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Sample records for randomly selected subset

  1. Distributional and efficiency results for subset selection

    NARCIS (Netherlands)

    Laan, van der P.

    1996-01-01

    Assume k (??k \\geq 2) populations are given. The associated independent random variables have continuous distribution functions with an unknown location parameter. The statistical selec??tion goal is to select a non??empty subset which contains the best population,?? that is the pop??ulation with

  2. Optimization of the Dutch Matrix Test by Random Selection of Sentences From a Preselected Subset

    Directory of Open Access Journals (Sweden)

    Rolph Houben

    2015-04-01

    Full Text Available Matrix tests are available for speech recognition testing in many languages. For an accurate measurement, a steep psychometric function of the speech materials is required. For existing tests, it would be beneficial if it were possible to further optimize the available materials by increasing the function’s steepness. The objective is to show if the steepness of the psychometric function of an existing matrix test can be increased by selecting a homogeneous subset of recordings with the steepest sentence-based psychometric functions. We took data from a previous multicenter evaluation of the Dutch matrix test (45 normal-hearing listeners. Based on half of the data set, first the sentences (140 out of 311 with a similar speech reception threshold and with the steepest psychometric function (≥9.7%/dB were selected. Subsequently, the steepness of the psychometric function for this selection was calculated from the remaining (unused second half of the data set. The calculation showed that the slope increased from 10.2%/dB to 13.7%/dB. The resulting subset did not allow the construction of enough balanced test lists. Therefore, the measurement procedure was changed to randomly select the sentences during testing. Random selection may interfere with a representative occurrence of phonemes. However, in our material, the median phonemic occurrence remained close to that of the original test. This finding indicates that phonemic occurrence is not a critical factor. The work highlights the possibility that existing speech tests might be improved by selecting sentences with a steep psychometric function.

  3. Subset Selection by Local Convex Approximation

    DEFF Research Database (Denmark)

    Øjelund, Henrik; Sadegh, Payman; Madsen, Henrik

    1999-01-01

    This paper concerns selection of the optimal subset of variables in a lenear regression setting. The posed problem is combinatiorial and the globally best subset can only be found in exponential time. We define a cost function for the subset selection problem by adding the penalty term to the usual...... of the subset selection problem so as to guarantee positive definiteness of the Hessian term, hence avoiding numerical instability. The backward Elemination type algorithm attempts to improve the results upon termination of the modified Newton-Raphson search by sing the current solution as an initial guess...

  4. On subset selection from Logistic populations

    NARCIS (Netherlands)

    Laan, van der P.

    1990-01-01

    Some distributional results are derived for subset selection from Logistic populations, differing only in their location parameter. The probability of correct selection is determined. Exact and numerical results concerning the expected subset size are presented.

  5. Subset selection in regression

    CERN Document Server

    Miller, Alan

    2002-01-01

    Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...

  6. Unsupervised Feature Subset Selection

    DEFF Research Database (Denmark)

    Søndberg-Madsen, Nicolaj; Thomsen, C.; Pena, Jose

    2003-01-01

    This paper studies filter and hybrid filter-wrapper feature subset selection for unsupervised learning (data clustering). We constrain the search for the best feature subset by scoring the dependence of every feature on the rest of the features, conjecturing that these scores discriminate some ir...... irrelevant features. We report experimental results on artificial and real data for unsupervised learning of naive Bayes models. Both the filter and hybrid approaches perform satisfactorily....

  7. Band Subset Selection for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Chunyan Yu

    2018-01-01

    Full Text Available This paper develops a new approach to band subset selection (BSS for hyperspectral image classification (HSIC which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS, rather than one band at a time sequentially, referred to as sequential multiple band selection (SQMBS, as most traditional band selection methods do. In doing so, a criterion is particularly developed for BSS that can be used for HSIC. It is a linearly constrained minimum variance (LCMV derived from adaptive beamforming in array signal processing which can be used to model misclassification errors as the minimum variance. To avoid an exhaustive search for all possible band subsets, two numerical algorithms, referred to as sequential (SQ and successive (SC algorithms are also developed for LCMV-based SMMBS, called SQ LCMV-BSS and SC LCMV-BSS. Experimental results demonstrate that LCMV-based BSS has advantages over SQMBS.

  8. Variable and subset selection in PLS regression

    DEFF Research Database (Denmark)

    Høskuldsson, Agnar

    2001-01-01

    The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion...... is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than...

  9. Stochastic subset selection for learning with kernel machines.

    Science.gov (United States)

    Rhinelander, Jason; Liu, Xiaoping P

    2012-06-01

    Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm.

  10. The efficiency of subset selection of an almost best treatment

    NARCIS (Netherlands)

    Laan, van der P.

    1991-01-01

    A generalized goal using subset selection is discussed for the location parameter case. This goal is to select a non-empty subset from a set of k (k \\geq 2) treatments that contains at least one \\epsilon-best treatment with confidence level P*. For a set of treatments an \\epsilon-best treatment is

  11. A Feature Subset Selection Method Based On High-Dimensional Mutual Information

    Directory of Open Access Journals (Sweden)

    Chee Keong Kwoh

    2011-04-01

    Full Text Available Feature selection is an important step in building accurate classifiers and provides better understanding of the data sets. In this paper, we propose a feature subset selection method based on high-dimensional mutual information. We also propose to use the entropy of the class attribute as a criterion to determine the appropriate subset of features when building classifiers. We prove that if the mutual information between a feature set X and the class attribute Y equals to the entropy of Y , then X is a Markov Blanket of Y . We show that in some cases, it is infeasible to approximate the high-dimensional mutual information with algebraic combinations of pairwise mutual information in any forms. In addition, the exhaustive searches of all combinations of features are prerequisite for finding the optimal feature subsets for classifying these kinds of data sets. We show that our approach outperforms existing filter feature subset selection methods for most of the 24 selected benchmark data sets.

  12. Chemical library subset selection algorithms: a unified derivation using spatial statistics.

    Science.gov (United States)

    Hamprecht, Fred A; Thiel, Walter; van Gunsteren, Wilfred F

    2002-01-01

    If similar compounds have similar activity, rational subset selection becomes superior to random selection in screening for pharmacological lead discovery programs. Traditional approaches to this experimental design problem fall into two classes: (i) a linear or quadratic response function is assumed (ii) some space filling criterion is optimized. The assumptions underlying the first approach are clear but not always defendable; the second approach yields more intuitive designs but lacks a clear theoretical foundation. We model activity in a bioassay as realization of a stochastic process and use the best linear unbiased estimator to construct spatial sampling designs that optimize the integrated mean square prediction error, the maximum mean square prediction error, or the entropy. We argue that our approach constitutes a unifying framework encompassing most proposed techniques as limiting cases and sheds light on their underlying assumptions. In particular, vector quantization is obtained, in dimensions up to eight, in the limiting case of very smooth response surfaces for the integrated mean square error criterion. Closest packing is obtained for very rough surfaces under the integrated mean square error and entropy criteria. We suggest to use either the integrated mean square prediction error or the entropy as optimization criteria rather than approximations thereof and propose a scheme for direct iterative minimization of the integrated mean square prediction error. Finally, we discuss how the quality of chemical descriptors manifests itself and clarify the assumptions underlying the selection of diverse or representative subsets.

  13. Subset selection for an epsilon-best population : efficiency results

    NARCIS (Netherlands)

    Laan, van der P.

    1991-01-01

    An almost best or an \\epsilon-best population is defined as a population with location parameter on a distance not larger than \\epsilon (\\geq 0) from the best population (with largest value of the location parameter). For the subset selection tables with the relative efficiency of selecting an

  14. Selecting Optimal Subset of Security Controls

    OpenAIRE

    Yevseyeva, I.; Basto-Fernandes, V.; Michael, Emmerich, T. M.; Moorsel, van, A.

    2015-01-01

    Open Access journal Choosing an optimal investment in information security is an issue most companies face these days. Which security controls to buy to protect the IT system of a company in the best way? Selecting a subset of security controls among many available ones can be seen as a resource allocation problem that should take into account conflicting objectives and constraints of the problem. In particular, the security of the system should be improved without hindering productivity, ...

  15. Two-stage atlas subset selection in multi-atlas based image segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Tingting, E-mail: tingtingzhao@mednet.ucla.edu; Ruan, Dan, E-mail: druan@mednet.ucla.edu [The Department of Radiation Oncology, University of California, Los Angeles, California 90095 (United States)

    2015-06-15

    Purpose: Fast growing access to large databases and cloud stored data presents a unique opportunity for multi-atlas based image segmentation and also presents challenges in heterogeneous atlas quality and computation burden. This work aims to develop a novel two-stage method tailored to the special needs in the face of large atlas collection with varied quality, so that high-accuracy segmentation can be achieved with low computational cost. Methods: An atlas subset selection scheme is proposed to substitute a significant portion of the computationally expensive full-fledged registration in the conventional scheme with a low-cost alternative. More specifically, the authors introduce a two-stage atlas subset selection method. In the first stage, an augmented subset is obtained based on a low-cost registration configuration and a preliminary relevance metric; in the second stage, the subset is further narrowed down to a fusion set of desired size, based on full-fledged registration and a refined relevance metric. An inference model is developed to characterize the relationship between the preliminary and refined relevance metrics, and a proper augmented subset size is derived to ensure that the desired atlases survive the preliminary selection with high probability. Results: The performance of the proposed scheme has been assessed with cross validation based on two clinical datasets consisting of manually segmented prostate and brain magnetic resonance images, respectively. The proposed scheme demonstrates comparable end-to-end segmentation performance as the conventional single-stage selection method, but with significant computation reduction. Compared with the alternative computation reduction method, their scheme improves the mean and medium Dice similarity coefficient value from (0.74, 0.78) to (0.83, 0.85) and from (0.82, 0.84) to (0.95, 0.95) for prostate and corpus callosum segmentation, respectively, with statistical significance. Conclusions: The authors

  16. Two-stage atlas subset selection in multi-atlas based image segmentation.

    Science.gov (United States)

    Zhao, Tingting; Ruan, Dan

    2015-06-01

    Fast growing access to large databases and cloud stored data presents a unique opportunity for multi-atlas based image segmentation and also presents challenges in heterogeneous atlas quality and computation burden. This work aims to develop a novel two-stage method tailored to the special needs in the face of large atlas collection with varied quality, so that high-accuracy segmentation can be achieved with low computational cost. An atlas subset selection scheme is proposed to substitute a significant portion of the computationally expensive full-fledged registration in the conventional scheme with a low-cost alternative. More specifically, the authors introduce a two-stage atlas subset selection method. In the first stage, an augmented subset is obtained based on a low-cost registration configuration and a preliminary relevance metric; in the second stage, the subset is further narrowed down to a fusion set of desired size, based on full-fledged registration and a refined relevance metric. An inference model is developed to characterize the relationship between the preliminary and refined relevance metrics, and a proper augmented subset size is derived to ensure that the desired atlases survive the preliminary selection with high probability. The performance of the proposed scheme has been assessed with cross validation based on two clinical datasets consisting of manually segmented prostate and brain magnetic resonance images, respectively. The proposed scheme demonstrates comparable end-to-end segmentation performance as the conventional single-stage selection method, but with significant computation reduction. Compared with the alternative computation reduction method, their scheme improves the mean and medium Dice similarity coefficient value from (0.74, 0.78) to (0.83, 0.85) and from (0.82, 0.84) to (0.95, 0.95) for prostate and corpus callosum segmentation, respectively, with statistical significance. The authors have developed a novel two-stage atlas

  17. Two-stage atlas subset selection in multi-atlas based image segmentation

    International Nuclear Information System (INIS)

    Zhao, Tingting; Ruan, Dan

    2015-01-01

    Purpose: Fast growing access to large databases and cloud stored data presents a unique opportunity for multi-atlas based image segmentation and also presents challenges in heterogeneous atlas quality and computation burden. This work aims to develop a novel two-stage method tailored to the special needs in the face of large atlas collection with varied quality, so that high-accuracy segmentation can be achieved with low computational cost. Methods: An atlas subset selection scheme is proposed to substitute a significant portion of the computationally expensive full-fledged registration in the conventional scheme with a low-cost alternative. More specifically, the authors introduce a two-stage atlas subset selection method. In the first stage, an augmented subset is obtained based on a low-cost registration configuration and a preliminary relevance metric; in the second stage, the subset is further narrowed down to a fusion set of desired size, based on full-fledged registration and a refined relevance metric. An inference model is developed to characterize the relationship between the preliminary and refined relevance metrics, and a proper augmented subset size is derived to ensure that the desired atlases survive the preliminary selection with high probability. Results: The performance of the proposed scheme has been assessed with cross validation based on two clinical datasets consisting of manually segmented prostate and brain magnetic resonance images, respectively. The proposed scheme demonstrates comparable end-to-end segmentation performance as the conventional single-stage selection method, but with significant computation reduction. Compared with the alternative computation reduction method, their scheme improves the mean and medium Dice similarity coefficient value from (0.74, 0.78) to (0.83, 0.85) and from (0.82, 0.84) to (0.95, 0.95) for prostate and corpus callosum segmentation, respectively, with statistical significance. Conclusions: The authors

  18. Massively-parallel best subset selection for ordinary least-squares regression

    DEFF Research Database (Denmark)

    Gieseke, Fabian; Polsterer, Kai Lars; Mahabal, Ashish

    2017-01-01

    Selecting an optimal subset of k out of d features for linear regression models given n training instances is often considered intractable for feature spaces with hundreds or thousands of dimensions. We propose an efficient massively-parallel implementation for selecting such optimal feature...

  19. Adenovirus-specific T-cell Subsets in Human Peripheral Blood and After IFN-γ Immunomagnetic Selection.

    Science.gov (United States)

    Qian, Chongsheng; Wang, Yingying; Cai, Huili; Laroye, Caroline; De Carvalho Bittencourt, Marcelo; Clement, Laurence; Stoltz, Jean-François; Decot, Véronique; Reppel, Loïc; Bensoussan, Danièle

    2016-01-01

    Adoptive antiviral cellular immunotherapy by infusion of virus-specific T cells (VSTs) is becoming an alternative treatment for viral infection after hematopoietic stem cell transplantation. The T memory stem cell (TSCM) subset was recently described as exhibiting self-renewal and multipotency properties which are required for sustained efficacy in vivo. We wondered if such a crucial subset for immunotherapy was present in VSTs. We identified, by flow cytometry, TSCM in adenovirus (ADV)-specific interferon (IFN)-γ+ T cells before and after IFN-γ-based immunomagnetic selection, and analyzed the distribution of the main T-cell subsets in VSTs: naive T cells (TN), TSCM, T central memory cells (TCM), T effector memory cell (TEM), and effector T cells (TEFF). In this study all of the different T-cell subsets were observed in the blood sample from healthy donor ADV-VSTs, both before and after IFN-γ-based immunomagnetic selection. As the IFN-γ-based immunomagnetic selection system sorts mainly the most differentiated T-cell subsets, we observed that TEM was always the major T-cell subset of ADV-specific T cells after immunomagnetic isolation and especially after expansion in vitro. Comparing T-cell subpopulation profiles before and after in vitro expansion, we observed that in vitro cell culture with interleukin-2 resulted in a significant expansion of TN-like, TCM, TEM, and TEFF subsets in CD4IFN-γ T cells and of TCM and TEM subsets only in CD8IFN-γ T cells. We demonstrated the presence of all T-cell subsets in IFN-γ VSTs including the TSCM subpopulation, although this was weakly selected by the IFN-γ-based immunomagnetic selection system.

  20. Core Hunter 3: flexible core subset selection.

    Science.gov (United States)

    De Beukelaer, Herman; Davenport, Guy F; Fack, Veerle

    2018-05-31

    Core collections provide genebank curators and plant breeders a way to reduce size of their collections and populations, while minimizing impact on genetic diversity and allele frequency. Many methods have been proposed to generate core collections, often using distance metrics to quantify the similarity of two accessions, based on genetic marker data or phenotypic traits. Core Hunter is a multi-purpose core subset selection tool that uses local search algorithms to generate subsets relying on one or more metrics, including several distance metrics and allelic richness. In version 3 of Core Hunter (CH3) we have incorporated two new, improved methods for summarizing distances to quantify diversity or representativeness of the core collection. A comparison of CH3 and Core Hunter 2 (CH2) showed that these new metrics can be effectively optimized with less complex algorithms, as compared to those used in CH2. CH3 is more effective at maximizing the improved diversity metric than CH2, still ensures a high average and minimum distance, and is faster for large datasets. Using CH3, a simple stochastic hill-climber is able to find highly diverse core collections, and the more advanced parallel tempering algorithm further increases the quality of the core and further reduces variability across independent samples. We also evaluate the ability of CH3 to simultaneously maximize diversity, and either representativeness or allelic richness, and compare the results with those of the GDOpt and SimEli methods. CH3 can sample equally representative cores as GDOpt, which was specifically designed for this purpose, and is able to construct cores that are simultaneously more diverse, and either are more representative or have higher allelic richness, than those obtained by SimEli. In version 3, Core Hunter has been updated to include two new core subset selection metrics that construct cores for representativeness or diversity, with improved performance. It combines and outperforms the

  1. Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling.

    Science.gov (United States)

    Zhou, Fuqun; Zhang, Aining

    2016-10-25

    Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2-3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests' features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data.

  2. SOCP relaxation bounds for the optimal subset selection problem applied to robust linear regression

    OpenAIRE

    Flores, Salvador

    2015-01-01

    This paper deals with the problem of finding the globally optimal subset of h elements from a larger set of n elements in d space dimensions so as to minimize a quadratic criterion, with an special emphasis on applications to computing the Least Trimmed Squares Estimator (LTSE) for robust regression. The computation of the LTSE is a challenging subset selection problem involving a nonlinear program with continuous and binary variables, linked in a highly nonlinear fashion. The selection of a ...

  3. A Genetic Algorithm for Selection of Fixed-Size Subsets with Application to Design Problems

    Directory of Open Access Journals (Sweden)

    Mark A. Wolters

    2015-11-01

    Full Text Available The R function kofnGA conducts a genetic algorithm search for the best subset of k items from a set of n alternatives, given an objective function that measures the quality of a subset. The function fills a gap in the presently available subset selection software, which typically searches over a range of subset sizes, restricts the types of objective functions considered, or does not include freely available code. The new function is demonstrated on two types of problem where a fixed-size subset search is desirable: design of environmental monitoring networks, and D-optimal design of experiments. Additionally, the performance is evaluated on a class of constructed test problems with a novel design that is interesting in its own right.

  4. Random-subset fitting of digital holograms for fast three-dimensional particle tracking [invited].

    Science.gov (United States)

    Dimiduk, Thomas G; Perry, Rebecca W; Fung, Jerome; Manoharan, Vinothan N

    2014-09-20

    Fitting scattering solutions to time series of digital holograms is a precise way to measure three-dimensional dynamics of microscale objects such as colloidal particles. However, this inverse-problem approach is computationally expensive. We show that the computational time can be reduced by an order of magnitude or more by fitting to a random subset of the pixels in a hologram. We demonstrate our algorithm on experimentally measured holograms of micrometer-scale colloidal particles, and we show that 20-fold increases in speed, relative to fitting full frames, can be attained while introducing errors in the particle positions of 10 nm or less. The method is straightforward to implement and works for any scattering model. It also enables a parallelization strategy wherein random-subset fitting is used to quickly determine initial guesses that are subsequently used to fit full frames in parallel. This approach may prove particularly useful for studying rare events, such as nucleation, that can only be captured with high frame rates over long times.

  5. An Empirical Study of Wrappers for Feature Subset Selection based on a Parallel Genetic Algorithm: The Multi-Wrapper Model

    KAUST Repository

    Soufan, Othman

    2012-09-01

    Feature selection is the first task of any learning approach that is applied in major fields of biomedical, bioinformatics, robotics, natural language processing and social networking. In feature subset selection problem, a search methodology with a proper criterion seeks to find the best subset of features describing data (relevance) and achieving better performance (optimality). Wrapper approaches are feature selection methods which are wrapped around a classification algorithm and use a performance measure to select the best subset of features. We analyze the proper design of the objective function for the wrapper approach and highlight an objective based on several classification algorithms. We compare the wrapper approaches to different feature selection methods based on distance and information based criteria. Significant improvement in performance, computational time, and selection of minimally sized feature subsets is achieved by combining different objectives for the wrapper model. In addition, considering various classification methods in the feature selection process could lead to a global solution of desirable characteristics.

  6. A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest

    Directory of Open Access Journals (Sweden)

    Nantian Huang

    2016-09-01

    Full Text Available The prediction accuracy of short-term load forecast (STLF depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original feature set was used to train an RF as the original model. After the training process, the prediction error of the original model on the test set was recorded and the permutation importance (PI value of each feature was obtained. Then, an improved sequential backward search method was used to select the optimal forecasting feature subset based on the PI value of each feature. Finally, the optimal forecasting feature subset was used to train a new RF model as the final prediction model. Experiments showed that the prediction accuracy of RF trained by the optimal forecasting feature subset was higher than that of the original model and comparative models based on support vector regression and artificial neural network.

  7. L1-Penalized N-way PLS for subset of electrodes selection in BCI experiments

    Science.gov (United States)

    Eliseyev, Andrey; Moro, Cecile; Faber, Jean; Wyss, Alexander; Torres, Napoleon; Mestais, Corinne; Benabid, Alim Louis; Aksenova, Tetiana

    2012-08-01

    Recently, the N-way partial least squares (NPLS) approach was reported as an effective tool for neuronal signal decoding and brain-computer interface (BCI) system calibration. This method simultaneously analyzes data in several domains. It combines the projection of a data tensor to a low dimensional space with linear regression. In this paper the L1-Penalized NPLS is proposed for sparse BCI system calibration, allowing uniting the projection technique with an effective selection of subset of features. The L1-Penalized NPLS was applied for the binary self-paced BCI system calibration, providing selection of electrodes subset. Our BCI system is designed for animal research, in particular for research in non-human primates.

  8. Selective effects of alpha interferon on human T-lymphocyte subsets during mixed lymphocyte cultures

    DEFF Research Database (Denmark)

    Hokland, M; Hokland, P; Heron, I

    1983-01-01

    Mixed lymphocyte reaction (MLR) cultures of human lymphocyte subsets with or without the addition of physiological doses of human alpha interferon (IFN-alpha) were compared with respect to surface marker phenotypes and proliferative capacities of the responder cells. A selective depression on the T...... T4 cells and decreased numbers of T4 cells harvested from IFN MLRs (days 5-6 of culture). In contrast, it was shown that the T8 (cytotoxic/suppressor) subset in MLRs was either not affected or slightly stimulated by the addition of IFN. The depression of the T4 cells by IFN was accompanied...... by a decrease in the number of activated T cells expressing Ia antigens. On the other hand, IFN MLRs contained greater numbers of cells expressing the T10 differentiation antigen. In experiments with purified T-cell subsets the IFN effect was exerted directly on the T4 cells and not mediated by either T8...

  9. Blocked Randomization with Randomly Selected Block Sizes

    Directory of Open Access Journals (Sweden)

    Jimmy Efird

    2010-12-01

    Full Text Available When planning a randomized clinical trial, careful consideration must be given to how participants are selected for various arms of a study. Selection and accidental bias may occur when participants are not assigned to study groups with equal probability. A simple random allocation scheme is a process by which each participant has equal likelihood of being assigned to treatment versus referent groups. However, by chance an unequal number of individuals may be assigned to each arm of the study and thus decrease the power to detect statistically significant differences between groups. Block randomization is a commonly used technique in clinical trial design to reduce bias and achieve balance in the allocation of participants to treatment arms, especially when the sample size is small. This method increases the probability that each arm will contain an equal number of individuals by sequencing participant assignments by block. Yet still, the allocation process may be predictable, for example, when the investigator is not blind and the block size is fixed. This paper provides an overview of blocked randomization and illustrates how to avoid selection bias by using random block sizes.

  10. ProSelection: A Novel Algorithm to Select Proper Protein Structure Subsets for in Silico Target Identification and Drug Discovery Research.

    Science.gov (United States)

    Wang, Nanyi; Wang, Lirong; Xie, Xiang-Qun

    2017-11-27

    Molecular docking is widely applied to computer-aided drug design and has become relatively mature in the recent decades. Application of docking in modeling varies from single lead compound optimization to large-scale virtual screening. The performance of molecular docking is highly dependent on the protein structures selected. It is especially challenging for large-scale target prediction research when multiple structures are available for a single target. Therefore, we have established ProSelection, a docking preferred-protein selection algorithm, in order to generate the proper structure subset(s). By the ProSelection algorithm, protein structures of "weak selectors" are filtered out whereas structures of "strong selectors" are kept. Specifically, the structure which has a good statistical performance of distinguishing active ligands from inactive ligands is defined as a strong selector. In this study, 249 protein structures of 14 autophagy-related targets are investigated. Surflex-dock was used as the docking engine to distinguish active and inactive compounds against these protein structures. Both t test and Mann-Whitney U test were used to distinguish the strong from the weak selectors based on the normality of the docking score distribution. The suggested docking score threshold for active ligands (SDA) was generated for each strong selector structure according to the receiver operating characteristic (ROC) curve. The performance of ProSelection was further validated by predicting the potential off-targets of 43 U.S. Federal Drug Administration approved small molecule antineoplastic drugs. Overall, ProSelection will accelerate the computational work in protein structure selection and could be a useful tool for molecular docking, target prediction, and protein-chemical database establishment research.

  11. Selection of a representative subset of global climate models that captures the profile of regional changes for integrated climate impacts assessment

    Directory of Open Access Journals (Sweden)

    Alex C. Ruane

    2017-03-01

    Full Text Available Abstract We present the Representative Temperature and Precipitation (T&P GCM Subsetting Approach developed within the Agricultural Model Intercomparison and Improvement Project (AgMIP to select a practical subset of global climate models (GCMs for regional integrated assessment of climate impacts when resource limitations do not permit the full ensemble of GCMs to be evaluated given the need to also focus on impacts sector and economics models. Subsetting inherently leads to a loss of information but can free up resources to explore important uncertainties in the integrated assessment that would otherwise be prohibitive. The Representative T&P GCM Subsetting Approach identifies five individual GCMs that capture a profile of the full ensemble of temperature and precipitation change within the growing season while maintaining information about the probability that basic classes of climate changes (relatively cool/wet, cool/dry, middle, hot/wet, and hot/dry are projected in the full GCM ensemble. We demonstrate the selection methodology for maize impacts in Ames, Iowa, and discuss limitations and situations when additional information may be required to select representative GCMs. We then classify 29 GCMs over all land areas to identify regions and seasons with characteristic diagonal skewness related to surface moisture as well as extreme skewness connected to snow-albedo feedbacks and GCM uncertainty. Finally, we employ this basic approach to recognize that GCM projections demonstrate coherence across space, time, and greenhouse gas concentration pathway. The Representative T&P GCM Subsetting Approach provides a quantitative basis for the determination of useful GCM subsets, provides a practical and coherent approach where previous assessments selected solely on availability of scenarios, and may be extended for application to a range of scales and sectoral impacts.

  12. Estimating skin blood saturation by selecting a subset of hyperspectral imaging data

    Science.gov (United States)

    Ewerlöf, Maria; Salerud, E. Göran; Strömberg, Tomas; Larsson, Marcus

    2015-03-01

    Skin blood haemoglobin saturation (?b) can be estimated with hyperspectral imaging using the wavelength (λ) range of 450-700 nm where haemoglobin absorption displays distinct spectral characteristics. Depending on the image size and photon transport algorithm, computations may be demanding. Therefore, this work aims to evaluate subsets with a reduced number of wavelengths for ?b estimation. White Monte Carlo simulations are performed using a two-layered tissue model with discrete values for epidermal thickness (?epi) and the reduced scattering coefficient (μ's ), mimicking an imaging setup. A detected intensity look-up table is calculated for a range of model parameter values relevant to human skin, adding absorption effects in the post-processing. Skin model parameters, including absorbers, are; μ's (λ), ?epi, haemoglobin saturation (?b), tissue fraction blood (?b) and tissue fraction melanin (?mel). The skin model paired with the look-up table allow spectra to be calculated swiftly. Three inverse models with varying number of free parameters are evaluated: A(?b, ?b), B(?b, ?b, ?mel) and C(all parameters free). Fourteen wavelength candidates are selected by analysing the maximal spectral sensitivity to ?b and minimizing the sensitivity to ?b. All possible combinations of these candidates with three, four and 14 wavelengths, as well as the full spectral range, are evaluated for estimating ?b for 1000 randomly generated evaluation spectra. The results show that the simplified models A and B estimated ?b accurately using four wavelengths (mean error 2.2% for model B). If the number of wavelengths increased, the model complexity needed to be increased to avoid poor estimations.

  13. Cerebellins are differentially expressed in selective subsets of neurons throughout the brain.

    Science.gov (United States)

    Seigneur, Erica; Südhof, Thomas C

    2017-10-15

    Cerebellins are secreted hexameric proteins that form tripartite complexes with the presynaptic cell-adhesion molecules neurexins or 'deleted-in-colorectal-cancer', and the postsynaptic glutamate-receptor-related proteins GluD1 and GluD2. These tripartite complexes are thought to regulate synapses. However, cerebellins are expressed in multiple isoforms whose relative distributions and overall functions are not understood. Three of the four cerebellins, Cbln1, Cbln2, and Cbln4, autonomously assemble into homohexamers, whereas the Cbln3 requires Cbln1 for assembly and secretion. Here, we show that Cbln1, Cbln2, and Cbln4 are abundantly expressed in nearly all brain regions, but exhibit strikingly different expression patterns and developmental dynamics. Using newly generated knockin reporter mice for Cbln2 and Cbln4, we find that Cbln2 and Cbln4 are not universally expressed in all neurons, but only in specific subsets of neurons. For example, Cbln2 and Cbln4 are broadly expressed in largely non-overlapping subpopulations of excitatory cortical neurons, but only sparse expression was observed in excitatory hippocampal neurons of the CA1- or CA3-region. Similarly, Cbln2 and Cbln4 are selectively expressed, respectively, in inhibitory interneurons and excitatory mitral projection neurons of the main olfactory bulb; here, these two classes of neurons form dendrodendritic reciprocal synapses with each other. A few brain regions, such as the nucleus of the lateral olfactory tract, exhibit astoundingly high Cbln2 expression levels. Viewed together, our data show that cerebellins are abundantly expressed in relatively small subsets of neurons, suggesting specific roles restricted to subsets of synapses. © 2017 Wiley Periodicals, Inc.

  14. Strategyproof Peer Selection using Randomization, Partitioning, and Apportionment

    OpenAIRE

    Aziz, Haris; Lev, Omer; Mattei, Nicholas; Rosenschein, Jeffrey S.; Walsh, Toby

    2016-01-01

    Peer review, evaluation, and selection is a fundamental aspect of modern science. Funding bodies the world over employ experts to review and select the best proposals of those submitted for funding. The problem of peer selection, however, is much more general: a professional society may want to give a subset of its members awards based on the opinions of all members; an instructor for a MOOC or online course may want to crowdsource grading; or a marketing company may select ideas from group b...

  15. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data

    Directory of Open Access Journals (Sweden)

    Himmelreich Uwe

    2009-07-01

    Full Text Available Abstract Background Regularized regression methods such as principal component or partial least squares regression perform well in learning tasks on high dimensional spectral data, but cannot explicitly eliminate irrelevant features. The random forest classifier with its associated Gini feature importance, on the other hand, allows for an explicit feature elimination, but may not be optimally adapted to spectral data due to the topology of its constituent classification trees which are based on orthogonal splits in feature space. Results We propose to combine the best of both approaches, and evaluated the joint use of a feature selection based on a recursive feature elimination using the Gini importance of random forests' together with regularized classification methods on spectral data sets from medical diagnostics, chemotaxonomy, biomedical analytics, food science, and synthetically modified spectral data. Here, a feature selection using the Gini feature importance with a regularized classification by discriminant partial least squares regression performed as well as or better than a filtering according to different univariate statistical tests, or using regression coefficients in a backward feature elimination. It outperformed the direct application of the random forest classifier, or the direct application of the regularized classifiers on the full set of features. Conclusion The Gini importance of the random forest provided superior means for measuring feature relevance on spectral data, but – on an optimal subset of features – the regularized classifiers might be preferable over the random forest classifier, in spite of their limitation to model linear dependencies only. A feature selection based on Gini importance, however, may precede a regularized linear classification to identify this optimal subset of features, and to earn a double benefit of both dimensionality reduction and the elimination of noise from the classification task.

  16. Modeling the Effect of the Selective S1P1 Receptor Modulator Ponesimod on Subsets of Blood Lymphocytes.

    Science.gov (United States)

    Lott, Dominik; Krause, Andreas; Seemayer, Christian A; Strasser, Daniel S; Dingemanse, Jasper; Lehr, Thorsten

    2017-03-01

    This analysis aimed at describing the effect of the selective sphingosine-1-phosphate receptor 1 modulator ponesimod on lymphocyte subsets in peripheral blood. As the involvement of different lymphocyte subsets varies among different autoimmune diseases, characterizing the effect of ponesimod on these may be beneficial in better understanding treatment effects. Three phase 1 clinical studies in healthy human subjects were pooled. Non-linear mixed-effects modeling techniques were used to study the effect of ponesimod on lymphocyte subsets such as B cells, T helper cells, T cytotoxic cells, and natural killer cells in a qualitative and quantitative manner. Indirect-response I max models including circadian variation best described the effect of ponesimod on lymphocyte subsets. B cells and T helper cells were shown to be more affected compared to T cytotoxic cells with respect to the maximum possible reduction (100% for B and T helper cells, 95% for T cytotoxic cells) and the concentration required to reach half the maximum effect. Inter-individual variability was found to be larger for T cytotoxic compared to T helper, and B cells. These first models for ponesimod on the level of lymphocyte subsets offer a valuable tool for the analysis and interpretation of results from ponesimod trials in autoimmune diseases.

  17. 47 CFR 1.1602 - Designation for random selection.

    Science.gov (United States)

    2010-10-01

    ... 47 Telecommunication 1 2010-10-01 2010-10-01 false Designation for random selection. 1.1602 Section 1.1602 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL PRACTICE AND PROCEDURE Random Selection Procedures for Mass Media Services General Procedures § 1.1602 Designation for random selection...

  18. 47 CFR 1.1603 - Conduct of random selection.

    Science.gov (United States)

    2010-10-01

    ... 47 Telecommunication 1 2010-10-01 2010-10-01 false Conduct of random selection. 1.1603 Section 1.1603 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL PRACTICE AND PROCEDURE Random Selection Procedures for Mass Media Services General Procedures § 1.1603 Conduct of random selection. The...

  19. An Empirical Study of Wrappers for Feature Subset Selection based on a Parallel Genetic Algorithm: The Multi-Wrapper Model

    KAUST Repository

    Soufan, Othman

    2012-01-01

    proper criterion seeks to find the best subset of features describing data (relevance) and achieving better performance (optimality). Wrapper approaches are feature selection methods which are wrapped around a classification algorithm and use a

  20. The Subset Sum game.

    Science.gov (United States)

    Darmann, Andreas; Nicosia, Gaia; Pferschy, Ulrich; Schauer, Joachim

    2014-03-16

    In this work we address a game theoretic variant of the Subset Sum problem, in which two decision makers (agents/players) compete for the usage of a common resource represented by a knapsack capacity. Each agent owns a set of integer weighted items and wants to maximize the total weight of its own items included in the knapsack. The solution is built as follows: Each agent, in turn, selects one of its items (not previously selected) and includes it in the knapsack if there is enough capacity. The process ends when the remaining capacity is too small for including any item left. We look at the problem from a single agent point of view and show that finding an optimal sequence of items to select is an [Formula: see text]-hard problem. Therefore we propose two natural heuristic strategies and analyze their worst-case performance when (1) the opponent is able to play optimally and (2) the opponent adopts a greedy strategy. From a centralized perspective we observe that some known results on the approximation of the classical Subset Sum can be effectively adapted to the multi-agent version of the problem.

  1. Eigenvector Subset Selection Using Bayesian Optimization Algorithm%基于贝叶斯优化算法的脸面特征向量子集选择

    Institute of Scientific and Technical Information of China (English)

    郭卫锋; 林亚平; 罗光平

    2002-01-01

    Eigenvector subset selection is the key to face recognition. In this paper ,we propose ESS-BOA, a newrandomized, population-based evolutionary algorithm which deals with the Eigenvector Subset Selection (ESS)prob-lem on face recognition application. In ESS-BOA ,the ESS problem, stated as a search problem ,uses the BayesianOptimization Algorithm (BOA) as searching engine and the distance degree as the object function to select eigenvec-tor. Experimental results show that ESS-BOA outperforms the traditional the eigenface selection algorithm.

  2. Using ArcMap, Google Earth, and Global Positioning Systems to select and locate random households in rural Haiti.

    Science.gov (United States)

    Wampler, Peter J; Rediske, Richard R; Molla, Azizur R

    2013-01-18

    A remote sensing technique was developed which combines a Geographic Information System (GIS); Google Earth, and Microsoft Excel to identify home locations for a random sample of households in rural Haiti. The method was used to select homes for ethnographic and water quality research in a region of rural Haiti located within 9 km of a local hospital and source of health education in Deschapelles, Haiti. The technique does not require access to governmental records or ground based surveys to collect household location data and can be performed in a rapid, cost-effective manner. The random selection of households and the location of these households during field surveys were accomplished using GIS, Google Earth, Microsoft Excel, and handheld Garmin GPSmap 76CSx GPS units. Homes were identified and mapped in Google Earth, exported to ArcMap 10.0, and a random list of homes was generated using Microsoft Excel which was then loaded onto handheld GPS units for field location. The development and use of a remote sensing method was essential to the selection and location of random households. A total of 537 homes initially were mapped and a randomized subset of 96 was identified as potential survey locations. Over 96% of the homes mapped using Google Earth imagery were correctly identified as occupied dwellings. Only 3.6% of the occupants of mapped homes visited declined to be interviewed. 16.4% of the homes visited were not occupied at the time of the visit due to work away from the home or market days. A total of 55 households were located using this method during the 10 days of fieldwork in May and June of 2012. The method used to generate and field locate random homes for surveys and water sampling was an effective means of selecting random households in a rural environment lacking geolocation infrastructure. The success rate for locating households using a handheld GPS was excellent and only rarely was local knowledge required to identify and locate households. This

  3. Using ArcMap, Google Earth, and Global Positioning Systems to select and locate random households in rural Haiti

    Directory of Open Access Journals (Sweden)

    Wampler Peter J

    2013-01-01

    Full Text Available Abstract Background A remote sensing technique was developed which combines a Geographic Information System (GIS; Google Earth, and Microsoft Excel to identify home locations for a random sample of households in rural Haiti. The method was used to select homes for ethnographic and water quality research in a region of rural Haiti located within 9 km of a local hospital and source of health education in Deschapelles, Haiti. The technique does not require access to governmental records or ground based surveys to collect household location data and can be performed in a rapid, cost-effective manner. Methods The random selection of households and the location of these households during field surveys were accomplished using GIS, Google Earth, Microsoft Excel, and handheld Garmin GPSmap 76CSx GPS units. Homes were identified and mapped in Google Earth, exported to ArcMap 10.0, and a random list of homes was generated using Microsoft Excel which was then loaded onto handheld GPS units for field location. The development and use of a remote sensing method was essential to the selection and location of random households. Results A total of 537 homes initially were mapped and a randomized subset of 96 was identified as potential survey locations. Over 96% of the homes mapped using Google Earth imagery were correctly identified as occupied dwellings. Only 3.6% of the occupants of mapped homes visited declined to be interviewed. 16.4% of the homes visited were not occupied at the time of the visit due to work away from the home or market days. A total of 55 households were located using this method during the 10 days of fieldwork in May and June of 2012. Conclusions The method used to generate and field locate random homes for surveys and water sampling was an effective means of selecting random households in a rural environment lacking geolocation infrastructure. The success rate for locating households using a handheld GPS was excellent and only

  4. A Hybrid Feature Subset Selection Algorithm for Analysis of High Correlation Proteomic Data

    Science.gov (United States)

    Kordy, Hussain Montazery; Baygi, Mohammad Hossein Miran; Moradi, Mohammad Hassan

    2012-01-01

    Pathological changes within an organ can be reflected as proteomic patterns in biological fluids such as plasma, serum, and urine. The surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF MS) has been used to generate proteomic profiles from biological fluids. Mass spectrometry yields redundant noisy data that the most data points are irrelevant features for differentiating between cancer and normal cases. In this paper, we have proposed a hybrid feature subset selection algorithm based on maximum-discrimination and minimum-correlation coupled with peak scoring criteria. Our algorithm has been applied to two independent SELDI-TOF MS datasets of ovarian cancer obtained from the NCI-FDA clinical proteomics databank. The proposed algorithm has used to extract a set of proteins as potential biomarkers in each dataset. We applied the linear discriminate analysis to identify the important biomarkers. The selected biomarkers have been able to successfully diagnose the ovarian cancer patients from the noncancer control group with an accuracy of 100%, a sensitivity of 100%, and a specificity of 100% in the two datasets. The hybrid algorithm has the advantage that increases reproducibility of selected biomarkers and able to find a small set of proteins with high discrimination power. PMID:23717808

  5. Effect of cytomegalovirus co-infection on normalization of selected T-cell subsets in children with perinatally acquired HIV infection treated with combination antiretroviral therapy.

    Directory of Open Access Journals (Sweden)

    Suad Kapetanovic

    Full Text Available We examined the effect of cytomegalovirus (CMV co-infection and viremia on reconstitution of selected CD4+ and CD8+ T-cell subsets in perinatally HIV-infected (PHIV+ children ≥ 1-year old who participated in a partially randomized, open-label, 96-week combination antiretroviral therapy (cART-algorithm study.Participants were categorized as CMV-naïve, CMV-positive (CMV+ viremic, and CMV+ aviremic, based on blood, urine, or throat culture, CMV IgG and DNA polymerase chain reaction measured at baseline. At weeks 0, 12, 20 and 40, T-cell subsets including naïve (CD62L+CD45RA+; CD95-CD28+, activated (CD38+HLA-DR+ and terminally differentiated (CD62L-CD45RA+; CD95+CD28- CD4+ and CD8+ T-cells were measured by flow cytometry.Of the 107 participants included in the analysis, 14% were CMV+ viremic; 49% CMV+ aviremic; 37% CMV-naïve. In longitudinal adjusted models, compared with CMV+ status, baseline CMV-naïve status was significantly associated with faster recovery of CD8+CD62L+CD45RA+% and CD8+CD95-CD28+% and faster decrease of CD8+CD95+CD28-%, independent of HIV VL response to treatment, cART regimen and baseline CD4%. Surprisingly, CMV status did not have a significant impact on longitudinal trends in CD8+CD38+HLA-DR+%. CMV status did not have a significant impact on any CD4+ T-cell subsets.In this cohort of PHIV+ children, the normalization of naïve and terminally differentiated CD8+ T-cell subsets in response to cART was detrimentally affected by the presence of CMV co-infection. These findings may have implications for adjunctive treatment strategies targeting CMV co-infection in PHIV+ children, especially those that are now adults or reaching young adulthood and may have accelerated immunologic aging, increased opportunistic infections and aging diseases of the immune system.

  6. Effect of cytomegalovirus co-infection on normalization of selected T-cell subsets in children with perinatally acquired HIV infection treated with combination antiretroviral therapy.

    Science.gov (United States)

    Kapetanovic, Suad; Aaron, Lisa; Montepiedra, Grace; Anthony, Patricia; Thuvamontolrat, Kasalyn; Pahwa, Savita; Burchett, Sandra; Weinberg, Adriana; Kovacs, Andrea

    2015-01-01

    We examined the effect of cytomegalovirus (CMV) co-infection and viremia on reconstitution of selected CD4+ and CD8+ T-cell subsets in perinatally HIV-infected (PHIV+) children ≥ 1-year old who participated in a partially randomized, open-label, 96-week combination antiretroviral therapy (cART)-algorithm study. Participants were categorized as CMV-naïve, CMV-positive (CMV+) viremic, and CMV+ aviremic, based on blood, urine, or throat culture, CMV IgG and DNA polymerase chain reaction measured at baseline. At weeks 0, 12, 20 and 40, T-cell subsets including naïve (CD62L+CD45RA+; CD95-CD28+), activated (CD38+HLA-DR+) and terminally differentiated (CD62L-CD45RA+; CD95+CD28-) CD4+ and CD8+ T-cells were measured by flow cytometry. Of the 107 participants included in the analysis, 14% were CMV+ viremic; 49% CMV+ aviremic; 37% CMV-naïve. In longitudinal adjusted models, compared with CMV+ status, baseline CMV-naïve status was significantly associated with faster recovery of CD8+CD62L+CD45RA+% and CD8+CD95-CD28+% and faster decrease of CD8+CD95+CD28-%, independent of HIV VL response to treatment, cART regimen and baseline CD4%. Surprisingly, CMV status did not have a significant impact on longitudinal trends in CD8+CD38+HLA-DR+%. CMV status did not have a significant impact on any CD4+ T-cell subsets. In this cohort of PHIV+ children, the normalization of naïve and terminally differentiated CD8+ T-cell subsets in response to cART was detrimentally affected by the presence of CMV co-infection. These findings may have implications for adjunctive treatment strategies targeting CMV co-infection in PHIV+ children, especially those that are now adults or reaching young adulthood and may have accelerated immunologic aging, increased opportunistic infections and aging diseases of the immune system.

  7. Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models

    Directory of Open Access Journals (Sweden)

    Hui Wang

    2017-10-01

    Full Text Available Achieving relatively high-accuracy short-term wind speed forecasting estimates is a precondition for the construction and grid-connected operation of wind power forecasting systems for wind farms. Currently, most research is focused on the structure of forecasting models and does not consider the selection of input variables, which can have significant impacts on forecasting performance. This paper presents an input variable selection method for wind speed forecasting models. The candidate input variables for various leading periods are selected and random forests (RF is employed to evaluate the importance of all variable as features. The feature subset with the best evaluation performance is selected as the optimal feature set. Then, kernel-based extreme learning machine is constructed to evaluate the performance of input variables selection based on RF. The results of the case study show that by removing the uncorrelated and redundant features, RF effectively extracts the most strongly correlated set of features from the candidate input variables. By finding the optimal feature combination to represent the original information, RF simplifies the structure of the wind speed forecasting model, shortens the training time required, and substantially improves the model’s accuracy and generalization ability, demonstrating that the input variables selected by RF are effective.

  8. An active learning representative subset selection method using net analyte signal

    Science.gov (United States)

    He, Zhonghai; Ma, Zhenhe; Luan, Jingmin; Cai, Xi

    2018-05-01

    To guarantee accurate predictions, representative samples are needed when building a calibration model for spectroscopic measurements. However, in general, it is not known whether a sample is representative prior to measuring its concentration, which is both time-consuming and expensive. In this paper, a method to determine whether a sample should be selected into a calibration set is presented. The selection is based on the difference of Euclidean norm of net analyte signal (NAS) vector between the candidate and existing samples. First, the concentrations and spectra of a group of samples are used to compute the projection matrix, NAS vector, and scalar values. Next, the NAS vectors of candidate samples are computed by multiplying projection matrix with spectra of samples. Scalar value of NAS is obtained by norm computation. The distance between the candidate set and the selected set is computed, and samples with the largest distance are added to selected set sequentially. Last, the concentration of the analyte is measured such that the sample can be used as a calibration sample. Using a validation test, it is shown that the presented method is more efficient than random selection. As a result, the amount of time and money spent on reference measurements is greatly reduced.

  9. Subset specification of central serotonergic neurons

    Directory of Open Access Journals (Sweden)

    Marten P Smidt

    2013-10-01

    Full Text Available The last decade the serotonin (5-hydroxytryptamine; 5-HT system has received enormous attention due to its role in regulation of behavior, exemplified by the discovery that increased 5-HT tone in the central nervous system is able to alleviate affective disorders. Here, we review the developmental processes, with a special emphasis on subset specification, leading to the formation of the 5-HT system in the brain. Molecular classification of 5-HT neuronal groups leads to the definition of two independent rostral groups positioned in rhombomere 1 and 2/3 and a caudal group in rhombomere 5-8. In addition, more disperse refinement of these subsets is present as shown by the selective expression of the 5-HT1A autoreceptor, indicating functional diversity between 5-HT subsets. The functional significance of the molecular coding differences is not well known and the molecular basis of described specific connectivity patterns remain to be elucidated. Recent developments in genetic lineage tracing models will provide these data and form a major step-up towards the full understanding of the importance of developmental programming and function of 5-HT neuronal subsets.

  10. A metaheuristic optimization framework for informative gene selection

    Directory of Open Access Journals (Sweden)

    Kaberi Das

    Full Text Available This paper presents a metaheuristic framework using Harmony Search (HS with Genetic Algorithm (GA for gene selection. The internal architecture of the proposed model broadly works in two phases, in the first phase, the model allows the hybridization of HS with GA to compute and evaluate the fitness of the randomly selected solutions of binary strings and then HS ranks the solutions in descending order of their fitness. In the second phase, the offsprings are generated using crossover and mutation operations of GA and finally, those offsprings were selected for the next generation whose fitness value is more than their parents evaluated by SVM classifier. The accuracy of the final gene subsets obtained from this model has been evaluated using SVM classifiers. The merit of this approach is analyzed by experimental results on five benchmark datasets and the results showed an impressive accuracy over existing feature selection approaches. The occurrence of gene subsets selected from this model have also been computed and the most often selected gene subsets with the probability of [0.1–0.9] have been chosen as optimal sets of informative genes. Finally, the performance of those selected informative gene subsets have been measured and established through probabilistic measures. Keywords: Gene Selection, Metaheuristic, Harmony Search Algorithm, Genetic Algorithm, SVM

  11. Selecting a climate model subset to optimise key ensemble properties

    Directory of Open Access Journals (Sweden)

    N. Herger

    2018-02-01

    Full Text Available End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.

  12. Selecting a climate model subset to optimise key ensemble properties

    Science.gov (United States)

    Herger, Nadja; Abramowitz, Gab; Knutti, Reto; Angélil, Oliver; Lehmann, Karsten; Sanderson, Benjamin M.

    2018-02-01

    End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.

  13. Selectivity and sparseness in randomly connected balanced networks.

    Directory of Open Access Journals (Sweden)

    Cengiz Pehlevan

    Full Text Available Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the "paradoxical" effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.

  14. Testing, Selection, and Implementation of Random Number Generators

    National Research Council Canada - National Science Library

    Collins, Joseph C

    2008-01-01

    An exhaustive evaluation of state-of-the-art random number generators with several well-known suites of tests provides the basis for selection of suitable random number generators for use in stochastic simulations...

  15. Individual discriminative face recognition models based on subsets of features

    DEFF Research Database (Denmark)

    Clemmensen, Line Katrine Harder; Gomez, David Delgado; Ersbøll, Bjarne Kjær

    2007-01-01

    The accuracy of data classification methods depends considerably on the data representation and on the selected features. In this work, the elastic net model selection is used to identify meaningful and important features in face recognition. Modelling the characteristics which distinguish one...... person from another using only subsets of features will both decrease the computational cost and increase the generalization capacity of the face recognition algorithm. Moreover, identifying which are the features that better discriminate between persons will also provide a deeper understanding...... of the face recognition problem. The elastic net model is able to select a subset of features with low computational effort compared to other state-of-the-art feature selection methods. Furthermore, the fact that the number of features usually is larger than the number of images in the data base makes feature...

  16. Tracking a Subset of Skeleton Joints: An Effective Approach towards Complex Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Muhammad Latif Anjum

    2017-01-01

    Full Text Available We present a robust algorithm for complex human activity recognition for natural human-robot interaction. The algorithm is based on tracking the position of selected joints in human skeleton. For any given activity, only a few skeleton joints are involved in performing the activity, so a subset of joints contributing the most towards the activity is selected. Our approach of tracking a subset of skeleton joints (instead of tracking the whole skeleton is computationally efficient and provides better recognition accuracy. We have developed both manual and automatic approaches for the selection of these joints. The position of the selected joints is tracked for the duration of the activity and is used to construct feature vectors for each activity. Once the feature vectors have been constructed, we use a Support Vector Machines (SVM multiclass classifier for training and testing the algorithm. The algorithm has been tested on a purposely built dataset of depth videos recorded using Kinect camera. The dataset consists of 250 videos of 10 different activities being performed by different users. Experimental results show classification accuracy of 83% when tracking all skeleton joints, 95% when using manual selection of subset joints, and 89% when using automatic selection of subset joints.

  17. Lectin Ulex europaeus agglutinin I specifically labels a subset of primary afferent fibers which project selectively to the superficial dorsal horn of the spinal cord.

    Science.gov (United States)

    Mori, K

    1986-02-19

    To examine differential carbohydrate expression among different subsets of primary afferent fibers, several fluorescein-isothiocyanate conjugated lectins were used in a histochemical study of the dorsal root ganglion (DRG) and spinal cord of the rabbit. The lectin Ulex europaeus agglutinin I specifically labeled a subset of DRG cells and primary afferent fibers which projected to the superficial laminae of the dorsal horn. These results suggest that specific carbohydrates containing L-fucosyl residue is expressed selectively in small diameter primary afferent fibers which subserve nociception or thermoception.

  18. Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics.

    Science.gov (United States)

    Lin, Xiaohui; Li, Chao; Zhang, Yanhui; Su, Benzhe; Fan, Meng; Wei, Hai

    2017-12-26

    Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE) is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA) algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data.

  19. Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics

    Directory of Open Access Journals (Sweden)

    Xiaohui Lin

    2017-12-01

    Full Text Available Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data.

  20. Application of random effects to the study of resource selection by animals.

    Science.gov (United States)

    Gillies, Cameron S; Hebblewhite, Mark; Nielsen, Scott E; Krawchuk, Meg A; Aldridge, Cameron L; Frair, Jacqueline L; Saher, D Joanne; Stevens, Cameron E; Jerde, Christopher L

    2006-07-01

    1. Resource selection estimated by logistic regression is used increasingly in studies to identify critical resources for animal populations and to predict species occurrence. 2. Most frequently, individual animals are monitored and pooled to estimate population-level effects without regard to group or individual-level variation. Pooling assumes that both observations and their errors are independent, and resource selection is constant given individual variation in resource availability. 3. Although researchers have identified ways to minimize autocorrelation, variation between individuals caused by differences in selection or available resources, including functional responses in resource selection, have not been well addressed. 4. Here we review random-effects models and their application to resource selection modelling to overcome these common limitations. We present a simple case study of an analysis of resource selection by grizzly bears in the foothills of the Canadian Rocky Mountains with and without random effects. 5. Both categorical and continuous variables in the grizzly bear model differed in interpretation, both in statistical significance and coefficient sign, depending on how a random effect was included. We used a simulation approach to clarify the application of random effects under three common situations for telemetry studies: (a) discrepancies in sample sizes among individuals; (b) differences among individuals in selection where availability is constant; and (c) differences in availability with and without a functional response in resource selection. 6. We found that random intercepts accounted for unbalanced sample designs, and models with random intercepts and coefficients improved model fit given the variation in selection among individuals and functional responses in selection. Our empirical example and simulations demonstrate how including random effects in resource selection models can aid interpretation and address difficult assumptions

  1. Local randomization in neighbor selection improves PRM roadmap quality

    KAUST Repository

    McMahon, Troy; Jacobs, Sam; Boyd, Bryan; Tapia, Lydia; Amato, Nancy M.

    2012-01-01

    Probabilistic Roadmap Methods (PRMs) are one of the most used classes of motion planning methods. These sampling-based methods generate robot configurations (nodes) and then connect them to form a graph (roadmap) containing representative feasible pathways. A key step in PRM roadmap construction involves identifying a set of candidate neighbors for each node. Traditionally, these candidates are chosen to be the k-closest nodes based on a given distance metric. In this paper, we propose a new neighbor selection policy called LocalRand(k,K'), that first computes the K' closest nodes to a specified node and then selects k of those nodes at random. Intuitively, LocalRand attempts to benefit from random sampling while maintaining the higher levels of local planner success inherent to selecting more local neighbors. We provide a methodology for selecting the parameters k and K'. We perform an experimental comparison which shows that for both rigid and articulated robots, LocalRand results in roadmaps that are better connected than the traditional k-closest policy or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to k-closest. © 2012 IEEE.

  2. Local randomization in neighbor selection improves PRM roadmap quality

    KAUST Repository

    McMahon, Troy

    2012-10-01

    Probabilistic Roadmap Methods (PRMs) are one of the most used classes of motion planning methods. These sampling-based methods generate robot configurations (nodes) and then connect them to form a graph (roadmap) containing representative feasible pathways. A key step in PRM roadmap construction involves identifying a set of candidate neighbors for each node. Traditionally, these candidates are chosen to be the k-closest nodes based on a given distance metric. In this paper, we propose a new neighbor selection policy called LocalRand(k,K\\'), that first computes the K\\' closest nodes to a specified node and then selects k of those nodes at random. Intuitively, LocalRand attempts to benefit from random sampling while maintaining the higher levels of local planner success inherent to selecting more local neighbors. We provide a methodology for selecting the parameters k and K\\'. We perform an experimental comparison which shows that for both rigid and articulated robots, LocalRand results in roadmaps that are better connected than the traditional k-closest policy or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to k-closest. © 2012 IEEE.

  3. Decoys Selection in Benchmarking Datasets: Overview and Perspectives

    Science.gov (United States)

    Réau, Manon; Langenfeld, Florent; Zagury, Jean-François; Lagarde, Nathalie; Montes, Matthieu

    2018-01-01

    Virtual Screening (VS) is designed to prospectively help identifying potential hits, i.e., compounds capable of interacting with a given target and potentially modulate its activity, out of large compound collections. Among the variety of methodologies, it is crucial to select the protocol that is the most adapted to the query/target system under study and that yields the most reliable output. To this aim, the performance of VS methods is commonly evaluated and compared by computing their ability to retrieve active compounds in benchmarking datasets. The benchmarking datasets contain a subset of known active compounds together with a subset of decoys, i.e., assumed non-active molecules. The composition of both the active and the decoy compounds subsets is critical to limit the biases in the evaluation of the VS methods. In this review, we focus on the selection of decoy compounds that has considerably changed over the years, from randomly selected compounds to highly customized or experimentally validated negative compounds. We first outline the evolution of decoys selection in benchmarking databases as well as current benchmarking databases that tend to minimize the introduction of biases, and secondly, we propose recommendations for the selection and the design of benchmarking datasets. PMID:29416509

  4. Prediction based on mean subset

    DEFF Research Database (Denmark)

    Øjelund, Henrik; Brown, P. J.; Madsen, Henrik

    2002-01-01

    , it is found that the proposed mean subset method has superior prediction performance than prediction based on the best subset method, and in some settings also better than the ridge regression and lasso methods. The conclusions drawn from the Monte Carlo study is corroborated in an example in which prediction......Shrinkage methods have traditionally been applied in prediction problems. In this article we develop a shrinkage method (mean subset) that forms an average of regression coefficients from individual subsets of the explanatory variables. A Bayesian approach is taken to derive an expression of how...... the coefficient vectors from each subset should be weighted. It is not computationally feasible to calculate the mean subset coefficient vector for larger problems, and thus we suggest an algorithm to find an approximation to the mean subset coefficient vector. In a comprehensive Monte Carlo simulation study...

  5. [Effect of Sijunzi Decoction and enteral nutrition on T-cell subsets and nutritional status in patients with gastric cancer after operation: a randomized controlled trial].

    Science.gov (United States)

    Cai, Jun; Wang, Hua; Zhou, Sheng; Wu, Bin; Song, Hua-Rong; Xuan, Zheng-Rong

    2008-01-01

    To observe the effect of perioperative application of Sijunzi Decoction and enteral nutrition on T-cell subsets and nutritional status in patients with gastric cancer after operation. In this prospective, single-blinded, controlled clinical trial, fifty-nine patients with gastric cancer were randomly divided into three groups: control group (n=20) and two study groups (group A, n=21; group B, n=18). Sjunzi Decoction (100 ml) was administered via nasogastric tube to the patients in the study group B from the second postoperation day to the 9th postoperation day. Patients in the two study groups were given an isocaloric and isonitrogonous enteral diet, which was started on the second day after operation, and continued for eight days. Patients in the control group were given an isocaloric and isonitrogonous parenteral diet for 9 days. All variables of nutritional status such as serum albumin (ALB), prealbumin (PA), transferrin (TRF) and T-cell subsets were measured one day before operation, and one day and 10 days after operation. All the nutritional variables and the levels of CD3(+), CD4(+), CD4(+)/CD8(+) were decreased significantly after operation. Ten days after operation, T-cell subsets and nutritional variables in the two study groups were increased as compare with the control group. The levels of ALB, TRF and T-cell subsets in the study group B were increased significantly as compared with the study group A (Pnutrition assisted with Sijunzi Decoction can positively improve and optimize cellular immune function and nutritional status in the patients with gastric cancer after operation.

  6. Criteria to Extract High-Quality Protein Data Bank Subsets for Structure Users.

    Science.gov (United States)

    Carugo, Oliviero; Djinović-Carugo, Kristina

    2016-01-01

    It is often necessary to build subsets of the Protein Data Bank to extract structural trends and average values. For this purpose it is mandatory that the subsets are non-redundant and of high quality. The first problem can be solved relatively easily at the sequence level or at the structural level. The second, on the contrary, needs special attention. It is not sufficient, in fact, to consider the crystallographic resolution and other feature must be taken into account: the absence of strings of residues from the electron density maps and from the files deposited in the Protein Data Bank; the B-factor values; the appropriate validation of the structural models; the quality of the electron density maps, which is not uniform; and the temperature of the diffraction experiments. More stringent criteria produce smaller subsets, which can be enlarged with more tolerant selection criteria. The incessant growth of the Protein Data Bank and especially of the number of high-resolution structures is allowing the use of more stringent selection criteria, with a consequent improvement of the quality of the subsets of the Protein Data Bank.

  7. Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection

    Directory of Open Access Journals (Sweden)

    Jaesung Lee

    2016-11-01

    Full Text Available Multi-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings. In a multi-label feature selection problem, the algorithm may be faced with a dataset containing a large number of labels. Because the computational cost of multi-label feature selection increases according to the number of labels, the algorithm may suffer from a degradation in performance when processing very large datasets. In this study, we propose an efficient multi-label feature selection method based on an information-theoretic label selection strategy. By identifying a subset of labels that significantly influence the importance of features, the proposed method efficiently outputs a feature subset. Experimental results demonstrate that the proposed method can identify a feature subset much faster than conventional multi-label feature selection methods for large multi-label datasets.

  8. dbVOR: a database system for importing pedigree, phenotype and genotype data and exporting selected subsets.

    Science.gov (United States)

    Baron, Robert V; Conley, Yvette P; Gorin, Michael B; Weeks, Daniel E

    2015-03-18

    When studying the genetics of a human trait, we typically have to manage both genome-wide and targeted genotype data. There can be overlap of both people and markers from different genotyping experiments; the overlap can introduce several kinds of problems. Most times the overlapping genotypes are the same, but sometimes they are different. Occasionally, the lab will return genotypes using a different allele labeling scheme (for example 1/2 vs A/C). Sometimes, the genotype for a person/marker index is unreliable or missing. Further, over time some markers are merged and bad samples are re-run under a different sample name. We need a consistent picture of the subset of data we have chosen to work with even though there might possibly be conflicting measurements from multiple data sources. We have developed the dbVOR database, which is designed to hold data efficiently for both genome-wide and targeted experiments. The data are indexed for fast retrieval by person and marker. In addition, we store pedigree and phenotype data for our subjects. The dbVOR database allows us to select subsets of the data by several different criteria and to merge their results into a coherent and consistent whole. Data may be filtered by: family, person, trait value, markers, chromosomes, and chromosome ranges. The results can be presented in columnar, Mega2, or PLINK format. dbVOR serves our needs well. It is freely available from https://watson.hgen.pitt.edu/register . Documentation for dbVOR can be found at https://watson.hgen.pitt.edu/register/docs/dbvor.html .

  9. Hyperspectral band selection based on consistency-measure of neighborhood rough set theory

    International Nuclear Information System (INIS)

    Liu, Yao; Xie, Hong; Wang, Liguo; Tan, Kezhu; Chen, Yuehua; Xu, Zhen

    2016-01-01

    Band selection is a well-known approach for reducing dimensionality in hyperspectral imaging. In this paper, a band selection method based on consistency-measure of neighborhood rough set theory (CMNRS) was proposed to select informative bands from hyperspectral images. A decision-making information system was established by the reflection spectrum of soybeans’ hyperspectral data between 400 nm and 1000 nm wavelengths. The neighborhood consistency-measure, which reflects not only the size of the decision positive region, but also the sample distribution in the boundary region, was used as the evaluation function of band significance. The optimal band subset was selected by a forward greedy search algorithm. A post-pruning strategy was employed to overcome the over-fitting problem and find the minimum subset. To assess the effectiveness of the proposed band selection technique, two classification models (extreme learning machine (ELM) and random forests (RF)) were built. The experimental results showed that the proposed algorithm can effectively select key bands and obtain satisfactory classification accuracy. (paper)

  10. Lymphocyte subset contents in cerebrospinal fluid of children with viral encephalitis

    Directory of Open Access Journals (Sweden)

    An-Ran Xu

    2016-06-01

    Full Text Available Objective: To study the lymphocyte subset contents in cerebrospinal fluid of children with viral encephalitis and their correlation with disease. Methods: Children with viral encephalitis were selected as VE group, children excluded of central nervous system infection by lumbar puncture or children without central nervous system diseases but receiving surgery with spinal anesthesia were selected as control group, and then cerebrospinal fluid and serum were collected to detect lymphocyte subset contents, nerve injury molecule contents as well as inflammatory response indicators and oxidative stress response indicators. Results: CD3+, CD3+CD4+, CD4/CD8 and CD16+CD56+ in cerebrospinal fluid of VE group were lower than those of control group, and both CD3+CD8+ and CD19+ were higher than those of control group; CD3+, CD3+CD4+, CD4/CD8 and CD16+CD56+ in cerebrospinal fluid of children with abnormal MRI were lower than those of children with normal MRI, and both CD3+CD8+ and CD19+ were higher than those of children with normal MRI; NSE, MBP, S-100 and NPT contents in cerebrospinal fluid and serum of VE group were significantly higher than those of control group and had good correlation with lymphocyte subset contents; MMP9, TNF-α and IL-6 contents in cerebrospinal fluid of VE group were significantly higher than those of control group, and SOD and GSH-Px contents were significantly lower than those of control group and had good correlation with lymphocyte subset contents. Conclusions: CD4+/CD8+T lymphocyte ratio and NK cell content decrease, and B lymphocyte content increases in cerebrospinal fluid of children with viral encephalitis, and lymphocyte subset contents have inhibitory effect on MRI manifestation, degree of inflammatory response and oxidative stress response.

  11. Using a Feature Subset Selection method and Support Vector Machine to address curse of dimensionality and redundancy in Hyperion hyperspectral data classification

    Directory of Open Access Journals (Sweden)

    Amir Salimi

    2018-04-01

    Full Text Available The curse of dimensionality resulted from insufficient training samples and redundancy is considered as an important problem in the supervised classification of hyperspectral data. This problem can be handled by Feature Subset Selection (FSS methods and Support Vector Machine (SVM. The FSS methods can manage the redundancy by removing redundant spectral bands. Moreover, kernel based methods, especially SVM have a high ability to classify limited-sample data sets. This paper mainly aims to assess the capability of a FSS method and the SVM in curse of dimensional circumstances and to compare results with the Artificial Neural Network (ANN, when they are used to classify alteration zones of the Hyperion hyperspectral image acquired from the greatest Iranian porphyry copper complex. The results demonstrated that by decreasing training samples, the accuracy of SVM was just decreased 1.8% while the accuracy of ANN was highly reduced i.e. 14.01%. In addition, a hybrid FSS was applied to reduce the dimension of Hyperion. Accordingly, among the 165 useable spectral bands of Hyperion, 18 bands were only selected as the most important and informative bands. Although this dimensionality reduction could not intensively improve the performance of SVM, ANN revealed a significant improvement in the computational time and a slightly enhancement in the average accuracy. Therefore, SVM as a low-sensitive method respect to the size of training data set and feature space can be applied to classify the curse of dimensional problems. Also, the FSS methods can improve the performance of non-kernel based classifiers by eliminating redundant features. Keywords: Curse of dimensionality, Feature Subset Selection, Hydrothermal alteration, Hyperspectral, SVM

  12. Automatic endmember selection and nonlinear spectral unmixing of Lunar analog minerals

    Science.gov (United States)

    Rommel, Daniela; Grumpe, Arne; Felder, Marian Patrik; Wöhler, Christian; Mall, Urs; Kronz, Andreas

    2017-03-01

    While the interpretation of spectral reflectance data has been widely applied to detect the presence of minerals, determining and quantifying the abundances of minerals contained by planetary surfaces is still an open problem. With this paper we address one of the two main questions arising from the spectral unmixing problem. While the mathematical mixture model has been extensively researched, considerably less work has been committed to the selection of endmembers from a possibly huge database or catalog of potential endmembers. To solve the endmember selection problem we define a new spectral similarity measure that is not purely based on the reconstruction error, i.e. the squared difference between the modeled and the measured reflectance spectrum. To select reasonable endmembers, we extend the similarity measure by adding information extracted from the spectral absorption bands. This will allow for a better separation of spectrally similar minerals. Evaluating all possible subsets of a possibly very large catalog that contain at least one endmember leads to an exponential increase in computational complexity, rendering catalogs of 20-30 endmembers impractical. To overcome this computational limitation, we propose the usage of a genetic algorithm that, while initially starting with random subsets, forms new subsets by combining the best subsets and, to some extent, does a local search around the best subsets by randomly adding a few endmembers. A Monte-Carlo simulation based on synthetic mixtures and a catalog size varying from three to eight endmembers demonstrates that the genetic algorithm is expected to require less combinations to be evaluated than an exhaustive search if the catalog comprises 10 or more endmembers. Since the genetic algorithm evaluates some combinations multiple times, we propose a simple modification and store previously evaluated endmember combinations. The resulting algorithm is shown to never require more function evaluations than a

  13. There is a need for new systemic sclerosis subset criteria. A content analytic approach.

    Science.gov (United States)

    Johnson, S R; Soowamber, M L; Fransen, J; Khanna, D; Van Den Hoogen, F; Baron, M; Matucci-Cerinic, M; Denton, C P; Medsger, T A; Carreira, P E; Riemekasten, G; Distler, J; Gabrielli, A; Steen, V; Chung, L; Silver, R; Varga, J; Müller-Ladner, U; Vonk, M C; Walker, U A; Wollheim, F A; Herrick, A; Furst, D E; Czirjak, L; Kowal-Bielecka, O; Del Galdo, F; Cutolo, M; Hunzelmann, N; Murray, C D; Foeldvari, I; Mouthon, L; Damjanov, N; Kahaleh, B; Frech, T; Assassi, S; Saketkoo, L A; Pope, J E

    2018-01-01

    Systemic sclerosis (SSc) is heterogenous. The objectives of this study were to evaluate the purpose, strengths and limitations of existing SSc subset criteria, and identify ideas among experts about subsets. We conducted semi-structured interviews with randomly sampled international SSc experts. The interview transcripts underwent an iterative process with text deconstructed to single thought units until a saturated conceptual framework with coding was achieved and respondent occurrence tabulated. Serial cross-referential analyses of clusters were developed. Thirty experts from 13 countries were included; 67% were male, 63% were from Europe and 37% from North America; median experience of 22.5 years, with a median of 55 new SSc patients annually. Three thematic clusters regarding subsetting were identified: research and communication; management; and prognosis (prediction of internal organ involvement, survival). The strength of the limited/diffuse system was its ease of use, however 10% stated this system had marginal value. Shortcomings of the diffuse/limited classification were the risk of misclassification, predictions/generalizations did not always hold true, and that the elbow or knee threshold was arbitrary. Eighty-seven percent use more than 2 subsets including: SSc sine scleroderma, overlap conditions, antibody-determined subsets, speed of progression, and age of onset (juvenile, elderly). We have synthesized an international view of the construct of SSc subsets in the modern era. We found a number of factors underlying the construct of SSc subsets. Considerations for the next phase include rate of change and hierarchal clustering (e.g. limited/diffuse, then by antibodies).

  14. Comparison of subset-based local and FE-based global digital image correlation: Theoretical error analysis and validation

    KAUST Repository

    Pan, B.

    2016-03-22

    Subset-based local and finite-element-based (FE-based) global digital image correlation (DIC) approaches are the two primary image matching algorithms widely used for full-field displacement mapping. Very recently, the performances of these different DIC approaches have been experimentally investigated using numerical and real-world experimental tests. The results have shown that in typical cases, where the subset (element) size is no less than a few pixels and the local deformation within a subset (element) can be well approximated by the adopted shape functions, the subset-based local DIC outperforms FE-based global DIC approaches because the former provides slightly smaller root-mean-square errors and offers much higher computation efficiency. Here we investigate the theoretical origin and lay a solid theoretical basis for the previous comparison. We assume that systematic errors due to imperfect intensity interpolation and undermatched shape functions are negligibly small, and perform a theoretical analysis of the random errors or standard deviation (SD) errors in the displacements measured by two local DIC approaches (i.e., a subset-based local DIC and an element-based local DIC) and two FE-based global DIC approaches (i.e., Q4-DIC and Q8-DIC). The equations that govern the random errors in the displacements measured by these local and global DIC approaches are theoretically derived. The correctness of the theoretically predicted SD errors is validated through numerical translation tests under various noise levels. We demonstrate that the SD errors induced by the Q4-element-based local DIC, the global Q4-DIC and the global Q8-DIC are 4, 1.8-2.2 and 1.2-1.6 times greater, respectively, than that associated with the subset-based local DIC, which is consistent with our conclusions from previous work. © 2016 Elsevier Ltd. All rights reserved.

  15. 40 CFR 761.308 - Sample selection by random number generation on any two-dimensional square grid.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 30 2010-07-01 2010-07-01 false Sample selection by random number... § 761.79(b)(3) § 761.308 Sample selection by random number generation on any two-dimensional square... area created in accordance with paragraph (a) of this section, select two random numbers: one each for...

  16. Applications of random forest feature selection for fine-scale genetic population assignment.

    Science.gov (United States)

    Sylvester, Emma V A; Bentzen, Paul; Bradbury, Ian R; Clément, Marie; Pearce, Jon; Horne, John; Beiko, Robert G

    2018-02-01

    Genetic population assignment used to inform wildlife management and conservation efforts requires panels of highly informative genetic markers and sensitive assignment tests. We explored the utility of machine-learning algorithms (random forest, regularized random forest and guided regularized random forest) compared with F ST ranking for selection of single nucleotide polymorphisms (SNP) for fine-scale population assignment. We applied these methods to an unpublished SNP data set for Atlantic salmon ( Salmo salar ) and a published SNP data set for Alaskan Chinook salmon ( Oncorhynchus tshawytscha ). In each species, we identified the minimum panel size required to obtain a self-assignment accuracy of at least 90% using each method to create panels of 50-700 markers Panels of SNPs identified using random forest-based methods performed up to 7.8 and 11.2 percentage points better than F ST -selected panels of similar size for the Atlantic salmon and Chinook salmon data, respectively. Self-assignment accuracy ≥90% was obtained with panels of 670 and 384 SNPs for each data set, respectively, a level of accuracy never reached for these species using F ST -selected panels. Our results demonstrate a role for machine-learning approaches in marker selection across large genomic data sets to improve assignment for management and conservation of exploited populations.

  17. Interference-aware random beam selection for spectrum sharing systems

    KAUST Repository

    Abdallah, Mohamed M.

    2012-09-01

    Spectrum sharing systems have been introduced to alleviate the problem of spectrum scarcity by allowing secondary unlicensed networks to share the spectrum with primary licensed networks under acceptable interference levels to the primary users. In this paper, we develop interference-aware random beam selection schemes that provide enhanced throughput for the secondary link under the condition that the interference observed at the primary link is within a predetermined acceptable value. For a secondary transmitter equipped with multiple antennas, our schemes select a random beam, among a set of power- optimized orthogonal random beams, that maximizes the capacity of the secondary link while satisfying the interference constraint at the primary receiver for different levels of feedback information describing the interference level at the primary receiver. For the proposed schemes, we develop a statistical analysis for the signal-to-noise and interference ratio (SINR) statistics as well as the capacity of the secondary link. Finally, we present numerical results that study the effect of system parameters including number of beams and the maximum transmission power on the capacity of the secondary link attained using the proposed schemes. © 2012 IEEE.

  18. Selective disruption of acetylcholine synthesis in subsets of motor neurons: a new model of late-onset motor neuron disease.

    Science.gov (United States)

    Lecomte, Marie-José; Bertolus, Chloé; Santamaria, Julie; Bauchet, Anne-Laure; Herbin, Marc; Saurini, Françoise; Misawa, Hidemi; Maisonobe, Thierry; Pradat, Pierre-François; Nosten-Bertrand, Marika; Mallet, Jacques; Berrard, Sylvie

    2014-05-01

    Motor neuron diseases are characterized by the selective chronic dysfunction of a subset of motor neurons and the subsequent impairment of neuromuscular function. To reproduce in the mouse these hallmarks of diseases affecting motor neurons, we generated a mouse line in which ~40% of motor neurons in the spinal cord and the brainstem become unable to sustain neuromuscular transmission. These mice were obtained by conditional knockout of the gene encoding choline acetyltransferase (ChAT), the biosynthetic enzyme for acetylcholine. The mutant mice are viable and spontaneously display abnormal phenotypes that worsen with age including hunched back, reduced lifespan, weight loss, as well as striking deficits in muscle strength and motor function. This slowly progressive neuromuscular dysfunction is accompanied by muscle fiber histopathological features characteristic of neurogenic diseases. Unexpectedly, most changes appeared with a 6-month delay relative to the onset of reduction in ChAT levels, suggesting that compensatory mechanisms preserve muscular function for several months and then are overwhelmed. Deterioration of mouse phenotype after ChAT gene disruption is a specific aging process reminiscent of human pathological situations, particularly among survivors of paralytic poliomyelitis. These mutant mice may represent an invaluable tool to determine the sequence of events that follow the loss of function of a motor neuron subset as the disease progresses, and to evaluate therapeutic strategies. They also offer the opportunity to explore fundamental issues of motor neuron biology. Copyright © 2014 Elsevier Inc. All rights reserved.

  19. Interference-aware random beam selection for spectrum sharing systems

    KAUST Repository

    Abdallah, Mohamed M.; Sayed, Mostafa M.; Alouini, Mohamed-Slim; Qaraqe, Khalid A.

    2012-01-01

    . In this paper, we develop interference-aware random beam selection schemes that provide enhanced throughput for the secondary link under the condition that the interference observed at the primary link is within a predetermined acceptable value. For a secondary

  20. The signature of positive selection at randomly chosen loci.

    OpenAIRE

    Przeworski, Molly

    2002-01-01

    In Drosophila and humans, there are accumulating examples of loci with a significant excess of high-frequency-derived alleles or high levels of linkage disequilibrium, relative to a neutral model of a random-mating population of constant size. These are features expected after a recent selective sweep. Their prevalence suggests that positive directional selection may be widespread in both species. However, as I show here, these features do not persist long after the sweep ends: The high-frequ...

  1. Simulated Performance Evaluation of a Selective Tracker Through Random Scenario Generation

    DEFF Research Database (Denmark)

    Hussain, Dil Muhammad Akbar

    2006-01-01

    performance assessment. Therefore, a random target motion scenario is adopted. Its implementation in particular for testing the proposed selective track splitting algorithm using Kalman filters is investigated through a number of performance parameters which gives the activity profile of the tracking scenario......  The paper presents a simulation study on the performance of a target tracker using selective track splitting filter algorithm through a random scenario implemented on a digital signal processor.  In a typical track splitting filter all the observation which fall inside a likelihood ellipse...... are used for update, however, in our proposed selective track splitting filter less number of observations are used for track update.  Much of the previous performance work [1] has been done on specific (deterministic) scenarios. One of the reasons for considering the specific scenarios, which were...

  2. Minimization over randomly selected lines

    Directory of Open Access Journals (Sweden)

    Ismet Sahin

    2013-07-01

    Full Text Available This paper presents a population-based evolutionary optimization method for minimizing a given cost function. The mutation operator of this method selects randomly oriented lines in the cost function domain, constructs quadratic functions interpolating the cost function at three different points over each line, and uses extrema of the quadratics as mutated points. The crossover operator modifies each mutated point based on components of two points in population, instead of one point as is usually performed in other evolutionary algorithms. The stopping criterion of this method depends on the number of almost degenerate quadratics. We demonstrate that the proposed method with these mutation and crossover operations achieves faster and more robust convergence than the well-known Differential Evolution and Particle Swarm algorithms.

  3. Selection for altruism through random drift in variable size populations

    Directory of Open Access Journals (Sweden)

    Houchmandzadeh Bahram

    2012-05-01

    Full Text Available Abstract Background Altruistic behavior is defined as helping others at a cost to oneself and a lowered fitness. The lower fitness implies that altruists should be selected against, which is in contradiction with their widespread presence is nature. Present models of selection for altruism (kin or multilevel show that altruistic behaviors can have ‘hidden’ advantages if the ‘common good’ produced by altruists is restricted to some related or unrelated groups. These models are mostly deterministic, or assume a frequency dependent fitness. Results Evolutionary dynamics is a competition between deterministic selection pressure and stochastic events due to random sampling from one generation to the next. We show here that an altruistic allele extending the carrying capacity of the habitat can win by increasing the random drift of “selfish” alleles. In other terms, the fixation probability of altruistic genes can be higher than those of a selfish ones, even though altruists have a smaller fitness. Moreover when populations are geographically structured, the altruists advantage can be highly amplified and the fixation probability of selfish genes can tend toward zero. The above results are obtained both by numerical and analytical calculations. Analytical results are obtained in the limit of large populations. Conclusions The theory we present does not involve kin or multilevel selection, but is based on the existence of random drift in variable size populations. The model is a generalization of the original Fisher-Wright and Moran models where the carrying capacity depends on the number of altruists.

  4. Selection bias and subject refusal in a cluster-randomized controlled trial

    Directory of Open Access Journals (Sweden)

    Rochelle Yang

    2017-07-01

    Full Text Available Abstract Background Selection bias and non-participation bias are major methodological concerns which impact external validity. Cluster-randomized controlled trials are especially prone to selection bias as it is impractical to blind clusters to their allocation into intervention or control. This study assessed the impact of selection bias in a large cluster-randomized controlled trial. Methods The Improved Cardiovascular Risk Reduction to Enhance Rural Primary Care (ICARE study examined the impact of a remote pharmacist-led intervention in twelve medical offices. To assess eligibility, a standardized form containing patient demographics and medical information was completed for each screened patient. Eligible patients were approached by the study coordinator for recruitment. Both the study coordinator and the patient were aware of the site’s allocation prior to consent. Patients who consented or declined to participate were compared across control and intervention arms for differing characteristics. Statistical significance was determined using a two-tailed, equal variance t-test and a chi-square test with adjusted Bonferroni p-values. Results were adjusted for random cluster variation. Results There were 2749 completed screening forms returned to research staff with 461 subjects who had either consented or declined participation. Patients with poorly controlled diabetes were found to be significantly more likely to decline participation in intervention sites compared to those in control sites. A higher mean diastolic blood pressure was seen in patients with uncontrolled hypertension who declined in the control sites compared to those who declined in the intervention sites. However, these findings were no longer significant after adjustment for random variation among the sites. After this adjustment, females were now found to be significantly more likely to consent than males (odds ratio = 1.41; 95% confidence interval = 1.03, 1

  5. MODIS/Aqua Atmosphere Aeronet Subsetting Product

    Data.gov (United States)

    National Aeronautics and Space Administration — The MODIS/Aqua Atmosphere Aeronet Subsetting Product (MYDARNSS) consists of MODIS Atmosphere and Ancillary Products subsets that are generated over a number of...

  6. Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data

    Directory of Open Access Journals (Sweden)

    Arpita Nagpal

    2017-01-01

    Full Text Available Microarray data usually contain a large number of genes, but a small number of samples. Feature subset selection for microarray data aims at reducing the number of genes so that useful information can be extracted from the samples. Reducing the dimension of data sets further helps in improving the computational efficiency of the learning model. In this paper, we propose a modified algorithm based on the tabu search as local search procedures to a Greedy Randomized Adaptive Search Procedure (GRASP for high dimensional microarray data sets. The proposed Tabu based Greedy Randomized Adaptive Search Procedure algorithm is named as TGRASP. In TGRASP, a new parameter has been introduced named as Tabu Tenure and the existing parameters, NumIter and size have been modified. We observed that different parameter settings affect the quality of the optimum. The second proposed algorithm known as FFGRASP (Firefly Greedy Randomized Adaptive Search Procedure uses a firefly optimization algorithm in the local search optimzation phase of the greedy randomized adaptive search procedure (GRASP. Firefly algorithm is one of the powerful algorithms for optimization of multimodal applications. Experimental results show that the proposed TGRASP and FFGRASP algorithms are much better than existing algorithm with respect to three performance parameters viz. accuracy, run time, number of a selected subset of features. We have also compared both the approaches with a unified metric (Extended Adjusted Ratio of Ratios which has shown that TGRASP approach outperforms existing approach for six out of nine cancer microarray datasets and FFGRASP performs better on seven out of nine datasets.

  7. TCR tuning of T cell subsets.

    Science.gov (United States)

    Cho, Jae-Ho; Sprent, Jonathan

    2018-05-01

    After selection in the thymus, the post-thymic T cell compartments comprise heterogenous subsets of naive and memory T cells that make continuous T cell receptor (TCR) contact with self-ligands bound to major histocompatibility complex (MHC) molecules. T cell recognition of self-MHC ligands elicits covert TCR signaling and is particularly important for controlling survival of naive T cells. Such tonic TCR signaling is tightly controlled and maintains the cells in a quiescent state to avoid autoimmunity. Here, we review how naive and memory T cells are differentially tuned and wired for TCR sensitivity to self and foreign ligands. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  8. Interference-aware random beam selection schemes for spectrum sharing systems

    KAUST Repository

    Abdallah, Mohamed; Qaraqe, Khalid; Alouini, Mohamed-Slim

    2012-01-01

    users. In this work, we develop interference-aware random beam selection schemes that provide enhanced performance for the secondary network under the condition that the interference observed by the receivers of the primary network is below a

  9. Different hunting strategies select for different weights in red deer

    Science.gov (United States)

    Martínez, María; Rodríguez-Vigal, Carlos; Jones, Owen R; Coulson, Tim; Miguel, Alfonso San

    2005-01-01

    Much insight can be derived from records of shot animals. Most researchers using such data assume that their data represents a random sample of a particular demographic class. However, hunters typically select a non-random subset of the population and hunting is, therefore, not a random process. Here, with red deer (Cervus elaphus) hunting data from a ranch in Toledo, Spain, we demonstrate that data collection methods have a significant influence upon the apparent relationship between age and weight. We argue that a failure to correct for such methodological bias may have significant consequences for the interpretation of analyses involving weight or correlated traits such as breeding success, and urge researchers to explore methods to identify and correct for such bias in their data. PMID:17148205

  10. Toll-like receptor activation reveals developmental reorganization and unmasks responder subsets of microglia

    NARCIS (Netherlands)

    Scheffel, Joerg; Regen, Tommy; Van Rossum, Denise; Seifert, Stefanie; Ribes, Sandra; Nau, Roland; Parsa, Roham; Harris, Robert A.; Boddeke, Hendrikus W. G. M.; Chuang, Han-Ning; Pukrop, Tobias; Wessels, Johannes T.; Juergens, Tanja; Merkler, Doron; Brueck, Wolfgang; Schnaars, Mareike; Simons, Mikael; Kettenmann, Helmut; Hanisch, Uwe-Karsten

    2012-01-01

    The sentinel and immune functions of microglia require rapid and appropriate reactions to infection and damage. Their Toll-like receptors (TLRs) sense both as threats. However, whether activated microglia mount uniform responses or whether subsets conduct selective tasks is unknown. We demonstrate

  11. On Maximal Non-Disjoint Families of Subsets

    Directory of Open Access Journals (Sweden)

    Yu. A. Zuev

    2017-01-01

    Full Text Available The paper studies maximal non-disjoint families of subsets of a finite set. Non-disjointness means that any two subsets of a family have a nonempty intersection. The maximality is expressed by the fact that adding a new subset to the family cannot increase its power without violating a non-disjointness condition. Studying the properties of such families is an important section of the extreme theory of sets. Along with purely combinatorial interest, the problems considered here play an important role in informatics, anti-noise coding, and cryptography.In 1961 this problem saw the light of day in the Erdos, Ko and Rado paper, which established a maximum power of the non-disjoint family of subsets of equal power. In 1974 the Erdos and Claytman publication estimated the number of maximal non-disjoint families of subsets without involving the equality of their power. These authors failed to establish an asymptotics of the logarithm of the number of such families when the power of a basic finite set tends to infinity. However, they suggested such an asymptotics as a hypothesis. A.D. Korshunov in two publications in 2003 and 2005 established the asymptotics for the number of non-disjoint families of the subsets of arbitrary powers without maximality condition of these families.The basis for the approach used in the paper to study the families of subsets is their description in the language of Boolean functions. A one-to-one correspondence between a family of subsets and a Boolean function is established by the fact that the characteristic vectors of subsets of a family are considered to be the unit sets of a Boolean function. The main theoretical result of the paper is that the maximal non-disjoint families are in one-to-one correspondence with the monotonic self-dual Boolean functions. When estimating the number of maximal non-disjoint families, this allowed us to use the result of A.A. Sapozhenko, who established the asymptotics of the number of the

  12. SAR Subsets for Selected Field Sites, 2007-2010

    Data.gov (United States)

    National Aeronautics and Space Administration — ABSTRACT: This data set provides Synthetic Aperture Radar (SAR) images for 42 selected sites from various terrestrial ecology and meteorological monitoring networks...

  13. SAR Subsets for Selected Field Sites, 2007-2010

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set provides Synthetic Aperture Radar (SAR) images for 42 selected sites from various terrestrial ecology and meteorological monitoring networks including...

  14. Continuous-Time Mean-Variance Portfolio Selection with Random Horizon

    International Nuclear Information System (INIS)

    Yu, Zhiyong

    2013-01-01

    This paper examines the continuous-time mean-variance optimal portfolio selection problem with random market parameters and random time horizon. Treating this problem as a linearly constrained stochastic linear-quadratic optimal control problem, I explicitly derive the efficient portfolios and efficient frontier in closed forms based on the solutions of two backward stochastic differential equations. Some related issues such as a minimum variance portfolio and a mutual fund theorem are also addressed. All the results are markedly different from those in the problem with deterministic exit time. A key part of my analysis involves proving the global solvability of a stochastic Riccati equation, which is interesting in its own right

  15. Continuous-Time Mean-Variance Portfolio Selection with Random Horizon

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Zhiyong, E-mail: yuzhiyong@sdu.edu.cn [Shandong University, School of Mathematics (China)

    2013-12-15

    This paper examines the continuous-time mean-variance optimal portfolio selection problem with random market parameters and random time horizon. Treating this problem as a linearly constrained stochastic linear-quadratic optimal control problem, I explicitly derive the efficient portfolios and efficient frontier in closed forms based on the solutions of two backward stochastic differential equations. Some related issues such as a minimum variance portfolio and a mutual fund theorem are also addressed. All the results are markedly different from those in the problem with deterministic exit time. A key part of my analysis involves proving the global solvability of a stochastic Riccati equation, which is interesting in its own right.

  16. TEHRAN AIR POLLUTANTS PREDICTION BASED ON RANDOM FOREST FEATURE SELECTION METHOD

    Directory of Open Access Journals (Sweden)

    A. Shamsoddini

    2017-09-01

    Full Text Available Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.

  17. Tehran Air Pollutants Prediction Based on Random Forest Feature Selection Method

    Science.gov (United States)

    Shamsoddini, A.; Aboodi, M. R.; Karami, J.

    2017-09-01

    Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.

  18. High Aldehyde Dehydrogenase Activity Identifies a Subset of Human Mesenchymal Stromal Cells with Vascular Regenerative Potential.

    Science.gov (United States)

    Sherman, Stephen E; Kuljanin, Miljan; Cooper, Tyler T; Putman, David M; Lajoie, Gilles A; Hess, David A

    2017-06-01

    During culture expansion, multipotent mesenchymal stromal cells (MSCs) differentially express aldehyde dehydrogenase (ALDH), an intracellular detoxification enzyme that protects long-lived cells against oxidative stress. Thus, MSC selection based on ALDH-activity may be used to reduce heterogeneity and distinguish MSC subsets with improved regenerative potency. After expansion of human bone marrow-derived MSCs, cell progeny was purified based on low versus high ALDH-activity (ALDH hi ) by fluorescence-activated cell sorting, and each subset was compared for multipotent stromal and provascular regenerative functions. Both ALDH l ° and ALDH hi MSC subsets demonstrated similar expression of stromal cell (>95% CD73 + , CD90 + , CD105 + ) and pericyte (>95% CD146 + ) surface markers and showed multipotent differentiation into bone, cartilage, and adipose cells in vitro. Conditioned media (CDM) generated by ALDH hi MSCs demonstrated a potent proliferative and prosurvival effect on human microvascular endothelial cells (HMVECs) under serum-free conditions and augmented HMVEC tube-forming capacity in growth factor-reduced matrices. After subcutaneous transplantation within directed in vivo angiogenesis assay implants into immunodeficient mice, ALDH hi MSC or CDM produced by ALDH hi MSC significantly augmented murine vascular cell recruitment and perfused vessel infiltration compared with ALDH l ° MSC. Although both subsets demonstrated strikingly similar mRNA expression patterns, quantitative proteomic analyses performed on subset-specific CDM revealed the ALDH hi MSC subset uniquely secreted multiple proangiogenic cytokines (vascular endothelial growth factor beta, platelet derived growth factor alpha, and angiogenin) and actively produced multiple factors with chemoattractant (transforming growth factor-β, C-X-C motif chemokine ligand 1, 2, and 3 (GRO), C-C motif chemokine ligand 5 (RANTES), monocyte chemotactic protein 1 (MCP-1), interleukin [IL]-6, IL-8) and matrix

  19. Efficient Secure Multiparty Subset Computation

    Directory of Open Access Journals (Sweden)

    Sufang Zhou

    2017-01-01

    Full Text Available Secure subset problem is important in secure multiparty computation, which is a vital field in cryptography. Most of the existing protocols for this problem can only keep the elements of one set private, while leaking the elements of the other set. In other words, they cannot solve the secure subset problem perfectly. While a few studies have addressed actual secure subsets, these protocols were mainly based on the oblivious polynomial evaluations with inefficient computation. In this study, we first design an efficient secure subset protocol for sets whose elements are drawn from a known set based on a new encoding method and homomorphic encryption scheme. If the elements of the sets are taken from a large domain, the existing protocol is inefficient. Using the Bloom filter and homomorphic encryption scheme, we further present an efficient protocol with linear computational complexity in the cardinality of the large set, and this is considered to be practical for inputs consisting of a large number of data. However, the second protocol that we design may yield a false positive. This probability can be rapidly decreased by reexecuting the protocol with different hash functions. Furthermore, we present the experimental performance analyses of these protocols.

  20. Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex

    Science.gov (United States)

    Lindsay, Grace W.

    2017-01-01

    Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by the prefrontal cortex (PFC). Neural activity in the PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear “mixed” selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which the PFC exhibits computationally relevant properties, such as mixed selectivity, and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data show significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and enables the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results provide clues about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training. SIGNIFICANCE STATEMENT The prefrontal cortex is a brain region believed to support the ability of animals to engage in complex behavior. How neurons in this area respond to stimuli—and in particular, to combinations of stimuli (

  1. Blood Monocyte Subsets and Selected Cardiovascular Risk Markers in Rheumatoid Arthritis of Short Duration in relation to Disease Activity

    Directory of Open Access Journals (Sweden)

    Ewa Klimek

    2014-01-01

    Full Text Available Objectives. To evaluate blood monocyte subsets and functional monocyte properties in patients with rheumatoid arthritis (RA of short duration in the context of cardiovascular (CV risk and disease activity. Methods. We studied conventional markers of CV risk, intima media thickness (IMT, and blood monocyte subsets in 27 patients aged 41 ± 10 years with RA of short duration (median 12 months and 22 healthy controls. The RA subjects were divided into low (DAS28: 2.6–5.1 and high (DAS28 > 5.1 disease activity. Results. RA patients exhibited increased levels of intermediate (CD14++CD16+ monocytes with decreased CD45RA expression compared to controls, increased counts of classical (CD14++CD16− monocytes, and decreased percentages of nonclassical (CD14+CD16++ monocytes. Patients with high disease activity had lower HLA DR expression on classical monocytes compared to low disease activity patients. There were no differences in monocyte subsets between subjects with DAS > 5.1 and DAS ≤ 5.1. There were no significant intergroup differences in IMT and the majority of classical CV risk factors. Conclusions. Patients with RA of short duration show alteration in peripheral blood monocyte subsets despite the fact that there is no evidence of subclinical atherosclerosis. Disease activity assessed with DAS28 was associated with impaired functional properties but not with a shift in monocyte subpopulations.

  2. Performance Evaluation of User Selection Protocols in Random Networks with Energy Harvesting and Hardware Impairments

    Directory of Open Access Journals (Sweden)

    Tan Nhat Nguyen

    2016-01-01

    Full Text Available In this paper, we evaluate performances of various user selection protocols under impact of hardware impairments. In the considered protocols, a Base Station (BS selects one of available Users (US to serve, while the remaining USs harvest the energy from the Radio Frequency (RF transmitted by the BS. We assume that all of the US randomly appear around the BS. In the Random Selection Protocol (RAN, the BS randomly selects a US to transmit the data. In the second proposed protocol, named Minimum Distance Protocol (MIND, the US that is nearest to the BS will be chosen. In the Optimal Selection Protocol (OPT, the US providing the highest channel gain between itself and the BS will be served. For performance evaluation, we derive exact and asymptotic closed-form expressions of average Outage Probability (OP over Rayleigh fading channels. We also consider average harvested energy per a US. Finally, Monte-Carlo simulations are then performed to verify the theoretical results.

  3. Change of T, B lymphocyte subsets and Th1/Th2 indexes of patients with recurrent spontaneous abortion

    Institute of Scientific and Technical Information of China (English)

    Shi-Hua Zhou

    2016-01-01

    Objective:To analyze and investigate the change state of T, B lymphocyte subsets and Th1/Th2 indexes of patients with recurrent spontaneous abortion. Methods: A total of 92 patients with recurrent spontaneous abortion in our hospital from June 2013 to July 2015 were selected as the observation group and 92 women with health delivery history at the same time were selected as the control group,then the peripheral blood T, B lymphocyte subsets and Th1/Th2 indexes of two groups were detected and compared and the peripheral blood T, B lymphocyte subsets and Th1/Th2 indexes of patients with different gestational age at abortion and abortion times were compared too. Results:The peripheral blood T, B lymphocyte subsets and Th1/Th2 indexes of observation group and control group all had obvious differences,and those blood indexes levels' differences of patients with different gestational age at abortion and abortion times were obvious too, all P<0.05 and the differences were significant. Conclusions: The T, B lymphocyte subsets and Th1/Th2 indexes of patients with recurrent spontaneous abortion show abnormal state and the differences of detection results of patients with different gestational age at abortion and abortion times are relatively obvious,so those indexes should be monitored and improved intentinonally.

  4. The reliability of randomly selected final year pharmacy students in ...

    African Journals Online (AJOL)

    Employing ANOVA, factorial experimental analysis, and the theory of error, reliability studies were conducted on the assessment of the drug product chloroquine phosphate tablets. The G–Study employed equal numbers of the factors for uniform control, and involved three analysts (randomly selected final year Pharmacy ...

  5. Random selection of items. Selection of n1 samples among N items composing a stratum

    International Nuclear Information System (INIS)

    Jaech, J.L.; Lemaire, R.J.

    1987-02-01

    STR-224 provides generalized procedures to determine required sample sizes, for instance in the course of a Physical Inventory Verification at Bulk Handling Facilities. The present report describes procedures to generate random numbers and select groups of items to be verified in a given stratum through each of the measurement methods involved in the verification. (author). 3 refs

  6. Systematic wavelength selection for improved multivariate spectral analysis

    Science.gov (United States)

    Thomas, Edward V.; Robinson, Mark R.; Haaland, David M.

    1995-01-01

    Methods and apparatus for determining in a biological material one or more unknown values of at least one known characteristic (e.g. the concentration of an analyte such as glucose in blood or the concentration of one or more blood gas parameters) with a model based on a set of samples with known values of the known characteristics and a multivariate algorithm using several wavelength subsets. The method includes selecting multiple wavelength subsets, from the electromagnetic spectral region appropriate for determining the known characteristic, for use by an algorithm wherein the selection of wavelength subsets improves the model's fitness of the determination for the unknown values of the known characteristic. The selection process utilizes multivariate search methods that select both predictive and synergistic wavelengths within the range of wavelengths utilized. The fitness of the wavelength subsets is determined by the fitness function F=.function.(cost, performance). The method includes the steps of: (1) using one or more applications of a genetic algorithm to produce one or more count spectra, with multiple count spectra then combined to produce a combined count spectrum; (2) smoothing the count spectrum; (3) selecting a threshold count from a count spectrum to select these wavelength subsets which optimize the fitness function; and (4) eliminating a portion of the selected wavelength subsets. The determination of the unknown values can be made: (1) noninvasively and in vivo; (2) invasively and in vivo; or (3) in vitro.

  7. Different spectra of recurrent gene mutations in subsets of chronic lymphocytic leukemia harboring stereotyped B-cell receptors

    DEFF Research Database (Denmark)

    Sutton, Lesley-Ann; Young, Emma; Baliakas, Panagiotis

    2016-01-01

    We report on markedly different frequencies of genetic lesions within subsets of chronic lymphocytic leukemia patients carrying mutated or unmutated stereotyped B-cell receptor immunoglobulins in the largest cohort (n=565) studied for this purpose. By combining data on recurrent gene mutations...... subsets implies that the mechanisms underlying clinical aggressiveness are not uniform, but rather support the existence of distinct genetic pathways of clonal evolution governed by a particular stereotyped B-cell receptor selecting a certain molecular lesion(s)....

  8. ';Best' Practices for Aggregating Subset Results from Archived Datasets

    Science.gov (United States)

    Baskin, W. E.; Perez, J.

    2013-12-01

    In response to the exponential growth in science data analysis and visualization capabilities Data Centers have been developing new delivery mechanisms to package and deliver large volumes of aggregated subsets of archived data. New standards are evolving to help data providers and application programmers deal with growing needs of the science community. These standards evolve from the best practices gleaned from new products and capabilities. The NASA Atmospheric Sciences Data Center (ASDC) has developed and deployed production provider-specific search and subset web applications for the CALIPSO, CERES, TES, and MOPITT missions. This presentation explores several use cases that leverage aggregated subset results and examines the standards and formats ASDC developers applied to the delivered files as well as the implementation strategies for subsetting and processing the aggregated products. The following topics will be addressed: - Applications of NetCDF CF conventions to aggregated level 2 satellite subsets - Data-Provider-Specific format requirements vs. generalized standards - Organization of the file structure of aggregated NetCDF subset output - Global Attributes of individual subsetted files vs. aggregated results - Specific applications and framework used for subsetting and delivering derivative data files

  9. Data mining a small molecule drug screening representative subset from NIH PubChem.

    Science.gov (United States)

    Xie, Xiang-Qun; Chen, Jian-Zhong

    2008-03-01

    PubChem is a scientific showcase of the NIH Roadmap Initiatives. It is a compound repository created to facilitate information exchange and data sharing among the NIH Roadmap-funded Molecular Library Screening Center Network (MLSCN) and the scientific community. However, PubChem has more than 10 million records of compound information. It will be challenging to conduct a drug screening of the whole database of millions of compounds. Thus, the purpose of the present study was to develop a data mining cheminformatics approach in order to construct a representative and structure-diverse sublibrary from the large PubChem database. In this study, a new chemical diverse representative subset, rePubChem, was selected by whole-molecule chemistry-space matrix calculation using the cell-based partition algorithm. The representative subset was generated and was then subjected to evaluations by compound property analyses based on 1D and 2D molecular descriptors. The new subset was also examined and assessed for self-similarity analysis based on 2D molecular fingerprints in comparing with the source compound library. The new subset has a much smaller library size (540K compounds) with minimum similarity and redundancy without loss of the structural diversity and basic molecular properties of its parent library (5.3 million compounds). The new representative subset library generated could be a valuable structure-diverse compound resource for in silico virtual screening and in vitro HTS drug screening. In addition, the established subset generation method of using the combined cell-based chemistry-space partition metrics with pairwised 2D fingerprint-based similarity search approaches will also be important to a broad scientific community interested in acquiring structurally diverse compounds for efficient drug screening, building representative virtual combinatorial chemistry libraries for syntheses, and data mining large compound databases like the PubChem library in general.

  10. Bias in random forest variable importance measures: Illustrations, sources and a solution

    Directory of Open Access Journals (Sweden)

    Hothorn Torsten

    2007-01-01

    Full Text Available Abstract Background Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types, for example when predictors include both sequence data and continuous variables such as folding energy, or when amino acid sequence data show different numbers of categories. Results Simulation studies are presented illustrating that, when random forest variable importance measures are used with data of varying types, the results are misleading because suboptimal predictor variables may be artificially preferred in variable selection. The two mechanisms underlying this deficiency are biased variable selection in the individual classification trees used to build the random forest on one hand, and effects induced by bootstrap sampling with replacement on the other hand. Conclusion We propose to employ an alternative implementation of random forests, that provides unbiased variable selection in the individual classification trees. When this method is applied using subsampling without replacement, the resulting variable importance measures can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale of measurement or their number of categories. The usage of both random forest algorithms and their variable importance measures in the R system for statistical computing is illustrated and

  11. The mathematics of random mutation and natural selection for multiple simultaneous selection pressures and the evolution of antimicrobial drug resistance.

    Science.gov (United States)

    Kleinman, Alan

    2016-12-20

    The random mutation and natural selection phenomenon act in a mathematically predictable behavior, which when understood leads to approaches to reduce and prevent the failure of the use of these selection pressures when treating infections and cancers. The underlying principle to impair the random mutation and natural selection phenomenon is to use combination therapy, which forces the population to evolve to multiple selection pressures simultaneously that invoke the multiplication rule of probabilities simultaneously as well. Recently, it has been seen that combination therapy for the treatment of malaria has failed to prevent the emergence of drug-resistant variants. Using this empirical example and the principles of probability theory, the derivation of the equations describing this treatment failure is carried out. These equations give guidance as to how to use combination therapy for the treatment of cancers and infectious diseases and prevent the emergence of drug resistance. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  12. A Bayesian random effects discrete-choice model for resource selection: Population-level selection inference

    Science.gov (United States)

    Thomas, D.L.; Johnson, D.; Griffith, B.

    2006-01-01

    Modeling the probability of use of land units characterized by discrete and continuous measures, we present a Bayesian random-effects model to assess resource selection. This model provides simultaneous estimation of both individual- and population-level selection. Deviance information criterion (DIC), a Bayesian alternative to AIC that is sample-size specific, is used for model selection. Aerial radiolocation data from 76 adult female caribou (Rangifer tarandus) and calf pairs during 1 year on an Arctic coastal plain calving ground were used to illustrate models and assess population-level selection of landscape attributes, as well as individual heterogeneity of selection. Landscape attributes included elevation, NDVI (a measure of forage greenness), and land cover-type classification. Results from the first of a 2-stage model-selection procedure indicated that there is substantial heterogeneity among cow-calf pairs with respect to selection of the landscape attributes. In the second stage, selection of models with heterogeneity included indicated that at the population-level, NDVI and land cover class were significant attributes for selection of different landscapes by pairs on the calving ground. Population-level selection coefficients indicate that the pairs generally select landscapes with higher levels of NDVI, but the relationship is quadratic. The highest rate of selection occurs at values of NDVI less than the maximum observed. Results for land cover-class selections coefficients indicate that wet sedge, moist sedge, herbaceous tussock tundra, and shrub tussock tundra are selected at approximately the same rate, while alpine and sparsely vegetated landscapes are selected at a lower rate. Furthermore, the variability in selection by individual caribou for moist sedge and sparsely vegetated landscapes is large relative to the variability in selection of other land cover types. The example analysis illustrates that, while sometimes computationally intense, a

  13. Subset Analysis of a Multicenter, Randomized Controlled Trial to Compare Magnifying Chromoendoscopy with Endoscopic Ultrasonography for Stage Diagnosis of Early Stage Colorectal Cancer.

    Science.gov (United States)

    Yamada, Tomonori; Shimura, Takaya; Ebi, Masahide; Hirata, Yoshikazu; Nishiwaki, Hirotaka; Mizushima, Takashi; Asukai, Koki; Togawa, Shozo; Takahashi, Satoru; Joh, Takashi

    2015-01-01

    Our recent prospective study found equivalent accuracy of magnifying chromoendoscopy (MC) and endoscopic ultrasonography (EUS) for diagnosing the invasion depth of colorectal cancer (CRC); however, whether these tools show diagnostic differences in categories such as tumor size and morphology remains unclear. Hence, we conducted detailed subset analysis of the prospective data. In this multicenter, prospective, comparative trial, a total of 70 patients with early, flat CRC were enrolled from February 2011 to December 2012, and the results of 66 lesions were finally analyzed. Patients were randomly allocated to primary MC followed by EUS or to primary EUS followed by MC. Diagnoses of invasion depth by each tool were divided into intramucosal to slight submucosal invasion (invasion depth final pathological diagnosis by an independent pathologist blinded to clinical data. To standardize diagnoses among examiners, this trial was started after achievement of a mean κ value of ≥0.6 which was calculated from the average of κ values between each pair of participating endoscopists. Both MC and EUS showed similar diagnostic outcomes, with no significant differences in prediction of invasion depth in subset analyses according to tumor size, location, and morphology. Lesions that were consistently diagnosed as Tis/T1-SMS or ≥T1-SMD with both tools revealed accuracy of 76-78%. Accuracy was low in borderline lesions with irregular pit pattern in MC and distorted findings of the third layer in EUS (MC, 58.5%; EUS, 50.0%). MC and EUS showed the same limited accuracy for predicting invasion depth in all categories of early CRC. Since the irregular pit pattern in MC, distorted findings to the third layer in EUS and inconsistent diagnosis between both tools were associated with low accuracy, further refinements or even novel methods are still needed for such lesions. University hospital Medical Information Network Clinical Trials Registry UMIN 000005085.

  14. Randomized interpolative decomposition of separated representations

    Science.gov (United States)

    Biagioni, David J.; Beylkin, Daniel; Beylkin, Gregory

    2015-01-01

    We introduce an algorithm to compute tensor interpolative decomposition (dubbed CTD-ID) for the reduction of the separation rank of Canonical Tensor Decompositions (CTDs). Tensor ID selects, for a user-defined accuracy ɛ, a near optimal subset of terms of a CTD to represent the remaining terms via a linear combination of the selected terms. CTD-ID can be used as an alternative to or in combination with the Alternating Least Squares (ALS) algorithm. We present examples of its use within a convergent iteration to compute inverse operators in high dimensions. We also briefly discuss the spectral norm as a computational alternative to the Frobenius norm in estimating approximation errors of tensor ID. We reduce the problem of finding tensor IDs to that of constructing interpolative decompositions of certain matrices. These matrices are generated via randomized projection of the terms of the given tensor. We provide cost estimates and several examples of the new approach to the reduction of separation rank.

  15. The signature of positive selection at randomly chosen loci.

    Science.gov (United States)

    Przeworski, Molly

    2002-03-01

    In Drosophila and humans, there are accumulating examples of loci with a significant excess of high-frequency-derived alleles or high levels of linkage disequilibrium, relative to a neutral model of a random-mating population of constant size. These are features expected after a recent selective sweep. Their prevalence suggests that positive directional selection may be widespread in both species. However, as I show here, these features do not persist long after the sweep ends: The high-frequency alleles drift to fixation and no longer contribute to polymorphism, while linkage disequilibrium is broken down by recombination. As a result, loci chosen without independent evidence of recent selection are not expected to exhibit either of these features, even if they have been affected by numerous sweeps in their genealogical history. How then can we explain the patterns in the data? One possibility is population structure, with unequal sampling from different subpopulations. Alternatively, positive selection may not operate as is commonly modeled. In particular, the rate of fixation of advantageous mutations may have increased in the recent past.

  16. Characterization of a subset of oligodendrocytes separated on the basis of selective adherence properties.

    Science.gov (United States)

    Szuchet, S; Yim, S H

    1984-01-01

    A subset of oligodendrocytes (B3,f) was isolated by taking advantage of selective cell-substratum interaction. B3,f cells were characterized morphologically, biochemically, and immunocytochemically. Oligodendrocytes were isolated from 4-to-6-month-old lamb brains by a modified version of our published procedure [Szuchet et al, J Neurosci Methods 3:7-19, 1980]. Freshly isolated cells from band III were plated on plastic culture plates at a concentration of 2 X 10(6) cells/ml. Approximately 40% of the cells attached to the plate under these conditions. The remaining cells formed small floating clusters. We refer to the latter as B3,f oligodendrocytes. After 4 to 5 days, the supernatant containing B3,f cells was removed and centrifuged, and the pellet was resuspended in culture medium and replated on polylysine-coated petri dishes. B3,f oligodendrocytes attached to this surface and extended an intricate network of processes. The purity of the cultures, judged by the number of cells staining with a monoclonal antibody against galactocerebroside was 98-99%. This high degree of cell homogeneity was maintained throughout the life of the cultures. B3,f cells appeared to be highly differentiated and remained so in vitro. This is surmised by the expression of oligodendrocytic characteristic functions such as high levels of CNPase activity typically, 5 microM/min/mgP; high incorporation of H2 35SO4 into sulfatides, an overall lipid metabolism that mimics events associated with myelinogenesis [Szuchet et al, PNAS 80:7019-7023, 1983]; the presence, detected immunocytochemically, of myelin-associated glycoprotein and myelin basic proteins. It is concluded that this culture system offers an opportunity for studying the biology of interfascicular oligodendrocytes and their interaction with neurons and/or astrocytes. It also should open up a way of examining the relevance of oligodendrocyte polymorphism.

  17. Differential privacy-based evaporative cooling feature selection and classification with relief-F and random forests.

    Science.gov (United States)

    Le, Trang T; Simmons, W Kyle; Misaki, Masaya; Bodurka, Jerzy; White, Bill C; Savitz, Jonathan; McKinney, Brett A

    2017-09-15

    Classification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classification accuracy but must be incorporated carefully into cross-validation to avoid overfitting. Recently, feature selection methods based on differential privacy, such as differentially private random forests and reusable holdout sets, have been proposed. However, for domains such as bioinformatics, where the number of features is much larger than the number of observations p≫n , these differential privacy methods are susceptible to overfitting. We introduce private Evaporative Cooling, a stochastic privacy-preserving machine learning algorithm that uses Relief-F for feature selection and random forest for privacy preserving classification that also prevents overfitting. We relate the privacy-preserving threshold mechanism to a thermodynamic Maxwell-Boltzmann distribution, where the temperature represents the privacy threshold. We use the thermal statistical physics concept of Evaporative Cooling of atomic gases to perform backward stepwise privacy-preserving feature selection. On simulated data with main effects and statistical interactions, we compare accuracies on holdout and validation sets for three privacy-preserving methods: the reusable holdout, reusable holdout with random forest, and private Evaporative Cooling, which uses Relief-F feature selection and random forest classification. In simulations where interactions exist between attributes, private Evaporative Cooling provides higher classification accuracy without overfitting based on an independent validation set. In simulations without interactions, thresholdout with random forest and private Evaporative Cooling give comparable accuracies. We also apply these privacy methods to human brain resting-state fMRI data from a study of major depressive disorder. Code

  18. Effect of CPAP on Cardiac Function in Minimally Symptomatic Patients with OSA: Results from a Subset of the MOSAIC Randomized Trial.

    Science.gov (United States)

    Craig, Sonya; Kylintireas, Ilias; Kohler, Malcolm; Nicoll, Debby; Bratton, Daniel J; Nunn, Andrew J; Leeson, Paul; Neubauer, Stefan; Stradling, John R

    2015-09-15

    Minimally symptomatic obstructive sleep apnea (OSA) is highly prevalent, and the effects of continuous positive airway pressure (CPAP) on myocardial function in these patients are unknown. The MOSAIC randomized, controlled trial of CPAP for minimally symptomatic OSA assessed the effect of CPAP on myocardial function in a subset of patients. Two centers taking part in the MOSAIC trial randomized 238 patients in parallel to 6 months of CPAP (120) or standard care (118). Of these, 168 patients had echocardiograms, and 68 patients had a cardiac magnetic resonance scan (CMR). A larger group (314) from 4 centers had brain natriuretic peptide (BNP) measured. Mean (SD) baseline oxygen desaturation index (ODI) and Epworth sleepiness score (ESS) were 13.5 (13.2), and 8.4 (4.0), respectively. CPAP significantly reduced ESS and ODI. Baseline LV ejection fraction (LVEF) was well preserved (60.4%). CPAP had no significant effect on echo-derived left atrial (LA) area (-1.0 cm2, 95% CI -2.6 to +0.6, p = 0.23) or early to late left ventricular filling velocity (E/A) ratio (-0.01, 95% CI -0.07 to +0.05, p = 0.79). There was a small change in echo-derived LV end diastolic volume (EDV) with CPAP (-5.9 mL, 95% CI -10.6 to -1.2, p = 0.015). No significant changes were detected by CMR on LV mass index (+1.1 g/m(2), 95% CI -5.9 to +8.0, p = 0.76) or LVEF (+0.8%, 95% CI -1.2 to +2.8, p = 0.41). CPAP did not affect BNP levels (p = 0.16). Six months of CPAP therapy does not change cardiac functional or structural parameters measured by echocardiogram or CMR in patients with minimally symptomatic mild-to-moderate OSA. ISRCTN 34164388 (http://isrctn.org). © 2015 American Academy of Sleep Medicine.

  19. Interference-aware random beam selection schemes for spectrum sharing systems

    KAUST Repository

    Abdallah, Mohamed

    2012-10-19

    Spectrum sharing systems have been recently introduced to alleviate the problem of spectrum scarcity by allowing secondary unlicensed networks to share the spectrum with primary licensed networks under acceptable interference levels to the primary users. In this work, we develop interference-aware random beam selection schemes that provide enhanced performance for the secondary network under the condition that the interference observed by the receivers of the primary network is below a predetermined/acceptable value. We consider a secondary link composed of a transmitter equipped with multiple antennas and a single-antenna receiver sharing the same spectrum with a primary link composed of a single-antenna transmitter and a single-antenna receiver. The proposed schemes select a beam, among a set of power-optimized random beams, that maximizes the signal-to-interference-plus-noise ratio (SINR) of the secondary link while satisfying the primary interference constraint for different levels of feedback information describing the interference level at the primary receiver. For the proposed schemes, we develop a statistical analysis for the SINR statistics as well as the capacity and bit error rate (BER) of the secondary link.

  20. Subset-sum phase transitions and data compression

    Science.gov (United States)

    Merhav, Neri

    2011-09-01

    We propose a rigorous analysis approach for the subset-sum problem in the context of lossless data compression, where the phase transition of the subset-sum problem is directly related to the passage between ambiguous and non-ambiguous decompression, for a compression scheme that is based on specifying the sequence composition. The proposed analysis lends itself to straightforward extensions in several directions of interest, including non-binary alphabets, incorporation of side information at the decoder (Slepian-Wolf coding), and coding schemes based on multiple subset sums. It is also demonstrated that the proposed technique can be used to analyze the critical behavior in a more involved situation where the sequence composition is not specified by the encoder.

  1. Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms.

    Science.gov (United States)

    Žuvela, Petar; Liu, J Jay; Macur, Katarzyna; Bączek, Tomasz

    2015-10-06

    In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.

  2. Topology-selective jamming of fully-connected, code-division random-access networks

    Science.gov (United States)

    Polydoros, Andreas; Cheng, Unjeng

    1990-01-01

    The purpose is to introduce certain models of topology selective stochastic jamming and examine its impact on a class of fully-connected, spread-spectrum, slotted ALOHA-type random access networks. The theory covers dedicated as well as half-duplex units. The dominant role of the spatial duty factor is established, and connections with the dual concept of time selective jamming are discussed. The optimal choices of coding rate and link access parameters (from the users' side) and the jamming spatial fraction are numerically established for DS and FH spreading.

  3. Peculiarities of the statistics of spectrally selected fluorescence radiation in laser-pumped dye-doped random media

    Science.gov (United States)

    Yuvchenko, S. A.; Ushakova, E. V.; Pavlova, M. V.; Alonova, M. V.; Zimnyakov, D. A.

    2018-04-01

    We consider the practical realization of a new optical probe method of the random media which is defined as the reference-free path length interferometry with the intensity moments analysis. A peculiarity in the statistics of the spectrally selected fluorescence radiation in laser-pumped dye-doped random medium is discussed. Previously established correlations between the second- and the third-order moments of the intensity fluctuations in the random interference patterns, the coherence function of the probe radiation, and the path difference probability density for the interfering partial waves in the medium are confirmed. The correlations were verified using the statistical analysis of the spectrally selected fluorescence radiation emitted by a laser-pumped dye-doped random medium. Water solution of Rhodamine 6G was applied as the doping fluorescent agent for the ensembles of the densely packed silica grains, which were pumped by the 532 nm radiation of a solid state laser. The spectrum of the mean path length for a random medium was reconstructed.

  4. Atlas ranking and selection for automatic segmentation of the esophagus from CT scans

    Science.gov (United States)

    Yang, Jinzhong; Haas, Benjamin; Fang, Raymond; Beadle, Beth M.; Garden, Adam S.; Liao, Zhongxing; Zhang, Lifei; Balter, Peter; Court, Laurence

    2017-12-01

    In radiation treatment planning, the esophagus is an important organ-at-risk that should be spared in patients with head and neck cancer or thoracic cancer who undergo intensity-modulated radiation therapy. However, automatic segmentation of the esophagus from CT scans is extremely challenging because of the structure’s inconsistent intensity, low contrast against the surrounding tissues, complex and variable shape and location, and random air bubbles. The goal of this study is to develop an online atlas selection approach to choose a subset of optimal atlases for multi-atlas segmentation to the delineate esophagus automatically. We performed atlas selection in two phases. In the first phase, we used the correlation coefficient of the image content in a cubic region between each atlas and the new image to evaluate their similarity and to rank the atlases in an atlas pool. A subset of atlases based on this ranking was selected, and deformable image registration was performed to generate deformed contours and deformed images in the new image space. In the second phase of atlas selection, we used Kullback-Leibler divergence to measure the similarity of local-intensity histograms between the new image and each of the deformed images, and the measurements were used to rank the previously selected atlases. Deformed contours were overlapped sequentially, from the most to the least similar, and the overlap ratio was examined. We further identified a subset of optimal atlases by analyzing the variation of the overlap ratio versus the number of atlases. The deformed contours from these optimal atlases were fused together using a modified simultaneous truth and performance level estimation algorithm to produce the final segmentation. The approach was validated with promising results using both internal data sets (21 head and neck cancer patients and 15 thoracic cancer patients) and external data sets (30 thoracic patients).

  5. FEATURE SELECTION METHODS BASED ON MUTUAL INFORMATION FOR CLASSIFYING HETEROGENEOUS FEATURES

    Directory of Open Access Journals (Sweden)

    Ratri Enggar Pawening

    2016-06-01

    Full Text Available Datasets with heterogeneous features can affect feature selection results that are not appropriate because it is difficult to evaluate heterogeneous features concurrently. Feature transformation (FT is another way to handle heterogeneous features subset selection. The results of transformation from non-numerical into numerical features may produce redundancy to the original numerical features. In this paper, we propose a method to select feature subset based on mutual information (MI for classifying heterogeneous features. We use unsupervised feature transformation (UFT methods and joint mutual information maximation (JMIM methods. UFT methods is used to transform non-numerical features into numerical features. JMIM methods is used to select feature subset with a consideration of the class label. The transformed and the original features are combined entirely, then determine features subset by using JMIM methods, and classify them using support vector machine (SVM algorithm. The classification accuracy are measured for any number of selected feature subset and compared between UFT-JMIM methods and Dummy-JMIM methods. The average classification accuracy for all experiments in this study that can be achieved by UFT-JMIM methods is about 84.47% and Dummy-JMIM methods is about 84.24%. This result shows that UFT-JMIM methods can minimize information loss between transformed and original features, and select feature subset to avoid redundant and irrelevant features.

  6. Subset Analysis of a Multicenter, Randomized Controlled Trial to Compare Magnifying Chromoendoscopy with Endoscopic Ultrasonography for Stage Diagnosis of Early Stage Colorectal Cancer.

    Directory of Open Access Journals (Sweden)

    Tomonori Yamada

    Full Text Available Our recent prospective study found equivalent accuracy of magnifying chromoendoscopy (MC and endoscopic ultrasonography (EUS for diagnosing the invasion depth of colorectal cancer (CRC; however, whether these tools show diagnostic differences in categories such as tumor size and morphology remains unclear. Hence, we conducted detailed subset analysis of the prospective data.In this multicenter, prospective, comparative trial, a total of 70 patients with early, flat CRC were enrolled from February 2011 to December 2012, and the results of 66 lesions were finally analyzed. Patients were randomly allocated to primary MC followed by EUS or to primary EUS followed by MC. Diagnoses of invasion depth by each tool were divided into intramucosal to slight submucosal invasion (invasion depth <1000 μm and deep submucosal invasion (invasion depth ≥1000 μm, and then compared with the final pathological diagnosis by an independent pathologist blinded to clinical data. To standardize diagnoses among examiners, this trial was started after achievement of a mean κ value of ≥0.6 which was calculated from the average of κ values between each pair of participating endoscopists.Both MC and EUS showed similar diagnostic outcomes, with no significant differences in prediction of invasion depth in subset analyses according to tumor size, location, and morphology. Lesions that were consistently diagnosed as Tis/T1-SMS or ≥T1-SMD with both tools revealed accuracy of 76-78%. Accuracy was low in borderline lesions with irregular pit pattern in MC and distorted findings of the third layer in EUS (MC, 58.5%; EUS, 50.0%.MC and EUS showed the same limited accuracy for predicting invasion depth in all categories of early CRC. Since the irregular pit pattern in MC, distorted findings to the third layer in EUS and inconsistent diagnosis between both tools were associated with low accuracy, further refinements or even novel methods are still needed for such lesions

  7. Development of a synchronous subset of AADL

    DEFF Research Database (Denmark)

    Filali, Mamoun; Lawall, Julia

    2010-01-01

    We study the definition and the mapping of an AADL subset: the so called synchronous subset. We show that the data port protocol used for delayed and immediate connections between periodic threads can be interpreted in a  synchronous way. In this paper, we formalize this interpretation and study ...... the development of its mapping such that the original synchronous semantics is preserved. For that purpose, we use refinements through the Event B method....

  8. Hybrid feature selection for supporting lightweight intrusion detection systems

    Science.gov (United States)

    Song, Jianglong; Zhao, Wentao; Liu, Qiang; Wang, Xin

    2017-08-01

    Redundant and irrelevant features not only cause high resource consumption but also degrade the performance of Intrusion Detection Systems (IDS), especially when coping with big data. These features slow down the process of training and testing in network traffic classification. Therefore, a hybrid feature selection approach in combination with wrapper and filter selection is designed in this paper to build a lightweight intrusion detection system. Two main phases are involved in this method. The first phase conducts a preliminary search for an optimal subset of features, in which the chi-square feature selection is utilized. The selected set of features from the previous phase is further refined in the second phase in a wrapper manner, in which the Random Forest(RF) is used to guide the selection process and retain an optimized set of features. After that, we build an RF-based detection model and make a fair comparison with other approaches. The experimental results on NSL-KDD datasets show that our approach results are in higher detection accuracy as well as faster training and testing processes.

  9. Adaptive single-antenna transmit selection with interference suppression

    KAUST Repository

    Radaydeh, Redha Mahmoud Mesleh

    2011-10-01

    This paper studies the performance of adaptive transmit selection with co-channel interference suppression in multipath fading channels. The adaptive selection algorithms are configured for single-antenna bandwidth-efficient or power-efficient transmission with as low transmit channel estimations as possible. Due to the fact that the number of active co-channel interfering signals and their corresponding powers experience random behavior, the adaptation to channels conditions, assuming uniform buffer and traffic loading, is proposed to be jointly based on the transmit channels instantaneous signal-to-noise ratios (SNRs) and signal-to-interference-plus-noise ratios (SINRs). Two interference cancelation algorithms are considered. The first algorithm assumes that the receiver eliminates the impact of the strongest subset of interferers, whereas the second algorithm suggests random cancelation of interferers to further reduce processing complexity. The impact of outdated ordering of interferers powers on the efficiency of interference cancelation, and the effect of imperfect prediction of transmit channels for desired user adaptation are investigated. Analytical formulations for various performance measures and comparisons between the performance and processing complexity of different adaptation schemes are presented. © 2011 IEEE.

  10. Blind Measurement Selection: A Random Matrix Theory Approach

    KAUST Repository

    Elkhalil, Khalil

    2016-12-14

    This paper considers the problem of selecting a set of $k$ measurements from $n$ available sensor observations. The selected measurements should minimize a certain error function assessing the error in estimating a certain $m$ dimensional parameter vector. The exhaustive search inspecting each of the $n\\\\choose k$ possible choices would require a very high computational complexity and as such is not practical for large $n$ and $k$. Alternative methods with low complexity have recently been investigated but their main drawbacks are that 1) they require perfect knowledge of the measurement matrix and 2) they need to be applied at the pace of change of the measurement matrix. To overcome these issues, we consider the asymptotic regime in which $k$, $n$ and $m$ grow large at the same pace. Tools from random matrix theory are then used to approximate in closed-form the most important error measures that are commonly used. The asymptotic approximations are then leveraged to select properly $k$ measurements exhibiting low values for the asymptotic error measures. Two heuristic algorithms are proposed: the first one merely consists in applying the convex optimization artifice to the asymptotic error measure. The second algorithm is a low-complexity greedy algorithm that attempts to look for a sufficiently good solution for the original minimization problem. The greedy algorithm can be applied to both the exact and the asymptotic error measures and can be thus implemented in blind and channel-aware fashions. We present two potential applications where the proposed algorithms can be used, namely antenna selection for uplink transmissions in large scale multi-user systems and sensor selection for wireless sensor networks. Numerical results are also presented and sustain the efficiency of the proposed blind methods in reaching the performances of channel-aware algorithms.

  11. Characterization of a chromosome-specific chimpanzee alpha satellite subset: Evolutionary relationship to subsets on human chromosomes

    Energy Technology Data Exchange (ETDEWEB)

    Warburton, P.E.; Gosden, J.; Lawson, D. [Western General Hospital, Edinburgh (United Kingdom)] [and others

    1996-04-15

    Alpha satellite DNA is a tandemly repeated DNA family found at the centromeres of all primate chromosomes examined. The fundamental repeat units of alpha satellite DNA are diverged 169- to 172-bp monomers, often found to be organized in chromosome-specific higher-order repeat units. The chromosomes of human (Homo sapiens (HSA)), chimpanzee (Pan troglodytes (PTR) and Pan paniscus), and gorilla (Gorilla gorilla) share a remarkable similarity and synteny. It is of interest to ask if alpha satellite arrays at centromeres of homologous chromosomes between these species are closely related (evolving in an orthologous manner) or if the evolutionary processes that homogenize and spread these arrays within and between chromosomes result in nonorthologous evolution of arrays. By using PCR primers specific for human chromosome 17-specific alpha satellite DNA, we have amplified, cloned, and characterized a chromosome-specific subset from the PTR chimpanzee genome. Hybridization both on Southern blots and in situ as well as sequence analysis show that this subset is most closely related, as expected, to sequences on HSA 17. However, in situ hybridization reveals that this subset is not found on the homologous chromosome in chimpanzee (PTR 19), but instead on PTR 12, which is homologous to HSA 2p. 40 refs., 3 figs.

  12. Efficient Use of Historical Data for Genomic Selection: A Case Study of Stem Rust Resistance in Wheat

    Directory of Open Access Journals (Sweden)

    J. Rutkoski

    2015-03-01

    Full Text Available Genomic selection (GS is a methodology that can improve crop breeding efficiency. To implement GS, a training population (TP with phenotypic and genotypic data is required to train a statistical model used to predict genotyped selection candidates (SCs. A key factor impacting prediction accuracy is the relationship between the TP and the SCs. This study used empirical data for quantitative adult plant resistance to stem rust of wheat ( L. to investigate the utility of a historical TP (TP compared with a population-specific TP (TP, the potential for TP optimization, and the utility of TP data when close relative data is available for training. We found that, depending on the population size, a TP was 1.5 to 4.4 times more accurate than a TP, and TP optimization based on the mean of the generalized coefficient of determination or prediction error variance enabled the selection of subsets that led to significantly higher accuracy than randomly selected subsets. Retaining historical data when data on close relatives were available lead to a 11.9% increase in accuracy, at best, and a 12% decrease in accuracy, at worst, depending on the heritability. We conclude that historical data could be used successfully to initiate a GS program, especially if the dataset is very large and of high heritability. Training population optimization would be useful for the identification of TP subsets to phenotype additional traits. However, after model updating, discarding historical data may be warranted. More studies are needed to determine if these observations represent general trends.

  13. A search engine to access PubMed monolingual subsets: proof of concept and evaluation in French.

    Science.gov (United States)

    Griffon, Nicolas; Schuers, Matthieu; Soualmia, Lina Fatima; Grosjean, Julien; Kerdelhué, Gaétan; Kergourlay, Ivan; Dahamna, Badisse; Darmoni, Stéfan Jacques

    2014-12-01

    PubMed contains numerous articles in languages other than English. However, existing solutions to access these articles in the language in which they were written remain unconvincing. The aim of this study was to propose a practical search engine, called Multilingual PubMed, which will permit access to a PubMed subset in 1 language and to evaluate the precision and coverage for the French version (Multilingual PubMed-French). To create this tool, translations of MeSH were enriched (eg, adding synonyms and translations in French) and integrated into a terminology portal. PubMed subsets in several European languages were also added to our database using a dedicated parser. The response time for the generic semantic search engine was evaluated for simple queries. BabelMeSH, Multilingual PubMed-French, and 3 different PubMed strategies were compared by searching for literature in French. Precision and coverage were measured for 20 randomly selected queries. The results were evaluated as relevant to title and abstract, the evaluator being blind to search strategy. More than 650,000 PubMed citations in French were integrated into the Multilingual PubMed-French information system. The response times were all below the threshold defined for usability (2 seconds). Two search strategies (Multilingual PubMed-French and 1 PubMed strategy) showed high precision (0.93 and 0.97, respectively), but coverage was 4 times higher for Multilingual PubMed-French. It is now possible to freely access biomedical literature using a practical search tool in French. This tool will be of particular interest for health professionals and other end users who do not read or query sufficiently in English. The information system is theoretically well suited to expand the approach to other European languages, such as German, Spanish, Norwegian, and Portuguese.

  14. Identifying developmental toxicity pathways for a subset of ToxCast chemicals using human embryonic stem cells and metabolomics

    Science.gov (United States)

    Metabolomics analysis was performed on the supernatant of human embryonic stem (hES) cell cultures exposed to a blinded subset of 11 chemicals selected from the chemical library of EPA's ToxCast™ chemical screening and prioritization research project. Metabolites from hES cultur...

  15. Statistical selection : a way of thinking !

    NARCIS (Netherlands)

    Laan, van der P.; Aarts, E.H.L.; Eikelder, ten H.M.M.; Hemerik, C.; Rem, M.

    1995-01-01

    Statistical selection of the best population is discussed in general terms and the principles of statistical selection procedures are presented. Advantages and disadvantages of Subset Selection, one of the main approaches, are indicated. The selection of an almost best population is considered and

  16. Statistical selection : a way of thinking!

    NARCIS (Netherlands)

    Laan, van der P.

    1995-01-01

    Statistical selection of the best population is discussed in general terms and the principles of statistical selection procedures are presented. Advantages and disadvantages of Subset Selection, one of the main approaches, are indicated. The selection of an almost best population is considered and

  17. Optimizing Event Selection with the Random Grid Search

    Energy Technology Data Exchange (ETDEWEB)

    Bhat, Pushpalatha C. [Fermilab; Prosper, Harrison B. [Florida State U.; Sekmen, Sezen [Kyungpook Natl. U.; Stewart, Chip [Broad Inst., Cambridge

    2017-06-29

    The random grid search (RGS) is a simple, but efficient, stochastic algorithm to find optimal cuts that was developed in the context of the search for the top quark at Fermilab in the mid-1990s. The algorithm, and associated code, have been enhanced recently with the introduction of two new cut types, one of which has been successfully used in searches for supersymmetry at the Large Hadron Collider. The RGS optimization algorithm is described along with the recent developments, which are illustrated with two examples from particle physics. One explores the optimization of the selection of vector boson fusion events in the four-lepton decay mode of the Higgs boson and the other optimizes SUSY searches using boosted objects and the razor variables.

  18. Non-random mating for selection with restricted rates of inbreeding and overlapping generations

    NARCIS (Netherlands)

    Sonesson, A.K.; Meuwissen, T.H.E.

    2002-01-01

    Minimum coancestry mating with a maximum of one offspring per mating pair (MC1) is compared with random mating schemes for populations with overlapping generations. Optimum contribution selection is used, whereby $\\\\\\\\Delta F$ is restricted. For schemes with $\\\\\\\\Delta F$ restricted to 0.25% per

  19. Measurement error correction in the least absolute shrinkage and selection operator model when validation data are available.

    Science.gov (United States)

    Vasquez, Monica M; Hu, Chengcheng; Roe, Denise J; Halonen, Marilyn; Guerra, Stefano

    2017-01-01

    Measurement of serum biomarkers by multiplex assays may be more variable as compared to single biomarker assays. Measurement error in these data may bias parameter estimates in regression analysis, which could mask true associations of serum biomarkers with an outcome. The Least Absolute Shrinkage and Selection Operator (LASSO) can be used for variable selection in these high-dimensional data. Furthermore, when the distribution of measurement error is assumed to be known or estimated with replication data, a simple measurement error correction method can be applied to the LASSO method. However, in practice the distribution of the measurement error is unknown and is expensive to estimate through replication both in monetary cost and need for greater amount of sample which is often limited in quantity. We adapt an existing bias correction approach by estimating the measurement error using validation data in which a subset of serum biomarkers are re-measured on a random subset of the study sample. We evaluate this method using simulated data and data from the Tucson Epidemiological Study of Airway Obstructive Disease (TESAOD). We show that the bias in parameter estimation is reduced and variable selection is improved.

  20. Random sampling of elementary flux modes in large-scale metabolic networks.

    Science.gov (United States)

    Machado, Daniel; Soons, Zita; Patil, Kiran Raosaheb; Ferreira, Eugénio C; Rocha, Isabel

    2012-09-15

    The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks. Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler. dmachado@deb.uminho.pt Supplementary data are available at Bioinformatics online.

  1. Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Michael H. Thaut

    2005-11-01

    Full Text Available Most EEG-based BCI systems make use of well-studied patterns of brain activity. However, those systems involve tasks that indirectly map to simple binary commands such as “yes” or “no” or require many weeks of biofeedback training. We hypothesized that signal processing and machine learning methods can be used to discriminate EEG in a direct “yes”/“no” BCI from a single session. Blind source separation (BSS and spectral transformations of the EEG produced a 180-dimensional feature space. We used a modified genetic algorithm (GA wrapped around a support vector machine (SVM classifier to search the space of feature subsets. The GA-based search found feature subsets that outperform full feature sets and random feature subsets. Also, BSS transformations of the EEG outperformed the original time series, particularly in conjunction with a subset search of both spaces. The results suggest that BSS and feature selection can be used to improve the performance of even a “direct,” single-session BCI.

  2. Comparative Evaluations of Randomly Selected Four Point-of-Care Glucometer Devices in Addis Ababa, Ethiopia.

    Science.gov (United States)

    Wolde, Mistire; Tarekegn, Getahun; Kebede, Tedla

    2018-05-01

    Point-of-care glucometer (PoCG) devices play a significant role in self-monitoring of the blood sugar level, particularly in the follow-up of high blood sugar therapeutic response. The aim of this study was to evaluate blood glucose test results performed with four randomly selected glucometers on diabetes and control subjects versus standard wet chemistry (hexokinase) methods in Addis Ababa, Ethiopia. A prospective cross-sectional study was conducted on randomly selected 200 study participants (100 participants with diabetes and 100 healthy controls). Four randomly selected PoCG devices (CareSens N, DIAVUE Prudential, On Call Extra, i-QARE DS-W) were evaluated against hexokinase method and ISO 15197:2003 and ISO 15197:2013 standards. The minimum and maximum blood sugar values were recorded by CareSens N (21 mg/dl) and hexokinase method (498.8 mg/dl), respectively. The mean sugar values of all PoCG devices except On Call Extra showed significant differences compared with the reference hexokinase method. Meanwhile, all four PoCG devices had strong positive relationship (>80%) with the reference method (hexokinase). On the other hand, none of the four PoCG devices fulfilled the minimum accuracy measurement set by ISO 15197:2003 and ISO 15197:2013 standards. In addition, the linear regression analysis revealed that all four selected PoCG overestimated the glucose concentrations. The overall evaluation of the selected four PoCG measurements were poorly correlated with standard reference method. Therefore, before introducing PoCG devices to the market, there should be a standardized evaluation platform for validation. Further similar large-scale studies on other PoCG devices also need to be undertaken.

  3. Geography and genography: prediction of continental origin using randomly selected single nucleotide polymorphisms

    Directory of Open Access Journals (Sweden)

    Ramoni Marco F

    2007-03-01

    Full Text Available Abstract Background Recent studies have shown that when individuals are grouped on the basis of genetic similarity, group membership corresponds closely to continental origin. There has been considerable debate about the implications of these findings in the context of larger debates about race and the extent of genetic variation between groups. Some have argued that clustering according to continental origin demonstrates the existence of significant genetic differences between groups and that these differences may have important implications for differences in health and disease. Others argue that clustering according to continental origin requires the use of large amounts of genetic data or specifically chosen markers and is indicative only of very subtle genetic differences that are unlikely to have biomedical significance. Results We used small numbers of randomly selected single nucleotide polymorphisms (SNPs from the International HapMap Project to train naïve Bayes classifiers for prediction of ancestral continent of origin. Predictive accuracy was tested on two independent data sets. Genetically similar groups should be difficult to distinguish, especially if only a small number of genetic markers are used. The genetic differences between continentally defined groups are sufficiently large that one can accurately predict ancestral continent of origin using only a minute, randomly selected fraction of the genetic variation present in the human genome. Genotype data from only 50 random SNPs was sufficient to predict ancestral continent of origin in our primary test data set with an average accuracy of 95%. Genetic variations informative about ancestry were common and widely distributed throughout the genome. Conclusion Accurate characterization of ancestry is possible using small numbers of randomly selected SNPs. The results presented here show how investigators conducting genetic association studies can use small numbers of arbitrarily

  4. Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles

    Directory of Open Access Journals (Sweden)

    Liying Yang

    2016-01-01

    Full Text Available Background. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous challenges for its characteristics, such as high dimensionality, small sample size, and low Signal-to-Noise Ratio. Results. This paper proposes a method, termed RS_SVM, to predict gene expression profiles via aggregating SVM trained on random subspaces. After choosing gene features through statistical analysis, RS_SVM randomly selects feature subsets to yield random subspaces and training SVM classifiers accordingly and then aggregates SVM classifiers to capture the advantage of ensemble learning. Experiments on eight real gene expression datasets are performed to validate the RS_SVM method. Experimental results show that RS_SVM achieved better classification accuracy and generalization performance in contrast with single SVM, K-nearest neighbor, decision tree, Bagging, AdaBoost, and the state-of-the-art methods. Experiments also explored the effect of subspace size on prediction performance. Conclusions. The proposed RS_SVM method yielded superior performance in analyzing gene expression profiles, which demonstrates that RS_SVM provides a good channel for such biological data.

  5. Fuzzy Random λ-Mean SAD Portfolio Selection Problem: An Ant Colony Optimization Approach

    Science.gov (United States)

    Thakur, Gour Sundar Mitra; Bhattacharyya, Rupak; Mitra, Swapan Kumar

    2010-10-01

    To reach the investment goal, one has to select a combination of securities among different portfolios containing large number of securities. Only the past records of each security do not guarantee the future return. As there are many uncertain factors which directly or indirectly influence the stock market and there are also some newer stock markets which do not have enough historical data, experts' expectation and experience must be combined with the past records to generate an effective portfolio selection model. In this paper the return of security is assumed to be Fuzzy Random Variable Set (FRVS), where returns are set of random numbers which are in turn fuzzy numbers. A new λ-Mean Semi Absolute Deviation (λ-MSAD) portfolio selection model is developed. The subjective opinions of the investors to the rate of returns of each security are taken into consideration by introducing a pessimistic-optimistic parameter vector λ. λ-Mean Semi Absolute Deviation (λ-MSAD) model is preferred as it follows absolute deviation of the rate of returns of a portfolio instead of the variance as the measure of the risk. As this model can be reduced to Linear Programming Problem (LPP) it can be solved much faster than quadratic programming problems. Ant Colony Optimization (ACO) is used for solving the portfolio selection problem. ACO is a paradigm for designing meta-heuristic algorithms for combinatorial optimization problem. Data from BSE is used for illustration.

  6. Accuracy of genomic selection for alfalfa biomass yield in different reference populations.

    Science.gov (United States)

    Annicchiarico, Paolo; Nazzicari, Nelson; Li, Xuehui; Wei, Yanling; Pecetti, Luciano; Brummer, E Charles

    2015-12-01

    Genomic selection based on genotyping-by-sequencing (GBS) data could accelerate alfalfa yield gains, if it displayed moderate ability to predict parent breeding values. Its interest would be enhanced by predicting ability also for germplasm/reference populations other than those for which it was defined. Predicting accuracy may be influenced by statistical models, SNP calling procedures and missing data imputation strategies. Landrace and variety material from two genetically-contrasting reference populations, i.e., 124 elite genotypes adapted to the Po Valley (sub-continental climate; PV population) and 154 genotypes adapted to Mediterranean-climate environments (Me population), were genotyped by GBS and phenotyped in separate environments for dry matter yield of their dense-planted half-sib progenies. Both populations showed no sub-population genetic structure. Predictive accuracy was higher by joint rather than separate SNP calling for the two data sets, and using random forest imputation of missing data. Highest accuracy was obtained using Support Vector Regression (SVR) for PV, and Ridge Regression BLUP and SVR for Me germplasm. Bayesian methods (Bayes A, Bayes B and Bayesian Lasso) tended to be less accurate. Random Forest Regression was the least accurate model. Accuracy attained about 0.35 for Me in the range of 0.30-0.50 missing data, and 0.32 for PV at 0.50 missing data, using at least 10,000 SNP markers. Cross-population predictions based on a smaller subset of common SNPs implied a relative loss of accuracy of about 25% for Me and 30% for PV. Genome-wide association analyses based on large subsets of M. truncatula-aligned markers revealed many SNPs with modest association with yield, and some genome areas hosting putative QTLs. A comparison of genomic vs. conventional selection for parent breeding value assuming 1-year vs. 5-year selection cycles, respectively, indicated over three-fold greater predicted yield gain per unit time for genomic selection

  7. CD14CD16 Monocyte Subset Levels in Heart Failure Patients

    Directory of Open Access Journals (Sweden)

    Chiara Barisione

    2010-01-01

    Full Text Available Our aim was to define the distribution of monocyte subsets in a cohort of congestive heart failure (CHF patients, to verify whether increased severity of CHF is linked to the expansion of specific monocyte subsets, and finally to investigate the relationship between monocyte subset relative frequencies, laboratory parameters of inflammation, and monocyte ACE expression.

  8. Pediatric selective mutism therapy: a randomized controlled trial.

    Science.gov (United States)

    Esposito, Maria; Gimigliano, Francesca; Barillari, Maria R; Precenzano, Francesco; Ruberto, Maria; Sepe, Joseph; Barillari, Umberto; Gimigliano, Raffaele; Militerni, Roberto; Messina, Giovanni; Carotenuto, Marco

    2017-10-01

    Selective mutism (SM) is a rare disease in children coded by DSM-5 as an anxiety disorder. Despite the disabling nature of the disease, there is still no specific treatment. The aims of this study were to verify the efficacy of six-month standard psychomotor treatment and the positive changes in lifestyle, in a population of children affected by SM. Randomized controlled trial registered in the European Clinical Trials Registry (EuDract 2015-001161-36). University third level Centre (Child and Adolescent Neuropsychiatry Clinic). Study population was composed by 67 children in group A (psychomotricity treatment) (35 M, mean age 7.84±1.15) and 71 children in group B (behavioral and educational counseling) (37 M, mean age 7.75±1.36). Psychomotor treatment was administered by trained child therapists in residential settings three times per week. Each child was treated for the whole period by the same therapist and all the therapists shared the same protocol. The standard psychomotor session length is of 45 minutes. At T0 and after 6 months (T1) of treatments, patients underwent a behavioral and SM severity assessment. To verify the effects of the psychomotor management, the Child Behavior Checklist questionnaire (CBCL) and Selective Mutism Questionnaire (SMQ) were administered to the parents. After 6 months of psychomotor treatment SM children showed a significant reduction among CBCL scores such as in social relations, anxious/depressed, social problems and total problems (Pselective mutism, even if further studies are needed. The present study identifies in psychomotricity a safe and efficacy therapy for pediatric selective mutism.

  9. Effects of prey abundance, distribution, visual contrast and morphology on selection by a pelagic piscivore

    Science.gov (United States)

    Hansen, Adam G.; Beauchamp, David A.

    2014-01-01

    Most predators eat only a subset of possible prey. However, studies evaluating diet selection rarely measure prey availability in a manner that accounts for temporal–spatial overlap with predators, the sensory mechanisms employed to detect prey, and constraints on prey capture.We evaluated the diet selection of cutthroat trout (Oncorhynchus clarkii) feeding on a diverse planktivore assemblage in Lake Washington to test the hypothesis that the diet selection of piscivores would reflect random (opportunistic) as opposed to non-random (targeted) feeding, after accounting for predator–prey overlap, visual detection and capture constraints.Diets of cutthroat trout were sampled in autumn 2005, when the abundance of transparent, age-0 longfin smelt (Spirinchus thaleichthys) was low, and 2006, when the abundance of smelt was nearly seven times higher. Diet selection was evaluated separately using depth-integrated and depth-specific (accounted for predator–prey overlap) prey abundance. The abundance of different prey was then adjusted for differences in detectability and vulnerability to predation to see whether these factors could explain diet selection.In 2005, cutthroat trout fed non-randomly by selecting against the smaller, transparent age-0 longfin smelt, but for the larger age-1 longfin smelt. After adjusting prey abundance for visual detection and capture, cutthroat trout fed randomly. In 2006, depth-integrated and depth-specific abundance explained the diets of cutthroat trout well, indicating random feeding. Feeding became non-random after adjusting for visual detection and capture. Cutthroat trout selected strongly for age-0 longfin smelt, but against similar sized threespine stickleback (Gasterosteus aculeatus) and larger age-1 longfin smelt in 2006. Overlap with juvenile sockeye salmon (O. nerka) was minimal in both years, and sockeye salmon were rare in the diets of cutthroat trout.The direction of the shift between random and non-random selection

  10. Evaluating the Stability of Feature Selectors that Optimize Feature Subset Cardinality

    Czech Academy of Sciences Publication Activity Database

    Somol, Petr; Novovičová, Jana

    2008-01-01

    Roč. 2008, č. 5342 (2008), s. 956-966 ISSN 0302-9743. [Joint IAPR International Workshops SSPR 2008 and SPR 2008. Orlando , 04.12.2008-06.12.2008] R&D Projects: GA AV ČR 1ET400750407; GA MŠk 1M0572; GA ČR GA102/07/1594 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Feature selection * stability * relative weighted consistency measure * sequential search * floating search Subject RIV: IN - Informatics, Computer Science http://library.utia.cas.cz/separaty/2008/RO/somol-evaluating the stability of feature selectors that optimize feature subset cardinality.pdf

  11. Health-related quality of life after laparoscopic and open surgery for rectal cancer in a randomized trial

    DEFF Research Database (Denmark)

    Andersson, J; Angenete, E; Gellerstedt, M

    2013-01-01

    Previous studies comparing laparoscopic and open surgical techniques have reported improved health-related quality of life (HRQL). This analysis compared HRQL 12¿months after laparoscopic versus open surgery for rectal cancer in a subset of a randomized trial.......Previous studies comparing laparoscopic and open surgical techniques have reported improved health-related quality of life (HRQL). This analysis compared HRQL 12¿months after laparoscopic versus open surgery for rectal cancer in a subset of a randomized trial....

  12. Efficient sensor selection for active information fusion.

    Science.gov (United States)

    Zhang, Yongmian; Ji, Qiang

    2010-06-01

    In our previous paper, we formalized an active information fusion framework based on dynamic Bayesian networks to provide active information fusion. This paper focuses on a central issue of active information fusion, i.e., the efficient identification of a subset of sensors that are most decision relevant and cost effective. Determining the most informative and cost-effective sensors requires an evaluation of all the possible subsets of sensors, which is computationally intractable, particularly when information-theoretic criterion such as mutual information is used. To overcome this challenge, we propose a new quantitative measure for sensor synergy based on which a sensor synergy graph is constructed. Using the sensor synergy graph, we first introduce an alternative measure to multisensor mutual information for characterizing the sensor information gain. We then propose an approximated nonmyopic sensor selection method that can efficiently and near-optimally select a subset of sensors for active fusion. The simulation study demonstrates both the performance and the efficiency of the proposed sensor selection method.

  13. Primitive polynomials selection method for pseudo-random number generator

    Science.gov (United States)

    Anikin, I. V.; Alnajjar, Kh

    2018-01-01

    In this paper we suggested the method for primitive polynomials selection of special type. This kind of polynomials can be efficiently used as a characteristic polynomials for linear feedback shift registers in pseudo-random number generators. The proposed method consists of two basic steps: finding minimum-cost irreducible polynomials of the desired degree and applying primitivity tests to get the primitive ones. Finally two primitive polynomials, which was found by the proposed method, used in pseudorandom number generator based on fuzzy logic (FRNG) which had been suggested before by the authors. The sequences generated by new version of FRNG have low correlation magnitude, high linear complexity, less power consumption, is more balanced and have better statistical properties.

  14. On the size of the subset partial order

    DEFF Research Database (Denmark)

    Elmasry, Amr Ahmed Abd Elmoneim

    2012-01-01

    Given a family of k sets with cardinalities S 1,S 2,⋯, S k and N=Σ k i=1S i, we show that the size of the partial order graph induced by the subset relation (called the subset graph) is O(Σ si≤B 2si+N/lgN·Σ si>Blg(s i/B)), 2 where B=lg(N/lg 2N). This implies a simpler proof to the O(N 2/lg 2N...

  15. Effects of low dose radiation on regulatory function between lymphocyte subsets

    International Nuclear Information System (INIS)

    Tian Hailin; Su Liaoyuan; Du Zeji; Zou Huawei; Wang Aiqing

    1997-01-01

    Four kinds of McAbs (anti CD 4 , CD 8 , CD 19 and CD 57 ) were used to separate CD 4 , CD 8 , CD 19 (B) and CD 57 (NK) lymphocyte subsets from human peripheral blood by 'Panning-direct' method. First the natural killing activity of each subsets and the regulatory functions between CD 57 and other subsets were studied. Then the effects of low dose radiation on the function of CD 57 cells and the regulatory functions between CD 57 and other subsets were studied. The results showed that the NK activity was found in all of the four subsets, with CD 57 cell having the strongest activity. When CD 4 and CD 57 cells were co-cultured, the total NK activity was higher than that of the sum of these two single subsets, i.e. there was synergistic effect between CD 4 and CD 57 cells. When CD 8 or CD 19 cells were co-cultured separately with CD 57 cells, no synergistic effect was found. Irradiation by gamma rays at doses of 50 cGy and 80 cGy was able to stimulate the function of CD 57 cells. After Cd 4 or CD 57 cells were irradiated, the total NK activity of their co-culture increased significantly. This phenomenon was not found in other subsets. This suggested that low dose radiation can enhance the synergistic action between CD 4 and CD 57 cells. So at least four subsets (CD 4 , CD 8 , CD 19 , CD 57 ) contribute to the total NK activity of peripheral blood mononuclear cells. (15 refs., 4 tabs.)

  16. Adaptive feature selection using v-shaped binary particle swarm optimization.

    Science.gov (United States)

    Teng, Xuyang; Dong, Hongbin; Zhou, Xiurong

    2017-01-01

    Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers.

  17. Susceptibility and response of human blood monocyte subsets to primary dengue virus infection.

    Directory of Open Access Journals (Sweden)

    Kok Loon Wong

    Full Text Available Human blood monocytes play a central role in dengue infections and form the majority of virus infected cells in the blood. Human blood monocytes are heterogeneous and divided into CD16(- and CD16(+ subsets. Monocyte subsets play distinct roles during disease, but it is not currently known if monocyte subsets differentially contribute to dengue protection and pathogenesis. Here, we compared the susceptibility and response of the human CD16(- and CD16(+ blood monocyte subsets to primary dengue virus in vitro. We found that both monocyte subsets were equally susceptible to dengue virus (DENV2 NGC, and capable of supporting the initial production of new infective virus particles. Both monocyte subsets produced anti-viral factors, including IFN-α, CXCL10 and TRAIL. However, CD16(+ monocytes were the major producers of inflammatory cytokines and chemokines in response to dengue virus, including IL-1β, TNF-α, IL-6, CCL2, 3 and 4. The susceptibility of both monocyte subsets to infection was increased after IL-4 treatment, but this increase was more profound for the CD16(+ monocyte subset, particularly at early time points after virus exposure. These findings reveal the differential role that monocyte subsets might play during dengue disease.

  18. Randomizing Roaches: Exploring the "Bugs" of Randomization in Experimental Design

    Science.gov (United States)

    Wagler, Amy; Wagler, Ron

    2014-01-01

    Understanding the roles of random selection and random assignment in experimental design is a central learning objective in most introductory statistics courses. This article describes an activity, appropriate for a high school or introductory statistics course, designed to teach the concepts, values and pitfalls of random selection and assignment…

  19. Listeria arpJ gene modifies T helper type 2 subset differentiation.

    Science.gov (United States)

    Kanoh, Makoto; Maruyama, Saho; Shen, Hua; Matsumoto, Akira; Shinomiya, Hiroto; Przybilla, Karin; Gouin, Edith; Cossart, Pascale; Goebel, Werner; Asano, Yoshihiro

    2015-07-15

    Although the T-cell subset differentiation pathway has been characterized extensively from the view of host gene regulation, the effects of genes of the pathogen on T-cell subset differentiation during infection have yet to be elucidated. Especially, the bacterial genes that are responsible for this shift have not yet been determined. Utilizing a single-gene-mutation Listeria panel, we investigated genes involved in the host-pathogen interaction that are required for the initiation of T-cell subset differentiation in the early phase of pathogen infection. We demonstrate that the induction of T helper types 1 and 2 (Th1 and Th2) subsets are separate phenomena and are mediated by distinct Listeria genes. We identified several candidate Listeria genes that appear to be involved in the host-Listeria interaction. Among them, arpJ is the strongest candidate gene for inhibiting Th2 subset induction. Furthermore, the analysis utilizing arpJ-deficient Listeria monocytogenes (Lm) revealed that the tumor necrosis factor (TNF) superfamily (Tnfsf) 9-TNF receptor superfamily (Tnfrsf) 9 interaction inhibits the Th2 response during Lm infection. arpJ is the candidate gene for inhibiting Th2 T-cell subset induction. The arpJ gene product influences the expression of Tnfsf/Tnfrsf on antigen-presenting cells and inhibits the Th2 T-cell subset differentiation during Listeria infection. © The Author 2015. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  20. Materials selection for oxide-based resistive random access memories

    International Nuclear Information System (INIS)

    Guo, Yuzheng; Robertson, John

    2014-01-01

    The energies of atomic processes in resistive random access memories (RRAMs) are calculated for four typical oxides, HfO 2 , TiO 2 , Ta 2 O 5 , and Al 2 O 3 , to define a materials selection process. O vacancies have the lowest defect formation energy in the O-poor limit and dominate the processes. A band diagram defines the operating Fermi energy and O chemical potential range. It is shown how the scavenger metal can be used to vary the O vacancy formation energy, via controlling the O chemical potential, and the mean Fermi energy. The high endurance of Ta 2 O 5 RRAM is related to its more stable amorphous phase and the adaptive lattice rearrangements of its O vacancy

  1. Three distinct subsets of thymic epithelial cells in rats and mice defined by novel antibodies.

    Directory of Open Access Journals (Sweden)

    Yasushi Sawanobori

    Full Text Available Thymic epithelial cells (TECs are thought to play an essential role in T cell development and have been detected mainly in mice using lectin binding and antibodies to keratins. Our aim in the present study was to create a precise map of rat TECs using antibodies to putative markers and novel monoclonal antibodies (i.e., ED 18/19/21 and anti-CD205 antibodies and compare it with a map from mouse counterparts and that of rat thymic dendritic cells.Rat TECs were subdivided on the basis of phenotype into three subsets; ED18+ED19+/-keratin 5 (K5+K8+CD205+ class II MHC (MHCII+ cortical TECs (cTECs, ED18+ED21-K5-K8+Ulex europaeus lectin 1 (UEA-1+CD205- medullary TECs (mTEC1s, and ED18+ED21+K5+K8dullUEA-1-CD205- medullary TECs (mTEC2s. Thymic nurse cells were defined in cytosmears as an ED18+ED19+/-K5+K8+ subset of cTECs. mTEC1s preferentially expressed MHCII, claudin-3, claudin-4, and autoimmune regulator (AIRE. Use of ED18 and ED21 antibodies revealed three subsets of TECs in mice as well. We also detected two distinct TEC-free areas in the subcapsular cortex and in the medulla. Rat dendritic cells in the cortex were MHCII+CD103+ but negative for TEC markers, including CD205. Those in the medulla were MHCII+CD103+ and CD205+ cells were found only in the TEC-free area.Both rats and mice have three TEC subsets with similar phenotypes that can be identified using known markers and new monoclonal antibodies. These findings will facilitate further analysis of TEC subsets and DCs and help to define their roles in thymic selection and in pathological states such as autoimmune disorders.

  2. Emergence of multilevel selection in the prisoner's dilemma game on coevolving random networks

    International Nuclear Information System (INIS)

    Szolnoki, Attila; Perc, Matjaz

    2009-01-01

    We study the evolution of cooperation in the prisoner's dilemma game, whereby a coevolutionary rule is introduced that molds the random topology of the interaction network in two ways. First, existing links are deleted whenever a player adopts a new strategy or its degree exceeds a threshold value; second, new links are added randomly after a given number of game iterations. These coevolutionary processes correspond to the generic formation of new links and deletion of existing links that, especially in human societies, appear frequently as a consequence of ongoing socialization, change of lifestyle or death. Due to the counteraction of deletions and additions of links the initial heterogeneity of the interaction network is qualitatively preserved, and thus cannot be held responsible for the observed promotion of cooperation. Indeed, the coevolutionary rule evokes the spontaneous emergence of a powerful multilevel selection mechanism, which despite the sustained random topology of the evolving network, maintains cooperation across the whole span of defection temptation values.

  3. Evolution of a Modified Binomial Random Graph by Agglomeration

    Science.gov (United States)

    Kang, Mihyun; Pachon, Angelica; Rodríguez, Pablo M.

    2018-02-01

    In the classical Erdős-Rényi random graph G( n, p) there are n vertices and each of the possible edges is independently present with probability p. The random graph G( n, p) is homogeneous in the sense that all vertices have the same characteristics. On the other hand, numerous real-world networks are inhomogeneous in this respect. Such an inhomogeneity of vertices may influence the connection probability between pairs of vertices. The purpose of this paper is to propose a new inhomogeneous random graph model which is obtained in a constructive way from the Erdős-Rényi random graph G( n, p). Given a configuration of n vertices arranged in N subsets of vertices (we call each subset a super-vertex), we define a random graph with N super-vertices by letting two super-vertices be connected if and only if there is at least one edge between them in G( n, p). Our main result concerns the threshold for connectedness. We also analyze the phase transition for the emergence of the giant component and the degree distribution. Even though our model begins with G( n, p), it assumes the existence of some community structure encoded in the configuration. Furthermore, under certain conditions it exhibits a power law degree distribution. Both properties are important for real-world applications.

  4. Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology

    Science.gov (United States)

    Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological datasets there is limited guidance on variable selection methods for RF modeling. Typically, e...

  5. Spectra of random operators with absolutely continuous integrated density of states

    International Nuclear Information System (INIS)

    Rio, Rafael del

    2014-01-01

    The structure of the spectrum of random operators is studied. It is shown that if the density of states measure of some subsets of the spectrum is zero, then these subsets are empty. In particular follows that absolute continuity of the integrated density of states implies singular spectra of ergodic operators is either empty or of positive measure. Our results apply to Anderson and alloy type models, perturbed Landau Hamiltonians, almost periodic potentials, and models which are not ergodic

  6. Spectra of random operators with absolutely continuous integrated density of states

    Energy Technology Data Exchange (ETDEWEB)

    Rio, Rafael del, E-mail: delrio@iimas.unam.mx, E-mail: delriomagia@gmail.com [Departamento de Fisica Matematica, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, C.P. 04510, México D.F. (Mexico)

    2014-04-15

    The structure of the spectrum of random operators is studied. It is shown that if the density of states measure of some subsets of the spectrum is zero, then these subsets are empty. In particular follows that absolute continuity of the integrated density of states implies singular spectra of ergodic operators is either empty or of positive measure. Our results apply to Anderson and alloy type models, perturbed Landau Hamiltonians, almost periodic potentials, and models which are not ergodic.

  7. Random drift versus selection in academic vocabulary: an evolutionary analysis of published keywords.

    Science.gov (United States)

    Bentley, R Alexander

    2008-08-27

    The evolution of vocabulary in academic publishing is characterized via keyword frequencies recorded in the ISI Web of Science citations database. In four distinct case-studies, evolutionary analysis of keyword frequency change through time is compared to a model of random copying used as the null hypothesis, such that selection may be identified against it. The case studies from the physical sciences indicate greater selection in keyword choice than in the social sciences. Similar evolutionary analyses can be applied to a wide range of phenomena; wherever the popularity of multiple items through time has been recorded, as with web searches, or sales of popular music and books, for example.

  8. Roquin Paralogs Differentially Regulate Functional NKT Cell Subsets.

    Science.gov (United States)

    Drees, Christoph; Vahl, J Christoph; Bortoluzzi, Sabrina; Heger, Klaus D; Fischer, Julius C; Wunderlich, F Thomas; Peschel, Christian; Schmidt-Supprian, Marc

    2017-04-01

    NKT cells represent a small subset of glycolipid-recognizing T cells that are heavily implicated in human allergic, autoimmune, and malignant diseases. In the thymus, precursor cells recognize self-glycolipids by virtue of their semi-invariant TCR, which triggers NKT cell lineage commitment and maturation. During their development, NKT cells are polarized into the NKT1, NKT2, and NKT17 subsets, defined through their cytokine-secretion patterns and the expression of key transcription factors. However, we have largely ignored how the differentiation into the NKT cell subsets is regulated. In this article, we describe the mRNA-binding Roquin-1 and -2 proteins as central regulators of murine NKT cell fate decisions. In the thymus, T cell-specific ablation of the Roquin paralogs leads to a dramatic expansion of NKT17 cells, whereas peripheral mature NKT cells are essentially absent. Roquin-1/2-deficient NKT17 cells show exaggerated lineage-specific expression of nearly all NKT17-defining proteins tested. We show through mixed bone marrow chimera experiments that NKT17 polarization is mediated through cell-intrinsic mechanisms early during NKT cell development. In contrast, the loss of peripheral NKT cells is due to cell-extrinsic factors. Surprisingly, Roquin paralog-deficient NKT cells are, in striking contrast to conventional T cells, compromised in their ability to secrete cytokines. Altogether, we show that Roquin paralogs regulate the development and function of NKT cell subsets in the thymus and periphery. Copyright © 2017 by The American Association of Immunologists, Inc.

  9. Materials selection for oxide-based resistive random access memories

    Energy Technology Data Exchange (ETDEWEB)

    Guo, Yuzheng; Robertson, John [Engineering Department, Cambridge University, Cambridge CB2 1PZ (United Kingdom)

    2014-12-01

    The energies of atomic processes in resistive random access memories (RRAMs) are calculated for four typical oxides, HfO{sub 2}, TiO{sub 2}, Ta{sub 2}O{sub 5}, and Al{sub 2}O{sub 3}, to define a materials selection process. O vacancies have the lowest defect formation energy in the O-poor limit and dominate the processes. A band diagram defines the operating Fermi energy and O chemical potential range. It is shown how the scavenger metal can be used to vary the O vacancy formation energy, via controlling the O chemical potential, and the mean Fermi energy. The high endurance of Ta{sub 2}O{sub 5} RRAM is related to its more stable amorphous phase and the adaptive lattice rearrangements of its O vacancy.

  10. Investigating evolutionary conservation of dendritic cell subset identity and functions

    Directory of Open Access Journals (Sweden)

    Thien-Phong eVu Manh

    2015-06-01

    Full Text Available Dendritic cells (DC were initially defined as mononuclear phagocytes with a dendritic morphology and an exquisite efficiency for naïve T cell activation. DC encompass several subsets initially identified by their expression of specific cell surface molecules and later shown to excel in distinct functions and to develop under the instruction of different transcription factors or cytokines. Very few cell surface molecules are expressed in a specific manner on any immune cell type. Hence, to identify cell types, the sole use of a small number of cell surface markers in classical flow cytometry can be deceiving. Moreover, the markers currently used to define mononuclear phagocyte subsets vary depending on the tissue and animal species studied and even between laboratories. This has led to confusion in the definition of DC subset identity and in their attribution of specific functions. There is a strong need to identify a rigorous and consensus way to define mononuclear phagocyte subsets, with precise guidelines potentially applicable throughout tissues and species. We will discuss the advantages, drawbacks and complementarities of different methodologies: cell surface phenotyping, ontogeny, functional characterization and molecular profiling. We will advocate that gene expression profiling is a very rigorous, largely unbiased and accessible method to define the identity of mononuclear phagocyte subsets, which strengthens and refines surface phenotyping. It is uniquely powerful to yield new, experimentally testable, hypotheses on the ontogeny or functions of mononuclear phagocyte subsets, their molecular regulation and their evolutionary conservation. We propose defining cell populations based on a combination of cell surface phenotyping, expression analysis of hallmark genes and robust functional assays, in order to reach a consensus and integrate faster the huge but scattered knowledge accumulated by different laboratories on different cell types

  11. Evaluating Stability and Comparing Output of Feature Selectors that Optimize Feature Subset Cardinality

    Czech Academy of Sciences Publication Activity Database

    Somol, Petr; Novovičová, Jana

    2010-01-01

    Roč. 32, č. 11 (2010), s. 1921-1939 ISSN 0162-8828 R&D Projects: GA MŠk 1M0572; GA ČR GA102/08/0593; GA ČR GA102/07/1594 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : feature selection * feature stability * stability measures * similarity measures * sequential search * individual ranking * feature subset-size optimization * high dimensionality * small sample size Subject RIV: BD - Theory of Information Impact factor: 5.027, year: 2010 http://library.utia.cas.cz/separaty/2010/RO/somol-0348726.pdf

  12. Non-suppressive regulatory T cell subset expansion in pulmonary arterial hypertension.

    Science.gov (United States)

    Sada, Yoshiharu; Dohi, Yoshihiro; Uga, Sayuri; Higashi, Akifumi; Kinoshita, Hiroki; Kihara, Yasuki

    2016-08-01

    Regulatory T cells (Tregs) have been reported to play a pivotal role in the vascular remodeling of pulmonary arterial hypertension (PAH). Recent studies have revealed that Tregs are heterogeneous and can be characterized by three phenotypically and functionally different subsets. In this study, we investigated the roles of Treg subsets in the pathogenesis of PAH in eight patients with PAH and 14 healthy controls. Tregs and their subsets in peripheral blood samples were analyzed by flow cytometry. Treg subsets were defined as CD4(+)CD45RA(+)FoxP3(low) resting Tregs (rTregs), CD4(+)CD45RA(-)FoxP3(high) activated Tregs (aTregs), and CD4(+)CD45RA(-)FoxP3(low) non-suppressive Tregs (non-Tregs). The proportion of Tregs among CD4(+) T cells was significantly higher in PAH patients than in controls (6.54 ± 1.10 vs. 3.81 ± 0.28 %, p < 0.05). Of the three subsets, the proportion of non-Tregs was significantly elevated in PAH patients compared with controls (4.06 ± 0.40 vs. 2.79 ± 0.14 %, p < 0.01), whereas those of rTregs and aTregs were not different between the two groups. Moreover, the expression levels of cytotoxic T lymphocyte antigen 4, a functional cell surface molecule, in aTregs (p < 0.05) and non-Tregs (p < 0.05) were significantly higher in PAH patients compared with controls. These results suggested the non-Treg subset was expanded and functionally activated in peripheral lymphocytes obtained from IPAH patients. We hypothesize that immunoreactions involving the specific activation of the non-Treg subset might play a role in the vascular remodeling of PAH.

  13. Subset selection from Type-I and Type-II generalized logistic populations

    NARCIS (Netherlands)

    Laan, van der M.J.; Laan, van der P.

    1996-01-01

    We give an introduction to the logistic and generalized logistic distributions. We obtain exact results for the probability of correct selection from Type-I and Type-II generalized logistic populations which only differ in their location parameter. Some open problems are formulated.

  14. Probabilistic quantum cloning of a subset of linearly dependent states

    Science.gov (United States)

    Rui, Pinshu; Zhang, Wen; Liao, Yanlin; Zhang, Ziyun

    2018-02-01

    It is well known that a quantum state, secretly chosen from a certain set, can be probabilistically cloned with positive cloning efficiencies if and only if all the states in the set are linearly independent. In this paper, we focus on probabilistic quantum cloning of a subset of linearly dependent states. We show that a linearly-independent subset of linearly-dependent quantum states {| Ψ 1⟩,| Ψ 2⟩,…,| Ψ n ⟩} can be probabilistically cloned if and only if any state in the subset cannot be expressed as a linear superposition of the other states in the set {| Ψ 1⟩,| Ψ 2⟩,…,| Ψ n ⟩}. The optimal cloning efficiencies are also investigated.

  15. Statistical image reconstruction for transmission tomography using relaxed ordered subset algorithms

    International Nuclear Information System (INIS)

    Kole, J S

    2005-01-01

    Statistical reconstruction methods offer possibilities for improving image quality as compared to analytical methods, but current reconstruction times prohibit routine clinical applications in x-ray computed tomography (CT). To reduce reconstruction times, we have applied (under) relaxation to ordered subset algorithms. This enables us to use subsets consisting of only single projection angle, effectively increasing the number of image updates within an entire iteration. A second advantage of applying relaxation is that it can help improve convergence by removing the limit cycle behaviour of ordered subset algorithms, which normally do not converge to an optimal solution but rather a suboptimal limit cycle consisting of as many points as there are subsets. Relaxation suppresses the limit cycle behaviour by decreasing the stepsize for approaching the solution. A simulation study for a 2D mathematical phantom and three different ordered subset algorithms shows that all three algorithms benefit from relaxation: equal noise-to-resolution trade-off can be achieved using fewer iterations than the conventional algorithms, while a lower minimal normalized mean square error (NMSE) clearly indicates a better convergence. Two different schemes for setting the relaxation parameter are studied, and both schemes yield approximately the same minimal NMSE

  16. A numeric comparison of variable selection algorithms for supervised learning

    International Nuclear Information System (INIS)

    Palombo, G.; Narsky, I.

    2009-01-01

    Datasets in modern High Energy Physics (HEP) experiments are often described by dozens or even hundreds of input variables. Reducing a full variable set to a subset that most completely represents information about data is therefore an important task in analysis of HEP data. We compare various variable selection algorithms for supervised learning using several datasets such as, for instance, imaging gamma-ray Cherenkov telescope (MAGIC) data found at the UCI repository. We use classifiers and variable selection methods implemented in the statistical package StatPatternRecognition (SPR), a free open-source C++ package developed in the HEP community ( (http://sourceforge.net/projects/statpatrec/)). For each dataset, we select a powerful classifier and estimate its learning accuracy on variable subsets obtained by various selection algorithms. When possible, we also estimate the CPU time needed for the variable subset selection. The results of this analysis are compared with those published previously for these datasets using other statistical packages such as R and Weka. We show that the most accurate, yet slowest, method is a wrapper algorithm known as generalized sequential forward selection ('Add N Remove R') implemented in SPR.

  17. A Quantum Approach to Subset-Sum and Similar Problems

    OpenAIRE

    Daskin, Ammar

    2017-01-01

    In this paper, we study the subset-sum problem by using a quantum heuristic approach similar to the verification circuit of quantum Arthur-Merlin games. Under described certain assumptions, we show that the exact solution of the subset sum problem my be obtained in polynomial time and the exponential speed-up over the classical algorithms may be possible. We give a numerical example and discuss the complexity of the approach and its further application to the knapsack problem.

  18. Genomic selection for the improvement of antibody response to Newcastle disease and avian influenza virus in chickens.

    Directory of Open Access Journals (Sweden)

    Tianfei Liu

    Full Text Available Newcastle disease (ND and avian influenza (AI are the most feared diseases in the poultry industry worldwide. They can cause flock mortality up to 100%, resulting in a catastrophic economic loss. This is the first study to investigate the feasibility of genomic selection for antibody response to Newcastle disease virus (Ab-NDV and antibody response to Avian Influenza virus (Ab-AIV in chickens. The data were collected from a crossbred population. Breeding values for Ab-NDV and Ab-AIV were estimated using a pedigree-based best linear unbiased prediction model (BLUP and a genomic best linear unbiased prediction model (GBLUP. Single-trait and multiple-trait analyses were implemented. According to the analysis using the pedigree-based model, the heritability for Ab-NDV estimated from the single-trait and multiple-trait models was 0.478 and 0.487, respectively. The heritability for Ab-AIV estimated from the two models was 0.301 and 0.291, respectively. The estimated genetic correlation between the two traits was 0.438. A four-fold cross-validation was used to assess the accuracy of the estimated breeding values (EBV in the two validation scenarios. In the family sample scenario each half-sib family is randomly allocated to one of four subsets and in the random sample scenario the individuals are randomly divided into four subsets. In the family sample scenario, compared with the pedigree-based model, the accuracy of the genomic prediction increased from 0.086 to 0.237 for Ab-NDV and from 0.080 to 0.347 for Ab-AIV. In the random sample scenario, the accuracy was improved from 0.389 to 0.427 for Ab-NDV and from 0.281 to 0.367 for Ab-AIV. The multiple-trait GBLUP model led to a slightly higher accuracy of genomic prediction for both traits. These results indicate that genomic selection for antibody response to ND and AI in chickens is promising.

  19. Parameter subset selection for the dynamic calibration of activated sludge models (ASMs): experience versus systems analysis

    DEFF Research Database (Denmark)

    Ruano, MV; Ribes, J; de Pauw, DJW

    2007-01-01

    to describe nitrogen and phosphorus removal in the Haaren WWTP (The Netherlands). The parameter significance ranking shows that the temperature correction coefficients are among the most influential parameters on the model output. This outcome confronts the previous identifiability studies and the experience...... based approaches which excluded them from their analysis. Systems analysis reveals that parameter significance ranking and size of the identifiable parameter subset depend on the information content of data available for calibration. However, it suffers from heavy computational demand. In contrast......, although the experience-based approach is computationally affordable, it is unable to take into account the information content issue and therefore can be either too optimistic (giving poorly identifiable sets) or pessimistic (small size of sets while much more can be estimated from the data...

  20. 40 CFR 761.306 - Sampling 1 meter square surfaces by random selection of halves.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 30 2010-07-01 2010-07-01 false Sampling 1 meter square surfaces by...(b)(3) § 761.306 Sampling 1 meter square surfaces by random selection of halves. (a) Divide each 1 meter square portion where it is necessary to collect a surface wipe test sample into two equal (or as...

  1. From Protocols to Publications: A Study in Selective Reporting of Outcomes in Randomized Trials in Oncology

    Science.gov (United States)

    Raghav, Kanwal Pratap Singh; Mahajan, Sminil; Yao, James C.; Hobbs, Brian P.; Berry, Donald A.; Pentz, Rebecca D.; Tam, Alda; Hong, Waun K.; Ellis, Lee M.; Abbruzzese, James; Overman, Michael J.

    2015-01-01

    Purpose The decision by journals to append protocols to published reports of randomized trials was a landmark event in clinical trial reporting. However, limited information is available on how this initiative effected transparency and selective reporting of clinical trial data. Methods We analyzed 74 oncology-based randomized trials published in Journal of Clinical Oncology, the New England Journal of Medicine, and The Lancet in 2012. To ascertain integrity of reporting, we compared published reports with their respective appended protocols with regard to primary end points, nonprimary end points, unplanned end points, and unplanned analyses. Results A total of 86 primary end points were reported in 74 randomized trials; nine trials had greater than one primary end point. Nine trials (12.2%) had some discrepancy between their planned and published primary end points. A total of 579 nonprimary end points (median, seven per trial) were planned, of which 373 (64.4%; median, five per trial) were reported. A significant positive correlation was found between the number of planned and nonreported nonprimary end points (Spearman r = 0.66; P < .001). Twenty-eight studies (37.8%) reported a total of 65 unplanned end points; 52 (80.0%) of which were not identified as unplanned. Thirty-one (41.9%) and 19 (25.7%) of 74 trials reported a total of 52 unplanned analyses involving primary end points and 33 unplanned analyses involving nonprimary end points, respectively. Studies reported positive unplanned end points and unplanned analyses more frequently than negative outcomes in abstracts (unplanned end points odds ratio, 6.8; P = .002; unplanned analyses odd ratio, 8.4; P = .007). Conclusion Despite public and reviewer access to protocols, selective outcome reporting persists and is a major concern in the reporting of randomized clinical trials. To foster credible evidence-based medicine, additional initiatives are needed to minimize selective reporting. PMID:26304898

  2. Joint random beam and spectrum selection for spectrum sharing systems with partial channel state information

    KAUST Repository

    Abdallah, Mohamed M.

    2013-11-01

    In this work, we develop joint interference-aware random beam and spectrum selection scheme that provide enhanced performance for the secondary network under the condition that the interference observed at the primary receiver is below a predetermined acceptable value. We consider a secondary link composed of a transmitter equipped with multiple antennas and a single-antenna receiver sharing the same spectrum with a set of primary links composed of a single-antenna transmitter and a single-antenna receiver. The proposed schemes jointly select a beam, among a set of power-optimized random beams, as well as the primary spectrum that maximizes the signal-to-interference-plus-noise ratio (SINR) of the secondary link while satisfying the primary interference constraint. In particular, we consider the case where the interference level is described by a q-bit description of its magnitude, whereby we propose a technique to find the optimal quantizer thresholds in a mean square error (MSE) sense. © 2013 IEEE.

  3. Joint random beam and spectrum selection for spectrum sharing systems with partial channel state information

    KAUST Repository

    Abdallah, Mohamed M.; Sayed, Mostafa M.; Alouini, Mohamed-Slim; Qaraqe, Khalid A.

    2013-01-01

    In this work, we develop joint interference-aware random beam and spectrum selection scheme that provide enhanced performance for the secondary network under the condition that the interference observed at the primary receiver is below a predetermined acceptable value. We consider a secondary link composed of a transmitter equipped with multiple antennas and a single-antenna receiver sharing the same spectrum with a set of primary links composed of a single-antenna transmitter and a single-antenna receiver. The proposed schemes jointly select a beam, among a set of power-optimized random beams, as well as the primary spectrum that maximizes the signal-to-interference-plus-noise ratio (SINR) of the secondary link while satisfying the primary interference constraint. In particular, we consider the case where the interference level is described by a q-bit description of its magnitude, whereby we propose a technique to find the optimal quantizer thresholds in a mean square error (MSE) sense. © 2013 IEEE.

  4. The RANDOM computer program: A linear congruential random number generator

    Science.gov (United States)

    Miles, R. F., Jr.

    1986-01-01

    The RANDOM Computer Program is a FORTRAN program for generating random number sequences and testing linear congruential random number generators (LCGs). The linear congruential form of random number generator is discussed, and the selection of parameters of an LCG for a microcomputer described. This document describes the following: (1) The RANDOM Computer Program; (2) RANDOM.MOD, the computer code needed to implement an LCG in a FORTRAN program; and (3) The RANCYCLE and the ARITH Computer Programs that provide computational assistance in the selection of parameters for an LCG. The RANDOM, RANCYCLE, and ARITH Computer Programs are written in Microsoft FORTRAN for the IBM PC microcomputer and its compatibles. With only minor modifications, the RANDOM Computer Program and its LCG can be run on most micromputers or mainframe computers.

  5. Analysis and applications of a frequency selective surface via a random distribution method

    International Nuclear Information System (INIS)

    Xie Shao-Yi; Huang Jing-Jian; Yuan Nai-Chang; Liu Li-Guo

    2014-01-01

    A novel frequency selective surface (FSS) for reducing radar cross section (RCS) is proposed in this paper. This FSS is based on the random distribution method, so it can be called random surface. In this paper, the stacked patches serving as periodic elements are employed for RCS reduction. Previous work has demonstrated the efficiency by utilizing the microstrip patches, especially for the reflectarray. First, the relevant theory of the method is described. Then a sample of a three-layer variable-sized stacked patch random surface with a dimension of 260 mm×260 mm is simulated, fabricated, and measured in order to demonstrate the validity of the proposed design. For the normal incidence, the 8-dB RCS reduction can be achieved both by the simulation and the measurement in 8 GHz–13 GHz. The oblique incidence of 30° is also investigated, in which the 7-dB RCS reduction can be obtained in a frequency range of 8 GHz–14 GHz. (condensed matter: electronic structure, electrical, magnetic, and optical properties)

  6. Indirect Positive Evidence in the Acquisition of a Subset Grammar

    Science.gov (United States)

    Schwartz, Misha; Goad, Heather

    2017-01-01

    This article proposes that second language learners can use indirect positive evidence (IPE) to acquire a phonological grammar that is a subset of their L1 grammar. IPE is evidence from errors in the learner's L1 made by native speakers of the learner's L2. It has been assumed that subset grammars may be acquired using direct or indirect negative…

  7. Random drift versus selection in academic vocabulary: an evolutionary analysis of published keywords.

    Directory of Open Access Journals (Sweden)

    R Alexander Bentley

    Full Text Available The evolution of vocabulary in academic publishing is characterized via keyword frequencies recorded in the ISI Web of Science citations database. In four distinct case-studies, evolutionary analysis of keyword frequency change through time is compared to a model of random copying used as the null hypothesis, such that selection may be identified against it. The case studies from the physical sciences indicate greater selection in keyword choice than in the social sciences. Similar evolutionary analyses can be applied to a wide range of phenomena; wherever the popularity of multiple items through time has been recorded, as with web searches, or sales of popular music and books, for example.

  8. On theoretical models of gene expression evolution with random genetic drift and natural selection.

    Directory of Open Access Journals (Sweden)

    Osamu Ogasawara

    2009-11-01

    Full Text Available The relative contributions of natural selection and random genetic drift are a major source of debate in the study of gene expression evolution, which is hypothesized to serve as a bridge from molecular to phenotypic evolution. It has been suggested that the conflict between views is caused by the lack of a definite model of the neutral hypothesis, which can describe the long-run behavior of evolutionary change in mRNA abundance. Therefore previous studies have used inadequate analogies with the neutral prediction of other phenomena, such as amino acid or nucleotide sequence evolution, as the null hypothesis of their statistical inference.In this study, we introduced two novel theoretical models, one based on neutral drift and the other assuming natural selection, by focusing on a common property of the distribution of mRNA abundance among a variety of eukaryotic cells, which reflects the result of long-term evolution. Our results demonstrated that (1 our models can reproduce two independently found phenomena simultaneously: the time development of gene expression divergence and Zipf's law of the transcriptome; (2 cytological constraints can be explicitly formulated to describe long-term evolution; (3 the model assuming that natural selection optimized relative mRNA abundance was more consistent with previously published observations than the model of optimized absolute mRNA abundances.The models introduced in this study give a formulation of evolutionary change in the mRNA abundance of each gene as a stochastic process, on the basis of previously published observations. This model provides a foundation for interpreting observed data in studies of gene expression evolution, including identifying an adequate time scale for discriminating the effect of natural selection from that of random genetic drift of selectively neutral variations.

  9. Short communication: Inhibitory effects of dietary aflatoxin B1 on cytokines expression and T-cell subsets in the cecal tonsil of broiler chickens

    Energy Technology Data Exchange (ETDEWEB)

    Liu, C.; Jiang, M.; Fang, J.; Peng, X.; Cui, H.

    2016-11-01

    Afatoxin B1 (AFB1) is the most toxic form among the mycotoxins. Cytokines are important mediators of the immune system. T-cell subsets play a crucial role in cell-mediated immunity. The aim of present study was to evaluate the effects of dietary AFB1 on the cytokines expression and T-cell subsets in the cecal tonsil of broiler chickens throughout a 21-day experimental period. One hundred and fifty six one-day-old broiler chickens were randomly divided into control group (0 mg AFB1/kg feed) and AFB1 group (0.6 mg pure AFB1/kg feed). At 7, 14 and 21 days of age, the levels of seven cytokines (IL-2, IL-4, IL-6, IL-10, IL-17, IFN-γ and TNF-α) mRNA expression as well as the proportions of T-cell subsets (CD3+, CD3+CD4+, CD3+CD8+) by qRT-PCR and flow cytometry methods were assessed in the cecal tonsils. The levels of the seven cytokines mRNA expression and the percentages of T-cell subsets significantly decreased at 14 and 21 days of age in the AFB1 group compared with the control group. However, the CD4+/CD8+ ratio was not significantly changed. These results demonstrate that 0.6 mg/kg AFB1 dietary exposure reduced the levels of cytokines mRNA expression and the percentages of T-cell subsets in the cecal tonsils of broiler chickens, suggesting that the cell-mediated immunity of cecal tonsils might be impaired in broilers. (Author)

  10. Short communication: Inhibitory effects of dietary aflatoxin B1 on cytokines expression and T-cell subsets in the cecal tonsil of broiler chickens

    Directory of Open Access Journals (Sweden)

    Chunyu Liu

    2016-08-01

    Full Text Available Aflatoxin B1 (AFB1 is the most toxic form among the mycotoxins. Cytokines are important mediators of the immune system. T-cell subsets play a crucial role in cell-mediated immunity. The aim of present study was to evaluate the effects of dietary AFB1 on the cytokines expression and T-cell subsets in the cecal tonsil of broiler chickens throughout a 21-day experimental period. One hundred and fifty six one-day-old broiler chickens were randomly divided into control group (0 mg AFB1/kg feed and AFB1 group (0.6 mg pure AFB1/kg feed. At 7, 14 and 21 days of age, the levels of seven cytokines (IL-2, IL-4, IL-6, IL-10, IL-17, IFN-γ and TNF-α mRNA expression as well as the proportions of T-cell subsets (CD3+, CD3+CD4+, CD3+CD8+ by qRT-PCR and flow cytometry methods were assessed in the cecal tonsils. The levels of the seven cytokines mRNA expression and the percentages of T-cell subsets significantly decreased at 14 and 21 days of age in the AFB1 group compared with the control group. However, the CD4+/CD8+ ratio was not significantly changed. These results demonstrate that 0.6 mg/kg AFB1 dietary exposure reduced the levels of cytokines mRNA expression and the percentages of T-cell subsets in the cecal tonsils of broiler chickens, suggesting that the cell-mediated immunity of cecal tonsils might be impaired in broilers.

  11. From Protocols to Publications: A Study in Selective Reporting of Outcomes in Randomized Trials in Oncology.

    Science.gov (United States)

    Raghav, Kanwal Pratap Singh; Mahajan, Sminil; Yao, James C; Hobbs, Brian P; Berry, Donald A; Pentz, Rebecca D; Tam, Alda; Hong, Waun K; Ellis, Lee M; Abbruzzese, James; Overman, Michael J

    2015-11-01

    The decision by journals to append protocols to published reports of randomized trials was a landmark event in clinical trial reporting. However, limited information is available on how this initiative effected transparency and selective reporting of clinical trial data. We analyzed 74 oncology-based randomized trials published in Journal of Clinical Oncology, the New England Journal of Medicine, and The Lancet in 2012. To ascertain integrity of reporting, we compared published reports with their respective appended protocols with regard to primary end points, nonprimary end points, unplanned end points, and unplanned analyses. A total of 86 primary end points were reported in 74 randomized trials; nine trials had greater than one primary end point. Nine trials (12.2%) had some discrepancy between their planned and published primary end points. A total of 579 nonprimary end points (median, seven per trial) were planned, of which 373 (64.4%; median, five per trial) were reported. A significant positive correlation was found between the number of planned and nonreported nonprimary end points (Spearman r = 0.66; P medicine, additional initiatives are needed to minimize selective reporting. © 2015 by American Society of Clinical Oncology.

  12. Circulating regulatory B cell subsets in patients with neuromyelitis optica spectrum disorders.

    Science.gov (United States)

    Han, Jinming; Sun, Li; Wang, Zhongkun; Fan, Xueli; Wang, Lifang; Song, Yang-Yang; Zhu, Jie; Jin, Tao

    2017-07-01

    This study analyzed the populations of three different subsets of regulatory B cells (Bregs) in the peripheral blood mononuclear cells (PBMCs) of patients with neuromyelitis optica spectrum disorders (NMOSDs) and explored the relationship between the changes in these subsets of Bregs and the severity of NMOSD. A total of 22 patients with relapsed NMOSDs before treatment were recruited in our study, along with 20 age and gender-matched healthy controls, from May 2015 to March 2016. The percentages and numbers for three different subsets of Bregs including the CD19 + CD24 hi CD38 hi , CD19 + CD24 hi CD27 + , and CD19 + CD5 + CD1d hi populations were evaluated in parallel by flow cytometry. Afterwards, correlations between the change of three different subsets of Bregs and disease severity were analyzed. We found significantly lower percentages of CD19 + CD24 hi CD38 hi and CD19 + CD5 + CD1d hi Bregs in NMOSDs patients than in healthy individuals. In contrast, the CD19 + CD24 hi CD27 + Bregs population was significantly higher in NMOSDs patients than in healthy individuals. However, the three different Bregs subsets showed no significant correlation with expanded disability status scale (EDSS) or annualized relapse rate (ARR). Our findings suggest that the subsets of Bregs may play complex roles in the pathogenesis of NMOSDs and are not correlated with clinical disease severity. Further insights into the potential role of subsets of Bregs could increase our basic knowledge of NMOSDs pathogenesis.

  13. Antenna subset selection at multi-antenna relay with adaptive modulation

    KAUST Repository

    Choi, Seyeong

    2011-06-01

    In this paper, we proposed several antenna selection schemes for cooperative diversity systems with adaptive transmission. The proposed schemes were based on dual-hop relaying where a relay with multiple-antenna capabilities at reception and transmission is deployed between the source and the destination nodes. We analyzed the performance of the proposed schemes by quantifying the average spectral efficiency and the outage probability. We also investigated the trade-off of performance and complexity by comparing the average number of active antennas, path estimations, and signal-to-noise ratio comparisons of the different proposed schemes. Copyright © 2011 John Wiley & Sons, Ltd.

  14. Antenna subset selection at multi-antenna relay with adaptive modulation

    KAUST Repository

    Choi, Seyeong; Hasna, Mazen Omar; Yang, Hongchuan; Alouini, Mohamed-Slim

    2011-01-01

    In this paper, we proposed several antenna selection schemes for cooperative diversity systems with adaptive transmission. The proposed schemes were based on dual-hop relaying where a relay with multiple-antenna capabilities at reception and transmission is deployed between the source and the destination nodes. We analyzed the performance of the proposed schemes by quantifying the average spectral efficiency and the outage probability. We also investigated the trade-off of performance and complexity by comparing the average number of active antennas, path estimations, and signal-to-noise ratio comparisons of the different proposed schemes. Copyright © 2011 John Wiley & Sons, Ltd.

  15. The Amnesiac Lookback Option: Selectively Monitored Lookback Options and Cryptocurrencies

    Directory of Open Access Journals (Sweden)

    Ho-Chun Herbert Chang

    2018-05-01

    Full Text Available This study proposes a strategy to make the lookback option cheaper and more practical, and suggests the use of its properties to reduce risk exposure in cryptocurrency markets through blockchain enforced smart contracts and correct for informational inefficiencies surrounding prices and volatility. This paper generalizes partial, discretely-monitored lookback options that dilute premiums by selecting a subset of specified periods to determine payoff, which we call amnesiac lookback options. Prior literature on discretely-monitored lookback options considers the number of periods and assumes equidistant lookback periods in pricing partial lookback options. This study by contrast considers random sampling of lookback periods and compares resulting payoff of the call, put and spread options under floating and fixed strikes. Amnesiac lookbacks are priced with Monte Carlo simulations of Gaussian random walks under equidistant and random periods. Results are compared to analytic and binomial pricing models for the same derivatives. Simulations show diminishing marginal increases to the fair price as the number of selected periods is increased. The returns correspond to a Hill curve whose parameters are set by interest rate and volatility. We demonstrate over-pricing under equidistant monitoring assumptions with error increasing as the lookback periods decrease. An example of a direct implication for event trading is when shock is forecasted but its timing uncertain, equidistant sampling produces a lower error on the true maximum than random choice. We conclude that the instrument provides an ideal space for investors to balance their risk, and as a prime candidate to hedge extreme volatility. We discuss the application of the amnesiac lookback option and path-dependent options to cryptocurrencies and blockchain commodities in the context of smart contracts.

  16. Reliable Refuge: Two Sky Island Scorpion Species Select Larger, Thermally Stable Retreat Sites.

    Science.gov (United States)

    Becker, Jamie E; Brown, Christopher A

    2016-01-01

    Sky island scorpions shelter under rocks and other surface debris, but, as with other scorpions, it is unclear whether these species select retreat sites randomly. Furthermore, little is known about the thermal preferences of scorpions, and no research has been done to identify whether reproductive condition might influence retreat site selection. The objectives were to (1) identify physical or thermal characteristics for retreat sites occupied by two sky island scorpions (Vaejovis cashi Graham 2007 and V. electrum Hughes 2011) and those not occupied; (2) determine whether retreat site selection differs between the two study species; and (3) identify whether thermal selection differs between species and between gravid and non-gravid females of the same species. Within each scorpion's habitat, maximum dimensions of rocks along a transect line were measured and compared to occupied rocks to determine whether retreat site selection occurred randomly. Temperature loggers were placed under a subset of occupied and unoccupied rocks for 48 hours to compare the thermal characteristics of these rocks. Thermal gradient trials were conducted before parturition and after dispersal of young in order to identify whether gravidity influences thermal preference. Vaejovis cashi and V. electrum both selected larger retreat sites that had more stable thermal profiles. Neither species appeared to have thermal preferences influenced by reproductive condition. However, while thermal selection did not differ among non-gravid individuals, gravid V. electrum selected warmer temperatures than its gravid congener. Sky island scorpions appear to select large retreat sites to maintain thermal stability, although biotic factors (e.g., competition) could also be involved in this choice. Future studies should focus on identifying the various biotic or abiotic factors that could influence retreat site selection in scorpions, as well as determining whether reproductive condition affects thermal

  17. High Entropy Random Selection Protocols

    NARCIS (Netherlands)

    H. Buhrman (Harry); M. Christandl (Matthias); M. Koucky (Michal); Z. Lotker (Zvi); B. Patt-Shamir; M. Charikar; K. Jansen; O. Reingold; J. Rolim

    2007-01-01

    textabstractIn this paper, we construct protocols for two parties that do not trust each other, to generate random variables with high Shannon entropy. We improve known bounds for the trade off between the number of rounds, length of communication and the entropy of the outcome.

  18. T-lymphocyte subsets in West African children: impact of age, sex, and season

    DEFF Research Database (Denmark)

    Lisse, I M; Aaby, P; Whittle, H

    1997-01-01

    method to determine T-lymphocyte subsets. RESULTS: We found differences by age, sex, and season, whereas there were no significant differences by birth order, twinning, or ethnic group. The CD4+ percentage declined from birth to age 2 years, at which time it started to increase to higher levels at age 4......OBJECTIVE: There has been no reference material for T-lymphocyte subsets for normal children in developing countries. We therefore used T-lymphocyte subset determinations among children in three different studies in Guinea-Bissau to construct age-related reference material and to examine possible...... determinants of T-lymphocyte subset levels. METHODS: A total of 803 healthy West African children younger than 6 years were included in the three community studies of T-lymphocyte subsets among twins and singletons, after measles infection and after measles immunization. We used the immunoalkaline phosphatase...

  19. Integrated Behavior Therapy for Selective Mutism: a randomized controlled pilot study.

    Science.gov (United States)

    Bergman, R Lindsey; Gonzalez, Araceli; Piacentini, John; Keller, Melody L

    2013-10-01

    To evaluate the feasibility, acceptability, and preliminary efficacy of a novel behavioral intervention for reducing symptoms of selective mutism and increasing functional speech. A total of 21 children ages 4 to 8 with primary selective mutism were randomized to 24 weeks of Integrated Behavior Therapy for Selective Mutism (IBTSM) or a 12-week Waitlist control. Clinical outcomes were assessed using blind independent evaluators, parent-, and teacher-report, and an objective behavioral measure. Treatment recipients completed a three-month follow-up to assess durability of treatment gains. Data indicated increased functional speaking behavior post-treatment as rated by parents and teachers, with a high rate of treatment responders as rated by blind independent evaluators (75%). Conversely, children in the Waitlist comparison group did not experience significant improvements in speaking behaviors. Children who received IBTSM also demonstrated significant improvements in number of words spoken at school compared to baseline, however, significant group differences did not emerge. Treatment recipients also experienced significant reductions in social anxiety per parent, but not teacher, report. Clinical gains were maintained over 3 month follow-up. IBTSM appears to be a promising new intervention that is efficacious in increasing functional speaking behaviors, feasible, and acceptable to parents and teachers. Copyright © 2013 Elsevier Ltd. All rights reserved.

  20. Metastability of Reversible Random Walks in Potential Fields

    Science.gov (United States)

    Landim, C.; Misturini, R.; Tsunoda, K.

    2015-09-01

    Let be an open and bounded subset of , and let be a twice continuously differentiable function. Denote by the discretization of , , and denote by the continuous-time, nearest-neighbor, random walk on which jumps from to at rate . We examine in this article the metastable behavior of among the wells of the potential F.

  1. Biophysical subsets of embryonic stem cells display distinct phenotypic and morphological signatures.

    Directory of Open Access Journals (Sweden)

    Tom Bongiorno

    Full Text Available The highly proliferative and pluripotent characteristics of embryonic stem cells engender great promise for tissue engineering and regenerative medicine, but the rapid identification and isolation of target cell phenotypes remains challenging. Therefore, the objectives of this study were to characterize cell mechanics as a function of differentiation and to employ differences in cell stiffness to select population subsets with distinct mechanical, morphological, and biological properties. Biomechanical analysis with atomic force microscopy revealed that embryonic stem cells stiffened within one day of differentiation induced by leukemia inhibitory factor removal, with a lagging but pronounced change from spherical to spindle-shaped cell morphology. A microfluidic device was then employed to sort a differentially labeled mixture of pluripotent and differentiating cells based on stiffness, resulting in pluripotent cell enrichment in the soft device outlet. Furthermore, sorting an unlabeled population of partially differentiated cells produced a subset of "soft" cells that was enriched for the pluripotent phenotype, as assessed by post-sort characterization of cell mechanics, morphology, and gene expression. The results of this study indicate that intrinsic cell mechanical properties might serve as a basis for efficient, high-throughput, and label-free isolation of pluripotent stem cells, which will facilitate a greater biological understanding of pluripotency and advance the potential of pluripotent stem cell differentiated progeny as cell sources for tissue engineering and regenerative medicine.

  2. Field-based random sampling without a sampling frame: control selection for a case-control study in rural Africa.

    Science.gov (United States)

    Crampin, A C; Mwinuka, V; Malema, S S; Glynn, J R; Fine, P E

    2001-01-01

    Selection bias, particularly of controls, is common in case-control studies and may materially affect the results. Methods of control selection should be tailored both for the risk factors and disease under investigation and for the population being studied. We present here a control selection method devised for a case-control study of tuberculosis in rural Africa (Karonga, northern Malawi) that selects an age/sex frequency-matched random sample of the population, with a geographical distribution in proportion to the population density. We also present an audit of the selection process, and discuss the potential of this method in other settings.

  3. Treatment selection in a randomized clinical trial via covariate-specific treatment effect curves.

    Science.gov (United States)

    Ma, Yunbei; Zhou, Xiao-Hua

    2017-02-01

    For time-to-event data in a randomized clinical trial, we proposed two new methods for selecting an optimal treatment for a patient based on the covariate-specific treatment effect curve, which is used to represent the clinical utility of a predictive biomarker. To select an optimal treatment for a patient with a specific biomarker value, we proposed pointwise confidence intervals for each covariate-specific treatment effect curve and the difference between covariate-specific treatment effect curves of two treatments. Furthermore, to select an optimal treatment for a future biomarker-defined subpopulation of patients, we proposed confidence bands for each covariate-specific treatment effect curve and the difference between each pair of covariate-specific treatment effect curve over a fixed interval of biomarker values. We constructed the confidence bands based on a resampling technique. We also conducted simulation studies to evaluate finite-sample properties of the proposed estimation methods. Finally, we illustrated the application of the proposed method in a real-world data set.

  4. Expansion of mycobacterium-reactive gamma delta T cells by a subset of memory helper T cells.

    Science.gov (United States)

    Vila, L M; Haftel, H M; Park, H S; Lin, M S; Romzek, N C; Hanash, S M; Holoshitz, J

    1995-04-01

    Human gamma delta T cells expressing the V gamma 9/V delta 2 T-cell receptor have been previously found to proliferate in response to certain microorganisms and to expand throughout life, presumably because of extrathymic activation by foreign antigens. In vitro expansion of V gamma 9/V delta 2 cells by mycobacteria has been previously shown to be dependent on accessory cells. In order to gain an insight into the mechanisms involved in the expansion of these cells, we have undertaken to identify the peripheral blood subset of cells on which proliferation of V gamma 9/V delta 2 cells in response to mycobacteria is dependent. Contrary to their role in antigen presentation to alpha beta T cells, professional antigen-presenting cells, such as monocytes, B cells, and dendritic cells, were unable to provide the cellular support for the expansion of V gamma 9/V delta 2 cells. Selective depletion of T-cell subsets, as well as the use of highly purified T-cell populations, indicated that the only subset of peripheral blood cells that could expand V gamma 9/V delta 2 cells were CD4+ CD45RO+ CD7- alpha beta T cells. These cells underwent distinct intracellular signaling events after stimulation with the mycobacterial antigen. Expansion of V gamma 9/V delta 2 cells by alpha beta T cells was dependent on cell-cell contact. This is the first evidence that a small subset of the memory helper T-cell population is exclusively responsible for the peripheral expansion of V gamma 9/V delta 2 cells. These data illustrate a unique aspect of antigen recognition by gamma delta T cells and provide new means to study their immune defense role.

  5. A systems biology approach to the analysis of subset-specific responses to lipopolysaccharide in dendritic cells.

    Science.gov (United States)

    Hancock, David G; Shklovskaya, Elena; Guy, Thomas V; Falsafi, Reza; Fjell, Chris D; Ritchie, William; Hancock, Robert E W; Fazekas de St Groth, Barbara

    2014-01-01

    Dendritic cells (DCs) are critical for regulating CD4 and CD8 T cell immunity, controlling Th1, Th2, and Th17 commitment, generating inducible Tregs, and mediating tolerance. It is believed that distinct DC subsets have evolved to control these different immune outcomes. However, how DC subsets mount different responses to inflammatory and/or tolerogenic signals in order to accomplish their divergent functions remains unclear. Lipopolysaccharide (LPS) provides an excellent model for investigating responses in closely related splenic DC subsets, as all subsets express the LPS receptor TLR4 and respond to LPS in vitro. However, previous studies of the LPS-induced DC transcriptome have been performed only on mixed DC populations. Moreover, comparisons of the in vivo response of two closely related DC subsets to LPS stimulation have not been reported in the literature to date. We compared the transcriptomes of murine splenic CD8 and CD11b DC subsets after in vivo LPS stimulation, using RNA-Seq and systems biology approaches. We identified subset-specific gene signatures, which included multiple functional immune mediators unique to each subset. To explain the observed subset-specific differences, we used a network analysis approach. While both DC subsets used a conserved set of transcription factors and major signalling pathways, the subsets showed differential regulation of sets of genes that 'fine-tune' the network Hubs expressed in common. We propose a model in which signalling through common pathway components is 'fine-tuned' by transcriptional control of subset-specific modulators, thus allowing for distinct functional outcomes in closely related DC subsets. We extend this analysis to comparable datasets from the literature and confirm that our model can account for cell subset-specific responses to LPS stimulation in multiple subpopulations in mouse and man.

  6. Design of focused and restrained subsets from extremely large virtual libraries.

    Science.gov (United States)

    Jamois, Eric A; Lin, Chien T; Waldman, Marvin

    2003-11-01

    With the current and ever-growing offering of reagents along with the vast palette of organic reactions, virtual libraries accessible to combinatorial chemists can reach sizes of billions of compounds or more. Extracting practical size subsets for experimentation has remained an essential step in the design of combinatorial libraries. A typical approach to computational library design involves enumeration of structures and properties for the entire virtual library, which may be unpractical for such large libraries. This study describes a new approach termed as on the fly optimization (OTFO) where descriptors are computed as needed within the subset optimization cycle and without intermediate enumeration of structures. Results reported herein highlight the advantages of coupling an ultra-fast descriptor calculation engine to subset optimization capabilities. We also show that enumeration of properties for the entire virtual library may not only be unpractical but also wasteful. Successful design of focused and restrained subsets can be achieved while sampling only a small fraction of the virtual library. We also investigate the stability of the method and compare results obtained from simulated annealing (SA) and genetic algorithms (GA).

  7. A DYNAMIC FEATURE SELECTION METHOD FOR DOCUMENT RANKING WITH RELEVANCE FEEDBACK APPROACH

    Directory of Open Access Journals (Sweden)

    K. Latha

    2010-07-01

    Full Text Available Ranking search results is essential for information retrieval and Web search. Search engines need to not only return highly relevant results, but also be fast to satisfy users. As a result, not all available features can be used for ranking, and in fact only a small percentage of these features can be used. Thus, it is crucial to have a feature selection mechanism that can find a subset of features that both meets latency requirements and achieves high relevance. In this paper we describe a 0/1 knapsack procedure for automatically selecting features to use within Generalization model for Document Ranking. We propose an approach for Relevance Feedback using Expectation Maximization method and evaluate the algorithm on the TREC Collection for describing classes of feedback textual information retrieval features. Experimental results, evaluated on standard TREC-9 part of the OHSUMED collections, show that our feature selection algorithm produces models that are either significantly more effective than, or equally effective as, models such as Markov Random Field model, Correlation Co-efficient and Count Difference method

  8. Mapping Spatial Distribution of Larch Plantations from Multi-Seasonal Landsat-8 OLI Imagery and Multi-Scale Textures Using Random Forests

    Directory of Open Access Journals (Sweden)

    Tian Gao

    2015-02-01

    Full Text Available The knowledge about spatial distribution of plantation forests is critical for forest management, monitoring programs and functional assessment. This study demonstrates the potential of multi-seasonal (spring, summer, autumn and winter Landsat-8 Operational Land Imager imageries with random forests (RF modeling to map larch plantations (LP in a typical plantation forest landscape in North China. The spectral bands and two types of textures were applied for creating 675 input variables of RF. An accuracy of 92.7% for LP, with a Kappa coefficient of 0.834, was attained using the RF model. A RF-based importance assessment reveals that the spectral bands and bivariate textural features calculated by pseudo-cross variogram (PC strongly promoted forest class-separability, whereas the univariate textural features influenced weakly. A feature selection strategy eliminated 93% of variables, and then a subset of the 47 most essential variables was generated. In this subset, PC texture derived from summer and winter appeared the most frequently, suggesting that this variability in growing peak season and non-growing season can effectively enhance forest class-separability. A RF classifier applied to the subset led to 91.9% accuracy for LP, with a Kappa coefficient of 0.829. This study provides an insight into approaches for discriminating plantation forests with phenological behaviors.

  9. A Cancer Gene Selection Algorithm Based on the K-S Test and CFS

    Directory of Open Access Journals (Sweden)

    Qiang Su

    2017-01-01

    Full Text Available Background. To address the challenging problem of selecting distinguished genes from cancer gene expression datasets, this paper presents a gene subset selection algorithm based on the Kolmogorov-Smirnov (K-S test and correlation-based feature selection (CFS principles. The algorithm selects distinguished genes first using the K-S test, and then, it uses CFS to select genes from those selected by the K-S test. Results. We adopted support vector machines (SVM as the classification tool and used the criteria of accuracy to evaluate the performance of the classifiers on the selected gene subsets. This approach compared the proposed gene subset selection algorithm with the K-S test, CFS, minimum-redundancy maximum-relevancy (mRMR, and ReliefF algorithms. The average experimental results of the aforementioned gene selection algorithms for 5 gene expression datasets demonstrate that, based on accuracy, the performance of the new K-S and CFS-based algorithm is better than those of the K-S test, CFS, mRMR, and ReliefF algorithms. Conclusions. The experimental results show that the K-S test-CFS gene selection algorithm is a very effective and promising approach compared to the K-S test, CFS, mRMR, and ReliefF algorithms.

  10. Design of a verifiable subset for HAL/S

    Science.gov (United States)

    Browne, J. C.; Good, D. I.; Tripathi, A. R.; Young, W. D.

    1979-01-01

    An attempt to evaluate the applicability of program verification techniques to the existing programming language, HAL/S is discussed. HAL/S is a general purpose high level language designed to accommodate the software needs of the NASA Space Shuttle project. A diversity of features for scientific computing, concurrent and real-time programming, and error handling are discussed. The criteria by which features were evaluated for inclusion into the verifiable subset are described. Individual features of HAL/S with respect to these criteria are examined and justification for the omission of various features from the subset is provided. Conclusions drawn from the research are presented along with recommendations made for the use of HAL/S with respect to the area of program verification.

  11. Binding of peanut lectin to germinal-centre cells: a marker for B-cell subsets of follicular lymphoma?

    OpenAIRE

    Rose, M. L.; Habeshaw, J. A.; Kennedy, R.; Sloane, J.; Wiltshaw, E.; Davies, A. J.

    1981-01-01

    The binding of horseradish-peroxidase-labelled peanut lectin (HRP-PNL) to cryostat sections of tonsil, lymphoma lymph nodes, reactive lymph nodes and miscellaneous tumours demonstrated that PNL binds selectively to lymphocytes in germinal centres. Lymph nodes from 21 patients with non-Hodgkin's lymphomas were phenotyped as cell suspensions for PNL binding, and the following surface markers: E rosetting, C3d, SIg, OK markers of T-cell subsets, Ig heavy-chain and light-chain classes. There was ...

  12. Selective decontamination in pediatric liver transplants. A randomized prospective study.

    Science.gov (United States)

    Smith, S D; Jackson, R J; Hannakan, C J; Wadowsky, R M; Tzakis, A G; Rowe, M I

    1993-06-01

    Although it has been suggested that selective decontamination of the digestive tract (SDD) decreases postoperative aerobic Gram-negative and fungal infections in orthotopic liver transplantation (OLT), no controlled trials exist in pediatric patients. This prospective, randomized controlled study of 36 pediatric OLT patients examines the effect of short-term SDD on postoperative infection and digestive tract flora. Patients were randomized into two groups. The control group received perioperative parenteral antibiotics only. The SDD group received in addition polymyxin E, tobramycin, and amphotericin B enterally and by oropharyngeal swab postoperatively until oral intake was tolerated (6 +/- 4 days). Indications for operation, preoperative status, age, and intensive care unit and hospital length of stay were no different in SDD (n = 18) and control (n = 18) groups. A total of 14 Gram-negative infections (intraabdominal abscess 7, septicemia 5, pneumonia 1, urinary tract 1) developed in the 36 patients studied. Mortality was not significantly different in the two groups. However, there were significantly fewer patients with Gram-negative infections in the SDD group: 3/18 patients (11%) vs. 11/18 patients (50%) in the control group, P < 0.001. There was also significant reduction in aerobic Gram-negative flora in the stool and pharynx in patients receiving SDD. Gram-positive and anaerobic organisms were unaffected. We conclude that short-term postoperative SDD significantly reduces Gram-negative infections in pediatric OLT patients.

  13. Circulating TFH subset distribution is strongly affected in lupus patients with an active disease.

    Directory of Open Access Journals (Sweden)

    Carole Le Coz

    Full Text Available Follicular helper T cells (TFH represent a distinct subset of CD4(+ T cells specialized in providing help to B lymphocytes, which may play a central role in autoimmune diseases having a major B cell component such as systemic lupus erythematosus. Recently, TFH subsets that share common phenotypic and functional characteristics with TFH cells from germinal centers, have been described in the peripheral blood from healthy individuals. The aim of this study was to analyze the distribution of such populations in lupus patients. Circulating TFH cell subsets were defined by multicolor flow cytometry as TFH17 (CXCR3(-CCR6(+, TFH1 (CXCR3 (+ CCR6(- or TFH2 (CXCR3(-CCR6(- cells among CXCR5 (+ CD45RA(-CD4(+ T cells in the peripheral blood of 23 SLE patients and 23 sex and age-matched healthy controls. IL-21 receptor expression by B cells was analyzed by flow cytometry and the serum levels of IL-21 and Igs were determined by ELISA tests. We found that the TFH2 cell subset frequency is strongly and significantly increased in lupus patients with an active disease (SLEDAI score>8, while the TFH1 cell subset percentage is greatly decreased. The TFH2 and TFH1 cell subset frequency alteration is associated with the presence of high Ig levels and autoantibodies in patient's sera. Moreover, the TFH2 cell subset enhancement correlates with an increased frequency of double negative memory B cells (CD27(-IgD(-CD19(+ cells expressing the IL-21R. Finally, we found that IgE levels in lupus patients' sera correlate with disease activity and seem to be associated with high TFH2 cell subset frequency. In conclusion, our study describes for the first time the distribution of circulating TFH cell subsets in lupus patients. Interestingly, we found an increased frequency of TFH2 cells, which correlates with disease activity. Our results suggest that this subset might play a key role in lupus pathogenesis.

  14. PKH26 staining defines distinct subsets of normal human colon epithelial cells at different maturation stages.

    Directory of Open Access Journals (Sweden)

    Anna Pastò

    selection strategy, several distinct cell subsets of human colon epithelial cells, which recapitulate the phenotypic and molecular profile of cells in a discrete crypt location.

  15. Comparison of Subset-Based Local and Finite Element-Based Global Digital Image Correlation

    KAUST Repository

    Pan, Bing; Wang, B.; Lubineau, Gilles; Moussawi, Ali

    2015-01-01

    Digital image correlation (DIC) techniques require an image matching algorithm to register the same physical points represented in different images. Subset-based local DIC and finite element-based (FE-based) global DIC are the two primary image matching methods that have been extensively investigated and regularly used in the field of experimental mechanics. Due to its straightforward implementation and high efficiency, subset-based local DIC has been used in almost all commercial DIC packages. However, it is argued by some researchers that FE-based global DIC offers better accuracy because of the enforced continuity between element nodes. We propose a detailed performance comparison between these different DIC algorithms both in terms of measurement accuracy and computational efficiency. Then, by measuring displacements of the same calculation points using the same calculation algorithms (e.g., correlation criterion, initial guess estimation, subpixel interpolation, optimization algorithm and convergence conditions) and identical calculation parameters (e.g., subset or element size), the performances of subset-based local DIC and two FE-based global DIC approaches are carefully compared in terms of measurement error and computational efficiency using both numerical tests and real experiments. A detailed examination of the experimental results reveals that, when subset (element) size is not very small and the local deformation within a subset (element) can be well approximated by the shape function used, standard subset-based local DIC approach not only provides better results in measured displacements, but also demonstrates much higher computation efficiency. However, several special merits of FE-based global DIC approaches are indicated.

  16. Comparison of Subset-Based Local and Finite Element-Based Global Digital Image Correlation

    KAUST Repository

    Pan, Bing

    2015-02-12

    Digital image correlation (DIC) techniques require an image matching algorithm to register the same physical points represented in different images. Subset-based local DIC and finite element-based (FE-based) global DIC are the two primary image matching methods that have been extensively investigated and regularly used in the field of experimental mechanics. Due to its straightforward implementation and high efficiency, subset-based local DIC has been used in almost all commercial DIC packages. However, it is argued by some researchers that FE-based global DIC offers better accuracy because of the enforced continuity between element nodes. We propose a detailed performance comparison between these different DIC algorithms both in terms of measurement accuracy and computational efficiency. Then, by measuring displacements of the same calculation points using the same calculation algorithms (e.g., correlation criterion, initial guess estimation, subpixel interpolation, optimization algorithm and convergence conditions) and identical calculation parameters (e.g., subset or element size), the performances of subset-based local DIC and two FE-based global DIC approaches are carefully compared in terms of measurement error and computational efficiency using both numerical tests and real experiments. A detailed examination of the experimental results reveals that, when subset (element) size is not very small and the local deformation within a subset (element) can be well approximated by the shape function used, standard subset-based local DIC approach not only provides better results in measured displacements, but also demonstrates much higher computation efficiency. However, several special merits of FE-based global DIC approaches are indicated.

  17. Day-ahead load forecast using random forest and expert input selection

    International Nuclear Information System (INIS)

    Lahouar, A.; Ben Hadj Slama, J.

    2015-01-01

    Highlights: • A model based on random forests for short term load forecast is proposed. • An expert feature selection is added to refine inputs. • Special attention is paid to customers behavior, load profile and special holidays. • The model is flexible and able to handle complex load signal. • A technical comparison is performed to assess the forecast accuracy. - Abstract: The electrical load forecast is getting more and more important in recent years due to the electricity market deregulation and integration of renewable resources. To overcome the incoming challenges and ensure accurate power prediction for different time horizons, sophisticated intelligent methods are elaborated. Utilization of intelligent forecast algorithms is among main characteristics of smart grids, and is an efficient tool to face uncertainty. Several crucial tasks of power operators such as load dispatch rely on the short term forecast, thus it should be as accurate as possible. To this end, this paper proposes a short term load predictor, able to forecast the next 24 h of load. Using random forest, characterized by immunity to parameter variations and internal cross validation, the model is constructed following an online learning process. The inputs are refined by expert feature selection using a set of if–then rules, in order to include the own user specifications about the country weather or market, and to generalize the forecast ability. The proposed approach is tested through a real historical set from the Tunisian Power Company, and the simulation shows accurate and satisfactory results for one day in advance, with an average error exceeding rarely 2.3%. The model is validated for regular working days and weekends, and special attention is paid to moving holidays, following non Gregorian calendar

  18. A Semidefinite Programming Based Search Strategy for Feature Selection with Mutual Information Measure.

    Science.gov (United States)

    Naghibi, Tofigh; Hoffmann, Sarah; Pfister, Beat

    2015-08-01

    Feature subset selection, as a special case of the general subset selection problem, has been the topic of a considerable number of studies due to the growing importance of data-mining applications. In the feature subset selection problem there are two main issues that need to be addressed: (i) Finding an appropriate measure function than can be fairly fast and robustly computed for high-dimensional data. (ii) A search strategy to optimize the measure over the subset space in a reasonable amount of time. In this article mutual information between features and class labels is considered to be the measure function. Two series expansions for mutual information are proposed, and it is shown that most heuristic criteria suggested in the literature are truncated approximations of these expansions. It is well-known that searching the whole subset space is an NP-hard problem. Here, instead of the conventional sequential search algorithms, we suggest a parallel search strategy based on semidefinite programming (SDP) that can search through the subset space in polynomial time. By exploiting the similarities between the proposed algorithm and an instance of the maximum-cut problem in graph theory, the approximation ratio of this algorithm is derived and is compared with the approximation ratio of the backward elimination method. The experiments show that it can be misleading to judge the quality of a measure solely based on the classification accuracy, without taking the effect of the non-optimum search strategy into account.

  19. Selecting Sensitive Parameter Subsets in Dynamical Models With Application to Biomechanical System Identification.

    Science.gov (United States)

    Ramadan, Ahmed; Boss, Connor; Choi, Jongeun; Peter Reeves, N; Cholewicki, Jacek; Popovich, John M; Radcliffe, Clark J

    2018-07-01

    Estimating many parameters of biomechanical systems with limited data may achieve good fit but may also increase 95% confidence intervals in parameter estimates. This results in poor identifiability in the estimation problem. Therefore, we propose a novel method to select sensitive biomechanical model parameters that should be estimated, while fixing the remaining parameters to values obtained from preliminary estimation. Our method relies on identifying the parameters to which the measurement output is most sensitive. The proposed method is based on the Fisher information matrix (FIM). It was compared against the nonlinear least absolute shrinkage and selection operator (LASSO) method to guide modelers on the pros and cons of our FIM method. We present an application identifying a biomechanical parametric model of a head position-tracking task for ten human subjects. Using measured data, our method (1) reduced model complexity by only requiring five out of twelve parameters to be estimated, (2) significantly reduced parameter 95% confidence intervals by up to 89% of the original confidence interval, (3) maintained goodness of fit measured by variance accounted for (VAF) at 82%, (4) reduced computation time, where our FIM method was 164 times faster than the LASSO method, and (5) selected similar sensitive parameters to the LASSO method, where three out of five selected sensitive parameters were shared by FIM and LASSO methods.

  20. An Entropy-based gene selection method for cancer classification using microarray data

    Directory of Open Access Journals (Sweden)

    Krishnan Arun

    2005-03-01

    Full Text Available Abstract Background Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of non-redundant but relevant genes is difficult. The selected gene set should be small enough to allow diagnosis even in regular clinical laboratories and ideally identify genes involved in cancer-specific regulatory pathways. Here an entropy-based method is proposed that selects genes related to the different cancer classes while at the same time reducing the redundancy among the genes. Results The present study identifies a subset of features by maximizing the relevance and minimizing the redundancy of the selected genes. A merit called normalized mutual information is employed to measure the relevance and the redundancy of the genes. In order to find a more representative subset of features, an iterative procedure is adopted that incorporates an initial clustering followed by data partitioning and the application of the algorithm to each of the partitions. A leave-one-out approach then selects the most commonly selected genes across all the different runs and the gene selection algorithm is applied again to pare down the list of selected genes until a minimal subset is obtained that gives a satisfactory accuracy of classification. The algorithm was applied to three different data sets and the results obtained were compared to work done by others using the same data sets Conclusion This study presents an entropy-based iterative algorithm for selecting genes from microarray data that are able to classify various cancer sub-types with high accuracy. In addition, the feature set obtained is very compact, that is, the redundancy between genes is reduced to a large extent. This implies that classifiers can be built with a smaller subset of genes.

  1. Invariant subsets under compact quantum group actions

    OpenAIRE

    Huang, Huichi

    2012-01-01

    We investigate compact quantum group actions on unital $C^*$-algebras by analyzing invariant subsets and invariant states. In particular, we come up with the concept of compact quantum group orbits and use it to show that countable compact metrizable spaces with infinitely many points are not quantum homogeneous spaces.

  2. Two Distinct Myeloid Subsets at the Term Human Fetal–Maternal Interface

    Directory of Open Access Journals (Sweden)

    Maria Laura Costa

    2017-10-01

    Full Text Available During pregnancy, immune cells infiltrate the placenta at different stages of fetal development. NK cells and macrophages are the most predominant cell types. These immune cells play pleiotropic roles, as they control spiral artery remodeling to ensure appropriate blood supply and maintain long-term tolerance to a true allograft; yet, they must be able to mount appropriate immune defenses to pathogens that may threaten the fetus. Whether the same cell type accomplishes all these tasks or if there are dedicated subsets remains controversial. Here, we identify and characterize two distinct subsets of myeloid cells that differ in their pro-inflammatory/regulatory capacity. While one subset predominantly produces the immune-modulating cytokine IL-10, the second subset has superior capacity to secrete pro-inflammatory mediators, such as IL-1β and IL-6. The putative regulatory myeloid cells also express high levels of inhibitory receptors and their ligands, including programmed cell death 1 (PD1 ligands. Importantly, a large fraction of CD8 and CD4 cells in normal term human placenta are PD1 positive, suggesting that the PD1/PD1 ligands axis might be critical to maintain tolerance during pregnancy.

  3. Reference ranges for lymphocyte subsets in healthy adult male Oman is

    International Nuclear Information System (INIS)

    Al-Jabri, Ali A.; Al-Shukaili, Ahmed K.; Al-Rashdi, Zowaina T.; Ganguly, Shyam S.

    2008-01-01

    Objective was to determine the reference ranges of lymphocyte subsets in serologically HIV-seronegative healthy male adults in Oman. A cohort, of 118 healthy male blood donors ranging in age from 18-51 years, was included in the study. The average was 25 years. Blood samples collected into tubes containing ethylene-diamine-tetra acetic acid were investigated for lymphocyte subsets using flow cytometer. This study was conducted in the Immunology Laboratory of Sultan Qaboos University, College of Medicine and Health Sciences, Muscat, Oman during the year 2006. For the 118 males investigated, the mean percentage and absolute values of the lymphocyte subsets were as follows: Cd3: 68.53+-7.5%, 1701+-489 cells/ul; CD8: 25.8+-5.9%, 638+-225 cells/ul; CD19: 13.7+-4.7%, 349+-158 cells/ul, CD56: 12.2+-6.7%, 308+-204 cells/ul. The ratio of CD4/CD8 was 1.6. Immunophenotyping has been used to establish reference values of lymphocyte subsets in normal healthy adult males in Oman. The Omani male reference values obtained in this study show wide variations compared with kits values previously used as reference. (author)

  4. Distribution of orientation selectivity in recurrent networks of spiking neurons with different random topologies.

    Science.gov (United States)

    Sadeh, Sadra; Rotter, Stefan

    2014-01-01

    Neurons in the primary visual cortex are more or less selective for the orientation of a light bar used for stimulation. A broad distribution of individual grades of orientation selectivity has in fact been reported in all species. A possible reason for emergence of broad distributions is the recurrent network within which the stimulus is being processed. Here we compute the distribution of orientation selectivity in randomly connected model networks that are equipped with different spatial patterns of connectivity. We show that, for a wide variety of connectivity patterns, a linear theory based on firing rates accurately approximates the outcome of direct numerical simulations of networks of spiking neurons. Distance dependent connectivity in networks with a more biologically realistic structure does not compromise our linear analysis, as long as the linearized dynamics, and hence the uniform asynchronous irregular activity state, remain stable. We conclude that linear mechanisms of stimulus processing are indeed responsible for the emergence of orientation selectivity and its distribution in recurrent networks with functionally heterogeneous synaptic connectivity.

  5. Change-based test selection : An empirical evaluation

    NARCIS (Netherlands)

    Soetens, Quinten; Demeyer, Serge; Zaidman, A.E.; Perez, Javier

    2015-01-01

    Regression test selection (i.e., selecting a subset of a given regression test suite) is a problem that has been studied intensely over the last decade. However, with the increasing popularity of developer tests as the driver of the test process, more fine-grained solutions that work well within the

  6. Randomly and Non-Randomly Missing Renal Function Data in the Strong Heart Study: A Comparison of Imputation Methods.

    Directory of Open Access Journals (Sweden)

    Nawar Shara

    Full Text Available Kidney and cardiovascular disease are widespread among populations with high prevalence of diabetes, such as American Indians participating in the Strong Heart Study (SHS. Studying these conditions simultaneously in longitudinal studies is challenging, because the morbidity and mortality associated with these diseases result in missing data, and these data are likely not missing at random. When such data are merely excluded, study findings may be compromised. In this article, a subset of 2264 participants with complete renal function data from Strong Heart Exams 1 (1989-1991, 2 (1993-1995, and 3 (1998-1999 was used to examine the performance of five methods used to impute missing data: listwise deletion, mean of serial measures, adjacent value, multiple imputation, and pattern-mixture. Three missing at random models and one non-missing at random model were used to compare the performance of the imputation techniques on randomly and non-randomly missing data. The pattern-mixture method was found to perform best for imputing renal function data that were not missing at random. Determining whether data are missing at random or not can help in choosing the imputation method that will provide the most accurate results.

  7. Automatic learning-based beam angle selection for thoracic IMRT

    International Nuclear Information System (INIS)

    Amit, Guy; Marshall, Andrea; Purdie, Thomas G.; Jaffray, David A.; Levinshtein, Alex; Hope, Andrew J.; Lindsay, Patricia; Pekar, Vladimir

    2015-01-01

    Purpose: The treatment of thoracic cancer using external beam radiation requires an optimal selection of the radiation beam directions to ensure effective coverage of the target volume and to avoid unnecessary treatment of normal healthy tissues. Intensity modulated radiation therapy (IMRT) planning is a lengthy process, which requires the planner to iterate between choosing beam angles, specifying dose–volume objectives and executing IMRT optimization. In thorax treatment planning, where there are no class solutions for beam placement, beam angle selection is performed manually, based on the planner’s clinical experience. The purpose of this work is to propose and study a computationally efficient framework that utilizes machine learning to automatically select treatment beam angles. Such a framework may be helpful for reducing the overall planning workload. Methods: The authors introduce an automated beam selection method, based on learning the relationships between beam angles and anatomical features. Using a large set of clinically approved IMRT plans, a random forest regression algorithm is trained to map a multitude of anatomical features into an individual beam score. An optimization scheme is then built to select and adjust the beam angles, considering the learned interbeam dependencies. The validity and quality of the automatically selected beams evaluated using the manually selected beams from the corresponding clinical plans as the ground truth. Results: The analysis included 149 clinically approved thoracic IMRT plans. For a randomly selected test subset of 27 plans, IMRT plans were generated using automatically selected beams and compared to the clinical plans. The comparison of the predicted and the clinical beam angles demonstrated a good average correspondence between the two (angular distance 16.8° ± 10°, correlation 0.75 ± 0.2). The dose distributions of the semiautomatic and clinical plans were equivalent in terms of primary target volume

  8. Range-reference determination of lymphocyte subsets in Moroccan

    African Journals Online (AJOL)

    Administrator

    Centre de Transfusion Sanguine, Hôpital Militaire d'Instruction Med V Rabat, Morocco. 3. Service ... The percentage of CD3-CD56+ subsets was affected by smoking (p < 0.01). Our analysis .... lymphocyte subpopulations in non-smokers and.

  9. Auditing Complex Concepts in Overlapping Subsets of SNOMED

    Science.gov (United States)

    Wang, Yue; Wei, Duo; Xu, Junchuan; Elhanan, Gai; Perl, Yehoshua; Halper, Michael; Chen, Yan; Spackman, Kent A.; Hripcsak, George

    2008-01-01

    Limited resources and the sheer volume of concepts make auditing a large terminology, such as SNOMED CT, a daunting task. It is essential to devise techniques that can aid an auditor by automatically identifying concepts that deserve attention. A methodology for this purpose based on a previously introduced abstraction network (called the p-area taxonomy) for a SNOMED CT hierarchy is presented. The methodology algorithmically gathers concepts appearing in certain overlapping subsets, defined exclusively with respect to the p-area taxonomy, for review. The results of applying the methodology to SNOMED’s Specimen hierarchy are presented. These results are compared against a control sample composed of concepts residing in subsets without the overlaps. With the use of the double bootstrap, the concept group produced by our methodology is shown to yield a statistically significant higher proportion of error discoveries. PMID:18998838

  10. Massive two-loop Bhabha scattering - the factorizable subset

    International Nuclear Information System (INIS)

    Fleischer, J.; Tarasov, O.V.; Werthenbach, A.

    2002-11-01

    The experimental precision that will be reached at the next generation of colliders makes it indispensable to improve theoretical predictions significantly. Bhabha scattering (e + e - → e + e - ) is one of the prime processes calling for a better theoretical precision, in particular for non-zero electron masses. We present a first subset of the full two-loop calculation, namely the factorizable subset. Our calculation is based on DIANA. We reduce tensor integrals to scalar integrals in shifted (increased) dimensions and additional powers of various propagators, so-called dots-on-lines. Recurrence relations remove those dots-on-lines as well as genuine dots-on-lines (originating from mass renormalization) and reduce the dimension of the integrals to the generic d=4-2ε dimensions. The resulting master integrals have to be expanded to O(ε) to ensure proper treatment of all finite terms. (orig.)

  11. Sexual selection protects against extinction

    OpenAIRE

    Lumley, Alyson; Michalczyk, Lukasz; Kitson, James; Spurgin, Lewis; Morrison, Catriona; Godwin, Joanne; Dickinson, Matthew; Martin, Oliver; Emerson, Brent; Chapman, Tracey; Gage, Matthew

    2015-01-01

    Reproduction through sex carries substantial costs, mainly because only half of sexual adults produce offspring. It has been theorised that these costs could be countered if sex allows sexual selection to clear the universal fitness constraint of mutation load. Under sexual selection, competition between (usually) males, and mate choice by (usually) females create important intraspecific filters for reproductive success, so that only a subset of males gains paternity. If reproductive success ...

  12. [The study on the changes of serum IL- 6, TNF-α and peripheral blood T lymphocyte subsets in the pregnant women during perinatal period].

    Science.gov (United States)

    Li, Juan

    2011-03-01

    To study the change law of serum IL-6, TNF-α and peripheral blood T lymphocyte subsets in the pregnant women during perinatal period. 100 pregnant women in our hospital from November 2009 to October 2010 were selected as research object, and the serum IL-6, TNF-α and peripheral blood T lymphocyte subsets be-fore and at labor onset occurring, after delivery at the first and third day were analyzed and compared. According the study, the serum IL-6 and TNF-aat labor onset occurring were higher than those before labor onset and af-ter delivery at the first and third day , the CD3(+), CD4 (+), CD8(+) and CD4/CD8 decreased first and then increased, all P < 0. 05, there were significant differences. The changes of serum IL-6, TNF-α and peripheral blood T lymphocyte subsets in the pregnant women during perinatal period has a regular pattern, and it is worthy of.

  13. Subset Statistics in the linear IV regression model

    NARCIS (Netherlands)

    Kleibergen, F.R.

    2005-01-01

    We show that the limiting distributions of subset generalizations of the weak instrument robust instrumental variable statistics are boundedly similar when the remaining structural parameters are estimated using maximum likelihood. They are bounded from above by the limiting distributions which

  14. B cell subset distribution is altered in patients with severe periodontitis.

    Science.gov (United States)

    Demoersman, Julien; Pochard, Pierre; Framery, Camille; Simon, Quentin; Boisramé, Sylvie; Soueidan, Assem; Pers, Jacques-Olivier

    2018-01-01

    Several studies have recently highlighted the implication of B cells in physiopathogenesis of periodontal disease by showing that a B cell deficiency leads to improved periodontal parameters. However, the detailed profiles of circulating B cell subsets have not yet been investigated in patients with severe periodontitis (SP). We hypothesised that an abnormal distribution of B cell subsets could be detected in the blood of patients with severe periodontal lesions, as already reported for patients with chronic inflammatory diseases as systemic autoimmune diseases. Fifteen subjects with SP and 13 subjects without periodontitis, according to the definition proposed by the CDC periodontal disease surveillance work group, were enrolled in this pilot observational study. Two flow cytometry panels were designed to analyse the circulating B and B1 cell subset distribution in association with the RANKL expression. A significantly higher percentage of CD27+ memory B cells was observed in patients with SP. Among these CD27+ B cells, the proportion of the switched memory subset was significantly higher. At the same time, human B1 cells, which were previously associated with a regulatory function (CD20+CD69-CD43+CD27+CD11b+), decreased in SP patients. The RANKL expression increased in every B cell subset from the SP patients and was significantly greater in activated B cells than in the subjects without periodontitis. These preliminary results demonstrate the altered distribution of B cells in the context of severe periodontitis. Further investigations with a larger cohort of patients can elucidate if the analysis of the B cell compartment distribution can reflect the periodontal disease activity and be a reliable marker for its prognosis (clinical trial registration number: NCT02833285, B cell functions in periodontitis).

  15. B cell subset distribution is altered in patients with severe periodontitis

    Science.gov (United States)

    Demoersman, Julien; Pochard, Pierre; Framery, Camille; Simon, Quentin; Boisramé, Sylvie; Soueidan, Assem

    2018-01-01

    Several studies have recently highlighted the implication of B cells in physiopathogenesis of periodontal disease by showing that a B cell deficiency leads to improved periodontal parameters. However, the detailed profiles of circulating B cell subsets have not yet been investigated in patients with severe periodontitis (SP). We hypothesised that an abnormal distribution of B cell subsets could be detected in the blood of patients with severe periodontal lesions, as already reported for patients with chronic inflammatory diseases as systemic autoimmune diseases. Fifteen subjects with SP and 13 subjects without periodontitis, according to the definition proposed by the CDC periodontal disease surveillance work group, were enrolled in this pilot observational study. Two flow cytometry panels were designed to analyse the circulating B and B1 cell subset distribution in association with the RANKL expression. A significantly higher percentage of CD27+ memory B cells was observed in patients with SP. Among these CD27+ B cells, the proportion of the switched memory subset was significantly higher. At the same time, human B1 cells, which were previously associated with a regulatory function (CD20+CD69-CD43+CD27+CD11b+), decreased in SP patients. The RANKL expression increased in every B cell subset from the SP patients and was significantly greater in activated B cells than in the subjects without periodontitis. These preliminary results demonstrate the altered distribution of B cells in the context of severe periodontitis. Further investigations with a larger cohort of patients can elucidate if the analysis of the B cell compartment distribution can reflect the periodontal disease activity and be a reliable marker for its prognosis (clinical trial registration number: NCT02833285, B cell functions in periodontitis). PMID:29447240

  16. Implementing Modifed Burg Algorithms in Multivariate Subset Autoregressive Modeling

    Directory of Open Access Journals (Sweden)

    A. Alexandre Trindade

    2003-02-01

    Full Text Available The large number of parameters in subset vector autoregressive models often leads one to procure fast, simple, and efficient alternatives or precursors to maximum likelihood estimation. We present the solution of the multivariate subset Yule-Walker equations as one such alternative. In recent work, Brockwell, Dahlhaus, and Trindade (2002, show that the Yule-Walker estimators can actually be obtained as a special case of a general recursive Burg-type algorithm. We illustrate the structure of this Algorithm, and discuss its implementation in a high-level programming language. Applications of the Algorithm in univariate and bivariate modeling are showcased in examples. Univariate and bivariate versions of the Algorithm written in Fortran 90 are included in the appendix, and their use illustrated.

  17. Characteristics of CD8+ T cell subsets in Chinese patients with chronic HIV infection during initial ART.

    Science.gov (United States)

    Jiao, Yanmei; Hua, Wei; Zhang, Tong; Zhang, Yonghong; Ji, Yunxia; Zhang, Hongwei; Wu, Hao

    2011-03-25

    CD8+ T cells may play an important role in protecting against HIV. However, the changes of CD8+ T cell subsets during early period of ART have not been fully studied. Twenty-one asymptomatic treatment-naive HIV-infected patients with CD4 T+ cells less than 350 cells/μl were enrolled in the study. Naïve, central memory(CM), effective memory(EM) and terminally differentiated effector (EMRA) CD8+ cell subsets and their activation and proliferation subsets were evaluated in blood samples collected at base line, and week 2, 4, 8 and 12 of ART. The total CD8+ T cells declined and the Naïve and CM subsets had a tendency of increase. Activation levels of all CD8+ T cell subsets except EMRA subset decreased after ART. However, proliferation levels of total CD8+ T cells, EMRA, EM and CM subsets increased at the first 4 weeks of ART, then decreased. Proliferation level of the naïve cells decreased after ART. The changes of CD8+ T cell subsets during initial ART are complex. Our results display a complete phenotypical picture of CD8+ cell subsets during initial ART and provide insights for understanding of immune status during ART.

  18. Characteristics of CD8+ T cell subsets in Chinese patients with chronic HIV infection during initial ART

    Directory of Open Access Journals (Sweden)

    Zhang Hongwei

    2011-03-01

    Full Text Available Abstract Background CD8+ T cells may play an important role in protecting against HIV. However, the changes of CD8+ T cell subsets during early period of ART have not been fully studied. Methods Twenty-one asymptomatic treatment-naive HIV-infected patients with CD4 T+ cells less than 350 cells/μl were enrolled in the study. Naïve, central memory(CM, effective memory(EM and terminally differentiated effector (EMRA CD8+ cell subsets and their activation and proliferation subsets were evaluated in blood samples collected at base line, and week 2, 4, 8 and 12 of ART. Results The total CD8+ T cells declined and the Naïve and CM subsets had a tendency of increase. Activation levels of all CD8+ T cell subsets except EMRA subset decreased after ART. However, proliferation levels of total CD8+ T cells, EMRA, EM and CM subsets increased at the first 4 weeks of ART, then decreased. Proliferation level of the naïve cells decreased after ART. Conclusion The changes of CD8+ T cell subsets during initial ART are complex. Our results display a complete phenotypical picture of CD8+ cell subsets during initial ART and provide insights for understanding of immune status during ART.

  19. Application of subset simulation in reliability estimation of underground pipelines

    International Nuclear Information System (INIS)

    Tee, Kong Fah; Khan, Lutfor Rahman; Li, Hongshuang

    2014-01-01

    This paper presents a computational framework for implementing an advanced Monte Carlo simulation method, called Subset Simulation (SS) for time-dependent reliability prediction of underground flexible pipelines. The SS can provide better resolution for low failure probability level of rare failure events which are commonly encountered in pipeline engineering applications. Random samples of statistical variables are generated efficiently and used for computing probabilistic reliability model. It gains its efficiency by expressing a small probability event as a product of a sequence of intermediate events with larger conditional probabilities. The efficiency of SS has been demonstrated by numerical studies and attention in this work is devoted to scrutinise the robustness of the SS application in pipe reliability assessment and compared with direct Monte Carlo simulation (MCS) method. Reliability of a buried flexible steel pipe with time-dependent failure modes, namely, corrosion induced deflection, buckling, wall thrust and bending stress has been assessed in this study. The analysis indicates that corrosion induced excessive deflection is the most critical failure event whereas buckling is the least susceptible during the whole service life of the pipe. The study also shows that SS is robust method to estimate the reliability of buried pipelines and it is more efficient than MCS, especially in small failure probability prediction

  20. Comprehensive Ocean - Atmosphere Data Set (COADS) LMRF Arctic Subset

    Data.gov (United States)

    National Aeronautics and Space Administration — The Comprehensive Ocean - Atmosphere Data Set (COADS) LMRF Arctic subset contains marine surface weather reports for the region north of 65 degrees N from ships,...

  1. Role of distinct CD4(+) T helper subset in pathogenesis of oral lichen planus.

    Science.gov (United States)

    Wang, Hui; Zhang, Dunfang; Han, Qi; Zhao, Xin; Zeng, Xin; Xu, Yi; Sun, Zheng; Chen, Qianming

    2016-07-01

    Oral lichen planus (OLP) is one of the most common chronic inflammatory oral mucosal diseases with T-cell-mediated immune pathogenesis. In subepithelial and lamina propria of OLP local lesions, the presence of CD4(+) T helper (CD4(+) Th) cells appeared as the major lymphocytes. These CD4(+) T lymphocytes can differentiate into distinct Th cell types such as Th1, Th2, Treg, Th17, Th22, Th9, and Tfh within the context of certain cytokines environment. Growing evidence indicated that Th1/Th2 imbalance may greatly participate into the cytokine network of OLP immunopathology. In addition, Th1/Th2 imbalance can be regulated by the Treg subset and also greatly influenced by the emerging novel CD4(+) Th subset Th17. Furthermore, the presence of novel subsets Th22, Th9 and Tfh in OLP patients is yet to be clarified. All these Th subsets and their specific cytokines may play a critical role in determining the character, extent and duration of immune responses in OLP pathogenesis. Therefore, we review the roles of distinct CD4(+) Th subsets and their signature cytokines in determining disease severity and susceptibility of OLP and also reveal the novel therapeutic strategies based on T lymphocytes subsets in OLP treatment. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  2. Ontogeny of surface markers on functionally distinct T cell subsets in the chicken.

    Science.gov (United States)

    Traill, K N; Böck, G; Boyd, R L; Ratheiser, K; Wick, G

    1984-01-01

    Three subsets of chicken peripheral T cells (T1, T2 and T3) have been identified in peripheral blood of adult chickens on the basis of fluorescence intensity after staining with certain xenogeneic anti-thymus cell sera (from turkeys and rabbits). They differentiate between 3-10 weeks of age in parallel with development of responsiveness to the mitogens concanavalin A (Con A), phytohemagglutinin (PHA) and pokeweed mitogen (PWM). Functional tests on the T subsets, sorted with a fluorescence-activated cell sorter, have shown that T2, 3 cells respond to Con A, PHA and PWM and are capable of eliciting a graft-vs.-host reaction (GvHR). In contrast, although T1 cells respond to Con A, they respond poorly to PHA and not at all to PWM or in GvHR. There was some indication of cooperation between T1 and T2,3 cells for the PHA response. Parallels between these chicken subsets and helper and suppressor/cytotoxic subsets in mammalian systems are discussed.

  3. Identification of a novel stereotypic IGHV4-59/IGHJ5-encoded B-cell receptor subset expressed by various B-cell lymphomas with high affinity rheumatoid factor activity

    NARCIS (Netherlands)

    Bende, Richard J.; Janssen, Jerry; Wormhoudt, Thera A. M.; Wagner, Koen; Guikema, Jeroen E. J.; van Noesel, Carel J. M.

    2016-01-01

    Subsets of mucosa-associated lymphoid tissue (MALT) lymphoma, splenic marginal zone B-cell lymphoma (MZBCL) and chronic lymphocytic leukemia (CLL) have been identified that express near-identical B-cell receptors (BCRs), strongly suggesting selection by restricted antigenic epitopes. We here report

  4. The relationship of different respiratory virus infection with pediatric asthma attack as well as cytokine and lymphocyte subset levels

    OpenAIRE

    Hua Miao; Xiao-Rong Liu

    2017-01-01

    Objective: To study the relationship of different respiratory virus infection with pediatric asthma attack as well as cytokine and lymphocyte subset levels. Methods: A total of 85 children who were diagnosed with bronchial asthma in our hospital between May 2013 and March 2016 were selected as asthma group and further divided into asthma-RSV group, asthma-AV group, asthma-PIV group, asthma-IFV group and pure asthma group according to the condition of respiratory virus infection...

  5. Bayesian Subset Modeling for High-Dimensional Generalized Linear Models

    KAUST Repository

    Liang, Faming

    2013-06-01

    This article presents a new prior setting for high-dimensional generalized linear models, which leads to a Bayesian subset regression (BSR) with the maximum a posteriori model approximately equivalent to the minimum extended Bayesian information criterion model. The consistency of the resulting posterior is established under mild conditions. Further, a variable screening procedure is proposed based on the marginal inclusion probability, which shares the same properties of sure screening and consistency with the existing sure independence screening (SIS) and iterative sure independence screening (ISIS) procedures. However, since the proposed procedure makes use of joint information from all predictors, it generally outperforms SIS and ISIS in real applications. This article also makes extensive comparisons of BSR with the popular penalized likelihood methods, including Lasso, elastic net, SIS, and ISIS. The numerical results indicate that BSR can generally outperform the penalized likelihood methods. The models selected by BSR tend to be sparser and, more importantly, of higher prediction ability. In addition, the performance of the penalized likelihood methods tends to deteriorate as the number of predictors increases, while this is not significant for BSR. Supplementary materials for this article are available online. © 2013 American Statistical Association.

  6. Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection

    NARCIS (Netherlands)

    Satsangi, Y.; Whiteson, S.; Oliehoek, F.A.

    2015-01-01

    A key challenge in the design of multi-sensor systems is the efficient allocation of scarce resources such as bandwidth, CPU cycles, and energy, leading to the dynamic sensor selection problem in which a subset of the available sensors must be selected at each timestep. While partially observable

  7. Effect of DC-CIK treatment on tumor markers and T cell subsets in patients with advanced ovarian cancer

    Directory of Open Access Journals (Sweden)

    Jie-Qun Guo

    2016-10-01

    Full Text Available Objective: To investigate the effects of dendritic cells (DC and cytokine induced killer cells (CIK on tumor markers and T cell subsets in peripheral blood of patients with advanced ovarian cancer. Methods: A total of 100 cases of patients with advanced ovarian cancer who were proved by operation and pathology in the department of gynecologic oncology in our hospital were selected from April 2013 to April 20l6, and randomly divided into experimental group and control group, the control group was treated with TC (Taxinol+Cisplat chemotherapy alone, the experimental group was treated with DC-CIK combined with chemotherapy. Before and after treatment, the changes of CD3+, CD4+, CD8+, CD4+/CD8+, CD4+/CD25+, NK cells in peripheral blood and serum tumor markers (CA125, CA19-9, HE4 were detected. Results: Before treatment, the phenotypes of T cell subsets in the two groups were not significantly different; in the experimental group after treatment, the levels of CD3+, CD4+, CD4+/CD8+, and NK cells were increased,while the levels of CD4+/CD25+ and CD8+ were decreased, compared with before treatment, the differences were statistically significant; the phenotype changes of T cells were not statistically significant before and after treatment in the control group; after treatment, there were significant differences in the levels of CD4+, CD8+, CD4+/CD8+, CD4+/CD25+ and NK cells between the two groups. Before treatment, there were no significant differences in HE4 value, CA125 value and CA19-9 value between the two groups; after treatment, the tumor markers in the two groups were all decreased, and the difference was significant as compared with those before treatment; after treatment, the CA125 value, CA19-9 value and HE4 value were (73.68±79.46 U/mL, (54.32±32.85 U/mL and (69.57±39.85 pmol/L respectively, the values of three tumor markers were compared with the control group, with a statistical difference. Conclusion: DC-CIK treatment can improve the

  8. Stress-Induced In Vivo Recruitment of Human Cytotoxic Natural Killer Cells Favors Subsets with Distinct Receptor Profiles and Associates with Increased Epinephrine Levels.

    Directory of Open Access Journals (Sweden)

    Marc B Bigler

    Full Text Available Acute stress drives a 'high-alert' response in the immune system. Psychoactive drugs induce distinct stress hormone profiles, offering a sought-after opportunity to dissect the in vivo immunological effects of acute stress in humans.3,4-methylenedioxymethamphetamine (MDMA, methylphenidate (MPH, or both, were administered to healthy volunteers in a randomized, double-blind, placebo-controlled crossover-study. Lymphocyte subset frequencies, natural killer (NK cell immune-phenotypes, and changes in effector function were assessed, and linked to stress hormone levels and expression of CD62L, CX3CR1, CD18, and stress hormone receptors on NK cells.MDMA/MPH > MDMA > MPH robustly induced an epinephrine-dominant stress response. Immunologically, rapid redistribution of peripheral blood lymphocyte-subsets towards phenotypically mature NK cells occurred. NK cytotoxicity was unaltered, but they expressed slightly reduced levels of the activating receptor NKG2D. Preferential circulation of mature NK cells was associated with high epinephrine receptor expression among this subset, as well as expression of integrin ligands previously linked to epinephrine-induced endothelial detachment.The acute epinephrine-induced stress response was characterized by rapid accumulation of mature and functional NK cells in the peripheral circulation. This is in line with studies using other acute stressors and supports the role of the acute stress response in rapidly mobilizing the innate immune system to counteract incoming threats.

  9. Random catalytic reaction networks

    Science.gov (United States)

    Stadler, Peter F.; Fontana, Walter; Miller, John H.

    1993-03-01

    We study networks that are a generalization of replicator (or Lotka-Volterra) equations. They model the dynamics of a population of object types whose binary interactions determine the specific type of interaction product. Such a system always reduces its dimension to a subset that contains production pathways for all of its members. The network equation can be rewritten at a level of collectives in terms of two basic interaction patterns: replicator sets and cyclic transformation pathways among sets. Although the system contains well-known cases that exhibit very complicated dynamics, the generic behavior of randomly generated systems is found (numerically) to be extremely robust: convergence to a globally stable rest point. It is easy to tailor networks that display replicator interactions where the replicators are entire self-sustaining subsystems, rather than structureless units. A numerical scan of random systems highlights the special properties of elementary replicators: they reduce the effective interconnectedness of the system, resulting in enhanced competition, and strong correlations between the concentrations.

  10. Studies on the stimulating effect of low dose irradiation on lymphocyte subsets

    International Nuclear Information System (INIS)

    Du Zeji; Su Liaoyuan

    1994-01-01

    In the study, three kinds of monoclonal antibody were used to separate subsets of lymphocyte, and then the functional changes of the separated subsets after low dose irradiation (LDI) were studied. McAb CD4, CD8 and B were used to obtain CD 4 + , CD8 + and B cells respectively with 'Panning' method, the cells were irradiated with X-ray machine (200 kV, 10 mA) for 0, 0.02, 0.05, 0.1, 0.2, 0.5 Gy. 3 H-TdR incorporation was used to reflect functional changes of subsets after LDI. The results indicated that (1) three kinds of subsets could be stimulated by LDI (within 0.2 Gy). The peak effect for CD 4 + and B cells was induced by 0.1 Gy irradiation for CD8 + cell, the peak effect dose was 0.05 Gy; (2) between 0.02 Gy and 0.2 Gy, for same dose, the stimulating effect of CD4 + was higher than that of CD8 + . This result has an important significance in demonstrating the immune mechanism of radiation hormesis. The past viewpoint suggested that immune hormesis was caused by the damage of radiosensitive T cell (Ts) after LDI. Recently, some authors proved that no change of the ratio of Ts to T H existed after LDI. In the study presented, It is found that the values of 3 H-TdR incorporation in CD4 + was bigger than that in CD8 + after LDI. Obvious stimulating effect could still be observed after 0.2 Gy irradiation, it indicated that subsets separated by McAb could have a wide stimulating dose range for LDI

  11. T-Cell Subsets Predict Mortality in Malnourished Zambian Adults Initiating Antiretroviral Therapy.

    Directory of Open Access Journals (Sweden)

    Caroline C Chisenga

    Full Text Available To estimate the prognostic value of T-cell subsets in Zambian patients initiating antiretroviral therapy (ART, and to assess the impact of a nutritional intervention on T-cell subsets.This was a sub-study of a randomised clinical trial of a nutritional intervention for malnourished adults initiating ART. Participants in a randomised controlled trial (NUSTART trial were enrolled between April and December 2012. Participants received lipid-based nutritional supplement either with or without additional vitamins and minerals. Immunophenotyping was undertaken at baseline and, in survivors, after 12 weeks of ART to characterize T-cell subsets using the markers CD3, CD4, CD8, CD45RA, CCR7, CD28, CD57, CD31, α4β7, Ki67, CD25 and HLA-DR. Univariate and multivariate survival analysis was performed, and responses to treatment were analysed using the Wicoxon rank-sum test.Among 181 adults, 36 (20% died by 12 weeks after starting ART. In univariate analysis, patients who died had fewer proliferating, more naïve and fewer gut homing CD4+ T-cells compared to survivors; and more senescent and fewer proliferating CD8+ T-cells. In a multivariate Cox regression model high naïve CD4+, low proliferating CD4+, high senescent CD8+ and low proliferating CD8+ subsets were independently associated with increased risk of death. Recent CD4+ thymic emigrants increased less between recruitment and 12 weeks of ART in the intervention group compared to the control group.Specific CD4+ T-cell subsets are of considerable prognostic significance for patients initiating ART in Zambia, but only thymic output responded to this nutritional intervention.

  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. 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.

  14. Subset-row inequalities applied to the vehicle routing problem with time windows

    DEFF Research Database (Denmark)

    Jepsen, Mads Kehlet; Petersen, Bjørn; Spoorendonk, Simon

    2008-01-01

    constraints as the pricing problem. We introduce the subset-row inequalities, which are Chvatal-Gomory rank-1 cuts based on a subset of the constraints in the master problem. Applying a subset-row inequality in the master problem increases the complexity of the label-setting algorithm used to solve......This paper presents a branch-and-cut-and-price algorithm for the vehicle-routing problem with time windows. The standard Dantzig-Wolfe decomposition of the arc flow formulation leads to a set-partitioning problem as the master problem and an elementary shortest-path problem with resource...... the pricing problem because an additional resource is added for each inequality. We propose a modified dominance criterion that makes it possible to dominate more labels by exploiting the step-like structure of the objective function of the pricing problem. Computational experiments have been performed...

  15. Feature Selection via Chaotic Antlion Optimization.

    Directory of Open Access Journals (Sweden)

    Hossam M Zawbaa

    Full Text Available Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting while minimizing the number of features used.We propose an optimization approach for the feature selection problem that considers a "chaotic" version of the antlion optimizer method, a nature-inspired algorithm that mimics the hunting mechanism of antlions in nature. The balance between exploration of the search space and exploitation of the best solutions is a challenge in multi-objective optimization. The exploration/exploitation rate is controlled by the parameter I that limits the random walk range of the ants/prey. This variable is increased iteratively in a quasi-linear manner to decrease the exploration rate as the optimization progresses. The quasi-linear decrease in the variable I may lead to immature convergence in some cases and trapping in local minima in other cases. The chaotic system proposed here attempts to improve the tradeoff between exploration and exploitation. The methodology is evaluated using different chaotic maps on a number of feature selection datasets. To ensure generality, we used ten biological datasets, but we also used other types of data from various sources. The results are compared with the particle swarm optimizer and with genetic algorithm variants for feature selection using a set of quality metrics.

  16. Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

    Science.gov (United States)

    Vanderhaeghe, F; Smolders, A J P; Roelofs, J G M; Hoffmann, M

    2012-03-01

    Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

  17. Optimum unambiguous discrimination between subsets of nonorthogonal quantum states

    International Nuclear Information System (INIS)

    Sun Yuqing; Hillery, Mark; Bergou, Janos A.

    2002-01-01

    It is known that unambiguous discrimination among nonorthogonal but linearly independent quantum states is possible with a certain probability of success. Here, we consider a variant of that problem. Instead of discriminating among all of the different states, we shall only discriminate between two subsets of them. In particular, for the case of three nonorthogonal states, { vertical bar ψ 1 >, vertical bar ψ 2 >, vertical bar ψ 3 >}, we show that the optimal strategy to distinguish vertical bar ψ 1 > from the set { vertical bar ψ 2 >, vertical bar ψ 3 >} has a higher success rate than if we wish to discriminate among all three states. Somewhat surprisingly, for unambiguous discrimination the subsets need not be linearly independent. A fully analytical solution is presented, and we also show how to construct generalized interferometers (multiport) which provide an optical implementation of the optimal strategy

  18. Implications of structural genomics target selection strategies: Pfam5000, whole genome, and random approaches

    Energy Technology Data Exchange (ETDEWEB)

    Chandonia, John-Marc; Brenner, Steven E.

    2004-07-14

    The structural genomics project is an international effort to determine the three-dimensional shapes of all important biological macromolecules, with a primary focus on proteins. Target proteins should be selected according to a strategy which is medically and biologically relevant, of good value, and tractable. As an option to consider, we present the Pfam5000 strategy, which involves selecting the 5000 most important families from the Pfam database as sources for targets. We compare the Pfam5000 strategy to several other proposed strategies that would require similar numbers of targets. These include including complete solution of several small to moderately sized bacterial proteomes, partial coverage of the human proteome, and random selection of approximately 5000 targets from sequenced genomes. We measure the impact that successful implementation of these strategies would have upon structural interpretation of the proteins in Swiss-Prot, TrEMBL, and 131 complete proteomes (including 10 of eukaryotes) from the Proteome Analysis database at EBI. Solving the structures of proteins from the 5000 largest Pfam families would allow accurate fold assignment for approximately 68 percent of all prokaryotic proteins (covering 59 percent of residues) and 61 percent of eukaryotic proteins (40 percent of residues). More fine-grained coverage which would allow accurate modeling of these proteins would require an order of magnitude more targets. The Pfam5000 strategy may be modified in several ways, for example to focus on larger families, bacterial sequences, or eukaryotic sequences; as long as secondary consideration is given to large families within Pfam, coverage results vary only slightly. In contrast, focusing structural genomics on a single tractable genome would have only a limited impact in structural knowledge of other proteomes: a significant fraction (about 30-40 percent of the proteins, and 40-60 percent of the residues) of each proteome is classified in small

  19. Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests.

    Science.gov (United States)

    Nguyen, Thanh-Tung; Huang, Joshua; Wu, Qingyao; Nguyen, Thuy; Li, Mark

    2015-01-01

    Single-nucleotide polymorphisms (SNPs) selection and identification are the most important tasks in Genome-wide association data analysis. The problem is difficult because genome-wide association data is very high dimensional and a large portion of SNPs in the data is irrelevant to the disease. Advanced machine learning methods have been successfully used in Genome-wide association studies (GWAS) for identification of genetic variants that have relatively big effects in some common, complex diseases. Among them, the most successful one is Random Forests (RF). Despite of performing well in terms of prediction accuracy in some data sets with moderate size, RF still suffers from working in GWAS for selecting informative SNPs and building accurate prediction models. In this paper, we propose to use a new two-stage quality-based sampling method in random forests, named ts-RF, for SNP subspace selection for GWAS. The method first applies p-value assessment to find a cut-off point that separates informative and irrelevant SNPs in two groups. The informative SNPs group is further divided into two sub-groups: highly informative and weak informative SNPs. When sampling the SNP subspace for building trees for the forest, only those SNPs from the two sub-groups are taken into account. The feature subspaces always contain highly informative SNPs when used to split a node at a tree. This approach enables one to generate more accurate trees with a lower prediction error, meanwhile possibly avoiding overfitting. It allows one to detect interactions of multiple SNPs with the diseases, and to reduce the dimensionality and the amount of Genome-wide association data needed for learning the RF model. Extensive experiments on two genome-wide SNP data sets (Parkinson case-control data comprised of 408,803 SNPs and Alzheimer case-control data comprised of 380,157 SNPs) and 10 gene data sets have demonstrated that the proposed model significantly reduced prediction errors and outperformed

  20. A Feynman graph selection tool in GRACE system

    International Nuclear Information System (INIS)

    Yuasa, Fukuko; Ishikawa, Tadashi; Kaneko, Toshiaki

    2001-01-01

    We present a Feynman graph selection tool grcsel, which is an interpreter written in C language. In the framework of GRACE, it enables us to get a subset of Feynman graphs according to given conditions

  1. Changes in T-cell subsets after radiation therapy

    International Nuclear Information System (INIS)

    Yang, S.J.; Rafla, S.; Youssef, E.; Selim, H.; Salloum, N.; Chuang, J.Y.

    1988-01-01

    The T-cell subsets of 129 patients with cancer were counted before and after radiation therapy. The cells were labeled with monoclonal antibodies that were specific for each type of T cell. Significant changes after therapy were decreases in the proportion of T-helper/inducer cells, pan-T cells, and in the ratio of T-helper/inducer to T-suppressor/cytotoxic cells. There was an increase in the percentage of T-suppressor/cytotoxic cells. When the site of the primary cancer was considered, genitourinary cancer and cancer of the head and neck both showed a decreased percentage of T-helper/inducer cells and a reduced ratio of T-helper/inducer to T-suppressor/cytotoxic cells. The percentage of pan-T cells in head and neck cancer and the ratio of T-helper/inducer to T-suppressor/cytotoxic cells in breast cancer were decreased. The percentage of T-helper cells was particularly decreased by radiation therapy in advanced stages of cancer, in higher grade tumors, and in larger tumors. The absolute numbers of various T-cell subsets were decreased in all groups

  2. Mast cell subsets and neuropeptides in leprosy reactions

    Directory of Open Access Journals (Sweden)

    Antunes Sérgio Luiz Gomes

    2003-01-01

    Full Text Available The immunohistochemical identification of neuropeptides (calcitonin gene-related peptide, vasoactive intestinal polypeptide, substance P, alpha-melanocyte stimulating hormone and gamma-melanocyte stimulating hormone quantification of mast cells and their subsets (tryptase/chymase-immunoreactive mast cells = TCMC and tryptase-immunoreactive mast cells = TMC were determined in biopsies of six patients with leprosy reactions (three patients with type I reaction and three with type II. Biopsies were compared with those taken from the same body site in the remission stage of the same patient. We found a relative increase of TMC in the inflammatory infiltrate of the reactional biopsies compared to the post-reactional biopsy. Also, the total number of mast cells and the TMC/TCMC ratio in the inflammatory infiltrate was significantly higher than in the intervening dermis of the biopsies of both periods. No significant difference was found regarding neuroptide expression in the reactional and post-reactional biopsies. The relative increase of TMC in the reactional infiltrates could implicate this mast cell subset in the reported increase of the immune response in leprosy reactions.

  3. The relationship of different respiratory virus infection with pediatric asthma attack as well as cytokine and lymphocyte subset levels

    Directory of Open Access Journals (Sweden)

    Hua Miao

    2017-04-01

    Full Text Available Objective: To study the relationship of different respiratory virus infection with pediatric asthma attack as well as cytokine and lymphocyte subset levels. Methods: A total of 85 children who were diagnosed with bronchial asthma in our hospital between May 2013 and March 2016 were selected as asthma group and further divided into asthma-RSV group, asthma-AV group, asthma-PIV group, asthma-IFV group and pure asthma group according to the condition of respiratory virus infection, and 70 healthy children who received physical examination in our hospital during the same period were selected as the control group. Spirometer was used to determine airway function parameters, enzyme-linked immunosorbent assay kits were used to determine serum cytokine contents, and flow cytometry was used to determine peripheral blood lymphocyte subset contents. Results: FEV1/FVC, FEF25, FEF50 and FEF75 levels, serum IL-2, IFN-γ and TGF-β1 contents as well as peripheral blood Th1 and Treg cell contents of asthma groups were significantly lower than those of control group while serum IL-4, IL-5 and IL-17 contents as well as peripheral blood Th2 and Th17 cell contents were significantly higher than those of control group; FEV1/FVC, FEF25, FEF50 and FEF75 levels, serum IL-2, IFN-γ and TGF-β1 contents as well as peripheral blood Th1 and Treg cell contents of asthma-RSV group and asthma-IFV group were significantly lower than those of pure asthma group while serum IL-4, IL-5 and IL-17 contents as well as peripheral blood Th2 and Th17 cell contents were significantly higher than those of pure asthma group; these indexes of asthma-AV group and asthma-PIV group were not significantly different from those of pure asthma group. Conclusion: RSV and IFV infection can affect the airway function and the balance of CD4+T cell subsets to promote the development of asthma.

  4. Participant-selected music and physical activity in older adults following cardiac rehabilitation: a randomized controlled trial.

    Science.gov (United States)

    Clark, Imogen N; Baker, Felicity A; Peiris, Casey L; Shoebridge, Georgie; Taylor, Nicholas F

    2017-03-01

    To evaluate effects of participant-selected music on older adults' achievement of activity levels recommended in the physical activity guidelines following cardiac rehabilitation. A parallel group randomized controlled trial with measurements at Weeks 0, 6 and 26. A multisite outpatient rehabilitation programme of a publicly funded metropolitan health service. Adults aged 60 years and older who had completed a cardiac rehabilitation programme. Experimental participants selected music to support walking with guidance from a music therapist. Control participants received usual care only. The primary outcome was the proportion of participants achieving activity levels recommended in physical activity guidelines. Secondary outcomes compared amounts of physical activity, exercise capacity, cardiac risk factors, and exercise self-efficacy. A total of 56 participants, mean age 68.2 years (SD = 6.5), were randomized to the experimental ( n = 28) and control groups ( n = 28). There were no differences between groups in proportions of participants achieving activity recommended in physical activity guidelines at Week 6 or 26. Secondary outcomes demonstrated between-group differences in male waist circumference at both measurements (Week 6 difference -2.0 cm, 95% CI -4.0 to 0; Week 26 difference -2.8 cm, 95% CI -5.4 to -0.1), and observed effect sizes favoured the experimental group for amounts of physical activity (d = 0.30), exercise capacity (d = 0.48), and blood pressure (d = -0.32). Participant-selected music did not increase the proportion of participants achieving recommended amounts of physical activity, but may have contributed to exercise-related benefits.

  5. Defining an essence of structure determining residue contacts in proteins.

    Science.gov (United States)

    Sathyapriya, R; Duarte, Jose M; Stehr, Henning; Filippis, Ioannis; Lappe, Michael

    2009-12-01

    The network of native non-covalent residue contacts determines the three-dimensional structure of a protein. However, not all contacts are of equal structural significance, and little knowledge exists about a minimal, yet sufficient, subset required to define the global features of a protein. Characterisation of this "structural essence" has remained elusive so far: no algorithmic strategy has been devised to-date that could outperform a random selection in terms of 3D reconstruction accuracy (measured as the Ca RMSD). It is not only of theoretical interest (i.e., for design of advanced statistical potentials) to identify the number and nature of essential native contacts-such a subset of spatial constraints is very useful in a number of novel experimental methods (like EPR) which rely heavily on constraint-based protein modelling. To derive accurate three-dimensional models from distance constraints, we implemented a reconstruction pipeline using distance geometry. We selected a test-set of 12 protein structures from the four major SCOP fold classes and performed our reconstruction analysis. As a reference set, series of random subsets (ranging from 10% to 90% of native contacts) are generated for each protein, and the reconstruction accuracy is computed for each subset. We have developed a rational strategy, termed "cone-peeling" that combines sequence features and network descriptors to select minimal subsets that outperform the reference sets. We present, for the first time, a rational strategy to derive a structural essence of residue contacts and provide an estimate of the size of this minimal subset. Our algorithm computes sparse subsets capable of determining the tertiary structure at approximately 4.8 A Ca RMSD with as little as 8% of the native contacts (Ca-Ca and Cb-Cb). At the same time, a randomly chosen subset of native contacts needs about twice as many contacts to reach the same level of accuracy. This "structural essence" opens new avenues in the

  6. Inter-donor variation in cell subset specific immune signaling responses in healthy individuals.

    Science.gov (United States)

    Longo, Diane M; Louie, Brent; Wang, Ena; Pos, Zoltan; Marincola, Francesco M; Hawtin, Rachael E; Cesano, Alessandra

    2012-01-01

    Single cell network profiling (SCNP) is a multi-parameter flow cytometry based approach that allows for the simultaneous interrogation of intracellular signaling pathways in multiple cell subpopulations within heterogeneous tissues, without the need for individual cell subset isolation. Thus, the technology is extremely well-suited for characterizing the multitude of interconnected signaling pathways and immune cell subpopulations that regulate the function of the immune system. Recently, SCNP was applied to generate a functional map of the healthy human immune cell signaling network by profiling immune signaling pathways downstream of 12 immunomodulators in 7 distinct immune cell subsets within peripheral blood mononuclear cells (PBMCs) from 60 healthy donors. In the study reported here, the degree of inter-donor variation in the magnitude of the immune signaling responses was analyzed. The highest inter-donor differences in immune signaling pathway activity occurred following perturbation of the immune signaling network, rather than in basal signaling. When examining the full panel of immune signaling responses, as one may expect, the overall degree of inter-donor variation was positively correlated (r = 0.727) with the magnitude of node response (i.e. a larger median signaling response was associated with greater inter-donor variation). However, when examining the degree of heterogeneity across cell subpopulations for individual signaling nodes, cell subset specificity in the degree of inter-donor variation was observed for several nodes. For such nodes, relatively weak correlations between inter-donor variation and the magnitude of the response were observed. Further, within the phenotypically distinct subpopulations, a fraction of the immune signaling responses had bimodal response profiles in which (a) only a portion of the cells had elevated phospho-protein levels following modulation and (b) the proportion of responsive cells varied by donor. These data

  7. r2VIM: A new variable selection method for random forests in genome-wide association studies.

    Science.gov (United States)

    Szymczak, Silke; Holzinger, Emily; Dasgupta, Abhijit; Malley, James D; Molloy, Anne M; Mills, James L; Brody, Lawrence C; Stambolian, Dwight; Bailey-Wilson, Joan E

    2016-01-01

    Machine learning methods and in particular random forests (RFs) are a promising alternative to standard single SNP analyses in genome-wide association studies (GWAS). RFs provide variable importance measures (VIMs) to rank SNPs according to their predictive power. However, in contrast to the established genome-wide significance threshold, no clear criteria exist to determine how many SNPs should be selected for downstream analyses. We propose a new variable selection approach, recurrent relative variable importance measure (r2VIM). Importance values are calculated relative to an observed minimal importance score for several runs of RF and only SNPs with large relative VIMs in all of the runs are selected as important. Evaluations on simulated GWAS data show that the new method controls the number of false-positives under the null hypothesis. Under a simple alternative hypothesis with several independent main effects it is only slightly less powerful than logistic regression. In an experimental GWAS data set, the same strong signal is identified while the approach selects none of the SNPs in an underpowered GWAS. The novel variable selection method r2VIM is a promising extension to standard RF for objectively selecting relevant SNPs in GWAS while controlling the number of false-positive results.

  8. GGRaSP: A R-package for selecting representative genomes using Gaussian mixture models.

    Science.gov (United States)

    Clarke, Thomas H; Brinkac, Lauren M; Sutton, Granger; Fouts, Derrick E

    2018-04-14

    The vast number of available sequenced bacterial genomes occasionally exceeds the facilities of comparative genomic methods or is dominated by a single outbreak strain, and thus a diverse and representative subset is required. Generation of the reduced subset currently requires a priori supervised clustering and sequence-only selection of medoid genomic sequences, independent of any additional genome metrics or strain attributes. The GGRaSP R-package described below generates a reduced subset of genomes that prioritizes maintaining genomes of interest to the user as well as minimizing the loss of genetic variation. The package also allows for unsupervised clustering by modeling the genomic relationships using a Gaussian Mixture Model to select an appropriate cluster threshold. We demonstrate the capabilities of GGRaSP by generating a reduced list of 315 genomes from a genomic dataset of 4600 Escherichia coli genomes, prioritizing selection by type strain and by genome completeness. GGRaSP is available at https://github.com/JCVenterInstitute/ggrasp/. tclarke@jcvi.org. Supplementary data are available at the GitHub site.

  9. Reliability-based design optimization using a generalized subset simulation method and posterior approximation

    Science.gov (United States)

    Ma, Yuan-Zhuo; Li, Hong-Shuang; Yao, Wei-Xing

    2018-05-01

    The evaluation of the probabilistic constraints in reliability-based design optimization (RBDO) problems has always been significant and challenging work, which strongly affects the performance of RBDO methods. This article deals with RBDO problems using a recently developed generalized subset simulation (GSS) method and a posterior approximation approach. The posterior approximation approach is used to transform all the probabilistic constraints into ordinary constraints as in deterministic optimization. The assessment of multiple failure probabilities required by the posterior approximation approach is achieved by GSS in a single run at all supporting points, which are selected by a proper experimental design scheme combining Sobol' sequences and Bucher's design. Sequentially, the transformed deterministic design optimization problem can be solved by optimization algorithms, for example, the sequential quadratic programming method. Three optimization problems are used to demonstrate the efficiency and accuracy of the proposed method.

  10. SU-E-J-128: Two-Stage Atlas Selection in Multi-Atlas-Based Image Segmentation

    International Nuclear Information System (INIS)

    Zhao, T; Ruan, D

    2015-01-01

    Purpose: In the new era of big data, multi-atlas-based image segmentation is challenged by heterogeneous atlas quality and high computation burden from extensive atlas collection, demanding efficient identification of the most relevant atlases. This study aims to develop a two-stage atlas selection scheme to achieve computational economy with performance guarantee. Methods: We develop a low-cost fusion set selection scheme by introducing a preliminary selection to trim full atlas collection into an augmented subset, alleviating the need for extensive full-fledged registrations. More specifically, fusion set selection is performed in two successive steps: preliminary selection and refinement. An augmented subset is first roughly selected from the whole atlas collection with a simple registration scheme and the corresponding preliminary relevance metric; the augmented subset is further refined into the desired fusion set size, using full-fledged registration and the associated relevance metric. The main novelty of this work is the introduction of an inference model to relate the preliminary and refined relevance metrics, based on which the augmented subset size is rigorously derived to ensure the desired atlases survive the preliminary selection with high probability. Results: The performance and complexity of the proposed two-stage atlas selection method were assessed using a collection of 30 prostate MR images. It achieved comparable segmentation accuracy as the conventional one-stage method with full-fledged registration, but significantly reduced computation time to 1/3 (from 30.82 to 11.04 min per segmentation). Compared with alternative one-stage cost-saving approach, the proposed scheme yielded superior performance with mean and medium DSC of (0.83, 0.85) compared to (0.74, 0.78). Conclusion: This work has developed a model-guided two-stage atlas selection scheme to achieve significant cost reduction while guaranteeing high segmentation accuracy. The benefit

  11. Abortive lytic Epstein–Barr virus replication in tonsil-B lymphocytes in infectious mononucleosis and a subset of the chronic fatigue syndrome

    Directory of Open Access Journals (Sweden)

    Lerner AM

    2012-11-01

    Full Text Available A Martin Lerner,1 Safedin Beqaj21Department of Medicine, Oakland University William Beaumont School of Medicine, Rochester, MI, USA; 2Pathology Inc, Torrance, CA, USAAbstract: A systematic 2001–2007 review of 142 chronic fatigue syndrome (CFS patients identified 106 CFS patients with elevated serum IgG antibodies to the herpesviruses Epstein–Barr virus (EBV, cytomegalovirus, or human herpesvirus (HHV 6 in single or multiple infections, with no other co-infections detected. We named these 106 patients group-A CFS. Eighty-six of these 106 group-A CFS patients (81% had elevated EBV early antibody, early antigen (diffuse, serum titers. A small group of six patients in the group-A EBV subset of CFS, additionally, had repetitive elevated-serum titers of antibody to the early lytic replication-encoded proteins, EBV dUTPase, and EBV DNA polymerase. The presence of these serum antibodies to EBV dUTPase and EBV DNA polymerase indicated EBV abortive lytic replication in these 6 CFS patients. None of 20 random control people (age- and sex-matched, with blood drawn at a commercial laboratory had elevated serum titers of antibody to EBV dUTPase or EBV DNA polymerase (P < 0.01. This finding needs verification in a larger group of EBV CFS subset patients, but if corroborated, it may represent a molecular marker for diagnosing the EBV subset of CFS. We review evidence that EBV abortive lytic replication with unassembled viral proteins in the blood may be the same in infectious mononucleosis (IM and a subset of CFS. EBV-abortive lytic replication in tonsil plasma cells is dominant in IM. No complete lytic virion is in the blood of IM or CFS patients. Complications of CFS and IM include cardiomyopathy and encephalopathy. Circulating abortive lytic-encoded EBV proteins (eg, EBV dUTPase, EBV DNA polymerase, and others may be common to IM and CFS. The intensity and duration of the circulating EBV-encoded proteins might differentiate the IM and EBV subsets of CFS

  12. Strong Selective Adsorption of Polymers.

    Science.gov (United States)

    Ge, Ting; Rubinstein, Michael

    2015-06-09

    A scaling theory is developed for selective adsorption of polymers induced by the strong binding between specific monomers and complementary surface adsorption sites. By "selective" we mean specific attraction between a subset of all monomers, called "sticky", and a subset of surface sites, called "adsorption sites". We demonstrate that, in addition to the expected dependence on the polymer volume fraction ϕ bulk in the bulk solution, selective adsorption strongly depends on the ratio between two characteristic length scales, the root-mean-square distance l between neighboring sticky monomers along the polymer, and the average distance d between neighboring surface adsorption sites. The role of the ratio l / d arises from the fact that a polymer needs to deform to enable the spatial commensurability between its sticky monomers and the surface adsorption sites for selective adsorption. We study strong selective adsorption of both telechelic polymers with two end monomers being sticky and multisticker polymers with many sticky monomers between sticky ends. For telechelic polymers, we identify four adsorption regimes at l / d 1, we expect that the adsorption layer at exponentially low ϕ bulk consists of separated unstretched loops, while as ϕ bulk increases the layer crosses over to a brush of extended loops with a second layer of weakly overlapping tails. For multisticker chains, in the limit of exponentially low ϕ bulk , adsorbed polymers are well separated from each other. As l / d increases, the conformation of an individual polymer changes from a single-end-adsorbed "mushroom" to a random walk of loops. For high ϕ bulk , adsorbed polymers at small l / d are mushrooms that cover all the adsorption sites. At sufficiently large l / d , adsorbed multisticker polymers strongly overlap. We anticipate the formation of a self-similar carpet and with increasing l / d a two-layer structure with a brush of loops covered by a self-similar carpet. As l / d exceeds the

  13. Improving breast cancer classification with mammography, supported on an appropriate variable selection analysis

    Science.gov (United States)

    Pérez, Noel; Guevara, Miguel A.; Silva, Augusto

    2013-02-01

    This work addresses the issue of variable selection within the context of breast cancer classification with mammography. A comprehensive repository of feature vectors was used including a hybrid subset gathering image-based and clinical features. It aimed to gather experimental evidence of variable selection in terms of cardinality, type and find a classification scheme that provides the best performance over the Area Under Receiver Operating Characteristics Curve (AUC) scores using the ranked features subset. We evaluated and classified a total of 300 subsets of features formed by the application of Chi-Square Discretization, Information-Gain, One-Rule and RELIEF methods in association with Feed-Forward Backpropagation Neural Network (FFBP), Support Vector Machine (SVM) and Decision Tree J48 (DTJ48) Machine Learning Algorithms (MLA) for a comparative performance evaluation based on AUC scores. A variable selection analysis was performed for Single-View Ranking and Multi-View Ranking groups of features. Features subsets representing Microcalcifications (MCs), Masses and both MCs and Masses lesions achieved AUC scores of 0.91, 0.954 and 0.934 respectively. Experimental evidence demonstrated that classification performance was improved by combining image-based and clinical features. The most important clinical and image-based features were StromaDistortion and Circularity respectively. Other less important but worth to use due to its consistency were Contrast, Perimeter, Microcalcification, Correlation and Elongation.

  14. The detection of influential subsets in linear regression using an influence matrix

    OpenAIRE

    Peña, Daniel; Yohai, Víctor J.

    1991-01-01

    This paper presents a new method to identify influential subsets in linear regression problems. The procedure uses the eigenstructure of an influence matrix which is defined as the matrix of uncentered covariance of the effect on the whole data set of deleting each observation, normalized to include the univariate Cook's statistics in the diagonal. It is shown that points in an influential subset will appear with large weight in at least one of the eigenvector linked to the largest eigenvalue...

  15. Regulation of EMMPRIN (CD147) on monocyte subsets in patients with symptomatic coronary artery disease.

    Science.gov (United States)

    Sturhan, Henrik; Ungern-Sternberg, Saskia N I v; Langer, Harald; Gawaz, Meinrad; Geisler, Tobias; May, Andreas E; Seizer, Peter

    2015-06-01

    The role of individual monocyte subsets in inflammatory cardiovascular diseases is insufficiently understood. Although the Extracellular Matrix Metalloproteinase Inducer (EMMPRIN) regulates important processes for inflammation such as MMP-release, its expression and regulation on monocyte subsets has not been characterized. In this clinical study, blood was obtained from 80 patients with stable coronary artery disease (CAD), 49 with acute myocardial infarction (AMI) and 34 healthy controls. Monocytes were divided into 3 subsets: CD14(++)CD16(-) (low), CD14(++)CD16(+) (intermediate), CD14(+)CD16(++) (high) according to phenotypic markers analyzed by flow cytometry. Surface expression of EMMPRIN was evaluated and compared with CD36 and CD47 expression. In all patients, EMMPRIN expression was significantly different among monocyte subsets with the highest expression on "classical" CD14(++)CD16(-) monocytes. EMMPRIN was upregulated on all monocyte subsets in patients with AMI as compared to patients with stable CAD. Notably, neither CD47 nor CD36 revealed a significant difference in patients with AMI compared to patients with stable CAD. EMMPRIN could serve as a marker for classical monocytes, which is upregulated in patients with acute myocardial infarction. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Two-year Randomized Clinical Trial of Self-etching Adhesives and Selective Enamel Etching.

    Science.gov (United States)

    Pena, C E; Rodrigues, J A; Ely, C; Giannini, M; Reis, A F

    2016-01-01

    The aim of this randomized, controlled prospective clinical trial was to evaluate the clinical effectiveness of restoring noncarious cervical lesions with two self-etching adhesive systems applied with or without selective enamel etching. A one-step self-etching adhesive (Xeno V(+)) and a two-step self-etching system (Clearfil SE Bond) were used. The effectiveness of phosphoric acid selective etching of enamel margins was also evaluated. Fifty-six cavities were restored with each adhesive system and divided into two subgroups (n=28; etch and non-etch). All 112 cavities were restored with the nanohybrid composite Esthet.X HD. The clinical effectiveness of restorations was recorded in terms of retention, marginal integrity, marginal staining, caries recurrence, and postoperative sensitivity after 3, 6, 12, 18, and 24 months (modified United States Public Health Service). The Friedman test detected significant differences only after 18 months for marginal staining in the groups Clearfil SE non-etch (p=0.009) and Xeno V(+) etch (p=0.004). One restoration was lost during the trial (Xeno V(+) etch; p>0.05). Although an increase in marginal staining was recorded for groups Clearfil SE non-etch and Xeno V(+) etch, the clinical effectiveness of restorations was considered acceptable for the single-step and two-step self-etching systems with or without selective enamel etching in this 24-month clinical trial.

  17. B-cell subset alterations and correlated factors in HIV-1 infection.

    Science.gov (United States)

    Pensieroso, Simone; Galli, Laura; Nozza, Silvia; Ruffin, Nicolas; Castagna, Antonella; Tambussi, Giuseppe; Hejdeman, Bo; Misciagna, Donatella; Riva, Agostino; Malnati, Mauro; Chiodi, Francesca; Scarlatti, Gabriella

    2013-05-15

    During HIV-1 infection, the development, phenotype, and functionality of B cells are impaired. Transitional B cells and aberrant B-cell populations arise in blood, whereas a declined percentage of resting memory B cells is detected. Our study aimed at pinpointing the demographic, immunological, and viral factors driving these pathological findings, and the role of antiretroviral therapy in reverting these alterations. B-cell phenotype and correlating factors were evaluated. Variations in B-cell subsets were evaluated by flow cytometry in HIV-1-infected individuals naive to therapy, elite controllers, and patients treated with antiretroviral drugs (virological control or failure). Multivariable analysis was performed to identify variables independently associated with the B-cell alterations. Significant differences were observed among patients' groups in relation to all B-cell subsets. Resting memory B cells were preserved in patients naive to therapy and elite controllers, but reduced in treated patients. Individuals naive to therapy and experiencing multidrug failure, as well as elite controllers, had significantly higher levels of activated memory B cells compared to healthy controls. In the multivariate analysis, plasma viral load and nadir CD4 T cells independently correlated with major B-cell alterations. Coinfection with hepatitis C but not hepatitis B virus also showed an impact on specific B-cell subsets. Successful protracted antiretroviral treatment led to normalization of all B-cell subsets with exception of resting memory B cells. Our results indicate that viremia and nadir CD4 T cells are important prognostic markers of B-cell perturbations and provide evidence that resting memory B-cell depletion during chronic infection is not reverted upon successful antiretroviral therapy.

  18. Towards Qualifiable Code Generation from a Clocked Synchronous Subset of Modelica

    Directory of Open Access Journals (Sweden)

    Bernhard Thiele

    2015-01-01

    Full Text Available So far no qualifiable automatic code generators (ACGs are available for Modelica. Hence, digital control applications can be modeled and simulated in Modelica, but require tedious additional efforts (e.g., manual reprogramming to produce qualifiable target system production code. In order to more fully leverage the potential of a model-based development (MBD process in Modelica, a qualifiable automatic code generator is needed. Typical Modelica code generation is a fairly complex process which imposes a huge development burden to any efforts of tool qualification. This work aims at mapping a Modelica subset for digital control function development to a well-understood synchronous data-flow kernel language. This kernel language allows to resort to established compilation techniques for data-flow languages which are understood enough to be accepted by certification authorities. The mapping is established by providing a translational semantics from the Modelica subset to the synchronous data-flow kernel language. However, this translation turned out to be more intricate than initially expected and has given rise to several interesting issues that require suitable design decisions regarding the mapping and the language subset.

  19. Reduction of the Random Variables of the Turbulent Wind Field

    DEFF Research Database (Denmark)

    Sichani, Mahdi Teimouri; Nielsen, Søren R.K.

    2012-01-01

    .e. Importance Sampling (IS) or Subset Simulation (SS), will be deteriorated on problems with many random variables. The problem with PDEM is that a multidimensional integral has to be carried out over the space defined by the random variables of the system. The numerical procedure requires discretization......Applicability of the Probability Density Evolution Method (PDEM) for realizing evolution of the probability density for the wind turbines has rather strict bounds on the basic number of the random variables involved in the model. The efficiency of most of the Advanced Monte Carlo (AMC) methods, i...... of the integral domain; this becomes increasingly difficult as the dimensions of the integral domain increase. On the other hand efficiency of the AMC methods is closely dependent on the design points of the problem. Presence of many random variables may increase the number of the design points, hence affects...

  20. Random effect selection in generalised linear models

    DEFF Research Database (Denmark)

    Denwood, Matt; Houe, Hans; Forkman, Björn

    We analysed abattoir recordings of meat inspection codes with possible relevance to onfarm animal welfare in cattle. Random effects logistic regression models were used to describe individual-level data obtained from 461,406 cattle slaughtered in Denmark. Our results demonstrate that the largest...

  1. Regularity of random attractors for fractional stochastic reaction-diffusion equations on Rn

    Science.gov (United States)

    Gu, Anhui; Li, Dingshi; Wang, Bixiang; Yang, Han

    2018-06-01

    We investigate the regularity of random attractors for the non-autonomous non-local fractional stochastic reaction-diffusion equations in Hs (Rn) with s ∈ (0 , 1). We prove the existence and uniqueness of the tempered random attractor that is compact in Hs (Rn) and attracts all tempered random subsets of L2 (Rn) with respect to the norm of Hs (Rn). The main difficulty is to show the pullback asymptotic compactness of solutions in Hs (Rn) due to the noncompactness of Sobolev embeddings on unbounded domains and the almost sure nondifferentiability of the sample paths of the Wiener process. We establish such compactness by the ideas of uniform tail-estimates and the spectral decomposition of solutions in bounded domains.

  2. User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms

    Directory of Open Access Journals (Sweden)

    Aitzol Astigarraga

    2016-01-01

    Full Text Available Brain-Computer Interfaces (BCIs have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG- based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.

  3. A Hybrid Soft Computing Approach for Subset Problems

    Directory of Open Access Journals (Sweden)

    Broderick Crawford

    2013-01-01

    Full Text Available Subset problems (set partitioning, packing, and covering are formal models for many practical optimization problems. A set partitioning problem determines how the items in one set (S can be partitioned into smaller subsets. All items in S must be contained in one and only one partition. Related problems are set packing (all items must be contained in zero or one partitions and set covering (all items must be contained in at least one partition. Here, we present a hybrid solver based on ant colony optimization (ACO combined with arc consistency for solving this kind of problems. ACO is a swarm intelligence metaheuristic inspired on ants behavior when they search for food. It allows to solve complex combinatorial problems for which traditional mathematical techniques may fail. By other side, in constraint programming, the solving process of Constraint Satisfaction Problems can dramatically reduce the search space by means of arc consistency enforcing constraint consistencies either prior to or during search. Our hybrid approach was tested with set covering and set partitioning dataset benchmarks. It was observed that the performance of ACO had been improved embedding this filtering technique in its constructive phase.

  4. Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm

    Science.gov (United States)

    Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider

    2016-01-01

    Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm. PMID:27822174

  5. Mouse NK cell-mediated rejection of bone marrow allografts exhibits patterns consistent with Ly49 subset licensing.

    Science.gov (United States)

    Sun, Kai; Alvarez, Maite; Ames, Erik; Barao, Isabel; Chen, Mingyi; Longo, Dan L; Redelman, Doug; Murphy, William J

    2012-02-09

    Natural killer (NK) cells can mediate the rejection of bone marrow allografts and exist as subsets based on expression of inhibitory/activating receptors that can bind MHC. In vitro data have shown that NK subsets bearing Ly49 receptors for self-MHC class I have intrinsically higher effector function, supporting the hypothesis that NK cells undergo a host MHC-dependent functional education. These subsets also play a role in bone marrow cell (BMC) allograft rejection. Thus far, little in vivo evidence for this preferential licensing across mouse strains with different MHC haplotypes has been shown. We assessed the intrinsic response potential of the different Ly49(+) subsets in BMC rejection by using β2-microglobulin deficient (β2m(-/-)) mice as donors. Using congenic and allogeneic mice as recipients and depleting the different Ly49 subsets, we found that NK subsets bearing Ly49s, which bind "self-MHC" were found to be the dominant subset responsible for β2m(-/-) BMC rejection. This provides in vivo evidence for host MHC class I-dependent functional education. Interestingly, all H2(d) strain mice regardless of background were able to resist significantly greater amounts of β2m(-/-), but not wild-type BMC than H2(b) mice, providing evidence that the rheostat hypothesis regarding Ly49 affinities for MHC and NK-cell function impacts BMC rejection capability.

  6. A Select Subset of Electron Transport Chain Genes Associated with Optic Atrophy Link Mitochondria to Axon Regeneration in Caenorhabditis elegans.

    Science.gov (United States)

    Knowlton, Wendy M; Hubert, Thomas; Wu, Zilu; Chisholm, Andrew D; Jin, Yishi

    2017-01-01

    The role of mitochondria within injured neurons is an area of active interest since these organelles are vital for the production of cellular energy in the form of ATP. Using mechanosensory neurons of the nematode Caenorhabditis elegans to test regeneration after neuronal injury in vivo , we surveyed genes related to mitochondrial function for effects on axon regrowth after laser axotomy. Genes involved in mitochondrial transport, calcium uptake, mitophagy, or fission and fusion were largely dispensable for axon regrowth, with the exception of eat-3/Opa1 . Surprisingly, many genes encoding components of the electron transport chain were dispensable for regrowth, except for the iron-sulfur proteins gas-1, nduf-2.2, nduf-7 , and isp-1 , and the putative oxidoreductase rad-8 . In these mutants, axonal development was essentially normal and axons responded normally to injury by forming regenerative growth cones, but were impaired in subsequent axon extension. Overexpression of nduf-2.2 or isp-1 was sufficient to enhance regrowth, suggesting that mitochondrial function is rate-limiting in axon regeneration. Moreover, loss of function in isp-1 reduced the enhanced regeneration caused by either a gain-of-function mutation in the calcium channel EGL-19 or overexpression of the MAP kinase DLK-1. While the cellular function of RAD-8 remains unclear, our genetic analyses place rad-8 in the same pathway as other electron transport genes in axon regeneration. Unexpectedly, rad-8 regrowth defects were suppressed by altered function in the ubiquinone biosynthesis gene clk-1 . Furthermore, we found that inhibition of the mitochondrial unfolded protein response via deletion of atfs-1 suppressed the defective regrowth in nduf-2.2 mutants. Together, our data indicate that while axon regeneration is not significantly affected by general dysfunction of cellular respiration, it is sensitive to the proper functioning of a select subset of electron transport chain genes, or to the

  7. T-lymphocyte subsets, thymic size and breastfeeding in infancy

    DEFF Research Database (Denmark)

    Jeppesen, Dorthe Lisbeth; Hasselbalch, Helle; Lisse, Ida M

    2004-01-01

    We followed the changes in concentration of T-lymphocyte subsets (CD4+ and CD8+ cells) in peripheral blood and thymus size during infancy. Previous studies have found increased thymus size in breastfed infants. The present study analyzed the association between breastfeeding and the number of CD4...

  8. Enhancement web proxy cache performance using Wrapper Feature Selection methods with NB and J48

    Science.gov (United States)

    Mahmoud Al-Qudah, Dua'a.; Funke Olanrewaju, Rashidah; Wong Azman, Amelia

    2017-11-01

    Web proxy cache technique reduces response time by storing a copy of pages between client and server sides. If requested pages are cached in the proxy, there is no need to access the server. Due to the limited size and excessive cost of cache compared to the other storages, cache replacement algorithm is used to determine evict page when the cache is full. On the other hand, the conventional algorithms for replacement such as Least Recently Use (LRU), First in First Out (FIFO), Least Frequently Use (LFU), Randomized Policy etc. may discard important pages just before use. Furthermore, using conventional algorithm cannot be well optimized since it requires some decision to intelligently evict a page before replacement. Hence, most researchers propose an integration among intelligent classifiers and replacement algorithm to improves replacement algorithms performance. This research proposes using automated wrapper feature selection methods to choose the best subset of features that are relevant and influence classifiers prediction accuracy. The result present that using wrapper feature selection methods namely: Best First (BFS), Incremental Wrapper subset selection(IWSS)embedded NB and particle swarm optimization(PSO)reduce number of features and have a good impact on reducing computation time. Using PSO enhance NB classifier accuracy by 1.1%, 0.43% and 0.22% over using NB with all features, using BFS and using IWSS embedded NB respectively. PSO rises J48 accuracy by 0.03%, 1.91 and 0.04% over using J48 classifier with all features, using IWSS-embedded NB and using BFS respectively. While using IWSS embedded NB fastest NB and J48 classifiers much more than BFS and PSO. However, it reduces computation time of NB by 0.1383 and reduce computation time of J48 by 2.998.

  9. The expanding universe of T-cell subsets: Th1, Th2 and more.

    Science.gov (United States)

    Mosmann, T R; Sad, S

    1996-03-01

    Since their discovery nearly ten years ago, T helper 1 (Th1) and Th2 subsets have been implicated in the regulation of many immune responses. In this article, Tim Mosmann and Subash Sad discuss the increasing number of T-cell subsets defined by cytokine patterns; the differentiation pathways of CD4+ and CD8+ T cells; the contribution of other cell types to these patterns; and the cytokine interactions during infection and pregnancy.

  10. Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa

    Directory of Open Access Journals (Sweden)

    Thandi Kapwata

    2016-11-01

    Full Text Available Malaria is an environmentally driven disease. In order to quantify the spatial variability of malaria transmission, it is imperative to understand the interactions between environmental variables and malaria epidemiology at a micro-geographic level using a novel statistical approach. The random forest (RF statistical learning method, a relatively new variable-importance ranking method, measures the variable importance of potentially influential parameters through the percent increase of the mean squared error. As this value increases, so does the relative importance of the associated variable. The principal aim of this study was to create predictive malaria maps generated using the selected variables based on the RF algorithm in the Ehlanzeni District of Mpumalanga Province, South Africa. From the seven environmental variables used [temperature, lag temperature, rainfall, lag rainfall, humidity, altitude, and the normalized difference vegetation index (NDVI], altitude was identified as the most influential predictor variable due its high selection frequency. It was selected as the top predictor for 4 out of 12 months of the year, followed by NDVI, temperature and lag rainfall, which were each selected twice. The combination of climatic variables that produced the highest prediction accuracy was altitude, NDVI, and temperature. This suggests that these three variables have high predictive capabilities in relation to malaria transmission. Furthermore, it is anticipated that the predictive maps generated from predictions made by the RF algorithm could be used to monitor the progression of malaria and assist in intervention and prevention efforts with respect to malaria.

  11. An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling

    Science.gov (United States)

    Moghadamfalahi, Mohammad; Akcakaya, Murat; Nezamfar, Hooman; Sourati, Jamshid; Erdogmus, Deniz

    2017-10-01

    A class of brain computer interfaces (BCIs) employs noninvasive recordings of electroencephalography (EEG) signals to enable users with severe speech and motor impairments to interact with their environment and social network. For example, EEG based BCIs for typing popularly utilize event related potentials (ERPs) for inference. Presentation paradigm design in current ERP-based letter by letter typing BCIs typically query the user with an arbitrary subset characters. However, the typing accuracy and also typing speed can potentially be enhanced with more informed subset selection and flash assignment. In this manuscript, we introduce the active recursive Bayesian state estimation (active-RBSE) framework for inference and sequence optimization. Prior to presentation in each iteration, rather than showing a subset of randomly selected characters, the developed framework optimally selects a subset based on a query function. Selected queries are made adaptively specialized for users during each intent detection. Through a simulation-based study, we assess the effect of active-RBSE on the performance of a language-model assisted typing BCI in terms of typing speed and accuracy. To provide a baseline for comparison, we also utilize standard presentation paradigms namely, row and column matrix presentation paradigm and also random rapid serial visual presentation paradigms. The results show that utilization of active-RBSE can enhance the online performance of the system, both in terms of typing accuracy and speed.

  12. Chemokine-mediated distribution of dendritic cell subsets in renal cell carcinoma

    Directory of Open Access Journals (Sweden)

    Meyer Werner

    2010-10-01

    Full Text Available Abstract Background Renal cell carcinoma (RCC represents one of the most immunoresponsive cancers. Antigen-specific vaccination with dendritic cells (DCs in patients with metastatic RCC has been shown to induce cytotoxic T-cell responses associated with objective clinical responses. Thus, clinical trials utilizing DCs for immunotherapy of advanced RCCs appear to be promising; however, detailed analyses concerning the distribution and function of DC subsets in RCCs are lacking. Methods We characterized the distribution of the different immature and mature myeloid DC subsets in RCC tumour tissue and the corresponding normal kidney tissues. In further analyses, the expression of various chemokines and chemokine receptors controlling the migration of DC subsets was investigated. Results The highest numbers of immature CD1a+ DCs were found within RCC tumour tissue. In contrast, the accumulation of mature CD83+/DC-LAMP+ DCs were restricted to the invasive margin of the RCCs. The mature DCs formed clusters with proliferating T-cells. Furthermore, a close association was observed between MIP-3α-producing tumour cells and immature CCR6+ DC recruitment to the tumour bed. Conversely, MIP-3β and SLC expression was only detected at the tumour border, where CCR7-expressing T-cells and mature DCs formed clusters. Conclusion Increased numbers of immature DCs were observed within the tumour tissue of RCCs, whereas mature DCs were found in increased numbers at the tumour margin. Our results strongly implicate that the distribution of DC subsets is controlled by local lymphoid chemokine expression. Thus, increased expression of MIP-3α favours recruitment of immature DCs to the tumour bed, whereas de novo local expression of SLC and MIP-3β induces accumulation of mature DCs at the tumour margin forming clusters with proliferating T-cells reflecting a local anti-tumour immune response.

  13. Chemokine-mediated distribution of dendritic cell subsets in renal cell carcinoma

    International Nuclear Information System (INIS)

    Middel, Peter; Brauneck, Sven; Meyer, Werner; Radzun, Heinz-Joachim

    2010-01-01

    Renal cell carcinoma (RCC) represents one of the most immunoresponsive cancers. Antigen-specific vaccination with dendritic cells (DCs) in patients with metastatic RCC has been shown to induce cytotoxic T-cell responses associated with objective clinical responses. Thus, clinical trials utilizing DCs for immunotherapy of advanced RCCs appear to be promising; however, detailed analyses concerning the distribution and function of DC subsets in RCCs are lacking. We characterized the distribution of the different immature and mature myeloid DC subsets in RCC tumour tissue and the corresponding normal kidney tissues. In further analyses, the expression of various chemokines and chemokine receptors controlling the migration of DC subsets was investigated. The highest numbers of immature CD1a+ DCs were found within RCC tumour tissue. In contrast, the accumulation of mature CD83+/DC-LAMP+ DCs were restricted to the invasive margin of the RCCs. The mature DCs formed clusters with proliferating T-cells. Furthermore, a close association was observed between MIP-3α-producing tumour cells and immature CCR6+ DC recruitment to the tumour bed. Conversely, MIP-3β and SLC expression was only detected at the tumour border, where CCR7-expressing T-cells and mature DCs formed clusters. Increased numbers of immature DCs were observed within the tumour tissue of RCCs, whereas mature DCs were found in increased numbers at the tumour margin. Our results strongly implicate that the distribution of DC subsets is controlled by local lymphoid chemokine expression. Thus, increased expression of MIP-3α favours recruitment of immature DCs to the tumour bed, whereas de novo local expression of SLC and MIP-3β induces accumulation of mature DCs at the tumour margin forming clusters with proliferating T-cells reflecting a local anti-tumour immune response

  14. Subsetting Tools for Enabling Easy Access to International Airborne Chemistry Data

    Science.gov (United States)

    Northup, E. A.; Chen, G.; Quam, B. M.; Beach, A. L., III; Silverman, M. L.; Early, A. B.

    2017-12-01

    In response to the Research Opportunities in Earth and Space Science (ROSES) 2015 release announcement for Advancing Collaborative Connections for Earth System Science (ACCESS), researchers at NASA Langley Research Center (LaRC) proposed to extend the capabilities of the existing Toolsets for Airborne Data (TAD) to include subsetting functionality to allow for easier access to international airborne field campaign data. Airborne field studies are commonly used to gain a detailed understanding of atmospheric processes for scientific research on international climate change and air quality issues. To accommodate the rigorous process for manipulating airborne field study chemistry data, and to lessen barriers for researchers, TAD was created with the ability to geolocate data from various sources measured on different time scales from a single flight. The analysis of airborne chemistry data typically requires data subsetting, which can be challenging and resource-intensive for end users. In an effort to streamline this process, new data subsetting features and updates to the current database model will be added to the TAD toolset. These will include two subsetters: temporal and spatial, and vertical profile. The temporal and spatial subsetter will allow users to both focus on data from a specific location and/or time period. The vertical profile subsetter will retrieve data collected during an individual aircraft ascent or descent spiral. These new web-based tools will allow for automation of the typically labor-intensive manual data subsetting process, which will provide users with data tailored to their specific research interests. The system has been designed to allow for new in-situ airborne missions to be added as they become available, with only minor pre-processing required. The development of these enhancements will be discussed in this presentation.

  15. Selecting Optimal Parameters of Random Linear Network Coding for Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Heide, J; Zhang, Qi; Fitzek, F H P

    2013-01-01

    This work studies how to select optimal code parameters of Random Linear Network Coding (RLNC) in Wireless Sensor Networks (WSNs). With Rateless Deluge [1] the authors proposed to apply Network Coding (NC) for Over-the-Air Programming (OAP) in WSNs, and demonstrated that with NC a significant...... reduction in the number of transmitted packets can be achieved. However, NC introduces additional computations and potentially a non-negligible transmission overhead, both of which depend on the chosen coding parameters. Therefore it is necessary to consider the trade-off that these coding parameters...... present in order to obtain the lowest energy consumption per transmitted bit. This problem is analyzed and suitable coding parameters are determined for the popular Tmote Sky platform. Compared to the use of traditional RLNC, these parameters enable a reduction in the energy spent per bit which grows...

  16. Differential adipokine receptor expression on circulating leukocyte subsets in lean and obese children.

    Directory of Open Access Journals (Sweden)

    Genoveva Keustermans

    Full Text Available Childhood obesity prevalence has increased worldwide and is an important risk factor for type 2 diabetes (T2D and cardiovascular disease (CVD. The production of inflammatory adipokines by obese adipose tissue contributes to the development of T2D and CVD. While levels of circulating adipokines such as adiponectin and leptin have been established in obese children and adults, the expression of adiponectin and leptin receptors on circulating immune cells can modulate adipokine signalling, but has not been studied so far. Here, we aim to establish the expression of adiponectin and leptin receptors on circulating immune cells in obese children pre and post-lifestyle intervention compared to normal weight control children.13 obese children before and after a 1-year lifestyle intervention were compared with an age and sex-matched normal weight control group of 15 children. Next to routine clinical and biochemical parameters, circulating adipokines were measured, and flow cytometric analysis of adiponectin receptor 1 and 2 (AdipoR1, AdipoR2 and leptin receptor expression on peripheral blood mononuclear cell subsets was performed.Obese children exhibited typical clinical and biochemical characteristics compared to controls, including a higher BMI-SD, blood pressure and circulating leptin levels, combined with a lower insulin sensitivity index (QUICKI. The 1-year lifestyle intervention resulted in stabilization of their BMI-SD. Overall, circulating leukocyte subsets showed distinct adipokine receptor expression profiles. While monocytes expressed high levels of all adipokine receptors, NK and iNKT cells predominantly expressed AdipoR2, and B-lymphocytes and CD4+ and CD8+ T-lymphocyte subsets expressed AdipoR2 as well as leptin receptor. Strikingly though, leukocyte subset numbers and adipokine receptor expression profiles were largely similar in obese children and controls. Obese children showed higher naïve B-cell numbers, and pre-intervention also

  17. A Feature Selection Method for Large-Scale Network Traffic Classification Based on Spark

    Directory of Open Access Journals (Sweden)

    Yong Wang

    2016-02-01

    Full Text Available Currently, with the rapid increasing of data scales in network traffic classifications, how to select traffic features efficiently is becoming a big challenge. Although a number of traditional feature selection methods using the Hadoop-MapReduce framework have been proposed, the execution time was still unsatisfactory with numeral iterative computations during the processing. To address this issue, an efficient feature selection method for network traffic based on a new parallel computing framework called Spark is proposed in this paper. In our approach, the complete feature set is firstly preprocessed based on Fisher score, and a sequential forward search strategy is employed for subsets. The optimal feature subset is then selected using the continuous iterations of the Spark computing framework. The implementation demonstrates that, on the precondition of keeping the classification accuracy, our method reduces the time cost of modeling and classification, and improves the execution efficiency of feature selection significantly.

  18. Application of random coherence order selection in gradient-enhanced multidimensional NMR

    International Nuclear Information System (INIS)

    Bostock, Mark J.; Nietlispach, Daniel

    2016-01-01

    Development of multidimensional NMR is essential to many applications, for example in high resolution structural studies of biomolecules. Multidimensional techniques enable separation of NMR signals over several dimensions, improving signal resolution, whilst also allowing identification of new connectivities. However, these advantages come at a significant cost. The Fourier transform theorem requires acquisition of a grid of regularly spaced points to satisfy the Nyquist criterion, while frequency discrimination and acquisition of a pure phase spectrum require acquisition of both quadrature components for each time point in every indirect (non-acquisition) dimension, adding a factor of 2 N -1 to the number of free- induction decays which must be acquired, where N is the number of dimensions. Compressed sensing (CS) ℓ 1 -norm minimisation in combination with non-uniform sampling (NUS) has been shown to be extremely successful in overcoming the Nyquist criterion. Previously, maximum entropy reconstruction has also been used to overcome the limitation of frequency discrimination, processing data acquired with only one quadrature component at a given time interval, known as random phase detection (RPD), allowing a factor of two reduction in the number of points for each indirect dimension (Maciejewski et al. 2011 PNAS 108 16640). However, whilst this approach can be easily applied in situations where the quadrature components are acquired as amplitude modulated data, the same principle is not easily extended to phase modulated (P-/N-type) experiments where data is acquired in the form exp (iωt) or exp (-iωt), and which make up many of the multidimensional experiments used in modern NMR. Here we demonstrate a modification of the CS ℓ 1 -norm approach to allow random coherence order selection (RCS) for phase modulated experiments; we generalise the nomenclature for RCS and RPD as random quadrature detection (RQD). With this method, the power of RQD can be extended

  19. Subset selection from generalized logistic populations

    NARCIS (Netherlands)

    Laan, van der M.J.; Laan, van der P.

    1997-01-01

    We give an introduction to the logistic and generalized logistic distributions. These generalized logistic distributions Type-I, Type-II and Type-III are indexed by a real valued parameter. They have been derived as mixtures with the standard logistic distribution and for discrete values of the

  20. Phenotypic, ultra-structural, and functional characterization of bovine peripheral blood dendritic cell subsets.

    Directory of Open Access Journals (Sweden)

    Janet J Sei

    Full Text Available Dendritic cells (DC are multi-functional cells that bridge the gap between innate and adaptive immune systems. In bovine, significant information is lacking on the precise identity and role of peripheral blood DC subsets. In this study, we identify and characterize bovine peripheral blood DC subsets directly ex vivo, without further in vitro manipulation. Multi-color flow cytometric analysis revealed that three DC subsets could be identified. Bovine plasmacytoid DC were phenotypically identified by a unique pattern of cell surface protein expression including CD4, exhibited an extensive endoplasmic reticulum and Golgi apparatus, efficiently internalized and degraded exogenous antigen, and were the only peripheral blood cells specialized in the production of type I IFN following activation with Toll-like receptor (TLR agonists. Conventional DC were identified by expression of a different pattern of cell surface proteins including CD11c, MHC class II, and CD80, among others, the display of extensive dendritic protrusions on their plasma membrane, expression of very high levels of MHC class II and co-stimulatory molecules, efficient internalization and degradation of exogenous antigen, and ready production of detectable levels of TNF-alpha in response to TLR activation. Our investigations also revealed a third novel DC subset that may be a precursor of conventional DC that were MHC class II+ and CD11c-. These cells exhibited a smooth plasma membrane with a rounded nucleus, produced TNF-alpha in response to TLR-activation (albeit lower than CD11c+ DC, and were the least efficient in internalization/degradation of exogenous antigen. These studies define three bovine blood DC subsets with distinct phenotypic and functional characteristics which can be analyzed during immune responses to pathogens and vaccinations of cattle.

  1. On Random Numbers and Design

    Science.gov (United States)

    Ben-Ari, Morechai

    2004-01-01

    The term "random" is frequently used in discussion of the theory of evolution, even though the mathematical concept of randomness is problematic and of little relevance in the theory. Therefore, since the core concept of the theory of evolution is the non-random process of natural selection, the term random should not be used in teaching the…

  2. Feature selection for splice site prediction: A new method using EDA-based feature ranking

    Directory of Open Access Journals (Sweden)

    Rouzé Pierre

    2004-05-01

    Full Text Available Abstract Background The identification of relevant biological features in large and complex datasets is an important step towards gaining insight in the processes underlying the data. Other advantages of feature selection include the ability of the classification system to attain good or even better solutions using a restricted subset of features, and a faster classification. Thus, robust methods for fast feature selection are of key importance in extracting knowledge from complex biological data. Results In this paper we present a novel method for feature subset selection applied to splice site prediction, based on estimation of distribution algorithms, a more general framework of genetic algorithms. From the estimated distribution of the algorithm, a feature ranking is derived. Afterwards this ranking is used to iteratively discard features. We apply this technique to the problem of splice site prediction, and show how it can be used to gain insight into the underlying biological process of splicing. Conclusion We show that this technique proves to be more robust than the traditional use of estimation of distribution algorithms for feature selection: instead of returning a single best subset of features (as they normally do this method provides a dynamical view of the feature selection process, like the traditional sequential wrapper methods. However, the method is faster than the traditional techniques, and scales better to datasets described by a large number of features.

  3. Characterization of the myeloid-derived suppressor cell subset regulated by NK cells in malignant lymphoma.

    Science.gov (United States)

    Sato, Yusuke; Shimizu, Kanako; Shinga, Jun; Hidaka, Michihiro; Kawano, Fumio; Kakimi, Kazuhiro; Yamasaki, Satoru; Asakura, Miki; Fujii, Shin-Ichiro

    2015-03-01

    Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population with the ability to suppress immune responses and are currently classified into three distinct MDSC subsets: monocytic, granulocytic and non-monocytic, and non-granulocytic MDSCs. Although NK cells provide an important first-line defense against newly transformed cancer cells, it is unknown whether NK cells can regulate MDSC populations in the context of cancer. In this study, we initially found that the frequency of MDSCs in non-Hodgkin lymphoma (NHL) patients was increased and inversely correlated with that of NK cells, but not that of T cells. To investigate the regulation of MDSC subsets by NK cells, we used an EL4 murine lymphoma model and found the non-monocytic and non-granulocytic MDSC subset, i.e., Gr1 + CD11b + Ly6G med Ly6C med MDSC, is increased after NK cell depletion. The MDSC population that expresses MHC class II, CD80, CD124, and CCR2 is regulated mainly by CD27 + CD11b + NK cells. In addition, this MDSC subset produces some immunosuppressive cytokines, including IL-10 but not nitric oxide (NO) or arginase. We also examined two subsets of MDSCs (CD14 + HLA-DR - and CD14 - HLA-DR - MDSC) in NHL patients and found that higher IL-10-producing CD14 + HLA-DR - MDSC subset can be seen in lymphoma patients with reduced NK cell frequency in peripheral blood. Our analyses of MDSCs in this study may enable a better understanding of how MDSCs manipulate the tumor microenvironment and are regulated by NK cells in patients with lymphoma.

  4. Selecting for Fast Protein-Protein Association As Demonstrated on a Random TEM1 Yeast Library Binding BLIP.

    Science.gov (United States)

    Cohen-Khait, Ruth; Schreiber, Gideon

    2018-04-27

    Protein-protein interactions mediate the vast majority of cellular processes. Though protein interactions obey basic chemical principles also within the cell, the in vivo physiological environment may not allow for equilibrium to be reached. Thus, in vitro measured thermodynamic affinity may not provide a complete picture of protein interactions in the biological context. Binding kinetics composed of the association and dissociation rate constants are relevant and important in the cell. Therefore, changes in protein-protein interaction kinetics have a significant impact on the in vivo activity of the proteins. The common protocol for the selection of tighter binders from a mutant library selects for protein complexes with slower dissociation rate constants. Here we describe a method to specifically select for variants with faster association rate constants by using pre-equilibrium selection, starting from a large random library. Toward this end, we refine the selection conditions of a TEM1-β-lactamase library against its natural nanomolar affinity binder β-lactamase inhibitor protein (BLIP). The optimal selection conditions depend on the ligand concentration and on the incubation time. In addition, we show that a second sort of the library helps to separate signal from noise, resulting in a higher percent of faster binders in the selected library. Fast associating protein variants are of particular interest for drug development and other biotechnological applications.

  5. The stock selection problem: Is the stock selection approach more important than the optimization method? Evidence from the Danish stock market

    OpenAIRE

    Grobys, Klaus

    2011-01-01

    Passive investment strategies basically aim to replicate an underlying benchmark. Thereby, the management usually selects a subset of stocks being employed in the optimization procedure. Apart from the optimization procedure, the stock selection approach determines the stock portfolios' out-of-sample performance. The empirical study here takes into account the Danish stock market from 2000-2010 and gives evidence that stock portfolios including small companies' stocks being estimated via coin...

  6. Channel selection for simultaneous and proportional myoelectric prosthesis control of multiple degrees-of-freedom

    Science.gov (United States)

    Hwang, Han-Jeong; Hahne, Janne Mathias; Müller, Klaus-Robert

    2014-10-01

    Objective. Recent studies have shown the possibility of simultaneous and proportional control of electrically powered upper-limb prostheses, but there has been little investigation on optimal channel selection. The objective of this study is to find a robust channel selection method and the channel subsets most suitable for simultaneous and proportional myoelectric prosthesis control of multiple degrees-of-freedom (DoFs). Approach. Ten able-bodied subjects and one person with congenital upper-limb deficiency took part in this study, and performed wrist movements with various combinations of two DoFs (flexion/extension and radial/ulnar deviation). During the experiment, high density electromyographic (EMG) signals and the actual wrist angles were recorded with an 8 × 24 electrode array and a motion tracking system, respectively. The wrist angles were estimated from EMG features with ridge regression using the subsets of channels chosen by three different channel selection methods: (1) least absolute shrinkage and selection operator (LASSO), (2) sequential feature selection (SFS), and (3) uniform selection (UNI). Main results. SFS generally showed higher estimation accuracy than LASSO and UNI, but LASSO always outperformed SFS in terms of robustness, such as noise addition, channel shift and training data reduction. It was also confirmed that about 95% of the original performance obtained using all channels can be retained with only 12 bipolar channels individually selected by LASSO and SFS. Significance. From the analysis results, it can be concluded that LASSO is a promising channel selection method for accurate simultaneous and proportional prosthesis control. We expect that our results will provide a useful guideline to select optimal channel subsets when developing clinical myoelectric prosthesis control systems based on continuous movements with multiple DoFs.

  7. Feature Subset Selection and Instance Filtering for Cross-project Defect Prediction - Classification and Ranking

    Directory of Open Access Journals (Sweden)

    Faimison Porto

    2016-12-01

    Full Text Available The defect prediction models can be a good tool on organizing the project's test resources. The models can be constructed with two main goals: 1 to classify the software parts - defective or not; or 2 to rank the most defective parts in a decreasing order. However, not all companies maintain an appropriate set of historical defect data. In this case, a company can build an appropriate dataset from known external projects - called Cross-project Defect Prediction (CPDP. The CPDP models, however, present low prediction performances due to the heterogeneity of data. Recently, Instance Filtering methods were proposed in order to reduce this heterogeneity by selecting the most similar instances from the training dataset. Originally, the similarity is calculated based on all the available dataset features (or independent variables. We propose that using only the most relevant features on the similarity calculation can result in more accurate filtered datasets and better prediction performances. In this study we extend our previous work. We analyse both prediction goals - Classification and Ranking. We present an empirical evaluation of 41 different methods by associating Instance Filtering methods with Feature Selection methods. We used 36 versions of 11 open source projects on experiments. The results show similar evidences for both prediction goals. First, the defect prediction performance of CPDP models can be improved by associating Feature Selection and Instance Filtering. Second, no evaluated method presented general better performances. Indeed, the most appropriate method can vary according to the characteristics of the project being predicted.

  8. Psychological stress during exercise: lymphocyte subset redistribution in firefighters.

    Science.gov (United States)

    Huang, Chun-Jung; Webb, Heather E; Garten, Ryan S; Kamimori, Gary H; Acevedo, Edmund O

    2010-10-05

    The purpose of this study examined the changes in heart rate (HR), catecholamines (NE, EPI) and percentages of blood lymphocyte subsets (CD3+ T cells, CD3+CD4+ helper T cells, CD3+CD8+ cytotoxic T cells, CD3- CD56+ NK cells, CD4/CD8 ratio, CD19+ B cells, and total lymphocytes [NK cells+T cells+B cells]) in firefighters exposed to a computerized firefighting strategies and tactics decision-making challenge while participating in moderate intensity exercise. Furthermore, this study also examined the possible relationships between catecholamines (NE and EPI) and blood lymphocyte subsets following combined mental and physical challenge. Ten professional male firefighters participated in two counterbalanced exercise conditions on a cycle ergometer: (1) 37min of cycle ergometry at 60% VO(2max) (exercise alone condition; EAC) and (2) 37min of cycle ergometry at 60% VO(2max) along with 20min of a computerized firefighting strategies and tactics decision-making challenge (firefighting strategies condition; FSC). FSC elicited significantly greater HR, NE, and EPI when compared to EAC. Both EAC and FSC elicited increases in CD3- CD56+ NK cells. The percentages of CD3+ T cells, CD3+CD4+ helper T cells, CD4/CD8 ratio, CD19+ B cells, and total lymphocytes were lower immediately following both conditions. Following dual challenge NE AUC was negatively correlated with percentage of CD19+ B cells immediately post challenge, and HR was negatively associated with the percent change in the CD4/CD8 ratio from pre to post challenge. These elevations in NE and heart rate simultaneously in response to the dual challenge suggest greater sympathetic activation that in turn would possibly explain the alteration in the distribution of lymphocyte subsets. Published by Elsevier Inc.

  9. Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.

    Directory of Open Access Journals (Sweden)

    Andrew Cron

    Full Text Available Flow cytometry is the prototypical assay for multi-parameter single cell analysis, and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies (0.1% or less. Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts, a process that is subjective and often difficult to reproduce. An alternative and more objective approach is the use of statistical models to identify cell subsets of interest in an automated fashion. Two specific challenges for automated analysis are to detect extremely low frequency event subsets without biasing the estimate by pre-processing enrichment, and the ability to align cell subsets across multiple data samples for comparative analysis. In this manuscript, we develop hierarchical modeling extensions to the Dirichlet Process Gaussian Mixture Model (DPGMM approach we have previously described for cell subset identification, and show that the hierarchical DPGMM (HDPGMM naturally generates an aligned data model that captures both commonalities and variations across multiple samples. HDPGMM also increases the sensitivity to extremely low frequency events by sharing information across multiple samples analyzed simultaneously. We validate the accuracy and reproducibility of HDPGMM estimates of antigen-specific T cells on clinically relevant reference peripheral blood mononuclear cell (PBMC samples with known frequencies of antigen-specific T cells. These cell samples take advantage of retrovirally TCR-transduced T cells spiked into autologous PBMC samples to give a defined number of antigen-specific T cells detectable by HLA-peptide multimer binding. We provide open source software that can take advantage of both multiple processors and GPU-acceleration to perform the numerically-demanding computations. We show that hierarchical modeling is a useful probabilistic approach that can provide a

  10. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

    Directory of Open Access Journals (Sweden)

    Nicole A Capela

    Full Text Available Human activity recognition (HAR, using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter. The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree. Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.

  11. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

    Science.gov (United States)

    Capela, Nicole A; Lemaire, Edward D; Baddour, Natalie

    2015-01-01

    Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.

  12. The regulatory roles of B cell subsets in transplantation.

    Science.gov (United States)

    Chu, Zhulang; Zou, Weilong; Xu, Yanan; Sun, Qiquan; Zhao, Yong

    2018-02-01

    B cells mediate allograft rejection through antigen presentation, and production of cytokines and antibodies. More and more immunosuppressive agents specifically targeting B cells and plasma cells have been applied in clinical transplantation. However, recent studies have indicated the regulatory roles of B cells. Therefore, it is vital to clarify the different effects of B cell subsets in organ transplantation so that we can completely understand the diverse functions of B cells in transplantation. Areas covered: This review focuses on the regulatory roles of B cells in transplantation. B cell subsets with immune modulation and factors mediating immunosuppressive functions of regulatory B (Breg) cells were analyzed. Therapies targeting B cells and the application of B cells for transplant tolerance induction were discussed. Expert commentary: Besides involving rejection, B cells could also play regulatory roles in transplantation. Breg cells and the related markers may be used to predict the immune tolerant state in transplant recipients. New therapeutic strategies targeting B cells should be explored to promote tolerance induction with less impact on the host's protective immunity in organ transplanted patients.

  13. Estimation of functional failure probability of passive systems based on subset simulation method

    International Nuclear Information System (INIS)

    Wang Dongqing; Wang Baosheng; Zhang Jianmin; Jiang Jing

    2012-01-01

    In order to solve the problem of multi-dimensional epistemic uncertainties and small functional failure probability of passive systems, an innovative reliability analysis algorithm called subset simulation based on Markov chain Monte Carlo was presented. The method is found on the idea that a small failure probability can be expressed as a product of larger conditional failure probabilities by introducing a proper choice of intermediate failure events. Markov chain Monte Carlo simulation was implemented to efficiently generate conditional samples for estimating the conditional failure probabilities. Taking the AP1000 passive residual heat removal system, for example, the uncertainties related to the model of a passive system and the numerical values of its input parameters were considered in this paper. And then the probability of functional failure was estimated with subset simulation method. The numerical results demonstrate that subset simulation method has the high computing efficiency and excellent computing accuracy compared with traditional probability analysis methods. (authors)

  14. Reconfiguration of NKT Cell Subset Compartment Is Associated with Plaque Development in Patients with Carotid Artery Stenosis.

    Science.gov (United States)

    Cai, Lun; Yu, Lei; Liu, Sa; Li, Tongxun; Zhang, Xiaoping; Cui, Wei; Du, Jie; Zhang, Qinyi

    2017-02-01

    Accumulating evidence shows that immune cells play an important role in carotid atherosclerotic plaque development. In this study, we assessed the association of 6 different natural killer T (NKT) cell subsets, based on CD57 and CD8 expression, with risk for development of carotid atherosclerotic plaque (CAP). Molecular expression by peripheral NKT cells was evaluated in 13 patients with high-risk CAP and control without carotid stenosis (n = 18). High-risk CAP patients, compared with healthy subjects, had less percentage of CD57+CD8- NKT cell subsets (8.64 ± 10.15 versus 19.62 ± 10.8 %; P = 0.01) and CD57+CD8int NKT cell subsets (4.32 ± 3.04 versus 11.87 ± 8.56 %; P = 0.002), with a corresponding increase in the CD57-CD8high NKT cell subsets (33.22 ± 11.87 versus 18.66 ± 13.68 %; P = 0.007). Intracellular cytokine staining showed that CD8+ NKT cell subset was the main cytokine-producing NKT cell. Cytokine production in plasma was measured with Bio-Plex assay. The expression levels of pro-inflammatory mediators (IFN-γ, IL-17, IP-10) were significantly higher in CAP patients as compared to that from controls. These data provide evidence that NKT cell subset compartment reconfiguration in patients with carotid stenosis seems to be associated with the occurrence of carotid atherosclerotic plaque and suggest that both pathogenic and protective NKT cell subsets exist.

  15. Direct random insertion mutagenesis of Helicobacter pylori

    NARCIS (Netherlands)

    de Jonge, Ramon; Bakker, Dennis; van Vliet, Arnoud H. M.; Kuipers, Ernst J.; Vandenbroucke-Grauls, Christina M. J. E.; Kusters, Johannes G.

    2003-01-01

    Random insertion mutagenesis is a widely used technique for the identification of bacterial virulence genes. Most strategies for random mutagenesis involve cloning in Escherichia coli for passage of plasmids or for phenotypic selection. This can result in biased selection due to restriction or

  16. Direct random insertion mutagenesis of Helicobacter pylori.

    NARCIS (Netherlands)

    Jonge, de R.; Bakker, D.; Vliet, van AH; Kuipers, E.J.; Vandenbroucke-Grauls, C.M.J.E.; Kusters, J.G.

    2003-01-01

    Random insertion mutagenesis is a widely used technique for the identification of bacterial virulence genes. Most strategies for random mutagenesis involve cloning in Escherichia coli for passage of plasmids or for phenotypic selection. This can result in biased selection due to restriction or

  17. Analysis of Peripheral B Cell Subsets in Patients With Allergic Rhinitis.

    Science.gov (United States)

    Luo, Jing; Guo, Huanhuan; Liu, Zhuofu; Peng, Tao; Hu, Xianting; Han, Miaomiao; Yang, Xiangping; Zhou, Xuhong; Li, Huabin

    2018-05-01

    Recent evidence suggests that B cells can both promote and inhibit the development and progression of allergic disease. However, the characteristics of B cell subsets in patients with allergic rhinitis (AR) have not been well documented. This study aimed to analyze the characteristics of B cell subsets in the peripheral blood of AR patients. Forty-seven AR patients and 54 healthy controls were enrolled in this study, and the B cell subsets in peripheral blood of all subjects were analyzed by flow cytometry. Moreover, the serum total immunoglobulin E (IgE) and IgE concentrations secreted into the cultured peripheral blood mononuclear cells (PBMCs) were measured by using enzyme-linked immunosorbent assay. We found the peripheral blood of AR patients contained higher percentages of memory B cells, plasma cells, and CD19⁺CD24(hi)CD27⁺ regulatory B cells (Bregs) than those of age-matched healthy controls (PB cells and CD19⁺CD24(hi)CD38(hi) Bregs were significantly lower in AR patients than in healthy individuals (PB cells or plasma cells and decreases in CD19⁺CD24(hi)CD38(hi) Breg cells in the peripheral blood. Copyright © 2018 The Korean Academy of Asthma, Allergy and Clinical Immunology · The Korean Academy of Pediatric Allergy and Respiratory Disease.

  18. The total graph of a module with respect to multiplicative-prime subsets

    Directory of Open Access Journals (Sweden)

    H. Heydarinejad Astaneh

    2014-09-01

    Full Text Available Let M be a module over a commutative ring R and U a nonempty proper subset of M. In this paper, a generalization of the total graph T(Γ(M, denoted by T(Γ_U (M is presented, where U is a multiplicative prime subset of M. It is the graph with all elements of M as vertices, and for two distinct elements m, n ∈ M, the vertices m and n are adjacent if and only if m + n ∈ U. The main purpose of this paper is to extend the definitions and properties given in [1] and [10] to a more general case.

  19. Identification of petroleum hydrocarbons using a reduced number of PAHs selected by Procrustes rotation.

    Science.gov (United States)

    Fernández-Varela, R; Andrade, J M; Muniategui, S; Prada, D; Ramírez-Villalobos, F

    2010-04-01

    Identifying petroleum-related products released into the environment is a complex and difficult task. To achieve this, polycyclic aromatic hydrocarbons (PAHs) are of outstanding importance nowadays. Despite traditional quantitative fingerprinting uses straightforward univariate statistical analyses to differentiate among oils and to assess their sources, a multivariate strategy based on Procrustes rotation (PR) was applied in this paper. The aim of PR is to select a reduced subset of PAHs still capable of performing a satisfactory identification of petroleum-related hydrocarbons. PR selected two subsets of three (C(2)-naphthalene, C(2)-dibenzothiophene and C(2)-phenanthrene) and five (C(1)-decahidronaphthalene, naphthalene, C(2)-phenanthrene, C(3)-phenanthrene and C(2)-fluoranthene) PAHs for each of the two datasets studied here. The classification abilities of each subset of PAHs were tested using principal components analysis, hierarchical cluster analysis and Kohonen neural networks and it was demonstrated that they unraveled the same patterns as the overall set of PAHs. (c) 2009 Elsevier Ltd. All rights reserved.

  20. Identifying developmental toxicity pathways for a subset of ToxCast chemicals using human embryonic stem cells and metabolomics

    Energy Technology Data Exchange (ETDEWEB)

    Kleinstreuer, N.C., E-mail: kleinstreuer.nicole@epa.gov [NCCT, US EPA, RTP, NC 27711 (United States); Smith, A.M.; West, P.R.; Conard, K.R.; Fontaine, B.R. [Stemina Biomarker Discovery, Inc., Madison, WI 53719 (United States); Weir-Hauptman, A.M. [Covance, Inc., Madison, WI 53704 (United States); Palmer, J.A. [Stemina Biomarker Discovery, Inc., Madison, WI 53719 (United States); Knudsen, T.B.; Dix, D.J. [NCCT, US EPA, RTP, NC 27711 (United States); Donley, E.L.R. [Stemina Biomarker Discovery, Inc., Madison, WI 53719 (United States); Cezar, G.G. [Stemina Biomarker Discovery, Inc., Madison, WI 53719 (United States); University of Wisconsin-Madison, Madison, WI 53706 (United States)

    2011-11-15

    Metabolomics analysis was performed on the supernatant of human embryonic stem (hES) cell cultures exposed to a blinded subset of 11 chemicals selected from the chemical library of EPA's ToxCast Trade-Mark-Sign chemical screening and prioritization research project. Metabolites from hES cultures were evaluated for known and novel signatures that may be indicative of developmental toxicity. Significant fold changes in endogenous metabolites were detected for 83 putatively annotated mass features in response to the subset of ToxCast chemicals. The annotations were mapped to specific human metabolic pathways. This revealed strong effects on pathways for nicotinate and nicotinamide metabolism, pantothenate and CoA biosynthesis, glutathione metabolism, and arginine and proline metabolism pathways. Predictivity for adverse outcomes in mammalian prenatal developmental toxicity studies used ToxRefDB and other sources of information, including Stemina Biomarker Discovery's predictive DevTox Registered-Sign model trained on 23 pharmaceutical agents of known developmental toxicity and differing potency. The model initially predicted developmental toxicity from the blinded ToxCast compounds in concordance with animal data with 73% accuracy. Retraining the model with data from the unblinded test compounds at one concentration level increased the predictive accuracy for the remaining concentrations to 83%. These preliminary results on a 11-chemical subset of the ToxCast chemical library indicate that metabolomics analysis of the hES secretome provides information valuable for predictive modeling and mechanistic understanding of mammalian developmental toxicity. -- Highlights: Black-Right-Pointing-Pointer We tested 11 environmental compounds in a hESC metabolomics platform. Black-Right-Pointing-Pointer Significant changes in secreted small molecule metabolites were observed. Black-Right-Pointing-Pointer Perturbed mass features map to pathways critical for normal

  1. Identifying developmental toxicity pathways for a subset of ToxCast chemicals using human embryonic stem cells and metabolomics

    International Nuclear Information System (INIS)

    Kleinstreuer, N.C.; Smith, A.M.; West, P.R.; Conard, K.R.; Fontaine, B.R.; Weir-Hauptman, A.M.; Palmer, J.A.; Knudsen, T.B.; Dix, D.J.; Donley, E.L.R.; Cezar, G.G.

    2011-01-01

    Metabolomics analysis was performed on the supernatant of human embryonic stem (hES) cell cultures exposed to a blinded subset of 11 chemicals selected from the chemical library of EPA's ToxCast™ chemical screening and prioritization research project. Metabolites from hES cultures were evaluated for known and novel signatures that may be indicative of developmental toxicity. Significant fold changes in endogenous metabolites were detected for 83 putatively annotated mass features in response to the subset of ToxCast chemicals. The annotations were mapped to specific human metabolic pathways. This revealed strong effects on pathways for nicotinate and nicotinamide metabolism, pantothenate and CoA biosynthesis, glutathione metabolism, and arginine and proline metabolism pathways. Predictivity for adverse outcomes in mammalian prenatal developmental toxicity studies used ToxRefDB and other sources of information, including Stemina Biomarker Discovery's predictive DevTox® model trained on 23 pharmaceutical agents of known developmental toxicity and differing potency. The model initially predicted developmental toxicity from the blinded ToxCast compounds in concordance with animal data with 73% accuracy. Retraining the model with data from the unblinded test compounds at one concentration level increased the predictive accuracy for the remaining concentrations to 83%. These preliminary results on a 11-chemical subset of the ToxCast chemical library indicate that metabolomics analysis of the hES secretome provides information valuable for predictive modeling and mechanistic understanding of mammalian developmental toxicity. -- Highlights: ► We tested 11 environmental compounds in a hESC metabolomics platform. ► Significant changes in secreted small molecule metabolites were observed. ► Perturbed mass features map to pathways critical for normal development and pregnancy. ► Arginine, proline, nicotinate, nicotinamide and glutathione pathways were affected.

  2. Enhancing the Performance of LibSVM Classifier by Kernel F-Score Feature Selection

    Science.gov (United States)

    Sarojini, Balakrishnan; Ramaraj, Narayanasamy; Nickolas, Savarimuthu

    Medical Data mining is the search for relationships and patterns within the medical datasets that could provide useful knowledge for effective clinical decisions. The inclusion of irrelevant, redundant and noisy features in the process model results in poor predictive accuracy. Much research work in data mining has gone into improving the predictive accuracy of the classifiers by applying the techniques of feature selection. Feature selection in medical data mining is appreciable as the diagnosis of the disease could be done in this patient-care activity with minimum number of significant features. The objective of this work is to show that selecting the more significant features would improve the performance of the classifier. We empirically evaluate the classification effectiveness of LibSVM classifier on the reduced feature subset of diabetes dataset. The evaluations suggest that the feature subset selected improves the predictive accuracy of the classifier and reduce false negatives and false positives.

  3. Selective oropharyngeal decontamination versus selective digestive decontamination in critically ill patients: a meta-analysis of randomized controlled trials

    Directory of Open Access Journals (Sweden)

    Zhao D

    2015-07-01

    Full Text Available Di Zhao,1,* Jian Song,2,* Xuan Gao,3 Fei Gao,4 Yupeng Wu,2 Yingying Lu,5 Kai Hou1 1Department of Neurosurgery, The First Hospital of Hebei Medical University, 2Department of Neurosurgery, 3Department of Neurology, The Second Hospital of Hebei Medical University, 4Hebei Provincial Procurement Centers for Medical Drugs and Devices, 5Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang People’s Republic of China *These authors contributed equally to this work Background: Selective digestive decontamination (SDD and selective oropharyngeal decontamination (SOD are associated with reduced mortality and infection rates among patients in intensive care units (ICUs; however, whether SOD has a superior effect than SDD remains uncertain. Hence, we conducted a meta-analysis of randomized controlled trials (RCTs to compare SOD with SDD in terms of clinical outcomes and antimicrobial resistance rates in patients who were critically ill. Methods: RCTs published in PubMed, Embase, and Web of Science were systematically reviewed to compare the effects of SOD and SDD in patients who were critically ill. Outcomes included day-28 mortality, length of ICU stay, length of hospital stay, duration of mechanical ventilation, ICU-acquired bacteremia, and prevalence of antibiotic-resistant Gram-negative bacteria. Results were expressed as risk ratio (RR with 95% confidence intervals (CIs, and weighted mean differences (WMDs with 95% CIs. Pooled estimates were performed using a fixed-effects model or random-effects model, depending on the heterogeneity among studies. Results: A total of four RCTs involving 23,822 patients met the inclusion criteria and were included in this meta-analysis. Among patients whose admitting specialty was surgery, cardiothoracic surgery (57.3% and neurosurgery (29.7% were the two main types of surgery being performed. Pooled results showed that SOD had similar effects as SDD in day-28 mortality (RR =1

  4. Phenotypic and functional characterization of earthworm coelomocyte subsets

    DEFF Research Database (Denmark)

    Engelmann, Péter; Hayashi, Yuya; Bodo, Kornélia

    2016-01-01

    Flow cytometry is a common approach to study invertebrate immune cells including earthworm coelomocytes. However, the link between light-scatter- and microscopy-based phenotyping remains obscured. Here we show, by means of light scatter-based cell sorting, both subpopulations (amoebocytes...... amoebocytes and eleocytes, with the former being in favor of bacterial engulfment. This study has proved successful in linking flow cytometry and microscopy analysis and provides further experimental evidence of phenotypic and functional heterogeneity in earthworm coelomocyte subsets....

  5. Evolutionary Feature Selection for Big Data Classification: A MapReduce Approach

    Directory of Open Access Journals (Sweden)

    Daniel Peralta

    2015-01-01

    Full Text Available Nowadays, many disciplines have to deal with big datasets that additionally involve a high number of features. Feature selection methods aim at eliminating noisy, redundant, or irrelevant features that may deteriorate the classification performance. However, traditional methods lack enough scalability to cope with datasets of millions of instances and extract successful results in a delimited time. This paper presents a feature selection algorithm based on evolutionary computation that uses the MapReduce paradigm to obtain subsets of features from big datasets. The algorithm decomposes the original dataset in blocks of instances to learn from them in the map phase; then, the reduce phase merges the obtained partial results into a final vector of feature weights, which allows a flexible application of the feature selection procedure using a threshold to determine the selected subset of features. The feature selection method is evaluated by using three well-known classifiers (SVM, Logistic Regression, and Naive Bayes implemented within the Spark framework to address big data problems. In the experiments, datasets up to 67 millions of instances and up to 2000 attributes have been managed, showing that this is a suitable framework to perform evolutionary feature selection, improving both the classification accuracy and its runtime when dealing with big data problems.

  6. Monocyte Subsets in Schistosomiasis Patients with Periportal Fibrosis

    Directory of Open Access Journals (Sweden)

    Jamille Souza Fernandes

    2014-01-01

    Full Text Available A major issue with Schistosoma mansoni infection is the development of periportal fibrosis, which is predominantly caused by the host immune response to egg antigens. Experimental studies have pointed to the participation of monocytes in the pathogenesis of liver fibrosis. The aim of this study was to characterize the subsets of monocytes in individuals with different degrees of periportal fibrosis secondary to schistosomiasis. Monocytes were classified into classical (CD14++CD16−, intermediate (CD14++CD16+, and nonclassical (CD14+CD16++. The expressions of monocyte markers and cytokines were assessed using flow cytometry. The frequency of classical monocytes was higher than the other subsets. The expression of HLA-DR, IL-6, TNF-α, and TGF-β was higher in monocytes from individuals with moderate to severe fibrosis as compared to other groups. Although no differences were observed in receptors expression (IL-4R and IL-10R between groups of patients, the expression of IL-12 was lower in monocytes from individuals with moderate to severe fibrosis, suggesting a protective role of this cytokine in the development of fibrosis. Our data support the hypothesis that the three different monocyte populations participate in the immunopathogenesis of periportal fibrosis, since they express high levels of proinflammatory and profibrotic cytokines and low levels of regulatory markers.

  7. Lymphocyte subsets are influenced by positivity levels in healthy subjects before and after mild acute stress.

    Science.gov (United States)

    Caprara, Gian Vittorio; Nisini, Roberto; Castellani, Valeria; Vittorio, Pasquali; Alessandri, Guido; Vincenzo, Ziparo; Claudia, Ferlito; Valentina, Germano; Andrea, Picchianti Diamanti; Biondo, Michela Ileen; Milanetti, Francesca; Salerno, Gerardo; Vincenzo, Visco; Mario, Pietrosanti; Aniballi, Eros; Simonetta, Salemi; Angela, Santoni; D'Amelio, Raffaele

    2017-08-01

    In the current study, the possible association of positivity (POS), recently defined as general disposition to view life under positive outlook, with immune markers and post-stress modifications, was analyzed. Circulating lymphocyte subsets and serum cytokine levels were evaluated before and after a standard mild acute stress test, in 41 healthy students, previously selected by a questionnaire for their level of POS (high [POS-H] and low [POS-L]). The CD3 + and CD4 + cell frequency was higher in the POS-H students before and after acute stress. CD4 + subpopulation analysis revealed baseline higher terminally differentiated frequency in the POS-H, whereas higher effector memory frequency was present in the POS-L students. Moreover, the frequency of post-stress B cells was higher in the POS-H students. The mild-stress test was associated to an increase of the IL-10 mean values, while mean values of the other cytokines tested did not change significantly. It is tempting to speculate that IL-10 may work as biomarker of response to acute mild stress and that POS-H may be associated to a better capacity of the immune system to contrast the disturbing effects of mild acute stress. Yet further studies on lymphocyte subset absolute number and function of larger and different populations are needed to definitively prove these preliminary observations. Copyright © 2017 European Federation of Immunological Societies. Published by Elsevier B.V. All rights reserved.

  8. NKp46 identifies an NKT cell subset susceptible to leukemic transformation in mouse and human

    Science.gov (United States)

    Yu, Jianhua; Mitsui, Takeki; Wei, Min; Mao, Hsiaoyin; Butchar, Jonathan P.; Shah, Mithun Vinod; Zhang, Jianying; Mishra, Anjali; Alvarez-Breckenridge, Christopher; Liu, Xingluo; Liu, Shujun; Yokohama, Akihiko; Trotta, Rossana; Marcucci, Guido; Benson, Don M.; Loughran, Thomas P.; Tridandapani, Susheela; Caligiuri, Michael A.

    2011-01-01

    IL-15 may have a role in the development of T cell large granular lymphocyte (T-LGL) or NKT leukemias. However, the mechanisms of action and the identity of the cell subset that undergoes leukemic transformation remain elusive. Here we show that in both mice and humans, NKp46 expression marks a minute population of WT NKT cells with higher activity and potency to become leukemic. Virtually 100% of T-LGL leukemias in IL-15 transgenic mice expressed NKp46, as did a majority of human T-LGL leukemias. The minute NKp46+ NKT population, but not the NKp46– NKT population, was selectively expanded by overexpression of endogenous IL-15. Importantly, IL-15 transgenic NKp46– NKT cells did not become NKp46+ in vivo, suggesting that NKp46+ T-LGL leukemia cells were the malignant counterpart of the minute WT NKp46+ NKT population. Mechanistically, NKp46+ NKT cells possessed higher responsiveness to IL-15 in vitro and in vivo compared with that of their NKp46– NKT counterparts. Furthermore, interruption of IL-15 signaling using a neutralizing antibody could prevent LGL leukemia in IL-15 transgenic mice. Collectively, our data demonstrate that NKp46 identifies a functionally distinct NKT subset in mice and humans that appears to be directly susceptible to leukemic transformation when IL-15 is overexpressed. Thus, IL-15 signaling and NKp46 may be useful targets in the treatment of patients with T-LGL or NKT leukemia. PMID:21364281

  9. 47 CFR 1.1604 - Post-selection hearings.

    Science.gov (United States)

    2010-10-01

    ... 47 Telecommunication 1 2010-10-01 2010-10-01 false Post-selection hearings. 1.1604 Section 1.1604 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL PRACTICE AND PROCEDURE Random Selection Procedures for Mass Media Services General Procedures § 1.1604 Post-selection hearings. (a) Following the random...

  10. Cellular evidence for selfish spermatogonial selection in aged human testes.

    Science.gov (United States)

    Maher, G J; Goriely, A; Wilkie, A O M

    2014-05-01

    Owing to a recent trend for delayed paternity, the genomic integrity of spermatozoa of older men has become a focus of increased interest. Older fathers are at higher risk for their children to be born with several monogenic conditions collectively termed paternal age effect (PAE) disorders, which include achondroplasia, Apert syndrome and Costello syndrome. These disorders are caused by specific mutations originating almost exclusively from the male germline, in genes encoding components of the tyrosine kinase receptor/RAS/MAPK signalling pathway. These particular mutations, occurring randomly during mitotic divisions of spermatogonial stem cells (SSCs), are predicted to confer a selective/growth advantage on the mutant SSC. This selective advantage leads to a clonal expansion of the mutant cells over time, which generates mutant spermatozoa at levels significantly above the background mutation rate. This phenomenon, termed selfish spermatogonial selection, is likely to occur in all men. In rare cases, probably because of additional mutational events, selfish spermatogonial selection may lead to spermatocytic seminoma. The studies that initially predicted the clonal nature of selfish spermatogonial selection were based on DNA analysis, rather than the visualization of mutant clones in intact testes. In a recent study that aimed to identify these clones directly, we stained serial sections of fixed testes for expression of melanoma antigen family A4 (MAGEA4), a marker of spermatogonia. A subset of seminiferous tubules with an appearance and distribution compatible with the predicted mutant clones were identified. In these tubules, termed 'immunopositive tubules', there is an increased density of spermatogonia positive for markers related to selfish selection (FGFR3) and SSC self-renewal (phosphorylated AKT). Here we detail the properties of the immunopositive tubules and how they relate to the predicted mutant clones, as well as discussing the utility of

  11. Random genetic drift, natural selection, and noise in human cranial evolution.

    Science.gov (United States)

    Roseman, Charles C

    2016-08-01

    This study assesses the extent to which relationships among groups complicate comparative studies of adaptation in recent human cranial variation and the extent to which departures from neutral additive models of evolution hinder the reconstruction of population relationships among groups using cranial morphology. Using a maximum likelihood evolutionary model fitting approach and a mixed population genomic and cranial data set, I evaluate the relative fits of several widely used models of human cranial evolution. Moreover, I compare the goodness of fit of models of cranial evolution constrained by genomic variation to test hypotheses about population specific departures from neutrality. Models from population genomics are much better fits to cranial variation than are traditional models from comparative human biology. There is not enough evolutionary information in the cranium to reconstruct much of recent human evolution but the influence of population history on cranial variation is strong enough to cause comparative studies of adaptation serious difficulties. Deviations from a model of random genetic drift along a tree-like population history show the importance of environmental effects, gene flow, and/or natural selection on human cranial variation. Moreover, there is a strong signal of the effect of natural selection or an environmental factor on a group of humans from Siberia. The evolution of the human cranium is complex and no one evolutionary process has prevailed at the expense of all others. A holistic unification of phenome, genome, and environmental context, gives us a strong point of purchase on these problems, which is unavailable to any one traditional approach alone. Am J Phys Anthropol 160:582-592, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  12. Distinguishing Different Strategies of Across-Dimension Attentional Selection

    Science.gov (United States)

    Huang, Liqiang; Pashler, Harold

    2012-01-01

    Selective attention in multidimensional displays has usually been examined using search tasks requiring the detection of a single target. We examined the ability to perceive a spatial structure in multi-item subsets of a display that were defined either conjunctively or disjunctively. Observers saw two adjacent displays and indicated whether the…

  13. Learning a constrained conditional random field for enhanced segmentation of fallen trees in ALS point clouds

    Science.gov (United States)

    Polewski, Przemyslaw; Yao, Wei; Heurich, Marco; Krzystek, Peter; Stilla, Uwe

    2018-06-01

    In this study, we present a method for improving the quality of automatic single fallen tree stem segmentation in ALS data by applying a specialized constrained conditional random field (CRF). The entire processing pipeline is composed of two steps. First, short stem segments of equal length are detected and a subset of them is selected for further processing, while in the second step the chosen segments are merged to form entire trees. The first step is accomplished using the specialized CRF defined on the space of segment labelings, capable of finding segment candidates which are easier to merge subsequently. To achieve this, the CRF considers not only the features of every candidate individually, but incorporates pairwise spatial interactions between adjacent segments into the model. In particular, pairwise interactions include a collinearity/angular deviation probability which is learned from training data as well as the ratio of spatial overlap, whereas unary potentials encode a learned probabilistic model of the laser point distribution around each segment. Each of these components enters the CRF energy with its own balance factor. To process previously unseen data, we first calculate the subset of segments for merging on a grid of balance factors by minimizing the CRF energy. Then, we perform the merging and rank the balance configurations according to the quality of their resulting merged trees, obtained from a learned tree appearance model. The final result is derived from the top-ranked configuration. We tested our approach on 5 plots from the Bavarian Forest National Park using reference data acquired in a field inventory. Compared to our previous segment selection method without pairwise interactions, an increase in detection correctness and completeness of up to 7 and 9 percentage points, respectively, was observed.

  14. Cytokine profile and lymphocyte subsets in type 2 diabetes

    Directory of Open Access Journals (Sweden)

    C.O. Francisco

    2016-01-01

    Full Text Available Type 2 diabetes mellitus (T2D is a metabolic disease with inflammation as an important pathogenic background. However, the pattern of immune cell subsets and the cytokine profile associated with development of T2D are unclear. The objective of this study was to evaluate different components of the immune system in T2D patients' peripheral blood by quantifying the frequency of lymphocyte subsets and intracellular pro- and anti-inflammatory cytokine production by T cells. Clinical data and blood samples were collected from 22 men (51.6±6.3 years old with T2D and 20 nonsmoking men (49.4±7.6 years old who were matched for age and sex as control subjects. Glycated hemoglobin, high-sensitivity C-reactive protein concentrations, and the lipid profile were measured by a commercially available automated system. Frequencies of lymphocyte subsets in peripheral blood and intracellular production of interleukin (IL-4, IL-10, IL-17, tumor necrosis factor-α, and interferon-γ cytokines by CD3+ T cells were assessed by flow cytometry. No differences were observed in the frequency of CD19+ B cells, CD3+CD8+ and CD3+CD4+ T cells, CD16+56+ NK cells, and CD4+CD25+Foxp3+ T regulatory cells in patients with T2D compared with controls. The numbers of IL-10- and IL-17-producing CD3+ T cells were significantly higher in patients with T2D than in controls (P<0.05. The frequency of interferon-γ-producing CD3+ T cells was positively correlated with body mass index (r=0.59; P=0.01. In conclusion, this study shows increased numbers of circulating IL-10- and IL-17-producing CD3+ T cells in patients with T2D, suggesting that these cytokines are involved in the immune pathology of this disease.

  15. Optimizing Training Population Data and Validation of Genomic Selection for Economic Traits in Soft Winter Wheat

    Directory of Open Access Journals (Sweden)

    Amber Hoffstetter

    2016-09-01

    Full Text Available Genomic selection (GS is a breeding tool that estimates breeding values (GEBVs of individuals based solely on marker data by using a model built using phenotypic and marker data from a training population (TP. The effectiveness of GS increases as the correlation of GEBVs and phenotypes (accuracy increases. Using phenotypic and genotypic data from a TP of 470 soft winter wheat lines, we assessed the accuracy of GS for grain yield, Fusarium Head Blight (FHB resistance, softness equivalence (SE, and flour yield (FY. Four TP data sampling schemes were tested: (1 use all TP data, (2 use subsets of TP lines with low genotype-by-environment interaction, (3 use subsets of markers significantly associated with quantitative trait loci (QTL, and (4 a combination of 2 and 3. We also correlated the phenotypes of relatives of the TP to their GEBVs calculated from TP data. The GS accuracy within the TP using all TP data ranged from 0.35 (FHB to 0.62 (FY. On average, the accuracy of GS from using subsets of data increased by 54% relative to using all TP data. Using subsets of markers selected for significant association with the target trait had the greatest impact on GS accuracy. Between-environment prediction accuracy was also increased by using data subsets. The accuracy of GS when predicting the phenotypes of TP relatives ranged from 0.00 to 0.85. These results suggest that GS could be useful for these traits and GS accuracy can be greatly improved by using subsets of TP data.

  16. Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

    Science.gov (United States)

    Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...

  17. Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology.

    Science.gov (United States)

    Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H

    2017-07-01

    Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in

  18. Effect of edaravone on T lymphocyte subsets and oxidative stress level in patients with cardiogenic cerebral embolism

    Directory of Open Access Journals (Sweden)

    Li Guo

    2016-07-01

    Full Text Available Objective: To explore the effect of edaravone on T lymphocyte subsets and oxidative stress level in patients with cardiogenic cerebral embolism. Methods: A total of 100 patients with cardiogenic cerebral embolism who were admitted in our hospital from June, 2013 to June, 2015 were included in the study and randomized into the observation group and the control group. The patients in the observation group were given edaravone, while the patients in the control group were given the conventional treatments. CD4, CD8, GSH-Px, and ROS levels, the occurrence of adverse reactions, and the clinical efficacy after treatment in the two groups were observed and compared. Results: The comparison of CD4 and CD8 levels before treatment between the two groups was not statistically significant (P>0.05. After treatment, CD4 level was significantly elevated, while CD8 level was significantly reduced when compared with before treatment (P0.05. After treatment, GSH-Px level was significantly elevated, while ROS level was significantly reduced when compared with before treatment (P<0.05. The improvement of GSH-Px and ROS levels after treatment in the observation group was significantly superior to that in the control group (P<0.05. The occurrence rate of adverse reactions in the observation group was significantly lower than that in the control group, while the treatment effective rate was significantly higher than that in the control group (P<0.05. Conclusions: Edaravone in the treatment of cardiogenic cerebral embolism can effectively correct the imbalance of T lymphocyte subsets, and reduce the oxidative stress level, with less adverse reactions and significant therapeutic effect.

  19. Accelerated time-of-flight (TOF) PET image reconstruction using TOF bin subsetization and TOF weighting matrix pre-computation

    International Nuclear Information System (INIS)

    Mehranian, Abolfazl; Kotasidis, Fotis; Zaidi, Habib

    2016-01-01

    Time-of-flight (TOF) positron emission tomography (PET) technology has recently regained popularity in clinical PET studies for improving image quality and lesion detectability. Using TOF information, the spatial location of annihilation events is confined to a number of image voxels along each line of response, thereby the cross-dependencies of image voxels are reduced, which in turns results in improved signal-to-noise ratio and convergence rate. In this work, we propose a novel approach to further improve the convergence of the expectation maximization (EM)-based TOF PET image reconstruction algorithm through subsetization of emission data over TOF bins as well as azimuthal bins. Given the prevalence of TOF PET, we elaborated the practical and efficient implementation of TOF PET image reconstruction through the pre-computation of TOF weighting coefficients while exploiting the same in-plane and axial symmetries used in pre-computation of geometric system matrix. In the proposed subsetization approach, TOF PET data were partitioned into a number of interleaved TOF subsets, with the aim of reducing the spatial coupling of TOF bins and therefore to improve the convergence of the standard maximum likelihood expectation maximization (MLEM) and ordered subsets EM (OSEM) algorithms. The comparison of on-the-fly and pre-computed TOF projections showed that the pre-computation of the TOF weighting coefficients can considerably reduce the computation time of TOF PET image reconstruction. The convergence rate and bias-variance performance of the proposed TOF subsetization scheme were evaluated using simulated, experimental phantom and clinical studies. Simulations demonstrated that as the number of TOF subsets is increased, the convergence rate of MLEM and OSEM algorithms is improved. It was also found that for the same computation time, the proposed subsetization gives rise to further convergence. The bias-variance analysis of the experimental NEMA phantom and a clinical

  20. Lymphocytic subsets and low-dose exposure

    International Nuclear Information System (INIS)

    Tuschl, H.; Kovac, R.; Eybl, E.

    1993-03-01

    The present investigations proved the differential radiosensitivity of lymphocytic subpopulations: From in vivo and in vitro irradiations it may be followed that the most sensitive subset are CD8 positive suppressor T cells. CD4/CD8 ratios are increased both in peripheral blood and after mitogen stimulation of lymphocytes of exposed persons. The decrease in B cells is pronounced only at higher radiation doses. Though the rate of DNA synthesis after mitogen stimulation was reduced in some exposed persons, that was no general phenomenon. Especially after tritium exposure, the observed lymphopenia correlated with an increased stimulation by PHA and an increased rate of DNA synthesis in some probands. Thus the present investigations indicate that - despite an inhibition of some immune parameters by radioexposure - the body is able to maintain its immunological homoeostasis. (authors)

  1. Correction of abnormal B-cell subset distribution by interleukin-6 receptor blockade in polymyalgia rheumatica.

    Science.gov (United States)

    Carvajal Alegria, Guillermo; Devauchelle-Pensec, Valérie; Renaudineau, Yves; Saraux, Alain; Pers, Jacques-Olivier; Cornec, Divi

    2017-08-01

    The aim was to study lymphocyte subsets and circulating cytokines at diagnosis of PMR and after tocilizumab monotherapy. Eighteen untreated patients with PMR were included in a prospective study and received 3-monthly tocilizumab infusions without glucocorticoids. Lymphocyte subset distribution was assessed by flow cytometry and serum cytokines were assayed by a 34-cytokine array and ELISA, at baseline and during follow-up. Baseline data were also compared with age- and sex-matched controls. At baseline, total lymphocytes, T-cell subsets and NK cell counts were similar in patients and controls, but patients had significantly lower B-cell counts attributable to lower transitional, naïve and post-switch memory B-cell subsets. Circulating B-cell counts were positively correlated with the PMR activity score (PMR-AS) in untreated active patients at baseline, but subsequently increased to normal values while disease activity was controlled after tocilizumab therapy. Among serum cytokines, IL-6 showed the largest concentration difference between patients and controls, and the serum IL-6 concentration was correlated with baseline PMR-AS. The effects of tocilizumab on serum IL-6 concentration were heterogeneous, and the patients whose serum IL-6 decreased after tocilizumab therapy exhibited a significant increase in circulating B-cell counts. In patients with PMR, B-cell lymphopenia and abnormal B-cell subset distribution are associated with disease activity and IL-6 concentration, and both are corrected by the IL-6 antagonist tocilizumab. © The Author 2017. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  2. Optimized hyperspectral band selection using hybrid genetic algorithm and gravitational search algorithm

    Science.gov (United States)

    Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

    2015-12-01

    The serious information redundancy in hyperspectral images (HIs) cannot contribute to the data analysis accuracy, instead it require expensive computational resources. Consequently, to identify the most useful and valuable information from the HIs, thereby improve the accuracy of data analysis, this paper proposed a novel hyperspectral band selection method using the hybrid genetic algorithm and gravitational search algorithm (GA-GSA). In the proposed method, the GA-GSA is mapped to the binary space at first. Then, the accuracy of the support vector machine (SVM) classifier and the number of selected spectral bands are utilized to measure the discriminative capability of the band subset. Finally, the band subset with the smallest number of spectral bands as well as covers the most useful and valuable information is obtained. To verify the effectiveness of the proposed method, studies conducted on an AVIRIS image against two recently proposed state-of-the-art GSA variants are presented. The experimental results revealed the superiority of the proposed method and indicated that the method can indeed considerably reduce data storage costs and efficiently identify the band subset with stable and high classification precision.

  3. NKT-cell subsets: promoters and protectors in inflammatory liver disease.

    Science.gov (United States)

    Kumar, Vipin

    2013-09-01

    Natural killer T cells (NKT) are innate-like cells which are abundant in liver sinusoids and express the cell surface receptors of NK cells (e.g., NK1.1 (mouse) or CD161+/CD56+(human)) as well as an antigen receptor (TCR) characteristic of conventional T cells. NKT cells recognize lipid antigens in the context of CD1d, a non-polymorphic MHC class I-like molecule. Activation of NKT cells has a profound influence on the immune response against tumors and infectious organisms and in autoimmune diseases. NKT cells can be categorized into at least two distinct subsets: iNKT or type I use a semi-invariant TCR, whereas type II NKT TCRs are more diverse. Recent evidence suggests that NKT-cell subsets can play opposing roles early in non-microbial liver inflammation in that type I NKT are proinflammatory whereas type II NKT cells inhibit type I NKT-mediated liver injury. Copyright © 2013 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

  4. Three distinct developmental pathways for adaptive and two IFN-γ-producing γδ T subsets in adult thymus

    DEFF Research Database (Denmark)

    Buus, Terkild Brink; Ødum, Niels; Geisler, Carsten

    2017-01-01

    -producing γδ T cells (γδNKT). Developmental progression towards both IFN-γ-producing subsets can be induced by TCR signalling, and each pathway results in thymic emigration at a different stage. Finally, we show that γδT1 cells are the predominating IFN-γ-producing subset developing in the adult thymus. Thus......, this study maps out three distinct development pathways that result in the programming of γδTn, γδT1 and γδNKT cells.......Murine γδ T cells include subsets that are programmed for distinct effector functions during their development in the thymus. Under pathological conditions, different γδ T cell subsets can be protective or can exacerbate a disease. Here we show that CD117, CD200 and CD371, together with other...

  5. FCNN-MR: A Parallel Instance Selection Method Based on Fast Condensed Nearest Neighbor Rule

    OpenAIRE

    Lu Si; Jie Yu; Shasha Li; Jun Ma; Lei Luo; Qingbo Wu; Yongqi Ma; Zhengji Liu

    2017-01-01

    Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rul...

  6. Varied effects of thoracic irradiation on peripheral lymphocyte subsets in lung cancer patients

    International Nuclear Information System (INIS)

    Nakayama, Yasuhiro; Makino, Shigeki; Fukuda, Yasuki; Min, Kyong-Yob; Ikemoto, Toshiyuki; Shimizu, Akira; Ohsawa, Nakaaki

    1995-01-01

    To investigate the influence of thoracic irradiation on immunological competence in patients with lung cancer, we examined the changes in peripheral blood lymphocyte subsets in 15 patients before and after radiation therapy by two-color flow cytometry techniques. After radiation therapy, the percentage and the absolute number of CD4+CD45RA+ cells (naive T cells) and CD56+and/orCD16+ cells (NK cells) decreased. The percentage of CD4+ human leukocyte antigen-DR(HLA-DR)+ cells (activated CD4T cells) and CD8+HLA-DR+ cells (activated CD8T cells) increased, although the absolute number did not change significantly. Naive T cells may be more selectively damaged than memory T cells by thoracic irradiation, through their recirculation behavior. The reduction of natural killer (NK) cells is disadvantageous for anti-tumor immunity. The percentage of HLA-DR positive T lymphocytes was significantly increased, and thus the possibility of HLA-DR enhancement by irradiation cannot be excluded. Therefore, thoracic irradiation has numerous varied effects on the immunological system of lung cancer patients. (author)

  7. POLDER/Parasol L2 Radiation Budget subset along CloudSat track V001 (PARASOLRB_CPR) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This is the POLDER/Parasol Level-2 Radiation Budget Subset, collocated with the CloudSat track. The subset is processed at the A-Train Data Depot of the GES DISC,...

  8. Blind Measurement Selection: A Random Matrix Theory Approach

    KAUST Repository

    Elkhalil, Khalil; Kammoun, Abla; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

    2016-01-01

    -aware fashions. We present two potential applications where the proposed algorithms can be used, namely antenna selection for uplink transmissions in large scale multi-user systems and sensor selection for wireless sensor networks. Numerical results are also

  9. Feature selection using genetic algorithms for fetal heart rate analysis

    International Nuclear Information System (INIS)

    Xu, Liang; Redman, Christopher W G; Georgieva, Antoniya; Payne, Stephen J

    2014-01-01

    The fetal heart rate (FHR) is monitored on a paper strip (cardiotocogram) during labour to assess fetal health. If necessary, clinicians can intervene and assist with a prompt delivery of the baby. Data-driven computerized FHR analysis could help clinicians in the decision-making process. However, selecting the best computerized FHR features that relate to labour outcome is a pressing research problem. The objective of this study is to apply genetic algorithms (GA) as a feature selection method to select the best feature subset from 64 FHR features and to integrate these best features to recognize unfavourable FHR patterns. The GA was trained on 404 cases and tested on 106 cases (both balanced datasets) using three classifiers, respectively. Regularization methods and backward selection were used to optimize the GA. Reasonable classification performance is shown on the testing set for the best feature subset (Cohen's kappa values of 0.45 to 0.49 using different classifiers). This is, to our knowledge, the first time that a feature selection method for FHR analysis has been developed on a database of this size. This study indicates that different FHR features, when integrated, can show good performance in predicting labour outcome. It also gives the importance of each feature, which will be a valuable reference point for further studies. (paper)

  10. Effects of choice architecture and chef-enhanced meals on the selection and consumption of healthier school foods: a randomized clinical trial.

    Science.gov (United States)

    Cohen, Juliana F W; Richardson, Scott A; Cluggish, Sarah A; Parker, Ellen; Catalano, Paul J; Rimm, Eric B

    2015-05-01

    Little is known about the long-term effect of a chef-enhanced menu on healthier food selection and consumption in school lunchrooms. In addition, it remains unclear if extended exposure to other strategies to promote healthier foods (eg, choice architecture) also improves food selection or consumption. To evaluate the short- and long-term effects of chef-enhanced meals and extended exposure to choice architecture on healthier school food selection and consumption. A school-based randomized clinical trial was conducted during the 2011-2012 school year among 14 elementary and middle schools in 2 urban, low-income school districts (intent-to-treat analysis). Included in the study were 2638 students in grades 3 through 8 attending participating schools (38.4% of eligible participants). Schools were first randomized to receive a professional chef to improve school meal palatability (chef schools) or to a delayed intervention (control group). To assess the effect of choice architecture (smart café), all schools after 3 months were then randomized to the smart café intervention or to the control group. School food selection was recorded, and consumption was measured using plate waste methods. After 3 months, vegetable selection increased in chef vs control schools (odds ratio [OR], 1.75; 95% CI, 1.36-2.24), but there was no effect on the selection of other components or on meal consumption. After long-term or extended exposure to the chef or smart café intervention, fruit selection increased in the chef (OR, 3.08; 95% CI, 2.23-4.25), smart café (OR, 1.45; 95% CI, 1.13-1.87), and chef plus smart café (OR, 3.10; 95% CI, 2.26-4.25) schools compared with the control schools, and consumption increased in the chef schools (OR, 0.17; 95% CI, 0.03-0.30 cups/d). Vegetable selection increased in the chef (OR, 2.54; 95% CI, 1.83-3.54), smart café (OR, 1.91; 95% CI, 1.46-2.50), and chef plus smart café schools (OR, 7.38, 95% CI, 5.26-10.35) compared with the control schools

  11. Modified random hinge transport mechanics and multiple scattering step-size selection in EGS5

    International Nuclear Information System (INIS)

    Wilderman, S.J.; Bielajew, A.F.

    2005-01-01

    The new transport mechanics in EGS5 allows for significantly longer electron transport step sizes and hence shorter computation times than required for identical problems in EGS4. But as with all Monte Carlo electron transport algorithms, certain classes of problems exhibit step-size dependencies even when operating within recommended ranges, sometimes making selection of step-sizes a daunting task for novice users. Further contributing to this problem, because of the decoupling of multiple scattering and continuous energy loss in the dual random hinge transport mechanics of EGS5, there are two independent step sizes in EGS5, one for multiple scattering and one for continuous energy loss, each of which influences speed and accuracy in a different manner. Further, whereas EGS4 used a single value of fractional energy loss (ESTEPE) to determine step sizes at all energies, to increase performance by decreasing the amount of effort expended simulating lower energy particles, EGS5 permits the fractional energy loss values which are used to determine both the multiple scattering and continuous energy loss step sizes to vary with energy. This results in requiring the user to specify four fractional energy loss values when optimizing computations for speed. Thus, in order to simplify step-size selection and to mitigate step-size dependencies, a method has been devised to automatically optimize step-size selection based on a single material dependent input related to the size of problem tally region. In this paper we discuss the new transport mechanics in EGS5 and describe the automatic step-size optimization algorithm. (author)

  12. Transposed-Letter Priming Effects with Masked Subset Primes: A Re-Examination of the "Relative Position Priming Constraint"

    Science.gov (United States)

    Stinchcombe, Eric J.; Lupker, Stephen J.; Davis, Colin J.

    2012-01-01

    Three experiments are reported investigating the role of letter order in orthographic subset priming (e.g., "grdn"-GARDEN) using both the conventional masked priming technique as well as the sandwich priming technique in a lexical decision task. In all three experiments, subset primes produced priming with the effect being considerably…

  13. Generation of a novel regulatory NK cell subset from peripheral blood CD34+ progenitors promoted by membrane-bound IL-15.

    Directory of Open Access Journals (Sweden)

    Massimo Giuliani

    Full Text Available BACKGROUND: NK cells have been long time considered as cytotoxic lymphocytes competent in killing virus-infected cells and tumors. However, NK cells may also play essential immuno-regulatory functions. In this context, the real existence of a defined NK subset with negative regulatory properties has been hypothesized but never clearly demonstrated. METHODOLOGY/PRINCIPAL FINDINGS: Herein, we show the in vitro generation from human peripheral blood haematopoietic progenitors (PB-HP, of a novel subset of non-cytolytic NK cells displaying a mature phenotype and remarkable immuno-regulatory functions (NK-ireg. The main functional hallmark of these NK-ireg cells is represented by the surface expression/release of HLA-G, a major immunosuppressive molecule. In addition, NK-ireg cells secrete two powerful immuno-regulatory factors: IL-10 and IL-21. Through these factors, NK-ireg cells act as effectors of the down-regulation of the immune response: reconverting mature myeloid DC (mDC into immature/tolerogenic DC, blocking cytolytic functions on conventional NK cells and inducing HLA-G membrane expression on PB-derived monocytes. The generation of "NK-ireg" cells is obtained, by default, in culture conditions favouring cell-to-cell contacts, and it is strictly dependent on reciprocal trans-presentation of membrane-bound IL-15 forms constitutively and selectively expressed by human CD34(+ PB-HP. Finally, a small subset of NKp46(+ HLA-G(+ IL-10(+ is detected within freshly isolated decidual NK cells, suggesting that these cells could represent an in vivo counterpart of the NK-ireg cells. CONCLUSIONS/SIGNIFICANCE: In conclusion, NK-ireg cells represent a novel truly differentiated non-cytolytic NK subset with a self-sustainable phenotype (CD56(+ CD16(+ NKp30(+ NKp44(+ NKp46(+ CD94(+ CD69(+ CCR7(+ generated from specific pSTAT6(+ GATA3(+ precursors. NK-ireg cells could be employed to develop new immuno-suppressive strategies in autoimmune diseases, transplant

  14. Gene selection heuristic algorithm for nutrigenomics studies.

    Science.gov (United States)

    Valour, D; Hue, I; Grimard, B; Valour, B

    2013-07-15

    Large datasets from -omics studies need to be deeply investigated. The aim of this paper is to provide a new method (LEM method) for the search of transcriptome and metabolome connections. The heuristic algorithm here described extends the classical canonical correlation analysis (CCA) to a high number of variables (without regularization) and combines well-conditioning and fast-computing in "R." Reduced CCA models are summarized in PageRank matrices, the product of which gives a stochastic matrix that resumes the self-avoiding walk covered by the algorithm. Then, a homogeneous Markov process applied to this stochastic matrix converges the probabilities of interconnection between genes, providing a selection of disjointed subsets of genes. This is an alternative to regularized generalized CCA for the determination of blocks within the structure matrix. Each gene subset is thus linked to the whole metabolic or clinical dataset that represents the biological phenotype of interest. Moreover, this selection process reaches the aim of biologists who often need small sets of genes for further validation or extended phenotyping. The algorithm is shown to work efficiently on three published datasets, resulting in meaningfully broadened gene networks.

  15. Intraclonal Cell Expansion and Selection Driven by B Cell Receptor in Chronic Lymphocytic Leukemia

    Science.gov (United States)

    Colombo, Monica; Cutrona, Giovanna; Reverberi, Daniele; Fabris, Sonia; Neri, Antonino; Fabbi, Marina; Quintana, Giovanni; Quarta, Giovanni; Ghiotto, Fabio; Fais, Franco; Ferrarini, Manlio

    2011-01-01

    The mutational status of the immunoglobulin heavy-chain variable region (IGHV) genes utilized by chronic lymphocytic leukemia (CLL) clones defines two disease subgroups. Patients with unmutated IGHV have a more aggressive disease and a worse outcome than patients with cells having somatic IGHV gene mutations. Moreover, up to 30% of the unmutated CLL clones exhibit very similar or identical B cell receptors (BcR), often encoded by the same IG genes. These “stereotyped” BcRs have been classified into defined subsets. The presence of an IGHV gene somatic mutation and the utilization of a skewed gene repertoire compared with normal B cells together with the expression of stereotyped receptors by unmutated CLL clones may indicate stimulation/selection by antigenic epitopes. This antigenic stimulation may occur prior to or during neoplastic transformation, but it is unknown whether this stimulation/selection continues after leukemogenesis has ceased. In this study, we focused on seven CLL cases with stereotyped BcR Subset #8 found among a cohort of 700 patients; in six, the cells expressed IgG and utilized IGHV4-39 and IGKV1-39/IGKV1D-39 genes, as reported for Subset #8 BcR. One case exhibited special features, including expression of IgM or IgG by different subclones consequent to an isotype switch, allelic inclusion at the IGH locus in the IgM-expressing cells and a particular pattern of cytogenetic lesions. Collectively, the data indicate a process of antigenic stimulation/selection of the fully transformed CLL cells leading to the expansion of the Subset #8 IgG-bearing subclone. PMID:21541442

  16. SPECIFICITIES OF THE SUBSET PROFILE OF PERIPHERAL BLOOD IN PATIENTS WITH GLIOBLASTOMA: PATHOGENETIC AND CLINICAL ASSESSMENTS

    Directory of Open Access Journals (Sweden)

    V. A. Chumakov

    2006-01-01

    Full Text Available Abstract. In glioblastoma (GB, it is necessary to take into consideration GB-associated secondary immunodeficiency (SID, so-called syndrome of tumor-associated SID (STASID. Cell subsets having effector and regulatory functions, play an important role in developing STASID, and their proportions in patients with different forms of GB can be of pathogenetic importance and have clinical value for treatment and rehabilitation scheduling as well. The most pathogenically and clinically important features of cell subsets profile of peripheral blood were analyzed in patients with different clinical and morphological types of GB. The patients were divided into three groups, i.e., groups I and II were formed by patients with STASID (marked and slightly marked SID, accordingly; group III – patients with SIDTAS (tumor-associated autoimmune syndrome, associated with SID. Marked suppression of cell immunity is typical of group I - imbalance in T-lymphocytes, in a number of specific subsets, and in subsets clusters, as well as disproportions in the immunoregulatory indexes. In group II, the subset profiles of blood were slightly different from the norm. In patients with SIDTAS, activation of cell immunity was evident, forming SID with signs of autoimmune syndrome, affecting effector and regulatory chains of immunity, and influencing the severity and forecast of the disease. Specific features of the immune status in patients with GB identified can be resulted from different clinicalmorphological types of the tumor; the latter are to be considered in differential diagnostics of clinical course of GB and in scheduling of clinical-immunological efficient anti-tumor pharmacotherapy in pre- and postoperative periods.

  17. Transmit Antenna Selection for Multi-User Underlay Cognitive Transmission with Zero-Forcing Beamforming

    KAUST Repository

    Hanif, Muhammad; Yang, Hong-Chuan; Alouini, Mohamed-Slim

    2017-01-01

    We present a transmit antenna subset selection scheme for an underlay cognitive system serving multiple secondary receivers. The secondary system employs zero-forcing beamforming to nullify the interference to multiple primary users and eliminate

  18. T lymphocyte subsets in prostate cancer subjects in south eastern ...

    African Journals Online (AJOL)

    Humoral and cellular mechanisms play roles in immune response to foreign antigens. The present study was designed to determine the T lymphocyte subsets (CD4 + T cells, CD8 + T cells and CD4/CD8 ratio) in the prostate cancer subjects and control subjects. CD4 + T cells (`l/count) and CD8 + T cells (`l/count) were ...

  19. Improving the chances of successful protein structure determination with a random forest classifier

    Energy Technology Data Exchange (ETDEWEB)

    Jahandideh, Samad [Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92307 (United States); Joint Center for Structural Genomics, (United States); Jaroszewski, Lukasz; Godzik, Adam, E-mail: adam@burnham.org [Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92307 (United States); Joint Center for Structural Genomics, (United States); University of California, San Diego, La Jolla, California (United States)

    2014-03-01

    Using an extended set of protein features calculated separately for protein surface and interior, a new version of XtalPred based on a random forest classifier achieves a significant improvement in predicting the success of structure determination from the primary amino-acid sequence. Obtaining diffraction quality crystals remains one of the major bottlenecks in structural biology. The ability to predict the chances of crystallization from the amino-acid sequence of the protein can, at least partly, address this problem by allowing a crystallographer to select homologs that are more likely to succeed and/or to modify the sequence of the target to avoid features that are detrimental to successful crystallization. In 2007, the now widely used XtalPred algorithm [Slabinski et al. (2007 ▶), Protein Sci.16, 2472–2482] was developed. XtalPred classifies proteins into five ‘crystallization classes’ based on a simple statistical analysis of the physicochemical features of a protein. Here, towards the same goal, advanced machine-learning methods are applied and, in addition, the predictive potential of additional protein features such as predicted surface ruggedness, hydrophobicity, side-chain entropy of surface residues and amino-acid composition of the predicted protein surface are tested. The new XtalPred-RF (random forest) achieves significant improvement of the prediction of crystallization success over the original XtalPred. To illustrate this, XtalPred-RF was tested by revisiting target selection from 271 Pfam families targeted by the Joint Center for Structural Genomics (JCSG) in PSI-2, and it was estimated that the number of targets entered into the protein-production and crystallization pipeline could have been reduced by 30% without lowering the number of families for which the first structures were solved. The prediction improvement depends on the subset of targets used as a testing set and reaches 100% (i.e. twofold) for the top class of predicted

  20. Evolving artificial metalloenzymes via random mutagenesis

    Science.gov (United States)

    Yang, Hao; Swartz, Alan M.; Park, Hyun June; Srivastava, Poonam; Ellis-Guardiola, Ken; Upp, David M.; Lee, Gihoon; Belsare, Ketaki; Gu, Yifan; Zhang, Chen; Moellering, Raymond E.; Lewis, Jared C.

    2018-03-01

    Random mutagenesis has the potential to optimize the efficiency and selectivity of protein catalysts without requiring detailed knowledge of protein structure; however, introducing synthetic metal cofactors complicates the expression and screening of enzyme libraries, and activity arising from free cofactor must be eliminated. Here we report an efficient platform to create and screen libraries of artificial metalloenzymes (ArMs) via random mutagenesis, which we use to evolve highly selective dirhodium cyclopropanases. Error-prone PCR and combinatorial codon mutagenesis enabled multiplexed analysis of random mutations, including at sites distal to the putative ArM active site that are difficult to identify using targeted mutagenesis approaches. Variants that exhibited significantly improved selectivity for each of the cyclopropane product enantiomers were identified, and higher activity than previously reported ArM cyclopropanases obtained via targeted mutagenesis was also observed. This improved selectivity carried over to other dirhodium-catalysed transformations, including N-H, S-H and Si-H insertion, demonstrating that ArMs evolved for one reaction can serve as starting points to evolve catalysts for others.

  1. Testing over-representation of observations in subsets of a DEA technology

    DEFF Research Database (Denmark)

    Asmild, Mette; Hougaard, Jens Leth; Olesen, Ole Bent

    2013-01-01

    This paper proposes a test for whether data are over-represented in a given production zone, i.e. a subset of a production possibility set which has been estimated using the non-parametric Data Envelopment Analysis (DEA) approach. A binomial test is used that relates the number of observations...

  2. An efficient algorithm to compute subsets of points in ℤ n

    OpenAIRE

    Pacheco Martínez, Ana María; Real Jurado, Pedro

    2012-01-01

    In this paper we show a more efficient algorithm than that in [8] to compute subsets of points non-congruent by isometries. This algorithm can be used to reconstruct the object from the digital image. Both algorithms are compared, highlighting the improvements obtained in terms of CPU time.

  3. Randomized Block Cubic Newton Method

    KAUST Repository

    Doikov, Nikita; Richtarik, Peter

    2018-01-01

    We study the problem of minimizing the sum of three convex functions: a differentiable, twice-differentiable and a non-smooth term in a high dimensional setting. To this effect we propose and analyze a randomized block cubic Newton (RBCN) method, which in each iteration builds a model of the objective function formed as the sum of the natural models of its three components: a linear model with a quadratic regularizer for the differentiable term, a quadratic model with a cubic regularizer for the twice differentiable term, and perfect (proximal) model for the nonsmooth term. Our method in each iteration minimizes the model over a random subset of blocks of the search variable. RBCN is the first algorithm with these properties, generalizing several existing methods, matching the best known bounds in all special cases. We establish ${\\cal O}(1/\\epsilon)$, ${\\cal O}(1/\\sqrt{\\epsilon})$ and ${\\cal O}(\\log (1/\\epsilon))$ rates under different assumptions on the component functions. Lastly, we show numerically that our method outperforms the state-of-the-art on a variety of machine learning problems, including cubically regularized least-squares, logistic regression with constraints, and Poisson regression.

  4. Randomized Block Cubic Newton Method

    KAUST Repository

    Doikov, Nikita

    2018-02-12

    We study the problem of minimizing the sum of three convex functions: a differentiable, twice-differentiable and a non-smooth term in a high dimensional setting. To this effect we propose and analyze a randomized block cubic Newton (RBCN) method, which in each iteration builds a model of the objective function formed as the sum of the natural models of its three components: a linear model with a quadratic regularizer for the differentiable term, a quadratic model with a cubic regularizer for the twice differentiable term, and perfect (proximal) model for the nonsmooth term. Our method in each iteration minimizes the model over a random subset of blocks of the search variable. RBCN is the first algorithm with these properties, generalizing several existing methods, matching the best known bounds in all special cases. We establish ${\\\\cal O}(1/\\\\epsilon)$, ${\\\\cal O}(1/\\\\sqrt{\\\\epsilon})$ and ${\\\\cal O}(\\\\log (1/\\\\epsilon))$ rates under different assumptions on the component functions. Lastly, we show numerically that our method outperforms the state-of-the-art on a variety of machine learning problems, including cubically regularized least-squares, logistic regression with constraints, and Poisson regression.

  5. Identification of the Gene for Scleroderma in the Tsk/2 Mouse Strain: Implications for Human Scleroderma Pathogenesis and subset Distinctions

    Science.gov (United States)

    2014-07-01

    in) the) fibroproliferative) subset) and) Tsk2/+) mice) we) analyzed) the) expression)of)the)TGFβFregulated)gene) tumor )necrosis)factor)receptor... esophagus , and gastrointestinal tract (LeRoy et al., 1988). Survival rates vary significantly between and within subsets of disease, and are usually...patients in the fibroproliferative subset and Tsk2/+ mice we analyzed the expression of the TGFβ- regulated gene tumor necrosis factor receptor superfamily

  6. Gene selection for microarray data classification via subspace learning and manifold regularization.

    Science.gov (United States)

    Tang, Chang; Cao, Lijuan; Zheng, Xiao; Wang, Minhui

    2017-12-19

    With the rapid development of DNA microarray technology, large amount of genomic data has been generated. Classification of these microarray data is a challenge task since gene expression data are often with thousands of genes but a small number of samples. In this paper, an effective gene selection method is proposed to select the best subset of genes for microarray data with the irrelevant and redundant genes removed. Compared with original data, the selected gene subset can benefit the classification task. We formulate the gene selection task as a manifold regularized subspace learning problem. In detail, a projection matrix is used to project the original high dimensional microarray data into a lower dimensional subspace, with the constraint that the original genes can be well represented by the selected genes. Meanwhile, the local manifold structure of original data is preserved by a Laplacian graph regularization term on the low-dimensional data space. The projection matrix can serve as an importance indicator of different genes. An iterative update algorithm is developed for solving the problem. Experimental results on six publicly available microarray datasets and one clinical dataset demonstrate that the proposed method performs better when compared with other state-of-the-art methods in terms of microarray data classification. Graphical Abstract The graphical abstract of this work.

  7. CD45RC isoform expression identifies functionally distinct T cell subsets differentially distributed between healthy individuals and AAV patients.

    Directory of Open Access Journals (Sweden)

    Laurence Ordonez

    Full Text Available In animal models of anti-neutrophil cytoplasmic antibody (ANCA-associated vasculitis (AAV, the proportion of CD45RC T cell subsets is important for disease susceptibility. Their human counterparts are, however, functionally ill defined. In this report, we studied their distribution in healthy controls (HC, AAV patients and in Systemic lupus erythematous (SLE patients as disease controls. We showed that CD45RC expression level on human CD4 and CD8 T cells identifies subsets that are highly variable among individuals. Interestingly, AAV patients exhibit an increased proportion of CD45RC(low CD4 T cells as compared to HC and SLE patients. This increase is stable over time and independent of AAV subtype, ANCA specificity, disease duration, or number of relapses. We also analyzed the cytokine profile of purified CD4 and CD8 CD45RC T cell subsets from HC, after stimulation with anti-CD3 and anti-CD28 mAbs. The CD45RC subsets exhibit different cytokine profiles. Type-1 cytokines (IL-2, IFN-gamma and TNF-alpha were produced by all CD45RC T cell subsets, while the production of IL-17, type-2 (IL-4, IL-5 and regulatory (IL-10 cytokines was restricted to the CD45RC(low subset. In conclusion, we have shown that CD45RC expression divides human T cells in functionally distinct subsets that are imbalanced in AAV. Since this imbalance is stable over time and independent of several disease parameters, we hypothesize that this is a pre-existing immune abnormality involved in the etiology of AAV.

  8. Crossing the Vascular Wall: Common and Unique Mechanisms Exploited by Different Leukocyte Subsets during Extravasation

    Directory of Open Access Journals (Sweden)

    Michael Schnoor

    2015-01-01

    Full Text Available Leukocyte extravasation is one of the essential and first steps during the initiation of inflammation. Therefore, a better understanding of the key molecules that regulate this process may help to develop novel therapeutics for treatment of inflammation-based diseases such as atherosclerosis or rheumatoid arthritis. The endothelial adhesion molecules ICAM-1 and VCAM-1 are known as the central mediators of leukocyte adhesion to and transmigration across the endothelium. Engagement of these molecules by their leukocyte integrin receptors initiates the activation of several signaling pathways within both leukocytes and endothelium. Several of such events have been described to occur during transendothelial migration of all leukocyte subsets, whereas other mechanisms are known only for a single leukocyte subset. Here, we summarize current knowledge on regulatory mechanisms of leukocyte extravasation from a leukocyte and endothelial point of view, respectively. Specifically, we will focus on highlighting common and unique mechanisms that specific leukocyte subsets exploit to succeed in crossing endothelial monolayers.

  9. Endothelial progenitor cell subsets and preeclampsia: Findings and controversies

    Directory of Open Access Journals (Sweden)

    Armin Attar

    2017-10-01

    Full Text Available Vascular remodeling is an essential component of gestation. Endothelial progenitor cells (EPCs play an important role in the regulation of vascular homeostasis. The results of studies measuring the number of EPCs in normal pregnancies and in preeclampsia have been highly controversial or even contradictory because of some variations in technical issues and different methodologies enumerating three distinct subsets of EPCs: circulating angiogenic cells (CAC, colony forming unit endothelial cells (CFU-ECs, and endothelial colony-forming cells (ECFCs. In general, most studies have shown an increase in the number of CACs in the maternal circulation with a progression in the gestational age in normal pregnancies, while functional capacities measured by CFU-ECs and ECFCs remain intact. In the case of preeclampsia, mobilization of CACs and ECFCs occurs in the peripheral blood of pregnant women, but the functional capacities shown by culture of the derived colony-forming assays (CFU-EC and ECFC assays are altered. Furthermore, the number of all EPC subsets will be reduced in umbilical cord blood in the case of preeclampsia. As EPCs play an important role in the homeostasis of vascular networks, the difference in their frequency and functionality in normal pregnancies and those with preeclampsia can be expected. In this review, there was an attempt to provide a justification for these controversies.

  10. Characterization of Peripheral Immune Cell Subsets in Patients with Acute and Chronic Cerebrovascular Disease: A Case-Control Study

    Directory of Open Access Journals (Sweden)

    Peter Kraft

    2015-10-01

    Full Text Available Immune cells (IC play a crucial role in murine stroke pathophysiology. However, data are limited on the role of these cells in ischemic stroke in humans. We therefore aimed to characterize and compare peripheral IC subsets in patients with acute ischemic stroke/transient ischemic attack (AIS/TIA, chronic cerebrovascular disease (CCD and healthy volunteers (HV. We conducted a case-control study of patients with AIS/TIA (n = 116 or CCD (n = 117, and HV (n = 104 who were enrolled at the University Hospital Würzburg from 2010 to 2013. We determined the expression and quantity of IC subsets in the three study groups and performed correlation analyses with demographic and clinical parameters. The quantity of several IC subsets differed between the AIS/TIA, CCD, and HV groups. Several clinical and demographic variables independently predicted the quantity of IC subsets in patients with AIS/TIA. No significant changes in the quantity of IC subsets occurred within the first three days after AIS/TIA. Overall, these findings strengthen the evidence for a pathophysiologic role of IC in human ischemic stroke and the potential use of IC-based biomarkers for the prediction of stroke risk. A comprehensive description of IC kinetics is crucial to enable the design of targeted treatment strategies.

  11. Fast Branch & Bound algorithms for optimal feature selection

    Czech Academy of Sciences Publication Activity Database

    Somol, Petr; Pudil, Pavel; Kittler, J.

    2004-01-01

    Roč. 26, č. 7 (2004), s. 900-912 ISSN 0162-8828 R&D Projects: GA ČR GA402/02/1271; GA ČR GA402/03/1310; GA AV ČR KSK1019101 Institutional research plan: CEZ:AV0Z1075907 Keywords : subset search * feature selection * search tree Subject RIV: BD - Theory of Information Impact factor: 4.352, year: 2004

  12. Characterization of αβ and γδ T cell subsets expressing IL-17A in ruminants and swine.

    Science.gov (United States)

    Elnaggar, Mahmoud M; Abdellrazeq, Gaber S; Dassanayake, Rohana P; Fry, Lindsay M; Hulubei, Victoria; Davis, William C

    2018-08-01

    As part of our ongoing program to expand immunological reagents available for research in cattle, we developed a monoclonal antibody (mAb) to bovine interleukin-17A (IL-17A), a multifunctional cytokine centrally involved in regulating innate and adaptive immune responses. Initial comparative studies demonstrated the mAb recognizes a conserved epitope expressed on orthologues of IL-17A in sheep, goats and pigs. Comparative flow cytometric analyses of lymphocyte subsets stimulated with phorbol 12-myristate 13-acetate (PMA) and ionomycin revealed differences in expression of IL-17A by CD4, CD8, and γδ T cells across ruminants and swine species. Results in cattle showed the largest proportion of IL-17A + cells were CD4 + followed by γδ and CD8 + T cells. Further analysis revealed the IL-17A + γδ T cell subset was comprised of WC1.1 + , WC1.2 + , and WC1 - subsets. Analysis of the IL-17A + CD8 + T cell subset revealed it was comprised of αβ and γδ T cell subsets. Results in sheep and goats revealed IL-17A is expressed mainly by CD4 + and CD8 + T cells, with little expression by γδ T cells. Analysis of IL-17A + CD8 + T cells showed the majority were CD8 + αβ in sheep, whereas they were CD8 + γδ in goats. The majority of the sheep and goat IL-17A + γδ T cells were WC1 + . Results obtained in swine showed expression of IL-17A by CD4, CD8, and γδ T cell subsets were similar to results reported in other studies. Comparison of expression of IL-17A with IFN-γ revealed subsets co-expressed IL-17A and IFN-γ in cattle, sheep, and goats. The new mAb expands opportunities for immunology research in ruminants and swine. Copyright © 2018 Elsevier Ltd. All rights reserved.

  13. The role of dendritic cell subsets and innate immunity in the pathogenesis of type 1 diabetes and other autoimmune diseases

    Directory of Open Access Journals (Sweden)

    Jeffrey D. Price

    2015-06-01

    Full Text Available Dendritic cells (DCs are key antigen presenting cells that have an important role in autoimmune pathogenesis. DCs control both steady-state T cell tolerance and activation of pathogenic responses. The balance between these two outcomes depends on several factors, including genetic susceptibility, environmental signals that stimulate varied innate responses, and which DC subset is presenting antigen. Although the specific DC phenotype can diverge depending on the tissue location and context, there are 4 main subsets identified in both mouse and human: conventional cDC1 and cDC2, plasmacytoid DCs, and monocyte-derived DCs. In this review, we will discuss the role of these subsets in autoimmune pathogenesis and regulation, as well as the genetic and environmental signals that influence their function. Specific topics to be addressed include: impact of susceptibility loci on DC subsets, alterations in DC subset development, the role of infection- and host-derived innate inflammatory signals, and the role of the intestinal microbiota on DC phenotype. The effects of these various signals on disease progression and the relative effects of DC subset composition and maturation level of DCs will be examined. These areas will be explored using examples from several autoimmune diseases but will focus mainly on type 1 diabetes.

  14. Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms

    Directory of Open Access Journals (Sweden)

    Kuan-Cheng Lin

    2015-01-01

    Full Text Available Rapid advances in information and communication technology have made ubiquitous computing and the Internet of Things popular and practicable. These applications create enormous volumes of data, which are available for analysis and classification as an aid to decision-making. Among the classification methods used to deal with big data, feature selection has proven particularly effective. One common approach involves searching through a subset of the features that are the most relevant to the topic or represent the most accurate description of the dataset. Unfortunately, searching through this kind of subset is a combinatorial problem that can be very time consuming. Meaheuristic algorithms are commonly used to facilitate the selection of features. The artificial fish swarm algorithm (AFSA employs the intelligence underlying fish swarming behavior as a means to overcome optimization of combinatorial problems. AFSA has proven highly successful in a diversity of applications; however, there remain shortcomings, such as the likelihood of falling into a local optimum and a lack of multiplicity. This study proposes a modified AFSA (MAFSA to improve feature selection and parameter optimization for support vector machine classifiers. Experiment results demonstrate the superiority of MAFSA in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original FASA.

  15. Flow Cytometry Study of Lymphocyte Subsets in Malnourished and Well-Nourished Children with Bacterial Infections

    Science.gov (United States)

    Nájera, Oralia; González, Cristina; Toledo, Guadalupe; López, Laura; Ortiz, Rocío

    2004-01-01

    Protein-energy malnutrition is the primary cause of immune deficiency in children across the world. It has been related to changes in peripheral T-lymphocyte subsets. The aim of the present study was to evaluate the effects of infection and malnutrition on the proportion of peripheral-lymphocyte subsets in well-nourished non-bacterium-infected (WN), well-nourished bacterium-infected (WNI), and malnourished bacterium-infected (MNI) children by flow cytometry. A prospectively monitored cohort of 15 MNI, 12 WNI, and 17 WN children was studied. All the children were 3 years old or younger and had only bacterial infections. Results showed a significant decrease in the proportion of T CD3+ (P < 0.05 for relative and P < 0.03 for absolute values), CD4+ (P < 0.01 for relative and absolute values), and CD8+ (P < 0.05 for relative values) lymphocyte subsets in WNI children compared to the results seen with WN children. Additionally, B lymphocytes in MNI children showed significant lower values (CD20+ P < 0.02 for relative and P < 0.05 for absolute values) in relation to the results seen with WNI children. These results suggest that the decreased proportions of T-lymphocyte subsets observed in WNI children were associated with infection diseases and that the incapacity to increase the proportion of B lymphocyte was associated with malnutrition. This low proportion of B lymphocytes may be associated with the mechanisms involved in the immunodeficiency of malnourished children. PMID:15138185

  16. Study of T cell subsets in patients with chronic glomerulonephritis by immuno-labelling technique

    International Nuclear Information System (INIS)

    Dong Jixiang; Zhang Xueguang; Liu Zhida; Han Huiqin; Xie Wei

    1998-12-01

    As the developing of nuclear industry science, the possibility of nuclear radiation has increased rapidly. Treatments of diseases caused by radiation, especially acute radiation injury, rely heavily on bone marrow transplantation. The usage of immunology inhibitors is crucial to successfully carrying out bone marrow transplantation. So it is important to find out and research on immunology inhibitors. Using the changes of T cell subsets as a marker of immunology function before and after treatment of chronic glomerulonephritis, the authors observed the effect of Tripterygium wilfordii (TW)--an Chinese traditional drug which may probably become an important immunology inhibitor--on the treatment of chronic glomerulonephritis. Methods: immuno-labelling technique was used to measure the changes of T cell subsets in 77 CGN patients before and after treated with TW. Results: CD3 + and CD4 + cells in CGN patients were lower than those in healthy control (p + to CD8 + (CD4 + /CD8 + ) cells reduced significantly (p + , CD4 + cells and the ratio of CD4 + /CD8 + in most of the patients with CGN were further reduced. In patients with uremia, only CD3 + cell level was lower than the level before treatment, while the ratio of CD4 + to CD8 + (CD4 + /CD8 + ) did not change markedly. Conclusion: The imbalance of various T cell subsets and dysfunction of these T cells may play an important role in the pathogenesis of CGN. the increase in γδT cells may be related with the development of CGN. The pharmacological mechanism of TW in the treatment of CGN patients may involve regulation of balance of T cell subsets and inhibition of the T helper functions

  17. Maximizing Consensus in Portfolio Selection in Multicriteria Group Decision Making

    NARCIS (Netherlands)

    Michael, Emmerich T. M.; Deutz, A.H.; Li, L.; Asep, Maulana A.; Yevseyeva, I.

    2016-01-01

    This paper deals with a scenario of decision making where a moderator selects a (sub)set (aka portfolio) of decision alternatives from a larger set. The larger the number of decision makers who agree on a solution in the portfolio the more successful the moderator is. We assume that decision makers

  18. Experimentally-derived fibroblast gene signatures identify molecular pathways associated with distinct subsets of systemic sclerosis patients in three independent cohorts.

    Directory of Open Access Journals (Sweden)

    Michael E Johnson

    Full Text Available Genome-wide expression profiling in systemic sclerosis (SSc has identified four 'intrinsic' subsets of disease (fibroproliferative, inflammatory, limited, and normal-like, each of which shows deregulation of distinct signaling pathways; however, the full set of pathways contributing to this differential gene expression has not been fully elucidated. Here we examine experimentally derived gene expression signatures in dermal fibroblasts for thirteen different signaling pathways implicated in SSc pathogenesis. These data show distinct and overlapping sets of genes induced by each pathway, allowing for a better understanding of the molecular relationship between profibrotic and immune signaling networks. Pathway-specific gene signatures were analyzed across a compendium of microarray datasets consisting of skin biopsies from three independent cohorts representing 80 SSc patients, 4 morphea, and 26 controls. IFNα signaling showed a strong association with early disease, while TGFβ signaling spanned the fibroproliferative and inflammatory subsets, was associated with worse MRSS, and was higher in lesional than non-lesional skin. The fibroproliferative subset was most strongly associated with PDGF signaling, while the inflammatory subset demonstrated strong activation of innate immune pathways including TLR signaling upstream of NF-κB. The limited and normal-like subsets did not show associations with fibrotic and inflammatory mediators such as TGFβ and TNFα. The normal-like subset showed high expression of genes associated with lipid signaling, which was absent in the inflammatory and limited subsets. Together, these data suggest a model by which IFNα is involved in early disease pathology, and disease severity is associated with active TGFβ signaling.

  19. The type I interferon signature in leukocyte subsets from peripheral blood of patients with early arthritis: a major contribution by granulocytes.

    Science.gov (United States)

    de Jong, Tamarah D; Lübbers, Joyce; Turk, Samina; Vosslamber, Saskia; Mantel, Elise; Bontkes, Hetty J; van der Laken, Conny J; Bijlsma, Johannes W; van Schaardenburg, Dirkjan; Verweij, Cornelis L

    2016-07-13

    The type I interferon (IFN) signature in rheumatoid arthritis (RA) has shown clinical relevance in relation to disease onset and therapeutic response. Identification of the cell type(s) contributing to this IFN signature could provide insight into the signature's functional consequences. The aim of this study was to investigate the contribution of peripheral leukocyte subsets to the IFN signature in early arthritis. Blood was collected from 26 patients with early arthritis and lysed directly or separated into peripheral blood mononuclear cells (PBMCs) and polymorphonuclear granulocytes (PMNs). PBMCs were sorted into CD4(+) T cells, CD8(+) T cells, CD19(+) B cells, and CD14(+) monocytes by flow cytometry. Messenger RNA expression of three interferon response genes (IRGs RSAD2, IFI44L, and MX1) and type I interferon receptors (IFNAR1 and IFNAR2) was determined in whole blood and blood cell subsets by quantitative polymerase chain reaction. IRG expression was averaged to calculate an IFN score for each sample. Patients were designated "IFN(high)" (n = 8) or "IFN(low)" (n = 18) on the basis of an IFN score cutoff in whole peripheral blood from healthy control subjects. The difference in IFN score between IFN(high) and IFN(low) patients was remarkably large for the PMN fraction (mean 25-fold) compared with the other subsets (mean 6- to 9-fold), indicating that PMNs are the main inducers of IRGs. Moreover, the relative contribution of the PMN fraction to the whole-blood IFN score was threefold higher than expected from its abundance in blood (p = 0.008), whereas it was three- to sixfold lower for the other subsets (p ≤ 0.063), implying that the PMNs are most sensitive to IFN signaling. Concordantly, IFNAR1 and IFNAR2 were upregulated compared with healthy controls selectively in patient PMNs (p ≤ 0.0077) but not in PBMCs. PMNs are the main contributors to the whole-blood type I IFN signature in patients with early arthritis, which seems due to

  20. Feature selection for Bayesian network classifiers using the MDL-FS score

    NARCIS (Netherlands)

    Drugan, Madalina M.; Wiering, Marco A.

    When constructing a Bayesian network classifier from data, the more or less redundant features included in a dataset may bias the classifier and as a consequence may result in a relatively poor classification accuracy. In this paper, we study the problem of selecting appropriate subsets of features

  1. Object-based random forest classification of Landsat ETM+ and WorldView-2 satellite imagery for mapping lowland native grassland communities in Tasmania, Australia

    Science.gov (United States)

    Melville, Bethany; Lucieer, Arko; Aryal, Jagannath

    2018-04-01

    This paper presents a random forest classification approach for identifying and mapping three types of lowland native grassland communities found in the Tasmanian Midlands region. Due to the high conservation priority assigned to these communities, there has been an increasing need to identify appropriate datasets that can be used to derive accurate and frequently updateable maps of community extent. Therefore, this paper proposes a method employing repeat classification and statistical significance testing as a means of identifying the most appropriate dataset for mapping these communities. Two datasets were acquired and analysed; a Landsat ETM+ scene, and a WorldView-2 scene, both from 2010. Training and validation data were randomly subset using a k-fold (k = 50) approach from a pre-existing field dataset. Poa labillardierei, Themeda triandra and lowland native grassland complex communities were identified in addition to dry woodland and agriculture. For each subset of randomly allocated points, a random forest model was trained based on each dataset, and then used to classify the corresponding imagery. Validation was performed using the reciprocal points from the independent subset that had not been used to train the model. Final training and classification accuracies were reported as per class means for each satellite dataset. Analysis of Variance (ANOVA) was undertaken to determine whether classification accuracy differed between the two datasets, as well as between classifications. Results showed mean class accuracies between 54% and 87%. Class accuracy only differed significantly between datasets for the dry woodland and Themeda grassland classes, with the WorldView-2 dataset showing higher mean classification accuracies. The results of this study indicate that remote sensing is a viable method for the identification of lowland native grassland communities in the Tasmanian Midlands, and that repeat classification and statistical significant testing can be

  2. Maximum parsimony on subsets of taxa.

    Science.gov (United States)

    Fischer, Mareike; Thatte, Bhalchandra D

    2009-09-21

    In this paper we investigate mathematical questions concerning the reliability (reconstruction accuracy) of Fitch's maximum parsimony algorithm for reconstructing the ancestral state given a phylogenetic tree and a character. In particular, we consider the question whether the maximum parsimony method applied to a subset of taxa can reconstruct the ancestral state of the root more accurately than when applied to all taxa, and we give an example showing that this indeed is possible. A surprising feature of our example is that ignoring a taxon closer to the root improves the reliability of the method. On the other hand, in the case of the two-state symmetric substitution model, we answer affirmatively a conjecture of Li, Steel and Zhang which states that under a molecular clock the probability that the state at a single taxon is a correct guess of the ancestral state is a lower bound on the reconstruction accuracy of Fitch's method applied to all taxa.

  3. Predictive Feature Selection for Genetic Policy Search

    Science.gov (United States)

    2014-05-22

    limited manual intervention are becoming increasingly desirable as more complex tasks in dynamic and high- tempo environments are explored. Reinforcement...states in many domains causes features relevant to the reward variations to be overlooked, which hinders the policy search. 3.4 Parameter Selection PFS...the current feature subset. This local minimum may be “deceptive,” meaning that it does not clearly lead to the global optimal policy ( Goldberg and

  4. Automatic Target Recognition: Statistical Feature Selection of Non-Gaussian Distributed Target Classes

    Science.gov (United States)

    2011-06-01

    implementing, and evaluating many feature selection algorithms. Mucciardi and Gose compared seven different techniques for choosing subsets of pattern...122 THIS PAGE INTENTIONALLY LEFT BLANK 123 LIST OF REFERENCES [1] A. Mucciardi and E. Gose , “A comparison of seven techniques for

  5. Inferences about nested subsets structure when not all species are detected

    Science.gov (United States)

    Cam, E.; Nichols, J.D.; Hines, J.E.; Sauer, J.R.

    2000-01-01

    Comparisons of species composition among ecological communities of different size have often provided evidence that the species in communities with lower species richness form nested subsets of the species in larger communities. In the vast majority of studies, the question of nested subsets has been addressed using information on presence-absence, where a '0' is interpreted as the absence of a given species from a given location. Most of the methodological discussion in earlier studies investigating nestedness concerns the approach to generation of model-based matrices. However, it is most likely that in many situations investigators cannot detect all the species present in the location sampled. The possibility that zeros in incidence matrices reflect nondetection rather than absence of species has not been considered in studies addressing nested subsets, even though the position of zeros in these matrices forms the basis of earlier inference methods. These sampling artifacts are likely to lead to erroneous conclusions about both variation over space in species richness and the degree of similarity of the various locations. Here we propose an approach to investigation of nestedness, based on statistical inference methods explicitly incorporating species detection probability, that take into account the probabilistic nature of the sampling process. We use presence-absence data collected under Pollock?s robust capture-recapture design, and resort to an estimator of species richness originally developed for closed populations to assess the proportion of species shared by different locations. We develop testable predictions corresponding to the null hypothesis of a nonnested pattern, and an alternative hypothesis of perfect nestedness. We also present an index for assessing the degree of nestedness of a system of ecological communities. We illustrate our approach using avian data from the North American Breeding Bird Survey collected in Florida Keys.

  6. NKT cell subsets as key participants in liver physiology and pathology

    Science.gov (United States)

    Bandyopadhyay, Keya; Marrero, Idania; Kumar, Vipin

    2016-01-01

    Natural killer T (NKT) cells are innate-like lymphocytes that generally recognize lipid antigens and are enriched in microvascular compartments of the liver. NKT cells can be activated by self- or microbial-lipid antigens and by signaling through toll-like receptors. Following activation, NKT cells rapidly secrete pro-inflammatory or anti-inflammatory cytokines and chemokines, and thereby determine the milieu for subsequent immunity or tolerance. It is becoming clear that two different subsets of NKT cells—type I and type II—have different modes of antigen recognition and have opposing roles in inflammatory liver diseases. Here we focus mainly on the roles of both NKT cell subsets in the maintenance of immune tolerance and inflammatory diseases in liver. Furthermore, how the differential activation of type I and type II NKT cells influences other innate cells and adaptive immune cells to result in important consequences for tissue integrity is discussed. It is crucial that better reagents, including CD1d tetramers, be used in clinical studies to define the roles of NKT cells in liver diseases in patients. PMID:26972772

  7. NKT cell subsets as key participants in liver physiology and pathology.

    Science.gov (United States)

    Bandyopadhyay, Keya; Marrero, Idania; Kumar, Vipin

    2016-05-01

    Natural killer T (NKT) cells are innate-like lymphocytes that generally recognize lipid antigens and are enriched in microvascular compartments of the liver. NKT cells can be activated by self- or microbial-lipid antigens and by signaling through toll-like receptors. Following activation, NKT cells rapidly secrete pro-inflammatory or anti-inflammatory cytokines and chemokines, and thereby determine the milieu for subsequent immunity or tolerance. It is becoming clear that two different subsets of NKT cells-type I and type II-have different modes of antigen recognition and have opposing roles in inflammatory liver diseases. Here we focus mainly on the roles of both NKT cell subsets in the maintenance of immune tolerance and inflammatory diseases in liver. Furthermore, how the differential activation of type I and type II NKT cells influences other innate cells and adaptive immune cells to result in important consequences for tissue integrity is discussed. It is crucial that better reagents, including CD1d tetramers, be used in clinical studies to define the roles of NKT cells in liver diseases in patients.

  8. Restoration of STORM images from sparse subset of localizations (Conference Presentation)

    Science.gov (United States)

    Moiseev, Alexander A.; Gelikonov, Grigory V.; Gelikonov, Valentine M.

    2016-02-01

    To construct a Stochastic Optical Reconstruction Microscopy (STORM) image one should collect sufficient number of localized fluorophores to satisfy Nyquist criterion. This requirement limits time resolution of the method. In this work we propose a probabalistic approach to construct STORM images from a subset of localized fluorophores 3-4 times sparser than required from Nyquist criterion. Using a set of STORM images constructed from number of localizations sufficient for Nyquist criterion we derive a model which allows us to predict the probability for every location to be occupied by a fluorophore at the end of hypothetical acquisition, having as an input parameters distribution of already localized fluorophores in the proximity of this location. We show that probability map obtained from number of fluorophores 3-4 times less than required by Nyquist criterion may be used as superresolution image itself. Thus we are able to construct STORM image from a subset of localized fluorophores 3-4 times sparser than required from Nyquist criterion, proportionaly decreasing STORM data acquisition time. This method may be used complementary with other approaches desined for increasing STORM time resolution.

  9. Data on correlations between T cell subset frequencies and length of partial remission in type 1 diabetes

    Directory of Open Access Journals (Sweden)

    Aditi Narsale

    2016-09-01

    Full Text Available Partial remission in patients newly diagnosed with type 1 diabetes is a period of good glucose control that can last from several weeks to over a year. The clinical significance of the remission period is that patients might be more responsive to immunotherapy if treated within this period. This article provides clinical data that indicates the level of glucose control and insulin-secreting β-cell function of each patient in the study at baseline (within 3 months of diagnosis, and at 3, 6, 9, 12, 18 and 24 months post-baseline. The relative frequency of immune cell subsets in the PBMC of each patient and the association between the frequency of immune cell subsets measured and length of remission is also shown. These data support the findings reported in the accompanying publication, “A pilot study showing associations between frequency of CD4+ memory cell subsets at diagnosis and duration of partial remission in type 1 diabetes” (Moya et al., 2016 [1], where a full interpretation, including biological relevance of the study can be found. Keywords: Type 1 diabetes, T cell subsets, Partial remission

  10. A QSAR Study of Environmental Estrogens Based on a Novel Variable Selection Method

    Directory of Open Access Journals (Sweden)

    Aiqian Zhang

    2012-05-01

    Full Text Available A large number of descriptors were employed to characterize the molecular structure of 53 natural, synthetic, and environmental chemicals which are suspected of disrupting endocrine functions by mimicking or antagonizing natural hormones and may thus pose a serious threat to the health of humans and wildlife. In this work, a robust quantitative structure-activity relationship (QSAR model with a novel variable selection method has been proposed for the effective estrogens. The variable selection method is based on variable interaction (VSMVI with leave-multiple-out cross validation (LMOCV to select the best subset. During variable selection, model construction and assessment, the Organization for Economic Co-operation and Development (OECD principles for regulation of QSAR acceptability were fully considered, such as using an unambiguous multiple-linear regression (MLR algorithm to build the model, using several validation methods to assessment the performance of the model, giving the define of applicability domain and analyzing the outliers with the results of molecular docking. The performance of the QSAR model indicates that the VSMVI is an effective, feasible and practical tool for rapid screening of the best subset from large molecular descriptors.

  11. Lymphocyte subsets in human immunodeficiency virus-unexposed Brazilian individuals from birth to adulthood

    Directory of Open Access Journals (Sweden)

    Maria Isabel de Moraes-Pinto

    2014-12-01

    Full Text Available Ethnic origin, genetics, gender and environmental factors have been shown to influence some immunologic indices, so that development of reference values for populations of different backgrounds may be necessary. We have determined the distribution of lymphocyte subsets in healthy Brazilian individuals from birth to adulthood. Lymphocyte subsets were determined using four-colour cytometry in a cross-sectional study of 463 human immunodeficiency virus-unexposed children and adults from birth through 49 years of age. Lymphocyte subsets varied according to age, as previously observed in other studies. However, total CD4+ T cell numbers were lower than what was described in the Pediatric AIDS Clinical Trials Group P1009 (PACTG P1009, which assessed an American population of predominantly African and Hispanic backgrounds until the 12-18 year age range, when values were comparable. Naïve percentages and absolute values of CD8+ T cells, as assessed by CD45RA expression, were also lower than the PACTG P1009 data for all analysed age ranges. CD38 expression on both CD4+ and CD8+ T cells was lower than the PACTG P1009 values, with a widening gap between the two studies at older age ranges. Different patterns of cell differentiation seem to occur in different settings and may have characteristic expression within each population.

  12. NKT Cell Subsets Can Exert Opposing Effects in Autoimmunity, Tumor Surveillance and Inflammation

    Science.gov (United States)

    Viale, Rachael; Ware, Randle; Maricic, Igor; Chaturvedi, Varun; Kumar, Vipin

    2014-01-01

    The innate-like natural killer T (NKT) cells are essential regulators of immunity. These cells comprise at least two distinct subsets and recognize different lipid antigens presented by the MHC class I like molecules CD1d. The CD1d-dependent recognition pathway of NKT cells is highly conserved from mouse to humans. While most type I NKT cells can recognize αGalCer and express a semi-invariant T cell receptor (TCR), a major population of type II NKT cells reactive to sulfatide utilizes an oligoclonal TCR. Furthermore TCR recognition features of NKT subsets are also distinctive with almost parallel as opposed to perpendicular footprints on the CD1d molecules for the type I and type II NKT cells respectively. Here we present a view based upon the recent studies in different clinical and experimental settings that while type I NKT cells are more often pathogenic, they may also be regulatory. On the other hand, sulfatide-reactive type II NKT cells mostly play an inhibitory role in the control of autoimmune and inflammatory diseases. Since the activity and cytokine secretion profiles of NKT cell subsets can be modulated differently by lipid ligands or their analogs, novel immunotherapeutic strategies are being developed for their differential activation for potential intervention in inflammatory diseases. PMID:25288922

  13. Molecular marker differences relate to developmental position and subsets of mesodiencephalic dopaminergic neurons.

    Directory of Open Access Journals (Sweden)

    Simone M Smits

    Full Text Available The development of mesodiencephalic dopaminergic (mdDA neurons located in the substantia nigra compacta (SNc and ventral tegmental area (VTA follow a number of stages marked by distinct events. After preparation of the region by signals that provide induction and patterning, several transcription factors have been identified, which are involved in specifying the neuronal fate of these cells. The specific vulnerability of SNc neurons is thought to root in these specific developmental programs. The present study examines the positions of young postmitotic mdDA neurons to relate developmental position to mdDA subset specific markers. MdDA neurons were mapped relative to the neuromeric domains (prosomeres 1-3 (P1-3, midbrain, and hindbrain as well as the longitudinal subdivisions (floor plate, basal plate, alar plate, as proposed by the prosomeric model. We found that postmitotic mdDA neurons are located mainly in the floorplate domain and very few in slightly more lateral domains. Moreover, mdDA neurons are present along a large proportion of the anterior/posterior axis extending from the midbrain to P3 in the diencephalon. The specific positions relate to some extent to the presence of specific subset markers as Ahd2. In the adult stage more of such subsets specific expressed genes are present and may represent a molecular map defining molecularly distinct groups of mdDA neurons.

  14. Comparison of confirmed inactive and randomly selected compounds as negative training examples in support vector machine-based virtual screening.

    Science.gov (United States)

    Heikamp, Kathrin; Bajorath, Jürgen

    2013-07-22

    The choice of negative training data for machine learning is a little explored issue in chemoinformatics. In this study, the influence of alternative sets of negative training data and different background databases on support vector machine (SVM) modeling and virtual screening has been investigated. Target-directed SVM models have been derived on the basis of differently composed training sets containing confirmed inactive molecules or randomly selected database compounds as negative training instances. These models were then applied to search background databases consisting of biological screening data or randomly assembled compounds for available hits. Negative training data were found to systematically influence compound recall in virtual screening. In addition, different background databases had a strong influence on the search results. Our findings also indicated that typical benchmark settings lead to an overestimation of SVM-based virtual screening performance compared to search conditions that are more relevant for practical applications.

  15. Variable Selection in Time Series Forecasting Using Random Forests

    Directory of Open Access Journals (Sweden)

    Hristos Tyralis

    2017-10-01

    Full Text Available Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.

  16. Features Selection for Skin Micro-Image Symptomatic Recognition

    Institute of Scientific and Technical Information of China (English)

    HUYue-li; CAOJia-lin; ZHAOQian; FENGXu

    2004-01-01

    Automatic recognition of skin micro-image symptom is important in skin diagnosis and treatment. Feature selection is to improve the classification performance of skin micro-image symptom.This paper proposes a hybrid approach based on the support vector machine (SVM) technique and genetic algorithm (GA) to select an optimum feature subset from the feature group extracted from the skin micro-images. An adaptive GA is introduced for maintaining the convergence rate. With the proposed method, the average cross validation accuracy is increased from 88.25% using all features to 96.92% using only selected features provided by a classifier for classification of 5 classes of skin symptoms. The experimental results are satisfactory.

  17. Features Selection for Skin Micro-Image Symptomatic Recognition

    Institute of Scientific and Technical Information of China (English)

    HU Yue-li; CAO Jia-lin; ZHAO Qian; FENG Xu

    2004-01-01

    Automatic recognition of skin micro-image symptom is important in skin diagnosis and treatment. Feature selection is to improve the classification performance of skin micro-image symptom.This paper proposes a hybrid approach based on the support vector machine (SVM) technique and genetic algorithm (GA) to select an optimum feature subset from the feature group extracted from the skin micro-images. An adaptive GA is introduced for maintaining the convergence rate. With the proposed method, the average cross validation accuracy is increased from 88.25% using all features to 96.92 % using only selected features provided by a classifier for classification of 5 classes of skin symptoms. The experimental results are satisfactory.

  18. The Long-Term Effectiveness of a Selective, Personality-Targeted Prevention Program in Reducing Alcohol Use and Related Harms: A Cluster Randomized Controlled Trial

    Science.gov (United States)

    Newton, Nicola C.; Conrod, Patricia J.; Slade, Tim; Carragher, Natacha; Champion, Katrina E.; Barrett, Emma L.; Kelly, Erin V.; Nair, Natasha K.; Stapinski, Lexine; Teesson, Maree

    2016-01-01

    Background: This study investigated the long-term effectiveness of Preventure, a selective personality-targeted prevention program, in reducing the uptake of alcohol, harmful use of alcohol, and alcohol-related harms over a 3-year period. Methods: A cluster randomized controlled trial was conducted to assess the effectiveness of Preventure.…

  19. Peripheral blood and milk leukocytes subsets of lactating Sarda ewes

    Directory of Open Access Journals (Sweden)

    Piero Bonelli

    2013-05-01

    Full Text Available Leukocytes subpopulations in blood and milk of lactating Sarda ewes were investigated. Animals characterized by a SSC level <500×103cells/mL and a negative bacteriological examination were sampled in early, mid and late lactation. Milk differential cell count evidenced that macrophage represented the main population (42.8%±3.5 followed by lymphocytes (40.2%±3.4 and neutrophils (8,6%±2.1. Flow cytometry analysis showed that lymphocytes subsets in milk were quite different from blood. High CD8+ and low CD4+ lymphocytes percentages determined a CD4/CD8 ratio inversion in milk compared to blood (0.3%±0.03 vs 1.8%±0.08. CD8+ decreased while, conversely, CD4+ increased in late lactation. γδ T cells were more represented in milk (12.6%±1.3 than in blood (6.8%±0.3 and their proportions appeared similar throughout lactation in both compartments. IL-2 receptor was mainly expressed in milk on T cytotoxic lymphocytes. Data obtained in uninfected mammary glands could allow an early discrimination between physiological and pathological changes occurring in ewe milk. Further phenotypical and functional studies on milk leukocytes subsets might help to understand defense mechanisms of the ovine mammary gland against IMI.

  20. Differential Aspartate Usage Identifies a Subset of Cancer Cells Particularly Dependent on OGDH

    Directory of Open Access Journals (Sweden)

    Eric L. Allen

    2016-10-01

    Full Text Available Although aberrant metabolism in tumors has been well described, the identification of cancer subsets with particular metabolic vulnerabilities has remained challenging. Here, we conducted an siRNA screen focusing on enzymes involved in the tricarboxylic acid (TCA cycle and uncovered a striking range of cancer cell dependencies on OGDH, the E1 subunit of the alpha-ketoglutarate dehydrogenase complex. Using an integrative metabolomics approach, we identified differential aspartate utilization, via the malate-aspartate shuttle, as a predictor of whether OGDH is required for proliferation in 3D culture assays and for the growth of xenograft tumors. These findings highlight an anaplerotic role of aspartate and, more broadly, suggest that differential nutrient utilization patterns can identify subsets of cancers with distinct metabolic dependencies for potential pharmacological intervention.

  1. Generating equilateral random polygons in confinement III

    International Nuclear Information System (INIS)

    Diao, Y; Ernst, C; Montemayor, A; Ziegler, U

    2012-01-01

    In this paper we continue our earlier studies (Diao et al 2011 J. Phys. A: Math. Theor. 44 405202, Diao et al J. Phys. A: Math. Theor. 45 275203) on the generation methods of random equilateral polygons confined in a sphere. The first half of this paper is concerned with the generation of confined equilateral random walks. We show that if the selection of a vertex is uniform subject to the position of its previous vertex and the confining condition, then the distributions of the vertices are not uniform, although there exists a distribution such that if the initial vertex is selected following this distribution, then all vertices of the random walk follow this same distribution. Thus in order to generate a confined equilateral random walk, the selection of a vertex cannot be uniform subject to the position of its previous vertex and the confining condition. We provide a simple algorithm capable of generating confined equilateral random walks whose vertex distribution is almost uniform in the confinement sphere. In the second half of this paper we show that any process generating confined equilateral random walks can be turned into a process generating confined equilateral random polygons with the property that the vertex distribution of the polygons approaches the vertex distribution of the walks as the polygons get longer and longer. In our earlier studies, the starting point of the confined polygon is fixed at the center of the sphere. The new approach here allows us to move the starting point of the confined polygon off the center of the sphere. (paper)

  2. Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.

    Science.gov (United States)

    Liang, Ja-Der; Ping, Xiao-Ou; Tseng, Yi-Ju; Huang, Guan-Tarn; Lai, Feipei; Yang, Pei-Ming

    2014-12-01

    Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment. From January 2007 to December 2009, 83 newly diagnosed HCC patients receiving RFA as their first treatment were enrolled. Five feature selection methods including genetic algorithm (GA), simulated annealing (SA) algorithm, random forests (RF) and hybrid methods (GA+RF and SA+RF) were utilized for selecting an important subset of features from a total of 16 clinical features. These feature selection methods were combined with support vector machine (SVM) for developing predictive models with better performance. Five-fold cross-validation was used to train and test SVM models. The developed SVM-based predictive models with hybrid feature selection methods and 5-fold cross-validation had averages of the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the ROC curve as 67%, 86%, 82%, 69%, 90%, and 0.69, respectively. The SVM derived predictive model can provide suggestive high-risk recurrent patients, who should be closely followed up after complete RFA treatment. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  3. Diagnosing Warm Frontal Cloud Formation in a GCM: A Novel Approach Using Conditional Subsetting

    Science.gov (United States)

    Booth, James F.; Naud, Catherine M.; DelGenio, Anthony D.

    2013-01-01

    This study analyzes characteristics of clouds and vertical motion across extratropical cyclone warm fronts in the NASA Goddard Institute for Space Studies general circulation model. The validity of the modeled clouds is assessed using a combination of satellite observations from CloudSat, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis. The analysis focuses on developing cyclones, to test the model's ability to generate their initial structure. To begin, the extratropical cyclones and their warm fronts are objectively identified and cyclone-local fields are mapped into a vertical transect centered on the surface warm front. To further isolate specific physics, the cyclones are separated using conditional subsetting based on additional cyclone-local variables, and the differences between the subset means are analyzed. Conditional subsets are created based on 1) the transect clouds and 2) vertical motion; 3) the strength of the temperature gradient along the warm front, as well as the storm-local 4) wind speed and 5) precipitable water (PW). The analysis shows that the model does not generate enough frontal cloud, especially at low altitude. The subsetting results reveal that, compared to the observations, the model exhibits a decoupling between cloud formation at high and low altitudes across warm fronts and a weak sensitivity to moisture. These issues are caused in part by the parameterized convection and assumptions in the stratiform cloud scheme that are valid in the subtropics. On the other hand, the model generates proper covariability of low-altitude vertical motion and cloud at the warm front and a joint dependence of cloudiness on wind and PW.

  4. Comparison of the Functional microRNA Expression in Immune Cell Subsets of Neonates and Adults

    Science.gov (United States)

    Yu, Hong-Ren; Hsu, Te-Yao; Huang, Hsin-Chun; Kuo, Ho-Chang; Li, Sung-Chou; Yang, Kuender D.; Hsieh, Kai-Sheng

    2016-01-01

    Diversity of biological molecules in newborn and adult immune cells contributes to differences in cell function and atopic properties. Micro RNAs (miRNAs) are reported to involve in the regulation of immune system. Therefore, determining the miRNA expression profile of leukocyte subpopulations is important for understanding immune system regulation. In order to explore the unique miRNA profiling that contribute to altered immune in neonates, we comprehensively analyzed the functional miRNA signatures of eight leukocyte subsets (polymorphonuclear cells, monocytes, CD4+ T cells, CD8+ T cells, natural killer cells, B cells, plasmacytoid dendritic cells, and myeloid dendritic cells) from both neonatal and adult umbilical cord and peripheral blood samples, respectively. We observed distinct miRNA profiles between adult and neonatal blood leukocyte subsets, including unique miRNA signatures for each cell lineage. Leukocyte miRNA signatures were altered after stimulation. Adult peripheral leukocytes had higher let-7b-5p expression levels compared to neonatal cord leukocytes across multiple subsets, irrespective of stimulation. Transfecting neonatal monocytes with a let-7b-5p mimic resulted in a reduction of LPS-induced interleukin (IL)-6 and TNF-α production, while transfection of a let-7b-5p inhibitor into adult monocytes enhanced IL-6 and TNF-α production. With this functional approach, we provide intact differential miRNA expression profiling of specific immune cell subsets between neonates and adults. These studies serve as a basis to further understand the altered immune response observed in neonates and advance the development of therapeutic strategies. PMID:28066425

  5. Comparison of the functional microRNA expression in immune cell subsets of neonates and adults

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    Hong-Ren Yu

    2016-12-01

    Full Text Available Diversity of biological molecules in newborn and adult immune cells contributes to differences in cell function and atopic properties. Micro RNAs (miRNAs are reported involve in the regulation of immune system. Therefore, determining the miRNA expression profile of leukocyte sub-populations is important for understanding immune system regulation. In order to explore the unique microRNA profiling that contribute to altered immune in neonates, we comprehensively analyzed the functional miRNA signatures of eight leukocyte subsets (polymorphonuclear cells, monocytes, CD4+ T cells, CD8+ T cells, natural killer cells, B cells, plasmacytoid dendritic cells (pDCs, and myeloid dendritic cells (mDCs from both neonatal and adult umbilical cord and peripheral blood samples, respectively. We observed distinct miRNA profiles between adult and neonatal blood leukocyte subsets, including unique miRNA signatures for each cell lineage. Leukocyte miRNA signatures were altered after stimulation. Adult peripheral leukocytes had higher let-7b-5p expression levels compared to neonatal cord leukocytes across multiple subsets, irrespective of stimulation. Transfecting neonatal monocytes with a let-7b-5p mimic resulted in a reduction of LPS-induced IL-6 and TNF-alpha production, while transfection of a let-7b-5p inhibitor into adult monocytes enhanced IL-6 and TNF-alpha production. With this functional approach, we provide intact differential microRNA expression profiling of specific immune cell subsets between neonates and adults. These studies serve as a basis to further understand the altered immune response observed in neonates and advance the development of therapeutic strategies.

  6. Affinity selection of Nipah and Hendra virus-related vaccine candidates from a complex random peptide library displayed on bacteriophage virus-like particles

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    Peabody, David S.; Chackerian, Bryce; Ashley, Carlee; Carnes, Eric; Negrete, Oscar

    2017-01-24

    The invention relates to virus-like particles of bacteriophage MS2 (MS2 VLPs) displaying peptide epitopes or peptide mimics of epitopes of Nipah Virus envelope glycoprotein that elicit an immune response against Nipah Virus upon vaccination of humans or animals. Affinity selection on Nipah Virus-neutralizing monoclonal antibodies using random sequence peptide libraries on MS2 VLPs selected peptides with sequence similarity to peptide sequences found within the envelope glycoprotein of Nipah itself, thus identifying the epitopes the antibodies recognize. The selected peptide sequences themselves are not necessarily identical in all respects to a sequence within Nipah Virus glycoprotein, and therefore may be referred to as epitope mimics VLPs displaying these epitope mimics can serve as vaccine. On the other hand, display of the corresponding wild-type sequence derived from Nipah Virus and corresponding to the epitope mapped by affinity selection, may also be used as a vaccine.

  7. Gene expression changes reflect clinical response in a placebo-controlled randomized trial of abatacept in patients with diffuse cutaneous systemic sclerosis.

    Science.gov (United States)

    Chakravarty, Eliza F; Martyanov, Viktor; Fiorentino, David; Wood, Tammara A; Haddon, David James; Jarrell, Justin Ansel; Utz, Paul J; Genovese, Mark C; Whitfield, Michael L; Chung, Lorinda

    2015-06-13

    Systemic sclerosis is an autoimmune disease characterized by inflammation and fibrosis of the skin and internal organs. We sought to assess the clinical and molecular effects associated with response to intravenous abatacept in patients with diffuse cutaneous systemic. Adult diffuse cutaneous systemic sclerosis patients were randomized in a 2:1 double-blinded fashion to receive abatacept or placebo over 24 weeks. Primary outcomes were safety and the change in modified Rodnan Skin Score (mRSS) at week 24 compared with baseline. Improvers were defined as patients with a decrease in mRSS of ≥30% post-treatment compared to baseline. Skin biopsies were obtained for differential gene expression and pathway enrichment analyses and intrinsic gene expression subset assignment. Ten subjects were randomized to abatacept (n = 7) or placebo (n = 3). Disease duration from first non-Raynaud's symptom was significantly longer (8.8 ± 3.8 years vs. 2.4 ± 1.6 years, p = 0.004) and median mRSS was higher (30 vs. 22, p = 0.05) in the placebo compared to abatacept group. Adverse events were similar in the two groups. Five out of seven patients (71%) randomized to abatacept and one out of three patients (33%) randomized to placebo experienced ≥30% improvement in skin score. Subjects receiving abatacept showed a trend toward improvement in mRSS at week 24 (-8.6 ± 7.5, p = 0.0625) while those in the placebo group did not (-2.3 ± 15, p = 0.75). After adjusting for disease duration, mRSS significantly improved in the abatacept compared with the placebo group (abatacept vs. placebo mRSS decrease estimate -9.8, 95% confidence interval -16.7 to -3.0, p = 0.0114). In the abatacept group, the patients in the inflammatory intrinsic subset showed a trend toward greater improvement in skin score at 24 weeks compared with the patients in the normal-like intrinsic subset (-13.5 ± 3.1 vs. -4.5 ± 6.4, p = 0.067). Abatacept resulted in decreased CD28 co-stimulatory gene expression in improvers

  8. Impact of prebiotic supplementation on T-cell subsets and their related cytokines, anthropometric features and blood pressure in patients with type 2 diabetes mellitus: A randomized placebo-controlled Trial.

    Science.gov (United States)

    Dehghan, Parvin; Farhangi, Mahdieh Abbasalizad; Tavakoli, Farnaz; Aliasgarzadeh, Akbar; Akbari, Aliakbar Movasaghpour

    2016-02-01

    Type 2 diabetic mellitus (T2DM) asone of the main causes of morbidity and mortality is associated with immune system disturbances and metabolic abnormalities. In the current study we aimed to evaluate the effects of oligofructose-enriched inulin on T-cell subsets and their related cytokines, anthropometric and metabolic parameters in patients with T2DM. Forty-six diabetic females patients were randomly allocated into intervention (n=27) and control (n=22) groups. Subjects in the intervention group received a daily dose of 10g of oligofructose-enriched inulin and subjects in control group received a placebo for two months. Anthropometric variables, metabolic parameters including fasting serum glucose (FSG), hemoglobin A1c (HbA1c), lipid profile and blood pressure were measured at the beginning and after two months. Immune markers also included serum interleukin (IL)-4, IL-12 and interferon (IFN)-γ concentrations were assessed and CD3(+), CD4(+), CD8(+) and CD11b(+)T-cell counts were determined by flow cytometry at baseline and end of the trial. After two months intervention, significant improvements in anthropometric variables, blood pressure and serum lipids occurred in prebiotic-treated group (P<0.001). Serum IL-4, IL-12 and IFN-γ concentrationsalso significantly decreased in intervention group (P<0.001). No significant changes in CD3(+), CD4(+), CD8(+) and CD11b(+) T-cell counts were observed in treatment groups after intervention. The present study showed several beneficial effects of oligofructose-enriched inulin on the improvement of the glycemic status, lipid profile, and immune markers in patients with T2DM. Further studies are needed to confirming our findings and to better clarify the underlying mechanisms. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Thy1.2 YFP-16 transgenic mouse labels a subset of large-diameter sensory neurons that lack TRPV1 expression.

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    Thomas E Taylor-Clark

    Full Text Available The Thy1.2 YFP-16 mouse expresses yellow fluorescent protein (YFP in specific subsets of peripheral and central neurons. The original characterization of this model suggested that YFP was expressed in all sensory neurons, and this model has been subsequently used to study sensory nerve structure and function. Here, we have characterized the expression of YFP in the sensory ganglia (DRG, trigeminal and vagal of the Thy1.2 YFP-16 mouse, using biochemical, functional and anatomical analyses. Despite previous reports, we found that YFP was only expressed in approximately half of DRG and trigeminal neurons and less than 10% of vagal neurons. YFP-expression was only found in medium and large-diameter neurons that expressed neurofilament but not TRPV1. YFP-expressing neurons failed to respond to selective agonists for TRPV1, P2X(2/3 and TRPM8 channels in Ca2+ imaging assays. Confocal analysis of glabrous skin, hairy skin of the back and ear and skeletal muscle indicated that YFP was expressed in some peripheral terminals with structures consistent with their presumed non-nociceptive nature. In summary, the Thy1.2 YFP-16 mouse expresses robust YFP expression in only a subset of sensory neurons. But this mouse model is not suitable for the study of nociceptive nerves or the function of such nerves in pain and neuropathies.

  10. AVC: Selecting discriminative features on basis of AUC by maximizing variable complementarity.

    Science.gov (United States)

    Sun, Lei; Wang, Jun; Wei, Jinmao

    2017-03-14

    The Receiver Operator Characteristic (ROC) curve is well-known in evaluating classification performance in biomedical field. Owing to its superiority in dealing with imbalanced and cost-sensitive data, the ROC curve has been exploited as a popular metric to evaluate and find out disease-related genes (features). The existing ROC-based feature selection approaches are simple and effective in evaluating individual features. However, these approaches may fail to find real target feature subset due to their lack of effective means to reduce the redundancy between features, which is essential in machine learning. In this paper, we propose to assess feature complementarity by a trick of measuring the distances between the misclassified instances and their nearest misses on the dimensions of pairwise features. If a misclassified instance and its nearest miss on one feature dimension are far apart on another feature dimension, the two features are regarded as complementary to each other. Subsequently, we propose a novel filter feature selection approach on the basis of the ROC analysis. The new approach employs an efficient heuristic search strategy to select optimal features with highest complementarities. The experimental results on a broad range of microarray data sets validate that the classifiers built on the feature subset selected by our approach can get the minimal balanced error rate with a small amount of significant features. Compared with other ROC-based feature selection approaches, our new approach can select fewer features and effectively improve the classification performance.

  11. Fast selection of miRNA candidates based on large-scale pre-computed MFE sets of randomized sequences.

    Science.gov (United States)

    Warris, Sven; Boymans, Sander; Muiser, Iwe; Noback, Michiel; Krijnen, Wim; Nap, Jan-Peter

    2014-01-13

    Small RNAs are important regulators of genome function, yet their prediction in genomes is still a major computational challenge. Statistical analyses of pre-miRNA sequences indicated that their 2D structure tends to have a minimal free energy (MFE) significantly lower than MFE values of equivalently randomized sequences with the same nucleotide composition, in contrast to other classes of non-coding RNA. The computation of many MFEs is, however, too intensive to allow for genome-wide screenings. Using a local grid infrastructure, MFE distributions of random sequences were pre-calculated on a large scale. These distributions follow a normal distribution and can be used to determine the MFE distribution for any given sequence composition by interpolation. It allows on-the-fly calculation of the normal distribution for any candidate sequence composition. The speedup achieved makes genome-wide screening with this characteristic of a pre-miRNA sequence practical. Although this particular property alone will not be able to distinguish miRNAs from other sequences sufficiently discriminative, the MFE-based P-value should be added to the parameters of choice to be included in the selection of potential miRNA candidates for experimental verification.

  12. Tachykinins stimulate a subset of mouse taste cells.

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    Jeff Grant

    Full Text Available The tachykinins substance P (SP and neurokinin A (NKA are present in nociceptive sensory fibers expressing transient receptor potential cation channel, subfamily V, member 1 (TRPV1. These fibers are found extensively in and around the taste buds of several species. Tachykinins are released from nociceptive fibers by irritants such as capsaicin, the active compound found in chili peppers commonly associated with the sensation of spiciness. Using real-time Ca(2+-imaging on isolated taste cells, it was observed that SP induces Ca(2+ -responses in a subset of taste cells at concentrations in the low nanomolar range. These responses were reversibly inhibited by blocking the SP receptor NK-1R. NKA also induced Ca(2+-responses in a subset of taste cells, but only at concentrations in the high nanomolar range. These responses were only partially inhibited by blocking the NKA receptor NK-2R, and were also inhibited by blocking NK-1R indicating that NKA is only active in taste cells at concentrations that activate both receptors. In addition, it was determined that tachykinin signaling in taste cells requires Ca(2+-release from endoplasmic reticulum stores. RT-PCR analysis further confirmed that mouse taste buds express NK-1R and NK-2R. Using Ca(2+-imaging and single cell RT-PCR, it was determined that the majority of tachykinin-responsive taste cells were Type I (Glial-like and umami-responsive Type II (Receptor cells. Importantly, stimulating NK-1R had an additive effect on Ca(2+ responses evoked by umami stimuli in Type II (Receptor cells. This data indicates that tachykinin release from nociceptive sensory fibers in and around taste buds may enhance umami and other taste modalities, providing a possible mechanism for the increased palatability of spicy foods.

  13. Selecting the optimum plot size for a California design-based stream and wetland mapping program.

    Science.gov (United States)

    Lackey, Leila G; Stein, Eric D

    2014-04-01

    Accurate estimates of the extent and distribution of wetlands and streams are the foundation of wetland monitoring, management, restoration, and regulatory programs. Traditionally, these estimates have relied on comprehensive mapping. However, this approach is prohibitively resource-intensive over large areas, making it both impractical and statistically unreliable. Probabilistic (design-based) approaches to evaluating status and trends provide a more cost-effective alternative because, compared with comprehensive mapping, overall extent is inferred from mapping a statistically representative, randomly selected subset of the target area. In this type of design, the size of sample plots has a significant impact on program costs and on statistical precision and accuracy; however, no consensus exists on the appropriate plot size for remote monitoring of stream and wetland extent. This study utilized simulated sampling to assess the performance of four plot sizes (1, 4, 9, and 16 km(2)) for three geographic regions of California. Simulation results showed smaller plot sizes (1 and 4 km(2)) were most efficient for achieving desired levels of statistical accuracy and precision. However, larger plot sizes were more likely to contain rare and spatially limited wetland subtypes. Balancing these considerations led to selection of 4 km(2) for the California status and trends program.

  14. Variable Selection for Regression Models of Percentile Flows

    Science.gov (United States)

    Fouad, G.

    2017-12-01

    Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high

  15. Cortisol increases CXCR4 expression but does not affect CD62L and CCR7 levels on specific T cell subsets in humans.

    Science.gov (United States)

    Besedovsky, Luciana; Linz, Barbara; Dimitrov, Stoyan; Groch, Sabine; Born, Jan; Lange, Tanja

    2014-06-01

    Glucocorticoids are well known to affect T cell migration, leading to a redistribution of the cells from blood to the bone marrow, accompanied by a concurrent suppression of lymph node homing. Despite numerous studies in this context, with most of them employing synthetic glucocorticoids in nonphysiological doses, the mechanisms of this redistribution are not well understood. Here, we investigated in healthy men the impact of cortisol at physiological concentrations on the expression of different migration molecules on eight T cell subpopulations in vivo and in vitro. Hydrocortisone (cortisol, 22 mg) infused during nocturnal rest when endogenous cortisol levels are low, compared with placebo, differentially reduced numbers of T cell subsets, with naive CD4(+) and CD8(+) subsets exhibiting the strongest reduction. Hydrocortisone in vivo and in vitro increased CXCR4 expression, which presumably mediates the recruitment of T cells to the bone marrow. Expression of the lymph node homing receptor CD62L on total CD3(+) and CD8(+) T cells appeared reduced following hydrocortisone infusion. However, this was due to a selective extravasation of CD62L(+) T cell subsets, as hydrocortisone affected neither CD62L expression on a subpopulation level nor CD62L expression in vitro. Corresponding results in the opposite direction were observed after blocking of endogenous cortisol synthesis by metyrapone. CCR7, another lymph node homing receptor, was also unaffected by hydrocortisone in vitro. Thus, cortisol seems to redirect T cells to the bone marrow by upregulating their CXCR4 expression, whereas its inhibiting effect on T cell homing to lymph nodes is apparently regulated independently of the expression of classical homing receptors. Copyright © 2014 the American Physiological Society.

  16. Translational selection is ubiquitous in prokaryotes.

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    Fran Supek

    2010-06-01

    Full Text Available Codon usage bias in prokaryotic genomes is largely a consequence of background substitution patterns in DNA, but highly expressed genes may show a preference towards codons that enable more efficient and/or accurate translation. We introduce a novel approach based on supervised machine learning that detects effects of translational selection on genes, while controlling for local variation in nucleotide substitution patterns represented as sequence composition of intergenic DNA. A cornerstone of our method is a Random Forest classifier that outperformed previous distance measure-based approaches, such as the codon adaptation index, in the task of discerning the (highly expressed ribosomal protein genes by their codon frequencies. Unlike previous reports, we show evidence that translational selection in prokaryotes is practically universal: in 460 of 461 examined microbial genomes, we find that a subset of genes shows a higher codon usage similarity to the ribosomal proteins than would be expected from the local sequence composition. These genes constitute a substantial part of the genome--between 5% and 33%, depending on genome size--while also exhibiting higher experimentally measured mRNA abundances and tending toward codons that match tRNA anticodons by canonical base pairing. Certain gene functional categories are generally enriched with, or depleted of codon-optimized genes, the trends of enrichment/depletion being conserved between Archaea and Bacteria. Prominent exceptions from these trends might indicate genes with alternative physiological roles; we speculate on specific examples related to detoxication of oxygen radicals and ammonia and to possible misannotations of asparaginyl-tRNA synthetases. Since the presence of codon optimizations on genes is a valid proxy for expression levels in fully sequenced genomes, we provide an example of an "adaptome" by highlighting gene functions with expression levels elevated specifically in

  17. Peripheral lymphocyte subset variation predicts prostate cancer carbon ion radiotherapy outcomes

    Science.gov (United States)

    Shi, Ze-Liang; Li, Bing-Xin; Wu, Xian-Wei; Li, Ping; Zhang, Qing; Wei, Xun-Bin; Fu, Shen

    2016-01-01

    The immune system plays a complementary role in the cytotoxic activity of radiotherapy. Here, we examined changes in immune cell subsets after heavy ion therapy for prostate cancer. The lymphocyte counts were compared with acute radiotherapy-related toxicity, defined according to the Common Terminology Criteria for Adverse Events, and short-term local efficacy, defined based on prostate-specific antigen concentrations. Confirmed prostate cancer patients who had not received previous radiotherapy were administered carbon ion radiotherapy (CIR) in daily fractions of 2.74 GyE with a total dose of 63-66 GyE. Lymphocyte subset counts were investigated before, during and after radiotherapy, and at a 1 month follow-up. Most notable among our findings, the CD4/CD8 ratio and CD19+ cell counts were consistently higher in patients with a complete response (CR) or partial response (PR) to CIR than in those classified in the stable disease (SD) group (P<0.05 for both). But CD3+ and CD8+ cell counts were lower in the CR and PR groups than in the SD group. These results indicate that variations in peripheral lymphocyte subpopulations are predictive of outcome after CIR for prostate cancer. PMID:27029063

  18. Varying levels of difficulty index of skills-test items randomly selected by examinees on the Korean emergency medical technician licensing examination.

    Science.gov (United States)

    Koh, Bongyeun; Hong, Sunggi; Kim, Soon-Sim; Hyun, Jin-Sook; Baek, Milye; Moon, Jundong; Kwon, Hayran; Kim, Gyoungyong; Min, Seonggi; Kang, Gu-Hyun

    2016-01-01

    The goal of this study was to characterize the difficulty index of the items in the skills test components of the class I and II Korean emergency medical technician licensing examination (KEMTLE), which requires examinees to select items randomly. The results of 1,309 class I KEMTLE examinations and 1,801 class II KEMTLE examinations in 2013 were subjected to analysis. Items from the basic and advanced skills test sections of the KEMTLE were compared to determine whether some were significantly more difficult than others. In the class I KEMTLE, all 4 of the items on the basic skills test showed significant variation in difficulty index (P<0.01), as well as 4 of the 5 items on the advanced skills test (P<0.05). In the class II KEMTLE, 4 of the 5 items on the basic skills test showed significantly different difficulty index (P<0.01), as well as all 3 of the advanced skills test items (P<0.01). In the skills test components of the class I and II KEMTLE, the procedure in which examinees randomly select questions should be revised to require examinees to respond to a set of fixed items in order to improve the reliability of the national licensing examination.

  19. Varying levels of difficulty index of skills-test items randomly selected by examinees on the Korean emergency medical technician licensing examination

    Directory of Open Access Journals (Sweden)

    Bongyeun Koh

    2016-01-01

    Full Text Available Purpose: The goal of this study was to characterize the difficulty index of the items in the skills test components of the class I and II Korean emergency medical technician licensing examination (KEMTLE, which requires examinees to select items randomly. Methods: The results of 1,309 class I KEMTLE examinations and 1,801 class II KEMTLE examinations in 2013 were subjected to analysis. Items from the basic and advanced skills test sections of the KEMTLE were compared to determine whether some were significantly more difficult than others. Results: In the class I KEMTLE, all 4 of the items on the basic skills test showed significant variation in difficulty index (P<0.01, as well as 4 of the 5 items on the advanced skills test (P<0.05. In the class II KEMTLE, 4 of the 5 items on the basic skills test showed significantly different difficulty index (P<0.01, as well as all 3 of the advanced skills test items (P<0.01. Conclusion: In the skills test components of the class I and II KEMTLE, the procedure in which examinees randomly select questions should be revised to require examinees to respond to a set of fixed items in order to improve the reliability of the national licensing examination.

  20. Glucocorticoid induced TNFR-related protein (GITR as marker of human regulatory T cells: expansion of the GITR+CD25- cell subset in patients with systemic lupus erythematosus

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    E. Bartoloni Bocci

    2011-06-01

    Full Text Available Objectives: Regulatory T cells (TREG represent a T cell subset able to modulate immune response by suppressing autoreactive T-lymphocytes. The evidence of a reduced number and an impaired function of this cell population in autoimmune/ inflammatory chronic diseases led to the hypothesis of its involvement in the pathogenesis of these disorders. Glucocorticoid-induced TNFR-related protein (GITR is a well known marker of murine TREG cells, but little is known in humans. The aim of this study was to investigate the characteristics of TREG cells in systemic lupus erythematosus (SLE and the potential role of GITR as marker of human TREG. Methods: Nineteen SLE patients and 15 sex- and age-matched normal controls (NC were enrolled. CD4+ T cells were magnetic sorted from peripheral blood by negative selection. Cell phenotype was analyzed through flow-cytometry using primary and secondary antibodies and real time polymerase-chain reaction (PCR using TaqMan probes. Results: The CD25highGITRhigh subset was significantly decreased in SLE patients with respect to NC (0.37±0.21% vs 0.72±0.19%; p<0.05. On the opposite, the CD25-GITRhigh cell population was expanded in the peripheral blood of SLE patients (3.5±2.25 vs 0.70±0.32%, p<0.01. Interestingly, FoxP3 at mRNA level was expressed in both CD25- GITRhigh and CD25highGITRhigh cells, suggesting that both cell subsets have regulatory activity. Conclusions: CD4+CD25-GITRhigh cells are increased in SLE as compared to NC. The expression of high level of GITR, but not CD25, on FoxP3+ cells appears to point to a regulatory phenotype of this peculiar T cell subset.

  1. Entropy-based gene ranking without selection bias for the predictive classification of microarray data

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    Serafini Maria

    2003-11-01

    Full Text Available Abstract Background We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process. Results With E-RFE, we speed up the recursive feature elimination (RFE with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. Conclusions Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.

  2. The human Vδ2+ T-cell compartment comprises distinct innate-like Vγ9+ and adaptive Vγ9- subsets.

    Science.gov (United States)

    Davey, Martin S; Willcox, Carrie R; Hunter, Stuart; Kasatskaya, Sofya A; Remmerswaal, Ester B M; Salim, Mahboob; Mohammed, Fiyaz; Bemelman, Frederike J; Chudakov, Dmitriy M; Oo, Ye H; Willcox, Benjamin E

    2018-05-02

    Vδ2 + T cells form the predominant human γδ T-cell population in peripheral blood and mediate T-cell receptor (TCR)-dependent anti-microbial and anti-tumour immunity. Here we show that the Vδ2 + compartment comprises both innate-like and adaptive subsets. Vγ9 + Vδ2 + T cells display semi-invariant TCR repertoires, featuring public Vγ9 TCR sequences equivalent in cord and adult blood. By contrast, we also identify a separate, Vγ9 - Vδ2 + T-cell subset that typically has a CD27 hi CCR7 + CD28 + IL-7Rα + naive-like phenotype and a diverse TCR repertoire, however in response to viral infection, undergoes clonal expansion and differentiation to a CD27 lo CD45RA + CX 3 CR1 + granzymeA/B + effector phenotype. Consistent with a function in solid tissue immunosurveillance, we detect human intrahepatic Vγ9 - Vδ2 + T cells featuring dominant clonal expansions and an effector phenotype. These findings redefine human γδ T-cell subsets by delineating the Vδ2 + T-cell compartment into innate-like (Vγ9 + ) and adaptive (Vγ9 - ) subsets, which have distinct functions in microbial immunosurveillance.

  3. Bond-selective control of a gas-surface reaction

    Science.gov (United States)

    Killelea, Daniel R.

    The prospect of using light to selectively control chemical reactions has tantalized chemists since the development of the laser. Unfortunately, the realization of laser-directed chemistry is frequently thwarted by the randomization of energy within the molecule through intramolecular vibrational energy distribution (IVR). However, recent results showing vibrational mode-specific reactivity on metal surfaces suggest that IVR may not always be complete for gas-surface reactions. Here, we combine molecular beam techniques and direct laser excitation to characterize the bond-specific reactivity of trideuteromethane on a Ni(111) surface. Our results reveal important details about how vibrational energy is distributed in the reactive molecule. We use a molecular beam to direct state-selected trideuteromethane (CHD 3) molecules onto a nickel single crystal sample and use the results we obtain to describe the flow of vibrational energy in the methane-surface reaction complex. We show that CHD3 molecules initially excited to v=1, J=2, K=0 of the v 1 symmetric C-H stretching mode will dissociate exclusively via C-H cleavage on Ni(111). This result highlights the localization of vibrational energy in the reaction complex, despite the presence of many energy exchange channels with the high state-density surface. We demonstrate, for the first time, highly parallel bond-selective control of a heterogeneously catalyzed reaction. We place our results in the context of recent experiments investigating IVR for molecules in both the gas phase and liquid solutions. If IVR is fast on the reaction timescale, vibrational energy would be randomly distributed throughout the nascent methane-surface reaction complex and vibrational mode-specific behavior would not occur. The short timescale of a direct gas-surface collision may explain how the exchange of energy via IVR is limited to only a small subset of the energetic configurations available to the reaction complex. This framework

  4. Selection of core animals in the Algorithm for Proven and Young using a simulation model.

    Science.gov (United States)

    Bradford, H L; Pocrnić, I; Fragomeni, B O; Lourenco, D A L; Misztal, I

    2017-12-01

    The Algorithm for Proven and Young (APY) enables the implementation of single-step genomic BLUP (ssGBLUP) in large, genotyped populations by separating genotyped animals into core and non-core subsets and creating a computationally efficient inverse for the genomic relationship matrix (G). As APY became the choice for large-scale genomic evaluations in BLUP-based methods, a common question is how to choose the animals in the core subset. We compared several core definitions to answer this question. Simulations comprised a moderately heritable trait for 95,010 animals and 50,000 genotypes for animals across five generations. Genotypes consisted of 25,500 SNP distributed across 15 chromosomes. Genotyping errors and missing pedigree were also mimicked. Core animals were defined based on individual generations, equal representation across generations, and at random. For a sufficiently large core size, core definitions had the same accuracies and biases, even if the core animals had imperfect genotypes. When genotyped animals had unknown parents, accuracy and bias were significantly better (p ≤ .05) for random and across generation core definitions. © 2017 The Authors. Journal of Animal Breeding and Genetics Published by Blackwell Verlag GmbH.

  5. Discrete Biogeography Based Optimization for Feature Selection in Molecular Signatures.

    Science.gov (United States)

    Liu, Bo; Tian, Meihong; Zhang, Chunhua; Li, Xiangtao

    2015-04-01

    Biomarker discovery from high-dimensional data is a complex task in the development of efficient cancer diagnoses and classification. However, these data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a discrete biogeography based optimization is proposed to select the good subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the fisher-markov selector is used to choose fixed number of gene data. Secondly, to make biogeography based optimization suitable for the feature selection problem; discrete migration model and discrete mutation model are proposed to balance the exploration and exploitation ability. Then, discrete biogeography based optimization, as we called DBBO, is proposed by integrating discrete migration model and discrete mutation model. Finally, the DBBO method is used for feature selection, and three classifiers are used as the classifier with the 10 fold cross-validation method. In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on four breast cancer dataset benchmarks. Comparison with genetic algorithm, particle swarm optimization, differential evolution algorithm and hybrid biogeography based optimization, experimental results demonstrate that the proposed method is better or at least comparable with previous method from literature when considering the quality of the solutions obtained. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  6. Multispectral iris recognition based on group selection and game theory

    Science.gov (United States)

    Ahmad, Foysal; Roy, Kaushik

    2017-05-01

    A commercially available iris recognition system uses only a narrow band of the near infrared spectrum (700-900 nm) while iris images captured in the wide range of 405 nm to 1550 nm offer potential benefits to enhance recognition performance of an iris biometric system. The novelty of this research is that a group selection algorithm based on coalition game theory is explored to select the best patch subsets. In this algorithm, patches are divided into several groups based on their maximum contribution in different groups. Shapley values are used to evaluate the contribution of patches in different groups. Results show that this group selection based iris recognition

  7. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

    Directory of Open Access Journals (Sweden)

    Mariela Cerrada

    2015-09-01

    Full Text Available There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%.

  8. Efficacy and safety assessment of the addition of bevacizumab to adjuvant therapy agents in cancer patients: A systematic review and meta-analysis of randomized controlled trials

    NARCIS (Netherlands)

    Ahmadizar, Fariba; Onland-Moret, N. Charlotte; De Boer, Anthonius; Liu, Geoffrey; Maitland-Van Der Zee, Anke H.

    2015-01-01

    Aim: To evaluate the efficacy and safety of bevacizumab in the adjuvant cancer therapy setting within different subset of patients. Methods & Design/Results: PubMed, EMBASE, Cochrane and Clinical trials.gov databases were searched for English language studies of randomized controlled trials

  9. Statistical Redundancy Testing for Improved Gene Selection in Cancer Classification Using Microarray Data

    Directory of Open Access Journals (Sweden)

    J. Sunil Rao

    2007-01-01

    Full Text Available In gene selection for cancer classifi cation using microarray data, we define an eigenvalue-ratio statistic to measure a gene’s contribution to the joint discriminability when this gene is included into a set of genes. Based on this eigenvalueratio statistic, we define a novel hypothesis testing for gene statistical redundancy and propose two gene selection methods. Simulation studies illustrate the agreement between statistical redundancy testing and gene selection methods. Real data examples show the proposed gene selection methods can select a compact gene subset which can not only be used to build high quality cancer classifiers but also show biological relevance.

  10. Subset simulation for structural reliability sensitivity analysis

    International Nuclear Information System (INIS)

    Song Shufang; Lu Zhenzhou; Qiao Hongwei

    2009-01-01

    Based on two procedures for efficiently generating conditional samples, i.e. Markov chain Monte Carlo (MCMC) simulation and importance sampling (IS), two reliability sensitivity (RS) algorithms are presented. On the basis of reliability analysis of Subset simulation (Subsim), the RS of the failure probability with respect to the distribution parameter of the basic variable is transformed as a set of RS of conditional failure probabilities with respect to the distribution parameter of the basic variable. By use of the conditional samples generated by MCMC simulation and IS, procedures are established to estimate the RS of the conditional failure probabilities. The formulae of the RS estimator, its variance and its coefficient of variation are derived in detail. The results of the illustrations show high efficiency and high precision of the presented algorithms, and it is suitable for highly nonlinear limit state equation and structural system with single and multiple failure modes

  11. A Meta-Heuristic Regression-Based Feature Selection for Predictive Analytics

    Directory of Open Access Journals (Sweden)

    Bharat Singh

    2014-11-01

    Full Text Available A high-dimensional feature selection having a very large number of features with an optimal feature subset is an NP-complete problem. Because conventional optimization techniques are unable to tackle large-scale feature selection problems, meta-heuristic algorithms are widely used. In this paper, we propose a particle swarm optimization technique while utilizing regression techniques for feature selection. We then use the selected features to classify the data. Classification accuracy is used as a criterion to evaluate classifier performance, and classification is accomplished through the use of k-nearest neighbour (KNN and Bayesian techniques. Various high dimensional data sets are used to evaluate the usefulness of the proposed approach. Results show that our approach gives better results when compared with other conventional feature selection algorithms.

  12. Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search

    Directory of Open Access Journals (Sweden)

    Simon Fong

    2013-01-01

    Full Text Available Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a long array of features. Many feature subset selection algorithms have been proposed in the past, but not all of them are effective. Since it takes seemingly forever to use brute force in exhaustively trying every possible combination of features, stochastic optimization may be a solution. In this paper, we propose a new feature selection scheme called Swarm Search to find an optimal feature set by using metaheuristics. The advantage of Swarm Search is its flexibility in integrating any classifier into its fitness function and plugging in any metaheuristic algorithm to facilitate heuristic search. Simulation experiments are carried out by testing the Swarm Search over some high-dimensional datasets, with different classification algorithms and various metaheuristic algorithms. The comparative experiment results show that Swarm Search is able to attain relatively low error rates in classification without shrinking the size of the feature subset to its minimum.

  13. Random survival forests for competing risks

    DEFF Research Database (Denmark)

    Ishwaran, Hemant; Gerds, Thomas A; Kogalur, Udaya B

    2014-01-01

    We introduce a new approach to competing risks using random forests. Our method is fully non-parametric and can be used for selecting event-specific variables and for estimating the cumulative incidence function. We show that the method is highly effective for both prediction and variable selection...

  14. Mediators of Effects of a Selective Family-Focused Violence Prevention Approach for Middle School Students

    Science.gov (United States)

    2013-01-01

    This study examined how parenting and family characteristics targeted in a selective prevention program mediated effects on key youth proximal outcomes related to violence perpetration. The selective intervention was evaluated within the context of a multi-site trial involving random assignment of 37 schools to four conditions: a universal intervention composed of a student social-cognitive curriculum and teacher training, a selective family-focused intervention with a subset of high-risk students, a condition combining these two interventions, and a no-intervention control condition. Two cohorts of sixth-grade students (total N=1,062) exhibiting high levels of aggression and social influence were the sample for this study. Analyses of pre-post change compared to controls using intent-to-treat analyses found no significant effects. However, estimates incorporating participation of those assigned to the intervention and predicted participation among those not assigned revealed significant positive effects on student aggression, use of aggressive strategies for conflict management, and parental estimation of student’s valuing of achievement. Findings also indicated intervention effects on two targeted family processes: discipline practices and family cohesion. Mediation analyses found evidence that change in these processes mediated effects on some outcomes, notably aggressive behavior and valuing of school achievement. Results support the notion that changing parenting practices and the quality of family relationships can prevent the escalation in aggression and maintain positive school engagement for high-risk youth. PMID:21932067

  15. Feature selection for high-dimensional integrated data

    KAUST Repository

    Zheng, Charles

    2012-04-26

    Motivated by the problem of identifying correlations between genes or features of two related biological systems, we propose a model of feature selection in which only a subset of the predictors Xt are dependent on the multidimensional variate Y, and the remainder of the predictors constitute a “noise set” Xu independent of Y. Using Monte Carlo simulations, we investigated the relative performance of two methods: thresholding and singular-value decomposition, in combination with stochastic optimization to determine “empirical bounds” on the small-sample accuracy of an asymptotic approximation. We demonstrate utility of the thresholding and SVD feature selection methods to with respect to a recent infant intestinal gene expression and metagenomics dataset.

  16. Feature selection for high-dimensional integrated data

    KAUST Repository

    Zheng, Charles; Schwartz, Scott; Chapkin, Robert S.; Carroll, Raymond J.; Ivanov, Ivan

    2012-01-01

    Motivated by the problem of identifying correlations between genes or features of two related biological systems, we propose a model of feature selection in which only a subset of the predictors Xt are dependent on the multidimensional variate Y, and the remainder of the predictors constitute a “noise set” Xu independent of Y. Using Monte Carlo simulations, we investigated the relative performance of two methods: thresholding and singular-value decomposition, in combination with stochastic optimization to determine “empirical bounds” on the small-sample accuracy of an asymptotic approximation. We demonstrate utility of the thresholding and SVD feature selection methods to with respect to a recent infant intestinal gene expression and metagenomics dataset.

  17. Classification Using Markov Blanket for Feature Selection

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Luo, Jian

    2009-01-01

    Selecting relevant features is in demand when a large data set is of interest in a classification task. It produces a tractable number of features that are sufficient and possibly improve the classification performance. This paper studies a statistical method of Markov blanket induction algorithm...... for filtering features and then applies a classifier using the Markov blanket predictors. The Markov blanket contains a minimal subset of relevant features that yields optimal classification performance. We experimentally demonstrate the improved performance of several classifiers using a Markov blanket...... induction as a feature selection method. In addition, we point out an important assumption behind the Markov blanket induction algorithm and show its effect on the classification performance....

  18. Using Random Numbers in Science Research Activities.

    Science.gov (United States)

    Schlenker, Richard M.; And Others

    1996-01-01

    Discusses the importance of science process skills and describes ways to select sets of random numbers for selection of subjects for a research study in an unbiased manner. Presents an activity appropriate for grades 5-12. (JRH)

  19. Randomly Generating Four Mixed Bell-Diagonal States with a Concurrences Sum to Unity

    International Nuclear Information System (INIS)

    Toh, S. P.; Zainuddin Hishamuddin; Foo Kim Eng

    2012-01-01

    A two-qubit system in quantum information theory is the simplest bipartite quantum system and its concurrence for pure and mixed states is well known. As a subset of two-qubit systems, Bell-diagonal states can be depicted by a very simple geometrical representation of a tetrahedron with sides of length 2√2. Based on this geometric representation, we propose a simple approach to randomly generate four mixed Bell decomposable states in which the sum of their concurrence is equal to one. (general)

  20. Solubility on compact subsets for differential equations with real principal pencil of symbols

    International Nuclear Information System (INIS)

    Shananin, N A

    2006-01-01

    The central result is a theorem on the solubility on compact subsets for differential equations of quasiprincipal type with real principal pencil of symbols. The proof is based on the analysis of the microlocal structure of the singularities of solutions of equations in this class.

  1. Increased hepatic Th2 and Treg subsets are associated with biliary fibrosis in different strains of mice caused by Clonorchis sinensis.

    Directory of Open Access Journals (Sweden)

    Bei-Bei Zhang

    Full Text Available Previous studies showed that CD4+T cells responses might be involved in the process of biliary fibrosis. However, the underlying mechanism resulting in biliary fibrosis caused by Clonorchis sinensis remains not yet fully elucidated. The objectives of the present study were to investigate the different profiles of hepatic CD4+T cell subsets (Th1, Th2, Th17 and Treg cells and their possible roles in the biliary fibrosis of different strains of mice (C57BL/6, BALB/c and FVB mice induced by C. sinensis infection. C57BL/6, BALB/c and FVB mice were orally gavaged with 45 metacercariae. All mice were sacrificed on 28 days post infection in deep anesthesia conditions. The leukocytes in the liver were separated to examine CD4+T cell subsets by flow cytometry and the left lobe of liver was used to observe pathological changes, collagen depositions and the concentrations of hydroxyproline. The most serious cystic and fibrotic changes appeared in FVB infected mice indicated by gross observation, Masson's trichrome staining and hydroxyproline content detection. In contrast to C57BL/6 infected mice, diffuse nodules and more intensive fibrosis were observed in the BALB/c infected mice. No differences of the hepatic Th1 subset and Th17 subset were found among the three strains, but the hepatic Th2 and Treg cells and their relative cytokines were dramatically increased in the BALB/c and FVB infected groups compared with the C57BL/6 infected group (P<0.01. Importantly, increased Th2 subset and Treg subset all positively correlated with hydroxyproline contents (P<0.01. This result for the first time implied that the increased hepatic Th2 and Treg cell subsets were likely to play potential roles in the formation of biliary fibrosis in C. sinensis-infected mice.

  2. Naive Bayes-Guided Bat Algorithm for Feature Selection

    Directory of Open Access Journals (Sweden)

    Ahmed Majid Taha

    2013-01-01

    Full Text Available When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets.

  3. Naive Bayes-Guided Bat Algorithm for Feature Selection

    Science.gov (United States)

    Taha, Ahmed Majid; Mustapha, Aida; Chen, Soong-Der

    2013-01-01

    When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets. PMID:24396295

  4. Encapsulation of an EP67-Conjugated CTL Peptide Vaccine in Nanoscale Biodegradable Particles Increases the Efficacy of Respiratory Immunization and Affects the Magnitude and Memory Subsets of Vaccine-Generated Mucosal and Systemic CD8+ T Cells in a Diameter-Dependent Manner.

    Science.gov (United States)

    Karuturi, Bala V K; Tallapaka, Shailendra B; Yeapuri, Pravin; Curran, Stephen M; Sanderson, Sam D; Vetro, Joseph A

    2017-05-01

    The diameter of biodegradable particles used to coencapsulate immunostimulants and subunit vaccines affects the magnitude of memory CD8 + T cells generated by systemic immunization. Possible effects on the magnitude of CD8 + T cells generated by mucosal immunization or memory subsets that potentially correlate more strongly with protection against certain pathogens, however, are unknown. In this study, we conjugated our novel host-derived mucosal immunostimulant, EP67, to the protective MCMV CTL epitope, pp89, through a lysosomal protease-labile double arginine linker (pp89-RR-EP67) and encapsulated in PLGA 50:50 micro- or nanoparticles. We then compared total magnitude, effector/central memory (CD127/KRLG1/CD62L), and IFN-γ/TNF-α/IL-2 secreting subsets of pp89-specific CD8 + T cells as well as protection of naive female BALB/c mice against primary respiratory infection with MCMV 21 days after respiratory immunization. We found that decreasing the diameter of encapsulating particle from ∼5.4 μm to ∼350 nm (i) increased the magnitude of pp89-specific CD8 + T cells in the lungs and spleen; (ii) partially changed CD127/KLRG1 effector memory subsets in the lungs but not the spleen; (iii) changed CD127/KRLG1/CD62L effector/central memory subsets in the spleen; (iv) changed pp89-responsive IFN-γ/TNF-α/IL-2 secreting subsets in the lungs and spleen; (v) did not affect the extent to which encapsulation increased efficacy against primary MCMV respiratory infection over unencapsulated pp89-RR-EP67. Thus, although not observed under our current experimental conditions with MCMV, varying the diameter of nanoscale biodegradable particles may increase the efficacy of mucosal immunization with coencapsulated immunostimulant/subunit vaccines against certain pathogens by selectively increasing memory subset(s) of CD8 + T cells that correlate the strongest with protection.

  5. Analysis of T Cell Subsets in Adult Primary/Idiopathic Minimal Change Disease: A Pilot Study

    Directory of Open Access Journals (Sweden)

    Francisco Salcido-Ochoa

    2017-01-01

    Full Text Available Aim. To characterise infiltrating T cells in kidneys and circulating lymphocyte subsets of adult patients with primary/idiopathic minimal change disease. Methods. In a cohort of 9 adult patients with primary/idiopathic minimal change recruited consecutively at disease onset, we characterized (1 infiltrating immune cells in the kidneys using immunohistochemistry and (2 circulating lymphocyte subsets using flow cytometry. As an exploratory analysis, association of the numbers and percentages of both kidney-infiltrating immune cells and the circulating lymphocyte subsets with kidney outcomes including deterioration of kidney function and proteinuria, as well as time to complete clinical remission up to 48 months of follow-up, was investigated. Results. In the recruited patients with primary/idiopathic minimal change disease, we observed (a a dominance of infiltrating T helper 17 cells and cytotoxic cells, comprising cytotoxic T cells and natural killer cells, over Foxp3+ Treg cells in the renal interstitium; (b an increase in the circulating total CD8+ T cells in peripheral blood; and (c an association of some of these parameters with kidney function and proteinuria. Conclusions. In primary/idiopathic minimal change disease, a relative numerical dominance of effector over regulatory T cells can be observed in kidney tissue and peripheral blood. However, larger confirmatory studies are necessary.

  6. Toolsets for Airborne Data (TAD): Enhanced Airborne Data Merging Functionality through Spatial and Temporal Subsetting

    Science.gov (United States)

    Early, A. B.; Chen, G.; Beach, A. L., III; Northup, E. A.

    2016-12-01

    NASA has conducted airborne tropospheric chemistry studies for over three decades. These field campaigns have generated a great wealth of observations, including a wide range of the trace gases and aerosol properties. The Atmospheric Science Data Center (ASDC) at NASA Langley Research Center in Hampton Virginia originally developed the Toolsets for Airborne Data (TAD) web application in September 2013 to meet the user community needs for manipulating aircraft data for scientific research on climate change and air quality relevant issues. The analysis of airborne data typically requires data subsetting, which can be challenging and resource intensive for end users. In an effort to streamline this process, the TAD toolset enhancements will include new data subsetting features and updates to the current database model. These will include two subsetters: temporal and spatial, and vertical profile. The temporal and spatial subsetter will allow users to both focus on data from a specific location and/or time period. The vertical profile subsetter will retrieve data collected during an individual aircraft ascent or descent spiral. This effort will allow for the automation of the typically labor-intensive manual data subsetting process, which will provide users with data tailored to their specific research interests. The development of these enhancements will be discussed in this presentation.

  7. Pivotal statistics for testing subsets of structural parameters in the IV Regression Model

    NARCIS (Netherlands)

    Kleibergen, F.R.

    2000-01-01

    We construct a novel statistic to test hypothezes on subsets of the structural parameters in anInstrumental Variables (IV) regression model. We derive the chi squared limiting distribution of thestatistic and show that it has a degrees of freedom parameter that is equal to the number ofstructural

  8. Modeling Liner Shipping Service Selection and Container Flows using a Multi-layer Network

    DEFF Research Database (Denmark)

    Karsten, Christian Vad; Balakrishnan, Anant

    We introduce a new formulation for the tactical planning problem facing container shipping companies of selecting the best subset of sailing routes from a given pool of candidate routes so as to maximize profit. Since most containers are sent directly or transshipped at most twice in current liner...

  9. Correlates of smoking with socioeconomic status, leisure time physical activity and alcohol consumption among Polish adults from randomly selected regions.

    Science.gov (United States)

    Woitas-Slubowska, Donata; Hurnik, Elzbieta; Skarpańska-Stejnborn, Anna

    2010-12-01

    To determine the association between smoking status and leisure time physical activity (LTPA), alcohol consumption, and socioeconomic status (SES) among Polish adults. 466 randomly selected men and women (aged 18-66 years) responded to an anonymous questionnaire regarding smoking, alcohol consumption, LTPA, and SES. Multiple logistic regression was used to examine the association of smoking status with six socioeconomic measures, level of LTPA, and frequency and type of alcohol consumed. Smokers were defined as individuals smoking occasionally or daily. The odds of being smoker were 9 times (men) and 27 times (women) higher among respondents who drink alcohol several times/ week or everyday in comparison to non-drinkers (p times higher compared to those with the high educational attainment (p = 0.007). Among women we observed that students were the most frequent smokers. Female students were almost three times more likely to smoke than non-professional women, and two times more likely than physical workers (p = 0.018). The findings of this study indicated that among randomly selected Polish man and women aged 18-66 smoking and alcohol consumption tended to cluster. These results imply that intervention strategies need to target multiple risk factors simultaneously. The highest risk of smoking was observed among low educated men, female students, and both men and women drinking alcohol several times a week or every day. Information on subgroups with the high risk of smoking will help in planning future preventive strategies.

  10. Selection on meiosis genes in diploid and tetraploid Arabidopsis arenosa.

    Science.gov (United States)

    Wright, Kevin M; Arnold, Brian; Xue, Katherine; Šurinová, Maria; O'Connell, Jeremy; Bomblies, Kirsten

    2015-04-01

    Meiotic chromosome segregation is critical for fertility across eukaryotes, and core meiotic processes are well conserved even between kingdoms. Nevertheless, recent work in animals has shown that at least some meiosis genes are highly diverse or strongly differentiated among populations. What drives this remains largely unknown. We previously showed that autotetraploid Arabidopsis arenosa evolved stable meiosis, likely through reduced crossover rates, and that associated with this there is strong evidence for selection in a subset of meiosis genes known to affect axis formation, synapsis, and crossover frequency. Here, we use genome-wide data to study the molecular evolution of 70 meiosis genes in a much wider sample of A. arenosa. We sample the polyploid lineage, a diploid lineage from the Carpathian Mountains, and a more distantly related diploid lineage from the adjacent, but biogeographically distinct Pannonian Basin. We find that not only did selection act on meiosis genes in the polyploid lineage but also independently on a smaller subset of meiosis genes in Pannonian diploids. Functionally related genes are targeted by selection in these distinct contexts, and in two cases, independent sweeps occurred in the same loci. The tetraploid lineage has sustained selection on more genes, has more amino acid changes in each, and these more often affect conserved or potentially functional sites. We hypothesize that Pannonian diploid and tetraploid A. arenosa experienced selection on structural proteins that mediate sister chromatid cohesion, the formation of meiotic chromosome axes, and synapsis, likely for different underlying reasons. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  11. Hydraulic head interpolation using ANFIS—model selection and sensitivity analysis

    Science.gov (United States)

    Kurtulus, Bedri; Flipo, Nicolas

    2012-01-01

    The aim of this study is to investigate the efficiency of ANFIS (adaptive neuro fuzzy inference system) for interpolating hydraulic head in a 40-km 2 agricultural watershed of the Seine basin (France). Inputs of ANFIS are Cartesian coordinates and the elevation of the ground. Hydraulic head was measured at 73 locations during a snapshot campaign on September 2009, which characterizes low-water-flow regime in the aquifer unit. The dataset was then split into three subsets using a square-based selection method: a calibration one (55%), a training one (27%), and a test one (18%). First, a method is proposed to select the best ANFIS model, which corresponds to a sensitivity analysis of ANFIS to the type and number of membership functions (MF). Triangular, Gaussian, general bell, and spline-based MF are used with 2, 3, 4, and 5 MF per input node. Performance criteria on the test subset are used to select the 5 best ANFIS models among 16. Then each is used to interpolate the hydraulic head distribution on a (50×50)-m grid, which is compared to the soil elevation. The cells where the hydraulic head is higher than the soil elevation are counted as "error cells." The ANFIS model that exhibits the less "error cells" is selected as the best ANFIS model. The best model selection reveals that ANFIS models are very sensitive to the type and number of MF. Finally, a sensibility analysis of the best ANFIS model with four triangular MF is performed on the interpolation grid, which shows that ANFIS remains stable to error propagation with a higher sensitivity to soil elevation.

  12. Selection of morphological features of pollen grains for chosen tree taxa

    Directory of Open Access Journals (Sweden)

    Agnieszka Kubik-Komar

    2018-05-01

    Full Text Available The basis of aerobiological studies is to monitor airborne pollen concentrations and pollen season timing. This task is performed by appropriately trained staff and is difficult and time consuming. The goal of this research is to select morphological characteristics of grains that are the most discriminative for distinguishing between birch, hazel and alder taxa and are easy to determine automatically from microscope images. This selection is based on the split attributes of the J4.8 classification trees built for different subsets of features. Determining the discriminative features by this method, we provide specific rules for distinguishing between individual taxa, at the same time obtaining a high percentage of correct classification. The most discriminative among the 13 morphological characteristics studied are the following: number of pores, maximum axis, minimum axis, axes difference, maximum oncus width, and number of lateral pores. The classification result of the tree based on this subset is better than the one built on the whole feature set and it is almost 94%. Therefore, selection of attributes before tree building is recommended. The classification results for the features easiest to obtain from the image, i.e. maximum axis, minimum axis, axes difference, and number of lateral pores, are only 2.09 pp lower than those obtained for the complete set, but 3.23 pp lower than the results obtained for the selected most discriminating attributes only.

  13. Mahalanobis distance and variable selection to optimize dose response

    International Nuclear Information System (INIS)

    Moore, D.H. II; Bennett, D.E.; Wyrobek, A.J.; Kranzler, D.

    1979-01-01

    A battery of statistical techniques are combined to improve detection of low-level dose response. First, Mahalanobis distances are used to classify objects as normal or abnormal. Then the proportion classified abnormal is regressed on dose. Finally, a subset of regressor variables is selected which maximizes the slope of the dose response line. Use of the techniques is illustrated by application to mouse sperm damaged by low doses of x-rays

  14. PPARγ partial agonist GQ-16 strongly represses a subset of genes in 3T3-L1 adipocytes

    Energy Technology Data Exchange (ETDEWEB)

    Milton, Flora Aparecida [Faculdade de Ciências da Saúde, Laboratório de Farmacologia Molecular, Universidade de Brasília (Brazil); Genomic Medicine, Houston Methodist Research Institute, Houston, TX (United States); Cvoro, Aleksandra [Genomic Medicine, Houston Methodist Research Institute, Houston, TX (United States); Amato, Angelica A. [Faculdade de Ciências da Saúde, Laboratório de Farmacologia Molecular, Universidade de Brasília (Brazil); Sieglaff, Douglas H.; Filgueira, Carly S.; Arumanayagam, Anithachristy Sigamani [Genomic Medicine, Houston Methodist Research Institute, Houston, TX (United States); Caro Alves de Lima, Maria do; Rocha Pitta, Ivan [Laboratório de Planejamento e Síntese de Fármacos – LPSF, Universidade Federal de Pernambuco (Brazil); Assis Rocha Neves, Francisco de [Faculdade de Ciências da Saúde, Laboratório de Farmacologia Molecular, Universidade de Brasília (Brazil); Webb, Paul, E-mail: pwebb@HoustonMethodist.org [Genomic Medicine, Houston Methodist Research Institute, Houston, TX (United States)

    2015-08-28

    Thiazolidinediones (TZDs) are peroxisome proliferator-activated receptor gamma (PPARγ) agonists that improve insulin resistance but trigger side effects such as weight gain, edema, congestive heart failure and bone loss. GQ-16 is a PPARγ partial agonist that improves glucose tolerance and insulin sensitivity in mouse models of obesity and diabetes without inducing weight gain or edema. It is not clear whether GQ-16 acts as a partial agonist at all PPARγ target genes, or whether it displays gene-selective actions. To determine how GQ-16 influences PPARγ activity on a gene by gene basis, we compared effects of rosiglitazone (Rosi) and GQ-16 in mature 3T3-L1 adipocytes using microarray and qRT-PCR. Rosi changed expression of 1156 genes in 3T3-L1, but GQ-16 only changed 89 genes. GQ-16 generally showed weak effects upon Rosi induced genes, consistent with partial agonist actions, but a subset of modestly Rosi induced and strongly repressed genes displayed disproportionately strong GQ-16 responses. PPARγ partial agonists MLR24 and SR1664 also exhibit disproportionately strong effects on transcriptional repression. We conclude that GQ-16 displays a continuum of weak partial agonist effects but efficiently represses some negatively regulated PPARγ responsive genes. Strong repressive effects could contribute to physiologic actions of GQ-16. - Highlights: • GQ-16 is an insulin sensitizing PPARγ ligand with reduced harmful side effects. • GQ-16 displays a continuum of weak partial agonist activities at PPARγ-induced genes. • GQ-16 exerts strong repressive effects at a subset of genes. • These inhibitor actions should be evaluated in models of adipose tissue inflammation.

  15. PPARγ partial agonist GQ-16 strongly represses a subset of genes in 3T3-L1 adipocytes

    International Nuclear Information System (INIS)

    Milton, Flora Aparecida; Cvoro, Aleksandra; Amato, Angelica A.; Sieglaff, Douglas H.; Filgueira, Carly S.; Arumanayagam, Anithachristy Sigamani; Caro Alves de Lima, Maria do; Rocha Pitta, Ivan; Assis Rocha Neves, Francisco de; Webb, Paul

    2015-01-01

    Thiazolidinediones (TZDs) are peroxisome proliferator-activated receptor gamma (PPARγ) agonists that improve insulin resistance but trigger side effects such as weight gain, edema, congestive heart failure and bone loss. GQ-16 is a PPARγ partial agonist that improves glucose tolerance and insulin sensitivity in mouse models of obesity and diabetes without inducing weight gain or edema. It is not clear whether GQ-16 acts as a partial agonist at all PPARγ target genes, or whether it displays gene-selective actions. To determine how GQ-16 influences PPARγ activity on a gene by gene basis, we compared effects of rosiglitazone (Rosi) and GQ-16 in mature 3T3-L1 adipocytes using microarray and qRT-PCR. Rosi changed expression of 1156 genes in 3T3-L1, but GQ-16 only changed 89 genes. GQ-16 generally showed weak effects upon Rosi induced genes, consistent with partial agonist actions, but a subset of modestly Rosi induced and strongly repressed genes displayed disproportionately strong GQ-16 responses. PPARγ partial agonists MLR24 and SR1664 also exhibit disproportionately strong effects on transcriptional repression. We conclude that GQ-16 displays a continuum of weak partial agonist effects but efficiently represses some negatively regulated PPARγ responsive genes. Strong repressive effects could contribute to physiologic actions of GQ-16. - Highlights: • GQ-16 is an insulin sensitizing PPARγ ligand with reduced harmful side effects. • GQ-16 displays a continuum of weak partial agonist activities at PPARγ-induced genes. • GQ-16 exerts strong repressive effects at a subset of genes. • These inhibitor actions should be evaluated in models of adipose tissue inflammation

  16. Age-dependent alterations of monocyte subsets and monocyte-related chemokine pathways in healthy adults

    Directory of Open Access Journals (Sweden)

    Trautwein Christian

    2010-06-01

    Full Text Available Abstract Background Recent experimental approaches have unraveled essential migratory and functional differences of monocyte subpopulations in mice. In order to possibly translate these findings into human physiology and pathophysiology, human monocyte subsets need to be carefully revisited in health and disease. In analogy to murine studies, we hypothesized that human monocyte subsets dynamically change during ageing, potentially influencing their functionality and contributing to immunosenescence. Results Circulating monocyte subsets, surface marker and chemokine receptor expression were analyzed in 181 healthy volunteers (median age 42, range 18-88. Unlike the unaffected total leukocyte or total monocyte counts, non-classical CD14+CD16+ monocytes significantly increased with age, but displayed reduced HLA-DR and CX3CR1 surface expression in the elderly. Classical CD14++CD16- monocyte counts did not vary dependent on age. Serum MCP-1 (CCL2, but not MIP1α (CCL3, MIP1β (CCL4 or fractalkine (CX3CL1 concentrations increased with age. Monocyte-derived macrophages from old or young individuals did not differ with respect to cytokine release in vitro at steady state or upon LPS stimulation. Conclusions Our study demonstrates dynamic changes of circulating monocytes during ageing in humans. The expansion of the non-classical CD14+CD16+ subtype, alterations of surface protein and chemokine receptor expression as well as circulating monocyte-related chemokines possibly contribute to the preserved functionality of the monocyte pool throughout adulthood.

  17. REST represses a subset of the pancreatic endocrine differentiation program

    DEFF Research Database (Denmark)

    Martin, David; Kim, Yung-Hae; Sever, Dror

    2015-01-01

    in neurons and in endocrine cells, which is necessary for their normal function. During development, REST represses a subset of genes in the neuronal differentiation program and Rest is down-regulated as neurons differentiate. Here, we investigate the role of REST in the differentiation of pancreatic...... endocrine cells, which are molecularly close to neurons. We show that Rest is widely expressed in pancreas progenitors and that it is down-regulated in differentiated endocrine cells. Sustained expression of REST in Pdx1(+) progenitors impairs the differentiation of endocrine-committed Neurog3...

  18. Cytokines and T-lymphocyte subsets in healthy post-menopausal women: estrogen retards bone loss without affecting the release of IL-1 or IL-1ra

    DEFF Research Database (Denmark)

    Abrahamsen, Bo; Bendtzen, Klaus; Beck-Nielsen, H

    1997-01-01

    resorptive cytokines and have also been linked with bone metabolism and the development of osteoporosis. Cytokine secretion from whole blood cell cultures was compared between two randomized groups of healthy early post-menopausal women (mean age 52.5 yrs, N = 91) and lymphocyte subsets were quantitated....... There was no association between cytokine release and bone mass or loss assessed over 2 yrs. The only exception was a weak estrogen-independent correlation between basal IL-1ra secretion and bone loss (r = -0.21, p loss...... cells may be important in the pathophysiology of post-menopausal bone loss. The possibility that IL-1ra acts as an independent bone-sparing factor unrelated to estrogen withdrawal warrants further investigation. In conclusion, ERT maintains bone without affecting the release of the IL-1 family...

  19. Some Observations on the Subset Simulation Related to the Wind Turbine Mechanics

    DEFF Research Database (Denmark)

    Sichani, Mahdi Teimouri; Nielsen, Søren R.K.; Thoft-Christensen, Palle

    2012-01-01

    systems. It is interesting to determine applicability of the Subset Simulation (SS) techniques, as a powerful representative of Variance Reduction Monte Carlo (VRMC) methods, on the wind turbine systems specifically with an active controller. Hence in this paper we apply and discuss these methods...... on a benchmark wind turbine model and analyze the results in view of their applicability....

  20. Effects of one versus two bouts of moderate intensity physical activity on selective attention during a school morning in Dutch primary schoolchildren: A randomized controlled trial.

    Science.gov (United States)

    Altenburg, Teatske M; Chinapaw, Mai J M; Singh, Amika S

    2016-10-01

    Evidence suggests that physical activity is positively related to several aspects of cognitive functioning in children, among which is selective attention. To date, no information is available on the optimal frequency of physical activity on cognitive functioning in children. The current study examined the acute effects of one and two bouts of moderate-intensity physical activity on children's selective attention. Randomized controlled trial (ISRCTN97975679). Thirty boys and twenty-six girls, aged 10-13 years, were randomly assigned to three conditions: (A) sitting all morning working on simulated school tasks; (B) one 20-min physical activity bout after 90min; and (C) two 20-min physical activity bouts, i.e. at the start and after 90min. Selective attention was assessed at five time points during the morning (i.e. at baseline and after 20, 110, 130 and 220min), using the 'Sky Search' subtest of the 'Test of Selective Attention in Children'. We used GEE analysis to examine differences in Sky Search scores between the three experimental conditions, adjusting for school, baseline scores, self-reported screen time and time spent in sports. Children who performed two 20-min bouts of moderate-intensity physical activity had significantly better Sky Search scores compared to children who performed one physical activity bout or remained seated the whole morning (B=-0.26; 95% CI=[-0.52; -0.00]). Our findings support the importance of repeated physical activity during the school day for beneficial effects on selective attention in children. Copyright © 2015 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

  1. Preliminary analysis of the nestedness patterns of Montane forest ...

    African Journals Online (AJOL)

    Results show that the species ordering is significantly non-random. The discussion and conclusions focus on the nested subset patterns exhibited by 14 species and, to a lesser extent, 'idiosyncratic' species and islands. Factors that may have contributed to this pattern include selective extinction and colonisation; however, ...

  2. Neuromorphic VLSI Models of Selective Attention: From Single Chip Vision Sensors to Multi-chip Systems

    OpenAIRE

    Giacomo Indiveri

    2008-01-01

    Biological organisms perform complex selective attention operations continuously and effortlessly. These operations allow them to quickly determine the motor actions to take in response to combinations of external stimuli and internal states, and to pay attention to subsets of sensory inputs suppressing non salient ones. Selective attention strategies are extremely effective in both natural and artificial systems which have to cope with large amounts of input data and have limited computation...

  3. The performance of tests on endogeneity of subsets of explanatory variables scanned by simulation

    NARCIS (Netherlands)

    Kiviet, J.F.; Pleus, M.

    2011-01-01

    Tests for classification as endogenous or predetermined of arbitrary subsets of regressors are formulated as significance tests in auxiliary IV regressions and their relationships with various more classic test procedures are examined. Simulation experiments are designed by solving the data

  4. Treg subsets in inflammatory bowel disease and colorectal carcinoma: Characteristics, role, and therapeutic targets

    NARCIS (Netherlands)

    van Herk, Egbert H.; te Velde, Anje A.

    2016-01-01

    T regulatory cells (Tregs) play an important role in the regulation of autoimmunity, autoinflammation, allergic diseases, infection, and the tumor environment. Different subsets are characterized that use a number of regulatory mechanisms. Tregs can influence the progression of inflammatory bowel

  5. A Longitudinal Study of the Role of T Cell subset, Th1/Th2 cytokines ...

    African Journals Online (AJOL)

    A Longitudinal Study of the Role of T Cell subset, Th1/Th2 cytokines and antiplasmodial antibodies in uncomplicated Malaria in a Village Population Chronically Exposed to Plasmodium falciparum Malaria.

  6. Selection of Representative Models for Decision Analysis Under Uncertainty

    Science.gov (United States)

    Meira, Luis A. A.; Coelho, Guilherme P.; Santos, Antonio Alberto S.; Schiozer, Denis J.

    2016-03-01

    The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request.

  7. Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction.

    Science.gov (United States)

    Marques, Yuri Bento; de Paiva Oliveira, Alcione; Ribeiro Vasconcelos, Ana Tereza; Cerqueira, Fabio Ribeiro

    2016-12-15

    MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets. By comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools. The extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool.

  8. Beneficial Effects of cART Initiated during Primary and Chronic HIV-1 Infection on Immunoglobulin-Expression of Memory B-Cell Subsets.

    Directory of Open Access Journals (Sweden)

    Manuela Pogliaghi

    Full Text Available During HIV-1 infection the B-cell compartment undergoes profound changes towards terminal differentiation, which are only partially restored by antiretroviral therapy (cART.To investigate the impact of infection as early as during primary HIV-1 infection (PHI we assessed distribution of B-cell subsets in 19 PHI and 25 chronic HIV-1-infected (CHI individuals before and during 48 weeks of cART as compared to healthy controls (n = 23. We also analysed Immunoglobulin-expression of memory B-cell subsets to identify alterations in Immunoglobulin-maturation.Determination of B-cell subsets at baseline showed that total and Naive B-cells were decreased whereas Activated Memory (AM, Tissue-like Memory (TLM B-cells and Plasma cells were increased in both PHI and CHI patients. After 4 weeks of cART total B-cells increased, while AM, TLM B-cells and Plasma cells decreased, although without reaching normal levels in either group of individuals. This trend was maintained until week 48, though only total B-cells normalized in both PHI and CHI. Resting Memory (RM B-cells were preserved since baseline. This subset remained stable in CHI, while was expanded by an early initiation of cART during PHI. Untreated CHI patients showed IgM-overexpression at the expenses of switched (IgM-IgD- phenotypes of the memory subsets. Interestingly, in PHI patients a significant alteration of Immunoglobulin-expression was evident at BL in TLM cells, and after 4 weeks, despite treatment, in AM and RM subsets. After 48 weeks of therapy, Immunoglobulin-expression of AM and RM almost normalized, but remained perturbed in TLM cells in both groups.In conclusion, aberrant activated and exhausted B-cell phenotypes rose already during PHI, while most of the alterations in Ig-expression seen in CHI appeared later, despite 4 weeks of effective cART. After 48 weeks of cART B-cell subsets distribution improved although without full normalization, while Immunoglobulin-expression normalized

  9. Beneficial Effects of cART Initiated during Primary and Chronic HIV-1 Infection on Immunoglobulin-Expression of Memory B-Cell Subsets.

    Science.gov (United States)

    Pogliaghi, Manuela; Ripa, Marco; Pensieroso, Simone; Tolazzi, Monica; Chiappetta, Stefania; Nozza, Silvia; Lazzarin, Adriano; Tambussi, Giuseppe; Scarlatti, Gabriella

    2015-01-01

    During HIV-1 infection the B-cell compartment undergoes profound changes towards terminal differentiation, which are only partially restored by antiretroviral therapy (cART). To investigate the impact of infection as early as during primary HIV-1 infection (PHI) we assessed distribution of B-cell subsets in 19 PHI and 25 chronic HIV-1-infected (CHI) individuals before and during 48 weeks of cART as compared to healthy controls (n = 23). We also analysed Immunoglobulin-expression of memory B-cell subsets to identify alterations in Immunoglobulin-maturation. Determination of B-cell subsets at baseline showed that total and Naive B-cells were decreased whereas Activated Memory (AM), Tissue-like Memory (TLM) B-cells and Plasma cells were increased in both PHI and CHI patients. After 4 weeks of cART total B-cells increased, while AM, TLM B-cells and Plasma cells decreased, although without reaching normal levels in either group of individuals. This trend was maintained until week 48, though only total B-cells normalized in both PHI and CHI. Resting Memory (RM) B-cells were preserved since baseline. This subset remained stable in CHI, while was expanded by an early initiation of cART during PHI. Untreated CHI patients showed IgM-overexpression at the expenses of switched (IgM-IgD-) phenotypes of the memory subsets. Interestingly, in PHI patients a significant alteration of Immunoglobulin-expression was evident at BL in TLM cells, and after 4 weeks, despite treatment, in AM and RM subsets. After 48 weeks of therapy, Immunoglobulin-expression of AM and RM almost normalized, but remained perturbed in TLM cells in both groups. In conclusion, aberrant activated and exhausted B-cell phenotypes rose already during PHI, while most of the alterations in Ig-expression seen in CHI appeared later, despite 4 weeks of effective cART. After 48 weeks of cART B-cell subsets distribution improved although without full normalization, while Immunoglobulin-expression normalized among AM and

  10. Randomized algorithms in automatic control and data mining

    CERN Document Server

    Granichin, Oleg; Toledano-Kitai, Dvora

    2015-01-01

    In the fields of data mining and control, the huge amount of unstructured data and the presence of uncertainty in system descriptions have always been critical issues. The book Randomized Algorithms in Automatic Control and Data Mining introduces the readers to the fundamentals of randomized algorithm applications in data mining (especially clustering) and in automatic control synthesis. The methods proposed in this book guarantee that the computational complexity of classical algorithms and the conservativeness of standard robust control techniques will be reduced. It is shown that when a problem requires "brute force" in selecting among options, algorithms based on random selection of alternatives offer good results with certain probability for a restricted time and significantly reduce the volume of operations.

  11. Exposure to di(n-butyl)phthalate and benzo(a)pyrene alters IL-1β secretion and subset expression of testicular macrophages, resulting in decreased testosterone production in rats

    International Nuclear Information System (INIS)

    Zheng Shanjun; Tian Huaijun; Cao Jia; Gao Yuqi

    2010-01-01

    Di(n-butyl)phthalate (DBP) and benzo(a)pyrene (BaP) are environmental endocrine disruptors that are potentially hazardous to humans. These chemicals affect testicular macrophage immuno-endocrine function and testosterone production. However, the underlying mechanisms for these effects are not fully understood. It is well known that interleukin-1 beta (IL-1β), which is secreted by testicular macrophages, plays a trigger role in regulating Leydig cell steroidogenesis. The purpose of this study was to reveal the effects of co-exposure to DBP and BaP on testicular macrophage subset expression, IL-1β secretion and testosterone production. Adult male Sprague-Dawley rats were randomly divided into seven groups; two groups received DBP plus BaP (DBP + BaP: 50 + 1 or 250 + 5 mg/kg/day) four groups received DBP or BaP alone (DBP: 50 or 250 mg/kg/day; BaP: 1 or 5 mg/kg/day), and one group received vehicle alone (control). After co-exposure for 90 days, the relative expression of macrophage subsets and their functions changed. ED2 + testicular macrophages (reactive with a differentiation-related antigen present on the resident macrophages) were activated and IL-1β secretion was enhanced. DBP and BaP acted additively, as demonstrated by greater IL-1β secretion relative to each compound alone. These observations suggest that exposure to DBP plus BaP exerted greater suppression on testosterone production compared with each compound alone. The altered balance in the subsets of testicular macrophages and the enhanced ability of resident testicular macrophages to secrete IL-1β, resulted in enhanced production of IL-1β as a potent steroidogenesis repressor. This may represent an important mechanism by which DBP and BaP repress steroidogenesis.

  12. The basic science and mathematics of random mutation and natural selection.

    Science.gov (United States)

    Kleinman, Alan

    2014-12-20

    The mutation and natural selection phenomenon can and often does cause the failure of antimicrobial, herbicidal, pesticide and cancer treatments selection pressures. This phenomenon operates in a mathematically predictable behavior, which when understood leads to approaches to reduce and prevent the failure of the use of these selection pressures. The mathematical behavior of mutation and selection is derived using the principles given by probability theory. The derivation of the equations describing the mutation and selection phenomenon is carried out in the context of an empirical example. Copyright © 2014 John Wiley & Sons, Ltd.

  13. A Typical Immune T/B Subset Profile Characterizes Bicuspid Aortic Valve: In an Old Status?

    Directory of Open Access Journals (Sweden)

    Carmela R. Balistreri

    2018-01-01

    Full Text Available Bicuspid valve disease is associated with the development of thoracic aortic aneurysm. The molecular mechanisms underlying this association still need to be clarified. Here, we evaluated the circulating levels of T and B lymphocyte subsets associated with the development of vascular diseases in patients with bicuspid aortic valve or tricuspid aortic valve with and without thoracic aortic aneurysm. We unveiled that the circulating levels of the MAIT, CD4+IL−17A+, and NKT T cell subsets were significantly reduced in bicuspid valve disease cases, when compared to tricuspid aortic valve cases in either the presence or the absence of thoracic aortic aneurysm. Among patients with tricuspid aortic valve, these cells were higher in those also affected by thoracic aortic aneurysm. Similar data were obtained by examining CD19+ B cells, naïve B cells (IgD+CD27−, memory unswitched B cells (IgD+CD27+, memory switched B cells (IgD−CD27+, and double-negative B cells (DN (IgD−CD27−. These cells resulted to be lower in subjects with bicuspid valve disease with respect to patients with tricuspid aortic valve. In whole, our data indicate that patients with bicuspid valve disease show a quantitative reduction of T and B lymphocyte cell subsets. Future studies are encouraged to understand the molecular mechanisms underlying this observation and its pathophysiological significance.

  14. PyCCF: Python Cross Correlation Function for reverberation mapping studies

    Science.gov (United States)

    Sun, Mouyuan; Grier, C. J.; Peterson, B. M.

    2018-05-01

    PyCCF emulates a Fortran program written by B. Peterson for use with reverberation mapping. The code cross correlates two light curves that are unevenly sampled using linear interpolation and measures the peak and centroid of the cross-correlation function. In addition, it is possible to run Monto Carlo iterations using flux randomization and random subset selection (RSS) to produce cross-correlation centroid distributions to estimate the uncertainties in the cross correlation results.

  15. Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness

    Science.gov (United States)

    Li, Jin; Tran, Maggie; Siwabessy, Justy

    2016-01-01

    Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia’s marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to ‘small p and large n’ problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and

  16. T cell subsets in human airways prior to and following endobronchial administration of endotoxin

    DEFF Research Database (Denmark)

    Ronit, Andreas; Plovsing, Ronni R; Gaardbo, Julie C

    2015-01-01

    BACKGROUND AND OBJECTIVES: Bronchial instillation of lipopolysaccharide (LPS) provides a reversible model of lung inflammation that may resemble early stages of acute respiratory distress syndrome (ARDS). We investigated the distributions of T-cell subsets in the human airways and sought to deter...

  17. Meditative Movement as a treatment for pulmonary dysfunction in flight attendants exposed to second-hand cigarette smoke: Study protocol for a randomized trial.

    Directory of Open Access Journals (Sweden)

    Peter ePayne

    2016-03-01

    Full Text Available A study protocol is presented for the investigation of Meditative Movement (MM as a treatment for pulmonary dysfunction in Flight Attendants (FA who were exposed to second-hand cigarette smoke (SHCS while flying before the smoking ban. The study will have three parts, some of which will run concurrently. The first is a data gathering and screening phase, which will gather data on pulmonary and other aspects of the health of FA, and will also serve to screen participants for the other phases. Second is an exercise selection phase, in which a variety of MM exercises will be taught, over a 16-week period, to a cohort of 20 FA. A subset of these exercises will be selected on the basis of participant feedback on effectiveness and compliance. Third is a 52-week randomized controlled trial (RCT to evaluate the effectiveness of a digitally delivered form of the previously selected exercises on a group of 20 FA, as compared with an attention control group. Outcome measures to be used in all three parts of the study include the six-minute walk test as a primary measure, as well as a range of biomarkers, tests and questionnaires documenting hormonal, cardio-respiratory, autonomic and affective state. This study is registered at ClinicalTrials.gov. Identifier: NCT02612389.

  18. Clinically Relevant Subsets Identified by Gene Expression Patterns Support a Revised Ontogenic Model of Wilms Tumor: A Children's Oncology Group Study

    Directory of Open Access Journals (Sweden)

    Samantha Gadd

    2012-08-01

    Full Text Available Wilms tumors (WT have provided broad insights into the interface between development and tumorigenesis. Further understanding is confounded by their genetic, histologic, and clinical heterogeneity, the basis of which remains largely unknown. We evaluated 224 WT for global gene expression patterns; WT1, CTNNB1, and WTX mutation; and 11p15 copy number and methylation patterns. Five subsets were identified showing distinct differences in their pathologic and clinical features: these findings were validated in 100 additional WT. The gene expression pattern of each subset was compared with published gene expression profiles during normal renal development. A novel subset of epithelial WT in infants lacked WT1, CTNNB1, and WTX mutations and nephrogenic rests and displayed a gene expression pattern of the postinduction nephron, and none recurred. Three subsets were characterized by a low expression of WT1 and intralobar nephrogenic rests. These differed in their frequency of WT1 and CTNNB1 mutations, in their age, in their relapse rate, and in their expression similarities with the intermediate mesoderm versus the metanephric mesenchyme. The largest subset was characterized by biallelic methylation of the imprint control region 1, a gene expression profile of the metanephric mesenchyme, and both interlunar and perilobar nephrogenic rests. These data provide a biologic explanation for the clinical and pathologic heterogeneity seen within WT and enable the future development of subset-specific therapeutic strategies. Further, these data support a revision of the current model of WT ontogeny, which allows for an interplay between the type of initiating event and the developmental stage in which it occurs.

  19. Tensor-based Multi-view Feature Selection with Applications to Brain Diseases

    Science.gov (United States)

    Cao, Bokai; He, Lifang; Kong, Xiangnan; Yu, Philip S.; Hao, Zhifeng; Ragin, Ann B.

    2015-01-01

    In the era of big data, we can easily access information from multiple views which may be obtained from different sources or feature subsets. Generally, different views provide complementary information for learning tasks. Thus, multi-view learning can facilitate the learning process and is prevalent in a wide range of application domains. For example, in medical science, measurements from a series of medical examinations are documented for each subject, including clinical, imaging, immunologic, serologic and cognitive measures which are obtained from multiple sources. Specifically, for brain diagnosis, we can have different quantitative analysis which can be seen as different feature subsets of a subject. It is desirable to combine all these features in an effective way for disease diagnosis. However, some measurements from less relevant medical examinations can introduce irrelevant information which can even be exaggerated after view combinations. Feature selection should therefore be incorporated in the process of multi-view learning. In this paper, we explore tensor product to bring different views together in a joint space, and present a dual method of tensor-based multi-view feature selection (dual-Tmfs) based on the idea of support vector machine recursive feature elimination. Experiments conducted on datasets derived from neurological disorder demonstrate the features selected by our proposed method yield better classification performance and are relevant to disease diagnosis. PMID:25937823

  20. RANDOM WALK HYPOTHESIS IN FINANCIAL MARKETS

    Directory of Open Access Journals (Sweden)

    Nicolae-Marius JULA

    2017-05-01

    Full Text Available Random walk hypothesis states that the stock market prices do not follow a predictable trajectory, but are simply random. If you are trying to predict a random set of data, one should test for randomness, because, despite the power and complexity of the used models, the results cannot be trustworthy. There are several methods for testing these hypotheses and the use of computational power provided by the R environment makes the work of the researcher easier and with a cost-effective approach. The increasing power of computing and the continuous development of econometric tests should give the potential investors new tools in selecting commodities and investing in efficient markets.

  1. Surface Estimation, Variable Selection, and the Nonparametric Oracle Property.

    Science.gov (United States)

    Storlie, Curtis B; Bondell, Howard D; Reich, Brian J; Zhang, Hao Helen

    2011-04-01

    Variable selection for multivariate nonparametric regression is an important, yet challenging, problem due, in part, to the infinite dimensionality of the function space. An ideal selection procedure should be automatic, stable, easy to use, and have desirable asymptotic properties. In particular, we define a selection procedure to be nonparametric oracle (np-oracle) if it consistently selects the correct subset of predictors and at the same time estimates the smooth surface at the optimal nonparametric rate, as the sample size goes to infinity. In this paper, we propose a model selection procedure for nonparametric models, and explore the conditions under which the new method enjoys the aforementioned properties. Developed in the framework of smoothing spline ANOVA, our estimator is obtained via solving a regularization problem with a novel adaptive penalty on the sum of functional component norms. Theoretical properties of the new estimator are established. Additionally, numerous simulated and real examples further demonstrate that the new approach substantially outperforms other existing methods in the finite sample setting.

  2. Sexual selection protects against extinction.

    Science.gov (United States)

    Lumley, Alyson J; Michalczyk, Łukasz; Kitson, James J N; Spurgin, Lewis G; Morrison, Catriona A; Godwin, Joanne L; Dickinson, Matthew E; Martin, Oliver Y; Emerson, Brent C; Chapman, Tracey; Gage, Matthew J G

    2015-06-25

    Reproduction through sex carries substantial costs, mainly because only half of sexual adults produce offspring. It has been theorized that these costs could be countered if sex allows sexual selection to clear the universal fitness constraint of mutation load. Under sexual selection, competition between (usually) males and mate choice by (usually) females create important intraspecific filters for reproductive success, so that only a subset of males gains paternity. If reproductive success under sexual selection is dependent on individual condition, which is contingent to mutation load, then sexually selected filtering through 'genic capture' could offset the costs of sex because it provides genetic benefits to populations. Here we test this theory experimentally by comparing whether populations with histories of strong versus weak sexual selection purge mutation load and resist extinction differently. After evolving replicate populations of the flour beetle Tribolium castaneum for 6 to 7 years under conditions that differed solely in the strengths of sexual selection, we revealed mutation load using inbreeding. Lineages from populations that had previously experienced strong sexual selection were resilient to extinction and maintained fitness under inbreeding, with some families continuing to survive after 20 generations of sib × sib mating. By contrast, lineages derived from populations that experienced weak or non-existent sexual selection showed rapid fitness declines under inbreeding, and all were extinct after generation 10. Multiple mutations across the genome with individually small effects can be difficult to clear, yet sum to a significant fitness load; our findings reveal that sexual selection reduces this load, improving population viability in the face of genetic stress.

  3. Flow cytometry analysis of T-cell subsets in cerebrospinal fluid of narcolepsy type 1 patients with long-lasting disease

    DEFF Research Database (Denmark)

    Moresco, Monica; Lecciso, Mariangela; Ocadlikova, Darina

    2018-01-01

    BACKGROUND: Type 1 narcolepsy (NT1) is a central hypersomnia linked to the destruction of hypocretin-producing neurons. A great body of genetic and epidemiological data points to likely autoimmune disease aetiology. Recent reports have characterized peripheral blood T-cell subsets in NT1, whereas...... data regarding the cerebrospinal fluid (CSF) immune cell composition are lacking. The current study aimed to characterize the T-cell and natural killer (NK) cell subsets in NT1 patients with long disease course. METHODS: Immune cell subsets from CSF and peripheral blood mononuclear cell (PBMC) samples...... were analysed by flow cytometry in two age-balanced and sex-balanced groups of 14 NT1 patients versus 14 healthy controls. The frequency of CSF cell groups was compared with PBMCs. Non-parametric tests were used for statistical analyses. RESULTS: The NT1 patients did not show significant differences...

  4. Using variable combination population analysis for variable selection in multivariate calibration.

    Science.gov (United States)

    Yun, Yong-Huan; Wang, Wei-Ting; Deng, Bai-Chuan; Lai, Guang-Bi; Liu, Xin-bo; Ren, Da-Bing; Liang, Yi-Zeng; Fan, Wei; Xu, Qing-Song

    2015-03-03

    Variable (wavelength or feature) selection techniques have become a critical step for the analysis of datasets with high number of variables and relatively few samples. In this study, a novel variable selection strategy, variable combination population analysis (VCPA), was proposed. This strategy consists of two crucial procedures. First, the exponentially decreasing function (EDF), which is the simple and effective principle of 'survival of the fittest' from Darwin's natural evolution theory, is employed to determine the number of variables to keep and continuously shrink the variable space. Second, in each EDF run, binary matrix sampling (BMS) strategy that gives each variable the same chance to be selected and generates different variable combinations, is used to produce a population of subsets to construct a population of sub-models. Then, model population analysis (MPA) is employed to find the variable subsets with the lower root mean squares error of cross validation (RMSECV). The frequency of each variable appearing in the best 10% sub-models is computed. The higher the frequency is, the more important the variable is. The performance of the proposed procedure was investigated using three real NIR datasets. The results indicate that VCPA is a good variable selection strategy when compared with four high performing variable selection methods: genetic algorithm-partial least squares (GA-PLS), Monte Carlo uninformative variable elimination by PLS (MC-UVE-PLS), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV). The MATLAB source code of VCPA is available for academic research on the website: http://www.mathworks.com/matlabcentral/fileexchange/authors/498750. Copyright © 2015 Elsevier B.V. All rights reserved.

  5. The Goodness of Covariance Selection Problem from AUC Bounds

    OpenAIRE

    Khajavi, Navid Tafaghodi; Kuh, Anthony

    2016-01-01

    We conduct a study of graphical models and discuss the quality of model selection approximation by formulating the problem as a detection problem and examining the area under the curve (AUC). We are specifically looking at the model selection problem for jointly Gaussian random vectors. For Gaussian random vectors, this problem simplifies to the covariance selection problem which is widely discussed in literature by Dempster [1]. In this paper, we give the definition for the correlation appro...

  6. Leukocyte counts and lymphocyte subsets in relation to pregnancy and HIV infection in Malawian women.

    Science.gov (United States)

    Mandala, Wilson L; Gondwe, Esther N; Molyneux, Malcolm E; MacLennan, Jenny M; MacLennan, Calman A

    2017-09-01

    We investigated leukocyte and lymphocyte subsets in HIV-infected or HIV-uninfected, pregnant or non-pregnant Malawian women to explore whether HIV infection and pregnancy may act synergistically to impair cellular immunity. We recruited 54 pregnant and 48 non-pregnant HIV-uninfected women and 24 pregnant and 20 non-pregnant HIV-infected Malawian women. We compared peripheral blood leukocyte and lymphocyte subsets between women in the four groups. Parturient HIV-infected and HIV-uninfected women had more neutrophils (each PHIV-uninfected parturient women had fewer CD4 + and γδ T cells, B and NK cells (each Ppregnancy. Malawian women at parturition have an increased total white cell count due to neutrophilia and an HIV-unrelated pan-lymphopenia. © 2017 The Author. American Journal of Reproductive Immunology Published by John Wiley & Sons Ltd.

  7. Molecular subsets in the gene expression signatures of scleroderma skin.

    Directory of Open Access Journals (Sweden)

    Ausra Milano

    2008-07-01

    Full Text Available Scleroderma is a clinically heterogeneous disease with a complex phenotype. The disease is characterized by vascular dysfunction, tissue fibrosis, internal organ dysfunction, and immune dysfunction resulting in autoantibody production.We analyzed the genome-wide patterns of gene expression with DNA microarrays in skin biopsies from distinct scleroderma subsets including 17 patients with systemic sclerosis (SSc with diffuse scleroderma (dSSc, 7 patients with SSc with limited scleroderma (lSSc, 3 patients with morphea, and 6 healthy controls. 61 skin biopsies were analyzed in a total of 75 microarray hybridizations. Analysis by hierarchical clustering demonstrates nearly identical patterns of gene expression in 17 out of 22 of the forearm and back skin pairs of SSc patients. Using this property of the gene expression, we selected a set of 'intrinsic' genes and analyzed the inherent data-driven groupings. Distinct patterns of gene expression separate patients with dSSc from those with lSSc and both are easily distinguished from normal controls. Our data show three distinct patient groups among the patients with dSSc and two groups among patients with lSSc. Each group can be distinguished by unique gene expression signatures indicative of proliferating cells, immune infiltrates and a fibrotic program. The intrinsic groups are statistically significant (p<0.001 and each has been mapped to clinical covariates of modified Rodnan skin score, interstitial lung disease, gastrointestinal involvement, digital ulcers, Raynaud's phenomenon and disease duration. We report a 177-gene signature that is associated with severity of skin disease in dSSc.Genome-wide gene expression profiling of skin biopsies demonstrates that the heterogeneity in scleroderma can be measured quantitatively with DNA microarrays. The diversity in gene expression demonstrates multiple distinct gene expression programs in the skin of patients with scleroderma.

  8. Multi-lingual search engine to access PubMed monolingual subsets: a feasibility study.

    Science.gov (United States)

    Darmoni, Stéfan J; Soualmia, Lina F; Griffon, Nicolas; Grosjean, Julien; Kerdelhué, Gaétan; Kergourlay, Ivan; Dahamna, Badisse

    2013-01-01

    PubMed contains many articles in languages other than English but it is difficult to find them using the English version of the Medical Subject Headings (MeSH) Thesaurus. The aim of this work is to propose a tool allowing access to a PubMed subset in one language, and to evaluate its performance. Translations of MeSH were enriched and gathered in the information system. PubMed subsets in main European languages were also added in our database, using a dedicated parser. The CISMeF generic semantic search engine was evaluated on the response time for simple queries. MeSH descriptors are currently available in 11 languages in the information system. All the 654,000 PubMed citations in French were integrated into CISMeF database. None of the response times exceed the threshold defined for usability (2 seconds). It is now possible to freely access biomedical literature in French using a tool in French; health professionals and lay people with a low English language may find it useful. It will be expended to several European languages: German, Spanish, Norwegian and Portuguese.

  9. Comprehensive Approach for Identifying the T Cell Subset Origin of CD3 and CD28 Antibody-Activated Chimeric Antigen Receptor-Modified T Cells.

    Science.gov (United States)

    Schmueck-Henneresse, Michael; Omer, Bilal; Shum, Thomas; Tashiro, Haruko; Mamonkin, Maksim; Lapteva, Natalia; Sharma, Sandhya; Rollins, Lisa; Dotti, Gianpietro; Reinke, Petra; Volk, Hans-Dieter; Rooney, Cliona M

    2017-07-01

    The outcome of therapy with chimeric Ag receptor (CAR)-modified T cells is strongly influenced by the subset origin of the infused T cells. However, because polyclonally activated T cells acquire a largely CD45RO + CCR7 - effector memory phenotype after expansion, regardless of subset origin, it is impossible to know which subsets contribute to the final T cell product. To determine the contribution of naive T cell, memory stem T cell, central memory T cell, effector memory T cell, and terminally differentiated effector T cell populations to the CD3 and CD28-activated CAR-modified T cells that we use for therapy, we followed the fate and function of individually sorted CAR-modified T cell subsets after activation with CD3 and CD28 Abs (CD3/28), transduction and culture alone, or after reconstitution into the relevant subset-depleted population. We show that all subsets are sensitive to CAR transduction, and each developed a distinct T cell functional profile during culture. Naive-derived T cells showed the greatest rate of proliferation but had more limited effector functions and reduced killing compared with memory-derived populations. When cultured in the presence of memory T cells, naive-derived T cells show increased differentiation, reduced effector cytokine production, and a reduced reproliferative response to CAR stimulation. CD3/28-activated T cells expanded in IL-7 and IL-15 produced greater expansion of memory stem T cells and central memory T cell-derived T cells compared with IL-2. Our strategy provides a powerful tool to elucidate the characteristics of CAR-modified T cells, regardless of the protocol used for expansion, reveals the functional properties of each expanded T cell subset, and paves the way for a more detailed evaluation of the effects of manufacturing changes on the subset contribution to in vitro-expanded T cells. Copyright © 2017 by The American Association of Immunologists, Inc.

  10. Organic Ferroelectric-Based 1T1T Random Access Memory Cell Employing a Common Dielectric Layer Overcoming the Half-Selection Problem.

    Science.gov (United States)

    Zhao, Qiang; Wang, Hanlin; Ni, Zhenjie; Liu, Jie; Zhen, Yonggang; Zhang, Xiaotao; Jiang, Lang; Li, Rongjin; Dong, Huanli; Hu, Wenping

    2017-09-01

    Organic electronics based on poly(vinylidenefluoride/trifluoroethylene) (P(VDF-TrFE)) dielectric is facing great challenges in flexible circuits. As one indispensable part of integrated circuits, there is an urgent demand for low-cost and easy-fabrication nonvolatile memory devices. A breakthrough is made on a novel ferroelectric random access memory cell (1T1T FeRAM cell) consisting of one selection transistor and one ferroelectric memory transistor in order to overcome the half-selection problem. Unlike complicated manufacturing using multiple dielectrics, this system simplifies 1T1T FeRAM cell fabrication using one common dielectric. To achieve this goal, a strategy for semiconductor/insulator (S/I) interface modulation is put forward and applied to nonhysteretic selection transistors with high performances for driving or addressing purposes. As a result, high hole mobility of 3.81 cm 2 V -1 s -1 (average) for 2,6-diphenylanthracene (DPA) and electron mobility of 0.124 cm 2 V -1 s -1 (average) for N,N'-1H,1H-perfluorobutyl dicyanoperylenecarboxydiimide (PDI-FCN 2 ) are obtained in selection transistors. In this work, we demonstrate this technology's potential for organic ferroelectric-based pixelated memory module fabrication. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. Feature Extraction and Selection Strategies for Automated Target Recognition

    Science.gov (United States)

    Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

    2010-01-01

    Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

  12. Effect of radiotherapy on serum SCC, CEA, CRFRA21-1, TAG72, CA199 and lymphocyte subsets in patients with esophageal squamous cell carcinoma

    Directory of Open Access Journals (Sweden)

    Sha Sha

    2016-09-01

    Full Text Available Objective: To study the effect of radiotherapy on serum SCC, CEA, CRFRA21-1, TAG72, CA199 and lymphocyte subsets in patients with esophageal squamous cell carcinoma. Methods: A total of 60 patients with esophageal squamous cell carcinoma in our hospital from January 2013 to January 2016 were selected as experiment group and 40 healthy subjects were selected as control group. Patients in experiment group were treated with 6MV X-ray radiation therapy. Serum SCC, CEA, CRFRA21-1, TAG72, CA199 and the cell percentage of peripheral blood CD4+, CD8+ were compared in control group and the experimental group before and after 1 month radiotherapy. Results: Before treatment, the levels of serum SCC, CEA and CRFRA21-1 in the experimental group were significantly higher than those in the control group (P0.05. Before treatment, the cell percentage of peripheral blood CD4+, CD8+ and the ratio of CD4+/CD8+ in experimental group was significantly lower than that of the control group, the percentage of peripheral blood CD8+ in the experimental group was significantly higher than that in the control group (P0.05, and in the experimental group, the proportion of CD4+ cells and the tatio of CD4+/CD8+ in peripheral blood was significantly lower than that of the control group, the proportion of CD8+ was significantly higher than that of the control group (P<0.05. Conclusions: Radiotherapy can significantly reduce the serum SCC, CEA, CRFRA21-1, TAG72 and CA199 levels of the patients with esophageal squamous cell carcinoma, but have less influence on the T lymphocyte subsets.

  13. Random-walk simulation of selected aspects of dissipative collisions

    International Nuclear Information System (INIS)

    Toeke, J.; Gobbi, A.; Matulewicz, T.

    1984-11-01

    Internuclear thermal equilibrium effects and shell structure effects in dissipative collisions are studied numerically within the framework of the model of stochastic exchanges by applying the random-walk technique. Effective blocking of the drift through the mass flux induced by the temperature difference, while leaving the variances of the mass distributions unaltered is found possible, provided an internuclear potential barrier is present. Presence of the shell structure is found to lead to characteristic correlations between the consecutive exchanges. Experimental evidence for the predicted effects is discussed. (orig.)

  14. Integrin αMβ2 is differently expressed by subsets of human osteoclast precursors and mediates adhesion of classical monocytes to bone

    Energy Technology Data Exchange (ETDEWEB)

    Sprangers, Sara, E-mail: s.l.sprangers@acta.nl [Department of Oral Cell Biology and Functional Anatomy, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, MOVE Research Institute Amsterdam, Gustav Mahlerlaan 3004, 1081 LA Amsterdam The Netherlands (Netherlands); Schoenmaker, Ton, E-mail: t.schoenmaker@acta.nl [Department of Oral Cell Biology and Functional Anatomy, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, MOVE Research Institute Amsterdam, Gustav Mahlerlaan 3004, 1081 LA Amsterdam The Netherlands (Netherlands); Department of Periodontology, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, MOVE Research Institute Amsterdam, Gustav Mahlerlaan 3004, 1081 LA Amsterdam The Netherlands (Netherlands); Cao, Yixuan, E-mail: y.cao@acta.nl [Department of Oral Cell Biology and Functional Anatomy, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, MOVE Research Institute Amsterdam, Gustav Mahlerlaan 3004, 1081 LA Amsterdam The Netherlands (Netherlands); Everts, Vincent, E-mail: v.everts@acta.nl [Department of Oral Cell Biology and Functional Anatomy, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, MOVE Research Institute Amsterdam, Gustav Mahlerlaan 3004, 1081 LA Amsterdam The Netherlands (Netherlands); Vries, Teun J. de, E-mail: teun.devries@acta.nl [Department of Oral Cell Biology and Functional Anatomy, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, MOVE Research Institute Amsterdam, Gustav Mahlerlaan 3004, 1081 LA Amsterdam The Netherlands (Netherlands); Department of Periodontology, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, MOVE Research Institute Amsterdam, Gustav Mahlerlaan 3004, 1081 LA Amsterdam The Netherlands (Netherlands)

    2017-01-01

    Bone-degrading osteoclasts are formed through fusion of their monocytic precursors. In the population of human peripheral blood monocytes, three distinct subsets have been identified: classical, intermediate and non-classical monocytes. We have previously shown that when the monocyte subsets are cultured on bone, significantly more osteoclasts are formed from classical monocytes than from intermediate or non-classical monocytes. Considering that this difference does not exist when monocyte subsets are cultured on plastic, we hypothesized that classical monocytes adhere better to the bone surface compared to intermediate and non-classical monocytes. To investigate this, the different monocyte subsets were isolated from human peripheral blood and cultured on slices of human bone in the presence of the cytokine M-CSF. We found that classical monocytes adhere better to bone due to a higher expression of the integrin αMβ2 and that their ability to attach to bone is significantly decreased when the integrin is blocked. This suggests that integrin αMβ2 mediates attachment of osteoclast precursors to bone and thereby enables the formation of osteoclasts.

  15. Selective attention without a neocortex.

    Science.gov (United States)

    Krauzlis, Richard J; Bogadhi, Amarender R; Herman, James P; Bollimunta, Anil

    2018-05-01

    Selective attention refers to the ability to restrict neural processing and behavioral responses to a relevant subset of available stimuli, while simultaneously excluding other valid stimuli from consideration. In primates and other mammals, descriptions of this ability typically emphasize the neural processing that takes place in the cerebral neocortex. However, non-mammals such as birds, reptiles, amphibians and fish, which completely lack a neocortex, also have the ability to selectively attend. In this article, we survey the behavioral evidence for selective attention in non-mammals, and review the midbrain and forebrain structures that are responsible. The ancestral forms of selective attention are presumably selective orienting behaviors, such as prey-catching and predator avoidance. These behaviors depend critically on a set of subcortical structures, including the optic tectum (OT), thalamus and striatum, that are highly conserved across vertebrate evolution. In contrast, the contributions of different pallial regions in the forebrain to selective attention have been subject to more substantial changes and reorganization. This evolutionary perspective makes plain that selective attention is not a function achieved de novo with the emergence of the neocortex, but instead is implemented by circuits accrued and modified over hundreds of millions of years, beginning well before the forebrain contained a neocortex. Determining how older subcortical circuits interact with the more recently evolved components in the neocortex will likely be crucial for understanding the complex properties of selective attention in primates and other mammals, and for identifying the etiology of attention disorders. Published by Elsevier Ltd.

  16. Time-dependent migration of citations through PubMed and OvidSP subsets: a study on a series of simultaneous PubMed and OvidSP searches.

    Science.gov (United States)

    Boeker, Martin; Vach, Werner; Motschall, Edith

    2013-01-01

    To quantitatively describe (1) differences between search results derived at consecutive time points with the PubMed and OvidSP literature search interfaces over a five day interval, and (2) the migration of citations through different subsets to estimate the timeliness of OvidSP. PubMed-Identifiers (PMIDs) of the following subsets were retrieved from PubMed and OvidSP simultaneously (within 8 h) at 11 days in March and April 2010 including 5 consecutive days: as supplied by publisher, in process, PubMed not MEDLINE, and OLDMEDLINE. Search results were compared for difference and intersection sets. The migration of citations on individual level was determined by comparison of corresponding sets over several days. The "in process" set was stable with about 446,000 - 452,000 citations; a small fraction of up to 3 % of the total subsets were in PubMed only and OvidSP only subsets. About 96 % of the ca. 10,500 citations in the OvidSP only subset migrated within 2 days out of the "in process" subset. The database of OvidSP is updated within a period of two days.

  17. Random number generation and creativity.

    Science.gov (United States)

    Bains, William

    2008-01-01

    A previous paper suggested that humans can generate genuinely random numbers. I tested this hypothesis by repeating the experiment with a larger number of highly numerate subjects, asking them to call out a sequence of digits selected from 0 through 9. The resulting sequences were substantially non-random, with an excess of sequential pairs of numbers and a deficit of repeats of the same number, in line with previous literature. However, the previous literature suggests that humans generate random numbers with substantial conscious effort, and distractions which reduce that effort reduce the randomness of the numbers. I reduced my subjects' concentration by asking them to call out in another language, and with alcohol - neither affected the randomness of their responses. This suggests that the ability to generate random numbers is a 'basic' function of the human mind, even if those numbers are not mathematically 'random'. I hypothesise that there is a 'creativity' mechanism, while not truly random, provides novelty as part of the mind's defence against closed programming loops, and that testing for the effects seen here in people more or less familiar with numbers or with spontaneous creativity could identify more features of this process. It is possible that training to perform better at simple random generation tasks could help to increase creativity, through training people to reduce the conscious mind's suppression of the 'spontaneous', creative response to new questions.

  18. Selection and characterization of DNA aptamers

    NARCIS (Netherlands)

    Ruigrok, V.J.B.

    2013-01-01

    This thesis focusses on the selection and characterisation of DNA aptamers and the various aspects related to their selection from large pools of randomized oligonucleotides. Aptamers are affinity tools that can specifically recognize and bind predefined target molecules; this ability, however,

  19. Flurbiprofen improves dysfunction of T-lymphocyte subsets and natural killer cells in cancer patients receiving post-operative morphine analgesia.

    Science.gov (United States)

    Shen, Jin-Chun; Sun, He-Liang; Zhang, Ming-Qiang; Liu, Xiao-Yu; Wang, Zhong- Yun; Yang, Jian-Jun

    2014-08-01

    Acute pain can lead to immune dysfunction, which can be partly ameliorated by successful pain management. Opioids, which are widely used for analgesia, can result in the deterioration of immune function. This study aimed to investigate the influence of morphine with or without flurbiprofen as post-operative analgesics on the immune systems of patients undergoing gastric cancer surgery. 60 patients undergoing gastric cancer surgery were equally randomized into two groups. They received post-operative patient-controlled intravenous (IV) analgesia using morphine either with or without flurbiprofen. Visual analogue scale (VAS) scores, Bruggemann comfort scale (BCS) scores, morphine consumption, time of first flatus, incidence of nausea/vomiting, and T-lymphocyte subsets (CD3⁺, CD4⁺, and CD8⁺) and natural killer cells (CD3⁻CD16⁺CD56⁺) were evaluated. No significant difference was observed in the VAS scores, BCS scores, and nausea/vomiting incidence between groups. Less morphine was consumed and the time of first flatus was earlier in patients receiving morphine with flurbiprofen than morphine alone. The expression of CD3⁺, CD4⁺, CD4⁺/CD8⁺, and CD3⁻CD16⁺CD56⁺ decreased at 2 hours after incision and, except for CD3⁻CD16⁺CD56⁺, returned to baseline at 120 hours after surgery. Moreover, the expression of CD3⁻CD16⁺CD56⁺ at 2 hours after incision and the expression of CD3⁺, CD4⁺, CD4⁺/CD8⁺, and CD3⁻CD16⁺CD56⁺ at 24 hours after surgery were higher in patients receiving morphine with flurbiprofen than morphine alone. The combination of morphine and flurbiprofen ameliorates the immune depression in Tlymphocyte subsets and natural killer cells and provides a similar analgesic efficacy to morphine alone in patients undergoing gastric cancer surgery.

  20. Online Feature Selection for Classifying Emphysema in HRCT Images

    Directory of Open Access Journals (Sweden)

    M. Prasad

    2008-06-01

    Full Text Available Feature subset selection, applied as a pre- processing step to machine learning, is valuable in dimensionality reduction, eliminating irrelevant data and improving classifier performance. In the classic formulation of the feature selection problem, it is assumed that all the features are available at the beginning. However, in many real world problems, there are scenarios where not all features are present initially and must be integrated as they become available. In such scenarios, online feature selection provides an efficient way to sort through a large space of features. It is in this context that we introduce online feature selection for the classification of emphysema, a smoking related disease that appears as low attenuation regions in High Resolution Computer Tomography (HRCT images. The technique was successfully evaluated on 61 HRCT scans and compared with different online feature selection approaches, including hill climbing, best first search, grafting, and correlation-based feature selection. The results were also compared against ldensity maskr, a standard approach used for emphysema detection in medical image analysis.