systems. We propose a non-parametric Bayesian generative model for structuring binary feature data that does not depend on a specific choice of similarity measure. We jointly model all combinations of binary...... matches and structure the data into groups at the level in which they have statistical support. The model naturally extends to structuring and aligning an arbitrary number of systems. We analyze three datasets on educational concepts and their features and demonstrate how the proposed model can both...

  2. Nonparametric statistical structuring of knowledge systems using binary feature matches

    DEFF Research Database (Denmark)

    Mørup, Morten; Glückstad, Fumiko Kano; Herlau, Tue

    2014-01-01

    Structuring knowledge systems with binary features is often based on imposing a similarity measure and clustering objects according to this similarity. Unfortunately, such analyses can be heavily influenced by the choice of similarity measure. Furthermore, it is unclear at which level clusters have...... statistical support and how this approach generalizes to the structuring and alignment of knowledge systems. We propose a non-parametric Bayesian generative model for structuring binary feature data that does not depend on a specific choice of similarity measure. We jointly model all combinations of binary...... matches and structure the data into groups at the level in which they have statistical support. The model naturally extends to structuring and aligning an arbitrary number of systems. We analyze three datasets on educational concepts and their features and demonstrate how the proposed model can both...

  3. Nonparametric Estimation of Distributions in Random Effects Models

    KAUST Repository

    Hart, Jeffrey D.

    2011-01-01

    We propose using minimum distance to obtain nonparametric estimates of the distributions of components in random effects models. A main setting considered is equivalent to having a large number of small datasets whose locations, and perhaps scales, vary randomly, but which otherwise have a common distribution. Interest focuses on estimating the distribution that is common to all datasets, knowledge of which is crucial in multiple testing problems where a location/scale invariant test is applied to every small dataset. A detailed algorithm for computing minimum distance estimates is proposed, and the usefulness of our methodology is illustrated by a simulation study and an analysis of microarray data. Supplemental materials for the article, including R-code and a dataset, are available online. © 2011 American Statistical Association.

  4. Multi-Directional Non-Parametric Analysis of Agricultural Efficiency

    DEFF Research Database (Denmark)

    Balezentis, Tomas

    This thesis seeks to develop methodologies for assessment of agricultural efficiency and employ them to Lithuanian family farms. In particular, we focus on three particular objectives throughout the research: (i) to perform a fully non-parametric analysis of efficiency effects, (ii) to extend...... to the Multi-Directional Efficiency Analysis approach when the proposed models were employed to analyse empirical data of Lithuanian family farm performance, we saw substantial differences in efficiencies associated with different inputs. In particular, assets appeared to be the least efficiently used input...... relative to labour, intermediate consumption and land (in some cases land was not treated as a discretionary input). These findings call for further research on relationships among financial structure, investment decisions, and efficiency in Lithuanian family farms. Application of different techniques...

  5. Bayesian nonparametric modeling for comparison of single-neuron firing intensities.

    Science.gov (United States)

    Kottas, Athanasios; Behseta, Sam

    2010-03-01

    We propose a fully inferential model-based approach to the problem of comparing the firing patterns of a neuron recorded under two distinct experimental conditions. The methodology is based on nonhomogeneous Poisson process models for the firing times of each condition with flexible nonparametric mixture prior models for the corresponding intensity functions. We demonstrate posterior inferences from a global analysis, which may be used to compare the two conditions over the entire experimental time window, as well as from a pointwise analysis at selected time points to detect local deviations of firing patterns from one condition to another. We apply our method on two neurons recorded from the primary motor cortex area of a monkey's brain while performing a sequence of reaching tasks.

  6. Non-parametric Estimation of a Survival Function with Two-stage Design Studies.

    Science.gov (United States)

    Li, Gang; Tseng, Chi-Hong

    2008-06-01

    The two-stage design is popular in epidemiology studies and clinical trials due to its cost effectiveness. Typically, the first stage sample contains cheaper and possibly biased information, while the second stage validation sample consists of a subset of subjects with accurate and complete information. In this paper, we study estimation of a survival function with right-censored survival data from a two-stage design. A non-parametric estimator is derived by combining data from both stages. We also study its large sample properties and derive pointwise and simultaneous confidence intervals for the survival function. The proposed estimator effectively reduces the variance and finite-sample bias of the Kaplan-Meier estimator solely based on the second stage validation sample. Finally, we apply our method to a real data set from a medical device post-marketing surveillance study.

  7. Extending the linear model with R generalized linear, mixed effects and nonparametric regression models

    CERN Document Server

    Faraway, Julian J

    2005-01-01

    Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...

  8. A Nonparametric Shape Prior Constrained Active Contour Model for Segmentation of Coronaries in CTA Images

    Directory of Open Access Journals (Sweden)

    Yin Wang

    2014-01-01

    Full Text Available We present a nonparametric shape constrained algorithm for segmentation of coronary arteries in computed tomography images within the framework of active contours. An adaptive scale selection scheme, based on the global histogram information of the image data, is employed to determine the appropriate window size for each point on the active contour, which improves the performance of the active contour model in the low contrast local image regions. The possible leakage, which cannot be identified by using intensity features alone, is reduced through the application of the proposed shape constraint, where the shape of circular sampled intensity profile is used to evaluate the likelihood of current segmentation being considered vascular structures. Experiments on both synthetic and clinical datasets have demonstrated the efficiency and robustness of the proposed method. The results on clinical datasets have shown that the proposed approach is capable of extracting more detailed coronary vessels with subvoxel accuracy.

  9. Bounding the causal effect of unemployment on mental health: Nonparametric evidence from four countries.

    Science.gov (United States)

    Cygan-Rehm, Kamila; Kuehnle, Daniel; Oberfichtner, Michael

    2017-12-01

    An important, yet unsettled, question in public health policy is the extent to which unemployment causally impacts mental health. The recent literature yields varying findings, which are likely due to differences in data, methods, samples, and institutional settings. Taking a more general approach, we provide comparable evidence for four countries with different institutional settings-Australia, Germany, the UK, and the United States-using a nonparametric bounds analysis. Relying on fairly weak and partially testable assumptions, our paper shows that unemployment has a significant negative effect on mental health in all countries. Our results rule out effects larger than a quarter of a standard deviation for Germany and half a standard deviation for the Anglo-Saxon countries. The effect is significant for both men and women and materialises already for short periods of unemployment. Public policy should hence focus on early prevention of mental health problems among the unemployed. Copyright © 2017 John Wiley & Sons, Ltd.

  10. A non-parametric consistency test of the ΛCDM model with Planck CMB data

    Energy Technology Data Exchange (ETDEWEB)

    Aghamousa, Amir; Shafieloo, Arman [Korea Astronomy and Space Science Institute, Daejeon 305-348 (Korea, Republic of); Hamann, Jan, E-mail: amir@aghamousa.com, E-mail: jan.hamann@unsw.edu.au, E-mail: shafieloo@kasi.re.kr [School of Physics, The University of New South Wales, Sydney NSW 2052 (Australia)

    2017-09-01

    Non-parametric reconstruction methods, such as Gaussian process (GP) regression, provide a model-independent way of estimating an underlying function and its uncertainty from noisy data. We demonstrate how GP-reconstruction can be used as a consistency test between a given data set and a specific model by looking for structures in the residuals of the data with respect to the model's best-fit. Applying this formalism to the Planck temperature and polarisation power spectrum measurements, we test their global consistency with the predictions of the base ΛCDM model. Our results do not show any serious inconsistencies, lending further support to the interpretation of the base ΛCDM model as cosmology's gold standard.

  11. Efficiency and equity in private and public education: A nonparametric comparison

    NARCIS (Netherlands)

    Cherchye, L.; de Witte, K.; Ooghe, E.; Nicaise, I.

    2010-01-01

    We present a nonparametric approach for (1) efficiency and (2) equity evaluation in education. Firstly, we use a nonparametric (Data Envelopment Analysis) model that is specially tailored to assess educational efficiency at the pupil level. The model accounts for the fact that typically minimal

  12. Equity and efficiency in private and public education: a nonparametric comparison

    NARCIS (Netherlands)

    Cherchye, L.; de Witte, K.; Ooghe, E.; Nicaise, I.

    2007-01-01

    We present a nonparametric approach for the equity and efficiency evaluation of (private and public) primary schools in Flanders. First, we use a nonparametric (Data Envelopment Analysis) model that is specially tailored to assess educational efficiency at the pupil level. The model accounts for the

  13. Overview of NonParametric Combination-based permutation tests for Multivariate multi-sample problems

    Directory of Open Access Journals (Sweden)

    Rosa Arboretti Giancristofaro

    2014-09-01

    Full Text Available In this work we present a review on nonparametric combination-based permutation tests along with SAS macros allowing to deal with two-sample and one-way MANOVA design problems, within NonParametric Combination methodology framework. Applications to real case studies are also presented.

  14. Forecasting turbulent modes with nonparametric diffusion models: Learning from noisy data

    Science.gov (United States)

    Berry, Tyrus; Harlim, John

    2016-04-01

    In this paper, we apply a recently developed nonparametric modeling approach, the "diffusion forecast", to predict the time-evolution of Fourier modes of turbulent dynamical systems. While the diffusion forecasting method assumes the availability of a noise-free training data set observing the full state space of the dynamics, in real applications we often have only partial observations which are corrupted by noise. To alleviate these practical issues, following the theory of embedology, the diffusion model is built using the delay-embedding coordinates of the data. We show that this delay embedding biases the geometry of the data in a way which extracts the most stable component of the dynamics and reduces the influence of independent additive observation noise. The resulting diffusion forecast model approximates the semigroup solutions of the generator of the underlying dynamics in the limit of large data and when the observation noise vanishes. As in any standard forecasting problem, the forecasting skill depends crucially on the accuracy of the initial conditions. We introduce a novel Bayesian method for filtering the discrete-time noisy observations which works with the diffusion forecast to determine the forecast initial densities. Numerically, we compare this nonparametric approach with standard stochastic parametric models on a wide-range of well-studied turbulent modes, including the Lorenz-96 model in weakly chaotic to fully turbulent regimes and the barotropic modes of a quasi-geostrophic model with baroclinic instabilities. We show that when the only available data is the low-dimensional set of noisy modes that are being modeled, the diffusion forecast is indeed competitive to the perfect model.

  15. Reliable estimates of predictive uncertainty for an Alpine catchment using a non-parametric methodology

    Science.gov (United States)

    Matos, José P.; Schaefli, Bettina; Schleiss, Anton J.

    2017-04-01

    Uncertainty affects hydrological modelling efforts from the very measurements (or forecasts) that serve as inputs to the more or less inaccurate predictions that are produced. Uncertainty is truly inescapable in hydrology and yet, due to the theoretical and technical hurdles associated with its quantification, it is at times still neglected or estimated only qualitatively. In recent years the scientific community has made a significant effort towards quantifying this hydrologic prediction uncertainty. Despite this, most of the developed methodologies can be computationally demanding, are complex from a theoretical point of view, require substantial expertise to be employed, and are constrained by a number of assumptions about the model error distribution. These assumptions limit the reliability of many methods in case of errors that show particular cases of non-normality, heteroscedasticity, or autocorrelation. The present contribution builds on a non-parametric data-driven approach that was developed for uncertainty quantification in operational (real-time) forecasting settings. The approach is based on the concept of Pareto optimality and can be used as a standalone forecasting tool or as a postprocessor. By virtue of its non-parametric nature and a general operating principle, it can be applied directly and with ease to predictions of streamflow, water stage, or even accumulated runoff. Also, it is a methodology capable of coping with high heteroscedasticity and seasonal hydrological regimes (e.g. snowmelt and rainfall driven events in the same catchment). Finally, the training and operation of the model are very fast, making it a tool particularly adapted to operational use. To illustrate its practical use, the uncertainty quantification method is coupled with a process-based hydrological model to produce statistically reliable forecasts for an Alpine catchment located in Switzerland. Results are presented and discussed in terms of their reliability and

  16. CD19 CAR-T cells of defined CD4+:CD8+ composition in adult B cell ALL patients.

    Science.gov (United States)

    Turtle, Cameron J; Hanafi, Laïla-Aïcha; Berger, Carolina; Gooley, Theodore A; Cherian, Sindhu; Hudecek, Michael; Sommermeyer, Daniel; Melville, Katherine; Pender, Barbara; Budiarto, Tanya M; Robinson, Emily; Steevens, Natalia N; Chaney, Colette; Soma, Lorinda; Chen, Xueyan; Yeung, Cecilia; Wood, Brent; Li, Daniel; Cao, Jianhong; Heimfeld, Shelly; Jensen, Michael C; Riddell, Stanley R; Maloney, David G

    2016-06-01

    T cells that have been modified to express a CD19-specific chimeric antigen receptor (CAR) have antitumor activity in B cell malignancies; however, identification of the factors that determine toxicity and efficacy of these T cells has been challenging in prior studies in which phenotypically heterogeneous CAR-T cell products were prepared from unselected T cells. We conducted a clinical trial to evaluate CD19 CAR-T cells that were manufactured from defined CD4+ and CD8+ T cell subsets and administered in a defined CD4+:CD8+ composition to adults with B cell acute lymphoblastic leukemia after lymphodepletion chemotherapy. The defined composition product was remarkably potent, as 27 of 29 patients (93%) achieved BM remission, as determined by flow cytometry. We established that high CAR-T cell doses and tumor burden increase the risks of severe cytokine release syndrome and neurotoxicity. Moreover, we identified serum biomarkers that allow testing of early intervention strategies in patients at the highest risk of toxicity. Risk-stratified CAR-T cell dosing based on BM disease burden decreased toxicity. CD8+ T cell-mediated anti-CAR transgene product immune responses developed after CAR-T cell infusion in some patients, limited CAR-T cell persistence, and increased relapse risk. Addition of fludarabine to the lymphodepletion regimen improved CAR-T cell persistence and disease-free survival. Immunotherapy with a CAR-T cell product of defined composition enabled identification of factors that correlated with CAR-T cell expansion, persistence, and toxicity and facilitated design of lymphodepletion and CAR-T cell dosing strategies that mitigated toxicity and improved disease-free survival. ClinicalTrials.gov NCT01865617. R01-CA136551; Life Science Development Fund; Juno Therapeutics; Bezos Family Foundation.

  17. Increased Tryptophan Catabolism is Associated with Increased Frequency of CD161+Tc17/MAIT Cells, and Lower CD4+ T cell Count in HIV-1 infected Patients on cART after Two Years of Follow-up

    DEFF Research Database (Denmark)

    Gaardbo, Julie Christine; Trøseid, Marius; Stiksrud, Birgitte

    2015-01-01

    of KTR on CD4+ T cell recovery in HIV-infected patients on cART after two years of follow-up was investigated. METHODS: Forty-one HIV-infected individuals treated with cART for a minimum of two years were included. Tregs, CD161+Tc17/MAIT cells, naïve cells, immune activation, senescence and apoptosis...... divided in two groups defined by high vs. low KTR. The CD4+ T cell count was determined at inclusion and after two years of follow-up. RESULTS: The KTR decreased following cART initiation. Patients on cART with high KTR displayed an immunological profile with high sCD14, high percentage Tregs, low...... percentage CD161+Tc17/MAIT cells, low percentage naïve cells, low CD4/CD8 ratio and poor immune reconstitution after two years of follow-up compared to patients with low KTR. CONCLUSIONS: Our results support the hypothesis that tryptophan catabolism, Indoleamine 2,3-dioxygenase 1 (IDO1) activation, microbial...

  18. Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions.

    Science.gov (United States)

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

    2017-01-01

    We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

  19. Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions.

    Directory of Open Access Journals (Sweden)

    Tomoki Tokuda

    Full Text Available We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

  20. Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions

    Science.gov (United States)

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

    2017-01-01

    We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data. PMID:29049392

  1. New analogs of the CART peptide with anorexigenic potency: The importance of individual disulfide bridges

    Czech Academy of Sciences Publication Activity Database

    Blechová, Miroslava; Nagelová, Veronika; Žáková, Lenka; Demianova, Zuzana; Železná, Blanka; Maletínská, Lenka

    2013-01-01

    Roč. 39, January (2013), s. 138-144 ISSN 0196-9781 R&D Projects: GA ČR GAP303/10/1368 Institutional support: RVO:61388963 Keywords : CART peptide analogs * sulfitolysis * PC12 cells * binding * food intake Subject RIV: CE - Biochemistry Impact factor: 2.614, year: 2013

  2. Functions of Alx3, Alx4 and Cart1 during craniofacial development in the mouse

    NARCIS (Netherlands)

    Beverdam, A. (Annemiek)

    2001-01-01

    Aristaless-related genes are Paired-related homeobox genes that by definition encode a second strongly conserved domain at their C-terminal end, the aristaless-box . During my Ph. D. project I studied the functions of a specific subset of these highly related genes, Alx3, Alx4 and Cart1, during

  3. Anorexigenní neuropeptid CART v regulaci příjmu potravy

    Czech Academy of Sciences Publication Activity Database

    Nagelová, Veronika; Železná, Blanka; Maletínská, Lenka

    2014-01-01

    Roč. 108, č. 4 (2014), s. 354-357 ISSN 0009-2770 R&D Projects: GA ČR GAP303/10/1368 Institutional support: RVO:61388963 Keywords : CART * cocaine and amphetamine regulated transcript * anorexigenic neuropeptide Subject RIV: CE - Biochemistry Impact factor: 0.272, year: 2014

  4. Structure-activity relationship of CART (cocaine- and amphetamine-regulated transcript) peptide fragments

    Czech Academy of Sciences Publication Activity Database

    Maixnerová, Jana; Hlaváček, Jan; Blokešová, Darja; Kowalczyk, W.; Elbert, Tomáš; Šanda, Miloslav; Blechová, Miroslava; Železná, Blanka; Slaninová, Jiřina; Maletínská, Lenka

    2007-01-01

    Roč. 28, č. 10 (2007), s. 1945-1953 ISSN 0196-9781 R&D Projects: GA ČR GA303/05/0614 Institutional research plan: CEZ:AV0Z40550506 Keywords : CART peptide * fragments * binding * PC12 cells Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 2.368, year: 2007

  5. Robust Takagi-Sugeno Fuzzy Dynamic Regulator for Trajectory Tracking of a Pendulum-Cart System

    Directory of Open Access Journals (Sweden)

    Miguel A. Llama

    2015-01-01

    Full Text Available Starting from a nonlinear model for a pendulum-cart system, on which viscous friction is considered, a Takagi-Sugeno (T-S fuzzy augmented model (TSFAM as well as a TSFAM with uncertainty (TSFAMwU is proposed. Since the design of a T-S fuzzy controller is based on the T-S fuzzy model of the nonlinear system, then, to address the trajectory tracking problem of the pendulum-cart system, three T-S fuzzy controllers are proposed via parallel distributed compensation: (1 a T-S fuzzy servo controller (TSFSC designed from the TSFAM; (2 a robust TSFSC (RTSFSC designed from the TSFAMwU; and (3 a robust T-S fuzzy dynamic regulator (RTSFDR designed from the RTSFSC with the addition of a T-S fuzzy observer, which estimates cart and pendulum velocities. Both TSFAM and TSFAMwU are comprised of two fuzzy rules and designed via local approximation in fuzzy partition spaces technique. Feedback gains for the three fuzzy controllers are obtained via linear matrix inequalities approach. A swing-up controller is developed to swing the pendulum up from its pendant position to its upright position. Real-time experiments validate the effectiveness of the proposed schemes, keeping the pendulum in its upright position while the cart follows a reference signal, standing out the RTSFDR.

  6. KLASIFIKASI KARAKTERISTIK KECELAKAAN LALU LINTAS DI KOTA DENPASAR DENGAN PENDEKATAN CLASSIFICATION AND REGRESSION TREES (CART

    Directory of Open Access Journals (Sweden)

    I GEDE AGUS JIWADIANA

    2015-11-01

    Full Text Available The aim of this research is to determine the classification characteristics of traffic accidents in Denpasar city in January-July 2014 by using Classification And Regression Trees (CART. Then, for determine the explanatory variables into the main classifier of CART. The result showed that optimum CART generate three terminal node. First terminal node, there are 12 people were classified as heavy traffic accident characteritics with single accident, and second terminal nodes, there are 68 people were classified as minor traffic accident characteristics by type of traffic accident front-rear, front-front, front-side, pedestrians, side-side and location of traffic accident in district road and sub-district road. For third terminal node, there are 291 people were classified as medium traffic accident characteristics by type of traffic accident front-rear, front-front, front-side, pedestrians, side-side and location of traffic accident in municipality road and explanatory variables into the main splitter to make of CART is type of traffic accident with maximum homogeneity measure of 0.03252.

  7. Trunk muscle control in response to (un)expected turns in cart pushing

    NARCIS (Netherlands)

    Lee, Y.J.; Hoozemans, M.J.M.; van Dieen, J.H.

    2012-01-01

    Before altering the travel direction in normal gait, anticipatory activation in trunk muscles is observed, followed by a top-down sequence of rotation of body segments. Turning while pushing a cart is a more challenging task for the trunk because of its low stiffness in pushing while walking and the

  8. Health constraints of Cart Horses in the Dry warm, Sub-moist tepid ...

    African Journals Online (AJOL)

    The objectives of this study were to identify the major health and welfare constraints of cart horses in the dry warm, sub-moist tepid and moist cool climatic zones of Ethiopia. The study was cross sectional and a total of 837 horses were examined. Five major health problems and welfare issues were identified. Lymphangitis ...

  9. CAR-T cells and combination therapies: What's next in the immunotherapy revolution?

    Science.gov (United States)

    Ramello, Maria C; Haura, Eric B; Abate-Daga, Daniel

    2018-03-01

    Cancer immunotherapies are dramatically reshaping the clinical management of oncologic patients. For many of these therapies, the guidelines for administration, monitoring, and management of associated toxicities are still being established. This is especially relevant for adoptively transferred, genetically-modified T cells, which have unique pharmacokinetic properties, due to their ability to replicate and persist long-term, following a single administration. Furthermore, in the case of CAR-T cells, the use of synthetic immune receptors may impact signaling pathways involved in T cell function and survival in unexpected ways. We, herein, comment on the most salient aspects of CAR-T cell design and clinical experience in the treatment of solid tumors. In addition, we discuss different possible scenarios for combinations of CAR-T cells and other treatment modalities, with a special emphasis on kinase inhibitors, elaborating on the strategies to maximize synergism. Finally, we discuss some of the technologies that are available to explore the molecular events governing the success of these therapies. The young fields of synthetic and systems biology are likely to be major players in the advancement of CAR-T cell therapies, providing the tools and the knowledge to engineer patients' T lymphocytes into intelligent cancer-fighting micromachines. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Biomarkers of cytokine release syndrome and neurotoxicity related to CAR-T cell therapy.

    Science.gov (United States)

    Wang, Zhenguang; Han, Weidong

    2018-01-01

    Severe cytokine release syndrome (CRS) and neurotoxicity following chimeric antigen receptor T cell (CAR-T) therapy can be life-threatening in some cases, and management of those toxicities is still a great challenge for physicians. Researchers hope to understand the pathophysiology of CRS and neurotoxicity, and identify predictive biomarkers that can forecast those toxicities in advance. Some risk factors for severe CRS and/or neurotoxicity including patient and treatment characteristics have been identified in multiple clinical trials of CAR-T cell therapy. Moreover, several groups have identified some predictive biomarkers that are able to determine beforehand which patients may suffer severe CRS and/or neurotoxicity during CAR-T cell therapy, facilitating testing of early intervention strategies for those toxicities. However, further studies are needed to better understand the biology and related risk factors for CRS and/or neurotoxicity, and determine if those identified predictors can be extrapolated to other series. Herein, we review the pathophysiology of CRS and neurotoxicity, and summarize the progress of predictive biomarkers to improve CAR-T cell therapy in cancer.

  11. Assessing community resilience: A CART survey application in an impoverished urban community.

    Science.gov (United States)

    Pfefferbaum, Rose L; Pfefferbaum, Betty; Zhao, Yan D; Van Horn, Richard L; McCarter, Grady S Mack; Leonard, Michael B

    2016-01-01

    This article describes an application of the Communities Advancing Resilience Toolkit (CART) Assessment Survey which has been recognized as an important community tool to assist communities in their resilience-building efforts. Developed to assist communities in assessing their resilience to disasters and other adversities, the CART survey can be used to obtain baseline information about a community, to identify relative community strengths and challenges, and to re-examine a community after a disaster or post intervention. This article, which describes an application of the survey in a community of 5 poverty neighborhoods, illustrates the use of the instrument, explicates aspects of community resilience, and provides possible explanations for the results. The paper also demonstrates how a community agency that serves many of the functions of a broker organization can enhance community resilience. Survey results suggest various dimensions of community resilience (as represented by core CART community resilience items and CART domains) and potential predictors. Correlates included homeownership, engagement with local entities/activities, prior experience with a personal emergency or crisis while living in the neighborhood, and involvement with a community organization that focuses on building safe and caring communities through personal relationships. In addition to influencing residents' perceptions of their community, it is likely that the community organization, which served as a sponsor for this application, contributes directly to community resilience through programs and initiatives that enhance social capital and resource acquisition and mobilization.

  12. Automated anesthesia carts reduce drug recording errors in medication administrations - A single center study in the largest tertiary referral hospital in China.

    Science.gov (United States)

    Wang, Ying; Du, Yingying; Zhao, Yingying; Ren, Yang; Zhang, Wei

    2017-08-01

    To clinically evaluate a type of patented automated anesthesia cart in medication administrations in anesthesia. This was a prospectively randomized open label clinical trial. In 10 designated operating suits in the First Affiliated Hospital of Zhengzhou University, in China. 1066 cases originated from 10,812 medication administrations in anesthesia were randomized. 78 registered anesthesiologists managed the medication. The patients received medication administrations in anesthesia with either an automated or a conventional manual cart. American Society of Anesthesiologists (ASA) score, sex, duration of anesthesia and surgical specialty, errors in administration of medications (incorrect medication given (substitution), medication not given (omission) and drug recordings errors"), compliance and satisfaction were recorded. The total error rate was 7.3% with the automated anesthesia carts (1 in 14 administrations) and 11.9% with conventional manual carts (1 in 8 administrations). Automated anesthesia carts significantly reduced the drug recording error rate compared to conventional manual carts (Perrors omission errors was found between groups of automated anesthesia carts and conventional manual carts. The anesthesiologists' compliance with the automated anesthesia carts was unsatisfactory, and all the errors in medication recordings with the automated anesthesia carts were due to the incorrect use of the carts. Most of the participating anesthesiologists preferred the automated anesthesia carts (Perrors in medication administrations of anesthesia. Copyright © 2017 Elsevier Inc. All rights reserved.

  13. The nucleus accumbens 5-HTR4-CART pathway ties anorexia to hyperactivity

    Science.gov (United States)

    Jean, A; Laurent, L; Bockaert, J; Charnay, Y; Dusticier, N; Nieoullon, A; Barrot, M; Neve, R; Compan, V

    2012-01-01

    In mental diseases, the brain does not systematically adjust motor activity to feeding. Probably, the most outlined example is the association between hyperactivity and anorexia in Anorexia nervosa. The neural underpinnings of this ‘paradox', however, are poorly elucidated. Although anorexia and hyperactivity prevail over self-preservation, both symptoms rarely exist independently, suggesting commonalities in neural pathways, most likely in the reward system. We previously discovered an addictive molecular facet of anorexia, involving production, in the nucleus accumbens (NAc), of the same transcripts stimulated in response to cocaine and amphetamine (CART) upon stimulation of the 5-HT4 receptors (5-HTR4) or MDMA (ecstasy). Here, we tested whether this pathway predisposes not only to anorexia but also to hyperactivity. Following food restriction, mice are expected to overeat. However, selecting hyperactive and addiction-related animal models, we observed that mice lacking 5-HTR1B self-imposed food restriction after deprivation and still displayed anorexia and hyperactivity after ecstasy. Decryption of the mechanisms showed a gain-of-function of 5-HTR4 in the absence of 5-HTR1B, associated with CART surplus in the NAc and not in other brain areas. NAc-5-HTR4 overexpression upregulated NAc-CART, provoked anorexia and hyperactivity. NAc-5-HTR4 knockdown or blockade reduced ecstasy-induced hyperactivity. Finally, NAc-CART knockdown suppressed hyperactivity upon stimulation of the NAc-5-HTR4. Additionally, inactivating NAc-5-HTR4 suppressed ecstasy's preference, strengthening the rewarding facet of anorexia. In conclusion, the NAc-5-HTR4/CART pathway establishes a ‘tight-junction' between anorexia and hyperactivity, suggesting the existence of a primary functional unit susceptible to limit overeating associated with resting following homeostasis rules. PMID:23233022

  14. The nucleus accumbens 5-HTR₄-CART pathway ties anorexia to hyperactivity.

    Science.gov (United States)

    Jean, A; Laurent, L; Bockaert, J; Charnay, Y; Dusticier, N; Nieoullon, A; Barrot, M; Neve, R; Compan, V

    2012-12-11

    In mental diseases, the brain does not systematically adjust motor activity to feeding. Probably, the most outlined example is the association between hyperactivity and anorexia in Anorexia nervosa. The neural underpinnings of this 'paradox', however, are poorly elucidated. Although anorexia and hyperactivity prevail over self-preservation, both symptoms rarely exist independently, suggesting commonalities in neural pathways, most likely in the reward system. We previously discovered an addictive molecular facet of anorexia, involving production, in the nucleus accumbens (NAc), of the same transcripts stimulated in response to cocaine and amphetamine (CART) upon stimulation of the 5-HT(4) receptors (5-HTR(4)) or MDMA (ecstasy). Here, we tested whether this pathway predisposes not only to anorexia but also to hyperactivity. Following food restriction, mice are expected to overeat. However, selecting hyperactive and addiction-related animal models, we observed that mice lacking 5-HTR(1B) self-imposed food restriction after deprivation and still displayed anorexia and hyperactivity after ecstasy. Decryption of the mechanisms showed a gain-of-function of 5-HTR(4) in the absence of 5-HTR(1B), associated with CART surplus in the NAc and not in other brain areas. NAc-5-HTR(4) overexpression upregulated NAc-CART, provoked anorexia and hyperactivity. NAc-5-HTR(4) knockdown or blockade reduced ecstasy-induced hyperactivity. Finally, NAc-CART knockdown suppressed hyperactivity upon stimulation of the NAc-5-HTR(4). Additionally, inactivating NAc-5-HTR(4) suppressed ecstasy's preference, strengthening the rewarding facet of anorexia. In conclusion, the NAc-5-HTR(4)/CART pathway establishes a 'tight-junction' between anorexia and hyperactivity, suggesting the existence of a primary functional unit susceptible to limit overeating associated with resting following homeostasis rules.

  15. River suspended sediment modelling using the CART model: A comparative study of machine learning techniques.

    Science.gov (United States)

    Choubin, Bahram; Darabi, Hamid; Rahmati, Omid; Sajedi-Hosseini, Farzaneh; Kløve, Bjørn

    2018-02-15

    Suspended sediment load (SSL) modelling is an important issue in integrated environmental and water resources management, as sediment affects water quality and aquatic habitats. Although classification and regression tree (CART) algorithms have been applied successfully to ecological and geomorphological modelling, their applicability to SSL estimation in rivers has not yet been investigated. In this study, we evaluated use of a CART model to estimate SSL based on hydro-meteorological data. We also compared the accuracy of the CART model with that of the four most commonly used models for time series modelling of SSL, i.e. adaptive neuro-fuzzy inference system (ANFIS), multi-layer perceptron (MLP) neural network and two kernels of support vector machines (RBF-SVM and P-SVM). The models were calibrated using river discharge, stage, rainfall and monthly SSL data for the Kareh-Sang River gauging station in the Haraz watershed in northern Iran, where sediment transport is a considerable issue. In addition, different combinations of input data with various time lags were explored to estimate SSL. The best input combination was identified through trial and error, percent bias (PBIAS), Taylor diagrams and violin plots for each model. For evaluating the capability of the models, different statistics such as Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE) and percent bias (PBIAS) were used. The results showed that the CART model performed best in predicting SSL (NSE=0.77, KGE=0.8, PBIAS<±15), followed by RBF-SVM (NSE=0.68, KGE=0.72, PBIAS<±15). Thus the CART model can be a helpful tool in basins where hydro-meteorological data are readily available. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. In Vivo Expansion and Antitumor Activity of Coinfused CD28- and 4-1BB-Engineered CAR-T Cells in Patients with B Cell Leukemia.

    Science.gov (United States)

    Cheng, Zhi; Wei, Runhong; Ma, Qiuling; Shi, Lin; He, Feng; Shi, Zixiao; Jin, Tao; Xie, Ronglin; Wei, Baofeng; Chen, Jing; Fang, Hongliang; Han, Xiaolu; Rohrs, Jennifer A; Bryson, Paul; Liu, Yarong; Li, Qi-Jing; Zhu, Bo; Wang, Pin

    2018-04-04

    Several recent clinical trials have successfully incorporated a costimulatory domain derived from either CD28 or 4-1BB with the original CD3ζ T cell activating domain to form second-generation chimeric antigen receptors (CARs) that can increase the responsiveness and survival of CAR-engineered T (CAR-T) cells. However, a rigorous assessment of the individual benefits of these costimulatory components relative to the in vivo performance of infused T cells in patients is still lacking. Therefore, we have designed a study that allows us to investigate and compare the impact of different costimulatory signal domains on CAR-T cells in vivo. Patients with B cell leukemia were infused with a mixture of two types of CD19-specific CAR-T cells, individually bearing CD28 (28ζ) and 4-1BB (BBζ) costimulatory signaling domains. We found that such a clinical procedure was feasible and safe. Complete remission (CR) was observed in five of seven enrolled patients, with two patients exhibiting durable CR lasting more than 15 months. The in vivo expansion pattern of 28ζ and BBζ CAR-T cells varied significantly among individual patients. These results confirm a feasible method of comparing different CAR designs within individual patients, potentially offering objective insights that may facilitate the development of optimal CAR-T cell-based immunotherapies. Copyright © 2018 The American Society of Gene and Cell Therapy. Published by Elsevier Inc. All rights reserved.

  17. Prediction of radiation levels in residences: A methodological comparison of CART [Classification and Regression Tree Analysis] and conventional regression

    International Nuclear Information System (INIS)

    Janssen, I.; Stebbings, J.H.

    1990-01-01

    In environmental epidemiology, trace and toxic substance concentrations frequently have very highly skewed distributions ranging over one or more orders of magnitude, and prediction by conventional regression is often poor. Classification and Regression Tree Analysis (CART) is an alternative in such contexts. To compare the techniques, two Pennsylvania data sets and three independent variables are used: house radon progeny (RnD) and gamma levels as predicted by construction characteristics in 1330 houses; and ∼200 house radon (Rn) measurements as predicted by topographic parameters. CART may identify structural variables of interest not identified by conventional regression, and vice versa, but in general the regression models are similar. CART has major advantages in dealing with other common characteristics of environmental data sets, such as missing values, continuous variables requiring transformations, and large sets of potential independent variables. CART is most useful in the identification and screening of independent variables, greatly reducing the need for cross-tabulations and nested breakdown analyses. There is no need to discard cases with missing values for the independent variables because surrogate variables are intrinsic to CART. The tree-structured approach is also independent of the scale on which the independent variables are measured, so that transformations are unnecessary. CART identifies important interactions as well as main effects. The major advantages of CART appear to be in exploring data. Once the important variables are identified, conventional regressions seem to lead to results similar but more interpretable by most audiences. 12 refs., 8 figs., 10 tabs

  18. Chimeric antigen receptor T cell (CAR-T) immunotherapy for solid tumors: lessons learned and strategies for moving forward.

    Science.gov (United States)

    Li, Jian; Li, Wenwen; Huang, Kejia; Zhang, Yang; Kupfer, Gary; Zhao, Qi

    2018-02-13

    Recently, the US Food and Drug Administration (FDA) approved the first chimeric antigen receptor T cell (CAR-T) therapy for the treatment CD19-positive B cell acute lymphoblastic leukemia. While CAR-T has achieved remarkable success in the treatment of hematopoietic malignancies, whether it can benefit solid tumor patients to the same extent is still uncertain. Even though hundreds of clinical trials are undergoing exploring a variety of tumor-associated antigens (TAA), no such antigen with comparable properties like CD19 has yet been identified regarding solid tumors CAR-T immunotherapy. Inefficient T cell trafficking, immunosuppressive tumor microenvironment, suboptimal antigen recognition specificity, and lack of safety control are currently considered as the main obstacles in solid tumor CAR-T therapy. Here, we reviewed the solid tumor CAR-T clinical trials, emphasizing the studies with published results. We further discussed the challenges that CAR-T is facing for solid tumor treatment and proposed potential strategies to improve the efficacy of CAR-T as promising immunotherapy.

  19. Nonparametric adaptive age replacement with a one-cycle criterion

    International Nuclear Information System (INIS)

    Coolen-Schrijner, P.; Coolen, F.P.A.

    2007-01-01

    Age replacement of technical units has received much attention in the reliability literature over the last four decades. Mostly, the failure time distribution for the units is assumed to be known, and minimal costs per unit of time is used as optimality criterion, where renewal reward theory simplifies the mathematics involved but requires the assumption that the same process and replacement strategy continues over a very large ('infinite') period of time. Recently, there has been increasing attention to adaptive strategies for age replacement, taking into account the information from the process. Although renewal reward theory can still be used to provide an intuitively and mathematically attractive optimality criterion, it is more logical to use minimal costs per unit of time over a single cycle as optimality criterion for adaptive age replacement. In this paper, we first show that in the classical age replacement setting, with known failure time distribution with increasing hazard rate, the one-cycle criterion leads to earlier replacement than the renewal reward criterion. Thereafter, we present adaptive age replacement with a one-cycle criterion within the nonparametric predictive inferential framework. We study the performance of this approach via simulations, which are also used for comparisons with the use of the renewal reward criterion within the same statistical framework

  20. Nonparametric Integrated Agrometeorological Drought Monitoring: Model Development and Application

    Science.gov (United States)

    Zhang, Qiang; Li, Qin; Singh, Vijay P.; Shi, Peijun; Huang, Qingzhong; Sun, Peng

    2018-01-01

    Drought is a major natural hazard that has massive impacts on the society. How to monitor drought is critical for its mitigation and early warning. This study proposed a modified version of the multivariate standardized drought index (MSDI) based on precipitation, evapotranspiration, and soil moisture, i.e., modified multivariate standardized drought index (MMSDI). This study also used nonparametric joint probability distribution analysis. Comparisons were done between standardized precipitation evapotranspiration index (SPEI), standardized soil moisture index (SSMI), MSDI, and MMSDI, and real-world observed drought regimes. Results indicated that MMSDI detected droughts that SPEI and/or SSMI failed to do. Also, MMSDI detected almost all droughts that were identified by SPEI and SSMI. Further, droughts detected by MMSDI were similar to real-world observed droughts in terms of drought intensity and drought-affected area. When compared to MMSDI, MSDI has the potential to overestimate drought intensity and drought-affected area across China, which should be attributed to exclusion of the evapotranspiration components from estimation of drought intensity. Therefore, MMSDI is proposed for drought monitoring that can detect agrometeorological droughts. Results of this study provide a framework for integrated drought monitoring in other regions of the world and can help to develop drought mitigation.

  1. Bayesian Nonparametric Model for Estimating Multistate Travel Time Distribution

    Directory of Open Access Journals (Sweden)

    Emmanuel Kidando

    2017-01-01

    Full Text Available Multistate models, that is, models with more than two distributions, are preferred over single-state probability models in modeling the distribution of travel time. Literature review indicated that the finite multistate modeling of travel time using lognormal distribution is superior to other probability functions. In this study, we extend the finite multistate lognormal model of estimating the travel time distribution to unbounded lognormal distribution. In particular, a nonparametric Dirichlet Process Mixture Model (DPMM with stick-breaking process representation was used. The strength of the DPMM is that it can choose the number of components dynamically as part of the algorithm during parameter estimation. To reduce computational complexity, the modeling process was limited to a maximum of six components. Then, the Markov Chain Monte Carlo (MCMC sampling technique was employed to estimate the parameters’ posterior distribution. Speed data from nine links of a freeway corridor, aggregated on a 5-minute basis, were used to calculate the corridor travel time. The results demonstrated that this model offers significant flexibility in modeling to account for complex mixture distributions of the travel time without specifying the number of components. The DPMM modeling further revealed that freeway travel time is characterized by multistate or single-state models depending on the inclusion of onset and offset of congestion periods.

  2. Cocaine- and amphetamine-regulated transcript (CART signaling within the paraventricular thalamus modulates cocaine-seeking behaviour.

    Directory of Open Access Journals (Sweden)

    Morgan H James

    Full Text Available BACKGROUND: Cocaine- and amphetamine-regulated transcript (CART has been demonstrated to play a role in regulating the rewarding and reinforcing effects of various drugs of abuse. A recent study demonstrated that i.c.v. administration of CART negatively modulates reinstatement of alcohol seeking, however, the site(s of action remains unclear. We investigated the paraventricular thalamus (PVT as a potential site of relapse-relevant CART signaling, as this region is known to receive dense innervation from CART-containing hypothalamic cells and to project to a number of regions known to be involved in mediating reinstatement, including the nucleus accumbens (NAC, medial prefrontal cortex (mPFC and basolateral amygdala (BLA. METHODOLOGY/PRINCIPAL FINDINGS: Male rats were trained to self-administer cocaine before being extinguished to a set criterion. One day following extinction, animals received intra-PVT infusions of saline, tetrodotoxin (TTX; 2.5 ng, CART (0.625 µg or 2.5 µg or no injection, followed by a cocaine prime (10 mg/kg, i.p.. Animals were then tested under extinction conditions for one hour. Treatment with either TTX or CART resulted in a significant attenuation of drug-seeking behaviour following cocaine-prime, with the 2.5 µg dose of CART having the greatest effect. This effect was specific to the PVT region, as misplaced injections of both TTX and CART resulted in responding that was identical to controls. CONCLUSIONS/SIGNIFICANCE: We show for the first time that CART signaling within the PVT acts to inhibit drug-primed reinstatement of cocaine seeking behaviour, presumably by negatively modulating PVT efferents that are important for drug seeking, including the NAC, mPFC and BLA. In this way, we identify a possible target for future pharmacological interventions designed to suppress drug seeking.

  3. Chromatin immunoprecipitation assays revealed CREB and serine 133 phospho-CREB binding to the CART gene proximal promoter.

    Science.gov (United States)

    Rogge, George A; Shen, Li-Ling; Kuhar, Michael J

    2010-07-16

    Both over expression of cyclic AMP response element binding protein (CREB) in the nucleus accumbens (NAc), and intra-accumbal injection of cocaine- and amphetamine-regulated transcript (CART) peptides, have been shown to decrease cocaine reward. Also, over expression of CREB in the rat NAc increased CART mRNA and peptide levels, but it is not known if this was due to a direct action of P-CREB on the CART gene promoter. The goal of this study was to test if CREB and P-CREB bound directly to the CRE site in the CART promoter, using chromatin immunoprecipitation (ChIP) assays. ChIP assay with anti-CREB antibodies showed an enrichment of the CART promoter fragment containing the CRE region over IgG precipitated material, a non-specific control. Forskolin, which was known to increase CART mRNA levels in GH3 cells, was utilized to show that the drug increased levels of P-CREB protein and P-CREB binding to the CART promoter CRE-containing region. A region of the c-Fos promoter containing a CRE cis-regulatory element was previously shown to bind P-CREB, and it was used here as a positive control. These data suggest that the effects of CREB over expression on blunting cocaine reward could be, at least in part, attributed to the increased expression of the CART gene by direct interaction of P-CREB with the CART promoter CRE site, rather than by some indirect action. Copyright (c) 2010 Elsevier B.V. All rights reserved.

  4. A nonparametric fiducial interval for the Youden index in multi-state diagnostic settings.

    Science.gov (United States)

    Batterton, Katherine A; Schubert, Christine M

    2016-01-15

    The Youden index is a commonly employed metric to characterize the performance of a diagnostic test at its optimal point. For tests with three or more outcome classes, the Youden index has been extended; however, there are limited methods to compute a confidence interval (CI) about its value. Often, outcome classes are assumed to be normally distributed, which facilitates computational formulas for the CI bounds; however, many scenarios exist for which these assumptions cannot be made. In addition, many of these existing CI methods do not work well for small sample sizes. We propose a method to compute a nonparametric interval about the Youden index utilizing the fiducial argument. This fiducial interval ensures that CI coverage is met regardless of sample size, underlying distributional assumptions, or use of a complex classifier for diagnosis. Two alternate fiducial intervals are also considered. A simulation was conducted, which demonstrates the coverage and interval length for the proposed methods. Comparisons were made using no distributional assumptions on the outcome classes and for when outcomes were assumed to be normally distributed. In general, coverage probability was consistently met, and interval length was reasonable. The proposed fiducial method was also demonstrated in data examining biomarkers in subjects to predict diagnostic stages ranging from normal kidney function to chronic allograph nephropathy. Published 2015. This article is a U.S. Government work and is in the public domain in the USA. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

  5. Geometry and Structural Properties for the Controls Advanced Research Turbine (CART) from Model Tuning: August 25, 2003--November 30, 2003

    Energy Technology Data Exchange (ETDEWEB)

    Stol, K. A.

    2004-09-01

    The Controls Advanced Research Turbine (CART) is a modified Westinghouse WWG-0600 machine rated at 600 kW. It is located at the National Wind Technology Center (NWTC) in Boulder, Colorado, and has been installed to test new control schemes for power and load regulation. In its original configuration, the WWG-0600 uses a synchronous generator, fluid coupling, and hydraulic collective pitch actuation. However, the CART is fitted with an induction generator, rigid coupling, and individual electromechanical pitch actuators. The rotor runs upwind of the tower and consists of two blades and a teetering hub. In order to design advanced control schemes for the CART, representative computational models are essential.

  6. Safety, tumor trafficking and immunogenicity of chimeric antigen receptor (CAR)-T cells specific for TAG-72 in colorectal cancer.

    Science.gov (United States)

    Hege, Kristen M; Bergsland, Emily K; Fisher, George A; Nemunaitis, John J; Warren, Robert S; McArthur, James G; Lin, Andy A; Schlom, Jeffrey; June, Carl H; Sherwin, Stephen A

    2017-01-01

    T cells engineered to express chimeric antigen receptors (CARs) have established efficacy in the treatment of B-cell malignancies, but their relevance in solid tumors remains undefined. Here we report results of the first human trials of CAR-T cells in the treatment of solid tumors performed in the 1990s. Patients with metastatic colorectal cancer (CRC) were treated in two phase 1 trials with first-generation retroviral transduced CAR-T cells targeting tumor-associated glycoprotein (TAG)-72 and including a CD3-zeta intracellular signaling domain (CART72 cells). In trial C-9701 and C-9702, CART72 cells were administered in escalating doses up to 10 10 total cells; in trial C-9701 CART72 cells were administered by intravenous infusion. In trial C-9702, CART72 cells were administered via direct hepatic artery infusion in patients with colorectal liver metastases. In both trials, a brief course of interferon-alpha (IFN-α) was given with each CART72 infusion to upregulate expression of TAG-72. Fourteen patients were enrolled in C-9701 and nine in C-9702. CART72 manufacturing success rate was 100% with an average transduction efficiency of 38%. Ten patients were treated in CC-9701 and 6 in CC-9702. Symptoms consistent with low-grade, cytokine release syndrome were observed in both trials without clear evidence of on target/off tumor toxicity. Detectable, but mostly short-term (≤14 weeks), persistence of CART72 cells was observed in blood; one patient had CART72 cells detectable at 48 weeks. Trafficking to tumor tissues was confirmed in a tumor biopsy from one of three patients. A subset of patients had 111 Indium-labeled CART72 cells injected, and trafficking could be detected to liver, but T cells appeared largely excluded from large metastatic deposits. Tumor biomarkers carcinoembryonic antigen (CEA) and TAG-72 were measured in serum; there was a precipitous decline of TAG-72, but not CEA, in some patients due to induction of an interfering antibody to the TAG-72

  7. La carte postale coloniale dans l’album : Nona des sables, un château de cartes mémorielles

    Directory of Open Access Journals (Sweden)

    Anne Schneider

    2012-01-01

    Full Text Available L’album Nona des sables de Françoise Kérisel raconte la recherche mémorielle de la petite Manuela interrogeant sa grand-mère Nona sur son passé dans son Algérie natale. À partir de cartes postales coloniales qui se superposent au texte de l’album, elle renoue petit à petit avec son histoire familiale et algérienne et son identité mixte en croisant les approches iconographiques et langagières.

  8. Separating environmental efficiency into production and abatement efficiency. A nonparametric model with application to U.S. power plants

    Energy Technology Data Exchange (ETDEWEB)

    Hampf, Benjamin

    2011-08-15

    In this paper we present a new approach to evaluate the environmental efficiency of decision making units. We propose a model that describes a two-stage process consisting of a production and an end-of-pipe abatement stage with the environmental efficiency being determined by the efficiency of both stages. Taking the dependencies between the two stages into account, we show how nonparametric methods can be used to measure environmental efficiency and to decompose it into production and abatement efficiency. For an empirical illustration we apply our model to an analysis of U.S. power plants.

  9. Emerging immunotherapeutics in adenocarcinomas: A focus on CAR-T cells.

    Science.gov (United States)

    Yazdanifar, Mahboubeh; Zhou, Ru; Mukherjee, Pinku

    2016-01-01

    More than 80% of all cancers arise from epithelial cells referred to as carcinomas. Adenocarcinomas are the most common type of carcinomas arising from the specialized epithelial cells that line the ducts of our major organs. Despite many advances in cancer therapies, metastatic and treatment-refractory cancers remain the 2 nd leading cause of death. Immunotherapy has offered potential opportunities with specific targeting of tumor cells and inducing remission in many cancer patients. Numerous therapies using antibodies as antagonists or checkpoint inhibitors/immune modulators, peptide or cell vaccines, cytokines, and adoptive T cell therapies have been developed. The most innovative immunotherapy approach so far has been the use of engineered T cell, also referred to as chimeric antigen receptor T cells (CAR-T cells). CAR-T cells are genetically modified naïve T cells that express a chimeric molecule which comprises of the antigen-recognition domains (scFv) of an anti-tumor antibody and one, two, or three intracellular signaling domains of the T cell receptor (TCR). When these engineered T cells recognize and bind to the tumor antigen target via the scFv fragment, a signal is sent to the intracellular TCR domains of the CAR, leading to activation of the T cells to become cytolytic against the tumor cells. CAR-T cell therapy has shown tremendous success for certain hematopoietic malignancies, but this success has not been extrapolated to adenocarcinomas. This is due to multiple factors associated with adenocarcinoma that are different from hematopoietic tumors. Although many advances have been made in targeting multiple cancers by CAR-T cells, clinical trials have shown adverse effects and toxicity related to this treatment. New strategies are yet to be devised to manage side effects associated with CAR-T cell therapies. In this review, we report some of the promising immunotherapeutic strategies being developed for treatment of most common adenocarcinomas with

  10. Nonparametric estimation for censored mixture data with application to the Cooperative Huntington’s Observational Research Trial

    Science.gov (United States)

    Wang, Yuanjia; Garcia, Tanya P.; Ma, Yanyuan

    2012-01-01

    This work presents methods for estimating genotype-specific distributions from genetic epidemiology studies where the event times are subject to right censoring, the genotypes are not directly observed, and the data arise from a mixture of scientifically meaningful subpopulations. Examples of such studies include kin-cohort studies and quantitative trait locus (QTL) studies. Current methods for analyzing censored mixture data include two types of nonparametric maximum likelihood estimators (NPMLEs) which do not make parametric assumptions on the genotype-specific density functions. Although both NPMLEs are commonly used, we show that one is inefficient and the other inconsistent. To overcome these deficiencies, we propose three classes of consistent nonparametric estimators which do not assume parametric density models and are easy to implement. They are based on the inverse probability weighting (IPW), augmented IPW (AIPW), and nonparametric imputation (IMP). The AIPW achieves the efficiency bound without additional modeling assumptions. Extensive simulation experiments demonstrate satisfactory performance of these estimators even when the data are heavily censored. We apply these estimators to the Cooperative Huntington’s Observational Research Trial (COHORT), and provide age-specific estimates of the effect of mutation in the Huntington gene on mortality using a sample of family members. The close approximation of the estimated non-carrier survival rates to that of the U.S. population indicates small ascertainment bias in the COHORT family sample. Our analyses underscore an elevated risk of death in Huntington gene mutation carriers compared to non-carriers for a wide age range, and suggest that the mutation equally affects survival rates in both genders. The estimated survival rates are useful in genetic counseling for providing guidelines on interpreting the risk of death associated with a positive genetic testing, and in facilitating future subjects at risk

  11. A parametric interpretation of Bayesian Nonparametric Inference from Gene Genealogies: Linking ecological, population genetics and evolutionary processes.

    Science.gov (United States)

    Ponciano, José Miguel

    2017-11-22

    Using a nonparametric Bayesian approach Palacios and Minin (2013) dramatically improved the accuracy, precision of Bayesian inference of population size trajectories from gene genealogies. These authors proposed an extension of a Gaussian Process (GP) nonparametric inferential method for the intensity function of non-homogeneous Poisson processes. They found that not only the statistical properties of the estimators were improved with their method, but also, that key aspects of the demographic histories were recovered. The authors' work represents the first Bayesian nonparametric solution to this inferential problem because they specify a convenient prior belief without a particular functional form on the population trajectory. Their approach works so well and provides such a profound understanding of the biological process, that the question arises as to how truly "biology-free" their approach really is. Using well-known concepts of stochastic population dynamics, here I demonstrate that in fact, Palacios and Minin's GP model can be cast as a parametric population growth model with density dependence and environmental stochasticity. Making this link between population genetics and stochastic population dynamics modeling provides novel insights into eliciting biologically meaningful priors for the trajectory of the effective population size. The results presented here also bring novel understanding of GP as models for the evolution of a trait. Thus, the ecological principles foundation of Palacios and Minin (2013)'s prior adds to the conceptual and scientific value of these authors' inferential approach. I conclude this note by listing a series of insights brought about by this connection with Ecology. Copyright © 2017 The Author. Published by Elsevier Inc. All rights reserved.

  12. Nonparametric Stochastic Model for Uncertainty Quantifi cation of Short-term Wind Speed Forecasts

    Science.gov (United States)

    AL-Shehhi, A. M.; Chaouch, M.; Ouarda, T.

    2014-12-01

    Wind energy is increasing in importance as a renewable energy source due to its potential role in reducing carbon emissions. It is a safe, clean, and inexhaustible source of energy. The amount of wind energy generated by wind turbines is closely related to the wind speed. Wind speed forecasting plays a vital role in the wind energy sector in terms of wind turbine optimal operation, wind energy dispatch and scheduling, efficient energy harvesting etc. It is also considered during planning, design, and assessment of any proposed wind project. Therefore, accurate prediction of wind speed carries a particular importance and plays significant roles in the wind industry. Many methods have been proposed in the literature for short-term wind speed forecasting. These methods are usually based on modeling historical fixed time intervals of the wind speed data and using it for future prediction. The methods mainly include statistical models such as ARMA, ARIMA model, physical models for instance numerical weather prediction and artificial Intelligence techniques for example support vector machine and neural networks. In this paper, we are interested in estimating hourly wind speed measures in United Arab Emirates (UAE). More precisely, we predict hourly wind speed using a nonparametric kernel estimation of the regression and volatility functions pertaining to nonlinear autoregressive model with ARCH model, which includes unknown nonlinear regression function and volatility function already discussed in the literature. The unknown nonlinear regression function describe the dependence between the value of the wind speed at time t and its historical data at time t -1, t - 2, … , t - d. This function plays a key role to predict hourly wind speed process. The volatility function, i.e., the conditional variance given the past, measures the risk associated to this prediction. Since the regression and the volatility functions are supposed to be unknown, they are estimated using

  13. Economic decision making and the application of nonparametric prediction models

    Science.gov (United States)

    Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

    2008-01-01

    Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly. Copyright ?? 2008 Society of Petroleum Engineers.

  14. A multi-instrument non-parametric reconstruction of the electron pressure profile in the galaxy cluster CLJ1226.9+3332

    Science.gov (United States)

    Romero, C.; McWilliam, M.; Macías-Pérez, J.-F.; Adam, R.; Ade, P.; André, P.; Aussel, H.; Beelen, A.; Benoît, A.; Bideaud, A.; Billot, N.; Bourrion, O.; Calvo, M.; Catalano, A.; Coiffard, G.; Comis, B.; de Petris, M.; Désert, F.-X.; Doyle, S.; Goupy, J.; Kramer, C.; Lagache, G.; Leclercq, S.; Lestrade, J.-F.; Mauskopf, P.; Mayet, F.; Monfardini, A.; Pascale, E.; Perotto, L.; Pisano, G.; Ponthieu, N.; Revéret, V.; Ritacco, A.; Roussel, H.; Ruppin, F.; Schuster, K.; Sievers, A.; Triqueneaux, S.; Tucker, C.; Zylka, R.

    2018-04-01

    Context. In the past decade, sensitive, resolved Sunyaev-Zel'dovich (SZ) studies of galaxy clusters have become common. Whereas many previous SZ studies have parameterized the pressure profiles of galaxy clusters, non-parametric reconstructions will provide insights into the thermodynamic state of the intracluster medium. Aim. We seek to recover the non-parametric pressure profiles of the high redshift (z = 0.89) galaxy cluster CLJ 1226.9+3332 as inferred from SZ data from the MUSTANG, NIKA, Bolocam, and Planck instruments, which all probe different angular scales. Methods: Our non-parametric algorithm makes use of logarithmic interpolation, which under the assumption of ellipsoidal symmetry is analytically integrable. For MUSTANG, NIKA, and Bolocam we derive a non-parametric pressure profile independently and find good agreement among the instruments. In particular, we find that the non-parametric profiles are consistent with a fitted generalized Navaro-Frenk-White (gNFW) profile. Given the ability of Planck to constrain the total signal, we include a prior on the integrated Compton Y parameter as determined by Planck. Results: For a given instrument, constraints on the pressure profile diminish rapidly beyond the field of view. The overlap in spatial scales probed by these four datasets is therefore critical in checking for consistency between instruments. By using multiple instruments, our analysis of CLJ 1226.9+3332 covers a large radial range, from the central regions to the cluster outskirts: 0.05 R500 generation of SZ instruments such as NIKA2 and MUSTANG2.

  15. Zero- vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis.

    Science.gov (United States)

    Pataky, Todd C; Vanrenterghem, Jos; Robinson, Mark A

    2015-05-01

    Biomechanical processes are often manifested as one-dimensional (1D) trajectories. It has been shown that 1D confidence intervals (CIs) are biased when based on 0D statistical procedures, and the non-parametric 1D bootstrap CI has emerged in the Biomechanics literature as a viable solution. The primary purpose of this paper was to clarify that, for 1D biomechanics datasets, the distinction between 0D and 1D methods is much more important than the distinction between parametric and non-parametric procedures. A secondary purpose was to demonstrate that a parametric equivalent to the 1D bootstrap exists in the form of a random field theory (RFT) correction for multiple comparisons. To emphasize these points we analyzed six datasets consisting of force and kinematic trajectories in one-sample, paired, two-sample and regression designs. Results showed, first, that the 1D bootstrap and other 1D non-parametric CIs were qualitatively identical to RFT CIs, and all were very different from 0D CIs. Second, 1D parametric and 1D non-parametric hypothesis testing results were qualitatively identical for all six datasets. Last, we highlight the limitations of 1D CIs by demonstrating that they are complex, design-dependent, and thus non-generalizable. These results suggest that (i) analyses of 1D data based on 0D models of randomness are generally biased unless one explicitly identifies 0D variables before the experiment, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one׳s hypothesis explicitly or implicitly pertains to whole 1D trajectories. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Kernel bandwidth estimation for non-parametric density estimation: a comparative study

    CSIR Research Space (South Africa)

    Van der Walt, CM

    2013-12-01

    Full Text Available We investigate the performance of conventional bandwidth estimators for non-parametric kernel density estimation on a number of representative pattern-recognition tasks, to gain a better understanding of the behaviour of these estimators in high...

  17. One-dimensional collision carts computer model and its design ideas for productive experiential learning

    Science.gov (United States)

    Wee, Loo Kang

    2012-05-01

    We develop an Easy Java Simulation (EJS) model for students to experience the physics of idealized one-dimensional collision carts. The physics model is described and simulated by both continuous dynamics and discrete transition during collision. In designing the simulations, we discuss briefly three pedagogical considerations namely (1) a consistent simulation world view with a pen and paper representation, (2) a data table, scientific graphs and symbolic mathematical representations for ease of data collection and multiple representational visualizations and (3) a game for simple concept testing that can further support learning. We also suggest using a physical world setup augmented by simulation by highlighting three advantages of real collision carts equipment such as a tacit 3D experience, random errors in measurement and the conceptual significance of conservation of momentum applied to just before and after collision. General feedback from the students has been relatively positive, and we hope teachers will find the simulation useful in their own classes.

  18. Clinical pharmacology of CAR-T cells: Linking cellular pharmacodynamics to pharmacokinetics and antitumor effects.

    Science.gov (United States)

    Norelli, M; Casucci, M; Bonini, C; Bondanza, A

    2016-01-01

    Adoptive cell transfer of T cells genetically modified with tumor-reactive chimeric antigen receptors (CARs) is a rapidly emerging field in oncology, which in preliminary clinical trials has already shown striking antitumor efficacy. Despite these premises, there are still a number of open issues related to CAR-T cells, spanning from their exact mechanism of action (pharmacodynamics), to the factors associated with their in vivo persistence (pharmacokinetics), and, finally, to the relative contribution of each of the two in determining the antitumor effects and accompanying toxicities. In light of the unprecedented curative potential of CAR-T cells and of their predicted wide availability in the next few years, in this review we will summarize the current knowledge on the clinical pharmacology aspects of what is anticipated to be a brand new class of biopharmaceuticals to join the therapeutic armamentarium of cancer doctors. Copyright © 2015. Published by Elsevier B.V.

  19. Enjeux juridiques du contrôle des émissions personnelles de gaz à effet de serre par un dispositif de carte carbone

    Directory of Open Access Journals (Sweden)

    Sandrine Rousseaux

    2011-02-01

    Full Text Available La carte carbone individuelle est un dispositif innovant, qui permet d’impliquer les particuliers dans la lutte face au changement climatique. Ce dispositif consiste à limiter leurs émissions de gaz à effet de serre, ce qui suppose de suivre leur consommation de certains produits et services. Ce mode de contrôle des émissions personnelles soulève parfois des controverses, la carte carbone pouvant être perçue comme un instrument de rationnement potentiellement liberticide. Ces deux aspects sont discutés sur la base d’une analyse comparative de la conception et des modalités de mise en œuvre des différents dispositifs de carte carbone, établis ou envisagés en Europe et aux États-Unis. Il en ressort que les controverses ne sont pas réellement fondées.The individual carbon card is an innovative mechanism that helps involve individuals in the fight against climate change. It consists in limiting their greenhouse gas emissions, which requires monitoring a part of their goods and services consumption. This method of personal emissions control is much debated since the carbon card may be perceived as a rationing tool and raises civil liberty issues. Both of these controversial aspects are discussed on the basis of a comparative analysis of how the various carbon card programs, established or contemplated in Europe and in the USA, are designed and implemented. The findings are these controversies are somewhat groundless.

  20. Nonparametric Identification and Estimation of Finite Mixture Models of Dynamic Discrete Choices

    OpenAIRE

    Hiroyuki Kasahara; Katsumi Shimotsu

    2006-01-01

    In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for unobserved heterogeneity. This paper studies nonparametric identifiability of type probabilities and type-specific component distributions in finite mixture models of dynamic discrete choices. We derive sufficient conditions for nonparametric identification for various finite mixture models of dynamic discrete choices used in appli...

  1. Examples of the Application of Nonparametric Information Geometry to Statistical Physics

    Directory of Open Access Journals (Sweden)

    Giovanni Pistone

    2013-09-01

    Full Text Available We review a nonparametric version of Amari’s information geometry in which the set of positive probability densities on a given sample space is endowed with an atlas of charts to form a differentiable manifold modeled on Orlicz Banach spaces. This nonparametric setting is used to discuss the setting of typical problems in machine learning and statistical physics, such as black-box optimization, Kullback-Leibler divergence, Boltzmann-Gibbs entropy and the Boltzmann equation.

  2. Experiment design for nonparametric models based on minimizing Bayes Risk: application to voriconazole¹.

    Science.gov (United States)

    Bayard, David S; Neely, Michael

    2017-04-01

    An experimental design approach is presented for individualized therapy in the special case where the prior information is specified by a nonparametric (NP) population model. Here, a NP model refers to a discrete probability model characterized by a finite set of support points and their associated weights. An important question arises as to how to best design experiments for this type of model. Many experimental design methods are based on Fisher information or other approaches originally developed for parametric models. While such approaches have been used with some success across various applications, it is interesting to note that they largely fail to address the fundamentally discrete nature of the NP model. Specifically, the problem of identifying an individual from a NP prior is more naturally treated as a problem of classification, i.e., to find a support point that best matches the patient's behavior. This paper studies the discrete nature of the NP experiment design problem from a classification point of view. Several new insights are provided including the use of Bayes Risk as an information measure, and new alternative methods for experiment design. One particular method, denoted as MMopt (multiple-model optimal), will be examined in detail and shown to require minimal computation while having distinct advantages compared to existing approaches. Several simulated examples, including a case study involving oral voriconazole in children, are given to demonstrate the usefulness of MMopt in pharmacokinetics applications.

  3. Nonparametric signal processing validation in T-wave alternans detection and estimation.

    Science.gov (United States)

    Goya-Esteban, R; Barquero-Pérez, O; Blanco-Velasco, M; Caamaño-Fernández, A J; García-Alberola, A; Rojo-Álvarez, J L

    2014-04-01

    Although a number of methods have been proposed for T-Wave Alternans (TWA) detection and estimation, their performance strongly depends on their signal processing stages and on their free parameters tuning. The dependence of the system quality with respect to the main signal processing stages in TWA algorithms has not yet been studied. This study seeks to optimize the final performance of the system by successive comparisons of pairs of TWA analysis systems, with one single processing difference between them. For this purpose, a set of decision statistics are proposed to evaluate the performance, and a nonparametric hypothesis test (from Bootstrap resampling) is used to make systematic decisions. Both the temporal method (TM) and the spectral method (SM) are analyzed in this study. The experiments were carried out in two datasets: first, in semisynthetic signals with artificial alternant waves and added noise; second, in two public Holter databases with different documented risk of sudden cardiac death. For semisynthetic signals (SNR = 15 dB), after the optimization procedure, a reduction of 34.0% (TM) and 5.2% (SM) of the power of TWA amplitude estimation errors was achieved, and the power of error probability was reduced by 74.7% (SM). For Holter databases, appropriate tuning of several processing blocks, led to a larger intergroup separation between the two populations for TWA amplitude estimation. Our proposal can be used as a systematic procedure for signal processing block optimization in TWA algorithmic implementations.

  4. Analysis of accelerated failure time data with dependent censoring using auxiliary variables via nonparametric multiple imputation.

    Science.gov (United States)

    Hsu, Chiu-Hsieh; Taylor, Jeremy M G; Hu, Chengcheng

    2015-08-30

    We consider the situation of estimating the marginal survival distribution from censored data subject to dependent censoring using auxiliary variables. We had previously developed a nonparametric multiple imputation approach. The method used two working proportional hazards (PH) models, one for the event times and the other for the censoring times, to define a nearest neighbor imputing risk set. This risk set was then used to impute failure times for censored observations. Here, we adapt the method to the situation where the event and censoring times follow accelerated failure time models and propose to use the Buckley-James estimator as the two working models. Besides studying the performances of the proposed method, we also compare the proposed method with two popular methods for handling dependent censoring through the use of auxiliary variables, inverse probability of censoring weighted and parametric multiple imputation methods, to shed light on the use of them. In a simulation study with time-independent auxiliary variables, we show that all approaches can reduce bias due to dependent censoring. The proposed method is robust to misspecification of either one of the two working models and their link function. This indicates that a working proportional hazards model is preferred because it is more cumbersome to fit an accelerated failure time model. In contrast, the inverse probability of censoring weighted method is not robust to misspecification of the link function of the censoring time model. The parametric imputation methods rely on the specification of the event time model. The approaches are applied to a prostate cancer dataset. Copyright © 2015 John Wiley & Sons, Ltd.

  5. Cocaine- and amphetamine-regulated transcript (CART) peptide specific binding in pheochromocytoma cells PC12

    Czech Academy of Sciences Publication Activity Database

    Maletínská, Lenka; Maixnerová, Jana; Matyšková, Resha; Haugvicová, Renata; Šloncová, Eva; Elbert, Tomáš; Slaninová, Jiřina; Železná, Blanka

    2007-01-01

    Roč. 559, 2/3 (2007), s. 109-114 ISSN 0014-2999 R&D Projects: GA ČR GA303/05/0614 Institutional research plan: CEZ:AV0Z40550506; CEZ:AV0Z50520514; CEZ:AV0Z50200510 Keywords : radioligand binding * CART * PC12 cells * food intake Subject RIV: CE - Biochemistry Impact factor: 2.376, year: 2007

  6. Peptid CART (cocaine- and amphetamine- regulated transcript) v signalizaci buněk PC12

    Czech Academy of Sciences Publication Activity Database

    Nagelová, Veronika; Železná, Blanka; Maletínská, Lenka

    2014-01-01

    Roč. 108, č. 5 (2014), s. 543 ISSN 0009-2770. [Mezioborové setkání mladých biologů, biochemiků a chemiků /14./. 13.05.2014-16.05.2014, Milovy] R&D Projects: GA ČR GAP303/10/1368 Institutional support: RVO:61388963 Keywords : peptide CART * PC12 * c-Jun * SAPK/JNK Subject RIV: CE - Biochemistry

  7. New developments of the CARTE thermochemical code: A two-phase equation of state for nanocarbons

    Science.gov (United States)

    Dubois, Vincent; Pineau, Nicolas

    2016-01-01

    We developed a new equation of state (EOS) for nanocarbons in the thermodynamic range of high explosives detonation products (up to 50 GPa and 4000 K). This EOS was fitted to an extensive database of thermodynamic properties computed by molecular dynamics simulations of nanodiamonds and nano-onions with the LCBOPII potential. We reproduced the detonation properties of a variety of high explosives with the CARTE thermochemical code, including carbon-poor and carbon-rich explosives, with excellent accuracy.

  8. Le CO.C.A.O: le commentaire de carte assisté par ordinateur

    Directory of Open Access Journals (Sweden)

    Joël CHARRE

    1991-12-01

    Full Text Available Le contenu d’une carte topographique peut être enregistré informatiquement sous forme d’un Système d’Information Géographique (SIG raster. En changeant de support, l’information change de nature: de fixe, elle devient manipulable, adaptable, vivante. L’analyse spatiale peut alors reposer sur des mesures de superficies, des fréquences de co-occurrences, des proximités...

  9. Development of prognostic indicators using Classification And Regression Trees (CART) for survival

    OpenAIRE

    Nunn, Martha E.; Fan, Juanjuan; Su, Xiaogang; McGuire, Michael K.

    2012-01-01

    The development of an accurate prognosis is an integral component of treatment planning in the practice of periodontics. Prior work has evaluated the validity of using various clinical measured parameters for assigning periodontal prognosis as well as for predicting tooth survival and change in clinical conditions over time. We critically review the application of multivariate Classification And Regression Trees (CART) for survival in developing evidence-based periodontal prognostic indicator...

  10. CAR-T cells targeting CLL-1 as an approach to treat acute myeloid leukemia.

    Science.gov (United States)

    Wang, Jinghua; Chen, Siyu; Xiao, Wei; Li, Wende; Wang, Liang; Yang, Shuo; Wang, Weida; Xu, Liping; Liao, Shuangye; Liu, Wenjian; Wang, Yang; Liu, Nawei; Zhang, Jianeng; Xia, Xiaojun; Kang, Tiebang; Chen, Gong; Cai, Xiuyu; Yang, Han; Zhang, Xing; Lu, Yue; Zhou, Penghui

    2018-01-10

    Acute myeloid leukemia (AML) is one of the most common types of adult acute leukemia. Standard chemotherapies can induce complete remission in selected patients; however, a majority of patients eventually relapse and succumb to the disease. Thus, the development of novel therapeutics for AML is urgently needed. Human C-type lectin-like molecule-1 (CLL-1) is a type II transmembrane glycoprotein, and its expression is restricted to myeloid cells and the majority of AML blasts. Moreover, CLL-1 is expressed in leukemia stem cells (LSCs), but absent in hematopoietic stem cells (HSCs), which may provide a potential therapeutic target for AML treatment. We tested the expression of CLL-1 antigen on peripheral blood cells and bone marrow cells in healthy donor and AML patients. Then, we developed a chimeric antigen receptor (CAR) containing a CLL1-specific single-chain variable fragment, in combination with CD28, 4-1BB costimulatory domains, and CD3-ζ signaling domain. We further investigate the function of CLL-1 CAR-T cells. The CLL-1 CAR-T cells specifically lysed CLL-1 + cell lines as well as primary AML patient samples in vitro. Strong anti-leukemic activity was observed in vivo by using a xenograft model of disseminated AML. Importantly, CLL-1 + myeloid progenitor cells and mature myeloid cells were specifically eliminated by CLL-1 CAR-T cells, while normal HSCs were not targeted due to the lack of CLL-1 expression. CLL-1 CAR-T represents a promising immunotherapy for the treatment of AML.

  11. Penyusunan Dan Penyelenggaran A La Carte Menu Pada Hotel Sinabung Dan Resort

    OpenAIRE

    Nasution, Rahmawaty

    2011-01-01

    Dalam operasional hotel, hotel memiliki beberapa departemen yang mempunyai peranan yang sangat penting dalam penjualan jasa dan pelayanan, dan salah satunya adalah departemen Food & Beverage. Food & Beverage mempunyai peran yang sangat besar dalam sebuah hotel, karena pendapatan sebuah hotel yang terbesar ada pada Food & Beverage terutama pada restoran. Adapun salah satu nama jenis restoran yang ada di Hotel Sinabung. Hotel Sinabung menyediakan jenis menu antara lain A La Carte Menu. M...

  12. CART modulates beta-amyloid metabolism-associated enzymes and attenuates memory deficits in APP/PS1 mice.

    Science.gov (United States)

    Yin, Kailin; Jin, Jiali; Zhu, Xiaolei; Yu, Linjie; Wang, Sulei; Qian, Lai; Han, Lijuan; Xu, Yun

    2017-10-01

    Cocaine- and amphetamine-regulated transcript (CART) peptide has been demonstrated to exert neuroprotective effects in stroke and some neurodegeneration diseases. In current study, we investigated the protective effects and underlying mechanisms of CART in APP/PS1 mice. The protein levels of CART, soluble Aβ 1-40 and Aβ 1-42 were measured in the hippocampus of APP/PS1 mice by enzyme-linked immunosorbent assay. We determined the mRNA and protein levels of Aβ metabolism-associated enzymes including neprilysin (NEP), insulin-degrading enzyme (IDE), receptor for advanced glycation end products (RAGE), and low-density lipoprotein receptor-related protein 1 (LRP-1) in the hippocampus of APP/PS1 mice using real-time PCR and western blotting. Spatial memory was measured in APP/PS1 mice using the Morris water maze. The phosphorylation of AKT, ERK, p38, and JNK was determined using western blotting. The levels of soluble Aβ 1-40 and Aβ 1-42 were significantly decreased in the hippocampus of APP/PS1 mice after CART treatment. CART modulated the levels of NEP, IDE, RAGE, and LRP-1. In addition, CART inhibited the MAPK pathways and activated the AKT pathway, whereas inhibition of the AKT pathway decreased the levels of IDE and LRP-1. Furthermore, CART attenuated spatial memory deficits in the APP/PS1 mice. CART decreases the levels of soluble Aβ in the hippocampus of APP/PS1 mice by modulating the expression of Aβ metabolism-associated enzymes, which may be associated with the MAPK and AKT pathways.

  13. Inventaire des cartes climatiques conservées à la cartothèque de l' IGN

    Directory of Open Access Journals (Sweden)

    Bernadette Joseph

    2010-12-01

    Full Text Available Cet inventaire présente la liste des cartes climatiques étrangères, conservées à la cartothèque de l' IGN. Ce fonds très riche en cartes thématiques n'est actuellement pas répertorié dans les catalogues SUDOC ou OPALE. Il n'est accessible qu'en interne sur fichiers papier.

  14. Synergistic effect of CART (cocaine- and amphetamine-regulated transcript peptide and cholecystokinin on food intake regulation in lean mice

    Directory of Open Access Journals (Sweden)

    Kiss Alexander

    2008-10-01

    Full Text Available Abstract Background CART (cocaine- and amphetamine-regulated transcript peptide and cholecystokinin (CCK are neuromodulators involved in feeding behavior. This study is based on previously found synergistic effect of leptin and CCK on food intake and our hypothesis on a co-operation of the CART peptide and CCK in food intake regulation and Fos activation in their common targets, the nucleus tractus solitarii of the brainstem (NTS, the paraventricular nucleus (PVN, and the dorsomedial nucleus (DMH of the hypothalamus. Results In fasted C57BL/6 mice, the anorexigenic effect of CART(61-102 in the doses of 0.1 or 0.5 μg/mouse was significantly enhanced by low doses of CCK-8 of 0.4 or 4 μg/kg, while 1 mg/kg dose of CCK-A receptor antagonist devazepide blocked the effect of CART(61-102 on food intake. After simultaneous administration of 0.1 μg/mouse CART(61-102 and of 4 μg/kg of CCK-8, the number of Fos-positive neurons in NTS, PVN, and DMH was significantly higher than after administration of each particular peptide. Besides, CART(61-102 and CCK-8 showed an additive effect on inhibition of the locomotor activity of mice in an open field test. Conclusion The synergistic and long-lasting effect of the CART peptide and CCK on food intake and their additive effect on Fos immunoreactivity in their common targets suggest a co-operative action of CART peptide and CCK which could be related to synergistic effect of leptin on CCK satiety.

  15. HIV-Associated Hodgkin's Lymphoma: Prognosis and Therapy in the Era of cART

    Directory of Open Access Journals (Sweden)

    Caron A. Jacobson

    2012-01-01

    Full Text Available Patients with human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS are at increased risk for developing Hodgkin's lymphoma (HL, a risk that has not decreased despite the success of combination antiretroviral therapy (cART in the modern era. HIV-associated HL (HIV-HL differs from HL in non-HIV-infected patients in that it is nearly always associated with Epstein-Barr virus (EBV and more often presents with high-risk features of advanced disease, systemic “B” symptoms, and extranodal involvement. Before the introduction of cART, patients with HIV-HL had lower response rates and worse outcomes than non-HIV-infected HL patients treated with conventional chemotherapy. The introduction of cART, however, has allowed for the delivery of full-dose and dose-intensive chemotherapy regimens with improved outcomes that approach those seen in non-HIV infected patients. Despite these significant advances, HIV-HL patients remain at increased risk for treatment-related toxicities and drug-drug interactions which require careful attention and supportive care to insure the safe administration of therapy. This paper will address the modern diagnosis, risk stratification, and therapy of HIV-associated HL.

  16. Analysis of performance measures to handle medical E-commerce shopping cart abandonment in cloud

    Directory of Open Access Journals (Sweden)

    Vedhanayagam Priya

    Full Text Available The E-commerce zone is crowded with many Internet users. Medical E-commerce has had significant growth in part because of a great deal of growth in the Indian E-commerce field. Medical E-commerce sites use cloud computing to guarantee a high quality of service anywhere and anytime in the world. For online access, the customer's expectations are very high. Medical E-commerce retailers are directed towards cloud service providers based on their quality of service. During online shopping, impatient customers may abandon a specific medical E-commerce shopping cart due to slow response. This is quite difficult to endure for a medical E-commerce firm. The research described herein observed the effect of shopping cart abandonment on medical E-commerce websites deployed in cloud computing. The impact of the idle virtual machine on customer impatience during medical E-commerce shopping was also studied. The ultimate aim of this study was to propose a stochastic queueing model and to yield results through probability generating functions. The results of the model may be highly useful for a medical E-commerce firm facing customer impatience, so as to design its service system to offer satisfactory quality of service. Keywords: Cloud computing, Queueing, Virtual machine, E-commerce, Cart abandonment, Quality of Service

  17. Automated Cart with VIS/NIR Hyperspectral Reflectance and Fluorescence Imaging Capabilities

    Directory of Open Access Journals (Sweden)

    Alan M. Lefcourt

    2016-12-01

    Full Text Available A system to take high-resolution Visible/Near Infra-Red (VIS/NIR hyperspectral reflectance and fluorescence images in outdoor fields using ambient lighting or a pulsed laser (355 nm, respectively, for illumination purposes was designed, built, and tested. Components of the system include a semi-autonomous cart, a gated-intensified camera, a spectral adapter, a frequency-triple Nd:YAG (Neodymium-doped Yttrium Aluminium Garnet laser, and optics to convert the Gaussian laser beam into a line-illumination source. The front wheels of the cart are independently powered by stepper motors that support stepping or continuous motion. When stepping, a spreadsheet is used to program parameters of image sets to be acquired at each step. For example, the spreadsheet can be used to set delays before the start of image acquisitions, acquisition times, and laser attenuation. One possible use of this functionality would be to establish acquisition parameters to facilitate the measurement of fluorescence decay-curve characteristics. The laser and camera are mounted on an aluminum plate that allows the optics to be calibrated in a laboratory setting and then moved to the cart. The system was validated by acquiring images of fluorescence responses of spinach leaves and dairy manure.

  18. Design of the CART data system for the US Department of Energy's ARM Program

    International Nuclear Information System (INIS)

    Melton, R.B.; Campbell, A.P.; Edwards, D.M.; Kanciruk, P.; Tichler, J.L.

    1991-01-01

    The Department of Energy (DOE) has initiated a major atmospheric research effort to reduce the uncertainties found in general circulation and other models due to the effects of clouds and radiation. The objective of the Atmospheric Radiation Measurement Program (ARM) is to provide an experimental testbed for the study of important atmospheric effects, particularly cloud and radiative processes, and testing parameterizations of the processes for use in atmospheric models. This experimental testbed, known as the Clouds and Radiation Testbed (CART), will include a complex data system, the CART Data Environment (CDE). The major functions of the CDE will be to: acquire environments from instruments and external data sources; perform quality assessments of the data streams; create data streams of known quality to be used as model input compared to model output; execute the models and capture their predictions; and make data streams associated with model tests available to ARM investigators in near real-time. The CDE will also be expected to capture ancillary information (''meta-data'') associated with the data streams, provide data management facilities for design of ARM experiments, and provide for archival data storage. The first section of this paper presents background information on CART. Next the process for the functional design of the system is described, the functional requirements summarized, and the conceptual architecture of the CDE is presented. Finally, the status of the CDE design activities is summarized, and major technical challenges are discussed

  19. CAR-T cell therapy in ovarian cancer: from the bench to the bedside.

    Science.gov (United States)

    Zhu, Xinxin; Cai, Han; Zhao, Ling; Ning, Li; Lang, Jinghe

    2017-09-08

    Ovarian cancer (OC) is the most lethal gynecological malignancy and is responsible for most gynecological cancer deaths. Apart from conventional surgery, chemotherapy, and radiotherapy, chimeric antigen receptor-modified T (CAR-T) cells as a representative of adoptive cellular immunotherapy have received considerable attention in the research field of cancer treatment. CARs combine antigen specificity and T-cell-activating properties in a single fusion molecule. Several preclinical experiments and clinical trials have confirmed that adoptive cell immunotherapy using typical CAR-engineered T cells for OC is a promising treatment approach with striking clinical efficacy; moreover, the emerging CAR-Ts targeting various antigens also exert great potential. However, such therapies have side effects and toxicities, such as cytokine-associated and "on-target, off-tumor" toxicities. In this review, we systematically detail and highlight the present knowledge of CAR-Ts including the constructions, vectors, clinical applications, development challenges, and solutions of CAR-T-cell therapy for OC. We hope to provide new insight into OC treatment for the future.

  20. New Strategies for the Treatment of Solid Tumors with CAR-T Cells.

    Science.gov (United States)

    Zhang, Hao; Ye, Zhen-Long; Yuan, Zhen-Gang; Luo, Zheng-Qiang; Jin, Hua-Jun; Qian, Qi-Jun

    2016-01-01

    Recent years, we have witnessed significant progresses in both basic and clinical studies regarding novel therapeutic strategies with genetically engineered T cells. Modification with chimeric antigen receptors (CARs) endows T cells with tumor specific cytotoxicity and thus induce anti-tumor immunity against malignancies. However, targeting solid tumors is more challenging than targeting B-cell malignancies with CAR-T cells because of the histopathological structure features, specific antigens shortage and strong immunosuppressive environment of solid tumors. Meanwhile, the on-target/off-tumor toxicity caused by relative expression of target on normal tissues is another issue that should be reckoned. Optimization of the design of CAR vectors, exploration of new targets, addition of safe switches and combination with other treatments bring new vitality to the CAR-T cell based immunotherapy against solid tumors. In this review, we focus on the major obstacles limiting the application of CAR-T cell therapy toward solid tumors and summarize the measures to refine this new cancer therapeutic modality.