Detection of Nitrogen Content in Rubber Leaves Using Near-Infrared (NIR) Spectroscopy with Correlation-Based Successive Projections Algorithm (SPA).

PubMed

Tang, Rongnian; Chen, Xupeng; Li, Chuang

2018-05-01

Near-infrared spectroscopy is an efficient, low-cost technology that has potential as an accurate method in detecting the nitrogen content of natural rubber leaves. Successive projections algorithm (SPA) is a widely used variable selection method for multivariate calibration, which uses projection operations to select a variable subset with minimum multi-collinearity. However, due to the fluctuation of correlation between variables, high collinearity may still exist in non-adjacent variables of subset obtained by basic SPA. Based on analysis to the correlation matrix of the spectra data, this paper proposed a correlation-based SPA (CB-SPA) to apply the successive projections algorithm in regions with consistent correlation. The result shows that CB-SPA can select variable subsets with more valuable variables and less multi-collinearity. Meanwhile, models established by the CB-SPA subset outperform basic SPA subsets in predicting nitrogen content in terms of both cross-validation and external prediction. Moreover, CB-SPA is assured to be more efficient, for the time cost in its selection procedure is one-twelfth that of the basic SPA.

Probabilistic streamflow forecasting for hydroelectricity production: A comparison of two non-parametric system identification algorithms

NASA Astrophysics Data System (ADS)

Pande, Saket; Sharma, Ashish

2014-05-01

This study is motivated by the need to robustly specify, identify, and forecast runoff generation processes for hydroelectricity production. It atleast requires the identification of significant predictors of runoff generation and the influence of each such significant predictor on runoff response. To this end, we compare two non-parametric algorithms of predictor subset selection. One is based on information theory that assesses predictor significance (and hence selection) based on Partial Information (PI) rationale of Sharma and Mehrotra (2014). The other algorithm is based on a frequentist approach that uses bounds on probability of error concept of Pande (2005), assesses all possible predictor subsets on-the-go and converges to a predictor subset in an computationally efficient manner. Both the algorithms approximate the underlying system by locally constant functions and select predictor subsets corresponding to these functions. The performance of the two algorithms is compared on a set of synthetic case studies as well as a real world case study of inflow forecasting. References: Sharma, A., and R. Mehrotra (2014), An information theoretic alternative to model a natural system using observational information alone, Water Resources Research, 49, doi:10.1002/2013WR013845. Pande, S. (2005), Generalized local learning in water resource management, PhD dissertation, Utah State University, UT-USA, 148p.

Muon tomography imaging improvement using optimized limited angle data

NASA Astrophysics Data System (ADS)

Bai, Chuanyong; Simon, Sean; Kindem, Joel; Luo, Weidong; Sossong, Michael J.; Steiger, Matthew

2014-05-01

Image resolution of muon tomography is limited by the range of zenith angles of cosmic ray muons and the flux rate at sea level. Low flux rate limits the use of advanced data rebinning and processing techniques to improve image quality. By optimizing the limited angle data, however, image resolution can be improved. To demonstrate the idea, physical data of tungsten blocks were acquired on a muon tomography system. The angular distribution and energy spectrum of muons measured on the system was also used to generate simulation data of tungsten blocks of different arrangement (geometry). The data were grouped into subsets using the zenith angle and volume images were reconstructed from the data subsets using two algorithms. One was a distributed PoCA (point of closest approach) algorithm and the other was an accelerated iterative maximal likelihood/expectation maximization (MLEM) algorithm. Image resolution was compared for different subsets. Results showed that image resolution was better in the vertical direction for subsets with greater zenith angles and better in the horizontal plane for subsets with smaller zenith angles. The overall image resolution appeared to be the compromise of that of different subsets. This work suggests that the acquired data can be grouped into different limited angle data subsets for optimized image resolution in desired directions. Use of multiple images with resolution optimized in different directions can improve overall imaging fidelity and the intended applications.

The minimal residual QR-factorization algorithm for reliably solving subset regression problems

NASA Technical Reports Server (NTRS)

Verhaegen, M. H.

1987-01-01

A new algorithm to solve test subset regression problems is described, called the minimal residual QR factorization algorithm (MRQR). This scheme performs a QR factorization with a new column pivoting strategy. Basically, this strategy is based on the change in the residual of the least squares problem. Furthermore, it is demonstrated that this basic scheme might be extended in a numerically efficient way to combine the advantages of existing numerical procedures, such as the singular value decomposition, with those of more classical statistical procedures, such as stepwise regression. This extension is presented as an advisory expert system that guides the user in solving the subset regression problem. The advantages of the new procedure are highlighted by a numerical example.

SU-E-J-262: Variability in Texture Analysis of Gynecological Tumors in the Context of An 18F-FDG PET Adaptive Protocol

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

Nawrocki, J; Chino, J; Das, S

Purpose: This study examines the effect on texture analysis due to variable reconstruction of PET images in the context of an adaptive FDG PET protocol for node positive gynecologic cancer patients. By measuring variability in texture features from baseline and intra-treatment PET-CT, we can isolate unreliable texture features due to large variation. Methods: A subset of seven patients with node positive gynecological cancers visible on PET was selected for this study. Prescribed dose varied between 45–50.4Gy, with a 55–70Gy boost to the PET positive nodes. A baseline and intratreatment (between 30–36Gy) PET-CT were obtained on a Siemens Biograph mCT. Eachmore » clinical PET image set was reconstructed 6 times using a TrueX+TOF algorithm with varying iterations and Gaussian filter. Baseline and intra-treatment primary GTVs were segmented using PET Edge (MIM Software Inc., Cleveland, OH), a semi-automatic gradient-based algorithm, on the clinical PET and transferred to the other reconstructed sets. Using an in-house MATLAB program, four 3D texture matrices describing relationships between voxel intensities in the GTV were generated: co-occurrence, run length, size zone, and neighborhood difference. From these, 39 textural features characterizing texture were calculated in addition to SUV histogram features. The percent variability among parameters was first calculated. Each reconstructed texture feature from baseline and intra-treatment per patient was normalized to the clinical baseline scan and compared using the Wilcoxon signed-rank test in order to isolate variations due to reconstruction parameters. Results: For the baseline scans, 13 texture features showed a mean range greater than 10%. For the intra scans, 28 texture features showed a mean range greater than 10%. Comparing baseline to intra scans, 25 texture features showed p <0.05. Conclusion: Variability due to different reconstruction parameters increased with treatment, however, the majority of texture features showed significant changes during treatment independent of reconstruction effects.« less

Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods.

PubMed

Polat, Huseyin; Danaei Mehr, Homay; Cetin, Aydin

2017-04-01

As Chronic Kidney Disease progresses slowly, early detection and effective treatment are the only cure to reduce the mortality rate. Machine learning techniques are gaining significance in medical diagnosis because of their classification ability with high accuracy rates. The accuracy of classification algorithms depend on the use of correct feature selection algorithms to reduce the dimension of datasets. In this study, Support Vector Machine classification algorithm was used to diagnose Chronic Kidney Disease. To diagnose the Chronic Kidney Disease, two essential types of feature selection methods namely, wrapper and filter approaches were chosen to reduce the dimension of Chronic Kidney Disease dataset. In wrapper approach, classifier subset evaluator with greedy stepwise search engine and wrapper subset evaluator with the Best First search engine were used. In filter approach, correlation feature selection subset evaluator with greedy stepwise search engine and filtered subset evaluator with the Best First search engine were used. The results showed that the Support Vector Machine classifier by using filtered subset evaluator with the Best First search engine feature selection method has higher accuracy rate (98.5%) in the diagnosis of Chronic Kidney Disease compared to other selected methods.

An evaluation of exact methods for the multiple subset maximum cardinality selection problem.

PubMed

Brusco, Michael J; Köhn, Hans-Friedrich; Steinley, Douglas

2016-05-01

The maximum cardinality subset selection problem requires finding the largest possible subset from a set of objects, such that one or more conditions are satisfied. An important extension of this problem is to extract multiple subsets, where the addition of one more object to a larger subset would always be preferred to increases in the size of one or more smaller subsets. We refer to this as the multiple subset maximum cardinality selection problem (MSMCSP). A recently published branch-and-bound algorithm solves the MSMCSP as a partitioning problem. Unfortunately, the computational requirement associated with the algorithm is often enormous, thus rendering the method infeasible from a practical standpoint. In this paper, we present an alternative approach that successively solves a series of binary integer linear programs to obtain a globally optimal solution to the MSMCSP. Computational comparisons of the methods using published similarity data for 45 food items reveal that the proposed sequential method is computationally far more efficient than the branch-and-bound approach. © 2016 The British Psychological Society.

The effect of tracking network configuration on GPS baseline estimates for the CASA Uno experiment

NASA Technical Reports Server (NTRS)

Wolf, S. Kornreich; Dixon, T. H.; Freymueller, J. T.

1990-01-01

The effect of the tracking network on long (greater than 100 km) GPS baseline estimates was estimated using various subsets of the global tracking network initiated by the first Central and South America (CASA Uno) experiment. It was found that best results could be obtained with a global tacking network consisting of three U.S. stations, two sites in the southwestern Pacific, and two sites in Europe. In comparison with smaller subsets, this global network improved the baseline repeatability, the resolution of carrier phase cycle ambiguities, and formal errors of the orbit estimates.

Impact of Time-of-Flight on PET Tumor Detection

PubMed Central

Kadrmas, Dan J.; Casey, Michael E.; Conti, Maurizio; Jakoby, Bjoern W.; Lois, Cristina; Townsend, David W.

2009-01-01

Time-of-flight (TOF) PET uses very fast detectors to improve localization of events along coincidence lines-of-response. This information is then utilized to improve the tomographic reconstruction. This work evaluates the effect of TOF upon an observer's performance for detecting and localizing focal warm lesions in noisy PET images. Methods An advanced anthropomorphic lesion-detection phantom was scanned 12 times over 3 days on a prototype TOF PET/CT scanner (Siemens Medical Solutions). The phantom was devised to mimic whole-body oncologic 18F-FDG PET imaging, and a number of spheric lesions (diameters 6–16 mm) were distributed throughout the phantom. The data were reconstructed with the baseline line-of-response ordered-subsets expectation-maximization algorithm, with the baseline algorithm plus point spread function model (PSF), baseline plus TOF, and with both PSF+TOF. The lesion-detection performance of each reconstruction was compared and ranked using localization receiver operating characteristics (LROC) analysis with both human and numeric observers. The phantom results were then subjectively compared to 2 illustrative patient scans reconstructed with PSF and with PSF+TOF. Results Inclusion of TOF information provides a significant improvement in the area under the LROC curve compared to the baseline algorithm without TOF data (P = 0.002), providing a degree of improvement similar to that obtained with the PSF model. Use of both PSF+TOF together provided a cumulative benefit in lesion-detection performance, significantly outperforming either PSF or TOF alone (P < 0.002). Example patient images reflected the same image characteristics that gave rise to improved performance in the phantom data. Conclusion Time-of-flight PET provides a significant improvement in observer performance for detecting focal warm lesions in a noisy background. These improvements in image quality can be expected to improve performance for the clinical tasks of detecting lesions and staging disease. Further study in a large clinical population is warranted to assess the benefit of TOF for various patient sizes and count levels, and to demonstrate effective performance in the clinical environment. PMID:19617317

Systematic wavelength selection for improved multivariate spectral analysis

DOEpatents

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.

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

PubMed

Mehranian, Abolfazl; Kotasidis, Fotis; Zaidi, Habib

2016-02-07

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 FDG-PET study also revealed that for the same noise level, a higher contrast recovery can be obtained by increasing the number of TOF subsets. It can be concluded that the proposed TOF weighting matrix pre-computation and subsetization approaches enable to further accelerate and improve the convergence properties of OSEM and MLEM algorithms, thus opening new avenues for accelerated TOF PET image reconstruction.

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

NASA Astrophysics Data System (ADS)

Mehranian, Abolfazl; Kotasidis, Fotis; Zaidi, Habib

2016-02-01

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 FDG-PET study also revealed that for the same noise level, a higher contrast recovery can be obtained by increasing the number of TOF subsets. It can be concluded that the proposed TOF weighting matrix pre-computation and subsetization approaches enable to further accelerate and improve the convergence properties of OSEM and MLEM algorithms, thus opening new avenues for accelerated TOF PET image reconstruction.

Selecting climate change scenarios for regional hydrologic impact studies based on climate extremes indices

NASA Astrophysics Data System (ADS)

Seo, Seung Beom; Kim, Young-Oh; Kim, Youngil; Eum, Hyung-Il

2018-04-01

When selecting a subset of climate change scenarios (GCM models), the priority is to ensure that the subset reflects the comprehensive range of possible model results for all variables concerned. Though many studies have attempted to improve the scenario selection, there is a lack of studies that discuss methods to ensure that the results from a subset of climate models contain the same range of uncertainty in hydrologic variables as when all models are considered. We applied the Katsavounidis-Kuo-Zhang (KKZ) algorithm to select a subset of climate change scenarios and demonstrated its ability to reduce the number of GCM models in an ensemble, while the ranges of multiple climate extremes indices were preserved. First, we analyzed the role of 27 ETCCDI climate extremes indices for scenario selection and selected the representative climate extreme indices. Before the selection of a subset, we excluded a few deficient GCM models that could not represent the observed climate regime. Subsequently, we discovered that a subset of GCM models selected by the KKZ algorithm with the representative climate extreme indices could not capture the full potential range of changes in hydrologic extremes (e.g., 3-day peak flow and 7-day low flow) in some regional case studies. However, the application of the KKZ algorithm with a different set of climate indices, which are correlated to the hydrologic extremes, enabled the overcoming of this limitation. Key climate indices, dependent on the hydrologic extremes to be projected, must therefore be determined prior to the selection of a subset of GCM models.

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.

2004-01-01

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.

2005-01-01

A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Increasing signal processing sophistication in the calculation of the respiratory modulation of the photoplethysmogram (DPOP).

PubMed

Addison, Paul S; Wang, Rui; Uribe, Alberto A; Bergese, Sergio D

2015-06-01

DPOP (∆POP or Delta-POP) is a non-invasive parameter which measures the strength of respiratory modulations present in the pulse oximetry photoplethysmogram (pleth) waveform. It has been proposed as a non-invasive surrogate parameter for pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. Many groups have reported on the DPOP parameter and its correlation with PPV using various semi-automated algorithmic implementations. The study reported here demonstrates the performance gains made by adding increasingly sophisticated signal processing components to a fully automated DPOP algorithm. A DPOP algorithm was coded and its performance systematically enhanced through a series of code module alterations and additions. Each algorithm iteration was tested on data from 20 mechanically ventilated OR patients. Correlation coefficients and ROC curve statistics were computed at each stage. For the purposes of the analysis we split the data into a manually selected 'stable' region subset of the data containing relatively noise free segments and a 'global' set incorporating the whole data record. Performance gains were measured in terms of correlation against PPV measurements in OR patients undergoing controlled mechanical ventilation. Through increasingly advanced pre-processing and post-processing enhancements to the algorithm, the correlation coefficient between DPOP and PPV improved from a baseline value of R = 0.347 to R = 0.852 for the stable data set, and, correspondingly, R = 0.225 to R = 0.728 for the more challenging global data set. Marked gains in algorithm performance are achievable for manually selected stable regions of the signals using relatively simple algorithm enhancements. Significant additional algorithm enhancements, including a correction for low perfusion values, were required before similar gains were realised for the more challenging global data set.

How do we choose the best model? The impact of cross-validation design on model evaluation for buried threat detection in ground penetrating radar

NASA Astrophysics Data System (ADS)

Malof, Jordan M.; Reichman, Daniël.; Collins, Leslie M.

2018-04-01

A great deal of research has been focused on the development of computer algorithms for buried threat detection (BTD) in ground penetrating radar (GPR) data. Most recently proposed BTD algorithms are supervised, and therefore they employ machine learning models that infer their parameters using training data. Cross-validation (CV) is a popular method for evaluating the performance of such algorithms, in which the available data is systematically split into ܰ disjoint subsets, and an algorithm is repeatedly trained on ܰ-1 subsets and tested on the excluded subset. There are several common types of CV in BTD, which vary principally upon the spatial criterion used to partition the data: site-based, lane-based, region-based, etc. The performance metrics obtained via CV are often used to suggest the superiority of one model over others, however, most studies utilize just one type of CV, and the impact of this choice is unclear. Here we employ several types of CV to evaluate algorithms from a recent large-scale BTD study. The results indicate that the rank-order of the performance of the algorithms varies substantially depending upon which type of CV is used. For example, the rank-1 algorithm for region-based CV is the lowest ranked algorithm for site-based CV. This suggests that any algorithm results should be interpreted carefully with respect to the type of CV employed. We discuss some potential interpretations of performance, given a particular type of CV.

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

NASA Astrophysics Data System (ADS)

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.

Research on allocation efficiency of the daisy chain allocation algorithm

NASA Astrophysics Data System (ADS)

Shi, Jingping; Zhang, Weiguo

2013-03-01

With the improvement of the aircraft performance in reliability, maneuverability and survivability, the number of the control effectors increases a lot. How to distribute the three-axis moments into the control surfaces reasonably becomes an important problem. Daisy chain method is simple and easy to be carried out in the design of the allocation system. But it can not solve the allocation problem for entire attainable moment subset. For the lateral-directional allocation problem, the allocation efficiency of the daisy chain can be directly measured by the area of its subset of attainable moments. Because of the non-linear allocation characteristic, the subset of attainable moments of daisy-chain method is a complex non-convex polygon, and it is difficult to solve directly. By analyzing the two-dimensional allocation problems with a "micro-element" idea, a numerical calculation algorithm is proposed to compute the area of the non-convex polygon. In order to improve the allocation efficiency of the algorithm, a genetic algorithm with the allocation efficiency chosen as the fitness function is proposed to find the best pseudo-inverse matrix.

Data reduction using cubic rational B-splines

NASA Technical Reports Server (NTRS)

Chou, Jin J.; Piegl, Les A.

1992-01-01

A geometric method is proposed for fitting rational cubic B-spline curves to data that represent smooth curves including intersection or silhouette lines. The algorithm is based on the convex hull and the variation diminishing properties of Bezier/B-spline curves. The algorithm has the following structure: it tries to fit one Bezier segment to the entire data set and if it is impossible it subdivides the data set and reconsiders the subset. After accepting the subset the algorithm tries to find the longest run of points within a tolerance and then approximates this set with a Bezier cubic segment. The algorithm uses this procedure repeatedly to the rest of the data points until all points are fitted. It is concluded that the algorithm delivers fitting curves which approximate the data with high accuracy even in cases with large tolerances.

Social Media: Menagerie of Metrics

DTIC Science & Technology

2010-01-27

intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm . An EA...Cloning - 22 Animals were cloned to date; genetic algorithms can help prediction (e.g. “elitism” - attempts to ensure selection by including performers...28, 2010 Evolutionary Algorithm • Evolutionary algorithm From Wikipedia, the free encyclopedia Artificial intelligence portal In artificial

A non-linear data mining parameter selection algorithm for continuous variables

PubMed Central

Razavi, Marianne; Brady, Sean

2017-01-01

In this article, we propose a new data mining algorithm, by which one can both capture the non-linearity in data and also find the best subset model. To produce an enhanced subset of the original variables, a preferred selection method should have the potential of adding a supplementary level of regression analysis that would capture complex relationships in the data via mathematical transformation of the predictors and exploration of synergistic effects of combined variables. The method that we present here has the potential to produce an optimal subset of variables, rendering the overall process of model selection more efficient. This algorithm introduces interpretable parameters by transforming the original inputs and also a faithful fit to the data. The core objective of this paper is to introduce a new estimation technique for the classical least square regression framework. This new automatic variable transformation and model selection method could offer an optimal and stable model that minimizes the mean square error and variability, while combining all possible subset selection methodology with the inclusion variable transformations and interactions. Moreover, this method controls multicollinearity, leading to an optimal set of explanatory variables. PMID:29131829

Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm.

PubMed

Martinez, Emmanuel; Alvarez, Mario Moises; Trevino, Victor

2010-08-01

Biomarker discovery is a typical application from functional genomics. Due to the large number of genes studied simultaneously in microarray data, feature selection is a key step. Swarm intelligence has emerged as a solution for the feature selection problem. However, swarm intelligence settings for feature selection fail to select small features subsets. We have proposed a swarm intelligence feature selection algorithm based on the initialization and update of only a subset of particles in the swarm. In this study, we tested our algorithm in 11 microarray datasets for brain, leukemia, lung, prostate, and others. We show that the proposed swarm intelligence algorithm successfully increase the classification accuracy and decrease the number of selected features compared to other swarm intelligence methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

Optimisation algorithms for ECG data compression.

PubMed

Haugland, D; Heber, J G; Husøy, J H

1997-07-01

The use of exact optimisation algorithms for compressing digital electrocardiograms (ECGs) is demonstrated. As opposed to traditional time-domain methods, which use heuristics to select a small subset of representative signal samples, the problem of selecting the subset is formulated in rigorous mathematical terms. This approach makes it possible to derive algorithms guaranteeing the smallest possible reconstruction error when a bounded selection of signal samples is interpolated. The proposed model resembles well-known network models and is solved by a cubic dynamic programming algorithm. When applied to standard test problems, the algorithm produces a compressed representation for which the distortion is about one-half of that obtained by traditional time-domain compression techniques at reasonable compression ratios. This illustrates that, in terms of the accuracy of decoded signals, existing time-domain heuristics for ECG compression may be far from what is theoretically achievable. The paper is an attempt to bridge this gap.

Serum BAFF and APRIL Levels, T-Lymphocyte Subsets, and Immunoglobulins after B-Cell Depletion Using the Monoclonal Anti-CD20 Antibody Rituximab in Myalgic Encephalopathy/Chronic Fatigue Syndrome.

PubMed

Lunde, Sigrid; Kristoffersen, Einar K; Sapkota, Dipak; Risa, Kristin; Dahl, Olav; Bruland, Ove; Mella, Olav; Fluge, Øystein

2016-01-01

Myalgic Encephalopathy/Chronic Fatigue Syndrome (ME/CFS) is a disease of unknown etiology. We have previously suggested clinical benefit from B-cell depletion using the monoclonal anti-CD20 antibody rituximab in a randomized and placebo-controlled study. Prolonged responses were then demonstrated in an open-label phase-II study with maintenance rituximab treatment. Using blood samples from patients in the previous two clinical trials, we investigated quantitative changes in T-lymphocyte subsets, in immunoglobulins, and in serum levels of two B-cell regulating cytokines during follow-up. B-lymphocyte activating factor of the tumor necrosis family (BAFF) in baseline serum samples was elevated in 70 ME/CFS patients as compared to 56 healthy controls (p = 0.011). There were no significant differences in baseline serum BAFF levels between patients with mild, moderate, or severe ME/CFS, or between responders and non-responders to rituximab. A proliferation-inducing ligand (APRIL) serum levels were not significantly different in ME/CFS patients compared to healthy controls at baseline, and no changes in serum levels were seen during follow-up. Immunophenotyping of peripheral blood T-lymphocyte subsets and T-cell activation markers at multiple time points during follow-up showed no significant differences over time, between rituximab and placebo groups, or between responders and non-responders to rituximab. Baseline serum IgG levels were significantly lower in patients with subsequent response after rituximab therapy compared to non-responders (p = 0.03). In the maintenance study, slight but significant reductions in mean serum immunoglobulin levels were observed at 24 months compared to baseline; IgG 10.6-9.5 g/L, IgA 1.8-1.5 g/L, and IgM 0.97-0.70 g/L. Although no functional assays were performed, the lack of significant associations of T- and NK-cell subset numbers with B-cell depletion, as well as the lack of associations to clinical responses, suggest that B-cell regulatory effects on T-cell or NK-cell subsets are not the main mechanisms for the observed improvements in ME/CFS symptoms observed in the two previous trials. The modest increase in serum BAFF levels at baseline may indicate an activated B-lymphocyte system in a subgroup of ME/CFS patients.

Serum BAFF and APRIL Levels, T-Lymphocyte Subsets, and Immunoglobulins after B-Cell Depletion Using the Monoclonal Anti-CD20 Antibody Rituximab in Myalgic Encephalopathy/Chronic Fatigue Syndrome

PubMed Central

Lunde, Sigrid; Kristoffersen, Einar K.; Sapkota, Dipak; Risa, Kristin; Dahl, Olav; Bruland, Ove; Mella, Olav; Fluge, Øystein

2016-01-01

Myalgic Encephalopathy/Chronic Fatigue Syndrome (ME/CFS) is a disease of unknown etiology. We have previously suggested clinical benefit from B-cell depletion using the monoclonal anti-CD20 antibody rituximab in a randomized and placebo-controlled study. Prolonged responses were then demonstrated in an open-label phase-II study with maintenance rituximab treatment. Using blood samples from patients in the previous two clinical trials, we investigated quantitative changes in T-lymphocyte subsets, in immunoglobulins, and in serum levels of two B-cell regulating cytokines during follow-up. B-lymphocyte activating factor of the tumor necrosis family (BAFF) in baseline serum samples was elevated in 70 ME/CFS patients as compared to 56 healthy controls (p = 0.011). There were no significant differences in baseline serum BAFF levels between patients with mild, moderate, or severe ME/CFS, or between responders and non-responders to rituximab. A proliferation-inducing ligand (APRIL) serum levels were not significantly different in ME/CFS patients compared to healthy controls at baseline, and no changes in serum levels were seen during follow-up. Immunophenotyping of peripheral blood T-lymphocyte subsets and T-cell activation markers at multiple time points during follow-up showed no significant differences over time, between rituximab and placebo groups, or between responders and non-responders to rituximab. Baseline serum IgG levels were significantly lower in patients with subsequent response after rituximab therapy compared to non-responders (p = 0.03). In the maintenance study, slight but significant reductions in mean serum immunoglobulin levels were observed at 24 months compared to baseline; IgG 10.6–9.5 g/L, IgA 1.8–1.5 g/L, and IgM 0.97–0.70 g/L. Although no functional assays were performed, the lack of significant associations of T- and NK-cell subset numbers with B-cell depletion, as well as the lack of associations to clinical responses, suggest that B-cell regulatory effects on T-cell or NK-cell subsets are not the main mechanisms for the observed improvements in ME/CFS symptoms observed in the two previous trials. The modest increase in serum BAFF levels at baseline may indicate an activated B-lymphocyte system in a subgroup of ME/CFS patients. PMID:27536947

SBAS-InSAR analysis of surface deformation at Mauna Loa and Kilauea volcanoes in Hawaii

USGS Publications Warehouse

Casu, F.; Lanari, Riccardo; Sansosti, E.; Solaro, G.; Tizzani, Pietro; Poland, M.; Miklius, Asta

2009-01-01

We investigate the deformation of Mauna Loa and K??lauea volcanoes, Hawai'i, by exploiting the advanced differential Synthetic Aperture Radar Interferometry (InSAR) technique referred to as the Small BAseline Subset (SBAS) algorithm. In particular, we present time series of line-of-sight (LOS) displacements derived from SAR data acquired by the ASAR instrument, on board the ENVISAT satellite, from the ascending (track 93) and descending (track 429) orbits between 2003 and 2008. For each coherent pixel of the radar images we compute time-dependent surface displacements as well as the average LOS deformation rate. Our results quantify, in space and time, the complex deformation of Mauna Loa and K??lauea volcanoes. The derived InSAR measurements are compared to continuous GPS data to asses the quality of the SBAS-InSAR products. ??2009 IEEE.

Rough sets and Laplacian score based cost-sensitive feature selection

PubMed Central

Yu, Shenglong

2018-01-01

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of “good” features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms. PMID:29912884

Rough sets and Laplacian score based cost-sensitive feature selection.

PubMed

Yu, Shenglong; Zhao, Hong

2018-01-01

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of "good" features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.

SCI model structure determination program (OSR) user's guide. [optimal subset regression

NASA Technical Reports Server (NTRS)

1979-01-01

The computer program, OSR (Optimal Subset Regression) which estimates models for rotorcraft body and rotor force and moment coefficients is described. The technique used is based on the subset regression algorithm. Given time histories of aerodynamic coefficients, aerodynamic variables, and control inputs, the program computes correlation between various time histories. The model structure determination is based on these correlations. Inputs and outputs of the program are given.

Baseline correction combined partial least squares algorithm and its application in on-line Fourier transform infrared quantitative analysis.

PubMed

Peng, Jiangtao; Peng, Silong; Xie, Qiong; Wei, Jiping

2011-04-01

In order to eliminate the lower order polynomial interferences, a new quantitative calibration algorithm "Baseline Correction Combined Partial Least Squares (BCC-PLS)", which combines baseline correction and conventional PLS, is proposed. By embedding baseline correction constraints into PLS weights selection, the proposed calibration algorithm overcomes the uncertainty in baseline correction and can meet the requirement of on-line attenuated total reflectance Fourier transform infrared (ATR-FTIR) quantitative analysis. The effectiveness of the algorithm is evaluated by the analysis of glucose and marzipan ATR-FTIR spectra. BCC-PLS algorithm shows improved prediction performance over PLS. The root mean square error of cross-validation (RMSECV) on marzipan spectra for the prediction of the moisture is found to be 0.53%, w/w (range 7-19%). The sugar content is predicted with a RMSECV of 2.04%, w/w (range 33-68%). Copyright © 2011 Elsevier B.V. All rights reserved.

On the Hardness of Subset Sum Problem from Different Intervals

NASA Astrophysics Data System (ADS)

Kogure, Jun; Kunihiro, Noboru; Yamamoto, Hirosuke

The subset sum problem, which is often called as the knapsack problem, is known as an NP-hard problem, and there are several cryptosystems based on the problem. Assuming an oracle for shortest vector problem of lattice, the low-density attack algorithm by Lagarias and Odlyzko and its variants solve the subset sum problem efficiently, when the “density” of the given problem is smaller than some threshold. When we define the density in the context of knapsack-type cryptosystems, weights are usually assumed to be chosen uniformly at random from the same interval. In this paper, we focus on general subset sum problems, where this assumption may not hold. We assume that weights are chosen from different intervals, and make analysis of the effect on the success probability of above algorithms both theoretically and experimentally. Possible application of our result in the context of knapsack cryptosystems is the security analysis when we reduce the data size of public keys.

Data on correlations between T cell subset frequencies and length of partial remission in type 1 diabetes.

PubMed

Narsale, Aditi; Moya, Rosita; Robertson, Hannah Kathryn; Davies, Joanna Davida

2016-09-01

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.

Sensor Network Localization by Eigenvector Synchronization Over the Euclidean Group

PubMed Central

CUCURINGU, MIHAI; LIPMAN, YARON; SINGER, AMIT

2013-01-01

We present a new approach to localization of sensors from noisy measurements of a subset of their Euclidean distances. Our algorithm starts by finding, embedding, and aligning uniquely realizable subsets of neighboring sensors called patches. In the noise-free case, each patch agrees with its global positioning up to an unknown rigid motion of translation, rotation, and possibly reflection. The reflections and rotations are estimated using the recently developed eigenvector synchronization algorithm, while the translations are estimated by solving an overdetermined linear system. The algorithm is scalable as the number of nodes increases and can be implemented in a distributed fashion. Extensive numerical experiments show that it compares favorably to other existing algorithms in terms of robustness to noise, sparse connectivity, and running time. While our approach is applicable to higher dimensions, in the current article, we focus on the two-dimensional case. PMID:23946700

Numerical algorithms for scatter-to-attenuation reconstruction in PET: empirical comparison of convergence, acceleration, and the effect of subsets.

PubMed

Berker, Yannick; Karp, Joel S; Schulz, Volkmar

2017-09-01

The use of scattered coincidences for attenuation correction of positron emission tomography (PET) data has recently been proposed. For practical applications, convergence speeds require further improvement, yet there exists a trade-off between convergence speed and the risk of non-convergence. In this respect, a maximum-likelihood gradient-ascent (MLGA) algorithm and a two-branch back-projection (2BP), which was previously proposed, were evaluated. MLGA was combined with the Armijo step size rule; and accelerated using conjugate gradients, Nesterov's momentum method, and data subsets of different sizes. In 2BP, we varied the subset size, an important determinant of convergence speed and computational burden. We used three sets of simulation data to evaluate the impact of a spatial scale factor. The Armijo step size allowed 10-fold increased step sizes compared to native MLGA. Conjugate gradients and Nesterov momentum lead to slightly faster, yet non-uniform convergence; improvements were mostly confined to later iterations, possibly due to the non-linearity of the problem. MLGA with data subsets achieved faster, uniform, and predictable convergence, with a speed-up factor equivalent to the number of subsets and no increase in computational burden. By contrast, 2BP computational burden increased linearly with the number of subsets due to repeated evaluation of the objective function, and convergence was limited to the case of many (and therefore small) subsets, which resulted in high computational burden. Possibilities of improving 2BP appear limited. While general-purpose acceleration methods appear insufficient for MLGA, results suggest that data subsets are a promising way of improving MLGA performance.

Phase retrieval based wavefront sensing experimental implementation and wavefront sensing accuracy calibration

NASA Astrophysics Data System (ADS)

Mao, Heng; Wang, Xiao; Zhao, Dazun

2009-05-01

As a wavefront sensing (WFS) tool, Baseline algorithm, which is classified as the iterative-transform algorithm of phase retrieval, estimates the phase distribution at pupil from some known PSFs at defocus planes. By using multiple phase diversities and appropriate phase unwrapping methods, this algorithm can accomplish reliable unique solution and high dynamic phase measurement. In the paper, a Baseline algorithm based wavefront sensing experiment with modification of phase unwrapping has been implemented, and corresponding Graphical User Interfaces (GUI) software has also been given. The adaptability and repeatability of Baseline algorithm have been validated in experiments. Moreover, referring to the ZYGO interferometric results, the WFS accuracy of this algorithm has been exactly calibrated.

Validation of a new algorithm for the BPM-100 electronic oscillometric office blood pressure monitor.

PubMed

Wright, J M; Mattu, G S; Perry, T L; Gelferc, M E; Strange, K D; Zorn, A; Chen, Y

2001-06-01

To test the accuracy of a new algorithm for the BPM-100, an automated oscillometric blood pressure (BP) monitor, using stored data from an independently conducted validation trial comparing the BPM-100(Beta) with a mercury sphygmomanometer. Raw pulse wave and cuff pressure data were stored electronically using embedded software in the BPM-100(Beta), during the validation trial. The 391 sets of measurements were separated objectively into two subsets. A subset of 136 measurements was used to develop a new algorithm to enhance the accuracy of the device when reading higher systolic pressures. The larger subset of 255 measurements (three readings for 85 subjects) was used as test data to validate the accuracy of the new algorithm. Differences between the new algorithm BPM-100 and the reference (mean of two observers) were determined and expressed as the mean difference +/- SD, plus the percentage of measurements within 5, 10, and 15 mmHg. The mean difference between the BPM-100 and reference systolic BP was -0.16 +/- 5.13 mmHg, with 73.7% < or = 5 mmHg, 94.9% < or = 10 mmHg and 98.8% < or = 15 mmHg. The mean difference between the BPM-100 and reference diastolic BP was -1.41 +/- 4.67 mmHg, with 78.4% < or = 5 mmHg, 92.5% < or = 10 mmHg, and 99.2% < or = 15 mmHg. These data improve upon that of the BPM-100(Beta) and pass the AAMI standard, and 'A' grade BHS protocol. This study illustrates a new method for developing and testing a change in an algorithm for an oscillometric BP monitor utilizing collected and stored electronic data and demonstrates that the new algorithm meets the AAMI standard and BHS protocol.

Project resource reallocation algorithm

NASA Technical Reports Server (NTRS)

Myers, J. E.

1981-01-01

A methodology for adjusting baseline cost estimates according to project schedule changes is described. An algorithm which performs a linear expansion or contraction of the baseline project resource distribution in proportion to the project schedule expansion or contraction is presented. Input to the algorithm consists of the deck of cards (PACE input data) prepared for the baseline project schedule as well as a specification of the nature of the baseline schedule change. Output of the algorithm is a new deck of cards with all work breakdown structure block and element of cost estimates redistributed for the new project schedule. This new deck can be processed through PACE to produce a detailed cost estimate for the new schedule.

The 2010 slow slip event and secular motion at Kilauea, Hawai`i inferred from TerraSAR-X InSAR data

USGS Publications Warehouse

Chen, Jingyi; Zebker, Howard A.; Segall, Paul; Miklius, Asta

2014-01-01

We present here an Small BAseline Subset (SBAS) algorithm to extract both transient and secular ground deformations on the order of millimeters in the presence of tropospheric noise on the order of centimeters, when the transient is of short duration and known time, and the background deformation is smooth in time. We applied this algorithm to study the 2010 slow slip event as well as the secular motion of Kīlauea's south flank using 49 TerraSAR-X images. We also estimate the tropospheric delay variation relative to a given reference pixel using an InSAR SBAS approach. We compare the InSAR SBAS solution for both ground deformation and tropospheric delays with existing GPS measurements and confirm that the ground deformation signal andtropospheric noise in InSAR data are successfully separated. We observe that the coastal region on the south side of the Hilina Pali moves at a higher background rate than the region north side of the Pali. We also conclude that the 2010 SSE displacement is mainly horizontal and the maximum magnitude of the 2010 SSE vertical component is less than 5 mm.

Multi-interferogram method for measuring interseismic deformation: Denali Fault, Alaska

USGS Publications Warehouse

Biggs, Juliet; Wright, Tim; Lu, Zhong; Parsons, Barry

2007-01-01

Studies of interseismic strain accumulation are crucial to our understanding of continental deformation, the earthquake cycle and seismic hazard. By mapping small amounts of ground deformation over large spatial areas, InSAR has the potential to produce continental-scale maps of strain accumulation on active faults. However, most InSAR studies to date have focused on areas where the coherence is relatively good (e.g. California, Tibet and Turkey) and most analysis techniques (stacking, small baseline subset algorithm, permanent scatterers, etc.) only include information from pixels which are coherent throughout the time-span of the study. In some areas, such as Alaska, where the deformation rate is small and coherence very variable, it is necessary to include information from pixels which are coherent in some but not all interferograms. We use a three-stage iterative algorithm based on distributed scatterer interferometry. We validate our method using synthetic data created using realistic parameters from a test site on the Denali Fault, Alaska, and present a preliminary result of 10.5 ?? 5.0 mm yr-1 for the slip rate on the Denali Fault based on a single track of radar data from ERS1/2. ?? 2007 The Authors Journal compilation ?? 2007 RAS.

Reversal of Glaucoma Hemifield Test Results and Visual Field Features in Glaucoma.

PubMed

Wang, Mengyu; Pasquale, Louis R; Shen, Lucy Q; Boland, Michael V; Wellik, Sarah R; De Moraes, Carlos Gustavo; Myers, Jonathan S; Wang, Hui; Baniasadi, Neda; Li, Dian; Silva, Rafaella Nascimento E; Bex, Peter J; Elze, Tobias

2018-03-01

To develop a visual field (VF) feature model to predict the reversal of glaucoma hemifield test (GHT) results to within normal limits (WNL) after 2 consecutive outside normal limits (ONL) results. Retrospective cohort study. Visual fields of 44 503 eyes from 26 130 participants. Eyes with 3 or more consecutive reliable VFs measured with the Humphrey Field Analyzer (Swedish interactive threshold algorithm standard 24-2) were included. Eyes with ONL GHT results for the 2 baseline VFs were selected. We extracted 3 categories of VF features from the baseline tests: (1) VF global indices (mean deviation [MD] and pattern standard deviation), (2) mismatch between baseline VFs, and (3) VF loss patterns (archetypes). Logistic regression was applied to predict the GHT results reversal. Cross-validation was applied to evaluate the model on testing data by the area under the receiver operating characteristic curve (AUC). We ascertained clinical glaucoma status on a patient subset (n = 97) to determine the usefulness of our model. Predictive models for GHT results reversal using VF features. For the 16 604 eyes with 2 initial ONL results, the prevalence of a subsequent WNL result increased from 0.1% for MD < -12 dB to 13.8% for MD ≥-3 dB. Compared with models with VF global indices, the AUC of predictive models increased from 0.669 (MD ≥-3 dB) and 0.697 (-6 dB ≤ MD < -3 dB) to 0.770 and 0.820, respectively, by adding VF mismatch features and computationally derived VF archetypes (P < 0.001 for both). The GHT results reversal was associated with a large mismatch between baseline VFs. Moreover, the GHT results reversal was associated more with VF archetypes of nonglaucomatous loss, severe widespread loss, and lens rim artifacts. For a subset of 97 eyes, using our model to predict absence of glaucoma based on clinical evidence after 2 ONL results yielded significantly better prediction accuracy (87.7%; P < 0.001) than predicting GHT results reversal (68.8%) with a prescribed specificity 67.7%. Using VF features may predict the GHT results reversal to WNL after 2 consecutive ONL results. Copyright © 2017 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III.

PubMed

Boon, K H; Khalil-Hani, M; Malarvili, M B

2018-01-01

This paper presents a method that able to predict the paroxysmal atrial fibrillation (PAF). The method uses shorter heart rate variability (HRV) signals when compared to existing methods, and achieves good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to electrically stabilize and prevent the onset of atrial arrhythmias with different pacing techniques. We propose a multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm III for optimizing the baseline PAF prediction system, that consists of the stages of pre-processing, HRV feature extraction, and support vector machine (SVM) model. The pre-processing stage comprises of heart rate correction, interpolation, and signal detrending. After that, time-domain, frequency-domain, non-linear HRV features are extracted from the pre-processed data in feature extraction stage. Then, these features are used as input to the SVM for predicting the PAF event. The proposed optimization algorithm is used to optimize the parameters and settings of various HRV feature extraction algorithms, select the best feature subsets, and tune the SVM parameters simultaneously for maximum prediction performance. The proposed method achieves an accuracy rate of 87.7%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 min to just 5 min (a reduction of 83%). Furthermore, another significant result is the sensitivity rate, which is considered more important that other performance metrics in this paper, can be improved with the trade-off of lower specificity. Copyright © 2017 Elsevier B.V. All rights reserved.

Minimizing the average distance to a closest leaf in a phylogenetic tree.

PubMed

Matsen, Frederick A; Gallagher, Aaron; McCoy, Connor O

2013-11-01

When performing an analysis on a collection of molecular sequences, it can be convenient to reduce the number of sequences under consideration while maintaining some characteristic of a larger collection of sequences. For example, one may wish to select a subset of high-quality sequences that represent the diversity of a larger collection of sequences. One may also wish to specialize a large database of characterized "reference sequences" to a smaller subset that is as close as possible on average to a collection of "query sequences" of interest. Such a representative subset can be useful whenever one wishes to find a set of reference sequences that is appropriate to use for comparative analysis of environmentally derived sequences, such as for selecting "reference tree" sequences for phylogenetic placement of metagenomic reads. In this article, we formalize these problems in terms of the minimization of the Average Distance to the Closest Leaf (ADCL) and investigate algorithms to perform the relevant minimization. We show that the greedy algorithm is not effective, show that a variant of the Partitioning Around Medoids (PAM) heuristic gets stuck in local minima, and develop an exact dynamic programming approach. Using this exact program we note that the performance of PAM appears to be good for simulated trees, and is faster than the exact algorithm for small trees. On the other hand, the exact program gives solutions for all numbers of leaves less than or equal to the given desired number of leaves, whereas PAM only gives a solution for the prespecified number of leaves. Via application to real data, we show that the ADCL criterion chooses chimeric sequences less often than random subsets, whereas the maximization of phylogenetic diversity chooses them more often than random. These algorithms have been implemented in publicly available software.

Combined endeavor of Neutrosophic Set and Chan-Vese model to extract accurate liver image from CT scan.

PubMed

Siri, Sangeeta K; Latte, Mrityunjaya V

2017-11-01

Many different diseases can occur in the liver, including infections such as hepatitis, cirrhosis, cancer and over effect of medication or toxins. The foremost stage for computer-aided diagnosis of liver is the identification of liver region. Liver segmentation algorithms extract liver image from scan images which helps in virtual surgery simulation, speedup the diagnosis, accurate investigation and surgery planning. The existing liver segmentation algorithms try to extort exact liver image from abdominal Computed Tomography (CT) scan images. It is an open problem because of ambiguous boundaries, large variation in intensity distribution, variability of liver geometry from patient to patient and presence of noise. A novel approach is proposed to meet challenges in extracting the exact liver image from abdominal CT scan images. The proposed approach consists of three phases: (1) Pre-processing (2) CT scan image transformation to Neutrosophic Set (NS) and (3) Post-processing. In pre-processing, the noise is removed by median filter. The "new structure" is designed to transform a CT scan image into neutrosophic domain which is expressed using three membership subset: True subset (T), False subset (F) and Indeterminacy subset (I). This transform approximately extracts the liver image structure. In post processing phase, morphological operation is performed on indeterminacy subset (I) and apply Chan-Vese (C-V) model with detection of initial contour within liver without user intervention. This resulted in liver boundary identification with high accuracy. Experiments show that, the proposed method is effective, robust and comparable with existing algorithm for liver segmentation of CT scan images. Copyright © 2017 Elsevier B.V. All rights reserved.

Nonuniform update for sparse target recovery in fluorescence molecular tomography accelerated by ordered subsets.

PubMed

Zhu, Dianwen; Li, Changqing

2014-12-01

Fluorescence molecular tomography (FMT) is a promising imaging modality and has been actively studied in the past two decades since it can locate the specific tumor position three-dimensionally in small animals. However, it remains a challenging task to obtain fast, robust and accurate reconstruction of fluorescent probe distribution in small animals due to the large computational burden, the noisy measurement and the ill-posed nature of the inverse problem. In this paper we propose a nonuniform preconditioning method in combination with L (1) regularization and ordered subsets technique (NUMOS) to take care of the different updating needs at different pixels, to enhance sparsity and suppress noise, and to further boost convergence of approximate solutions for fluorescence molecular tomography. Using both simulated data and phantom experiment, we found that the proposed nonuniform updating method outperforms its popular uniform counterpart by obtaining a more localized, less noisy, more accurate image. The computational cost was greatly reduced as well. The ordered subset (OS) technique provided additional 5 times and 3 times speed enhancements for simulation and phantom experiments, respectively, without degrading image qualities. When compared with the popular L (1) algorithms such as iterative soft-thresholding algorithm (ISTA) and Fast iterative soft-thresholding algorithm (FISTA) algorithms, NUMOS also outperforms them by obtaining a better image in much shorter period of time.

Using Parental Profiles to Predict Membership in a Subset of College Students Experiencing Excessive Alcohol Consequences: Findings From a Longitudinal Study

PubMed Central

Varvil-Weld, Lindsey; Mallett, Kimberly A.; Turrisi, Rob; Abar, Caitlin C.

2012-01-01

Objective: Previous research identified a high-risk subset of college students experiencing a disproportionate number of alcohol-related consequences at the end of their first year. With the goal of identifying pre-college predictors of membership in this high-risk subset, the present study used a prospective design to identify latent profiles of student-reported maternal and paternal parenting styles and alcohol-specific behaviors and to determine whether these profiles were associated with membership in the high-risk consequences subset. Method: A sample of randomly selected 370 incoming first-year students at a large public university reported on their mothers’ and fathers’ communication quality, monitoring, approval of alcohol use, and modeling of drinking behaviors and on consequences experienced across the first year of college. Results: Students in the high-risk subset comprised 15.5% of the sample but accounted for almost half (46.6%) of the total consequences reported by the entire sample. Latent profile analyses identified four parental profiles: positive pro-alcohol, positive anti-alcohol, negative mother, and negative father. Logistic regression analyses revealed that students in the negative-father profile were at greatest odds of being in the high-risk consequences subset at a follow-up assessment 1 year later, even after drinking at baseline was controlled for. Students in the positive pro-alcohol profile also were at increased odds of being in the high-risk subset, although this association was attenuated after baseline drinking was controlled for. Conclusions: These findings have important implications for the improvement of existing parent- and individual-based college student drinking interventions designed to reduce alcohol-related consequences. PMID:22456248

Using parental profiles to predict membership in a subset of college students experiencing excessive alcohol consequences: findings from a longitudinal study.

PubMed

Varvil-Weld, Lindsey; Mallett, Kimberly A; Turrisi, Rob; Abar, Caitlin C

2012-05-01

Previous research identified a high-risk subset of college students experiencing a disproportionate number of alcohol-related consequences at the end of their first year. With the goal of identifying pre-college predictors of membership in this high-risk subset, the present study used a prospective design to identify latent profiles of student-reported maternal and paternal parenting styles and alcohol-specific behaviors and to determine whether these profiles were associated with membership in the high-risk consequences subset. A sample of randomly selected 370 incoming first-year students at a large public university reported on their mothers' and fathers' communication quality, monitoring, approval of alcohol use, and modeling of drinking behaviors and on consequences experienced across the first year of college. Students in the high-risk subset comprised 15.5% of the sample but accounted for almost half (46.6%) of the total consequences reported by the entire sample. Latent profile analyses identified four parental profiles: positive pro-alcohol, positive anti-alcohol, negative mother, and negative father. Logistic regression analyses revealed that students in the negative-father profile were at greatest odds of being in the high-risk consequences subset at a follow-up assessment 1 year later, even after drinking at baseline was controlled for. Students in the positive pro-alcohol profile also were at increased odds of being in the high-risk subset, although this association was attenuated after baseline drinking was controlled for. These findings have important implications for the improvement of existing parent- and individual-based college student drinking interventions designed to reduce alcohol-related consequences.

Formal Semantics and Implementation of BPMN 2.0 Inclusive Gateways

NASA Astrophysics Data System (ADS)

Christiansen, David Raymond; Carbone, Marco; Hildebrandt, Thomas

We present the first direct formalization of the semantics of inclusive gateways as described in the Business Process Modeling Notation (BPMN) 2.0 Beta 1 specification. The formal semantics is given for a minimal subset of BPMN 2.0 containing just the inclusive and exclusive gateways and the start and stop events. By focusing on this subset we achieve a simple graph model that highlights the particular non-local features of the inclusive gateway semantics. We sketch two ways of implementing the semantics using algorithms based on incrementally updated data structures and also discuss distributed communication-based implementations of the two algorithms.

A Parameter Subset Selection Algorithm for Mixed-Effects Models

DOE PAGES

Schmidt, Kathleen L.; Smith, Ralph C.

2016-01-01

Mixed-effects models are commonly used to statistically model phenomena that include attributes associated with a population or general underlying mechanism as well as effects specific to individuals or components of the general mechanism. This can include individual effects associated with data from multiple experiments. However, the parameterizations used to incorporate the population and individual effects are often unidentifiable in the sense that parameters are not uniquely specified by the data. As a result, the current literature focuses on model selection, by which insensitive parameters are fixed or removed from the model. Model selection methods that employ information criteria are applicablemore » to both linear and nonlinear mixed-effects models, but such techniques are limited in that they are computationally prohibitive for large problems due to the number of possible models that must be tested. To limit the scope of possible models for model selection via information criteria, we introduce a parameter subset selection (PSS) algorithm for mixed-effects models, which orders the parameters by their significance. In conclusion, we provide examples to verify the effectiveness of the PSS algorithm and to test the performance of mixed-effects model selection that makes use of parameter subset selection.« less

Variable screening via quantile partial correlation

PubMed Central

Ma, Shujie; Tsai, Chih-Ling

2016-01-01

In quantile linear regression with ultra-high dimensional data, we propose an algorithm for screening all candidate variables and subsequently selecting relevant predictors. Specifically, we first employ quantile partial correlation for screening, and then we apply the extended Bayesian information criterion (EBIC) for best subset selection. Our proposed method can successfully select predictors when the variables are highly correlated, and it can also identify variables that make a contribution to the conditional quantiles but are marginally uncorrelated or weakly correlated with the response. Theoretical results show that the proposed algorithm can yield the sure screening set. By controlling the false selection rate, model selection consistency can be achieved theoretically. In practice, we proposed using EBIC for best subset selection so that the resulting model is screening consistent. Simulation studies demonstrate that the proposed algorithm performs well, and an empirical example is presented. PMID:28943683

Possibility expectation and its decision making algorithm

NASA Technical Reports Server (NTRS)

Keller, James M.; Yan, Bolin

1992-01-01

The fuzzy integral has been shown to be an effective tool for the aggregation of evidence in decision making. Of primary importance in the development of a fuzzy integral pattern recognition algorithm is the choice (construction) of the measure which embodies the importance of subsets of sources of evidence. Sugeno fuzzy measures have received the most attention due to the recursive nature of the fabrication of the measure on nested sequences of subsets. Possibility measures exhibit an even simpler generation capability, but usually require that one of the sources of information possess complete credibility. In real applications, such normalization may not be possible, or even desirable. In this report, both the theory and a decision making algorithm for a variation of the fuzzy integral are presented. This integral is based on a possibility measure where it is not required that the measure of the universe be unity. A training algorithm for the possibility densities in a pattern recognition application is also presented with the results demonstrated on the shuttle-earth-space training and testing images.

Decontaminate feature for tracking: adaptive tracking via evolutionary feature subset

NASA Astrophysics Data System (ADS)

Liu, Qiaoyuan; Wang, Yuru; Yin, Minghao; Ren, Jinchang; Li, Ruizhi

2017-11-01

Although various visual tracking algorithms have been proposed in the last 2-3 decades, it remains a challenging problem for effective tracking with fast motion, deformation, occlusion, etc. Under complex tracking conditions, most tracking models are not discriminative and adaptive enough. When the combined feature vectors are inputted to the visual models, this may lead to redundancy causing low efficiency and ambiguity causing poor performance. An effective tracking algorithm is proposed to decontaminate features for each video sequence adaptively, where the visual modeling is treated as an optimization problem from the perspective of evolution. Every feature vector is compared to a biological individual and then decontaminated via classical evolutionary algorithms. With the optimized subsets of features, the "curse of dimensionality" has been avoided while the accuracy of the visual model has been improved. The proposed algorithm has been tested on several publicly available datasets with various tracking challenges and benchmarked with a number of state-of-the-art approaches. The comprehensive experiments have demonstrated the efficacy of the proposed methodology.

Systemic inflammatory responses in progressing periodontitis during pregnancy in a baboon model

PubMed Central

Ebersole, J L; Steffen, M J; Holt, S C; Kesavalu, L; Chu, L; Cappelli, D

2010-01-01

This study tested the hypothesis that pregnant female baboons exhibit increased levels of various inflammatory mediators in serum resulting from ligature-induced periodontitis, and that these profiles would relate to periodontal disease severity/extent in the animals. The animals were sampled at baseline (B), mid-pregnancy (MP; two quadrants ligated) and at delivery (D; four quadrants ligated). All baboons developed increased plaque, gingival inflammation and bleeding, pocket depths and attachment loss following placement of the ligatures. By MP, both prostaglandin E2 (PGE2) and bactericidal permeability inducing factor (BPI) were greater than baseline, while increased levels of interleukin (IL)-6 occurred in the experimental animals by the time of delivery. IL-8, MCP-1 and LBP all decreased from baseline through the ligation phase of the study. Stratification of the animals by baseline clinical presentation demonstrated that PGE2, LBP, IL-8 and MCP-1 levels were altered throughout the ligation interval, irrespective of baseline clinical values. IL-6, IL-8 and LBP were significantly lower in the subset of animals that demonstrated the least clinical response to ligation, indicative of progressing periodontal disease. PGE2, macrophage chemotactic protein (MCP)-1, regulated upon activation, normal T cell expressed and secreted (RANTES) and LBP were decreased in the most diseased subset of animals at delivery. Systemic antibody responses to Fusobacterium nucleatum, Porphyromonas gingivalis, Actinobacillus actinomycetemcomitans and Campylobacter rectus were associated most frequently with variations in inflammatory mediator levels. These results provide a profile of systemic inflammatory mediators during ligature-induced periodontitis in pregnant baboons. The relationship of the oral clinical parameters to systemic inflammatory responses is consistent with a contribution to adverse pregnancy outcomes in a subset of the animals. PMID:21070210

Efficient Craig Interpolation for Linear Diophantine (Dis)Equations and Linear Modular Equations

DTIC Science & Technology

2008-02-01

Craig interpolants has enabled the development of powerful hardware and software model checking techniques. Efficient algorithms are known for computing...interpolants in rational and real linear arithmetic. We focus on subsets of integer linear arithmetic. Our main results are polynomial time algorithms ...congruences), and linear diophantine disequations. We show the utility of the proposed interpolation algorithms for discovering modular/divisibility predicates

The selection of the optimal baseline in the front-view monocular vision system

NASA Astrophysics Data System (ADS)

Xiong, Bincheng; Zhang, Jun; Zhang, Daimeng; Liu, Xiaomao; Tian, Jinwen

2018-03-01

In the front-view monocular vision system, the accuracy of solving the depth field is related to the length of the inter-frame baseline and the accuracy of image matching result. In general, a longer length of the baseline can lead to a higher precision of solving the depth field. However, at the same time, the difference between the inter-frame images increases, which increases the difficulty in image matching and the decreases matching accuracy and at last may leads to the failure of solving the depth field. One of the usual practices is to use the tracking and matching method to improve the matching accuracy between images, but this algorithm is easy to cause matching drift between images with large interval, resulting in cumulative error in image matching, and finally the accuracy of solving the depth field is still very low. In this paper, we propose a depth field fusion algorithm based on the optimal length of the baseline. Firstly, we analyze the quantitative relationship between the accuracy of the depth field calculation and the length of the baseline between frames, and find the optimal length of the baseline by doing lots of experiments; secondly, we introduce the inverse depth filtering technique for sparse SLAM, and solve the depth field under the constraint of the optimal length of the baseline. By doing a large number of experiments, the results show that our algorithm can effectively eliminate the mismatch caused by image changes, and can still solve the depth field correctly in the large baseline scene. Our algorithm is superior to the traditional SFM algorithm in time and space complexity. The optimal baseline obtained by a large number of experiments plays a guiding role in the calculation of the depth field in front-view monocular.

Comparison of Different EHG Feature Selection Methods for the Detection of Preterm Labor

PubMed Central

Alamedine, D.; Khalil, M.; Marque, C.

2013-01-01

Numerous types of linear and nonlinear features have been extracted from the electrohysterogram (EHG) in order to classify labor and pregnancy contractions. As a result, the number of available features is now very large. The goal of this study is to reduce the number of features by selecting only the relevant ones which are useful for solving the classification problem. This paper presents three methods for feature subset selection that can be applied to choose the best subsets for classifying labor and pregnancy contractions: an algorithm using the Jeffrey divergence (JD) distance, a sequential forward selection (SFS) algorithm, and a binary particle swarm optimization (BPSO) algorithm. The two last methods are based on a classifier and were tested with three types of classifiers. These methods have allowed us to identify common features which are relevant for contraction classification. PMID:24454536

High-dimensional cluster analysis with the Masked EM Algorithm

PubMed Central

Kadir, Shabnam N.; Goodman, Dan F. M.; Harris, Kenneth D.

2014-01-01

Cluster analysis faces two problems in high dimensions: first, the “curse of dimensionality” that can lead to overfitting and poor generalization performance; and second, the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. We describe a solution to these problems, designed for the application of “spike sorting” for next-generation high channel-count neural probes. In this problem, only a small subset of features provide information about the cluster member-ship of any one data vector, but this informative feature subset is not the same for all data points, rendering classical feature selection ineffective. We introduce a “Masked EM” algorithm that allows accurate and time-efficient clustering of up to millions of points in thousands of dimensions. We demonstrate its applicability to synthetic data, and to real-world high-channel-count spike sorting data. PMID:25149694

Negotiating Multicollinearity with Spike-and-Slab Priors.

PubMed

Ročková, Veronika; George, Edward I

2014-08-01

In multiple regression under the normal linear model, the presence of multicollinearity is well known to lead to unreliable and unstable maximum likelihood estimates. This can be particularly troublesome for the problem of variable selection where it becomes more difficult to distinguish between subset models. Here we show how adding a spike-and-slab prior mitigates this difficulty by filtering the likelihood surface into a posterior distribution that allocates the relevant likelihood information to each of the subset model modes. For identification of promising high posterior models in this setting, we consider three EM algorithms, the fast closed form EMVS version of Rockova and George (2014) and two new versions designed for variants of the spike-and-slab formulation. For a multimodal posterior under multicollinearity, we compare the regions of convergence of these three algorithms. Deterministic annealing versions of the EMVS algorithm are seen to substantially mitigate this multimodality. A single simple running example is used for illustration throughout.

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

NASA Technical Reports Server (NTRS)

Heisler, R.

1981-01-01

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

Analysis of facial motion patterns during speech using a matrix factorization algorithm

PubMed Central

Lucero, Jorge C.; Munhall, Kevin G.

2008-01-01

This paper presents an analysis of facial motion during speech to identify linearly independent kinematic regions. The data consists of three-dimensional displacement records of a set of markers located on a subject’s face while producing speech. A QR factorization with column pivoting algorithm selects a subset of markers with independent motion patterns. The subset is used as a basis to fit the motion of the other facial markers, which determines facial regions of influence of each of the linearly independent markers. Those regions constitute kinematic “eigenregions” whose combined motion produces the total motion of the face. Facial animations may be generated by driving the independent markers with collected displacement records. PMID:19062866

Statistical iterative reconstruction for streak artefact reduction when using multidetector CT to image the dento-alveolar structures.

PubMed

Dong, J; Hayakawa, Y; Kober, C

2014-01-01

When metallic prosthetic appliances and dental fillings exist in the oral cavity, the appearance of metal-induced streak artefacts is not avoidable in CT images. The aim of this study was to develop a method for artefact reduction using the statistical reconstruction on multidetector row CT images. Adjacent CT images often depict similar anatomical structures. Therefore, reconstructed images with weak artefacts were attempted using projection data of an artefact-free image in a neighbouring thin slice. Images with moderate and strong artefacts were continuously processed in sequence by successive iterative restoration where the projection data was generated from the adjacent reconstructed slice. First, the basic maximum likelihood-expectation maximization algorithm was applied. Next, the ordered subset-expectation maximization algorithm was examined. Alternatively, a small region of interest setting was designated. Finally, the general purpose graphic processing unit machine was applied in both situations. The algorithms reduced the metal-induced streak artefacts on multidetector row CT images when the sequential processing method was applied. The ordered subset-expectation maximization and small region of interest reduced the processing duration without apparent detriments. A general-purpose graphic processing unit realized the high performance. A statistical reconstruction method was applied for the streak artefact reduction. The alternative algorithms applied were effective. Both software and hardware tools, such as ordered subset-expectation maximization, small region of interest and general-purpose graphic processing unit achieved fast artefact correction.

Coverability graphs for a class of synchronously executed unbounded Petri net

NASA Technical Reports Server (NTRS)

Stotts, P. David; Pratt, Terrence W.

1990-01-01

After detailing a variant of the concurrent-execution rule for firing of maximal subsets, in which the simultaneous firing of conflicting transitions is prohibited, an algorithm is constructed for generating the coverability graph of a net executed under this synchronous firing rule. The omega insertion criteria in the algorithm are shown to be valid for any net on which the algorithm terminates. It is accordingly shown that the set of nets on which the algorithm terminates includes the 'conflict-free' class.

High Resolution Deformation Time Series Estimation for Distributed Scatterers Using Terrasar-X Data

NASA Astrophysics Data System (ADS)

Goel, K.; Adam, N.

2012-07-01

In recent years, several SAR satellites such as TerraSAR-X, COSMO-SkyMed and Radarsat-2 have been launched. These satellites provide high resolution data suitable for sophisticated interferometric applications. With shorter repeat cycles, smaller orbital tubes and higher bandwidth of the satellites; deformation time series analysis of distributed scatterers (DSs) is now supported by a practical data basis. Techniques for exploiting DSs in non-urban (rural) areas include the Small Baseline Subset Algorithm (SBAS). However, it involves spatial phase unwrapping, and phase unwrapping errors are typically encountered in rural areas and are difficult to detect. In addition, the SBAS technique involves a rectangular multilooking of the differential interferograms to reduce phase noise, resulting in a loss of resolution and superposition of different objects on ground. In this paper, we introduce a new approach for deformation monitoring with a focus on DSs, wherein, there is no need to unwrap the differential interferograms and the deformation is mapped at object resolution. It is based on a robust object adaptive parameter estimation using single look differential interferograms, where, the local tilts of deformation velocity and local slopes of residual DEM in range and azimuth directions are estimated. We present here the technical details and a processing example of this newly developed algorithm.

UAV Mission Planning under Uncertainty

DTIC Science & Technology

2006-06-01

algorithm , adapted from [13] . 57 4-5 Robust Optimization considers only a subset of the feasible region . 61 5-1 Overview of simulation with parameter...incorporates the robust optimization method suggested by Bertsimas and Sim [12], and is solved with a standard Branch- and-Cut algorithm . The chapter... algorithms , and the heuristic methods of Local Search methods and Simulated Annealing. With each method, we attempt to give a review of research that has

A Composite Algorithm for Mixed Integer Constrained Nonlinear Optimization.

DTIC Science & Technology

1980-01-01

de Silva [141, and Weisman and Wood [76). A particular direct search algorithm, the simplex method, has been cited for having the potential for...spaced discrete points on a line which makes the direction suitable for an efficient integer search technique based on Fibonacci numbers. Two...defined by a subset of variables. The complex algorithm is particularly well suited for this subspace search for two reasons. First, the complex method

Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data.

PubMed

Wang, Shuaiqun; Aorigele; Kong, Wei; Zeng, Weiming; Hong, Xiaomin

2016-01-01

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes.

Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data

PubMed Central

Aorigele; Zeng, Weiming; Hong, Xiaomin

2016-01-01

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes. PMID:27579323

Meaningless comparisons lead to false optimism in medical machine learning

PubMed Central

Kording, Konrad; Recht, Benjamin

2017-01-01

A new trend in medicine is the use of algorithms to analyze big datasets, e.g. using everything your phone measures about you for diagnostics or monitoring. However, these algorithms are commonly compared against weak baselines, which may contribute to excessive optimism. To assess how well an algorithm works, scientists typically ask how well its output correlates with medically assigned scores. Here we perform a meta-analysis to quantify how the literature evaluates their algorithms for monitoring mental wellbeing. We find that the bulk of the literature (∼77%) uses meaningless comparisons that ignore patient baseline state. For example, having an algorithm that uses phone data to diagnose mood disorders would be useful. However, it is possible to explain over 80% of the variance of some mood measures in the population by simply guessing that each patient has their own average mood—the patient-specific baseline. Thus, an algorithm that just predicts that our mood is like it usually is can explain the majority of variance, but is, obviously, entirely useless. Comparing to the wrong (population) baseline has a massive effect on the perceived quality of algorithms and produces baseless optimism in the field. To solve this problem we propose “user lift” that reduces these systematic errors in the evaluation of personalized medical monitoring. PMID:28949964

The effect of tracking network configuration on Global Positioning System (GPS) baseline estimates for the CASA (Central and South America) Uno experiment

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

Wolf, S.K.; Dixon, T.H.; Freymueller, J.T.

1990-04-01

Geodetic monitoring of subduction of the Nazca and Cocos plates is a goal of the CASA (Central and South America) Global Positioning System (GPS) experiments, and requires measurement of intersite distances (baselines) in excess of 500 km. The major error source in these measurements is the uncertainty in the position of the GPS satellites at the time of observation. A key aspect of the first CASA experiment, CASA Uno, was the initiation of a global network of tracking stations minimize these errors. The authors studied the effect of using various subsets of this global tracking network on long (>100 km)more » baseline estimates in the CASA region. Best results were obtained with a global tracking network consisting of three U.S. fiducial stations, two sites in the southwest pacific and two sites in Europe. Relative to smaller subsets, this global network improved baseline repeatability, resolution of carrier phase cycle ambiguities, and formal errors of the orbit estimates. Describing baseline repeatability for horizontal components as {sigma}=(a{sup 2} + b{sup 2}L{sup 2}){sup 1/2} where L is baseline length, the authors obtained a = 4 and 9 mm and b = 2.8{times}10{sup {minus}8} and 2.3{times}10{sup {minus}8} for north and east components, respectively, on CASA baselines up to 1,000 km in length with this global network.« less

Postinjection single photon transmission tomography with ordered-subset algorithms for whole-body PET imaging

NASA Astrophysics Data System (ADS)

Bai, Chuanyong; Kinahan, P. E.; Brasse, D.; Comtat, C.; Townsend, D. W.

2002-02-01

We have evaluated the penalized ordered-subset transmission reconstruction (OSTR) algorithm for postinjection single photon transmission scanning. The OSTR algorithm of Erdogan and Fessler (1999) uses a more accurate model for transmission tomography than ordered-subsets expectation-maximization (OSEM) when OSEM is applied to the logarithm of the transmission data. The OSTR algorithm is directly applicable to postinjection transmission scanning with a single photon source, as emission contamination from the patient mimics the effect, in the original derivation of OSTR, of random coincidence contamination in a positron source transmission scan. Multiple noise realizations of simulated postinjection transmission data were reconstructed using OSTR, filtered backprojection (FBP), and OSEM algorithms. Due to the nonspecific task performance, or multiple uses, of the transmission image, multiple figures of merit were evaluated, including image noise, contrast, uniformity, and root mean square (rms) error. We show that: 1) the use of a three-dimensional (3-D) regularizing image roughness penalty with OSTR improves the tradeoffs in noise, contrast, and rms error relative to the use of a two-dimensional penalty; 2) OSTR with a 3-D penalty has improved tradeoffs in noise, contrast, and rms error relative to FBP or OSEM; and 3) the use of image standard deviation from a single realization to estimate the true noise can be misleading in the case of OSEM. We conclude that using OSTR with a 3-D penalty potentially allows for shorter postinjection transmission scans in single photon transmission tomography in positron emission tomography (PET) relative to FBP or OSEM reconstructed images with the same noise properties. This combination of singles+OSTR is particularly suitable for whole-body PET oncology imaging.

A ℓ2, 1 norm regularized multi-kernel learning for false positive reduction in Lung nodule CAD.

PubMed

Cao, Peng; Liu, Xiaoli; Zhang, Jian; Li, Wei; Zhao, Dazhe; Huang, Min; Zaiane, Osmar

2017-03-01

The aim of this paper is to describe a novel algorithm for False Positive Reduction in lung nodule Computer Aided Detection(CAD). In this paper, we describes a new CT lung CAD method which aims to detect solid nodules. Specially, we proposed a multi-kernel classifier with a ℓ 2, 1 norm regularizer for heterogeneous feature fusion and selection from the feature subset level, and designed two efficient strategies to optimize the parameters of kernel weights in non-smooth ℓ 2, 1 regularized multiple kernel learning algorithm. The first optimization algorithm adapts a proximal gradient method for solving the ℓ 2, 1 norm of kernel weights, and use an accelerated method based on FISTA; the second one employs an iterative scheme based on an approximate gradient descent method. The results demonstrates that the FISTA-style accelerated proximal descent method is efficient for the ℓ 2, 1 norm formulation of multiple kernel learning with the theoretical guarantee of the convergence rate. Moreover, the experimental results demonstrate the effectiveness of the proposed methods in terms of Geometric mean (G-mean) and Area under the ROC curve (AUC), and significantly outperforms the competing methods. The proposed approach exhibits some remarkable advantages both in heterogeneous feature subsets fusion and classification phases. Compared with the fusion strategies of feature-level and decision level, the proposed ℓ 2, 1 norm multi-kernel learning algorithm is able to accurately fuse the complementary and heterogeneous feature sets, and automatically prune the irrelevant and redundant feature subsets to form a more discriminative feature set, leading a promising classification performance. Moreover, the proposed algorithm consistently outperforms the comparable classification approaches in the literature. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction

PubMed Central

Zhang, Daqing; Xiao, Jianfeng; Zhou, Nannan; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian

2015-01-01

Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy. In most studies, they are treated as two independent problems, but it has been proven that they could affect each other. We designed and implemented genetic algorithm (GA) to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction. The results show that our GA/SVM model is more accurate than other currently available log BB models. Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately. Analysis of our log BB model suggests that carboxylic acid group, polar surface area (PSA)/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration. Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration. PMID:26504797

Defining an essence of structure determining residue contacts in proteins.

PubMed

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 fields of structure prediction, empirical potentials and docking.

Defining an Essence of Structure Determining Residue Contacts in Proteins

PubMed Central

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

2009-01-01

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 Å 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 fields of structure prediction, empirical potentials and docking. PMID:19997489

A comparative analysis of biclustering algorithms for gene expression data

PubMed Central

Eren, Kemal; Deveci, Mehmet; Küçüktunç, Onur; Çatalyürek, Ümit V.

2013-01-01

The need to analyze high-dimension biological data is driving the development of new data mining methods. Biclustering algorithms have been successfully applied to gene expression data to discover local patterns, in which a subset of genes exhibit similar expression levels over a subset of conditions. However, it is not clear which algorithms are best suited for this task. Many algorithms have been published in the past decade, most of which have been compared only to a small number of algorithms. Surveys and comparisons exist in the literature, but because of the large number and variety of biclustering algorithms, they are quickly outdated. In this article we partially address this problem of evaluating the strengths and weaknesses of existing biclustering methods. We used the BiBench package to compare 12 algorithms, many of which were recently published or have not been extensively studied. The algorithms were tested on a suite of synthetic data sets to measure their performance on data with varying conditions, such as different bicluster models, varying noise, varying numbers of biclusters and overlapping biclusters. The algorithms were also tested on eight large gene expression data sets obtained from the Gene Expression Omnibus. Gene Ontology enrichment analysis was performed on the resulting biclusters, and the best enrichment terms are reported. Our analyses show that the biclustering method and its parameters should be selected based on the desired model, whether that model allows overlapping biclusters, and its robustness to noise. In addition, we observe that the biclustering algorithms capable of finding more than one model are more successful at capturing biologically relevant clusters. PMID:22772837

Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Admissibility and Termination Analysis.

PubMed

Wei, Qinglai; Liu, Derong; Lin, Qiao

In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.

Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm.

PubMed

Beheshti, Iman; Demirel, Hasan; Matsuda, Hiroshi

2017-04-01

We developed a novel computer-aided diagnosis (CAD) system that uses feature-ranking and a genetic algorithm to analyze structural magnetic resonance imaging data; using this system, we can predict conversion of mild cognitive impairment (MCI)-to-Alzheimer's disease (AD) at between one and three years before clinical diagnosis. The CAD system was developed in four stages. First, we used a voxel-based morphometry technique to investigate global and local gray matter (GM) atrophy in an AD group compared with healthy controls (HCs). Regions with significant GM volume reduction were segmented as volumes of interest (VOIs). Second, these VOIs were used to extract voxel values from the respective atrophy regions in AD, HC, stable MCI (sMCI) and progressive MCI (pMCI) patient groups. The voxel values were then extracted into a feature vector. Third, at the feature-selection stage, all features were ranked according to their respective t-test scores and a genetic algorithm designed to find the optimal feature subset. The Fisher criterion was used as part of the objective function in the genetic algorithm. Finally, the classification was carried out using a support vector machine (SVM) with 10-fold cross validation. We evaluated the proposed automatic CAD system by applying it to baseline values from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (160 AD, 162 HC, 65 sMCI and 71 pMCI subjects). The experimental results indicated that the proposed system is capable of distinguishing between sMCI and pMCI patients, and would be appropriate for practical use in a clinical setting. Copyright © 2017 Elsevier Ltd. All rights reserved.

Gene selection heuristic algorithm for nutrigenomics studies.

PubMed

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.

Graph Learning in Knowledge Bases

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

Goldberg, Sean; Wang, Daisy Zhe

The amount of text data has been growing exponentially in recent years, giving rise to automatic information extraction methods that store text annotations in a database. The current state-of-theart structured prediction methods, however, are likely to contain errors and it’s important to be able to manage the overall uncertainty of the database. On the other hand, the advent of crowdsourcing has enabled humans to aid machine algorithms at scale. As part of this project we introduced pi-CASTLE , a system that optimizes and integrates human and machine computing as applied to a complex structured prediction problem involving conditional random fieldsmore » (CRFs). We proposed strategies grounded in information theory to select a token subset, formulate questions for the crowd to label, and integrate these labelings back into the database using a method of constrained inference. On both a text segmentation task over academic citations and a named entity recognition task over tweets we showed an order of magnitude improvement in accuracy gain over baseline methods.« less

The effect of perceptual grouping on haptic numerosity perception.

PubMed

Verlaers, K; Wagemans, J; Overvliet, K E

2015-01-01

We used a haptic enumeration task to investigate whether enumeration can be facilitated by perceptual grouping in the haptic modality. Eight participants were asked to count tangible dots as quickly and accurately as possible, while moving their finger pad over a tactile display. In Experiment 1, we manipulated the number and organization of the dots, while keeping the total exploration area constant. The dots were either evenly distributed on a horizontal line (baseline condition) or organized into groups based on either proximity (dots placed in closer proximity to each other) or configural cues (dots placed in a geometric configuration). In Experiment 2, we varied the distance between the subsets of dots. We hypothesized that when subsets of dots can be grouped together, the enumeration time will be shorter and accuracy will be higher than in the baseline condition. The results of both experiments showed faster enumeration for the configural condition than for the baseline condition, indicating that configural grouping also facilitates haptic enumeration. In Experiment 2, faster enumeration was also observed for the proximity condition than for the baseline condition. Thus, perceptual grouping speeds up haptic enumeration by both configural and proximity cues, suggesting that similar mechanisms underlie perceptual grouping in both visual and haptic enumeration.

[The comparison of two different types of baseline data regarding the performance of aberration detection algorithm for infectious disease outbreaks].

PubMed

Lai, Sheng-jie; Li, Zhong-jie; Zhang, Hong-long; Lan, Ya-jia; Yang, Wei-zhong

2011-06-01

To compare the performance of aberration detection algorithm for infectious disease outbreaks, based on two different types of baseline data. Cases and outbreaks of hand-foot-and-mouth disease (HFMD) reported by six provinces of China in 2009 were used as the source of data. Two types of baseline data on algorithms of C1, C2 and C3 were tested, by distinguishing the baseline data of weekdays and weekends. Time to detection (TTD) and false alarm rate (FAR) were adopted as two evaluation indices to compare the performance of 3 algorithms based on these two types of baseline data. A total of 405 460 cases of HFMD were reported by 6 provinces in 2009. On average, each county reported 1.78 cases per day during the weekdays and 1.29 cases per day during weekends, with significant difference (P < 0.01) between them. When using the baseline data without distinguish weekdays and weekends, the optimal thresholds for C1, C2 and C3 was 0.2, 0.4 and 0.6 respectively while the TTD of C1, C2 and C3 was all 1 day and the FARs were 5.33%, 4.88% and 4.50% respectively. On the contrast, when using the baseline data to distinguish the weekdays and weekends, the optimal thresholds for C1, C2 and C3 became 0.4, 0.6 and 1.0 while the TTD of C1, C2 and C3 also appeared equally as 1 day. However, the FARs became 4.81%, 4.75% and 4.16% respectively, which were lower than the baseline data from the first type. The number of HFMD cases reported in weekdays and weekends were significantly different, suggesting that when using the baseline data to distinguish weekdays and weekends, the FAR of C1, C2 and C3 algorithm could effectively reduce so as to improve the accuracy of outbreak detection.

Elucidation of Seventeen Human Peripheral Blood B cell Subsets and Quantification of the Tetanus Response Using a Density-Based Method for the Automated Identification of Cell Populations in Multidimensional Flow Cytometry Data

PubMed Central

Qian, Yu; Wei, Chungwen; Lee, F. Eun-Hyung; Campbell, John; Halliley, Jessica; Lee, Jamie A.; Cai, Jennifer; Kong, Megan; Sadat, Eva; Thomson, Elizabeth; Dunn, Patrick; Seegmiller, Adam C.; Karandikar, Nitin J.; Tipton, Chris; Mosmann, Tim; Sanz, Iñaki; Scheuermann, Richard H.

2011-01-01

Background Advances in multi-parameter flow cytometry (FCM) now allow for the independent detection of larger numbers of fluorochromes on individual cells, generating data with increasingly higher dimensionality. The increased complexity of these data has made it difficult to identify cell populations from high-dimensional FCM data using traditional manual gating strategies based on single-color or two-color displays. Methods To address this challenge, we developed a novel program, FLOCK (FLOw Clustering without K), that uses a density-based clustering approach to algorithmically identify biologically relevant cell populations from multiple samples in an unbiased fashion, thereby eliminating operator-dependent variability. Results FLOCK was used to objectively identify seventeen distinct B cell subsets in a human peripheral blood sample and to identify and quantify novel plasmablast subsets responding transiently to tetanus and other vaccinations in peripheral blood. FLOCK has been implemented in the publically available Immunology Database and Analysis Portal – ImmPort (http://www.immport.org) for open use by the immunology research community. Conclusions FLOCK is able to identify cell subsets in experiments that use multi-parameter flow cytometry through an objective, automated computational approach. The use of algorithms like FLOCK for FCM data analysis obviates the need for subjective and labor intensive manual gating to identify and quantify cell subsets. Novel populations identified by these computational approaches can serve as hypotheses for further experimental study. PMID:20839340

Column Subset Selection, Matrix Factorization, and Eigenvalue Optimization

DTIC Science & Technology

2008-07-01

Pietsch and Grothendieck, which are regarded as basic instruments in modern functional analysis [Pis86]. • The methods for computing these... Pietsch factorization and the maxcut semi- definite program [GW95]. 1.2. Overview. We focus on the algorithmic version of the Kashin–Tzafriri theorem...will see that the desired subset is exposed by factoring the random submatrix. This factorization, which was invented by Pietsch , is regarded as a basic

Quantum digital-to-analog conversion algorithm using decoherence

NASA Astrophysics Data System (ADS)

SaiToh, Akira

2015-08-01

We consider the problem of mapping digital data encoded on a quantum register to analog amplitudes in parallel. It is shown to be unlikely that a fully unitary polynomial-time quantum algorithm exists for this problem; NP becomes a subset of BQP if it exists. In the practical point of view, we propose a nonunitary linear-time algorithm using quantum decoherence. It tacitly uses an exponentially large physical resource, which is typically a huge number of identical molecules. Quantumness of correlation appearing in the process of the algorithm is also discussed.

Beneficial Effects of cART Initiated during Primary and Chronic HIV-1 Infection on Immunoglobulin-Expression of Memory B-Cell Subsets

PubMed Central

Pensieroso, Simone; Tolazzi, Monica; Chiappetta, Stefania; Nozza, Silvia; Lazzarin, Adriano; Tambussi, Giuseppe; Scarlatti, Gabriella

2015-01-01

Introduction During HIV-1 infection the B-cell compartment undergoes profound changes towards terminal differentiation, which are only partially restored by antiretroviral therapy (cART). Materials and Methods 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. Results 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. Conclusions 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 RM, remaining perturbed in TLM B-cells of PHI and CHI. PMID:26474181

Beneficial Effects of cART Initiated during Primary and Chronic HIV-1 Infection on Immunoglobulin-Expression of Memory B-Cell Subsets.

PubMed

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 RM, remaining perturbed in TLM B-cells of PHI and CHI.

Missing value imputation in DNA microarrays based on conjugate gradient method.

PubMed

Dorri, Fatemeh; Azmi, Paeiz; Dorri, Faezeh

2012-02-01

Analysis of gene expression profiles needs a complete matrix of gene array values; consequently, imputation methods have been suggested. In this paper, an algorithm that is based on conjugate gradient (CG) method is proposed to estimate missing values. k-nearest neighbors of the missed entry are first selected based on absolute values of their Pearson correlation coefficient. Then a subset of genes among the k-nearest neighbors is labeled as the best similar ones. CG algorithm with this subset as its input is then used to estimate the missing values. Our proposed CG based algorithm (CGimpute) is evaluated on different data sets. The results are compared with sequential local least squares (SLLSimpute), Bayesian principle component analysis (BPCAimpute), local least squares imputation (LLSimpute), iterated local least squares imputation (ILLSimpute) and adaptive k-nearest neighbors imputation (KNNKimpute) methods. The average of normalized root mean squares error (NRMSE) and relative NRMSE in different data sets with various missing rates shows CGimpute outperforms other methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

Negotiating Multicollinearity with Spike-and-Slab Priors

PubMed Central

Ročková, Veronika

2014-01-01

In multiple regression under the normal linear model, the presence of multicollinearity is well known to lead to unreliable and unstable maximum likelihood estimates. This can be particularly troublesome for the problem of variable selection where it becomes more difficult to distinguish between subset models. Here we show how adding a spike-and-slab prior mitigates this difficulty by filtering the likelihood surface into a posterior distribution that allocates the relevant likelihood information to each of the subset model modes. For identification of promising high posterior models in this setting, we consider three EM algorithms, the fast closed form EMVS version of Rockova and George (2014) and two new versions designed for variants of the spike-and-slab formulation. For a multimodal posterior under multicollinearity, we compare the regions of convergence of these three algorithms. Deterministic annealing versions of the EMVS algorithm are seen to substantially mitigate this multimodality. A single simple running example is used for illustration throughout. PMID:25419004

Performance and Accuracy of LAPACK's Symmetric TridiagonalEigensolvers

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

Demmel, Jim W.; Marques, Osni A.; Parlett, Beresford N.

2007-04-19

We compare four algorithms from the latest LAPACK 3.1 release for computing eigenpairs of a symmetric tridiagonal matrix. These include QR iteration, bisection and inverse iteration (BI), the Divide-and-Conquer method (DC), and the method of Multiple Relatively Robust Representations (MR). Our evaluation considers speed and accuracy when computing all eigenpairs, and additionally subset computations. Using a variety of carefully selected test problems, our study includes a variety of today's computer architectures. Our conclusions can be summarized as follows. (1) DC and MR are generally much faster than QR and BI on large matrices. (2) MR almost always does the fewestmore » floating point operations, but at a lower MFlop rate than all the other algorithms. (3) The exact performance of MR and DC strongly depends on the matrix at hand. (4) DC and QR are the most accurate algorithms with observed accuracy O({radical}ne). The accuracy of BI and MR is generally O(ne). (5) MR is preferable to BI for subset computations.« less

Interband coding extension of the new lossless JPEG standard

NASA Astrophysics Data System (ADS)

Memon, Nasir D.; Wu, Xiaolin; Sippy, V.; Miller, G.

1997-01-01

Due to the perceived inadequacy of current standards for lossless image compression, the JPEG committee of the International Standards Organization (ISO) has been developing a new standard. A baseline algorithm, called JPEG-LS, has already been completed and is awaiting approval by national bodies. The JPEG-LS baseline algorithm despite being simple is surprisingly efficient, and provides compression performance that is within a few percent of the best and more sophisticated techniques reported in the literature. Extensive experimentations performed by the authors seem to indicate that an overall improvement by more than 10 percent in compression performance will be difficult to obtain even at the cost of great complexity; at least not with traditional approaches to lossless image compression. However, if we allow inter-band decorrelation and modeling in the baseline algorithm, nearly 30 percent improvement in compression gains for specific images in the test set become possible with a modest computational cost. In this paper we propose and investigate a few techniques for exploiting inter-band correlations in multi-band images. These techniques have been designed within the framework of the baseline algorithm, and require minimal changes to the basic architecture of the baseline, retaining its essential simplicity.

Informed baseline subtraction of proteomic mass spectrometry data aided by a novel sliding window algorithm.

PubMed

Stanford, Tyman E; Bagley, Christopher J; Solomon, Patty J

2016-01-01

Proteomic matrix-assisted laser desorption/ionisation (MALDI) linear time-of-flight (TOF) mass spectrometry (MS) may be used to produce protein profiles from biological samples with the aim of discovering biomarkers for disease. However, the raw protein profiles suffer from several sources of bias or systematic variation which need to be removed via pre-processing before meaningful downstream analysis of the data can be undertaken. Baseline subtraction, an early pre-processing step that removes the non-peptide signal from the spectra, is complicated by the following: (i) each spectrum has, on average, wider peaks for peptides with higher mass-to-charge ratios ( m / z ), and (ii) the time-consuming and error-prone trial-and-error process for optimising the baseline subtraction input arguments. With reference to the aforementioned complications, we present an automated pipeline that includes (i) a novel 'continuous' line segment algorithm that efficiently operates over data with a transformed m / z -axis to remove the relationship between peptide mass and peak width, and (ii) an input-free algorithm to estimate peak widths on the transformed m / z scale. The automated baseline subtraction method was deployed on six publicly available proteomic MS datasets using six different m/z-axis transformations. Optimality of the automated baseline subtraction pipeline was assessed quantitatively using the mean absolute scaled error (MASE) when compared to a gold-standard baseline subtracted signal. Several of the transformations investigated were able to reduce, if not entirely remove, the peak width and peak location relationship resulting in near-optimal baseline subtraction using the automated pipeline. The proposed novel 'continuous' line segment algorithm is shown to far outperform naive sliding window algorithms with regard to the computational time required. The improvement in computational time was at least four-fold on real MALDI TOF-MS data and at least an order of magnitude on many simulated datasets. The advantages of the proposed pipeline include informed and data specific input arguments for baseline subtraction methods, the avoidance of time-intensive and subjective piecewise baseline subtraction, and the ability to automate baseline subtraction completely. Moreover, individual steps can be adopted as stand-alone routines.

Algorithmic and user study of an autocompletion algorithm on a large medical vocabulary.

PubMed

Sevenster, Merlijn; van Ommering, Rob; Qian, Yuechen

2012-02-01

Autocompletion supports human-computer interaction in software applications that let users enter textual data. We will be inspired by the use case in which medical professionals enter ontology concepts, catering the ongoing demand for structured and standardized data in medicine. Goal is to give an algorithmic analysis of one particular autocompletion algorithm, called multi-prefix matching algorithm, which suggests terms whose words' prefixes contain all words in the string typed by the user, e.g., in this sense, opt ner me matches optic nerve meningioma. Second we aim to investigate how well it supports users entering concepts from a large and comprehensive medical vocabulary (snomed ct). We give a concise description of the multi-prefix algorithm, and sketch how it can be optimized to meet required response time. Performance will be compared to a baseline algorithm, which gives suggestions that extend the string typed by the user to the right, e.g. optic nerve m gives optic nerve meningioma, but opt ner me does not. We conduct a user experiment in which 12 participants are invited to complete 40 snomed ct terms with the baseline algorithm and another set of 40 snomed ct terms with the multi-prefix algorithm. Our results show that users need significantly fewer keystrokes when supported by the multi-prefix algorithm than when supported by the baseline algorithm. The proposed algorithm is a competitive candidate for searching and retrieving terms from a large medical ontology. Copyright © 2011 Elsevier Inc. All rights reserved.

Applying an efficient K-nearest neighbor search to forest attribute imputation

Treesearch

Andrew O. Finley; Ronald E. McRoberts; Alan R. Ek

2006-01-01

This paper explores the utility of an efficient nearest neighbor (NN) search algorithm for applications in multi-source kNN forest attribute imputation. The search algorithm reduces the number of distance calculations between a given target vector and each reference vector, thereby, decreasing the time needed to discover the NN subset. Results of five trials show gains...

Experimental data filtration algorithm

NASA Astrophysics Data System (ADS)

Oanta, E.; Tamas, R.; Danisor, A.

2017-08-01

Experimental data reduction is an important topic because the resulting information is used to calibrate the theoretical models and to verify the accuracy of their results. The paper presents some ideas used to extract a subset of points from the initial set of points which defines an experimentally acquired curve. The objective is to get a subset with significantly fewer points as the initial data set and which accurately defines a smooth curve that preserves the shape of the initial curve. Being a general study we used only data filtering criteria based geometric features that at a later stage may be related to upper level conditions specific to the phenomenon under investigation. Five algorithms were conceived and implemented in an original software consisting of more than 1800 computer code lines which has a flexible structure that allows us to easily update it using new algorithms. The software instrument was used to process the data of several case studies. Conclusions are drawn regarding the values of the parameters used in the algorithms to decide if a series of points may be considered either noise, or a relevant part of the curve. Being a general analysis, the result is a computer based trial-and-error method that efficiently solves this kind of problems.

Mining subspace clusters from DNA microarray data using large itemset techniques.

PubMed

Chang, Ye-In; Chen, Jiun-Rung; Tsai, Yueh-Chi

2009-05-01

Mining subspace clusters from the DNA microarrays could help researchers identify those genes which commonly contribute to a disease, where a subspace cluster indicates a subset of genes whose expression levels are similar under a subset of conditions. Since in a DNA microarray, the number of genes is far larger than the number of conditions, those previous proposed algorithms which compute the maximum dimension sets (MDSs) for any two genes will take a long time to mine subspace clusters. In this article, we propose the Large Itemset-Based Clustering (LISC) algorithm for mining subspace clusters. Instead of constructing MDSs for any two genes, we construct only MDSs for any two conditions. Then, we transform the task of finding the maximal possible gene sets into the problem of mining large itemsets from the condition-pair MDSs. Since we are only interested in those subspace clusters with gene sets as large as possible, it is desirable to pay attention to those gene sets which have reasonable large support values in the condition-pair MDSs. From our simulation results, we show that the proposed algorithm needs shorter processing time than those previous proposed algorithms which need to construct gene-pair MDSs.

Combinatorial-topological framework for the analysis of global dynamics.

PubMed

Bush, Justin; Gameiro, Marcio; Harker, Shaun; Kokubu, Hiroshi; Mischaikow, Konstantin; Obayashi, Ippei; Pilarczyk, Paweł

2012-12-01

We discuss an algorithmic framework based on efficient graph algorithms and algebraic-topological computational tools. The framework is aimed at automatic computation of a database of global dynamics of a given m-parameter semidynamical system with discrete time on a bounded subset of the n-dimensional phase space. We introduce the mathematical background, which is based upon Conley's topological approach to dynamics, describe the algorithms for the analysis of the dynamics using rectangular grids both in phase space and parameter space, and show two sample applications.

Combinatorial-topological framework for the analysis of global dynamics

NASA Astrophysics Data System (ADS)

Bush, Justin; Gameiro, Marcio; Harker, Shaun; Kokubu, Hiroshi; Mischaikow, Konstantin; Obayashi, Ippei; Pilarczyk, Paweł

2012-12-01

We discuss an algorithmic framework based on efficient graph algorithms and algebraic-topological computational tools. The framework is aimed at automatic computation of a database of global dynamics of a given m-parameter semidynamical system with discrete time on a bounded subset of the n-dimensional phase space. We introduce the mathematical background, which is based upon Conley's topological approach to dynamics, describe the algorithms for the analysis of the dynamics using rectangular grids both in phase space and parameter space, and show two sample applications.

Molecular stratification and precision medicine in systemic sclerosis from genomic and proteomic data.

PubMed

Martyanov, Viktor; Whitfield, Michael L

2016-01-01

The goal of this review is to summarize recent advances into the pathogenesis and treatment of systemic sclerosis (SSc) from genomic and proteomic studies. Intrinsic gene expression-driven molecular subtypes of SSc are reproducible across three independent datasets. These subsets are a consistent feature of SSc and are found in multiple end-target tissues, such as skin and esophagus. Intrinsic subsets as well as baseline levels of molecular target pathways are potentially predictive of clinical response to specific therapeutics, based on three recent clinical trials. A gene expression-based biomarker of modified Rodnan skin score, a measure of SSc skin severity, can be used as a surrogate outcome metric and has been validated in a recent trial. Proteome analyses have identified novel biomarkers of SSc that correlate with SSc clinical phenotypes. Integrating intrinsic gene expression subset data, baseline molecular pathway information, and serum biomarkers along with surrogate measures of modified Rodnan skin score provides molecular context in SSc clinical trials. With validation, these approaches could be used to match patients with the therapies from which they are most likely to benefit and thus increase the likelihood of clinical improvement.

[An Improved Cubic Spline Interpolation Method for Removing Electrocardiogram Baseline Drift].

PubMed

Wang, Xiangkui; Tang, Wenpu; Zhang, Lai; Wu, Minghu

2016-04-01

The selection of fiducial points has an important effect on electrocardiogram(ECG)denoise with cubic spline interpolation.An improved cubic spline interpolation algorithm for suppressing ECG baseline drift is presented in this paper.Firstly the first order derivative of original ECG signal is calculated,and the maximum and minimum points of each beat are obtained,which are treated as the position of fiducial points.And then the original ECG is fed into a high pass filter with 1.5Hz cutoff frequency.The difference between the original and the filtered ECG at the fiducial points is taken as the amplitude of the fiducial points.Then cubic spline interpolation curve fitting is used to the fiducial points,and the fitting curve is the baseline drift curve.For the two simulated case test,the correlation coefficients between the fitting curve by the presented algorithm and the simulated curve were increased by 0.242and0.13 compared with that from traditional cubic spline interpolation algorithm.And for the case of clinical baseline drift data,the average correlation coefficient from the presented algorithm achieved 0.972.

An efficient voting algorithm for finding additive biclusters with random background.

PubMed

Xiao, Jing; Wang, Lusheng; Liu, Xiaowen; Jiang, Tao

2008-12-01

The biclustering problem has been extensively studied in many areas, including e-commerce, data mining, machine learning, pattern recognition, statistics, and, more recently, computational biology. Given an n x m matrix A (n >or= m), the main goal of biclustering is to identify a subset of rows (called objects) and a subset of columns (called properties) such that some objective function that specifies the quality of the found bicluster (formed by the subsets of rows and of columns of A) is optimized. The problem has been proved or conjectured to be NP-hard for various objective functions. In this article, we study a probabilistic model for the implanted additive bicluster problem, where each element in the n x m background matrix is a random integer from [0, L - 1] for some integer L, and a k x k implanted additive bicluster is obtained from an error-free additive bicluster by randomly changing each element to a number in [0, L - 1] with probability theta. We propose an O(n(2)m) time algorithm based on voting to solve the problem. We show that when k >or= Omega(square root of (n log n)), the voting algorithm can correctly find the implanted bicluster with probability at least 1 - (9/n(2)). We also implement our algorithm as a C++ program named VOTE. The implementation incorporates several ideas for estimating the size of an implanted bicluster, adjusting the threshold in voting, dealing with small biclusters, and dealing with overlapping implanted biclusters. Our experimental results on both simulated and real datasets show that VOTE can find biclusters with a high accuracy and speed.

An Interferometry Imaging Beauty Contest

NASA Technical Reports Server (NTRS)

Lawson, Peter R.; Cotton, William D.; Hummel, Christian A.; Monnier, John D.; Zhaod, Ming; Young, John S.; Thorsteinsson, Hrobjartur; Meimon, Serge C.; Mugnier, Laurent; LeBesnerais, Guy;

A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal.

PubMed

Oosugi, Naoya; Kitajo, Keiichi; Hasegawa, Naomi; Nagasaka, Yasuo; Okanoya, Kazuo; Fujii, Naotaka

2017-09-01

Blind source separation (BSS) algorithms extract neural signals from electroencephalography (EEG) data. However, it is difficult to quantify source separation performance because there is no criterion to dissociate neural signals and noise in EEG signals. This study develops a method for evaluating BSS performance. The idea is neural signals in EEG can be estimated by comparison with simultaneously measured electrocorticography (ECoG). Because the ECoG electrodes cover the majority of the lateral cortical surface and should capture most of the original neural sources in the EEG signals. We measured real EEG and ECoG data and developed an algorithm for evaluating BSS performance. First, EEG signals are separated into EEG components using the BSS algorithm. Second, the EEG components are ranked using the correlation coefficients of the ECoG regression and the components are grouped into subsets based on their ranks. Third, canonical correlation analysis estimates how much information is shared between the subsets of the EEG components and the ECoG signals. We used our algorithm to compare the performance of BSS algorithms (PCA, AMUSE, SOBI, JADE, fastICA) via the EEG and ECoG data of anesthetized nonhuman primates. The results (Best case >JADE = fastICA >AMUSE = SOBI ≥ PCA >random separation) were common to the two subjects. To encourage the further development of better BSS algorithms, our EEG and ECoG data are available on our Web site (http://neurotycho.org/) as a common testing platform. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Characterization of Functional Antibody and Memory B-Cell Responses to pH1N1 Monovalent Vaccine in HIV-Infected Children and Youth

PubMed Central

Curtis, Donna J.; Muresan, Petronella; Nachman, Sharon; Fenton, Terence; Richardson, Kelly M.; Dominguez, Teresa; Flynn, Patricia M.; Spector, Stephen A.; Cunningham, Coleen K.; Bloom, Anthony; Weinberg, Adriana

2015-01-01

Objectives We investigated immune determinants of antibody responses and B-cell memory to pH1N1 vaccine in HIV-infected children. Methods Ninety subjects 4 to <25 years of age received two double doses of pH1N1 vaccine. Serum and cells were frozen at baseline, after each vaccination, and at 28 weeks post-immunization. Hemagglutination inhibition (HAI) titers, avidity indices (AI), B-cell subsets, and pH1N1 IgG and IgA antigen secreting cells (ASC) were measured at baseline and after each vaccination. Neutralizing antibodies and pH1N1-specific Th1, Th2 and Tfh cytokines were measured at baseline and post-dose 1. Results At entry, 26 (29%) subjects had pH1N1 protective HAI titers (≥1:40). pH1N1-specific HAI, neutralizing titers, AI, IgG ASC, IL-2 and IL-4 increased in response to vaccination (p<0.05), but IgA ASC, IL-5, IL-13, IL-21, IFNγ and B-cell subsets did not change. Subjects with baseline HAI ≥1:40 had significantly greater increases in IgG ASC and AI after immunization compared with those with HAI <1:40. Neutralizing titers and AI after vaccination increased with older age. High pH1N1 HAI responses were associated with increased IgG ASC, IFNγ, IL-2, microneutralizion titers, and AI. Microneutralization titers after vaccination increased with high IgG ASC and IL-2 responses. IgG ASC also increased with high IFNγ responses. CD4% and viral load did not predict the immune responses post-vaccination, but the B-cell distribution did. Notably, vaccine immunogenicity increased with high CD19+CD21+CD27+% resting memory, high CD19+CD10+CD27+% immature activated, low CD19+CD21-CD27-CD20-% tissue-like, low CD19+CD21-CD27-CD20-% transitional and low CD19+CD38+HLADR+% activated B-cell subsets. Conclusions HIV-infected children on HAART mount a broad B-cell memory response to pH1N1 vaccine, which was higher for subjects with baseline HAI≥1:40 and increased with age, presumably due to prior exposure to pH1N1 or to other influenza vaccination/infection. The response to the vaccine was dependent on B-cell subset distribution, but not on CD4 counts or viral load. Trial Registration ClinicalTrials.gov NCT00992836 PMID:25785995

Information technologies for taking into account risks in business development programme

NASA Astrophysics Data System (ADS)

Kalach, A. V.; Khasianov, R. R.; Rossikhina, L. V.; Zybin, D. G.; Melnik, A. A.

2018-05-01

The paper describes the information technologies for taking into account risks in business development programme, which rely on the algorithm for assessment of programme project risks and the algorithm of programme forming with constrained financing of high-risk projects taken into account. A method of lower-bound estimate is suggested for subsets of solutions. The corresponding theorem and lemma and their proofs are given.

Second thoughts on the final rule: An analysis of baseline participant characteristics reports on ClinicalTrials.gov.

PubMed

Cahan, Amos; Anand, Vibha

2017-01-01

ClinicalTrials.gov is valuable for aggregate-level analysis of trials. The recently published final rule aims to improve reporting of trial results. We aimed to assess variability in ClinicalTirals.gov records reporting participants' baseline measures. The September 2015 edition of the database for Aggregate Analysis of ClinicalTrials.gov (AACT), was used in this study. To date, AACT contains 186,941 trials of which 16,660 trials reporting baseline (participant) measures were analyzed. We also analyzed a subset of 13,818 Highly Likely Applicable Clinical Trials (HLACT), for which reporting of results is likely mandatory and compared a random sample of 30 trial records to their journal articles. We report counts for each mandatory baseline measure and variability reporting in their formats. The AACT dataset contains 8,161 baseline measures with 1206 unique measurement units. However, of these 6,940 (85%) variables appear only once in the dataset. Age and Gender are reported using many different formats (178 and 49 respectively). "Age" as the variable name is reported in 60 different formats. HLACT subset reports measures using 3,931 variables. The most frequent Age format (i.e. mean (years) ± sd) is found in only 45% of trials. Overall only 4 baseline measures (Region of Enrollment, Age, Number of Participants, and Gender) are reported by > 10% of trials. Discrepancies are found in both the types and formats of ClinicalTrials.gov records and their corresponding journal articles. On average, journal articles include twice the number of baseline measures (13.6±7.1 (sd) vs. 6.6±7.6) when compared to the ClinicalTrials.gov records that report any results. We found marked variability in baseline measures reporting. This is not addressed by the final rule. To support secondary use of ClinicalTrials.gov, a uniform format for baseline measures reporting is warranted.

Decision Variants for the Automatic Determination of Optimal Feature Subset in RF-RFE.

PubMed

Chen, Qi; Meng, Zhaopeng; Liu, Xinyi; Jin, Qianguo; Su, Ran

2018-06-15

Feature selection, which identifies a set of most informative features from the original feature space, has been widely used to simplify the predictor. Recursive feature elimination (RFE), as one of the most popular feature selection approaches, is effective in data dimension reduction and efficiency increase. A ranking of features, as well as candidate subsets with the corresponding accuracy, is produced through RFE. The subset with highest accuracy (HA) or a preset number of features (PreNum) are often used as the final subset. However, this may lead to a large number of features being selected, or if there is no prior knowledge about this preset number, it is often ambiguous and subjective regarding final subset selection. A proper decision variant is in high demand to automatically determine the optimal subset. In this study, we conduct pioneering work to explore the decision variant after obtaining a list of candidate subsets from RFE. We provide a detailed analysis and comparison of several decision variants to automatically select the optimal feature subset. Random forest (RF)-recursive feature elimination (RF-RFE) algorithm and a voting strategy are introduced. We validated the variants on two totally different molecular biology datasets, one for a toxicogenomic study and the other one for protein sequence analysis. The study provides an automated way to determine the optimal feature subset when using RF-RFE.

A Fully Customized Baseline Removal Framework for Spectroscopic Applications.

PubMed

Giguere, Stephen; Boucher, Thomas; Carey, C J; Mahadevan, Sridhar; Dyar, M Darby

2017-07-01

The task of proper baseline or continuum removal is common to nearly all types of spectroscopy. Its goal is to remove any portion of a signal that is irrelevant to features of interest while preserving any predictive information. Despite the importance of baseline removal, median or guessed default parameters are commonly employed, often using commercially available software supplied with instruments. Several published baseline removal algorithms have been shown to be useful for particular spectroscopic applications but their generalizability is ambiguous. The new Custom Baseline Removal (Custom BLR) method presented here generalizes the problem of baseline removal by combining operations from previously proposed methods to synthesize new correction algorithms. It creates novel methods for each technique, application, and training set, discovering new algorithms that maximize the predictive accuracy of the resulting spectroscopic models. In most cases, these learned methods either match or improve on the performance of the best alternative. Examples of these advantages are shown for three different scenarios: quantification of components in near-infrared spectra of corn and laser-induced breakdown spectroscopy data of rocks, and classification/matching of minerals using Raman spectroscopy. Software to implement this optimization is available from the authors. By removing subjectivity from this commonly encountered task, Custom BLR is a significant step toward completely automatic and general baseline removal in spectroscopic and other applications.

Applications and development of new algorithms for displacement analysis using InSAR time series

NASA Astrophysics Data System (ADS)

Osmanoglu, Batuhan

Time series analysis of Synthetic Aperture Radar Interferometry (InSAR) data has become an important scientific tool for monitoring and measuring the displacement of Earth's surface due to a wide range of phenomena, including earthquakes, volcanoes, landslides, changes in ground water levels, and wetlands. Time series analysis is a product of interferometric phase measurements, which become ambiguous when the observed motion is larger than half of the radar wavelength. Thus, phase observations must first be unwrapped in order to obtain physically meaningful results. Persistent Scatterer Interferometry (PSI), Stanford Method for Persistent Scatterers (StaMPS), Short Baselines Interferometry (SBAS) and Small Temporal Baseline Subset (STBAS) algorithms solve for this ambiguity using a series of spatio-temporal unwrapping algorithms and filters. In this dissertation, I improve upon current phase unwrapping algorithms, and apply the PSI method to study subsidence in Mexico City. PSI was used to obtain unwrapped deformation rates in Mexico City (Chapter 3),where ground water withdrawal in excess of natural recharge causes subsurface, clay-rich sediments to compact. This study is based on 23 satellite SAR scenes acquired between January 2004 and July 2006. Time series analysis of the data reveals a maximum line-of-sight subsidence rate of 300mm/yr at a high enough resolution that individual subsidence rates for large buildings can be determined. Differential motion and related structural damage along an elevated metro rail was evident from the results. Comparison of PSI subsidence rates with data from permanent GPS stations indicate root mean square (RMS) agreement of 6.9 mm/yr, about the level expected based on joint data uncertainty. The Mexico City results suggest negligible recharge, implying continuing degradation and loss of the aquifer in the third largest metropolitan area in the world. Chapters 4 and 5 illustrate the link between time series analysis and three-dimensional (3-D) phase unwrapping. Chapter 4 focuses on the unwrapping path. Unwrapping algorithms can be divided into two groups, path-dependent and path-independent algorithms. Path-dependent algorithms use local unwrapping functions applied pixel-by-pixel to the dataset. In contrast, path-independent algorithms use global optimization methods such as least squares, and return a unique solution. However, when aliasing and noise are present, path-independent algorithms can underestimate the signal in some areas due to global fitting criteria. Path-dependent algorithms do not underestimate the signal, but, as the name implies, the unwrapping path can affect the result. Comparison between existing path algorithms and a newly developed algorithm based on Fisher information theory was conducted. Results indicate that Fisher information theory does indeed produce lower misfit results for most tested cases. Chapter 5 presents a new time series analysis method based on 3-D unwrapping of SAR data using extended Kalman filters. Existing methods for time series generation using InSAR data employ special filters to combine two-dimensional (2-D) spatial unwrapping with one-dimensional (1-D) temporal unwrapping results. The new method, however, combines observations in azimuth, range and time for repeat pass interferometry. Due to the pixel-by-pixel characteristic of the filter, the unwrapping path is selected based on a quality map. This unwrapping algorithm is the first application of extended Kalman filters to the 3-D unwrapping problem. Time series analyses of InSAR data are used in a variety of applications with different characteristics. Consequently, it is difficult to develop a single algorithm that can provide optimal results in all cases, given that different algorithms possess a unique set of strengths and weaknesses. Nonetheless, filter-based unwrapping algorithms such as the one presented in this dissertation have the capability of joining multiple observations into a uniform solution, which is becoming an important feature with continuously growing datasets.

Efficient feature subset selection with probabilistic distance criteria. [pattern recognition

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

Recursive expressions are derived for efficiently computing the commonly used probabilistic distance measures as a change in the criteria both when a feature is added to and when a feature is deleted from the current feature subset. A combinatorial algorithm for generating all possible r feature combinations from a given set of s features in (s/r) steps with a change of a single feature at each step is presented. These expressions can also be used for both forward and backward sequential feature selection.

Optimization of Self-Directed Target Coverage in Wireless Multimedia Sensor Network

PubMed Central

Yang, Yang; Wang, Yufei; Pi, Dechang; Wang, Ruchuan

2014-01-01

Video and image sensors in wireless multimedia sensor networks (WMSNs) have directed view and limited sensing angle. So the methods to solve target coverage problem for traditional sensor networks, which use circle sensing model, are not suitable for WMSNs. Based on the FoV (field of view) sensing model and FoV disk model proposed, how expected multimedia sensor covers the target is defined by the deflection angle between target and the sensor's current orientation and the distance between target and the sensor. Then target coverage optimization algorithms based on expected coverage value are presented for single-sensor single-target, multisensor single-target, and single-sensor multitargets problems distinguishingly. Selecting the orientation that sensor rotated to cover every target falling in the FoV disk of that sensor for candidate orientations and using genetic algorithm to multisensor multitargets problem, which has NP-complete complexity, then result in the approximated minimum subset of sensors which covers all the targets in networks. Simulation results show the algorithm's performance and the effect of number of targets on the resulting subset. PMID:25136667

Analysis of genetic association in Listeria and Diabetes using Hierarchical Clustering and Silhouette Index

NASA Astrophysics Data System (ADS)

Pagnuco, Inti A.; Pastore, Juan I.; Abras, Guillermo; Brun, Marcel; Ballarin, Virginia L.

2016-04-01

It is usually assumed that co-expressed genes suggest co-regulation in the underlying regulatory network. Determining sets of co-expressed genes is an important task, where significative groups of genes are defined based on some criteria. This task is usually performed by clustering algorithms, where the whole family of genes, or a subset of them, are clustered into meaningful groups based on their expression values in a set of experiment. In this work we used a methodology based on the Silhouette index as a measure of cluster quality for individual gene groups, and a combination of several variants of hierarchical clustering to generate the candidate groups, to obtain sets of co-expressed genes for two real data examples. We analyzed the quality of the best ranked groups, obtained by the algorithm, using an online bioinformatics tool that provides network information for the selected genes. Moreover, to verify the performance of the algorithm, considering the fact that it doesn’t find all possible subsets, we compared its results against a full search, to determine the amount of good co-regulated sets not detected.

The application of cat swarm optimisation algorithm in classifying small loan performance

NASA Astrophysics Data System (ADS)

Kencana, Eka N.; Kiswanti, Nyoman; Sari, Kartika

2017-10-01

It is common for banking system to analyse the feasibility of credit application before its approval. Although this process has been carefully done, there is no warranty that all credits will be repaid smoothly. This study aimed to know the accuracy of Cat Swarm Optimisation (CSO) algorithm in classifying small loans’ performance that is approved by Bank Rakyat Indonesia (BRI), one of several public banks in Indonesia. Data collected from 200 lenders were used in this work. The data matrix consists of 9 independent variables that represent profile of the credit, and one categorical dependent variable reflects credit’s performance. Prior to the analyses, data was divided into two data subset with equal size. Ordinal logistic regression (OLR) procedure is applied for the first subset and gave 3 out of 9 independent variables i.e. the amount of credit, credit’s period, and income per month of lender proved significantly affect credit performance. By using significantly estimated parameters from OLR procedure as the initial values for observations at the second subset, CSO procedure started. This procedure gave 76 percent of classification accuracy of credit performance, slightly better compared to 64 percent resulted from OLR procedure.

Application of the artificial bee colony algorithm for solving the set covering problem.

PubMed

Crawford, Broderick; Soto, Ricardo; Cuesta, Rodrigo; Paredes, Fernando

2014-01-01

The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem.

Application of the Artificial Bee Colony Algorithm for Solving the Set Covering Problem

PubMed Central

Crawford, Broderick; Soto, Ricardo; Cuesta, Rodrigo; Paredes, Fernando

2014-01-01

The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem. PMID:24883356

Comparison of Algorithm-based Estimates of Occupational Diesel Exhaust Exposure to Those of Multiple Independent Raters in a Population-based Case–Control Study

PubMed Central

Friesen, Melissa C.

2013-01-01

Objectives: Algorithm-based exposure assessments based on patterns in questionnaire responses and professional judgment can readily apply transparent exposure decision rules to thousands of jobs quickly. However, we need to better understand how algorithms compare to a one-by-one job review by an exposure assessor. We compared algorithm-based estimates of diesel exhaust exposure to those of three independent raters within the New England Bladder Cancer Study, a population-based case–control study, and identified conditions under which disparities occurred in the assessments of the algorithm and the raters. Methods: Occupational diesel exhaust exposure was assessed previously using an algorithm and a single rater for all 14 983 jobs reported by 2631 study participants during personal interviews conducted from 2001 to 2004. Two additional raters independently assessed a random subset of 324 jobs that were selected based on strata defined by the cross-tabulations of the algorithm and the first rater’s probability assessments for each job, oversampling their disagreements. The algorithm and each rater assessed the probability, intensity and frequency of occupational diesel exhaust exposure, as well as a confidence rating for each metric. Agreement among the raters, their aggregate rating (average of the three raters’ ratings) and the algorithm were evaluated using proportion of agreement, kappa and weighted kappa (κw). Agreement analyses on the subset used inverse probability weighting to extrapolate the subset to estimate agreement for all jobs. Classification and Regression Tree (CART) models were used to identify patterns in questionnaire responses that predicted disparities in exposure status (i.e., unexposed versus exposed) between the first rater and the algorithm-based estimates. Results: For the probability, intensity and frequency exposure metrics, moderate to moderately high agreement was observed among raters (κw = 0.50–0.76) and between the algorithm and the individual raters (κw = 0.58–0.81). For these metrics, the algorithm estimates had consistently higher agreement with the aggregate rating (κw = 0.82) than with the individual raters. For all metrics, the agreement between the algorithm and the aggregate ratings was highest for the unexposed category (90–93%) and was poor to moderate for the exposed categories (9–64%). Lower agreement was observed for jobs with a start year <1965 versus ≥1965. For the confidence metrics, the agreement was poor to moderate among raters (κw = 0.17–0.45) and between the algorithm and the individual raters (κw = 0.24–0.61). CART models identified patterns in the questionnaire responses that predicted a fair-to-moderate (33–89%) proportion of the disagreements between the raters’ and the algorithm estimates. Discussion: The agreement between any two raters was similar to the agreement between an algorithm-based approach and individual raters, providing additional support for using the more efficient and transparent algorithm-based approach. CART models identified some patterns in disagreements between the first rater and the algorithm. Given the absence of a gold standard for estimating exposure, these patterns can be reviewed by a team of exposure assessors to determine whether the algorithm should be revised for future studies. PMID:23184256

Computing Intrinsic Images.

DTIC Science & Technology

1986-08-01

most of the algorithms fail when applied to real images. (2) Usually the constraints from the geometry and the physics of the problem are not enough...large subset of real images), and so most of the algorithms fail when applied to real images. (2) Usually the constraints from the geometry and the...constraints from the geometry and the physics of the problem are not enough to guarantee uniqueness of the computed parameters. In this case, strong

Topology design and performance analysis of an integrated communication network

NASA Technical Reports Server (NTRS)

Li, V. O. K.; Lam, Y. F.; Hou, T. C.; Yuen, J. H.

1985-01-01

A research study on the topology design and performance analysis for the Space Station Information System (SSIS) network is conducted. It is begun with a survey of existing research efforts in network topology design. Then a new approach for topology design is presented. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. The algorithm for generating subsets is described in detail, and various aspects of the overall design procedure are discussed. Two more efficient versions of this algorithm (applicable in specific situations) are also given. Next, two important aspects of network performance analysis: network reliability and message delays are discussed. A new model is introduced to study the reliability of a network with dependent failures. For message delays, a collection of formulas from existing research results is given to compute or estimate the delays of messages in a communication network without making the independence assumption. The design algorithm coded in PASCAL is included as an appendix.

Gastroenterologist and nurse management of symptoms after pelvic radiotherapy for cancer: an economic evaluation of a clinical randomized controlled trial (the ORBIT study).

PubMed

Jordan, Jake; Gage, Heather; Benton, Barbara; Lalji, Amyn; Norton, Christine; Andreyev, H Jervoise N

2017-01-01

Over 20 distressing gastrointestinal symptoms affect many patients after pelvic radiotherapy, but in the United Kingdom few are referred for assessment. Algorithmic-based treatment delivered by either a consultant gastroenterologist or a clinical nurse specialist has been shown in a randomized trial to be statistically and clinically more effective than provision of a self-help booklet. In this study, we assessed cost-effectiveness. Outcomes were measured at baseline (pre-randomization) and 6 months. Change in quality-adjusted life years (QALYs) was the primary outcome for the economic evaluation; a secondary analysis used change in the bowel subset score of the modified Inflammatory Bowel Disease Questionnaire (IBDQ-B). Intervention costs, British pounds 2013, covered visits with the gastroenterologist or nurse, investigations, medications and treatments. Incremental outcomes and incremental costs were estimated simultaneously using multivariate linear regression. Uncertainty was handled non-parametrically using bootstrap with replacement. The mean (SD) cost of treatment was £895 (499) for the nurse and £1101 (567) for the consultant. The nurse was dominated by usual care, which was cheaper and achieved better outcomes. The mean cost per QALY gained from the consultant, compared to usual care, was £250,455; comparing the consultant to the nurse, it was £25,875. Algorithmic care produced better outcomes compared to the booklet only, as reflected in the IBDQ-B results, at a cost of ~£1,000. Algorithmic treatment of radiation bowel injury by a consultant or a nurse results in significant symptom relief for patients but was not found to be cost-effective according to the National Institute for Health and Care Excellence (NICE) criteria.

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network.

PubMed

Janet, Jon Paul; Chan, Lydia; Kulik, Heather J

2018-03-01

Machine learning (ML) has emerged as a powerful complement to simulation for materials discovery by reducing time for evaluation of energies and properties at accuracy competitive with first-principles methods. We use genetic algorithm (GA) optimization to discover unconventional spin-crossover complexes in combination with efficient scoring from an artificial neural network (ANN) that predicts spin-state splitting of inorganic complexes. We explore a compound space of over 5600 candidate materials derived from eight metal/oxidation state combinations and a 32-ligand pool. We introduce a strategy for error-aware ML-driven discovery by limiting how far the GA travels away from the nearest ANN training points while maximizing property (i.e., spin-splitting) fitness, leading to discovery of 80% of the leads from full chemical space enumeration. Over a 51-complex subset, average unsigned errors (4.5 kcal/mol) are close to the ANN's baseline 3 kcal/mol error. By obtaining leads from the trained ANN within seconds rather than days from a DFT-driven GA, this strategy demonstrates the power of ML for accelerating inorganic material discovery.

Plate-based diversity subset screening generation 2: an improved paradigm for high-throughput screening of large compound files.

PubMed

Bell, Andrew S; Bradley, Joseph; Everett, Jeremy R; Loesel, Jens; McLoughlin, David; Mills, James; Peakman, Marie-Claire; Sharp, Robert E; Williams, Christine; Zhu, Hongyao

2016-11-01

High-throughput screening (HTS) is an effective method for lead and probe discovery that is widely used in industry and academia to identify novel chemical matter and to initiate the drug discovery process. However, HTS can be time consuming and costly and the use of subsets as an efficient alternative to screening entire compound collections has been investigated. Subsets may be selected on the basis of chemical diversity, molecular properties, biological activity diversity or biological target focus. Previously, we described a novel form of subset screening: plate-based diversity subset (PBDS) screening, in which the screening subset is constructed by plate selection (rather than individual compound cherry-picking), using algorithms that select for compound quality and chemical diversity on a plate basis. In this paper, we describe a second-generation approach to the construction of an updated subset: PBDS2, using both plate and individual compound selection, that has an improved coverage of the chemical space of the screening file, whilst only selecting the same number of plates for screening. We describe the validation of PBDS2 and its successful use in hit and lead discovery. PBDS2 screening became the default mode of singleton (one compound per well) HTS for lead discovery in Pfizer.

Towards semi-automatic rock mass discontinuity orientation and set analysis from 3D point clouds

NASA Astrophysics Data System (ADS)

Guo, Jiateng; Liu, Shanjun; Zhang, Peina; Wu, Lixin; Zhou, Wenhui; Yu, Yinan

2017-06-01

Obtaining accurate information on rock mass discontinuities for deformation analysis and the evaluation of rock mass stability is important. Obtaining measurements for high and steep zones with the traditional compass method is difficult. Photogrammetry, three-dimensional (3D) laser scanning and other remote sensing methods have gradually become mainstream methods. In this study, a method that is based on a 3D point cloud is proposed to semi-automatically extract rock mass structural plane information. The original data are pre-treated prior to segmentation by removing outlier points. The next step is to segment the point cloud into different point subsets. Various parameters, such as the normal, dip/direction and dip, can be calculated for each point subset after obtaining the equation of the best fit plane for the relevant point subset. A cluster analysis (a point subset that satisfies some conditions and thus forms a cluster) is performed based on the normal vectors by introducing the firefly algorithm (FA) and the fuzzy c-means (FCM) algorithm. Finally, clusters that belong to the same discontinuity sets are merged and coloured for visualization purposes. A prototype system is developed based on this method to extract the points of the rock discontinuity from a 3D point cloud. A comparison with existing software shows that this method is feasible. This method can provide a reference for rock mechanics, 3D geological modelling and other related fields.

The exploitation of large archives of space-borne C-band SAR data in the framework of FP7-DORIS Project

NASA Astrophysics Data System (ADS)

Del Ventisette, Chiara; Ciampalini, Andrea

2013-04-01

DORIS (Ground Deformations Risk Scenarios: an Advanced Assessment Service) is an advanced downstream service project within the seventh Framework Programme of the European Commission. A European team was set up in order to make the best views of the most advanced research and technologies outcomes in the field of Earth Observation (EO) for the improvement of risk management. The aim of the DORIS project is the development of new methodologies for the detection, mapping, monitoring and forecasting of ground deformations. DORIS integrates traditional and innovative EO and ground based (non-EO) data to improve our understanding of the complex phenomena at different temporal and spatial scales and in various physiographic and environmental settings that result in ground deformations, including landslides and ground subsidence, for civil protection purposes. One of the goal of the Doris Project is the exploitation of the large data archives for geohazards mapping. In this work the existing ESA Synthetic Aperture Radar (SAR) archives, operating in the microwave C-band (data collected by the ERS-1/2 and ENVISAT satellite) were analysed through new algorithms developed to reconstruct long time series (almost 20 years) and the obtained preliminary results are presented. The algorithms are based on Small BAseline Subset technique (SBAS; developed by CNR-IREA), ERS- ENVISAT Stitching (T.R.E.), Stable Point Network (SPN; Altamira) and ERS-ENVISAT Interferometric Point Target Analysis (IPTA; Gamma). The potentiality of these algorithms were evaluate in selected test sites characterized by different ground deformation phenomena (landslide and/or subsidence): i) Central Umbria (Italy); ii) Messina Province (Italy); iii) Rácalmás (Hungary); iv) Silesian Coal Basin (Poland); v) Tramuntana Range (Mallorca, Spain) and vi) St. Moritz (Switzerland). The results demonstrate the usefulness of the implemented algorithms, but in some cases there is a loss of the coherent points, especially in the most unstable areas.

Efficient algorithm for baseline wander and powerline noise removal from ECG signals based on discrete Fourier series.

PubMed

Bahaz, Mohamed; Benzid, Redha

2018-03-01

Electrocardiogram (ECG) signals are often contaminated with artefacts and noises which can lead to incorrect diagnosis when they are visually inspected by cardiologists. In this paper, the well-known discrete Fourier series (DFS) is re-explored and an efficient DFS-based method is proposed to reduce contribution of both baseline wander (BW) and powerline interference (PLI) noises in ECG records. In the first step, the determination of the exact number of low frequency harmonics contributing in BW is achieved. Next, the baseline drift is estimated by the sum of all associated Fourier sinusoids components. Then, the baseline shift is discarded efficiently by a subtraction of its approximated version from the original biased ECG signal. Concerning the PLI, the subtraction of the contributing harmonics calculated in the same manner reduces efficiently such type of noise. In addition of visual quality results, the proposed algorithm shows superior performance in terms of higher signal-to-noise ratio and smaller mean square error when faced to the DCT-based algorithm.

Wide-Field Imaging Interferometry Spatial-Spectral Image Synthesis Algorithms

NASA Technical Reports Server (NTRS)

Lyon, Richard G.; Leisawitz, David T.; Rinehart, Stephen A.; Memarsadeghi, Nargess; Sinukoff, Evan J.

2012-01-01

Developed is an algorithmic approach for wide field of view interferometric spatial-spectral image synthesis. The data collected from the interferometer consists of a set of double-Fourier image data cubes, one cube per baseline. These cubes are each three-dimensional consisting of arrays of two-dimensional detector counts versus delay line position. For each baseline a moving delay line allows collection of a large set of interferograms over the 2D wide field detector grid; one sampled interferogram per detector pixel per baseline. This aggregate set of interferograms, is algorithmically processed to construct a single spatial-spectral cube with angular resolution approaching the ratio of the wavelength to longest baseline. The wide field imaging is accomplished by insuring that the range of motion of the delay line encompasses the zero optical path difference fringe for each detector pixel in the desired field-of-view. Each baseline cube is incoherent relative to all other baseline cubes and thus has only phase information relative to itself. This lost phase information is recovered by having point, or otherwise known, sources within the field-of-view. The reference source phase is known and utilized as a constraint to recover the coherent phase relation between the baseline cubes and is key to the image synthesis. Described will be the mathematical formalism, with phase referencing and results will be shown using data collected from NASA/GSFC Wide-Field Imaging Interferometry Testbed (WIIT).

Evolutionary Approach for Relative Gene Expression Algorithms

PubMed Central

Czajkowski, Marcin

2014-01-01

A Relative Expression Analysis (RXA) uses ordering relationships in a small collection of genes and is successfully applied to classiffication using microarray data. As checking all possible subsets of genes is computationally infeasible, the RXA algorithms require feature selection and multiple restrictive assumptions. Our main contribution is a specialized evolutionary algorithm (EA) for top-scoring pairs called EvoTSP which allows finding more advanced gene relations. We managed to unify the major variants of relative expression algorithms through EA and introduce weights to the top-scoring pairs. Experimental validation of EvoTSP on public available microarray datasets showed that the proposed solution significantly outperforms in terms of accuracy other relative expression algorithms and allows exploring much larger solution space. PMID:24790574

Computing on quantum shared secrets

NASA Astrophysics Data System (ADS)

Ouyang, Yingkai; Tan, Si-Hui; Zhao, Liming; Fitzsimons, Joseph F.

2017-11-01

A (k ,n )-threshold secret-sharing scheme allows for a string to be split into n shares in such a way that any subset of at least k shares suffices to recover the secret string, but such that any subset of at most k -1 shares contains no information about the secret. Quantum secret-sharing schemes extend this idea to the sharing of quantum states. Here we propose a method of performing computation securely on quantum shared secrets. We introduce a (n ,n )-quantum secret sharing scheme together with a set of algorithms that allow quantum circuits to be evaluated securely on the shared secret without the need to decode the secret. We consider a multipartite setting, with each participant holding a share of the secret. We show that if there exists at least one honest participant, no group of dishonest participants can recover any information about the shared secret, independent of their deviations from the algorithm.

Performance Assessment of the Optical Transient Detector and Lightning Imaging Sensor. Part 2; Clustering Algorithm

NASA Technical Reports Server (NTRS)

Mach, Douglas M.; Christian, Hugh J.; Blakeslee, Richard; Boccippio, Dennis J.; Goodman, Steve J.; Boeck, William

2006-01-01

We describe the clustering algorithm used by the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) for combining the lightning pulse data into events, groups, flashes, and areas. Events are single pixels that exceed the LIS/OTD background level during a single frame (2 ms). Groups are clusters of events that occur within the same frame and in adjacent pixels. Flashes are clusters of groups that occur within 330 ms and either 5.5 km (for LIS) or 16.5 km (for OTD) of each other. Areas are clusters of flashes that occur within 16.5 km of each other. Many investigators are utilizing the LIS/OTD flash data; therefore, we test how variations in the algorithms for the event group and group-flash clustering affect the flash count for a subset of the LIS data. We divided the subset into areas with low (1-3), medium (4-15), high (16-63), and very high (64+) flashes to see how changes in the clustering parameters affect the flash rates in these different sizes of areas. We found that as long as the cluster parameters are within about a factor of two of the current values, the flash counts do not change by more than about 20%. Therefore, the flash clustering algorithm used by the LIS and OTD sensors create flash rates that are relatively insensitive to reasonable variations in the clustering algorithms.

Discrete Biogeography Based Optimization for Feature Selection in Molecular Signatures.

PubMed

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.

Estimating rare events in biochemical systems using conditional sampling.

PubMed

Sundar, V S

2017-01-28

The paper focuses on development of variance reduction strategies to estimate rare events in biochemical systems. Obtaining this probability using brute force Monte Carlo simulations in conjunction with the stochastic simulation algorithm (Gillespie's method) is computationally prohibitive. To circumvent this, important sampling tools such as the weighted stochastic simulation algorithm and the doubly weighted stochastic simulation algorithm have been proposed. However, these strategies require an additional step of determining the important region to sample from, which is not straightforward for most of the problems. In this paper, we apply the subset simulation method, developed as a variance reduction tool in the context of structural engineering, to the problem of rare event estimation in biochemical systems. The main idea is that the rare event probability is expressed as a product of more frequent conditional probabilities. These conditional probabilities are estimated with high accuracy using Monte Carlo simulations, specifically the Markov chain Monte Carlo method with the modified Metropolis-Hastings algorithm. Generating sample realizations of the state vector using the stochastic simulation algorithm is viewed as mapping the discrete-state continuous-time random process to the standard normal random variable vector. This viewpoint opens up the possibility of applying more sophisticated and efficient sampling schemes developed elsewhere to problems in stochastic chemical kinetics. The results obtained using the subset simulation method are compared with existing variance reduction strategies for a few benchmark problems, and a satisfactory improvement in computational time is demonstrated.

Dynamic Capacity Allocation Algorithms for iNET Link Manager

DTIC Science & Technology

2014-05-01

algorithm that can better cope with severe congestion and misbehaving users and traffic flows. We compare the E-LM with the LM baseline algorithm (B-LM...capacity allocation algorithm that can better cope with severe congestion and misbehaving users and traffic flows. We compare the E-LM with the LM

Development and testing of an algorithm to detect implantable cardioverter-defibrillator lead failure.

PubMed

Gunderson, Bruce D; Gillberg, Jeffrey M; Wood, Mark A; Vijayaraman, Pugazhendhi; Shepard, Richard K; Ellenbogen, Kenneth A

2006-02-01

Implantable cardioverter-defibrillator (ICD) lead failures often present as inappropriate shock therapy. An algorithm that can reliably discriminate between ventricular tachyarrhythmias and noise due to lead failure may prevent patient discomfort and anxiety and avoid device-induced proarrhythmia by preventing inappropriate ICD shocks. The goal of this analysis was to test an ICD tachycardia detection algorithm that differentiates noise due to lead failure from ventricular tachyarrhythmias. We tested an algorithm that uses a measure of the ventricular intracardiac electrogram baseline to discriminate the sinus rhythm isoelectric line from the right ventricular coil-can (i.e., far-field) electrogram during oversensing of noise caused by a lead failure. The baseline measure was defined as the product of the sum (mV) and standard deviation (mV) of the voltage samples for a 188-ms window centered on each sensed electrogram. If the minimum baseline measure of the last 12 beats was <0.35 mV-mV, then the detected rhythm was considered noise due to a lead failure. The first ICD-detected episode of lead failure and inappropriate detection from 24 ICD patients with a pace/sense lead failure and all ventricular arrhythmias from 56 ICD patients without a lead failure were selected. The stored data were analyzed to determine the sensitivity and specificity of the algorithm to detect lead failures. The minimum baseline measure for the 24 lead failure episodes (0.28 +/- 0.34 mV-mV) was smaller than the 135 ventricular tachycardia (40.8 +/- 43.0 mV-mV, P <.0001) and 55 ventricular fibrillation episodes (19.1 +/- 22.8 mV-mV, P <.05). A minimum baseline <0.35 mV-mV threshold had a sensitivity of 83% (20/24) with a 100% (190/190) specificity. A baseline measure of the far-field electrogram had a high sensitivity and specificity to detect lead failure noise compared with ventricular tachycardia or fibrillation.

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

PubMed

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).

CT image reconstruction with half precision floating-point values.

PubMed

Maaß, Clemens; Baer, Matthias; Kachelrieß, Marc

2011-07-01

Analytic CT image reconstruction is a computationally demanding task. Currently, the even more demanding iterative reconstruction algorithms find their way into clinical routine because their image quality is superior to analytic image reconstruction. The authors thoroughly analyze a so far unconsidered but valuable tool of tomorrow's reconstruction hardware (CPU and GPU) that allows implementing the forward projection and backprojection steps, which are the computationally most demanding parts of any reconstruction algorithm, much more efficiently. Instead of the standard 32 bit floating-point values (float), a recently standardized floating-point value with 16 bit (half) is adopted for data representation in image domain and in rawdata domain. The reduction in the total data amount reduces the traffic on the memory bus, which is the bottleneck of today's high-performance algorithms, by 50%. In CT simulations and CT measurements, float reconstructions (gold standard) and half reconstructions are visually compared via difference images and by quantitative image quality evaluation. This is done for analytical reconstruction (filtered backprojection) and iterative reconstruction (ordered subset SART). The magnitude of quantization noise, which is caused by a reduction in the data precision of both rawdata and image data during image reconstruction, is negligible. This is clearly shown for filtered backprojection and iterative ordered subset SART reconstruction. In filtered backprojection, the implementation of the backprojection should be optimized for low data precision if the image data are represented in half format. In ordered subset SART image reconstruction, no adaptations are necessary and the convergence speed remains unchanged. Half precision floating-point values allow to speed up CT image reconstruction without compromising image quality.

Tumor and liver determinants of prognosis in unresectable hepatocellular carcinoma: a case cohort study.

PubMed

Carr, Brian I; Buch, Shama C; Kondragunta, Venkateswarlu; Pancoska, Petr; Branch, Robert A

2008-08-01

A total of 967 patients with unresectable and untransplantable, biopsy-proven hepatocellular carcinoma (HCC) were prospectively evaluated at baseline and followed up till death. Survival was the end-point for all analyses. We found in our overall analysis, that male gender, ascites, cirrhosis, portal vein thrombosis (PVT), elevated alpha-fetoprotein (AFP) or bilirubin or alkaline phosphatases were each statistically significant adverse prognostic factors. Patients with normal AFP survived longer than those with elevated AFP, in the presence of PVT, large or bilobar tumors or cirrhosis. We used a bivariate analysis to separate patient subgroups based on poor liver function and aggressive tumor characteristics. In subgroup analysis based on these subsets, there was clear discrimination in survival between subsets; in addition both cirrhosis and presence of PVT were significant, independent but modest risk factors. The results of this large dataset show that amongst nonsurgical HCC patients, there are clear subsets with longer survival than other subsets. This data also supports the concept of heterogeneity of HCC.

Use of Hundreds of Electrocardiograhpic Biomarkers for Prediction of Mortality in Post-Menopausal Women: The Women’s Health Initiative

PubMed Central

Gorodeski, Eiran Z.; Ishwaran, Hemant; Kogalur, Udaya B.; Blackstone, Eugene H.; Hsich, Eileen; Zhang, Zhu-ming; Vitolins, Mara Z.; Manson, JoAnn E.; Curb, J. David; Martin, Lisa W.; Prineas, Ronald J.; Lauer, Michael S.

2013-01-01

Background Simultaneous contribution of hundreds of electrocardiographic biomarkers to prediction of long-term mortality in post-menopausal women with clinically normal resting electrocardiograms (ECGs) is unknown. Methods and Results We analyzed ECGs and all-cause mortality in 33,144 women enrolled in Women’s Health Initiative trials, who were without baseline cardiovascular disease or cancer, and had normal ECGs by Minnesota and Novacode criteria. Four hundred and seventy seven ECG biomarkers, encompassing global and individual ECG findings, were measured using computer algorithms. During a median follow-up of 8.1 years (range for survivors 0.5–11.2 years), 1,229 women died. For analyses cohort was randomly split into derivation (n=22,096, deaths=819) and validation (n=11,048, deaths=410) subsets. ECG biomarkers, demographic, and clinical characteristics were simultaneously analyzed using both traditional Cox regression and Random Survival Forest (RSF), a novel algorithmic machine-learning approach. Regression modeling failed to converge. RSF variable selection yielded 20 variables that were independently predictive of long-term mortality, 14 of which were ECG biomarkers related to autonomic tone, atrial conduction, and ventricular depolarization and repolarization. Conclusions We identified 14 ECG biomarkers from amongst hundreds that were associated with long-term prognosis using a novel random forest variable selection methodology. These were related to autonomic tone, atrial conduction, ventricular depolarization, and ventricular repolarization. Quantitative ECG biomarkers have prognostic importance, and may be markers of subclinical disease in apparently healthy post-menopausal women. PMID:21862719

Real coded genetic algorithm for fuzzy time series prediction

NASA Astrophysics Data System (ADS)

Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.

2017-10-01

Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.

Feature Selection Method Based on Neighborhood Relationships: Applications in EEG Signal Identification and Chinese Character Recognition

PubMed Central

Zhao, Yu-Xiang; Chou, Chien-Hsing

2016-01-01

In this study, a new feature selection algorithm, the neighborhood-relationship feature selection (NRFS) algorithm, is proposed for identifying rat electroencephalogram signals and recognizing Chinese characters. In these two applications, dependent relationships exist among the feature vectors and their neighboring feature vectors. Therefore, the proposed NRFS algorithm was designed for solving this problem. By applying the NRFS algorithm, unselected feature vectors have a high priority of being added into the feature subset if the neighboring feature vectors have been selected. In addition, selected feature vectors have a high priority of being eliminated if the neighboring feature vectors are not selected. In the experiments conducted in this study, the NRFS algorithm was compared with two feature algorithms. The experimental results indicated that the NRFS algorithm can extract the crucial frequency bands for identifying rat vigilance states and identifying crucial character regions for recognizing Chinese characters. PMID:27314346

QuantiFly: Robust Trainable Software for Automated Drosophila Egg Counting.

PubMed

Waithe, Dominic; Rennert, Peter; Brostow, Gabriel; Piper, Matthew D W

2015-01-01

We report the development and testing of software called QuantiFly: an automated tool to quantify Drosophila egg laying. Many laboratories count Drosophila eggs as a marker of fitness. The existing method requires laboratory researchers to count eggs manually while looking down a microscope. This technique is both time-consuming and tedious, especially when experiments require daily counts of hundreds of vials. The basis of the QuantiFly software is an algorithm which applies and improves upon an existing advanced pattern recognition and machine-learning routine. The accuracy of the baseline algorithm is additionally increased in this study through correction of bias observed in the algorithm output. The QuantiFly software, which includes the refined algorithm, has been designed to be immediately accessible to scientists through an intuitive and responsive user-friendly graphical interface. The software is also open-source, self-contained, has no dependencies and is easily installed (https://github.com/dwaithe/quantifly). Compared to manual egg counts made from digital images, QuantiFly achieved average accuracies of 94% and 85% for eggs laid on transparent (defined) and opaque (yeast-based) fly media. Thus, the software is capable of detecting experimental differences in most experimental situations. Significantly, the advanced feature recognition capabilities of the software proved to be robust to food surface artefacts like bubbles and crevices. The user experience involves image acquisition, algorithm training by labelling a subset of eggs in images of some of the vials, followed by a batch analysis mode in which new images are automatically assessed for egg numbers. Initial training typically requires approximately 10 minutes, while subsequent image evaluation by the software is performed in just a few seconds. Given the average time per vial for manual counting is approximately 40 seconds, our software introduces a timesaving advantage for experiments starting with as few as 20 vials. We also describe an optional acrylic box to be used as a digital camera mount and to provide controlled lighting during image acquisition which will guarantee the conditions used in this study.

QuantiFly: Robust Trainable Software for Automated Drosophila Egg Counting

PubMed Central

Waithe, Dominic; Rennert, Peter; Brostow, Gabriel; Piper, Matthew D. W.

2015-01-01

We report the development and testing of software called QuantiFly: an automated tool to quantify Drosophila egg laying. Many laboratories count Drosophila eggs as a marker of fitness. The existing method requires laboratory researchers to count eggs manually while looking down a microscope. This technique is both time-consuming and tedious, especially when experiments require daily counts of hundreds of vials. The basis of the QuantiFly software is an algorithm which applies and improves upon an existing advanced pattern recognition and machine-learning routine. The accuracy of the baseline algorithm is additionally increased in this study through correction of bias observed in the algorithm output. The QuantiFly software, which includes the refined algorithm, has been designed to be immediately accessible to scientists through an intuitive and responsive user-friendly graphical interface. The software is also open-source, self-contained, has no dependencies and is easily installed (https://github.com/dwaithe/quantifly). Compared to manual egg counts made from digital images, QuantiFly achieved average accuracies of 94% and 85% for eggs laid on transparent (defined) and opaque (yeast-based) fly media. Thus, the software is capable of detecting experimental differences in most experimental situations. Significantly, the advanced feature recognition capabilities of the software proved to be robust to food surface artefacts like bubbles and crevices. The user experience involves image acquisition, algorithm training by labelling a subset of eggs in images of some of the vials, followed by a batch analysis mode in which new images are automatically assessed for egg numbers. Initial training typically requires approximately 10 minutes, while subsequent image evaluation by the software is performed in just a few seconds. Given the average time per vial for manual counting is approximately 40 seconds, our software introduces a timesaving advantage for experiments starting with as few as 20 vials. We also describe an optional acrylic box to be used as a digital camera mount and to provide controlled lighting during image acquisition which will guarantee the conditions used in this study. PMID:25992957

Analyte quantification with comprehensive two-dimensional gas chromatography: assessment of methods for baseline correction, peak delineation, and matrix effect elimination for real samples.

PubMed

Samanipour, Saer; Dimitriou-Christidis, Petros; Gros, Jonas; Grange, Aureline; Samuel Arey, J

2015-01-02

Comprehensive two-dimensional gas chromatography (GC×GC) is used widely to separate and measure organic chemicals in complex mixtures. However, approaches to quantify analytes in real, complex samples have not been critically assessed. We quantified 7 PAHs in a certified diesel fuel using GC×GC coupled to flame ionization detector (FID), and we quantified 11 target chlorinated hydrocarbons in a lake water extract using GC×GC with electron capture detector (μECD), further confirmed qualitatively by GC×GC with electron capture negative chemical ionization time-of-flight mass spectrometer (ENCI-TOFMS). Target analyte peak volumes were determined using several existing baseline correction algorithms and peak delineation algorithms. Analyte quantifications were conducted using external standards and also using standard additions, enabling us to diagnose matrix effects. We then applied several chemometric tests to these data. We find that the choice of baseline correction algorithm and peak delineation algorithm strongly influence the reproducibility of analyte signal, error of the calibration offset, proportionality of integrated signal response, and accuracy of quantifications. Additionally, the choice of baseline correction and the peak delineation algorithm are essential for correctly discriminating analyte signal from unresolved complex mixture signal, and this is the chief consideration for controlling matrix effects during quantification. The diagnostic approaches presented here provide guidance for analyte quantification using GC×GC. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Analysis of the single-vehicle cyclic inventory routing problem

NASA Astrophysics Data System (ADS)

Aghezzaf, El-Houssaine; Zhong, Yiqing; Raa, Birger; Mateo, Manel

2012-11-01

The single-vehicle cyclic inventory routing problem (SV-CIRP) consists of a repetitive distribution of a product from a single depot to a selected subset of customers. For each customer, selected for replenishments, the supplier collects a corresponding fixed reward. The objective is to determine the subset of customers to replenish, the quantity of the product to be delivered to each and to design the vehicle route so that the resulting profit (difference between the total reward and the total logistical cost) is maximised while preventing stockouts at each of the selected customers. This problem appears often as a sub-problem in many logistical problems. In this article, the SV-CIRP is formulated as a mixed-integer program with a nonlinear objective function. After a thorough analysis of the structure of the problem and its features, an exact algorithm for its solution is proposed. This exact algorithm requires only solutions of linear mixed-integer programs. Values of a savings-based heuristic for this problem are compared to the optimal values obtained for a set of some test problems. In general, the gap may get as large as 25%, which justifies the effort to continue exploring and developing exact and approximation algorithms for the SV-CIRP.

Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

PubMed Central

Boonjing, Veera; Intakosum, Sarun

2016-01-01

This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span. PMID:27974883

Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend.

PubMed

Inthachot, Montri; Boonjing, Veera; Intakosum, Sarun

2016-01-01

This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

Strains, functions, and dynamics in the expanded Human Microbiome Project

PubMed Central

Lloyd-Price, Jason; Mahurkar, Anup; Rahnavard, Gholamali; Crabtree, Jonathan; Orvis, Joshua; Hall, A. Brantley; Brady, Arthur; Creasy, Heather H.; McCracken, Carrie; Giglio, Michelle G.; McDonald, Daniel; Franzosa, Eric A.; Knight, Rob; White, Owen; Huttenhower, Curtis

2018-01-01

Summary The characterization of baseline microbial and functional diversity in the human microbiome has enabled studies of microbiome-related disease, microbial population diversity, biogeography, and molecular function. The NIH Human Microbiome Project (HMP) has provided one of the broadest such characterizations to date. Here, we introduce an expanded second phase of the study, abbreviated HMP1-II, comprising 1,631 new metagenomic samples (2,355 total) targeting diverse body sites with multiple time points in 265 individuals. We applied updated profiling and assembly methods to these data to provide new characterizations of microbiome personalization. Strain identification revealed distinct subspecies clades specific to body sites; it also quantified species with phylogenetic diversity under-represented in isolate genomes. Body-wide functional profiling classified pathways into universal, human-enriched, and body site-enriched subsets. Finally, temporal analysis decomposed microbial variation into rapidly variable, moderately variable, and stable subsets. This study furthers our knowledge of baseline human microbial diversity, thus enabling an understanding of personalized microbiome function and dynamics. PMID:28953883

Small-scale loess landslide monitoring with small baseline subsets interferometric synthetic aperture radar technique-case study of Xingyuan landslide, Shaanxi, China

NASA Astrophysics Data System (ADS)

Zhao, Chaoying; Zhang, Qin; He, Yang; Peng, Jianbing; Yang, Chengsheng; Kang, Ya

2016-04-01

Small baseline subsets interferometric synthetic aperture radar technique is analyzed to detect and monitor the loess landslide in the southern bank of the Jinghe River, Shaanxi province, China. Aiming to achieve the accurate preslide time-series deformation results over small spatial scale and abrupt temporal deformation loess landslide, digital elevation model error, coherence threshold for phase unwrapping, and quality of unwrapping interferograms must be carefully checked in advance. In this experience, land subsidence accompanying a landslide with the distance <1 km is obtained, which gives a sound precursor for small-scale loess landslide detection. Moreover, the longer and continuous land subsidence has been monitored while deformation starting point for the landslide is successfully inverted, which is key to monitoring the similar loess landslide. In addition, the accelerated landslide deformation from one to two months before the landslide can provide a critical clue to early warning of this kind of landslide.

Evaluation of Bias and Variance in Low-count OSEM List Mode Reconstruction

PubMed Central

Jian, Y; Planeta, B; Carson, R E

2016-01-01

Statistical algorithms have been widely used in PET image reconstruction. The maximum likelihood expectation maximization (MLEM) reconstruction has been shown to produce bias in applications where images are reconstructed from a relatively small number of counts. In this study, image bias and variability in low-count OSEM reconstruction are investigated on images reconstructed with MOLAR (motion-compensation OSEM list-mode algorithm for resolution-recovery reconstruction) platform. A human brain ([11C]AFM) and a NEMA phantom are used in the simulation and real experiments respectively, for the HRRT and Biograph mCT. Image reconstructions were repeated with different combination of subsets and iterations. Regions of interest (ROIs) were defined on low-activity and high-activity regions to evaluate the bias and noise at matched effective iteration numbers (iterations x subsets). Minimal negative biases and no positive biases were found at moderate count levels and less than 5% negative bias was found using extremely low levels of counts (0.2 M NEC). At any given count level, other factors, such as subset numbers and frame-based scatter correction may introduce small biases (1–5%) in the reconstructed images. The observed bias was substantially lower than that reported in the literature, perhaps due to the use of point spread function and/or other implementation methods in MOLAR. PMID:25479254

Evaluation of bias and variance in low-count OSEM list mode reconstruction

NASA Astrophysics Data System (ADS)

Jian, Y.; Planeta, B.; Carson, R. E.

2015-01-01

Statistical algorithms have been widely used in PET image reconstruction. The maximum likelihood expectation maximization reconstruction has been shown to produce bias in applications where images are reconstructed from a relatively small number of counts. In this study, image bias and variability in low-count OSEM reconstruction are investigated on images reconstructed with MOLAR (motion-compensation OSEM list-mode algorithm for resolution-recovery reconstruction) platform. A human brain ([11C]AFM) and a NEMA phantom are used in the simulation and real experiments respectively, for the HRRT and Biograph mCT. Image reconstructions were repeated with different combinations of subsets and iterations. Regions of interest were defined on low-activity and high-activity regions to evaluate the bias and noise at matched effective iteration numbers (iterations × subsets). Minimal negative biases and no positive biases were found at moderate count levels and less than 5% negative bias was found using extremely low levels of counts (0.2 M NEC). At any given count level, other factors, such as subset numbers and frame-based scatter correction may introduce small biases (1-5%) in the reconstructed images. The observed bias was substantially lower than that reported in the literature, perhaps due to the use of point spread function and/or other implementation methods in MOLAR.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

PubMed Central

Arruti, Andoni; Cearreta, Idoia; Álvarez, Aitor; Lazkano, Elena; Sierra, Basilio

2014-01-01

Study of emotions in human–computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested. PMID:25279686

A time series deformation estimation in the NW Himalayas using SBAS InSAR technique

NASA Astrophysics Data System (ADS)

Kumar, V.; Venkataraman, G.

2012-12-01

A time series land deformation studies in north western Himalayan region has been presented in this study. Synthetic aperture radar (SAR) interferometry (InSAR) is an important tool for measuring the land displacement caused by different geological processes [1]. Frequent spatial and temporal decorrelation in the Himalayan region is a strong impediment in precise deformation estimation using conventional interferometric SAR approach. In such cases, advanced DInSAR approaches PSInSAR as well as Small base line subset (SBAS) can be used to estimate earth surface deformation. The SBAS technique [2] is a DInSAR approach which uses a twelve or more number of repeat SAR acquisitions in different combinations of a properly chosen data (subsets) for generation of DInSAR interferograms using two pass interferometric approach. Finally it leads to the generation of mean deformation velocity maps and displacement time series. Herein, SBAS algorithm has been used for time series deformation estimation in the NW Himalayan region. ENVISAT ASAR IS2 swath data from 2003 to 2008 have been used for quantifying slow deformation. Himalayan region is a very active tectonic belt and active orogeny play a significant role in land deformation process [3]. Geomorphology in the region is unique and reacts to the climate change adversely bringing with land slides and subsidence. Settlements on the hill slopes are prone to land slides, landslips, rockslides and soil creep. These hazardous features have hampered the over all progress of the region as they obstruct the roads and flow of traffic, break communication, block flowing water in stream and create temporary reservoirs and also bring down lot of soil cover and thus add enormous silt and gravel to the streams. It has been observed that average deformation varies from -30.0 mm/year to 10 mm/year in the NW Himalayan region . References [1] Massonnet, D., Feigl, K.L.,Rossi, M. and Adragna, F. (1994) Radar interferometry mapping of deformation in the year after the Landers earthquake. Nature 1994, 369, 227-230. [2] Berardino, P., Fornaro, G., Lanari, R., Sansosti, E. (2002). A new algorithm for surface deformation Monitoring based on Small Baseline Differential SAR Interferograms. IEEE Transactions on Geoscience and Remote Sensing, 40 (11), 2375-2383. [3] GEOLOGICAL SURVEY OF INDIA (GSI), (1999) Inventory of the Himalayan glaciers. Special publication, vol. 34, pp. 165-168. [4] Chen, C.W., and Zebker, H. A., (2000). Network approaches to two-dimensional phase unwrapping: intractability and two new algorithms. Journal of the Optical Society of America, A, 17, 401-414.

Predicting Positive and Negative Relationships in Large Social Networks.

PubMed

Wang, Guan-Nan; Gao, Hui; Chen, Lian; Mensah, Dennis N A; Fu, Yan

2015-01-01

In a social network, users hold and express positive and negative attitudes (e.g. support/opposition) towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships are likely to be latent as the scale of social network increases. The essence of predicting latent binary relationships have recently began to draw researchers' attention. In this paper, we propose a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory. More specifically, we show that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship between them deteriorates. Accordingly, in the training phase, we propose a segment-based training framework to divide the training data into two subsets according to the number of common neighbors between users, and build a prediction model for each subset based on support vector machine (SVM). Moreover, to deal with large-scale social network data, we employ a sampling strategy that selects small amount of training data while maintaining high accuracy of prediction. We compare our algorithm with traditional algorithms and adaptive boosting of them. Experimental results of typical data sets show that our algorithm can deal with large social networks and consistently outperforms other methods.

Feature selection for the classification of traced neurons.

PubMed

López-Cabrera, José D; Lorenzo-Ginori, Juan V

2018-06-01

The great availability of computational tools to calculate the properties of traced neurons leads to the existence of many descriptors which allow the automated classification of neurons from these reconstructions. This situation determines the necessity to eliminate irrelevant features as well as making a selection of the most appropriate among them, in order to improve the quality of the classification obtained. The dataset used contains a total of 318 traced neurons, classified by human experts in 192 GABAergic interneurons and 126 pyramidal cells. The features were extracted by means of the L-measure software, which is one of the most used computational tools in neuroinformatics to quantify traced neurons. We review some current feature selection techniques as filter, wrapper, embedded and ensemble methods. The stability of the feature selection methods was measured. For the ensemble methods, several aggregation methods based on different metrics were applied to combine the subsets obtained during the feature selection process. The subsets obtained applying feature selection methods were evaluated using supervised classifiers, among which Random Forest, C4.5, SVM, Naïve Bayes, Knn, Decision Table and the Logistic classifier were used as classification algorithms. Feature selection methods of types filter, embedded, wrappers and ensembles were compared and the subsets returned were tested in classification tasks for different classification algorithms. L-measure features EucDistanceSD, PathDistanceSD, Branch_pathlengthAve, Branch_pathlengthSD and EucDistanceAve were present in more than 60% of the selected subsets which provides evidence about their importance in the classification of this neurons. Copyright © 2018 Elsevier B.V. All rights reserved.

A baseline-free procedure for transformation models under interval censorship.

PubMed

Gu, Ming Gao; Sun, Liuquan; Zuo, Guoxin

2005-12-01

An important property of Cox regression model is that the estimation of regression parameters using the partial likelihood procedure does not depend on its baseline survival function. We call such a procedure baseline-free. Using marginal likelihood, we show that an baseline-free procedure can be derived for a class of general transformation models under interval censoring framework. The baseline-free procedure results a simplified and stable computation algorithm for some complicated and important semiparametric models, such as frailty models and heteroscedastic hazard/rank regression models, where the estimation procedures so far available involve estimation of the infinite dimensional baseline function. A detailed computational algorithm using Markov Chain Monte Carlo stochastic approximation is presented. The proposed procedure is demonstrated through extensive simulation studies, showing the validity of asymptotic consistency and normality. We also illustrate the procedure with a real data set from a study of breast cancer. A heuristic argument showing that the score function is a mean zero martingale is provided.

Fish swarm intelligent to optimize real time monitoring of chips drying using machine vision

NASA Astrophysics Data System (ADS)

Hendrawan, Y.; Hawa, L. C.; Damayanti, R.

2018-03-01

This study attempted to apply machine vision-based chips drying monitoring system which is able to optimise the drying process of cassava chips. The objective of this study is to propose fish swarm intelligent (FSI) optimization algorithms to find the most significant set of image features suitable for predicting water content of cassava chips during drying process using artificial neural network model (ANN). Feature selection entails choosing the feature subset that maximizes the prediction accuracy of ANN. Multi-Objective Optimization (MOO) was used in this study which consisted of prediction accuracy maximization and feature-subset size minimization. The results showed that the best feature subset i.e. grey mean, L(Lab) Mean, a(Lab) energy, red entropy, hue contrast, and grey homogeneity. The best feature subset has been tested successfully in ANN model to describe the relationship between image features and water content of cassava chips during drying process with R2 of real and predicted data was equal to 0.9.

When self-reliance is not safe: associations between reduced help-seeking and subsequent mental health symptoms in suicidal adolescents.

PubMed

Labouliere, Christa D; Kleinman, Marjorie; Gould, Madelyn S

2015-04-01

The majority of suicidal adolescents have no contact with mental health services, and reduced help-seeking in this population further lessens the likelihood of accessing treatment. A commonly-reported reason for not seeking help is youths' perception that they should solve problems on their own. In this study, we explore associations between extreme self-reliance behavior (i.e., solving problems on your own all of the time), help-seeking behavior, and mental health symptoms in a community sample of adolescents. Approximately 2150 adolescents, across six schools, participated in a school-based suicide prevention screening program, and a subset of at-risk youth completed a follow-up interview two years later. Extreme self-reliance was associated with reduced help-seeking, clinically-significant depressive symptoms, and serious suicidal ideation at the baseline screening. Furthermore, in a subset of youth identified as at-risk at the baseline screening, extreme self-reliance predicted level of suicidal ideation and depressive symptoms two years later even after controlling for baseline symptoms. Given these findings, attitudes that reinforce extreme self-reliance behavior may be an important target for youth suicide prevention programs. Reducing extreme self-reliance in youth with suicidality may increase their likelihood of appropriate help-seeking and concomitant reductions in symptoms.

Monocyte Phenotype and IFN-γ-Inducible Cytokine Responses Are Associated with Cryptococcal Immune Reconstitution Inflammatory Syndrome

PubMed Central

Meya, David B.; Okurut, Samuel; Zziwa, Godfrey; Cose, Stephen; Bohjanen, Paul R.; Mayanja-Kizza, Harriet; Joloba, Moses; Boulware, David R.; Yukari Manabe, Carol; Wahl, Sharon; Janoff, Edward N.

2017-01-01

A third of adults with AIDS and cryptococcal meningitis (CM) develop immune reconstitution inflammatory syndrome (IRIS) after initiating antiretroviral therapy (ART), which is thought to result from exaggerated inflammatory antigen-specific T cell responses. The contribution of monocytes to the immunopathogenesis of cryptococcal IRIS remains unclear. We compared monocyte subset frequencies and immune responses in HIV-infected Ugandans at time of CM diagnosis (IRIS-Baseline) for those who later developed CM-IRIS, controls who did not develop CM-IRIS (Control-Baseline) at CM-IRIS (IRIS-Event), and for controls at a time point matched for ART duration (Control-Event) to understand the association of monocyte distribution and immune responses with cryptococcal IRIS. At baseline, stimulation with IFN-γ ex vivo induced a higher frequency of TNF-α- and IL-6-producing monocytes among those who later developed IRIS. Among participants who developed IRIS, ex vivo IFN-γ stimulation induced higher frequencies of activated monocytes, IL-6+, TNF-α+ classical, and IL-6+ intermediate monocytes compared with controls. In conclusion, we have demonstrated that monocyte subset phenotype and cytokine responses prior to ART are associated with and may be predictive of CM-IRIS. Larger studies to further delineate innate immunological responses and the efficacy of immunomodulatory therapies during cryptococcal IRIS are warranted. PMID:29371546

When Self-Reliance Is Not Safe: Associations between Reduced Help-Seeking and Subsequent Mental Health Symptoms in Suicidal Adolescents

PubMed Central

Labouliere, Christa D.; Kleinman, Marjorie; Gould, Madelyn S.

2015-01-01

The majority of suicidal adolescents have no contact with mental health services, and reduced help-seeking in this population further lessens the likelihood of accessing treatment. A commonly-reported reason for not seeking help is youths’ perception that they should solve problems on their own. In this study, we explore associations between extreme self-reliance behavior (i.e., solving problems on your own all of the time), help-seeking behavior, and mental health symptoms in a community sample of adolescents. Approximately 2150 adolescents, across six schools, participated in a school-based suicide prevention screening program, and a subset of at-risk youth completed a follow-up interview two years later. Extreme self-reliance was associated with reduced help-seeking, clinically-significant depressive symptoms, and serious suicidal ideation at the baseline screening. Furthermore, in a subset of youth identified as at-risk at the baseline screening, extreme self-reliance predicted level of suicidal ideation and depressive symptoms two years later even after controlling for baseline symptoms. Given these findings, attitudes that reinforce extreme self-reliance behavior may be an important target for youth suicide prevention programs. Reducing extreme self-reliance in youth with suicidality may increase their likelihood of appropriate help-seeking and concomitant reductions in symptoms. PMID:25837350

MISR - Science Data Validation Plan

NASA Technical Reports Server (NTRS)

Conel, J.; Ledeboer, W.; Ackerman, T.; Marchand, R.; Clothiaux, E.

2000-01-01

This Science Data Validation Plan describes the plans for validating a subset of the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 algorithms and data products and supplying top-of-atmosphere (TOA) radiances to the In-flight Radiometric Calibration and Characterization (IFRCC) subsystem for vicarious calibration.

Novel and efficient tag SNPs selection algorithms.

PubMed

Chen, Wen-Pei; Hung, Che-Lun; Tsai, Suh-Jen Jane; Lin, Yaw-Ling

2014-01-01

SNPs are the most abundant forms of genetic variations amongst species; the association studies between complex diseases and SNPs or haplotypes have received great attention. However, these studies are restricted by the cost of genotyping all SNPs; thus, it is necessary to find smaller subsets, or tag SNPs, representing the rest of the SNPs. In fact, the existing tag SNP selection algorithms are notoriously time-consuming. An efficient algorithm for tag SNP selection was presented, which was applied to analyze the HapMap YRI data. The experimental results show that the proposed algorithm can achieve better performance than the existing tag SNP selection algorithms; in most cases, this proposed algorithm is at least ten times faster than the existing methods. In many cases, when the redundant ratio of the block is high, the proposed algorithm can even be thousands times faster than the previously known methods. Tools and web services for haplotype block analysis integrated by hadoop MapReduce framework are also developed using the proposed algorithm as computation kernels.

Generalization of some hidden subgroup algorithms for input sets of arbitrary size

NASA Astrophysics Data System (ADS)

Poslu, Damla; Say, A. C. Cem

2006-05-01

We consider the problem of generalizing some quantum algorithms so that they will work on input domains whose cardinalities are not necessarily powers of two. When analyzing the algorithms we assume that generating superpositions of arbitrary subsets of basis states whose cardinalities are not necessarily powers of two perfectly is possible. We have taken Ballhysa's model as a template and have extended it to Chi, Kim and Lee's generalizations of the Deutsch-Jozsa algorithm and to Simon's algorithm. With perfectly equal superpositions of input sets of arbitrary size, Chi, Kim and Lee's generalized Deutsch-Jozsa algorithms, both for evenly-distributed and evenly-balanced functions, worked with one-sided error property. For Simon's algorithm the success probability of the generalized algorithm is the same as that of the original for input sets of arbitrary cardinalities with equiprobable superpositions, since the property that the measured strings are all those which have dot product zero with the string we search, for the case where the function is 2-to-1, is not lost.

Changes in hematological indices and lymphocyte subsets in response to whole blood donation in healthy male donors.

PubMed

Borai, Anwar; Livingstone, Callum; Alsobhi, Enaam; Al Sofyani, Abeer; Balgoon, Dalal; Farzal, Anwar; Almohammadi, Mohammed; Al-Amri, Abdulafattah; Bahijri, Suhad; Alrowaili, Daad; Bassiuni, Wafaa; Saleh, Ayman; Alrowaili, Norah; Abdelaal, Mohamed

2017-04-01

Whole blood donation has immunomodulatory effects, and most of these have been observed at short intervals following blood donation. This study aimed to investigate the impact of whole blood donation on lymphocyte subsets over a typical inter-donation interval. Healthy male subjects were recruited to study changes in complete blood count (CBC) (n = 42) and lymphocyte subsets (n = 16) before and at four intervals up to 106 days following blood donation. Repeated measures ANOVA were used to compare quantitative variables between different visits. Following blood donation, changes in CBC and erythropoietin were as expected. The neutrophil count increased by 11.3% at 8 days (p < .001). Novel changes were observed in lymphocyte subsets as the CD4/CD8 ratio increased by 9.2% (p < .05) at 8 days and 13.7% (p < .05) at 22 days. CD16-56 cells decreased by 16.2% (p < .05) at 8 days. All the subsets had returned to baseline by 106 days. Regression analysis showed that the changes in CD16-56 cells and CD4/CD8 ratio were not significant (Wilk's lambda = 0.15 and 0.94, respectively) when adjusted for BMI. In conclusion, following whole blood donation, there are transient changes in lymphocyte subsets. The effect of BMI on lymphocyte subsets and the effect of this immunomodulation on the immune response merit further investigation.

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification.

PubMed

Haque, Mohammad Nazmul; Noman, Nasimul; Berretta, Regina; Moscato, Pablo

2016-01-01

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble's output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) - k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer's disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

PubMed Central

Haque, Mohammad Nazmul; Noman, Nasimul; Berretta, Regina; Moscato, Pablo

2016-01-01

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble’s output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) − k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer’s disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases. PMID:26764911

Comparison of machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes - ELSA-Brasil: accuracy study.

PubMed

Olivera, André Rodrigues; Roesler, Valter; Iochpe, Cirano; Schmidt, Maria Inês; Vigo, Álvaro; Barreto, Sandhi Maria; Duncan, Bruce Bartholow

2017-01-01

Type 2 diabetes is a chronic disease associated with a wide range of serious health complications that have a major impact on overall health. The aims here were to develop and validate predictive models for detecting undiagnosed diabetes using data from the Longitudinal Study of Adult Health (ELSA-Brasil) and to compare the performance of different machine-learning algorithms in this task. Comparison of machine-learning algorithms to develop predictive models using data from ELSA-Brasil. After selecting a subset of 27 candidate variables from the literature, models were built and validated in four sequential steps: (i) parameter tuning with tenfold cross-validation, repeated three times; (ii) automatic variable selection using forward selection, a wrapper strategy with four different machine-learning algorithms and tenfold cross-validation (repeated three times), to evaluate each subset of variables; (iii) error estimation of model parameters with tenfold cross-validation, repeated ten times; and (iv) generalization testing on an independent dataset. The models were created with the following machine-learning algorithms: logistic regression, artificial neural network, naïve Bayes, K-nearest neighbor and random forest. The best models were created using artificial neural networks and logistic regression. -These achieved mean areas under the curve of, respectively, 75.24% and 74.98% in the error estimation step and 74.17% and 74.41% in the generalization testing step. Most of the predictive models produced similar results, and demonstrated the feasibility of identifying individuals with highest probability of having undiagnosed diabetes, through easily-obtained clinical data.

Multibaseline interferometric SAR at millimeterwaves test of an algorithm on real data and a synthetic scene

NASA Astrophysics Data System (ADS)

Essen, Helmut; Brehm, Thorsten; Boehmsdorff, Stephan

2007-10-01

Interferometric Synthetic Aperture Radar has the capability to provide the user with the 3-D-Information of land surfaces. To gather data with high height estimation accuracy it is necessary to use a wide interferometric baseline or a high radar frequency. However the problem of resolving the phase ambiguity at smaller wavelengths is more critical than at longer wavelengths, as the unambiguous height interval is inversely proportional to the radar wavelength. To solve this shortcoming, a multiple baseline approach can be used with a number of neighbouring horns and an increasing baselength going from narrow to wide. The narrowest, corresponding to adjacent horns, is then assumed to be unambiguous in phase. This initial interferogram is used as a starting point for the algorithm, which in the next step, unwraps the interferogram with the next wider baseline using the coarse height information to solve the phase ambiguities. This process is repeated consecutively until the interferogram with highest precision is unwrapped. On the expense of this multi-channel-approach the algorithm is simple and robust, and even the amount of processing time is reduced considerably, compared to traditional methods. The multiple baseline approach is especially adequate for millimeterwave radars as antenna horns with relatively small aperture can be used, while a sufficient 3-dB beamwidth is maintained. The paper describes the multiple baseline algorithm and shows the results of tests on real data and a synthetic area. Possibilities and limitations of this approach are discussed. Examples of digital elevation maps derived from measured data at millimeterwaves are shown.

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

PubMed

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.

Sync-rank: Robust Ranking, Constrained Ranking and Rank Aggregation via Eigenvector and SDP Synchronization

DTIC Science & Technology

2015-04-28

the players . In addition, we compare the algorithms on three real data sets: the outcome of soccer games in the English Premier League, a Microsoft...Premier League soccer games, a Halo 2 game tournament and NCAA College Basketball games), which show that our proposed method compares favorably to...information on the ground truth rank of a subset of players , and propose an algorithm based on SDP which is able to recover the ranking of the remaining

Alternative to Ritt's pseudodivision for finding the input-output equations of multi-output models.

PubMed

Meshkat, Nicolette; Anderson, Chris; DiStefano, Joseph J

2012-09-01

Differential algebra approaches to structural identifiability analysis of a dynamic system model in many instances heavily depend upon Ritt's pseudodivision at an early step in analysis. The pseudodivision algorithm is used to find the characteristic set, of which a subset, the input-output equations, is used for identifiability analysis. A simpler algorithm is proposed for this step, using Gröbner Bases, along with a proof of the method that includes a reduced upper bound on derivative requirements. Efficacy of the new algorithm is illustrated with several biosystem model examples. Copyright © 2012 Elsevier Inc. All rights reserved.

An algorithm for a single machine scheduling problem with sequence dependent setup times and scheduling windows

NASA Technical Reports Server (NTRS)

Moore, J. E.

1975-01-01

An enumeration algorithm is presented for solving a scheduling problem similar to the single machine job shop problem with sequence dependent setup times. The scheduling problem differs from the job shop problem in two ways. First, its objective is to select an optimum subset of the available tasks to be performed during a fixed period of time. Secondly, each task scheduled is constrained to occur within its particular scheduling window. The algorithm is currently being used to develop typical observational timelines for a telescope that will be operated in earth orbit. Computational times associated with timeline development are presented.

An Image Processing Algorithm Based On FMAT

NASA Technical Reports Server (NTRS)

Wang, Lui; Pal, Sankar K.

1995-01-01

Information deleted in ways minimizing adverse effects on reconstructed images. New grey-scale generalization of medial axis transformation (MAT), called FMAT (short for Fuzzy MAT) proposed. Formulated by making natural extension to fuzzy-set theory of all definitions and conditions (e.g., characteristic function of disk, subset condition of disk, and redundancy checking) used in defining MAT of crisp set. Does not need image to have any kind of priori segmentation, and allows medial axis (and skeleton) to be fuzzy subset of input image. Resulting FMAT (consisting of maximal fuzzy disks) capable of reconstructing exactly original image.

Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

NASA Astrophysics Data System (ADS)

Lee, Chien-Cheng; Huang, Shin-Sheng; Shih, Cheng-Yuan

2010-12-01

This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB) with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO) algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.

An efficient hybrid method for stochastic reaction-diffusion biochemical systems with delay

NASA Astrophysics Data System (ADS)

Sayyidmousavi, Alireza; Ilie, Silvana

2017-12-01

Many chemical reactions, such as gene transcription and translation in living cells, need a certain time to finish once they are initiated. Simulating stochastic models of reaction-diffusion systems with delay can be computationally expensive. In the present paper, a novel hybrid algorithm is proposed to accelerate the stochastic simulation of delayed reaction-diffusion systems. The delayed reactions may be of consuming or non-consuming delay type. The algorithm is designed for moderately stiff systems in which the events can be partitioned into slow and fast subsets according to their propensities. The proposed algorithm is applied to three benchmark problems and the results are compared with those of the delayed Inhomogeneous Stochastic Simulation Algorithm. The numerical results show that the new hybrid algorithm achieves considerable speed-up in the run time and very good accuracy.

Core Hunter 3: flexible core subset selection.

PubMed

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 strengths of other methods, as it (simultaneously) optimizes a variety of metrics. In addition, CH3 is an improvement over CH2, with the option to use genetic marker data or phenotypic traits, or both, and improved speed. Core Hunter 3 is freely available on http://www.corehunter.org .

Data Mining Feature Subset Weighting and Selection Using Genetic Algorithms

DTIC Science & Technology

2002-03-01

seed-stain, anthracnose, phyllosticta-leaf-spot, alternarialeaf-spot, frog-eye-leaf- spot, diaporthe-pod-&-stem-blight, cyst - nematode , 2-4-d-injury...seed-discolor: absent,present,?. 33. seed-size: norm,lt-norm,?. 34. shriveling: absent,present,?. 35. roots: norm,rotted,galls- cysts

Auditing Complex Concepts in Overlapping Subsets of SNOMED

PubMed Central

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

Auditing complex concepts in overlapping subsets of SNOMED.

PubMed

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

2008-11-06

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.

How long will my mouse live? Machine learning approaches for prediction of mouse life span.

PubMed

Swindell, William R; Harper, James M; Miller, Richard A

2008-09-01

Prediction of individual life span based on characteristics evaluated at middle-age represents a challenging objective for aging research. In this study, we used machine learning algorithms to construct models that predict life span in a stock of genetically heterogeneous mice. Life-span prediction accuracy of 22 algorithms was evaluated using a cross-validation approach, in which models were trained and tested with distinct subsets of data. Using a combination of body weight and T-cell subset measures evaluated before 2 years of age, we show that the life-span quartile to which an individual mouse belongs can be predicted with an accuracy of 35.3% (+/-0.10%). This result provides a new benchmark for the development of life-span-predictive models, but improvement can be expected through identification of new predictor variables and development of computational approaches. Future work in this direction can provide tools for aging research and will shed light on associations between phenotypic traits and longevity.

Post-processing images from the WFIRST-AFTA coronagraph testbed

NASA Astrophysics Data System (ADS)

Zimmerman, Neil T.; Ygouf, Marie; Pueyo, Laurent; Soummer, Remi; Perrin, Marshall D.; Mennesson, Bertrand; Cady, Eric; Mejia Prada, Camilo

2016-01-01

The concept for the exoplanet imaging instrument on WFIRST-AFTA relies on the development of mission-specific data processing tools to reduce the speckle noise floor. No instruments have yet functioned on the sky in the planet-to-star contrast regime of the proposed coronagraph (1E-8). Therefore, starlight subtraction algorithms must be tested on a combination of simulated and laboratory data sets to give confidence that the scientific goals can be reached. The High Contrast Imaging Testbed (HCIT) at Jet Propulsion Lab has carried out several technology demonstrations for the instrument concept, demonstrating 1E-8 raw (absolute) contrast. Here, we have applied a mock reference differential imaging strategy to HCIT data sets, treating one subset of images as a reference star observation and another subset as a science target observation. We show that algorithms like KLIP (Karhunen-Loève Image Projection), by suppressing residual speckles, enable the recovery of exoplanet signals at contrast of order 2E-9.

A formulation of a matrix sparsity approach for the quantum ordered search algorithm

NASA Astrophysics Data System (ADS)

Parmar, Jupinder; Rahman, Saarim; Thiara, Jaskaran

One specific subset of quantum algorithms is Grovers Ordered Search Problem (OSP), the quantum counterpart of the classical binary search algorithm, which utilizes oracle functions to produce a specified value within an ordered database. Classically, the optimal algorithm is known to have a log2N complexity; however, Grovers algorithm has been found to have an optimal complexity between the lower bound of ((lnN-1)/π≈0.221log2N) and the upper bound of 0.433log2N. We sought to lower the known upper bound of the OSP. With Farhi et al. MITCTP 2815 (1999), arXiv:quant-ph/9901059], we see that the OSP can be resolved into a translational invariant algorithm to create quantum query algorithm restraints. With these restraints, one can find Laurent polynomials for various k — queries — and N — database sizes — thus finding larger recursive sets to solve the OSP and effectively reducing the upper bound. These polynomials are found to be convex functions, allowing one to make use of convex optimization to find an improvement on the known bounds. According to Childs et al. [Phys. Rev. A 75 (2007) 032335], semidefinite programming, a subset of convex optimization, can solve the particular problem represented by the constraints. We were able to implement a program abiding to their formulation of a semidefinite program (SDP), leading us to find that it takes an immense amount of storage and time to compute. To combat this setback, we then formulated an approach to improve results of the SDP using matrix sparsity. Through the development of this approach, along with an implementation of a rudimentary solver, we demonstrate how matrix sparsity reduces the amount of time and storage required to compute the SDP — overall ensuring further improvements will likely be made to reach the theorized lower bound.

A novel baseline correction method using convex optimization framework in laser-induced breakdown spectroscopy quantitative analysis

NASA Astrophysics Data System (ADS)

Yi, Cancan; Lv, Yong; Xiao, Han; Ke, Ke; Yu, Xun

2017-12-01

For laser-induced breakdown spectroscopy (LIBS) quantitative analysis technique, baseline correction is an essential part for the LIBS data preprocessing. As the widely existing cases, the phenomenon of baseline drift is generated by the fluctuation of laser energy, inhomogeneity of sample surfaces and the background noise, which has aroused the interest of many researchers. Most of the prevalent algorithms usually need to preset some key parameters, such as the suitable spline function and the fitting order, thus do not have adaptability. Based on the characteristics of LIBS, such as the sparsity of spectral peaks and the low-pass filtered feature of baseline, a novel baseline correction and spectral data denoising method is studied in this paper. The improved technology utilizes convex optimization scheme to form a non-parametric baseline correction model. Meanwhile, asymmetric punish function is conducted to enhance signal-noise ratio (SNR) of the LIBS signal and improve reconstruction precision. Furthermore, an efficient iterative algorithm is applied to the optimization process, so as to ensure the convergence of this algorithm. To validate the proposed method, the concentration analysis of Chromium (Cr),Manganese (Mn) and Nickel (Ni) contained in 23 certified high alloy steel samples is assessed by using quantitative models with Partial Least Squares (PLS) and Support Vector Machine (SVM). Because there is no prior knowledge of sample composition and mathematical hypothesis, compared with other methods, the method proposed in this paper has better accuracy in quantitative analysis, and fully reflects its adaptive ability.

Predictive classification of self-paced upper-limb analytical movements with EEG.

PubMed

Ibáñez, Jaime; Serrano, J I; del Castillo, M D; Minguez, J; Pons, J L

2015-11-01

The extent to which the electroencephalographic activity allows the characterization of movements with the upper limb is an open question. This paper describes the design and validation of a classifier of upper-limb analytical movements based on electroencephalographic activity extracted from intervals preceding self-initiated movement tasks. Features selected for the classification are subject specific and associated with the movement tasks. Further tests are performed to reject the hypothesis that other information different from the task-related cortical activity is being used by the classifiers. Six healthy subjects were measured performing self-initiated upper-limb analytical movements. A Bayesian classifier was used to classify among seven different kinds of movements. Features considered covered the alpha and beta bands. A genetic algorithm was used to optimally select a subset of features for the classification. An average accuracy of 62.9 ± 7.5% was reached, which was above the baseline level observed with the proposed methodology (30.2 ± 4.3%). The study shows how the electroencephalography carries information about the type of analytical movement performed with the upper limb and how it can be decoded before the movement begins. In neurorehabilitation environments, this information could be used for monitoring and assisting purposes.

Automated Identification of Abnormal Adult EEGs

PubMed Central

López, S.; Suarez, G.; Jungreis, D.; Obeid, I.; Picone, J.

2016-01-01

The interpretation of electroencephalograms (EEGs) is a process that is still dependent on the subjective analysis of the examiners. Though interrater agreement on critical events such as seizures is high, it is much lower on subtler events (e.g., when there are benign variants). The process used by an expert to interpret an EEG is quite subjective and hard to replicate by machine. The performance of machine learning technology is far from human performance. We have been developing an interpretation system, AutoEEG, with a goal of exceeding human performance on this task. In this work, we are focusing on one of the early decisions made in this process – whether an EEG is normal or abnormal. We explore two baseline classification algorithms: k-Nearest Neighbor (kNN) and Random Forest Ensemble Learning (RF). A subset of the TUH EEG Corpus was used to evaluate performance. Principal Components Analysis (PCA) was used to reduce the dimensionality of the data. kNN achieved a 41.8% detection error rate while RF achieved an error rate of 31.7%. These error rates are significantly lower than those obtained by random guessing based on priors (49.5%). The majority of the errors were related to misclassification of normal EEGs. PMID:27195311

Longitudinal Changes in Serum Glucose Levels are Associated with Metabolic Changes in Alzheimer's Disease Related Brain Regions.

PubMed

Burns, Christine M; Kaszniak, Alfred W; Chen, Kewei; Lee, Wendy; Bandy, Daniel J; Caselli, Richard J; Reiman, Eric M

2018-01-01

The association between longitudinal changes in serum glucose level and longitudinal changes in [18F] Fluorodeoxyglucose-PET (FDG PET) measurements of Alzheimer's disease (AD) risk are unknown. To investigate whether variation in serum glucose levels across time are associated with changes in FDG PET measurements of cerebral metabolic rate for glucose (rCMRgl) in brain regions preferentially affected by Alzheimer's disease (AD). Participants are a subset of a prospective cohort study investigating FDG PET, apolipoprotein E (APOE) ɛ4, and risk for AD which includes data from baseline, interim, and follow up visits over 4.4±1.0-years. An automated brain-mapping algorithm was utilized to characterize and compare associations between longitudinal changes in serum glucose levels and longitudinal changes in rCMRgl. This study included 80 adults aged 61.5±5 years, including 38 carriers and 42 non-carriers of the APOE ɛ4 allele. Longitudinal increases in serum glucose levels were associated with longitudinal CMRgl decline in the vicinity of parietotemporal, precuneus/posterior cingulate, and prefrontal brain regions preferentially affected by AD (p < 0.05, corrected for multiple comparisons). Findings remained significant when controlled for APOE ɛ4 status and baseline and advancing age. Additional studies are needed to clarify and confirm the relationship between longitudinal changes in peripheral glucose and FDG PET measurements of AD risk. Future findings will set the stage on the use of FDG PET in the evaluation of possible interventions that target risk factors for the development of AD.

Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification

PubMed Central

2012-01-01

Background Previous studies on tumor classification based on gene expression profiles suggest that gene selection plays a key role in improving the classification performance. Moreover, finding important tumor-related genes with the highest accuracy is a very important task because these genes might serve as tumor biomarkers, which is of great benefit to not only tumor molecular diagnosis but also drug development. Results This paper proposes a novel gene selection method with rich biomedical meaning based on Heuristic Breadth-first Search Algorithm (HBSA) to find as many optimal gene subsets as possible. Due to the curse of dimensionality, this type of method could suffer from over-fitting and selection bias problems. To address these potential problems, a HBSA-based ensemble classifier is constructed using majority voting strategy from individual classifiers constructed by the selected gene subsets, and a novel HBSA-based gene ranking method is designed to find important tumor-related genes by measuring the significance of genes using their occurrence frequencies in the selected gene subsets. The experimental results on nine tumor datasets including three pairs of cross-platform datasets indicate that the proposed method can not only obtain better generalization performance but also find many important tumor-related genes. Conclusions It is found that the frequencies of the selected genes follow a power-law distribution, indicating that only a few top-ranked genes can be used as potential diagnosis biomarkers. Moreover, the top-ranked genes leading to very high prediction accuracy are closely related to specific tumor subtype and even hub genes. Compared with other related methods, the proposed method can achieve higher prediction accuracy with fewer genes. Moreover, they are further justified by analyzing the top-ranked genes in the context of individual gene function, biological pathway, and protein-protein interaction network. PMID:22830977

Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

PubMed

Kesharaju, Manasa; Nagarajah, Romesh

2015-09-01

The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. All rights reserved.

Automatic Target Recognition: Statistical Feature Selection of Non-Gaussian Distributed Target Classes

DTIC Science & Technology

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

Querying Co-regulated Genes on Diverse Gene Expression Datasets Via Biclustering.

PubMed

Deveci, Mehmet; Küçüktunç, Onur; Eren, Kemal; Bozdağ, Doruk; Kaya, Kamer; Çatalyürek, Ümit V

2016-01-01

Rapid development and increasing popularity of gene expression microarrays have resulted in a number of studies on the discovery of co-regulated genes. One important way of discovering such co-regulations is the query-based search since gene co-expressions may indicate a shared role in a biological process. Although there exist promising query-driven search methods adapting clustering, they fail to capture many genes that function in the same biological pathway because microarray datasets are fraught with spurious samples or samples of diverse origin, or the pathways might be regulated under only a subset of samples. On the other hand, a class of clustering algorithms known as biclustering algorithms which simultaneously cluster both the items and their features are useful while analyzing gene expression data, or any data in which items are related in only a subset of their samples. This means that genes need not be related in all samples to be clustered together. Because many genes only interact under specific circumstances, biclustering may recover the relationships that traditional clustering algorithms can easily miss. In this chapter, we briefly summarize the literature using biclustering for querying co-regulated genes. Then we present a novel biclustering approach and evaluate its performance by a thorough experimental analysis.

Potential for false positive HIV test results with the serial rapid HIV testing algorithm.

PubMed

Baveewo, Steven; Kamya, Moses R; Mayanja-Kizza, Harriet; Fatch, Robin; Bangsberg, David R; Coates, Thomas; Hahn, Judith A; Wanyenze, Rhoda K

2012-03-19

Rapid HIV tests provide same-day results and are widely used in HIV testing programs in areas with limited personnel and laboratory infrastructure. The Uganda Ministry of Health currently recommends the serial rapid testing algorithm with Determine, STAT-PAK, and Uni-Gold for diagnosis of HIV infection. Using this algorithm, individuals who test positive on Determine, negative to STAT-PAK and positive to Uni-Gold are reported as HIV positive. We conducted further testing on this subgroup of samples using qualitative DNA PCR to assess the potential for false positive tests in this situation. Of the 3388 individuals who were tested, 984 were HIV positive on two consecutive tests, and 29 were considered positive by a tiebreaker (positive on Determine, negative on STAT-PAK, and positive on Uni-Gold). However, when the 29 samples were further tested using qualitative DNA PCR, 14 (48.2%) were HIV negative. Although this study was not primarily designed to assess the validity of rapid HIV tests and thus only a subset of the samples were retested, the findings show a potential for false positive HIV results in the subset of individuals who test positive when a tiebreaker test is used in serial testing. These findings highlight a need for confirmatory testing for this category of individuals.

Potential for false positive HIV test results with the serial rapid HIV testing algorithm

PubMed Central

2012-01-01

Background Rapid HIV tests provide same-day results and are widely used in HIV testing programs in areas with limited personnel and laboratory infrastructure. The Uganda Ministry of Health currently recommends the serial rapid testing algorithm with Determine, STAT-PAK, and Uni-Gold for diagnosis of HIV infection. Using this algorithm, individuals who test positive on Determine, negative to STAT-PAK and positive to Uni-Gold are reported as HIV positive. We conducted further testing on this subgroup of samples using qualitative DNA PCR to assess the potential for false positive tests in this situation. Results Of the 3388 individuals who were tested, 984 were HIV positive on two consecutive tests, and 29 were considered positive by a tiebreaker (positive on Determine, negative on STAT-PAK, and positive on Uni-Gold). However, when the 29 samples were further tested using qualitative DNA PCR, 14 (48.2%) were HIV negative. Conclusion Although this study was not primarily designed to assess the validity of rapid HIV tests and thus only a subset of the samples were retested, the findings show a potential for false positive HIV results in the subset of individuals who test positive when a tiebreaker test is used in serial testing. These findings highlight a need for confirmatory testing for this category of individuals. PMID:22429706

Leveraging probabilistic peak detection to estimate baseline drift in complex chromatographic samples.

PubMed

Lopatka, Martin; Barcaru, Andrei; Sjerps, Marjan J; Vivó-Truyols, Gabriel

2016-01-29

Accurate analysis of chromatographic data often requires the removal of baseline drift. A frequently employed strategy strives to determine asymmetric weights in order to fit a baseline model by regression. Unfortunately, chromatograms characterized by a very high peak saturation pose a significant challenge to such algorithms. In addition, a low signal-to-noise ratio (i.e. s/n<40) also adversely affects accurate baseline correction by asymmetrically weighted regression. We present a baseline estimation method that leverages a probabilistic peak detection algorithm. A posterior probability of being affected by a peak is computed for each point in the chromatogram, leading to a set of weights that allow non-iterative calculation of a baseline estimate. For extremely saturated chromatograms, the peak weighted (PW) method demonstrates notable improvement compared to the other methods examined. However, in chromatograms characterized by low-noise and well-resolved peaks, the asymmetric least squares (ALS) and the more sophisticated Mixture Model (MM) approaches achieve superior results in significantly less time. We evaluate the performance of these three baseline correction methods over a range of chromatographic conditions to demonstrate the cases in which each method is most appropriate. Copyright © 2016 Elsevier B.V. All rights reserved.

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

PubMed

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.

Adaptive feature selection using v-shaped binary particle swarm optimization

PubMed Central

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. PMID:28358850

Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease: Methodology and Baseline Sample Characteristics.

PubMed

Byun, Min Soo; Yi, Dahyun; Lee, Jun Ho; Choe, Young Min; Sohn, Bo Kyung; Lee, Jun-Young; Choi, Hyo Jung; Baek, Hyewon; Kim, Yu Kyeong; Lee, Yun-Sang; Sohn, Chul-Ho; Mook-Jung, Inhee; Choi, Murim; Lee, Yu Jin; Lee, Dong Woo; Ryu, Seung-Ho; Kim, Shin Gyeom; Kim, Jee Wook; Woo, Jong Inn; Lee, Dong Young

2017-11-01

The Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (KBASE) aimed to recruit 650 individuals, aged from 20 to 90 years, to search for new biomarkers of Alzheimer's disease (AD) and to investigate how multi-faceted lifetime experiences and bodily changes contribute to the brain changes or brain pathologies related to the AD process. All participants received comprehensive clinical and neuropsychological evaluations, multi-modal brain imaging, including magnetic resonance imaging, magnetic resonance angiography, [ 11 C]Pittsburgh compound B-positron emission tomography (PET), and [ 18 F]fluorodeoxyglucose-PET, blood and genetic marker analyses at baseline, and a subset of participants underwent actigraph monitoring and completed a sleep diary. Participants are to be followed annually with clinical and neuropsychological assessments, and biannually with the full KBASE assessment, including neuroimaging and laboratory tests. As of March 2017, in total, 758 individuals had volunteered for this study. Among them, in total, 591 participants-291 cognitively normal (CN) old-aged individuals, 74 CN young- and middle-aged individuals, 139 individuals with mild cognitive impairment (MCI), and 87 individuals with AD dementia (ADD)-were enrolled at baseline, after excluding 162 individuals. A subset of participants (n=275) underwent actigraph monitoring. The KBASE cohort is a prospective, longitudinal cohort study that recruited participants with a wide age range and a wide distribution of cognitive status (CN, MCI, and ADD) and it has several strengths in its design and methodologies. Details of the recruitment, study methodology, and baseline sample characteristics are described in this paper.

Potential inundated coastal area estimation in Shanghai with multi-platform SAR and altimetry data

NASA Astrophysics Data System (ADS)

Ma, Guanyu; Yang, Tianliang; Zhao, Qing; Kubanek, Julia; Pepe, Antonio; Dong, Hongbin; Sun, Zhibin

2017-09-01

As global warming problem is becoming serious in recent decades, the global sea level is continuously rising. This will cause damages to the coastal deltas with the characteristics of low-lying land, dense population, and developed economy. Continuously reclamation costal intertidal and wetland areas are making Shanghai, the mega city of Yangtze River Delta, more vulnerable to sea level rise. In this paper, we investigate the land subsidence temporal evolution of patterns and processes on a stretch of muddy coast located between the Yangtze River Estuary and Hangzou Bay with differential synthetic aperture radar interferometry (DInSAR) analyses. By exploiting a set of 31 SAR images acquired by the ENVISAT/ASAR from February 2007 to May 2010 and a set of 48 SAR images acquired by the COSMO-SkyMed (CSK) sensors from December 2013 to March 2016, coherent point targets as long as land subsidence velocity maps and time series are identified by using the Small Baseline Subset (SBAS) algorithm. With the DInSAR constrained land subsidence model, we predict the land subsidence trend and the expected cumulative subsidence in 2020, 2025 and 2030. Meanwhile, we used altimetrydata and densely distributed in the coastal region are identified (EEMD) algorithm to obtain the average sea level rise rate in the East China Sea. With the land subsidence predictions, sea level rise predictions, and high-precision digital elevation model (DEM), we analyze the combined risk of land subsidence and sea level rise on the coastal areas of Shanghai. The potential inundated areas are mapped under different scenarios.

A GeoNode-Based Multiscale Platform For Management, Visualization And Integration Of DInSAR Data With Different Geospatial Information Sources

NASA Astrophysics Data System (ADS)

Buonanno, Sabatino; Fusco, Adele; Zeni, Giovanni; Manunta, Michele; Lanari, Riccardo

2017-04-01

This work describes the implementation of an efficient system for managing, viewing, analyzing and updating remotely sensed data, with special reference to Differential Interferometric Synthetic Aperture Radar (DInSAR) data. The DInSAR products measure Earth surface deformation both in space and time, producing deformation maps and time series[1,2]. The use of these data in research or operational contexts requires tools that have to handle temporal and spatial variability with high efficiency. For this aim we present an implementation based on Spatial Data Infrastructure (SDI) for data integration, management and interchange, by using standard protocols[3]. SDI tools provide access to static datasets that operate only with spatial variability . In this paper we use the open source project GeoNode as framework to extend SDI infrastructure functionalities to ingest very efficiently DInSAR deformation maps and deformation time series. GeoNode allows to realize comprehensive and distributed infrastructure, following the standards of the Open Geospatial Consortium, Inc. - OGC, for remote sensing data management, analysis and integration [4,5]. In the current paper we explain the methodology used for manage the data complexity and data integration using the opens source project GeoNode. The solution presented in this work for the ingestion of DinSAR products is a very promising starting point for future developments of the OGC compliant implementation of a semi-automatic remote sensing data processing chain . [1] Berardino, P., Fornaro, G., Lanari, R., & Sansosti, E. (2002). A new Algorithm for Surface Deformation Monitoring based on Small Baseline Differential SAR Interferograms. IEEE Transactions on Geoscience and Remote Sensing, 40, 11, pp. 2375-2383. [2] Lanari R., F. Casu, M. Manzo, G. Zeni,, P. Berardino, M. Manunta and A. Pepe (2007), An overview of the Small Baseline Subset Algorithm: a DInSAR Technique for Surface Deformation Analysis, P. Appl. Geophys., 164, doi: 10.1007/s00024-007-0192-9. [3] Nebert, D.D. (ed). 2000. Developing Spatial data Infrastructures: The SDI Cookbook. [4] Geonode (www.geonode.org) [5] Kolodziej, k. (ed). 2004. OGC OpenGIS Web Map Server Cookbook. Open Geospatial Consortium, 1.0.2 edition.

An Integrated Ransac and Graph Based Mismatch Elimination Approach for Wide-Baseline Image Matching

NASA Astrophysics Data System (ADS)

Hasheminasab, M.; Ebadi, H.; Sedaghat, A.

2015-12-01

In this paper we propose an integrated approach in order to increase the precision of feature point matching. Many different algorithms have been developed as to optimizing the short-baseline image matching while because of illumination differences and viewpoints changes, wide-baseline image matching is so difficult to handle. Fortunately, the recent developments in the automatic extraction of local invariant features make wide-baseline image matching possible. The matching algorithms which are based on local feature similarity principle, using feature descriptor as to establish correspondence between feature point sets. To date, the most remarkable descriptor is the scale-invariant feature transform (SIFT) descriptor , which is invariant to image rotation and scale, and it remains robust across a substantial range of affine distortion, presence of noise, and changes in illumination. The epipolar constraint based on RANSAC (random sample consensus) method is a conventional model for mismatch elimination, particularly in computer vision. Because only the distance from the epipolar line is considered, there are a few false matches in the selected matching results based on epipolar geometry and RANSAC. Aguilariu et al. proposed Graph Transformation Matching (GTM) algorithm to remove outliers which has some difficulties when the mismatched points surrounded by the same local neighbor structure. In this study to overcome these limitations, which mentioned above, a new three step matching scheme is presented where the SIFT algorithm is used to obtain initial corresponding point sets. In the second step, in order to reduce the outliers, RANSAC algorithm is applied. Finally, to remove the remained mismatches, based on the adjacent K-NN graph, the GTM is implemented. Four different close range image datasets with changes in viewpoint are utilized to evaluate the performance of the proposed method and the experimental results indicate its robustness and capability.

Evaluation of the genotypic prediction of HIV-1 coreceptor use versus a phenotypic assay and correlation with the virological response to maraviroc: the ANRS GenoTropism study.

PubMed

Recordon-Pinson, Patricia; Soulié, Cathia; Flandre, Philippe; Descamps, Diane; Lazrek, Mouna; Charpentier, Charlotte; Montes, Brigitte; Trabaud, Mary-Anne; Cottalorda, Jacqueline; Schneider, Véronique; Morand-Joubert, Laurence; Tamalet, Catherine; Desbois, Delphine; Macé, Muriel; Ferré, Virginie; Vabret, Astrid; Ruffault, Annick; Pallier, Coralie; Raymond, Stéphanie; Izopet, Jacques; Reynes, Jacques; Marcelin, Anne-Geneviève; Masquelier, Bernard

2010-08-01

Genotypic algorithms for prediction of HIV-1 coreceptor usage need to be evaluated in a clinical setting. We aimed at studying (i) the correlation of genotypic prediction of coreceptor use in comparison with a phenotypic assay and (ii) the relationship between genotypic prediction of coreceptor use at baseline and the virological response (VR) to a therapy including maraviroc (MVC). Antiretroviral-experienced patients were included in the MVC Expanded Access Program if they had an R5 screening result with Trofile (Monogram Biosciences). V3 loop sequences were determined at screening, and coreceptor use was predicted using 13 genotypic algorithms or combinations of algorithms. Genotypic predictions were compared to Trofile; dual or mixed (D/M) variants were considered as X4 variants. Both genotypic and phenotypic results were obtained for 189 patients at screening, with 54 isolates scored as X4 or D/M and 135 scored as R5 with Trofile. The highest sensitivity (59.3%) for detection of X4 was obtained with the Geno2pheno algorithm, with a false-positive rate set up at 10% (Geno2pheno10). In the 112 patients receiving MVC, a plasma viral RNA load of <50 copies/ml was obtained in 68% of cases at month 6. In multivariate analysis, the prediction of the X4 genotype at baseline with the Geno2pheno10 algorithm including baseline viral load and CD4 nadir was independently associated with a worse VR at months 1 and 3. The baseline weighted genotypic sensitivity score was associated with VR at month 6. There were strong arguments in favor of using genotypic coreceptor use assays for determining which patients would respond to CCR5 antagonist.

Very long baseline interferometry applied to polar motion, relativity, and geodesy. Ph. D. thesis

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

Ma, C.

1978-01-01

The causes and effects of diurnal polar motion are described. An algorithm was developed for modeling the effects on very long baseline interferometry observables. A selection was made between two three-station networks for monitoring polar motion. The effects of scheduling and the number of sources observed on estimated baseline errors are discussed. New hardware and software techniques in very long baseline interferometry are described.

Adaptive thresholding with inverted triangular area for real-time detection of the heart rate from photoplethysmogram traces on a smartphone.

PubMed

Jiang, Wen Jun; Wittek, Peter; Zhao, Li; Gao, Shi Chao

2014-01-01

Photoplethysmogram (PPG) signals acquired by smartphone cameras are weaker than those acquired by dedicated pulse oximeters. Furthermore, the signals have lower sampling rates, have notches in the waveform and are more severely affected by baseline drift, leading to specific morphological characteristics. This paper introduces a new feature, the inverted triangular area, to address these specific characteristics. The new feature enables real-time adaptive waveform detection using an algorithm of linear time complexity. It can also recognize notches in the waveform and it is inherently robust to baseline drift. An implementation of the algorithm on Android is available for free download. We collected data from 24 volunteers and compared our algorithm in peak detection with two competing algorithms designed for PPG signals, Incremental-Merge Segmentation (IMS) and Adaptive Thresholding (ADT). A sensitivity of 98.0% and a positive predictive value of 98.8% were obtained, which were 7.7% higher than the IMS algorithm in sensitivity, and 8.3% higher than the ADT algorithm in positive predictive value. The experimental results confirmed the applicability of the proposed method.

A Comparison of Three Algorithms for Orion Drogue Parachute Release

NASA Technical Reports Server (NTRS)

Matz, Daniel A.; Braun, Robert D.

2015-01-01

The Orion Multi-Purpose Crew Vehicle is susceptible to ipping apex forward between drogue parachute release and main parachute in ation. A smart drogue release algorithm is required to select a drogue release condition that will not result in an apex forward main parachute deployment. The baseline algorithm is simple and elegant, but does not perform as well as desired in drogue failure cases. A simple modi cation to the baseline algorithm can improve performance, but can also sometimes fail to identify a good release condition. A new algorithm employing simpli ed rotational dynamics and a numeric predictor to minimize a rotational energy metric is proposed. A Monte Carlo analysis of a drogue failure scenario is used to compare the performance of the algorithms. The numeric predictor prevents more of the cases from ipping apex forward, and also results in an improvement in the capsule attitude at main bag extraction. The sensitivity of the numeric predictor to aerodynamic dispersions, errors in the navigated state, and execution rate is investigated, showing little degradation in performance.

Comparative Analysis of a Principal Component Analysis-Based and an Artificial Neural Network-Based Method for Baseline Removal.

PubMed

Carvajal, Roberto C; Arias, Luis E; Garces, Hugo O; Sbarbaro, Daniel G

2016-04-01

This work presents a non-parametric method based on a principal component analysis (PCA) and a parametric one based on artificial neural networks (ANN) to remove continuous baseline features from spectra. The non-parametric method estimates the baseline based on a set of sampled basis vectors obtained from PCA applied over a previously composed continuous spectra learning matrix. The parametric method, however, uses an ANN to filter out the baseline. Previous studies have demonstrated that this method is one of the most effective for baseline removal. The evaluation of both methods was carried out by using a synthetic database designed for benchmarking baseline removal algorithms, containing 100 synthetic composed spectra at different signal-to-baseline ratio (SBR), signal-to-noise ratio (SNR), and baseline slopes. In addition to deomonstrating the utility of the proposed methods and to compare them in a real application, a spectral data set measured from a flame radiation process was used. Several performance metrics such as correlation coefficient, chi-square value, and goodness-of-fit coefficient were calculated to quantify and compare both algorithms. Results demonstrate that the PCA-based method outperforms the one based on ANN both in terms of performance and simplicity. © The Author(s) 2016.

Multiparty Quantum Key Agreement Based on Quantum Search Algorithm

PubMed Central

Cao, Hao; Ma, Wenping

2017-01-01

Quantum key agreement is an important topic that the shared key must be negotiated equally by all participants, and any nontrivial subset of participants cannot fully determine the shared key. To date, the embed modes of subkey in all the previously proposed quantum key agreement protocols are based on either BB84 or entangled states. The research of the quantum key agreement protocol based on quantum search algorithms is still blank. In this paper, on the basis of investigating the properties of quantum search algorithms, we propose the first quantum key agreement protocol whose embed mode of subkey is based on a quantum search algorithm known as Grover’s algorithm. A novel example of protocols with 5 – party is presented. The efficiency analysis shows that our protocol is prior to existing MQKA protocols. Furthermore it is secure against both external attack and internal attacks. PMID:28332610

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

NASA Astrophysics Data System (ADS)

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.

Clustering of longitudinal data by using an extended baseline: A new method for treatment efficacy clustering in longitudinal data.

PubMed

Schramm, Catherine; Vial, Céline; Bachoud-Lévi, Anne-Catherine; Katsahian, Sandrine

2018-01-01

Heterogeneity in treatment efficacy is a major concern in clinical trials. Clustering may help to identify the treatment responders and the non-responders. In the context of longitudinal cluster analyses, sample size and variability of the times of measurements are the main issues with the current methods. Here, we propose a new two-step method for the Clustering of Longitudinal data by using an Extended Baseline. The first step relies on a piecewise linear mixed model for repeated measurements with a treatment-time interaction. The second step clusters the random predictions and considers several parametric (model-based) and non-parametric (partitioning, ascendant hierarchical clustering) algorithms. A simulation study compares all options of the clustering of longitudinal data by using an extended baseline method with the latent-class mixed model. The clustering of longitudinal data by using an extended baseline method with the two model-based algorithms was the more robust model. The clustering of longitudinal data by using an extended baseline method with all the non-parametric algorithms failed when there were unequal variances of treatment effect between clusters or when the subgroups had unbalanced sample sizes. The latent-class mixed model failed when the between-patients slope variability is high. Two real data sets on neurodegenerative disease and on obesity illustrate the clustering of longitudinal data by using an extended baseline method and show how clustering may help to identify the marker(s) of the treatment response. The application of the clustering of longitudinal data by using an extended baseline method in exploratory analysis as the first stage before setting up stratified designs can provide a better estimation of treatment effect in future clinical trials.

Reference-free automatic quality assessment of tracheoesophageal speech.

PubMed

Huang, Andy; Falk, Tiago H; Chan, Wai-Yip; Parsa, Vijay; Doyle, Philip

2009-01-01

Evaluation of the quality of tracheoesophageal (TE) speech using machines instead of human experts can enhance the voice rehabilitation process for patients who have undergone total laryngectomy and voice restoration. Towards the goal of devising a reference-free TE speech quality estimation algorithm, we investigate the efficacy of speech signal features that are used in standard telephone-speech quality assessment algorithms, in conjunction with a recently introduced speech modulation spectrum measure. Tests performed on two TE speech databases demonstrate that the modulation spectral measure and a subset of features in the standard ITU-T P.563 algorithm estimate TE speech quality with better correlation (up to 0.9) than previously proposed features.

A Note on Alternating Minimization Algorithm for the Matrix Completion Problem

DOE PAGES

Gamarnik, David; Misra, Sidhant

2016-06-06

Here, we consider the problem of reconstructing a low-rank matrix from a subset of its entries and analyze two variants of the so-called alternating minimization algorithm, which has been proposed in the past.We establish that when the underlying matrix has rank one, has positive bounded entries, and the graph underlying the revealed entries has diameter which is logarithmic in the size of the matrix, both algorithms succeed in reconstructing the matrix approximately in polynomial time starting from an arbitrary initialization.We further provide simulation results which suggest that the second variant which is based on the message passing type updates performsmore » significantly better.« less

Scheduling Algorithm for Mission Planning and Logistics Evaluation (SAMPLE). Volume 3: The GREEDY algorithm

NASA Technical Reports Server (NTRS)

Dupnick, E.; Wiggins, D.

1980-01-01

The functional specifications, functional design and flow, and the program logic of the GREEDY computer program are described. The GREEDY program is a submodule of the Scheduling Algorithm for Mission Planning and Logistics Evaluation (SAMPLE) program and has been designed as a continuation of the shuttle Mission Payloads (MPLS) program. The MPLS uses input payload data to form a set of feasible payload combinations; from these, GREEDY selects a subset of combinations (a traffic model) so all payloads can be included without redundancy. The program also provides the user a tutorial option so that he can choose an alternate traffic model in case a particular traffic model is unacceptable.

SVM-based feature extraction and classification of aflatoxin contaminated corn using fluorescence hyperspectral data

USDA-ARS?s Scientific Manuscript database

Support Vector Machine (SVM) was used in the Genetic Algorithms (GA) process to select and classify a subset of hyperspectral image bands. The method was applied to fluorescence hyperspectral data for the detection of aflatoxin contamination in Aspergillus flavus infected single corn kernels. In the...

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

USDA-ARS?s Scientific Manuscript database

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...

Diabetes Care Management Teams Did Not Reduce Utilization When Compared With Traditional Care: A Randomized Cluster Trial.

PubMed

Kearns, Patrick

2017-10-01

PURPOSE: Health services research evaluates redesign models for primary care. Care management is one alternative. Evaluation includes resource utilization as a criterion. Compare the impact of care-manager teams on resource utilization. The comparison includes entire panes of patients and the subset of patients with diabetes. DESIGN: Randomized, prospective, cohort study comparing change in utilization rates between groups, pre- and post-intervention. METHODOLOGY: Ten primary care physician panels in a safety-net setting. Ten physicians were randomized to either a care-management approach (Group 1) or a traditional approach (Group 2). Care managers focused on diabetes and the cardiovascular cluster of diseases. Analysis compared rates of hospitalization, 30-day readmission, emergency room visits, and urgent care visits. Analysis compared baseline rates to annual rates after a yearlong run-in for entire panels and the subset of patients with diabetes. RESULTS: Resource utilization showed no statistically significant change between baseline and Year 3 (P=.79). Emergency room visits and hospital readmission increased for both groups (P=.90), while hospital admissions and urgent care visits decreased (P=.73). Similarly, utilization was not significantly different for patients with diabetes (P=.69). CONCLUSIONS: A care-management team approach failed to improve resource utilization rates by entire panels and the subset of diabetic patients compared to traditional care. This reinforces the need for further evidentiary support for the care-management model's hypothesis in the safety net.

Clinical, hemispheric, and autonomic changes associated with use of closed-loop, allostatic neurotechnology by a case series of individuals with self-reported symptoms of post-traumatic stress.

PubMed

Tegeler, Charles H; Cook, Jared F; Tegeler, Catherine L; Hirsch, Joshua R; Shaltout, Hossam A; Simpson, Sean L; Fidali, Brian C; Gerdes, Lee; Lee, Sung W

2017-04-19

The objective of this pilot study was to explore the use of a closed-loop, allostatic, acoustic stimulation neurotechnology for individuals with self-reported symptoms of post-traumatic stress, as a potential means to impact symptomatology, temporal lobe high frequency asymmetry, heart rate variability (HRV), and baroreflex sensitivity (BRS). From a cohort of individuals participating in a naturalistic study to evaluate use of allostatic neurotechnology for diverse clinical conditions, a subset was identified who reported high scores on the Posttraumatic Stress Disorder Checklist (PCL). The intervention entailed a series of sessions wherein brain electrical activity was monitored noninvasively at high spectral resolutions, with software algorithms translating selected brain frequencies into acoustic stimuli (audible tones) that were delivered back to the user in real time, to support auto-calibration of neural oscillations. Participants completed symptom inventories before and after the intervention, and a subset underwent short-term blood pressure recordings for HRV and BRS. Changes in temporal lobe high frequency asymmetry were analyzed from baseline assessment through the first four sessions, and for the last four sessions. Nineteen individuals (mean age 47, 11 women) were enrolled, and the majority also reported symptom scores that exceeded inventory thresholds for depression. They undertook a median of 16 sessions over 16.5 days, and 18 completed the number of sessions recommended. After the intervention, 89% of the completers reported clinically significant decreases in post-traumatic stress symptoms, indicated by a change of at least 10 points on the PCL. At a group level, individuals with either rightward (n = 7) or leftward (n = 7) dominant baseline asymmetry in temporal lobe high frequency (23-36 Hz) activity demonstrated statistically significant reductions in their asymmetry scores over the course of their first four sessions. For 12 individuals who underwent short-term blood pressure recordings, there were statistically significant increases in HRV in the time domain and BRS (Sequence Up). There were no adverse events. Closed-loop, allostatic neurotechnology for auto-calibration of neural oscillations appears promising as an innovative therapeutic strategy for individuals with symptoms of post-traumatic stress. ClinicalTrials.gov #NCT02709369 , retrospectively registered on March 4, 2016.

QuateXelero: An Accelerated Exact Network Motif Detection Algorithm

PubMed Central

Khakabimamaghani, Sahand; Sharafuddin, Iman; Dichter, Norbert; Koch, Ina; Masoudi-Nejad, Ali

2013-01-01

Finding motifs in biological, social, technological, and other types of networks has become a widespread method to gain more knowledge about these networks’ structure and function. However, this task is very computationally demanding, because it is highly associated with the graph isomorphism which is an NP problem (not known to belong to P or NP-complete subsets yet). Accordingly, this research is endeavoring to decrease the need to call NAUTY isomorphism detection method, which is the most time-consuming step in many existing algorithms. The work provides an extremely fast motif detection algorithm called QuateXelero, which has a Quaternary Tree data structure in the heart. The proposed algorithm is based on the well-known ESU (FANMOD) motif detection algorithm. The results of experiments on some standard model networks approve the overal superiority of the proposed algorithm, namely QuateXelero, compared with two of the fastest existing algorithms, G-Tries and Kavosh. QuateXelero is especially fastest in constructing the central data structure of the algorithm from scratch based on the input network. PMID:23874498

A long baseline global stereo matching based upon short baseline estimation

NASA Astrophysics Data System (ADS)

Li, Jing; Zhao, Hong; Li, Zigang; Gu, Feifei; Zhao, Zixin; Ma, Yueyang; Fang, Meiqi

2018-05-01

In global stereo vision, balancing the matching efficiency and computing accuracy seems to be impossible because they contradict each other. In the case of a long baseline, this contradiction becomes more prominent. In order to solve this difficult problem, this paper proposes a novel idea to improve both the efficiency and accuracy in global stereo matching for a long baseline. In this way, the reference images located between the long baseline image pairs are firstly chosen to form the new image pairs with short baselines. The relationship between the disparities of pixels in the image pairs with different baselines is revealed by considering the quantized error so that the disparity search range under the long baseline can be reduced by guidance of the short baseline to gain matching efficiency. Then, the novel idea is integrated into the graph cuts (GCs) to form a multi-step GC algorithm based on the short baseline estimation, by which the disparity map under the long baseline can be calculated iteratively on the basis of the previous matching. Furthermore, the image information from the pixels that are non-occluded under the short baseline but are occluded for the long baseline can be employed to improve the matching accuracy. Although the time complexity of the proposed method depends on the locations of the chosen reference images, it is usually much lower for a long baseline stereo matching than when using the traditional GC algorithm. Finally, the validity of the proposed method is examined by experiments based on benchmark datasets. The results show that the proposed method is superior to the traditional GC method in terms of efficiency and accuracy, and thus it is suitable for long baseline stereo matching.

An Efficient Voting Algorithm for Finding Additive Biclusters with Random Background

PubMed Central

Xiao, Jing; Wang, Lusheng; Liu, Xiaowen

2008-01-01

Abstract The biclustering problem has been extensively studied in many areas, including e-commerce, data mining, machine learning, pattern recognition, statistics, and, more recently, computational biology. Given an n × m matrix A (n ≥ m), the main goal of biclustering is to identify a subset of rows (called objects) and a subset of columns (called properties) such that some objective function that specifies the quality of the found bicluster (formed by the subsets of rows and of columns of A) is optimized. The problem has been proved or conjectured to be NP-hard for various objective functions. In this article, we study a probabilistic model for the implanted additive bicluster problem, where each element in the n × m background matrix is a random integer from [0, L − 1] for some integer L, and a k × k implanted additive bicluster is obtained from an error-free additive bicluster by randomly changing each element to a number in [0, L − 1] with probability θ. We propose an O (n2m) time algorithm based on voting to solve the problem. We show that when \\documentclass{aastex}\\usepackage{amsbsy}\\usepackage{amsfonts}\\usepackage{amssymb}\\usepackage{bm}\\usepackage{mathrsfs}\\usepackage{pifont}\\usepackage{stmaryrd}\\usepackage{textcomp}\\usepackage{portland, xspace}\\usepackage{amsmath, amsxtra}\\pagestyle{empty}\\DeclareMathSizes{10}{9}{7}{6}\\begin{document}$$k \\geq \\Omega (\\sqrt{n \\log n})$$\\end{document}, the voting algorithm can correctly find the implanted bicluster with probability at least \\documentclass{aastex}\\usepackage{amsbsy}\\usepackage{amsfonts}\\usepackage{amssymb}\\usepackage{bm}\\usepackage{mathrsfs}\\usepackage{pifont}\\usepackage{stmaryrd}\\usepackage{textcomp}\\usepackage{portland, xspace}\\usepackage{amsmath, amsxtra}\\pagestyle{empty}\\DeclareMathSizes{10}{9}{7}{6}\\begin{document}$$1 - {\\frac {9} {n^ {2}}}$$\\end{document}. We also implement our algorithm as a C++ program named VOTE. The implementation incorporates several ideas for estimating the size of an implanted bicluster, adjusting the threshold in voting, dealing with small biclusters, and dealing with overlapping implanted biclusters. Our experimental results on both simulated and real datasets show that VOTE can find biclusters with a high accuracy and speed. PMID:19040364

Advances in Satellite Microwave Precipitation Retrieval Algorithms Over Land

NASA Astrophysics Data System (ADS)

Wang, N. Y.; You, Y.; Ferraro, R. R.

2015-12-01

Precipitation plays a key role in the earth's climate system, particularly in the aspect of its water and energy balance. Satellite microwave (MW) observations of precipitation provide a viable mean to achieve global measurement of precipitation with sufficient sampling density and accuracy. However, accurate precipitation information over land from satellite MW is a challenging problem. The Goddard Profiling Algorithm (GPROF) algorithm for the Global Precipitation Measurement (GPM) is built around the Bayesian formulation (Evans et al., 1995; Kummerow et al., 1996). GPROF uses the likelihood function and the prior probability distribution function to calculate the expected value of precipitation rate, given the observed brightness temperatures. It is particularly convenient to draw samples from a prior PDF from a predefined database of observations or models. GPROF algorithm does not search all database entries but only the subset thought to correspond to the actual observation. The GPM GPROF V1 database focuses on stratification by surface emissivity class, land surface temperature and total precipitable water. However, there is much uncertainty as to what is the optimal information needed to subset the database for different conditions. To this end, we conduct a database stratification study of using National Mosaic and Multi-Sensor Quantitative Precipitation Estimation, Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Technology Microwave Sounder (ATMS) and reanalysis data from Modern-Era Retrospective Analysis for Research and Applications (MERRA). Our database study (You et al., 2015) shows that environmental factors such as surface elevation, relative humidity, and storm vertical structure and height, and ice thickness can help in stratifying a single large database to smaller and more homogeneous subsets, in which the surface condition and precipitation vertical profiles are similar. It is found that the probability of detection (POD) increases about 8% and 12% by using stratified databases for rainfall and snowfall detection, respectively. In addition, by considering the relative humidity at lower troposphere and the vertical velocity at 700 hPa in the precipitation detection process, the POD for snowfall detection is further increased by 20.4% from 56.0% to 76.4%.

Fast Solution in Sparse LDA for Binary Classification

NASA Technical Reports Server (NTRS)

Moghaddam, Baback

2010-01-01

An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable- selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bio-informatics. Because of its combinatorial nature, feature- or variable-selection problems are NP-hard or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms. The prior Sparse-LDA algorithm was a greedy algorithm that considered the best variable or feature to add/ delete to/ from its subsets in order to maximally discriminate between multiple classes of data. The present algorithm is designed for the special but prevalent case of 2-class or binary classification (e.g. 1 vs. 0, functioning vs. malfunctioning, or change versus no change). The present algorithm provides near-optimal solutions on large real-world datasets having hundreds or even thousands of variables or features (e.g. selecting the fewest wavelength bands in a hyperspectral sensor to do terrain classification) and does so in typical computation times of minutes as compared to days or weeks as taken by the prior art. Sparse LDA requires solving generalized eigenvalue problems for a large number of variable subsets (represented by the submatrices of the input within-class and between-class covariance matrices). In the general (fullrank) case, the amount of computation scales at least cubically with the number of variables and thus the size of the problems that can be solved is limited accordingly. However, in binary classification, the principal eigenvalues can be found using a special analytic formula, without resorting to costly iterative techniques. The present algorithm exploits this analytic form along with the inherent sequential nature of greedy search itself. Together this enables the use of highly-efficient partitioned-matrix-inverse techniques that result in large speedups of computation in both the forward-selection and backward-elimination stages of greedy algorithms in general.

Reduced numbers of mucosal DR(int) macrophages and increased numbers of CD103(+) dendritic cells during anti-TNF-α treatment in patients with Crohn's disease.

PubMed

Dige, Anders; Magnusson, Maria K; Öhman, Lena; Hvas, Christian Lodberg; Kelsen, Jens; Wick, Mary Jo; Agnholt, Jørgen

2016-01-01

Anti-TNF-α treatment constitutes a mainstay in the treatment of Crohn's disease (CD), but its mechanisms of action are not fully understood. We aimed to investigate the effects of adalimumab, a human monoclonal TNF-α antibody, on macrophage (MQ) and dendritic cell (DC) subsets in mucosal biopsies and peripheral blood. Intestinal biopsies and blood samples were obtained from 12 different CD patients both before and 4 weeks after the initiation of the induction of adalimumab treatment. Endoscopic disease activity was estimated by the Simple Endoscopic Score for Crohn's Disease. Biopsies were obtained from inflamed and non-inflamed areas. The numbers of lamina propria CD14 (+) DR(int) and CD14 (+) DR(hi) MQs, CD141(+), CD141(-) and CD103(+) DCs subsets, and circulating monocytes and DCs were analyzed using flow cytometry. At baseline, we observed higher numbers of DR(int) MQs and lower numbers of CD103(+) DCs in inflamed versus non-inflamed mucosa [843 vs. 391/10(5) lamina propria mononuclear cells (LPMCs) (p < 0.05) and 9 vs. 19 × 10(5) LPMCs (p = 0.01), respectively]. After four weeks of adalimumab treatment, the numbers of DR(int) MQs decreased [843 to 379/10(5) LPMCs (p = 0.03)], whereas the numbers of CD103(+) DCs increased [9-20 × 10(5) LPMCs (p = 0.003)] compared with baseline. In peripheral blood, no alterations were observed in monocyte or DC numbers between baseline and week 4. In CD, mucosal inflammation is associated with high numbers of DR(int) MQs and low numbers of CD103(+) DCs. This composition of intestinal myeloid subsets is reversed by anti-TNF-α treatment. These results suggest that DR(int) MQs play a pivotal role in CD inflammation.

A divide-and-conquer algorithm for large-scale de novo transcriptome assembly through combining small assemblies from existing algorithms.

PubMed

Sze, Sing-Hoi; Parrott, Jonathan J; Tarone, Aaron M

2017-12-06

While the continued development of high-throughput sequencing has facilitated studies of entire transcriptomes in non-model organisms, the incorporation of an increasing amount of RNA-Seq libraries has made de novo transcriptome assembly difficult. Although algorithms that can assemble a large amount of RNA-Seq data are available, they are generally very memory-intensive and can only be used to construct small assemblies. We develop a divide-and-conquer strategy that allows these algorithms to be utilized, by subdividing a large RNA-Seq data set into small libraries. Each individual library is assembled independently by an existing algorithm, and a merging algorithm is developed to combine these assemblies by picking a subset of high quality transcripts to form a large transcriptome. When compared to existing algorithms that return a single assembly directly, this strategy achieves comparable or increased accuracy as memory-efficient algorithms that can be used to process a large amount of RNA-Seq data, and comparable or decreased accuracy as memory-intensive algorithms that can only be used to construct small assemblies. Our divide-and-conquer strategy allows memory-intensive de novo transcriptome assembly algorithms to be utilized to construct large assemblies.

Day-Ahead PM2.5 Concentration Forecasting Using WT-VMD Based Decomposition Method and Back Propagation Neural Network Improved by Differential Evolution

PubMed Central

Wang, Deyun; Liu, Yanling; Luo, Hongyuan; Yue, Chenqiang; Cheng, Sheng

2017-01-01

Accurate PM2.5 concentration forecasting is crucial for protecting public health and atmospheric environment. However, the intermittent and unstable nature of PM2.5 concentration series makes its forecasting become a very difficult task. In order to improve the forecast accuracy of PM2.5 concentration, this paper proposes a hybrid model based on wavelet transform (WT), variational mode decomposition (VMD) and back propagation (BP) neural network optimized by differential evolution (DE) algorithm. Firstly, WT is employed to disassemble the PM2.5 concentration series into a number of subsets with different frequencies. Secondly, VMD is applied to decompose each subset into a set of variational modes (VMs). Thirdly, DE-BP model is utilized to forecast all the VMs. Fourthly, the forecast value of each subset is obtained through aggregating the forecast results of all the VMs obtained from VMD decomposition of this subset. Finally, the final forecast series of PM2.5 concentration is obtained by adding up the forecast values of all subsets. Two PM2.5 concentration series collected from Wuhan and Tianjin, respectively, located in China are used to test the effectiveness of the proposed model. The results demonstrate that the proposed model outperforms all the other considered models in this paper. PMID:28704955

Efficient clustering aggregation based on data fragments.

PubMed

Wu, Ou; Hu, Weiming; Maybank, Stephen J; Zhu, Mingliang; Li, Bing

2012-06-01

Clustering aggregation, known as clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a single better clustering. Existing clustering aggregation algorithms are applied directly to data points, in what is referred to as the point-based approach. The algorithms are inefficient if the number of data points is large. We define an efficient approach for clustering aggregation based on data fragments. In this fragment-based approach, a data fragment is any subset of the data that is not split by any of the clustering results. To establish the theoretical bases of the proposed approach, we prove that clustering aggregation can be performed directly on data fragments under two widely used goodness measures for clustering aggregation taken from the literature. Three new clustering aggregation algorithms are described. The experimental results obtained using several public data sets show that the new algorithms have lower computational complexity than three well-known existing point-based clustering aggregation algorithms (Agglomerative, Furthest, and LocalSearch); nevertheless, the new algorithms do not sacrifice the accuracy.

Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation.

PubMed

Oliveira, Roberta B; Pereira, Aledir S; Tavares, João Manuel R S

2017-10-01

The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist dermatologists in early diagnosis of skin cancer, or even to monitor skin lesions. However, there still remains a challenge to improve classifiers for the diagnosis of such skin lesions. The main objective of this article is to evaluate different ensemble classification models based on input feature manipulation to diagnose skin lesions. Input feature manipulation processes are based on feature subset selections from shape properties, colour variation and texture analysis to generate diversity for the ensemble models. Three subset selection models are presented here: (1) a subset selection model based on specific feature groups, (2) a correlation-based subset selection model, and (3) a subset selection model based on feature selection algorithms. Each ensemble classification model is generated using an optimum-path forest classifier and integrated with a majority voting strategy. The proposed models were applied on a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by the first ensemble classification model that generates a feature subset ensemble based on specific feature groups. The skin lesion diagnosis computational system achieved 94.3% accuracy, 91.8% sensitivity and 96.7% specificity. The input feature manipulation process based on specific feature subsets generated the greatest diversity for the ensemble classification model with very promising results. Copyright © 2017 Elsevier B.V. All rights reserved.

Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories

NASA Technical Reports Server (NTRS)

Burchett, Bradley T.

2003-01-01

The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.

Approximate Bayesian Computation by Subset Simulation using hierarchical state-space models

NASA Astrophysics Data System (ADS)

Vakilzadeh, Majid K.; Huang, Yong; Beck, James L.; Abrahamsson, Thomas

2017-02-01

A new multi-level Markov Chain Monte Carlo algorithm for Approximate Bayesian Computation, ABC-SubSim, has recently appeared that exploits the Subset Simulation method for efficient rare-event simulation. ABC-SubSim adaptively creates a nested decreasing sequence of data-approximating regions in the output space that correspond to increasingly closer approximations of the observed output vector in this output space. At each level, multiple samples of the model parameter vector are generated by a component-wise Metropolis algorithm so that the predicted output corresponding to each parameter value falls in the current data-approximating region. Theoretically, if continued to the limit, the sequence of data-approximating regions would converge on to the observed output vector and the approximate posterior distributions, which are conditional on the data-approximation region, would become exact, but this is not practically feasible. In this paper we study the performance of the ABC-SubSim algorithm for Bayesian updating of the parameters of dynamical systems using a general hierarchical state-space model. We note that the ABC methodology gives an approximate posterior distribution that actually corresponds to an exact posterior where a uniformly distributed combined measurement and modeling error is added. We also note that ABC algorithms have a problem with learning the uncertain error variances in a stochastic state-space model and so we treat them as nuisance parameters and analytically integrate them out of the posterior distribution. In addition, the statistical efficiency of the original ABC-SubSim algorithm is improved by developing a novel strategy to regulate the proposal variance for the component-wise Metropolis algorithm at each level. We demonstrate that Self-regulated ABC-SubSim is well suited for Bayesian system identification by first applying it successfully to model updating of a two degree-of-freedom linear structure for three cases: globally, locally and un-identifiable model classes, and then to model updating of a two degree-of-freedom nonlinear structure with Duffing nonlinearities in its interstory force-deflection relationship.

Feature selection method based on multi-fractal dimension and harmony search algorithm and its application

NASA Astrophysics Data System (ADS)

Zhang, Chen; Ni, Zhiwei; Ni, Liping; Tang, Na

2016-10-01

Feature selection is an important method of data preprocessing in data mining. In this paper, a novel feature selection method based on multi-fractal dimension and harmony search algorithm is proposed. Multi-fractal dimension is adopted as the evaluation criterion of feature subset, which can determine the number of selected features. An improved harmony search algorithm is used as the search strategy to improve the efficiency of feature selection. The performance of the proposed method is compared with that of other feature selection algorithms on UCI data-sets. Besides, the proposed method is also used to predict the daily average concentration of PM2.5 in China. Experimental results show that the proposed method can obtain competitive results in terms of both prediction accuracy and the number of selected features.

A novel approach to locomotion learning: Actor-Critic architecture using central pattern generators and dynamic motor primitives.

PubMed

Li, Cai; Lowe, Robert; Ziemke, Tom

2014-01-01

In this article, we propose an architecture of a bio-inspired controller that addresses the problem of learning different locomotion gaits for different robot morphologies. The modeling objective is split into two: baseline motion modeling and dynamics adaptation. Baseline motion modeling aims to achieve fundamental functions of a certain type of locomotion and dynamics adaptation provides a "reshaping" function for adapting the baseline motion to desired motion. Based on this assumption, a three-layer architecture is developed using central pattern generators (CPGs, a bio-inspired locomotor center for the baseline motion) and dynamic motor primitives (DMPs, a model with universal "reshaping" functions). In this article, we use this architecture with the actor-critic algorithms for finding a good "reshaping" function. In order to demonstrate the learning power of the actor-critic based architecture, we tested it on two experiments: (1) learning to crawl on a humanoid and, (2) learning to gallop on a puppy robot. Two types of actor-critic algorithms (policy search and policy gradient) are compared in order to evaluate the advantages and disadvantages of different actor-critic based learning algorithms for different morphologies. Finally, based on the analysis of the experimental results, a generic view/architecture for locomotion learning is discussed in the conclusion.

A novel approach to locomotion learning: Actor-Critic architecture using central pattern generators and dynamic motor primitives

PubMed Central

Li, Cai; Lowe, Robert; Ziemke, Tom

2014-01-01

In this article, we propose an architecture of a bio-inspired controller that addresses the problem of learning different locomotion gaits for different robot morphologies. The modeling objective is split into two: baseline motion modeling and dynamics adaptation. Baseline motion modeling aims to achieve fundamental functions of a certain type of locomotion and dynamics adaptation provides a “reshaping” function for adapting the baseline motion to desired motion. Based on this assumption, a three-layer architecture is developed using central pattern generators (CPGs, a bio-inspired locomotor center for the baseline motion) and dynamic motor primitives (DMPs, a model with universal “reshaping” functions). In this article, we use this architecture with the actor-critic algorithms for finding a good “reshaping” function. In order to demonstrate the learning power of the actor-critic based architecture, we tested it on two experiments: (1) learning to crawl on a humanoid and, (2) learning to gallop on a puppy robot. Two types of actor-critic algorithms (policy search and policy gradient) are compared in order to evaluate the advantages and disadvantages of different actor-critic based learning algorithms for different morphologies. Finally, based on the analysis of the experimental results, a generic view/architecture for locomotion learning is discussed in the conclusion. PMID:25324773

A Submodularity Framework for Data Subset Selection

DTIC Science & Technology

2013-09-01

37 7 List of Language Modeling Corpora in thet Arabic -to-English NIST Task ............. 37 8...Task ( Arabic -to-English) ................. 39 10 Baseline BLEU (%) PER Scores on Transtac Task (English-to- Arabic ) ................. 39 11...Comparison of BLEU (%) PER Scores on Transtac Task ( Arabic -to-English) ....... 39 12 Comparison of BLEU (%) PER Scores on Transtac Task (English-to- Arabic

A Simple Algorithm for the Metric Traveling Salesman Problem

NASA Technical Reports Server (NTRS)

Grimm, M. J.

1984-01-01

An algorithm was designed for a wire list net sort problem. A branch and bound algorithm for the metric traveling salesman problem is presented for this. The algorithm is a best bound first recursive descent where the bound is based on the triangle inequality. The bounded subsets are defined by the relative order of the first K of the N cities (i.e., a K city subtour). When K equals N, the bound is the length of the tour. The algorithm is implemented as a one page subroutine written in the C programming language for the VAX 11/750. Average execution times for randomly selected planar points using the Euclidean metric are 0.01, 0.05, 0.42, and 3.13 seconds for ten, fifteen, twenty, and twenty-five cities, respectively. Maximum execution times for a hundred cases are less than eleven times the averages. The speed of the algorithms is due to an initial ordering algorithm that is a N squared operation. The algorithm also solves the related problem where the tour does not return to the starting city and the starting and/or ending cities may be specified. It is possible to extend the algorithm to solve a nonsymmetric problem satisfying the triangle inequality.

Mayo Registry for Telemetry Efficacy in Arrest (MR TEA) study: An analysis of code status change following cardiopulmonary arrest.

PubMed

Snipelisky, David; Ray, Jordan; Matcha, Gautam; Roy, Archana; Chirila, Razvan; Maniaci, Michael; Bosworth, Veronica; Whitman, Anastasia; Lewis, Patricia; Vadeboncoeur, Tyler; Kusumoto, Fred; Burton, M Caroline

2015-07-01

Code status discussions are important during a hospitalization, yet variation in its practice exists. No data have assessed the likelihood of patients to change code status following a cardiopulmonary arrest. A retrospective review of all patients that experienced a cardiopulmonary arrest between May 1, 2008 and June 30, 2014 at an academic medical center was performed. The proportion of code status modifications to do not resuscitate (DNR) from full code was assessed. Baseline clinical characteristics, resuscitation factors, and 24-h post-resuscitation, hospital, and overall survival rates were compared between the two subsets. A total of 157 patients survived the index event and were included. One hundred and fifteen (73.2%) patients did not have a change in code status following the index event, while 42 (26.8%) changed code status to DNR. Clinical characteristics were similar between subsets, although patients in the change to DNR subset were older (average age 67.7 years) compared to the full code subset (average age 59.2 years; p = 0.005). Patients in the DNR subset had longer overall resuscitation efforts with less attempts at defibrillation. Compared to the DNR subset, patients that remained full code demonstrated higher 24-h post-resuscitation (n = 108, 93.9% versus n = 32, 76.2%; p = 0.001) and hospital (n = 50, 43.5% versus n = 6, 14.3%; p = 0.001) survival rates. Patients in the DNR subset were more likely to have neurologic deficits on discharge and shorter overall survival. Patient code status wishes do tend to change during critical periods within a hospitalization, adding emphasis for continued code status evaluation. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Efficacy of T Regulatory Cells, Th17 Cells and the Associated Markers in Monitoring Tuberculosis Treatment Response

PubMed Central

Agrawal, Sonali; Parkash, Om; Palaniappan, Alangudi Natarajan; Bhatia, Ashok Kumar; Kumar, Santosh; Chauhan, Devendra Singh; Madhan Kumar, M.

2018-01-01

Treatment monitoring is an essential aspect for tuberculosis (TB) disease management. Sputum smear microscopy is the only available tool for monitoring, but it suffers from demerits. Therefore, we sought to evaluate markers and cellular subsets of T regulatory (Treg) cells and T helper (Th) 17 cells in pulmonary TB patients (PTB) for TB treatment monitoring. Peripheral blood mononuclear cells (PBMCs) were stimulated in vitro (with purified protein derivative (PPD)) overnight which was followed by a polychromatic flow cytometry approach to study Treg and Th17 markers and cellular subsets in PTB (n = 12) undergoing antituberculous treatment (ATT). The baseline levels of these markers and cellular subsets were evaluated in normal healthy subjects (NHS). We observed a significant decrease in the expression of CD25 (p<0.01) marker and percentage of T-cell subsets like CD4+CD25+ (p<0.001) and CD4+CD25+CD39+ (p<0.05) at the end of intensive phase (IP) as well as in the continuation phase (CP) of ATT. A decrease in CD25 marker expression and percentage of CD4+CD25+ T cell subset showed a positive correlation to sputum conversion both in high and low sputum positive PTB. In eight PTB with cavitary lesions, only CD4+CD25+FoxP3 Treg subset manifested a significant decrease at the end of CP. Thus, results of this study show that CD25 marker and CD4+CD25+ T cells can serve as better markers for monitoring TB treatment efficacy. The Treg subset CD4+CD25+FoxP3 may be useful for prediction of favorable response in PTB with extensive lung lesions. However, these findings have to be evaluated in a larger patient cohort. PMID:29472922

Validation of Manual Muscle Testing and a Subset of Eight Muscles (MMT8) for Adult and Juvenile Idiopathic Inflammatory Myopathies

PubMed Central

Rider, Lisa G.; Koziol, Deloris; Giannini, Edward H.; Jain, Minal S.; Smith, Michaele R.; Whitney-Mahoney, Kristi; Feldman, Brian M.; Wright, Susan J.; Lindsley, Carol B.; Pachman, Lauren M.; Villalba, Maria L.; Lovell, Daniel J.; Bowyer, Suzanne L.; Plotz, Paul H.; Miller, Frederick W.; Hicks, Jeanne E.

2010-01-01

Objective To validate manual muscle testing (MMT) for strength assessment in juvenile and adult dermatomyositis (DM) and polymyositis (PM). Methods Seventy-three children and 45 adult DM/PM patients were assessed at baseline and reevaluated 6–9 months later. We compared Total MMT (a group of 24 proximal, distal, and axial muscles) and Proximal MMT (7 proximal muscle groups) tested bilaterally on a 0–10 scale with 144 subsets of six and 96 subsets of eight muscle groups tested unilaterally. Expert consensus was used to rank the best abbreviated MMT subsets for face validity and ease of assessment. Results The Total, Proximal and best MMT subsets had excellent internal reliability (rs:Total MMT 0.91–0.98), and consistency (Cronbach’s α 0.78–0.97). Inter- and intra-rater reliability were acceptable (Kendall’s W 0.68–0.76; rs 0.84–0.95). MMT subset scores correlated highly with Total and Proximal MMT scores and with the Childhood Myositis Assessment Scale, and correlated moderately with physician global activity, functional disability, magnetic resonance imaging, axial and distal MMT scores and, in adults, with creatine kinase. The standardized response mean for Total MMT was 0.56 in juveniles and 0.75 in adults. Consensus was reached to use a subset of eight muscles (neck flexors, deltoids, biceps, wrist extensors, gluteus maximus and medius, quadriceps and ankle dorsiflexors) that performed as well as the Total and Proximal MMT, and had good face validity and ease of assessment. Conclusions These findings aid in standardizing the use of MMT for assessing strength as an outcome measure for myositis. PMID:20391500

Label-free haemogram using wavelength modulated Raman spectroscopy for identifying immune-cell subset

NASA Astrophysics Data System (ADS)

Ashok, Praveen C.; Praveen, Bavishna B.; Campbell, Elaine C.; Dholakia, Kishan; Powis, Simon J.

2014-03-01

Leucocytes in the blood of mammals form a powerful protective system against a wide range of dangerous pathogens. There are several types of immune cells that has specific role in the whole immune system. The number and type of immune cells alter in the disease state and identifying the type of immune cell provides information about a person's state of health. There are several immune cell subsets that are essentially morphologically identical and require external labeling to enable discrimination. Here we demonstrate the feasibility of using Wavelength Modulated Raman Spectroscopy (WMRS) with suitable machine learning algorithms as a label-free method to distinguish between different closely lying immune cell subset. Principal Component Analysis (PCA) was performed on WMRS data from single cells, obtained using confocal Raman microscopy for feature reduction, followed by Support Vector Machine (SVM) for binary discrimination of various cell subset, which yielded an accuracy >85%. The method was successful in discriminating between untouched and unfixed purified populations of CD4+CD3+ and CD8+CD3+ T lymphocyte subsets, and CD56+CD3- natural killer cells with a high degree of specificity. It was also proved sensitive enough to identify unique Raman signatures that allow clear discrimination between dendritic cell subsets, comprising CD303+CD45+ plasmacytoid and CD1c+CD141+ myeloid dendritic cells. The results of this study clearly show that WMRS is highly sensitive and can distinguish between cell types that are morphologically identical.

A high level interface to SCOP and ASTRAL implemented in python.

PubMed

Casbon, James A; Crooks, Gavin E; Saqi, Mansoor A S

2006-01-10

Benchmarking algorithms in structural bioinformatics often involves the construction of datasets of proteins with given sequence and structural properties. The SCOP database is a manually curated structural classification which groups together proteins on the basis of structural similarity. The ASTRAL compendium provides non redundant subsets of SCOP domains on the basis of sequence similarity such that no two domains in a given subset share more than a defined degree of sequence similarity. Taken together these two resources provide a 'ground truth' for assessing structural bioinformatics algorithms. We present a small and easy to use API written in python to enable construction of datasets from these resources. We have designed a set of python modules to provide an abstraction of the SCOP and ASTRAL databases. The modules are designed to work as part of the Biopython distribution. Python users can now manipulate and use the SCOP hierarchy from within python programs, and use ASTRAL to return sequences of domains in SCOP, as well as clustered representations of SCOP from ASTRAL. The modules make the analysis and generation of datasets for use in structural genomics easier and more principled.

Ensemble LUT classification for degraded document enhancement

NASA Astrophysics Data System (ADS)

Obafemi-Ajayi, Tayo; Agam, Gady; Frieder, Ophir

2008-01-01

The fast evolution of scanning and computing technologies have led to the creation of large collections of scanned paper documents. Examples of such collections include historical collections, legal depositories, medical archives, and business archives. Moreover, in many situations such as legal litigation and security investigations scanned collections are being used to facilitate systematic exploration of the data. It is almost always the case that scanned documents suffer from some form of degradation. Large degradations make documents hard to read and substantially deteriorate the performance of automated document processing systems. Enhancement of degraded document images is normally performed assuming global degradation models. When the degradation is large, global degradation models do not perform well. In contrast, we propose to estimate local degradation models and use them in enhancing degraded document images. Using a semi-automated enhancement system we have labeled a subset of the Frieder diaries collection.1 This labeled subset was then used to train an ensemble classifier. The component classifiers are based on lookup tables (LUT) in conjunction with the approximated nearest neighbor algorithm. The resulting algorithm is highly effcient. Experimental evaluation results are provided using the Frieder diaries collection.1

A method of 3D object recognition and localization in a cloud of points

NASA Astrophysics Data System (ADS)

Bielicki, Jerzy; Sitnik, Robert

2013-12-01

The proposed method given in this article is prepared for analysis of data in the form of cloud of points directly from 3D measurements. It is designed for use in the end-user applications that can directly be integrated with 3D scanning software. The method utilizes locally calculated feature vectors (FVs) in point cloud data. Recognition is based on comparison of the analyzed scene with reference object library. A global descriptor in the form of a set of spatially distributed FVs is created for each reference model. During the detection process, correlation of subsets of reference FVs with FVs calculated in the scene is computed. Features utilized in the algorithm are based on parameters, which qualitatively estimate mean and Gaussian curvatures. Replacement of differentiation with averaging in the curvatures estimation makes the algorithm more resistant to discontinuities and poor quality of the input data. Utilization of the FV subsets allows to detect partially occluded and cluttered objects in the scene, while additional spatial information maintains false positive rate at a reasonably low level.

When the bell tolls on Bell's palsy: finding occult malignancy in acute-onset facial paralysis.

PubMed

Quesnel, Alicia M; Lindsay, Robin W; Hadlock, Tessa A

2010-01-01

This study reports 4 cases of occult parotid malignancy presenting with sudden-onset facial paralysis to demonstrate that failure to regain tone 6 months after onset distinguishes these patients from Bell's palsy patients with delayed recovery and to propose a diagnostic algorithm for this subset of patients. A case series of 4 patients with occult parotid malignancies presenting with acute-onset unilateral facial paralysis is reported. Initial imaging on all 4 patients did not demonstrate a parotid mass. Diagnostic delays ranged from 7 to 36 months from time of onset of facial paralysis to time of diagnosis of parotid malignancy. Additional physical examination findings, especially failure to regain tone, as well as properly protocolled radiologic studies reviewed with dedicated head and neck radiologists, were helpful in arriving at the diagnosis. An algorithm to minimize diagnostic delays in this subset of acute facial paralysis patients is presented. Careful attention to facial tone, in addition to movement, is important in the diagnostic evaluation of acute-onset facial paralysis. Copyright 2010 Elsevier Inc. All rights reserved.

Developing a radiomics framework for classifying non-small cell lung carcinoma subtypes

NASA Astrophysics Data System (ADS)

Yu, Dongdong; Zang, Yali; Dong, Di; Zhou, Mu; Gevaert, Olivier; Fang, Mengjie; Shi, Jingyun; Tian, Jie

2017-03-01

Patient-targeted treatment of non-small cell lung carcinoma (NSCLC) has been well documented according to the histologic subtypes over the past decade. In parallel, recent development of quantitative image biomarkers has recently been highlighted as important diagnostic tools to facilitate histological subtype classification. In this study, we present a radiomics analysis that classifies the adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). We extract 52-dimensional, CT-based features (7 statistical features and 45 image texture features) to represent each nodule. We evaluate our approach on a clinical dataset including 324 ADCs and 110 SqCCs patients with CT image scans. Classification of these features is performed with four different machine-learning classifiers including Support Vector Machines with Radial Basis Function kernel (RBF-SVM), Random forest (RF), K-nearest neighbor (KNN), and RUSBoost algorithms. To improve the classifiers' performance, optimal feature subset is selected from the original feature set by using an iterative forward inclusion and backward eliminating algorithm. Extensive experimental results demonstrate that radiomics features achieve encouraging classification results on both complete feature set (AUC=0.89) and optimal feature subset (AUC=0.91).

Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching.

PubMed

Du, Pan; Kibbe, Warren A; Lin, Simon M

2006-09-01

A major problem for current peak detection algorithms is that noise in mass spectrometry (MS) spectra gives rise to a high rate of false positives. The false positive rate is especially problematic in detecting peaks with low amplitudes. Usually, various baseline correction algorithms and smoothing methods are applied before attempting peak detection. This approach is very sensitive to the amount of smoothing and aggressiveness of the baseline correction, which contribute to making peak detection results inconsistent between runs, instrumentation and analysis methods. Most peak detection algorithms simply identify peaks based on amplitude, ignoring the additional information present in the shape of the peaks in a spectrum. In our experience, 'true' peaks have characteristic shapes, and providing a shape-matching function that provides a 'goodness of fit' coefficient should provide a more robust peak identification method. Based on these observations, a continuous wavelet transform (CWT)-based peak detection algorithm has been devised that identifies peaks with different scales and amplitudes. By transforming the spectrum into wavelet space, the pattern-matching problem is simplified and in addition provides a powerful technique for identifying and separating the signal from the spike noise and colored noise. This transformation, with the additional information provided by the 2D CWT coefficients can greatly enhance the effective signal-to-noise ratio. Furthermore, with this technique no baseline removal or peak smoothing preprocessing steps are required before peak detection, and this improves the robustness of peak detection under a variety of conditions. The algorithm was evaluated with SELDI-TOF spectra with known polypeptide positions. Comparisons with two other popular algorithms were performed. The results show the CWT-based algorithm can identify both strong and weak peaks while keeping false positive rate low. The algorithm is implemented in R and will be included as an open source module in the Bioconductor project.

Modulation of dendritic cell and monocyte subsets in tuberculosis-diabetes co-morbidity upon standard tuberculosis treatment

PubMed Central

Kumar, Nathella Pavan; Moideen, Kadar; Sivakumar, Shanmugam; Menon, Pradeep A; Viswanathan, Vijay; Kornfeld, Hardy; Babu, Subash

2016-01-01

Type 2 diabetes mellitus (DM) is a major risk factor for the development of active pulmonary tuberculosis (PTB), with development of DM pandemic in countries where tuberculosis (TB) is also endemic. However, the effect of anti-TB treatment on the changes in dentritic cell (DC) and monocyte subset phenotype in TB-DM co-morbidity is not well understood. In this study, we characterized the frequency of DC and monocyte subsets in individuals with PTB with (PTB-DM) or without coincident diabetes mellitus (PTB-NDM) before, during and after completion of anti-TB treatment. PTB-DM is characterized by diminished frequencies of plasmacytoid and myeloid DCs and classical and intermediate monocytes at baseline and 2 months of anti-TB treatment but not following 6 months of treatment completion in comparison to PTB-NDM. DC and monocyte subsets exhibit significant but borderline correlation with fasting blood glucose and glycated hemoglobin levels. Finally, while minor changes in the DC and monocyte compartment were observed at 2 months of treatment, significantly increased frequencies of plasmacytoid and myeloid DCs and classical and intermediate monocytes were observed at the successful completion of anti-TB treatment. Our data show that coincident diabetes alters the frequencies of innate subset distribution of DC and monocytes in TB-DM co-morbidity and suggests that most of these changes are reversible following anti-TB therapy. PMID:27865391

Geographical traceability of wild Boletus edulis based on data fusion of FT-MIR and ICP-AES coupled with data mining methods (SVM)

NASA Astrophysics Data System (ADS)

Li, Yun; Zhang, Ji; Li, Tao; Liu, Honggao; Li, Jieqing; Wang, Yuanzhong

2017-04-01

In this work, the data fusion strategy of Fourier transform mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES) was used in combination with Support Vector Machine (SVM) to determine the geographic origin of Boletus edulis collected from nine regions of Yunnan Province in China. Firstly, competitive adaptive reweighted sampling (CARS) was used for selecting an optimal combination of key wavenumbers of second derivative FT-MIR spectra, and thirteen elements were sorted with variable importance in projection (VIP) scores. Secondly, thirteen subsets of multi-elements with the best VIP score were generated and each subset was used to fuse with FT-MIR. Finally, the classification models were established by SVM, and the combination of parameter C and γ (gamma) of SVM models was calculated by the approaches of grid search (GS) and genetic algorithm (GA). The results showed that both GS-SVM and GA-SVM models achieved good performances based on the #9 subset and the prediction accuracy in calibration and validation sets of the two models were 81.40% and 90.91%, correspondingly. In conclusion, it indicated that the data fusion strategy of FT-MIR and ICP-AES coupled with the algorithm of SVM can be used as a reliable tool for accurate identification of B. edulis, and it can provide a useful way of thinking for the quality control of edible mushrooms.

Geographical traceability of wild Boletus edulis based on data fusion of FT-MIR and ICP-AES coupled with data mining methods (SVM).

PubMed

Li, Yun; Zhang, Ji; Li, Tao; Liu, Honggao; Li, Jieqing; Wang, Yuanzhong

2017-04-15

In this work, the data fusion strategy of Fourier transform mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES) was used in combination with Support Vector Machine (SVM) to determine the geographic origin of Boletus edulis collected from nine regions of Yunnan Province in China. Firstly, competitive adaptive reweighted sampling (CARS) was used for selecting an optimal combination of key wavenumbers of second derivative FT-MIR spectra, and thirteen elements were sorted with variable importance in projection (VIP) scores. Secondly, thirteen subsets of multi-elements with the best VIP score were generated and each subset was used to fuse with FT-MIR. Finally, the classification models were established by SVM, and the combination of parameter C and γ (gamma) of SVM models was calculated by the approaches of grid search (GS) and genetic algorithm (GA). The results showed that both GS-SVM and GA-SVM models achieved good performances based on the #9 subset and the prediction accuracy in calibration and validation sets of the two models were 81.40% and 90.91%, correspondingly. In conclusion, it indicated that the data fusion strategy of FT-MIR and ICP-AES coupled with the algorithm of SVM can be used as a reliable tool for accurate identification of B. edulis, and it can provide a useful way of thinking for the quality control of edible mushrooms. Copyright © 2017. Published by Elsevier B.V.

Efficacy and safety of flavocoxid compared with naproxen in subjects with osteoarthritis of the knee- a subset analysis.

PubMed

Levy, Robert; Khokhlov, Alexander; Kopenkin, Sergey; Bart, Boris; Ermolova, Tatiana; Kantemirova, Raiasa; Mazurov, Vadim; Bell, Marjorie; Caldron, Paul; Pillai, Lakshmi; Burnett, Bruce

2010-12-01

twice-daily flavocoxid, a cyclooxygenase and 5-lipoxygenase inhibitor with potent antioxidant activity of botanical origin, was evaluated for 12 weeks in a randomized, double-blind, active-comparator study against naproxen in 220 subjects with moderate-severe osteoarthritis (OA) of the knee. As previously reported, both groups noted a significant reduction in the signs and symptoms of OA with no detectable differences in efficacy between the groups when the entire intent-to-treat population was considered. This post-hoc analysis compares the efficacy of flavocoxid to naproxen in different subsets of patients, specifically those related to age, gender, and disease severity as reported at baseline for individual response parameters. in the original randomized, double-blind study, 220 subjects were assigned to receive either flavocoxid (500 mg twice daily) or naproxen (500 mg twice daily) for 12 weeks. In this subgroup analysis, primary outcome measures including the Western Ontario and McMaster Universities OA index and subscales, timed walk, and secondary efficacy variables, including investigator global assessment for disease and global response to treatment, subject visual analog scale for discomfort, overall disease activity, global response to treatment, index joint tenderness and mobility, were evaluated for differing trends between the study groups. subset analyses revealed some statistically significant differences and some notable trends in favor of the flavocoxid group. These trends became stronger the longer the subjects continued on therapy. These observations were specifically noted in older subjects (>60 years), males and in subjects with milder disease, particularly those with lower subject global assessment of disease activity and investigator global assessment for disease and faster walking times at baseline. initial analysis of the entire intent-to-treat population revealed that flavocoxid was as effective as naproxen in managing the signs and symptoms of OA of the knee. Detailed analyses of subject subsets demonstrated distinct trends in favor of flavocoxid for specific groups of subjects.

Novel probabilistic neuroclassifier

NASA Astrophysics Data System (ADS)

Hong, Jiang; Serpen, Gursel

2003-09-01

A novel probabilistic potential function neural network classifier algorithm to deal with classes which are multi-modally distributed and formed from sets of disjoint pattern clusters is proposed in this paper. The proposed classifier has a number of desirable properties which distinguish it from other neural network classifiers. A complete description of the algorithm in terms of its architecture and the pseudocode is presented. Simulation analysis of the newly proposed neuro-classifier algorithm on a set of benchmark problems is presented. Benchmark problems tested include IRIS, Sonar, Vowel Recognition, Two-Spiral, Wisconsin Breast Cancer, Cleveland Heart Disease and Thyroid Gland Disease. Simulation results indicate that the proposed neuro-classifier performs consistently better for a subset of problems for which other neural classifiers perform relatively poorly.

A fast bottom-up algorithm for computing the cut sets of noncoherent fault trees

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

Corynen, G.C.

1987-11-01

An efficient procedure for finding the cut sets of large fault trees has been developed. Designed to address coherent or noncoherent systems, dependent events, shared or common-cause events, the method - called SHORTCUT - is based on a fast algorithm for transforming a noncoherent tree into a quasi-coherent tree (COHERE), and on a new algorithm for reducing cut sets (SUBSET). To assure sufficient clarity and precision, the procedure is discussed in the language of simple sets, which is also developed in this report. Although the new method has not yet been fully implemented on the computer, we report theoretical worst-casemore » estimates of its computational complexity. 12 refs., 10 figs.« less

Efficient Approximation Algorithms for Weighted $b$-Matching

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

Khan, Arif; Pothen, Alex; Mostofa Ali Patwary, Md.

2016-01-01

We describe a half-approximation algorithm, b-Suitor, for computing a b-Matching of maximum weight in a graph with weights on the edges. b-Matching is a generalization of the well-known Matching problem in graphs, where the objective is to choose a subset of M edges in the graph such that at most a specified number b(v) of edges in M are incident on each vertex v. Subject to this restriction we maximize the sum of the weights of the edges in M. We prove that the b-Suitor algorithm computes the same b-Matching as the one obtained by the greedy algorithm for themore » problem. We implement the algorithm on serial and shared-memory parallel processors, and compare its performance against a collection of approximation algorithms that have been proposed for the Matching problem. Our results show that the b-Suitor algorithm outperforms the Greedy and Locally Dominant edge algorithms by one to two orders of magnitude on a serial processor. The b-Suitor algorithm has a high degree of concurrency, and it scales well up to 240 threads on a shared memory multiprocessor. The b-Suitor algorithm outperforms the Locally Dominant edge algorithm by a factor of fourteen on 16 cores of an Intel Xeon multiprocessor.« less

Designing basin-customized combined drought indices via feature extraction

NASA Astrophysics Data System (ADS)

Zaniolo, Marta; Giuliani, Matteo; Castelletti, Andrea

2017-04-01

The socio-economic costs of drought are progressively increasing worldwide due to the undergoing alteration of hydro-meteorological regimes induced by climate change. Although drought management is largely studied in the literature, most of the traditional drought indexes fail in detecting critical events in highly regulated systems, which generally rely on ad-hoc formulations and cannot be generalized to different context. In this study, we contribute a novel framework for the design of a basin-customized drought index. This index represents a surrogate of the state of the basin and is computed by combining the available information about the water available in the system to reproduce a representative target variable for the drought condition of the basin (e.g., water deficit). To select the relevant variables and how to combine them, we use an advanced feature extraction algorithm called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS). The W-QEISS algorithm relies on a multi-objective evolutionary algorithm to find Pareto-efficient subsets of variables by maximizing the wrapper accuracy, minimizing the number of selected variables (cardinality) and optimizing relevance and redundancy of the subset. The accuracy objective is evaluated trough the calibration of a pre-defined model (i.e., an extreme learning machine) of the water deficit for each candidate subset of variables, with the index selected from the resulting solutions identifying a suitable compromise between accuracy, cardinality, relevance, and redundancy. The proposed methodology is tested in the case study of Lake Como in northern Italy, a regulated lake mainly operated for irrigation supply to four downstream agricultural districts. In the absence of an institutional drought monitoring system, we constructed the combined index using all the hydrological variables from the existing monitoring system as well as the most common drought indicators at multiple time aggregations. The soil moisture deficit in the root zone computed by a distributed-parameter water balance model of the agricultural districts is used as target variable. Numerical results show that our framework succeeds in constructing a combined drought index that reproduces the soil moisture deficit. Moreover, this index represents a valuable information for supporting appropriate drought management strategies, including the possibility of directly informing the lake operations about the drought conditions and improve the overall reliability of the irrigation supply system.

Dynamic partitioning for hybrid simulation of the bistable HIV-1 transactivation network.

PubMed

Griffith, Mark; Courtney, Tod; Peccoud, Jean; Sanders, William H

2006-11-15

The stochastic kinetics of a well-mixed chemical system, governed by the chemical Master equation, can be simulated using the exact methods of Gillespie. However, these methods do not scale well as systems become more complex and larger models are built to include reactions with widely varying rates, since the computational burden of simulation increases with the number of reaction events. Continuous models may provide an approximate solution and are computationally less costly, but they fail to capture the stochastic behavior of small populations of macromolecules. In this article we present a hybrid simulation algorithm that dynamically partitions the system into subsets of continuous and discrete reactions, approximates the continuous reactions deterministically as a system of ordinary differential equations (ODE) and uses a Monte Carlo method for generating discrete reaction events according to a time-dependent propensity. Our approach to partitioning is improved such that we dynamically partition the system of reactions, based on a threshold relative to the distribution of propensities in the discrete subset. We have implemented the hybrid algorithm in an extensible framework, utilizing two rigorous ODE solvers to approximate the continuous reactions, and use an example model to illustrate the accuracy and potential speedup of the algorithm when compared with exact stochastic simulation. Software and benchmark models used for this publication can be made available upon request from the authors.

Registration of interferometric SAR images

NASA Technical Reports Server (NTRS)

Lin, Qian; Vesecky, John F.; Zebker, Howard A.

1992-01-01

Interferometric synthetic aperture radar (INSAR) is a new way of performing topography mapping. Among the factors critical to mapping accuracy is the registration of the complex SAR images from repeated orbits. A new algorithm for registering interferometric SAR images is presented. A new figure of merit, the average fluctuation function of the phase difference image, is proposed to evaluate the fringe pattern quality. The process of adjusting the registration parameters according to the fringe pattern quality is optimized through a downhill simplex minimization algorithm. The results of applying the proposed algorithm to register two pairs of Seasat SAR images with a short baseline (75 m) and a long baseline (500 m) are shown. It is found that the average fluctuation function is a very stable measure of fringe pattern quality allowing very accurate registration.

Short-cut Methods versus Rigorous Methods for Performance-evaluation of Distillation Configurations

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

Ramapriya, Gautham Madenoor; Selvarajah, Ajiththaa; Jimenez Cucaita, Luis Eduardo

Here, this study demonstrates the efficacy of a short-cut method such as the Global Minimization Algorithm (GMA), that uses assumptions of ideal mixtures, constant molar overflow (CMO) and pinched columns, in pruning the search-space of distillation column configurations for zeotropic multicomponent separation, to provide a small subset of attractive configurations with low minimum heat duties. The short-cut method, due to its simplifying assumptions, is computationally efficient, yet reliable in identifying the small subset of useful configurations for further detailed process evaluation. This two-tier approach allows expedient search of the configuration space containing hundreds to thousands of candidate configurations for amore » given application.« less

Short-cut Methods versus Rigorous Methods for Performance-evaluation of Distillation Configurations

DOE PAGES

Ramapriya, Gautham Madenoor; Selvarajah, Ajiththaa; Jimenez Cucaita, Luis Eduardo; ...

2018-05-17

Here, this study demonstrates the efficacy of a short-cut method such as the Global Minimization Algorithm (GMA), that uses assumptions of ideal mixtures, constant molar overflow (CMO) and pinched columns, in pruning the search-space of distillation column configurations for zeotropic multicomponent separation, to provide a small subset of attractive configurations with low minimum heat duties. The short-cut method, due to its simplifying assumptions, is computationally efficient, yet reliable in identifying the small subset of useful configurations for further detailed process evaluation. This two-tier approach allows expedient search of the configuration space containing hundreds to thousands of candidate configurations for amore » given application.« less

geoknife: Reproducible web-processing of large gridded datasets

USGS Publications Warehouse

Read, Jordan S.; Walker, Jordan I.; Appling, Alison P.; Blodgett, David L.; Read, Emily K.; Winslow, Luke A.

2016-01-01

Geoprocessing of large gridded data according to overlap with irregular landscape features is common to many large-scale ecological analyses. The geoknife R package was created to facilitate reproducible analyses of gridded datasets found on the U.S. Geological Survey Geo Data Portal web application or elsewhere, using a web-enabled workflow that eliminates the need to download and store large datasets that are reliably hosted on the Internet. The package provides access to several data subset and summarization algorithms that are available on remote web processing servers. Outputs from geoknife include spatial and temporal data subsets, spatially-averaged time series values filtered by user-specified areas of interest, and categorical coverage fractions for various land-use types.

Identity and Diversity of Human Peripheral Th and T Regulatory Cells Defined by Single-Cell Mass Cytometry.

PubMed

Kunicki, Matthew A; Amaya Hernandez, Laura C; Davis, Kara L; Bacchetta, Rosa; Roncarolo, Maria-Grazia

2018-01-01

Human CD3 + CD4 + Th cells, FOXP3 + T regulatory (Treg) cells, and T regulatory type 1 (Tr1) cells are essential for ensuring peripheral immune response and tolerance, but the diversity of Th, Treg, and Tr1 cell subsets has not been fully characterized. Independent functional characterization of human Th1, Th2, Th17, T follicular helper (Tfh), Treg, and Tr1 cells has helped to define unique surface molecules, transcription factors, and signaling profiles for each subset. However, the adequacy of these markers to recapitulate the whole CD3 + CD4 + T cell compartment remains questionable. In this study, we examined CD3 + CD4 + T cell populations by single-cell mass cytometry. We characterize the CD3 + CD4 + Th, Treg, and Tr1 cell populations simultaneously across 23 memory T cell-associated surface and intracellular molecules. High-dimensional analysis identified several new subsets, in addition to the already defined CD3 + CD4 + Th, Treg, and Tr1 cell populations, for a total of 11 Th cell, 4 Treg, and 1 Tr1 cell subsets. Some of these subsets share markers previously thought to be selective for Treg, Th1, Th2, Th17, and Tfh cells, including CD194 (CCR4) + FOXP3 + Treg and CD183 (CXCR3) + T-bet + Th17 cell subsets. Unsupervised clustering displayed a phenotypic organization of CD3 + CD4 + T cells that confirmed their diversity but showed interrelation between the different subsets, including similarity between Th1-Th2-Tfh cell populations and Th17 cells, as well as similarity of Th2 cells with Treg cells. In conclusion, the use of single-cell mass cytometry provides a systems-level characterization of CD3 + CD4 + T cells in healthy human blood, which represents an important baseline reference to investigate abnormalities of different subsets in immune-mediated pathologies. Copyright © 2017 by The American Association of Immunologists, Inc.

Linear and nonlinear pattern selection in Rayleigh-Benard stability problems

NASA Technical Reports Server (NTRS)

Davis, Sanford S.

1993-01-01

A new algorithm is introduced to compute finite-amplitude states using primitive variables for Rayleigh-Benard convection on relatively coarse meshes. The algorithm is based on a finite-difference matrix-splitting approach that separates all physical and dimensional effects into one-dimensional subsets. The nonlinear pattern selection process for steady convection in an air-filled square cavity with insulated side walls is investigated for Rayleigh numbers up to 20,000. The internalization of disturbances that evolve into coherent patterns is investigated and transient solutions from linear perturbation theory are compared with and contrasted to the full numerical simulations.

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

PubMed Central

Nickolov, Radoslav Z; Milanov, Valentin B

2007-01-01

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

Random Partition Distribution Indexed by Pairwise Information

PubMed Central

Dahl, David B.; Day, Ryan; Tsai, Jerry W.

2017-01-01

We propose a random partition distribution indexed by pairwise similarity information such that partitions compatible with the similarities are given more probability. The use of pairwise similarities, in the form of distances, is common in some clustering algorithms (e.g., hierarchical clustering), but we show how to use this type of information to define a prior partition distribution for flexible Bayesian modeling. A defining feature of the distribution is that it allocates probability among partitions within a given number of subsets, but it does not shift probability among sets of partitions with different numbers of subsets. Our distribution places more probability on partitions that group similar items yet keeps the total probability of partitions with a given number of subsets constant. The distribution of the number of subsets (and its moments) is available in closed-form and is not a function of the similarities. Our formulation has an explicit probability mass function (with a tractable normalizing constant) so the full suite of MCMC methods may be used for posterior inference. We compare our distribution with several existing partition distributions, showing that our formulation has attractive properties. We provide three demonstrations to highlight the features and relative performance of our distribution. PMID:29276318

Precollege Predictors of Incapacitated Rape Among Female Students in Their First Year of College

PubMed Central

Carey, Kate B.; Durney, Sarah E.; Shepardson, Robyn L.; Carey, Michael P.

2015-01-01

Objective: The first year of college is an important transitional period for young adults; it is also a period associated with elevated risk of incapacitated rape (IR) for female students. The goal of this study was to identify prospective risk factors associated with experiencing attempted or completed IR during the first year of college. Method: Using a prospective cohort design, we recruited 483 incoming first-year female students. Participants completed a baseline survey and three follow-up surveys over the next year. At baseline, we assessed precollege alcohol use, marijuana use, sexual behavior, and, for the subset of sexually experienced participants, sex-related alcohol expectancies. At the baseline and all follow-ups, we assessed sexual victimization. Results: Approximately 1 in 6 women (18%) reported IR before entering college, and 15% reported IR during their first year of college. In bivariate analyses, precollege IR history, precollege heavy episodic drinking, number of precollege sexual partners, and sex-related alcohol expectancies (enhancement and disinhibition) predicted first-year IR. In multivariate analyses with the entire sample, only precollege IR (odds ratio = 4.98, p < .001) remained a significant predictor. However, among the subset of sexually experienced participants, both enhancement expectancies and precollege IR predicted IR during the study year. Conclusions: IR during the first year of college is independently associated with a history of IR and with expectancies about alcohol’s enhancement of sexual experience. Alcohol expectancies are a modifiable risk factor that may be a promising target for prevention efforts. PMID:26562590

Current Status of Japan's Activity for GPM/DPR and Global Rainfall Map algorithm development

NASA Astrophysics Data System (ADS)

Kachi, M.; Kubota, T.; Yoshida, N.; Kida, S.; Oki, R.; Iguchi, T.; Nakamura, K.

2012-04-01

The Global Precipitation Measurement (GPM) mission is composed of two categories of satellites; 1) a Tropical Rainfall Measuring Mission (TRMM)-like non-sun-synchronous orbit satellite (GPM Core Observatory); and 2) constellation of satellites carrying microwave radiometer instruments. The GPM Core Observatory carries the Dual-frequency Precipitation Radar (DPR), which is being developed by the Japan Aerospace Exploration Agency (JAXA) and the National Institute of Information and Communications Technology (NICT), and microwave radiometer provided by the National Aeronautics and Space Administration (NASA). GPM Core Observatory will be launched in February 2014, and development of algorithms is underway. DPR Level 1 algorithm, which provides DPR L1B product including received power, will be developed by the JAXA. The first version was submitted in March 2011. Development of the second version of DPR L1B algorithm (Version 2) will complete in March 2012. Version 2 algorithm includes all basic functions, preliminary database, HDF5 I/F, and minimum error handling. Pre-launch code will be developed by the end of October 2012. DPR Level 2 algorithm has been developing by the DPR Algorithm Team led by Japan, which is under the NASA-JAXA Joint Algorithm Team. The first version of GPM/DPR Level-2 Algorithm Theoretical Basis Document was completed on November 2010. The second version, "Baseline code", was completed in January 2012. Baseline code includes main module, and eight basic sub-modules (Preparation module, Vertical Profile module, Classification module, SRT module, DSD module, Solver module, Input module, and Output module.) The Level-2 algorithms will provide KuPR only products, KaPR only products, and Dual-frequency Precipitation products, with estimated precipitation rate, radar reflectivity, and precipitation information such as drop size distribution and bright band height. It is important to develop algorithm applicable to both TRMM/PR and KuPR in order to produce long-term continuous data set. Pre-launch code will be developed by autumn 2012. Global Rainfall Map algorithm has been developed by the Global Rainfall Map Algorithm Development Team in Japan. The algorithm succeeded heritages of the Global Satellite Mapping for Precipitation (GSMaP) project between 2002 and 2007, and near-real-time version operating at JAXA since 2007. "Baseline code" used current operational GSMaP code (V5.222,) and development completed in January 2012. Pre-launch code will be developed by autumn 2012, including update of database for rain type classification and rain/no-rain classification, and introduction of rain-gauge correction.

An OMIC biomarker detection algorithm TriVote and its application in methylomic biomarker detection.

PubMed

Xu, Cheng; Liu, Jiamei; Yang, Weifeng; Shu, Yayun; Wei, Zhipeng; Zheng, Weiwei; Feng, Xin; Zhou, Fengfeng

2018-04-01

Transcriptomic and methylomic patterns represent two major OMIC data sources impacted by both inheritable genetic information and environmental factors, and have been widely used as disease diagnosis and prognosis biomarkers. Modern transcriptomic and methylomic profiling technologies detect the status of tens of thousands or even millions of probing residues in the human genome, and introduce a major computational challenge for the existing feature selection algorithms. This study proposes a three-step feature selection algorithm, TriVote, to detect a subset of transcriptomic or methylomic residues with highly accurate binary classification performance. TriVote outperforms both filter and wrapper feature selection algorithms with both higher classification accuracy and smaller feature number on 17 transcriptomes and two methylomes. Biological functions of the methylome biomarkers detected by TriVote were discussed for their disease associations. An easy-to-use Python package is also released to facilitate the further applications.

A novel clinical decision support system using improved adaptive genetic algorithm for the assessment of fetal well-being.

PubMed

Ravindran, Sindhu; Jambek, Asral Bahari; Muthusamy, Hariharan; Neoh, Siew-Chin

2015-01-01

A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm.

Energy-Efficient Routing and Spectrum Assignment Algorithm with Physical-Layer Impairments Constraint in Flexible Optical Networks

NASA Astrophysics Data System (ADS)

Zhao, Jijun; Zhang, Nawa; Ren, Danping; Hu, Jinhua

2017-12-01

The recently proposed flexible optical network can provide more efficient accommodation of multiple data rates than the current wavelength-routed optical networks. Meanwhile, the energy efficiency has also been a hot topic because of the serious energy consumption problem. In this paper, the energy efficiency problem of flexible optical networks with physical-layer impairments constraint is studied. We propose a combined impairment-aware and energy-efficient routing and spectrum assignment (RSA) algorithm based on the link availability, in which the impact of power consumption minimization on signal quality is considered. By applying the proposed algorithm, the connection requests are established on a subset of network topology, reducing the number of transitions from sleep to active state. The simulation results demonstrate that our proposed algorithm can improve the energy efficiency and spectrum resources utilization with the acceptable blocking probability and average delay.

Selected-node stochastic simulation algorithm

NASA Astrophysics Data System (ADS)

Duso, Lorenzo; Zechner, Christoph

2018-04-01

Stochastic simulations of biochemical networks are of vital importance for understanding complex dynamics in cells and tissues. However, existing methods to perform such simulations are associated with computational difficulties and addressing those remains a daunting challenge to the present. Here we introduce the selected-node stochastic simulation algorithm (snSSA), which allows us to exclusively simulate an arbitrary, selected subset of molecular species of a possibly large and complex reaction network. The algorithm is based on an analytical elimination of chemical species, thereby avoiding explicit simulation of the associated chemical events. These species are instead described continuously in terms of statistical moments derived from a stochastic filtering equation, resulting in a substantial speedup when compared to Gillespie's stochastic simulation algorithm (SSA). Moreover, we show that statistics obtained via snSSA profit from a variance reduction, which can significantly lower the number of Monte Carlo samples needed to achieve a certain performance. We demonstrate the algorithm using several biological case studies for which the simulation time could be reduced by orders of magnitude.

Multi-task feature selection in microarray data by binary integer programming.

PubMed

Lan, Liang; Vucetic, Slobodan

2013-12-20

A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples. In this paper, we propose a novel feature filter algorithm to select the feature subset with maximal discriminative power and minimal redundancy by solving a quadratic objective function with binary integer constraints. To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied. The proposed feature selection algorithm was extended to solve multi-task microarray classification problems. We compared the single-task version of the proposed feature selection algorithm with 9 existing feature selection methods on 4 benchmark microarray data sets. The empirical results show that the proposed method achieved the most accurate predictions overall. We also evaluated the multi-task version of the proposed algorithm on 8 multi-task microarray datasets. The multi-task feature selection algorithm resulted in significantly higher accuracy than when using the single-task feature selection methods.

Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

PubMed

Li, Yang; Li, Guoqing; Wang, Zhenhao

2015-01-01

In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.

An efficient parallel-processing method for transposing large matrices in place.

PubMed

Portnoff, M R

1999-01-01

We have developed an efficient algorithm for transposing large matrices in place. The algorithm is efficient because data are accessed either sequentially in blocks or randomly within blocks small enough to fit in cache, and because the same indexing calculations are shared among identical procedures operating on independent subsets of the data. This inherent parallelism makes the method well suited for a multiprocessor computing environment. The algorithm is easy to implement because the same two procedures are applied to the data in various groupings to carry out the complete transpose operation. Using only a single processor, we have demonstrated nearly an order of magnitude increase in speed over the previously published algorithm by Gate and Twigg for transposing a large rectangular matrix in place. With multiple processors operating in parallel, the processing speed increases almost linearly with the number of processors. A simplified version of the algorithm for square matrices is presented as well as an extension for matrices large enough to require virtual memory.

Army Sustainability Report 2010

DTIC Science & Technology

2011-09-01

a vastly complex concept: it is an organizing principle that factors mission, environment, community and economic benefit into each of its...Report on a minimum of 10 performance indicators, including at least one from each of : economic , social and environmental Report on a minimum...FY09 in a partial performance baseline. It is based on a subset of the economic , environmental and social responsibility performance

Health evaluation of Columbian white-tailed deer on Julia Butler Hansen Refuge for the Columbian white-tailed deer

USGS Publications Warehouse

Creekmore, Terry E.; Glaser, Linda C.

1999-01-01

The objectives of this study were to: (1) gather baseline physiologic data on a subset of the population, (2) evaluate the data to determine the health of the animals sampled, (3) if possible, identify causes of poor health and, ( 4) provide refuge personnel with information that will aid them in managing the population.

Three consecutive days of interval runs to exhaustion affects lymphocyte subset apoptosis and migration.

PubMed

Navalta, James W; Tibana, Ramires Alsamir; Fedor, Elizabeth A; Vieira, Amilton; Prestes, Jonato

2014-01-01

This investigation assessed the lymphocyte subset response to three days of intermittent run exercise to exhaustion. Twelve healthy college-aged males (n = 8) and females (n = 4) (age = 26 ± 4 years; height = 170.2 ± 10 cm; body mass = 75 ± 18 kg) completed an exertion test (maximal running speed and VO2max) and later performed three consecutive days of an intermittent run protocol to exhaustion (30 sec at maximal running speed and 30 sec at half of the maximal running speed). Blood was collected before exercise (PRE) and immediately following the treadmill bout (POST) each day. When the absolute change from baseline was evaluated (i. e., Δ baseline), a significant change in CD4+ and CD8+ for CX3CR1 cells was observed by completion of the third day. Significant changes in both apoptosis and migration were observed following two consecutive days in CD19+ lymphocytes, and the influence of apoptosis persisted following the third day. Given these lymphocyte responses, it is recommended that a rest day be incorporated following two consecutive days of a high-intensity intermittent run program to minimize immune cell modulations and reduce potential susceptibility.

A novel statistical approach shows evidence for multi-system physiological dysregulation during aging.

PubMed

Cohen, Alan A; Milot, Emmanuel; Yong, Jian; Seplaki, Christopher L; Fülöp, Tamàs; Bandeen-Roche, Karen; Fried, Linda P

2013-03-01

Previous studies have identified many biomarkers that are associated with aging and related outcomes, but the relevance of these markers for underlying processes and their relationship to hypothesized systemic dysregulation is not clear. We address this gap by presenting a novel method for measuring dysregulation via the joint distribution of multiple biomarkers and assessing associations of dysregulation with age and mortality. Using longitudinal data from the Women's Health and Aging Study, we selected a 14-marker subset from 63 blood measures: those that diverged from the baseline population mean with age. For the 14 markers and all combinatorial sub-subsets we calculated a multivariate distance called the Mahalanobis distance (MHBD) for all observations, indicating how "strange" each individual's biomarker profile was relative to the baseline population mean. In most models, MHBD correlated positively with age, MHBD increased within individuals over time, and higher MHBD predicted higher risk of subsequent mortality. Predictive power increased as more variables were incorporated into the calculation of MHBD. Biomarkers from multiple systems were implicated. These results support hypotheses of simultaneous dysregulation in multiple systems and confirm the need for longitudinal, multivariate approaches to understanding biomarkers in aging. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Online damage detection using recursive principal component analysis and recursive condition indicators

NASA Astrophysics Data System (ADS)

Krishnan, M.; Bhowmik, B.; Tiwari, A. K.; Hazra, B.

2017-08-01

In this paper, a novel baseline free approach for continuous online damage detection of multi degree of freedom vibrating structures using recursive principal component analysis (RPCA) in conjunction with online damage indicators is proposed. In this method, the acceleration data is used to obtain recursive proper orthogonal modes in online using the rank-one perturbation method, and subsequently utilized to detect the change in the dynamic behavior of the vibrating system from its pristine state to contiguous linear/nonlinear-states that indicate damage. The RPCA algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants, using the rank-one perturbation method. An online condition indicator (CI) based on the L2 norm of the error between actual response and the response projected using recursive eigenvector matrix updates over successive iterations is proposed. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data. The proposed CI, named recursive residual error, is also adopted for simultaneous spatio-temporal damage detection. Numerical simulations performed on five-degree of freedom nonlinear system under white noise and El Centro excitations, with different levels of nonlinearity simulating the damage scenarios, demonstrate the robustness of the proposed algorithm. Successful results obtained from practical case studies involving experiments performed on a cantilever beam subjected to earthquake excitation, for full sensors and underdetermined cases; and data from recorded responses of the UCLA Factor building (full data and its subset) demonstrate the efficacy of the proposed methodology as an ideal candidate for real-time, reference free structural health monitoring.

An Evolutionary Algorithm for Feature Subset Selection in Hard Disk Drive Failure Prediction

ERIC Educational Resources Information Center

Bhasin, Harpreet

2011-01-01

Hard disk drives are used in everyday life to store critical data. Although they are reliable, failure of a hard disk drive can be catastrophic, especially in applications like medicine, banking, air traffic control systems, missile guidance systems, computer numerical controlled machines, and more. The use of Self-Monitoring, Analysis and…

Water quality parameter measurement using spectral signatures

NASA Technical Reports Server (NTRS)

White, P. E.

1973-01-01

Regression analysis is applied to the problem of measuring water quality parameters from remote sensing spectral signature data. The equations necessary to perform regression analysis are presented and methods of testing the strength and reliability of a regression are described. An efficient algorithm for selecting an optimal subset of the independent variables available for a regression is also presented.

Feature selection with harmony search.

PubMed

Diao, Ren; Shen, Qiang

2012-12-01

Many search strategies have been exploited for the task of feature selection (FS), in an effort to identify more compact and better quality subsets. Such work typically involves the use of greedy hill climbing (HC), or nature-inspired heuristics, in order to discover the optimal solution without going through exhaustive search. In this paper, a novel FS approach based on harmony search (HS) is presented. It is a general approach that can be used in conjunction with many subset evaluation techniques. The simplicity of HS is exploited to reduce the overall complexity of the search process. The proposed approach is able to escape from local solutions and identify multiple solutions owing to the stochastic nature of HS. Additional parameter control schemes are introduced to reduce the effort and impact of parameter configuration. These can be further combined with the iterative refinement strategy, tailored to enforce the discovery of quality subsets. The resulting approach is compared with those that rely on HC, genetic algorithms, and particle swarm optimization, accompanied by in-depth studies of the suggested improvements.

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

PubMed

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.

The Cross-Entropy Based Multi-Filter Ensemble Method for Gene Selection.

PubMed

Sun, Yingqiang; Lu, Chengbo; Li, Xiaobo

2018-05-17

The gene expression profile has the characteristics of a high dimension, low sample, and continuous type, and it is a great challenge to use gene expression profile data for the classification of tumor samples. This paper proposes a cross-entropy based multi-filter ensemble (CEMFE) method for microarray data classification. Firstly, multiple filters are used to select the microarray data in order to obtain a plurality of the pre-selected feature subsets with a different classification ability. The top N genes with the highest rank of each subset are integrated so as to form a new data set. Secondly, the cross-entropy algorithm is used to remove the redundant data in the data set. Finally, the wrapper method, which is based on forward feature selection, is used to select the best feature subset. The experimental results show that the proposed method is more efficient than other gene selection methods and that it can achieve a higher classification accuracy under fewer characteristic genes.

On the complexity and approximability of some Euclidean optimal summing problems

NASA Astrophysics Data System (ADS)

Eremeev, A. V.; Kel'manov, A. V.; Pyatkin, A. V.

2016-10-01

The complexity status of several well-known discrete optimization problems with the direction of optimization switching from maximum to minimum is analyzed. The task is to find a subset of a finite set of Euclidean points (vectors). In these problems, the objective functions depend either only on the norm of the sum of the elements from the subset or on this norm and the cardinality of the subset. It is proved that, if the dimension of the space is a part of the input, then all these problems are strongly NP-hard. Additionally, it is shown that, if the space dimension is fixed, then all the problems are NP-hard even for dimension 2 (on a plane) and there are no approximation algorithms with a guaranteed accuracy bound for them unless P = NP. It is shown that, if the coordinates of the input points are integer, then all the problems can be solved in pseudopolynomial time in the case of a fixed space dimension.

Incorporating HYPR de-noising within iterative PET reconstruction (HYPR-OSEM)

NASA Astrophysics Data System (ADS)

(Kevin Cheng, Ju-Chieh; Matthews, Julian; Sossi, Vesna; Anton-Rodriguez, Jose; Salomon, André; Boellaard, Ronald

2017-08-01

HighlY constrained back-PRojection (HYPR) is a post-processing de-noising technique originally developed for time-resolved magnetic resonance imaging. It has been recently applied to dynamic imaging for positron emission tomography and shown promising results. In this work, we have developed an iterative reconstruction algorithm (HYPR-OSEM) which improves the signal-to-noise ratio (SNR) in static imaging (i.e. single frame reconstruction) by incorporating HYPR de-noising directly within the ordered subsets expectation maximization (OSEM) algorithm. The proposed HYPR operator in this work operates on the target image(s) from each subset of OSEM and uses the sum of the preceding subset images as the composite which is updated every iteration. Three strategies were used to apply the HYPR operator in OSEM: (i) within the image space modeling component of the system matrix in forward-projection only, (ii) within the image space modeling component in both forward-projection and back-projection, and (iii) on the image estimate after the OSEM update for each subset thus generating three forms: (i) HYPR-F-OSEM, (ii) HYPR-FB-OSEM, and (iii) HYPR-AU-OSEM. Resolution and contrast phantom simulations with various sizes of hot and cold regions as well as experimental phantom and patient data were used to evaluate the performance of the three forms of HYPR-OSEM, and the results were compared to OSEM with and without a post reconstruction filter. It was observed that the convergence in contrast recovery coefficients (CRC) obtained from all forms of HYPR-OSEM was slower than that obtained from OSEM. Nevertheless, HYPR-OSEM improved SNR without degrading accuracy in terms of resolution and contrast. It achieved better accuracy in CRC at equivalent noise level and better precision than OSEM and better accuracy than filtered OSEM in general. In addition, HYPR-AU-OSEM has been determined to be the more effective form of HYPR-OSEM in terms of accuracy and precision based on the studies conducted in this work.

Adaptive Baseline Enhances EM-Based Policy Search: Validation in a View-Based Positioning Task of a Smartphone Balancer

PubMed Central

Wang, Jiexin; Uchibe, Eiji; Doya, Kenji

2017-01-01

EM-based policy search methods estimate a lower bound of the expected return from the histories of episodes and iteratively update the policy parameters using the maximum of a lower bound of expected return, which makes gradient calculation and learning rate tuning unnecessary. Previous algorithms like Policy learning by Weighting Exploration with the Returns, Fitness Expectation Maximization, and EM-based Policy Hyperparameter Exploration implemented the mechanisms to discard useless low-return episodes either implicitly or using a fixed baseline determined by the experimenter. In this paper, we propose an adaptive baseline method to discard worse samples from the reward history and examine different baselines, including the mean, and multiples of SDs from the mean. The simulation results of benchmark tasks of pendulum swing up and cart-pole balancing, and standing up and balancing of a two-wheeled smartphone robot showed improved performances. We further implemented the adaptive baseline with mean in our two-wheeled smartphone robot hardware to test its performance in the standing up and balancing task, and a view-based approaching task. Our results showed that with adaptive baseline, the method outperformed the previous algorithms and achieved faster, and more precise behaviors at a higher successful rate. PMID:28167910

Validation of the alternating conditional estimation algorithm for estimation of flexible extensions of Cox's proportional hazards model with nonlinear constraints on the parameters.

PubMed

Wynant, Willy; Abrahamowicz, Michal

2016-11-01

Standard optimization algorithms for maximizing likelihood may not be applicable to the estimation of those flexible multivariable models that are nonlinear in their parameters. For applications where the model's structure permits separating estimation of mutually exclusive subsets of parameters into distinct steps, we propose the alternating conditional estimation (ACE) algorithm. We validate the algorithm, in simulations, for estimation of two flexible extensions of Cox's proportional hazards model where the standard maximum partial likelihood estimation does not apply, with simultaneous modeling of (1) nonlinear and time-dependent effects of continuous covariates on the hazard, and (2) nonlinear interaction and main effects of the same variable. We also apply the algorithm in real-life analyses to estimate nonlinear and time-dependent effects of prognostic factors for mortality in colon cancer. Analyses of both simulated and real-life data illustrate good statistical properties of the ACE algorithm and its ability to yield new potentially useful insights about the data structure. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Can the BMS Algorithm Decode Up to \\lfloor \\frac{d_G-g-1}{2}\\rfloor Errors? Yes, but with Some Additional Remarks

NASA Astrophysics Data System (ADS)

Sakata, Shojiro; Fujisawa, Masaya

It is a well-known fact [7], [9] that the BMS algorithm with majority voting can decode up to half the Feng-Rao designed distance dFR. Since dFR is not smaller than the Goppa designed distance dG, that algorithm can correct up to \\lfloor \\frac{d_G-1}{2}\\rfloor errors. On the other hand, it has been considered to be evident that the original BMS algorithm (without voting) [1], [2] can correct up to \\lfloor \\frac{d_G-g-1}{2}\\rfloor errors similarly to the basic algorithm by Skorobogatov-Vladut. But, is it true? In this short paper, we show that it is true, although we need a few remarks and some additional procedures for determining the Groebner basis of the error locator ideal exactly. In fact, as the basic algorithm gives a set of polynomials whose zero set contains the error locators as a subset, it cannot always give the exact error locators, unless the syndrome equation is solved to find the error values in addition.

Metal-induced streak artifact reduction using iterative reconstruction algorithms in x-ray computed tomography image of the dentoalveolar region.

PubMed

Dong, Jian; Hayakawa, Yoshihiko; Kannenberg, Sven; Kober, Cornelia

2013-02-01

The objective of this study was to reduce metal-induced streak artifact on oral and maxillofacial x-ray computed tomography (CT) images by developing the fast statistical image reconstruction system using iterative reconstruction algorithms. Adjacent CT images often depict similar anatomical structures in thin slices. So, first, images were reconstructed using the same projection data of an artifact-free image. Second, images were processed by the successive iterative restoration method where projection data were generated from reconstructed image in sequence. Besides the maximum likelihood-expectation maximization algorithm, the ordered subset-expectation maximization algorithm (OS-EM) was examined. Also, small region of interest (ROI) setting and reverse processing were applied for improving performance. Both algorithms reduced artifacts instead of slightly decreasing gray levels. The OS-EM and small ROI reduced the processing duration without apparent detriments. Sequential and reverse processing did not show apparent effects. Two alternatives in iterative reconstruction methods were effective for artifact reduction. The OS-EM algorithm and small ROI setting improved the performance. Copyright © 2012 Elsevier Inc. All rights reserved.

Parallel algorithm of VLBI software correlator under multiprocessor environment

NASA Astrophysics Data System (ADS)

Zheng, Weimin; Zhang, Dong

2007-11-01

The correlator is the key signal processing equipment of a Very Lone Baseline Interferometry (VLBI) synthetic aperture telescope. It receives the mass data collected by the VLBI observatories and produces the visibility function of the target, which can be used to spacecraft position, baseline length measurement, synthesis imaging, and other scientific applications. VLBI data correlation is a task of data intensive and computation intensive. This paper presents the algorithms of two parallel software correlators under multiprocessor environments. A near real-time correlator for spacecraft tracking adopts the pipelining and thread-parallel technology, and runs on the SMP (Symmetric Multiple Processor) servers. Another high speed prototype correlator using the mixed Pthreads and MPI (Massage Passing Interface) parallel algorithm is realized on a small Beowulf cluster platform. Both correlators have the characteristic of flexible structure, scalability, and with 10-station data correlating abilities.

Computationally efficient real-time interpolation algorithm for non-uniform sampled biosignals

PubMed Central

Eftekhar, Amir; Kindt, Wilko; Constandinou, Timothy G.

2016-01-01

This Letter presents a novel, computationally efficient interpolation method that has been optimised for use in electrocardiogram baseline drift removal. In the authors’ previous Letter three isoelectric baseline points per heartbeat are detected, and here utilised as interpolation points. As an extension from linear interpolation, their algorithm segments the interpolation interval and utilises different piecewise linear equations. Thus, the algorithm produces a linear curvature that is computationally efficient while interpolating non-uniform samples. The proposed algorithm is tested using sinusoids with different fundamental frequencies from 0.05 to 0.7 Hz and also validated with real baseline wander data acquired from the Massachusetts Institute of Technology University and Boston's Beth Israel Hospital (MIT-BIH) Noise Stress Database. The synthetic data results show an root mean square (RMS) error of 0.9 μV (mean), 0.63 μV (median) and 0.6 μV (standard deviation) per heartbeat on a 1 mVp–p 0.1 Hz sinusoid. On real data, they obtain an RMS error of 10.9 μV (mean), 8.5 μV (median) and 9.0 μV (standard deviation) per heartbeat. Cubic spline interpolation and linear interpolation on the other hand shows 10.7 μV, 11.6 μV (mean), 7.8 μV, 8.9 μV (median) and 9.8 μV, 9.3 μV (standard deviation) per heartbeat. PMID:27382478

Computationally efficient real-time interpolation algorithm for non-uniform sampled biosignals.

PubMed

Guven, Onur; Eftekhar, Amir; Kindt, Wilko; Constandinou, Timothy G

2016-06-01

This Letter presents a novel, computationally efficient interpolation method that has been optimised for use in electrocardiogram baseline drift removal. In the authors' previous Letter three isoelectric baseline points per heartbeat are detected, and here utilised as interpolation points. As an extension from linear interpolation, their algorithm segments the interpolation interval and utilises different piecewise linear equations. Thus, the algorithm produces a linear curvature that is computationally efficient while interpolating non-uniform samples. The proposed algorithm is tested using sinusoids with different fundamental frequencies from 0.05 to 0.7 Hz and also validated with real baseline wander data acquired from the Massachusetts Institute of Technology University and Boston's Beth Israel Hospital (MIT-BIH) Noise Stress Database. The synthetic data results show an root mean square (RMS) error of 0.9 μV (mean), 0.63 μV (median) and 0.6 μV (standard deviation) per heartbeat on a 1 mVp-p 0.1 Hz sinusoid. On real data, they obtain an RMS error of 10.9 μV (mean), 8.5 μV (median) and 9.0 μV (standard deviation) per heartbeat. Cubic spline interpolation and linear interpolation on the other hand shows 10.7 μV, 11.6 μV (mean), 7.8 μV, 8.9 μV (median) and 9.8 μV, 9.3 μV (standard deviation) per heartbeat.

Evaluation of current tropospheric mapping functions by Deep Space Network very long baseline interferometry

NASA Technical Reports Server (NTRS)

Sovers, O. J.; Lanyi, G. E.

1994-01-01

To compare the validity of current algorithms that map zenith tropospheric delay to arbitrary elevation angles, 10 different tropospheric mapping functions are used to analyze the current data base of Deep Space Network Mark 3 intercontinental very long baseline interferometric (VLBI) data. This analysis serves as a stringent test because of the high proportion of low-elevation observations necessitated by the extremely long baselines. Postfit delay and delay-rate residuals are examined, as well as the scatter of baseline lengths about the time-linear model that characterizes tectonic motion. Among the functions that utilize surface meteorological data as input parameters, the Lanyi 1984 mapping shows the best performance both for residuals and baselines, through the 1985 Davis function is statistically nearly identical. The next best performance is shown by the recent function of Niell, which is based on an examination of global atmospheric characteristics as a function of season and uses no weather data at the time of the measurements. The Niell function shows a slight improvement in residuals relative to Lanyi, but also an increase in baseline scatter that is significant for the California-Spain baseline. Two variants of the Chao mapping function, as well as the Chao tables used with the interpolation algorithm employed in the Orbit Determination Program software, show substandard behavior for both VLBI residuals and baseline scatter. The length of the California-Australia baseline (10,600 km) in the VLBI solution can vary by as much as 5 to 10 cm for the 10 mapping functions.

In Silico Identification Software (ISIS): A Machine Learning Approach to Tandem Mass Spectral Identification of Lipids

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

Kangas, Lars J.; Metz, Thomas O.; Isaac, Georgis

2012-05-15

Liquid chromatography-mass spectrometry-based metabolomics has gained importance in the life sciences, yet it is not supported by software tools for high throughput identification of metabolites based on their fragmentation spectra. An algorithm (ISIS: in silico identification software) and its implementation are presented and show great promise in generating in silico spectra of lipids for the purpose of structural identification. Instead of using chemical reaction rate equations or rules-based fragmentation libraries, the algorithm uses machine learning to find accurate bond cleavage rates in a mass spectrometer employing collision-induced dissocia-tion tandem mass spectrometry. A preliminary test of the algorithm with 45 lipidsmore » from a subset of lipid classes shows both high sensitivity and specificity.« less

Genetic Algorithms and Classification Trees in Feature Discovery: Diabetes and the NHANES database

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

Heredia-Langner, Alejandro; Jarman, Kristin H.; Amidan, Brett G.

2013-09-01

This paper presents a feature selection methodology that can be applied to datasets containing a mixture of continuous and categorical variables. Using a Genetic Algorithm (GA), this method explores a dataset and selects a small set of features relevant for the prediction of a binary (1/0) response. Binary classification trees and an objective function based on conditional probabilities are used to measure the fitness of a given subset of features. The method is applied to health data in order to find factors useful for the prediction of diabetes. Results show that our algorithm is capable of narrowing down the setmore » of predictors to around 8 factors that can be validated using reputable medical and public health resources.« less

2006 Interferometry Imaging Beauty Contest

NASA Technical Reports Server (NTRS)

Lawson, Peter R.; Cotton, William D.; Hummel, Christian A.; Ireland, Michael; Monnier, John D.; Thiebaut, Eric; Rengaswamy, Sridharan; Baron, Fabien; Young, John S.; Kraus, Stefan;

Adaptive Two Dimensional RLS (Recursive Least Squares) Algorithms

DTIC Science & Technology

1989-03-01

in Monterey wonderful. IX I. INTRODUCTION Adaptive algorithms have been used successfully for many years in a wide range of digital signal...SIMULATION RESULTS The 2-D FRLS algorithm was tested both on computer-generated data and on digitized images. For a baseline reference the 2-D L:rv1S...Alexander, S. T. Adaptivt Signal Processing: Theory and Applications. Springer- Verlag, New York. 1986. 7. Bellanger, Maurice G. Adaptive Digital

Comparison of three algorithms for initiation and titration of insulin glargine in insulin-naive patients with type 2 diabetes mellitus.

PubMed

Dailey, George; Aurand, Lisa; Stewart, John; Ameer, Barbara; Zhou, Rong

2014-03-01

Several titration algorithms can be used to adjust insulin dose and attain blood glucose targets. We compared clinical outcomes using three initiation and titration algorithms for insulin glargine in insulin-naive patients with type 2 diabetes mellitus (T2DM); focusing on those receiving both metformin and sulfonylurea (SU) at baseline. This was a pooled analysis of patient-level data from prospective, randomized, controlled 24-week trials. Patients received algorithm 1 (1 IU increase once daily, if fasting plasma glucose [FPG] > target), algorithm 2 (2 IU increase every 3 days, if FPG > target), or algorithm 3 (treat-to-target, generally 2-8 IU increase weekly based on 2-day mean FPG levels). Glycemic control, insulin dose, and hypoglycemic events were compared between algorithms. Overall, 1380 patients were included. In patients receiving metformin and SU at baseline, there were no significant differences in glycemic control between algorithms. Weight-adjusted dose was higher for algorithm 2 vs algorithms 1 and 3 (P = 0.0037 and P < 0.0001, respectively), though results were not significantly different when adjusted for reductions in HbA1c (0.36 IU/kg, 0.43 IU/kg, and 0.31 IU/kg for algorithms 1, 2, and 3, respectively). Yearly hypoglycemic event rates (confirmed blood glucose <56 mg/dL) were higher for algorithm 3 than algorithms 1 (P = 0.0003) and 2 (P < 0.0001). Three algorithms for initiation and titration of insulin glargine in patients with T2DM resulted in similar levels of glycemic control, with lower rates of hypoglycemia for patients treated using simpler algorithms 1 and 2. © 2013 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and Wiley Publishing Asia Pty Ltd.

Subset selective search on the basis of color and preview.

PubMed

Donk, Mieke

2017-01-01

In the preview paradigm observers are presented with one set of elements (the irrelevant set) followed by the addition of a second set among which the target is presented (the relevant set). Search efficiency in such a preview condition has been demonstrated to be higher than that in a full-baseline condition in which both sets are simultaneously presented, suggesting that a preview of the irrelevant set reduces its influence on the search process. However, numbers of irrelevant and relevant elements are typically not independently manipulated. Moreover, subset selective search also occurs when both sets are presented simultaneously but differ in color. The aim of the present study was to investigate how numbers of irrelevant and relevant elements contribute to preview search in the absence and presence of a color difference between subsets. In two experiments it was demonstrated that a preview reduced the influence of the number of irrelevant elements in the absence but not in the presence of a color difference between subsets. In the presence of a color difference, a preview lowered the effect of the number of relevant elements but only when the target was defined by a unique feature within the relevant set (Experiment 1); when the target was defined by a conjunction of features (Experiment 2), search efficiency as a function of the number of relevant elements was not modulated by a preview. Together the results are in line with the idea that subset selective search is based on different simultaneously operating mechanisms.

PGA/MOEAD: a preference-guided evolutionary algorithm for multi-objective decision-making problems with interval-valued fuzzy preferences

NASA Astrophysics Data System (ADS)

Luo, Bin; Lin, Lin; Zhong, ShiSheng

2018-02-01

In this research, we propose a preference-guided optimisation algorithm for multi-criteria decision-making (MCDM) problems with interval-valued fuzzy preferences. The interval-valued fuzzy preferences are decomposed into a series of precise and evenly distributed preference-vectors (reference directions) regarding the objectives to be optimised on the basis of uniform design strategy firstly. Then the preference information is further incorporated into the preference-vectors based on the boundary intersection approach, meanwhile, the MCDM problem with interval-valued fuzzy preferences is reformulated into a series of single-objective optimisation sub-problems (each sub-problem corresponds to a decomposed preference-vector). Finally, a preference-guided optimisation algorithm based on MOEA/D (multi-objective evolutionary algorithm based on decomposition) is proposed to solve the sub-problems in a single run. The proposed algorithm incorporates the preference-vectors within the optimisation process for guiding the search procedure towards a more promising subset of the efficient solutions matching the interval-valued fuzzy preferences. In particular, lots of test instances and an engineering application are employed to validate the performance of the proposed algorithm, and the results demonstrate the effectiveness and feasibility of the algorithm.

A binary search approach to whole-genome data analysis.

PubMed

Brodsky, Leonid; Kogan, Simon; Benjacob, Eshel; Nevo, Eviatar

2010-09-28

A sequence analysis-oriented binary search-like algorithm was transformed to a sensitive and accurate analysis tool for processing whole-genome data. The advantage of the algorithm over previous methods is its ability to detect the margins of both short and long genome fragments, enriched by up-regulated signals, at equal accuracy. The score of an enriched genome fragment reflects the difference between the actual concentration of up-regulated signals in the fragment and the chromosome signal baseline. The "divide-and-conquer"-type algorithm detects a series of nonintersecting fragments of various lengths with locally optimal scores. The procedure is applied to detected fragments in a nested manner by recalculating the lower-than-baseline signals in the chromosome. The algorithm was applied to simulated whole-genome data, and its sensitivity/specificity were compared with those of several alternative algorithms. The algorithm was also tested with four biological tiling array datasets comprising Arabidopsis (i) expression and (ii) histone 3 lysine 27 trimethylation CHIP-on-chip datasets; Saccharomyces cerevisiae (iii) spliced intron data and (iv) chromatin remodeling factor binding sites. The analyses' results demonstrate the power of the algorithm in identifying both the short up-regulated fragments (such as exons and transcription factor binding sites) and the long--even moderately up-regulated zones--at their precise genome margins. The algorithm generates an accurate whole-genome landscape that could be used for cross-comparison of signals across the same genome in evolutionary and general genomic studies.

Coronary artery calcification identification and labeling in low-dose chest CT images

NASA Astrophysics Data System (ADS)

Xie, Yiting; Liu, Shuang; Miller, Albert; Miller, Jeffrey A.; Markowitz, Steven; Akhund, Ali; Reeves, Anthony P.

2017-03-01

A fully automated computer algorithm has been developed to evaluate coronary artery calcification (CAC) from lowdose CT scans. CAC is identified and evaluated in three main coronary artery groups: Left Main and Left Anterior Descending Artery (LM + LAD) CAC, Left Circumflex Artery (LCX) CAC, and Right Coronary Artery (RCA) CAC. The artery labeling is achieved by segmenting all CAC candidates in the heart region and applying geometric constraints on the candidates using locally pre-identified anatomy regions. This algorithm was evaluated on 1,359 low-dose ungated CT scans, in which each artery CAC content was categorically visually scored by a radiologist into none, mild, moderate and extensive. The Spearman correlation coefficient R was used to assess the agreement between three automated CAC scores (Agatston-weighted, volume, and mass) and categorical visual scores. For Agatston-weighted automated scores, R was 0.87 for total CAC, 0.82 for LM + LAD CAC, 0.66 for LCX CAC and 0.72 for RCA CAC; results using volume and mass scores were similar. CAC detection sensitivities were: 0.87 for total, 0.82 for LM + LAD, 0.65 for LCX and 0.74 for RCA. To assess the impact of image noise, the dataset was further partitioned into three subsets based on heart region noise level (low<=80HU, medium=(80HU, 110HU], high>110HU). The low and medium noise subsets had higher sensitivities and correlations than the high noise subset. These results indicate that location specific heart risk assessment is possible from low-dose chest CT images.

Classifier Subset Selection for the Stacked Generalization Method Applied to Emotion Recognition in Speech

PubMed Central

Álvarez, Aitor; Sierra, Basilio; Arruti, Andoni; López-Gil, Juan-Miguel; Garay-Vitoria, Nestor

2015-01-01

In this paper, a new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking), is presented to deal with speech emotion recognition. The new approach consists of an improvement of a bi-level multi-classifier system known as stacking generalization by means of an integration of an estimation of distribution algorithm (EDA) in the first layer to select the optimal subset from the standard base classifiers. The good performance of the proposed new paradigm was demonstrated over different configurations and datasets. First, several CSS stacking classifiers were constructed on the RekEmozio dataset, using some specific standard base classifiers and a total of 123 spectral, quality and prosodic features computed using in-house feature extraction algorithms. These initial CSS stacking classifiers were compared to other multi-classifier systems and the employed standard classifiers built on the same set of speech features. Then, new CSS stacking classifiers were built on RekEmozio using a different set of both acoustic parameters (extended version of the Geneva Minimalistic Acoustic Parameter Set (eGeMAPS)) and standard classifiers and employing the best meta-classifier of the initial experiments. The performance of these two CSS stacking classifiers was evaluated and compared. Finally, the new paradigm was tested on the well-known Berlin Emotional Speech database. We compared the performance of single, standard stacking and CSS stacking systems using the same parametrization of the second phase. All of the classifications were performed at the categorical level, including the six primary emotions plus the neutral one. PMID:26712757

Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes.

PubMed

Fleet, Jamie L; Dixon, Stephanie N; Shariff, Salimah Z; Quinn, Robert R; Nash, Danielle M; Harel, Ziv; Garg, Amit X

2013-04-05

Large, population-based administrative healthcare databases can be used to identify patients with chronic kidney disease (CKD) when serum creatinine laboratory results are unavailable. We examined the validity of algorithms that used combined hospital encounter and physician claims database codes for the detection of CKD in Ontario, Canada. We accrued 123,499 patients over the age of 65 from 2007 to 2010. All patients had a baseline serum creatinine value to estimate glomerular filtration rate (eGFR). We developed an algorithm of physician claims and hospital encounter codes to search administrative databases for the presence of CKD. We determined the sensitivity, specificity, positive and negative predictive values of this algorithm to detect our primary threshold of CKD, an eGFR <45 mL/min per 1.73 m² (15.4% of patients). We also assessed serum creatinine and eGFR values in patients with and without CKD codes (algorithm positive and negative, respectively). Our algorithm required evidence of at least one of eleven CKD codes and 7.7% of patients were algorithm positive. The sensitivity was 32.7% [95% confidence interval: (95% CI): 32.0 to 33.3%]. Sensitivity was lower in women compared to men (25.7 vs. 43.7%; p <0.001) and in the oldest age category (over 80 vs. 66 to 80; 28.4 vs. 37.6 %; p < 0.001). All specificities were over 94%. The positive and negative predictive values were 65.4% (95% CI: 64.4 to 66.3%) and 88.8% (95% CI: 88.6 to 89.0%), respectively. In algorithm positive patients, the median [interquartile range (IQR)] baseline serum creatinine value was 135 μmol/L (106 to 179 μmol/L) compared to 82 μmol/L (69 to 98 μmol/L) for algorithm negative patients. Corresponding eGFR values were 38 mL/min per 1.73 m² (26 to 51 mL/min per 1.73 m²) vs. 69 mL/min per 1.73 m² (56 to 82 mL/min per 1.73 m²), respectively. Patients with CKD as identified by our database algorithm had distinctly higher baseline serum creatinine values and lower eGFR values than those without such codes. However, because of limited sensitivity, the prevalence of CKD was underestimated.

Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes

PubMed Central

2013-01-01

Background Large, population-based administrative healthcare databases can be used to identify patients with chronic kidney disease (CKD) when serum creatinine laboratory results are unavailable. We examined the validity of algorithms that used combined hospital encounter and physician claims database codes for the detection of CKD in Ontario, Canada. Methods We accrued 123,499 patients over the age of 65 from 2007 to 2010. All patients had a baseline serum creatinine value to estimate glomerular filtration rate (eGFR). We developed an algorithm of physician claims and hospital encounter codes to search administrative databases for the presence of CKD. We determined the sensitivity, specificity, positive and negative predictive values of this algorithm to detect our primary threshold of CKD, an eGFR <45 mL/min per 1.73 m2 (15.4% of patients). We also assessed serum creatinine and eGFR values in patients with and without CKD codes (algorithm positive and negative, respectively). Results Our algorithm required evidence of at least one of eleven CKD codes and 7.7% of patients were algorithm positive. The sensitivity was 32.7% [95% confidence interval: (95% CI): 32.0 to 33.3%]. Sensitivity was lower in women compared to men (25.7 vs. 43.7%; p <0.001) and in the oldest age category (over 80 vs. 66 to 80; 28.4 vs. 37.6 %; p < 0.001). All specificities were over 94%. The positive and negative predictive values were 65.4% (95% CI: 64.4 to 66.3%) and 88.8% (95% CI: 88.6 to 89.0%), respectively. In algorithm positive patients, the median [interquartile range (IQR)] baseline serum creatinine value was 135 μmol/L (106 to 179 μmol/L) compared to 82 μmol/L (69 to 98 μmol/L) for algorithm negative patients. Corresponding eGFR values were 38 mL/min per 1.73 m2 (26 to 51 mL/min per 1.73 m2) vs. 69 mL/min per 1.73 m2 (56 to 82 mL/min per 1.73 m2), respectively. Conclusions Patients with CKD as identified by our database algorithm had distinctly higher baseline serum creatinine values and lower eGFR values than those without such codes. However, because of limited sensitivity, the prevalence of CKD was underestimated. PMID:23560464

Discrete Address Beacon System (DABS) Baseline Test and Evaluation.

DTIC Science & Technology

1980-04-01

Organization ReportNo 7. ~/ - 9. PorTorming Organisation Name and Address 10. Work Unit No. (TRALS) Federal Aviation Administration National Aviation...version of the Common International Civil Aviation Organization (ICAO) Data Interchange Network (CIDIN) protocol used in the DABS engineering model. 8. All...grouped into two subsets, one for surveillance data communications and one for Common International Civil Aviation Organization (ICAO) Data Interchange

Goldindec: A Novel Algorithm for Raman Spectrum Baseline Correction

PubMed Central

Liu, Juntao; Sun, Jianyang; Huang, Xiuzhen; Li, Guojun; Liu, Binqiang

2016-01-01

Raman spectra have been widely used in biology, physics, and chemistry and have become an essential tool for the studies of macromolecules. Nevertheless, the raw Raman signal is often obscured by a broad background curve (or baseline) due to the intrinsic fluorescence of the organic molecules, which leads to unpredictable negative effects in quantitative analysis of Raman spectra. Therefore, it is essential to correct this baseline before analyzing raw Raman spectra. Polynomial fitting has proven to be the most convenient and simplest method and has high accuracy. In polynomial fitting, the cost function used and its parameters are crucial. This article proposes a novel iterative algorithm named Goldindec, freely available for noncommercial use as noted in text, with a new cost function that not only conquers the influence of great peaks but also solves the problem of low correction accuracy when there is a high peak number. Goldindec automatically generates parameters from the raw data rather than by empirical choice, as in previous methods. Comparisons with other algorithms on the benchmark data show that Goldindec has a higher accuracy and computational efficiency, and is hardly affected by great peaks, peak number, and wavenumber. PMID:26037638

rhG-CSF in healthy donors: mobilization of peripheral hemopoietic progenitors and effect on peripheral blood leukocytes.

PubMed

Sica, S; Rutella, S; Di Mario, A; Salutari, P; Rumi, C; Ortu la Barbera, E; Etuk, B; Menichella, G; D'Onofrio, G; Leone, G

1996-08-01

Recombinant human granulocyte colony-stimulating factor (rhG-CSF) 16 micrograms/kg/day was given to 9 healthy donors to recruit hemopoietic progenitors (HP) for allogeneic transplantation or donor leukocyte infusion. rhG-CSF was administered s.c. for 5 days. No side effects were encountered except for moderate bone pain and lumbago. Mobilization was effective, reaching a peak median value of 187 x 10(3) CD34+ cells/ml (range 51.2-1127) and 2170 x 10(3) colony-forming units-granulocyte macrophage (CFU-GM)/ml (range 1138-4190). Peak values were obtained at a median of 4 days of rhG-CSF and represented, respectively, a 13-fold and a 37-fold increase from baseline values (p = 0.0007 and p = 0.006). White blood cell (WBC) counts increased 6-fold from baseline values (p < 0.0007) and reached a median peak of 34 x 10(6)/ml (23.5-59). Polymorphonuclear (PMN), and mononuclear (MNC) cells increased 10-fold and 2-fold, respectively (p = 0.0039 and p = 0.0026) and reached a median peak of 32.1 x 10(6)/ml (18.2-52) and 4.42 x 10(6)/ml (3.14-12.42). Absolute lymphocyte and monocyte counts increased at peak day in all donors 1.5-fold and 5.7-fold from baseline values (p = 0.0017 and p = 0.0018). In 7 of 9 donors, lymphocyte subsets were analyzed in detail. CD3+ and CD19+ lymphocytes increased 1.5-fold and 3-fold, respectively (p = 0.032 for both). NK and activated T lymphocytes doubled at a median of 4 days of rhG-CSF (p = 0.032 and p = NS, respectively). Similar changes were observed in lymphocytes collected in leukapheresis product. T helper and T suppressor subsets displayed a similar increase. Thus, besides the anticipated priming effect on HP and PMN, rhG-CSF in healthy donors produced an unexpected and still unexplained modification of lymphocyte subsets in peripheral blood.

Mammographic images segmentation based on chaotic map clustering algorithm

PubMed Central

2014-01-01

Background This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. The chaotic map clustering algorithm is used to group together similar subsets of image pixels resulting in a medically meaningful partition of the mammography. Methods The image is divided into pixels subsets characterized by a set of conveniently chosen features and each of the corresponding points in the feature space is associated to a map. A mutual coupling strength between the maps depending on the associated distance between feature space points is subsequently introduced. On the system of maps, the simulated evolution through chaotic dynamics leads to its natural partitioning, which corresponds to a particular segmentation scheme of the initial mammographic image. Results The system provides a high recognition rate for small mass lesions (about 94% correctly segmented inside the breast) and the reproduction of the shape of regions with denser micro-calcifications in about 2/3 of the cases, while being less effective on identification of larger mass lesions. Conclusions We can summarize our analysis by asserting that due to the particularities of the mammographic images, the chaotic map clustering algorithm should not be used as the sole method of segmentation. It is rather the joint use of this method along with other segmentation techniques that could be successfully used for increasing the segmentation performance and for providing extra information for the subsequent analysis stages such as the classification of the segmented ROI. PMID:24666766

Automation and Preclinical Evaluation of a Dedicated Emission Mammotomography System for Fully 3-D Molecular Breast Imaging

DTIC Science & Technology

2008-10-01

concentrated aqueous 99m Tc and taped to the exterior surface of the breast phantom to act as fiducial markers for registration purposes. Two...34 Physica Medica, vol. 21, pp. 48-55, 2006. [16] H. Erdogan and J. A. Fessler, "Ordered subsets algorithms for transmission tomography," Phys Med Biol

Vector dissimilarity and clustering.

PubMed

Lefkovitch, L P

1991-04-01

Based on the description of objects by m attributes, an m-element vector dissimilarity function is defined that, unlike scalar functions, retains the distinction among attributes. This function, which satisfies the conditions for a metric, allows the definition of betweenness, which can then be used for clustering. Applications to the subset-generation phase of conditional clustering and to nearest-neighbor-type algorithms are described.

Multivariate interactive digital analysis system /MIDAS/ - A new fast multispectral recognition system

NASA Technical Reports Server (NTRS)

Kriegler, F.; Marshall, R.; Lampert, S.; Gordon, M.; Cornell, C.; Kistler, R.

1973-01-01

The MIDAS system is a prototype, multiple-pipeline digital processor mechanizing the multivariate-Gaussian, maximum-likelihood decision algorithm operating at 200,000 pixels/second. It incorporates displays and film printer equipment under control of a general purpose midi-computer and possesses sufficient flexibility that operational versions of the equipment may be subsequently specified as subsets of the system.

Design Patterns to Achieve 300x Speedup for Oceanographic Analytics in the Cloud

NASA Astrophysics Data System (ADS)

Jacob, J. C.; Greguska, F. R., III; Huang, T.; Quach, N.; Wilson, B. D.

2017-12-01

We describe how we achieve super-linear speedup over standard approaches for oceanographic analytics on a cluster computer and the Amazon Web Services (AWS) cloud. NEXUS is an open source platform for big data analytics in the cloud that enables this performance through a combination of horizontally scalable data parallelism with Apache Spark and rapid data search, subset, and retrieval with tiled array storage in cloud-aware NoSQL databases like Solr and Cassandra. NEXUS is the engine behind several public portals at NASA and OceanWorks is a newly funded project for the ocean community that will mature and extend this capability for improved data discovery, subset, quality screening, analysis, matchup of satellite and in situ measurements, and visualization. We review the Python language API for Spark and how to use it to quickly convert existing programs to use Spark to run with cloud-scale parallelism, and discuss strategies to improve performance. We explain how partitioning the data over space, time, or both leads to algorithmic design patterns for Spark analytics that can be applied to many different algorithms. We use NEXUS analytics as examples, including area-averaged time series, time averaged map, and correlation map.

Fuzzy Subspace Clustering

NASA Astrophysics Data System (ADS)

Borgelt, Christian

In clustering we often face the situation that only a subset of the available attributes is relevant for forming clusters, even though this may not be known beforehand. In such cases it is desirable to have a clustering algorithm that automatically weights attributes or even selects a proper subset. In this paper I study such an approach for fuzzy clustering, which is based on the idea to transfer an alternative to the fuzzifier (Klawonn and Höppner, What is fuzzy about fuzzy clustering? Understanding and improving the concept of the fuzzifier, In: Proc. 5th Int. Symp. on Intelligent Data Analysis, 254-264, Springer, Berlin, 2003) to attribute weighting fuzzy clustering (Keller and Klawonn, Int J Uncertain Fuzziness Knowl Based Syst 8:735-746, 2000). In addition, by reformulating Gustafson-Kessel fuzzy clustering, a scheme for weighting and selecting principal axes can be obtained. While in Borgelt (Feature weighting and feature selection in fuzzy clustering, In: Proc. 17th IEEE Int. Conf. on Fuzzy Systems, IEEE Press, Piscataway, NJ, 2008) I already presented such an approach for a global selection of attributes and principal axes, this paper extends it to a cluster-specific selection, thus arriving at a fuzzy subspace clustering algorithm (Parsons, Haque, and Liu, 2004).

Selection of core animals in the Algorithm for Proven and Young using a simulation model.

PubMed

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.

An Iris Segmentation Algorithm based on Edge Orientation for Off-angle Iris Recognition

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

Karakaya, Mahmut; Barstow, Del R; Santos-Villalobos, Hector J

Iris recognition is known as one of the most accurate and reliable biometrics. However, the accuracy of iris recognition systems depends on the quality of data capture and is negatively affected by several factors such as angle, occlusion, and dilation. In this paper, we present a segmentation algorithm for off-angle iris images that uses edge detection, edge elimination, edge classification, and ellipse fitting techniques. In our approach, we first detect all candidate edges in the iris image by using the canny edge detector; this collection contains edges from the iris and pupil boundaries as well as eyelash, eyelids, iris texturemore » etc. Edge orientation is used to eliminate the edges that cannot be part of the iris or pupil. Then, we classify the remaining edge points into two sets as pupil edges and iris edges. Finally, we randomly generate subsets of iris and pupil edge points, fit ellipses for each subset, select ellipses with similar parameters, and average to form the resultant ellipses. Based on the results from real experiments, the proposed method shows effectiveness in segmentation for off-angle iris images.« less

Influence of Iterative Reconstruction Algorithms on PET Image Resolution

NASA Astrophysics Data System (ADS)

Karpetas, G. E.; Michail, C. M.; Fountos, G. P.; Valais, I. G.; Nikolopoulos, D.; Kandarakis, I. S.; Panayiotakis, G. S.

2015-09-01

The aim of the present study was to assess image quality of PET scanners through a thin layer chromatography (TLC) plane source. The source was simulated using a previously validated Monte Carlo model. The model was developed by using the GATE MC package and reconstructed images obtained with the STIR software for tomographic image reconstruction. The simulated PET scanner was the GE DiscoveryST. A plane source consisted of a TLC plate, was simulated by a layer of silica gel on aluminum (Al) foil substrates, immersed in 18F-FDG bath solution (1MBq). Image quality was assessed in terms of the modulation transfer function (MTF). MTF curves were estimated from transverse reconstructed images of the plane source. Images were reconstructed by the maximum likelihood estimation (MLE)-OSMAPOSL, the ordered subsets separable paraboloidal surrogate (OSSPS), the median root prior (MRP) and OSMAPOSL with quadratic prior, algorithms. OSMAPOSL reconstruction was assessed by using fixed subsets and various iterations, as well as by using various beta (hyper) parameter values. MTF values were found to increase with increasing iterations. MTF also improves by using lower beta values. The simulated PET evaluation method, based on the TLC plane source, can be useful in the resolution assessment of PET scanners.

Multimodal biometric approach for cancelable face template generation

NASA Astrophysics Data System (ADS)

Paul, Padma Polash; Gavrilova, Marina

2012-06-01

Due to the rapid growth of biometric technology, template protection becomes crucial to secure integrity of the biometric security system and prevent unauthorized access. Cancelable biometrics is emerging as one of the best solutions to secure the biometric identification and verification system. We present a novel technique for robust cancelable template generation algorithm that takes advantage of the multimodal biometric using feature level fusion. Feature level fusion of different facial features is applied to generate the cancelable template. A proposed algorithm based on the multi-fold random projection and fuzzy communication scheme is used for this purpose. In cancelable template generation, one of the main difficulties is keeping interclass variance of the feature. We have found that interclass variations of the features that are lost during multi fold random projection can be recovered using fusion of different feature subsets and projecting in a new feature domain. Applying the multimodal technique in feature level, we enhance the interclass variability hence improving the performance of the system. We have tested the system for classifier fusion for different feature subset and different cancelable template fusion. Experiments have shown that cancelable template improves the performance of the biometric system compared with the original template.

Non-iterative volumetric particle reconstruction near moving bodies

NASA Astrophysics Data System (ADS)

Mendelson, Leah; Techet, Alexandra

2017-11-01

When multi-camera 3D PIV experiments are performed around a moving body, the body often obscures visibility of regions of interest in the flow field in a subset of cameras. We evaluate the performance of non-iterative particle reconstruction algorithms used for synthetic aperture PIV (SAPIV) in these partially-occluded regions. We show that when partial occlusions are present, the quality and availability of 3D tracer particle information depends on the number of cameras and reconstruction procedure used. Based on these findings, we introduce an improved non-iterative reconstruction routine for SAPIV around bodies. The reconstruction procedure combines binary masks, already required for reconstruction of the body's 3D visual hull, and a minimum line-of-sight algorithm. This approach accounts for partial occlusions without performing separate processing for each possible subset of cameras. We combine this reconstruction procedure with three-dimensional imaging on both sides of the free surface to reveal multi-fin wake interactions generated by a jumping archer fish. Sufficient particle reconstruction in near-body regions is crucial to resolving the wake structures of upstream fins (i.e., dorsal and anal fins) before and during interactions with the caudal tail.

Optimization of a Turboprop UAV for Maximum Loiter and Specific Power Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Dinc, Ali

2016-09-01

In this study, a genuine code was developed for optimization of selected parameters of a turboprop engine for an unmanned aerial vehicle (UAV) by employing elitist genetic algorithm. First, preliminary sizing of a UAV and its turboprop engine was done, by the code in a given mission profile. Secondly, single and multi-objective optimization were done for selected engine parameters to maximize loiter duration of UAV or specific power of engine or both. In single objective optimization, as first case, UAV loiter time was improved with an increase of 17.5% from baseline in given boundaries or constraints of compressor pressure ratio and burner exit temperature. In second case, specific power was enhanced by 12.3% from baseline. In multi-objective optimization case, where previous two objectives are considered together, loiter time and specific power were increased by 14.2% and 9.7% from baseline respectively, for the same constraints.

Automated reduction of sub-millimetre single-dish heterodyne data from the James Clerk Maxwell Telescope using ORAC-DR

NASA Astrophysics Data System (ADS)

Jenness, Tim; Currie, Malcolm J.; Tilanus, Remo P. J.; Cavanagh, Brad; Berry, David S.; Leech, Jamie; Rizzi, Luca

2015-10-01

With the advent of modern multidetector heterodyne instruments that can result in observations generating thousands of spectra per minute it is no longer feasible to reduce these data as individual spectra. We describe the automated data reduction procedure used to generate baselined data cubes from heterodyne data obtained at the James Clerk Maxwell Telescope (JCMT). The system can automatically detect baseline regions in spectra and automatically determine regridding parameters, all without input from a user. Additionally, it can detect and remove spectra suffering from transient interference effects or anomalous baselines. The pipeline is written as a set of recipes using the ORAC-DR pipeline environment with the algorithmic code using Starlink software packages and infrastructure. The algorithms presented here can be applied to other heterodyne array instruments and have been applied to data from historical JCMT heterodyne instrumentation.

The performance of monotonic and new non-monotonic gradient ascent reconstruction algorithms for high-resolution neuroreceptor PET imaging.

PubMed

Angelis, G I; Reader, A J; Kotasidis, F A; Lionheart, W R; Matthews, J C

2011-07-07

Iterative expectation maximization (EM) techniques have been extensively used to solve maximum likelihood (ML) problems in positron emission tomography (PET) image reconstruction. Although EM methods offer a robust approach to solving ML problems, they usually suffer from slow convergence rates. The ordered subsets EM (OSEM) algorithm provides significant improvements in the convergence rate, but it can cycle between estimates converging towards the ML solution of each subset. In contrast, gradient-based methods, such as the recently proposed non-monotonic maximum likelihood (NMML) and the more established preconditioned conjugate gradient (PCG), offer a globally convergent, yet equally fast, alternative to OSEM. Reported results showed that NMML provides faster convergence compared to OSEM; however, it has never been compared to other fast gradient-based methods, like PCG. Therefore, in this work we evaluate the performance of two gradient-based methods (NMML and PCG) and investigate their potential as an alternative to the fast and widely used OSEM. All algorithms were evaluated using 2D simulations, as well as a single [(11)C]DASB clinical brain dataset. Results on simulated 2D data show that both PCG and NMML achieve orders of magnitude faster convergence to the ML solution compared to MLEM and exhibit comparable performance to OSEM. Equally fast performance is observed between OSEM and PCG for clinical 3D data, but NMML seems to perform poorly. However, with the addition of a preconditioner term to the gradient direction, the convergence behaviour of NMML can be substantially improved. Although PCG is a fast convergent algorithm, the use of a (bent) line search increases the complexity of the implementation, as well as the computational time involved per iteration. Contrary to previous reports, NMML offers no clear advantage over OSEM or PCG, for noisy PET data. Therefore, we conclude that there is little evidence to replace OSEM as the algorithm of choice for many applications, especially given that in practice convergence is often not desired for algorithms seeking ML estimates.

Automatic classification of protein structures relying on similarities between alignments

PubMed Central

2012-01-01

Background Identification of protein structural cores requires isolation of sets of proteins all sharing a same subset of structural motifs. In the context of an ever growing number of available 3D protein structures, standard and automatic clustering algorithms require adaptations so as to allow for efficient identification of such sets of proteins. Results When considering a pair of 3D structures, they are stated as similar or not according to the local similarities of their matching substructures in a structural alignment. This binary relation can be represented in a graph of similarities where a node represents a 3D protein structure and an edge states that two 3D protein structures are similar. Therefore, classifying proteins into structural families can be viewed as a graph clustering task. Unfortunately, because such a graph encodes only pairwise similarity information, clustering algorithms may include in the same cluster a subset of 3D structures that do not share a common substructure. In order to overcome this drawback we first define a ternary similarity on a triple of 3D structures as a constraint to be satisfied by the graph of similarities. Such a ternary constraint takes into account similarities between pairwise alignments, so as to ensure that the three involved protein structures do have some common substructure. We propose hereunder a modification algorithm that eliminates edges from the original graph of similarities and gives a reduced graph in which no ternary constraints are violated. Our approach is then first to build a graph of similarities, then to reduce the graph according to the modification algorithm, and finally to apply to the reduced graph a standard graph clustering algorithm. Such method was used for classifying ASTRAL-40 non-redundant protein domains, identifying significant pairwise similarities with Yakusa, a program devised for rapid 3D structure alignments. Conclusions We show that filtering similarities prior to standard graph based clustering process by applying ternary similarity constraints i) improves the separation of proteins of different classes and consequently ii) improves the classification quality of standard graph based clustering algorithms according to the reference classification SCOP. PMID:22974051

SU-E-T-252: Developing a Pencil Beam Dose Calculation Algorithm for CyberKnife System

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

Liang, B; Duke University Medical Center, Durham, NC; Liu, B

2015-06-15

Purpose: Currently there are two dose calculation algorithms available in the Cyberknife planning system: ray-tracing and Monte Carlo, which is either not accurate or time-consuming for irregular field shaped by the MLC that was recently introduced. The purpose of this study is to develop a fast and accurate pencil beam dose calculation algorithm which can handle irregular field. Methods: A pencil beam dose calculation algorithm widely used in Linac system is modified. The algorithm models both primary (short range) and scatter (long range) components with a single input parameter: TPR{sub 20}/{sub 10}. The TPR{sub 20}/{sub 20}/{sub 10} value was firstmore » estimated to derive an initial set of pencil beam model parameters (PBMP). The agreement between predicted and measured TPRs for all cones were evaluated using the root mean square of the difference (RMSTPR), which was then minimized by adjusting PBMPs. PBMPs are further tuned to minimize OCR RMS (RMSocr) by focusing at the outfield region. Finally, an arbitrary intensity profile is optimized by minimizing RMSocr difference at infield region. To test model validity, the PBMPs were obtained by fitting to only a subset of cones (4) and applied to all cones (12) for evaluation. Results: With RMS values normalized to the dmax and all cones combined, the average RMSTPR at build-up and descending region is 2.3% and 0.4%, respectively. The RMSocr at infield, penumbra and outfield region is 1.5%, 7.8% and 0.6%, respectively. Average DTA in penumbra region is 0.5mm. There is no trend found in TPR or OCR agreement among cones or depths. Conclusion: We have developed a pencil beam algorithm for Cyberknife system. The prediction agrees well with commissioning data. Only a subset of measurements is needed to derive the model. Further improvements are needed for TPR buildup region and OCR penumbra. Experimental validations on MLC shaped irregular field needs to be performed. This work was partially supported by the National Natural Science Foundation of China (61171005) and the China Scholarship Council (CSC)« less

Additional BCAA-enriched nutrient mixture improves the nutritional condition in cirrhotic patients with hypoalbuminemia despite treatment with regular BCAA granules: A pilot study.

PubMed

Fukui, Aiko; Kawabe, Naoto; Hashimoto, Senju; Murao, Michihito; Nakano, Takuji; Shimazaki, Hiroaki; Kan, Toshiki; Nakaoka, Kazunori; Ohki, Masashi; Takagawa, Yuka; Kamei, Hiroyuki; Yoshioka, Kentaro

2015-07-01

To elucidate the effect of adding branched-chain amino acid (BCAA)-enriched nutrient mixtures in cirrhotic patients with hypoalbuminemia despite the use of BCAA granules. A BCAA-enriched nutrient mixture containing 5.6 g of BCAA and 210 kcal was additionally administered in 40 cirrhotic patients with hypoalbuminemia despite their treatment with BCAA granules containing 12 g of BCAA. Laboratory data were assessed at 6 months before beginning additional therapy, at baseline, and at 6 months after baseline. Serum albumin levels significantly decreased from 6 months before baseline (3.14±0.47 g/dL) to baseline (2.83±0.46 g/dL), despite the treatment with BCAA granules (p<0.001), and tended to increase from baseline to 6 months after baseline (2.95±0.42 g/dL) (p=0.084). In the subset of 23 patients without hepatocellular carcinoma treatments, upper gastrointestinal tract bleeding, or albumin infusion, serum albumin levels significantly increased from baseline (2.93±0.38 g/dL) to 6 months after baseline (3.15±0.34 g/dL) (p=0.014). Additional therapy with BCAA-enriched nutrient mixtures increased serum albumin levels of the cirrhotic patients with hypoalbuminemia despite the treatment with BCAA granules and without hepatocellular carcinoma treatment, upper gastrointestinal tract bleeding, or albumin infusion.

Automatic Correction Algorithm of Hyfrology Feature Attribute in National Geographic Census

NASA Astrophysics Data System (ADS)

Li, C.; Guo, P.; Liu, X.

2017-09-01

A subset of the attributes of hydrologic features data in national geographic census are not clear, the current solution to this problem was through manual filling which is inefficient and liable to mistakes. So this paper proposes an automatic correction algorithm of hydrologic features attribute. Based on the analysis of the structure characteristics and topological relation, we put forward three basic principles of correction which include network proximity, structure robustness and topology ductility. Based on the WJ-III map workstation, we realize the automatic correction of hydrologic features. Finally, practical data is used to validate the method. The results show that our method is highly reasonable and efficient.

Multispectral iris recognition based on group selection and game theory

NASA Astrophysics Data System (ADS)

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

Personality and Career Success: Concurrent and Longitudinal Relations.

PubMed

Sutin, Angelina R; Costa, Paul T; Miech, Richard; Eaton, William W

2009-03-01

The present research addresses the dynamic transaction between extrinsic (occupational prestige, income) and intrinsic (job satisfaction) career success and the Five-Factor Model of personality. Participants (N = 731) completed a comprehensive measure of personality and reported their job title, annual income, and job satisfaction; a subset of these participants (n = 302) provided the same information approximately 10 years later. Measured concurrently, emotionally stable and conscientious participants reported higher incomes and job satisfaction. Longitudinal analyses revealed that, among younger participants, higher income at baseline predicted decreases in Neuroticism and baseline Extraversion predicted increases in income across the 10 years. Results suggest that the mutual influence of career success and personality is limited to income and occurs early in the career.

Development and use of touch-screen audio computer-assisted self-interviewing in a study of American Indians.

PubMed

Edwards, Sandra L; Slattery, Martha L; Murtaugh, Maureen A; Edwards, Roger L; Bryner, James; Pearson, Mindy; Rogers, Amy; Edwards, Alison M; Tom-Orme, Lillian

2007-06-01

This article describes the development and usability of an audio computer-assisted self-interviewing (ACASI) questionnaire created to collect dietary, physical activity, medical history, and other lifestyle data in a population of American Indians. Study participants were part of a cohort of American Indians living in the southwestern United States. Data were collected between March 2004 and July 2005. Information for evaluating questionnaire usability and acceptability was collected from three different sources: baseline study data, auxiliary background data, and a short questionnaire administered to a subset of study participants. For the subset of participants, 39.6% reported not having used a computer in the past year. The ACASI questionnaires were well accepted: 96.0% of the subset of participants reported finding them enjoyable to use, 97.2% reported that they were easy to use, and 82.6% preferred them for future questionnaires. A lower educational level and infrequent computer use in the past year were predictors of having usability trouble. These results indicate that the ACASI questionnaire is both an acceptable and a preferable mode of data collection in this population.

Validation of the Abdominal Pain Index Using a Revised Scoring Method

PubMed Central

Sherman, Amanda L.; Smith, Craig A.; Walker, Lynn S.

2015-01-01

Objective Evaluate the psychometric properties of child- and parent-report versions of the four-item Abdominal Pain Index (API) in children with functional abdominal pain (FAP) and healthy controls, using a revised scoring method that facilitates comparisons of scores across samples and time. Methods Pediatric patients aged 8–18 years with FAP and controls completed the API at baseline (N = 1,967); a subset of their parents (N = 290) completed the API regarding the child’s pain. Subsets of patients completed follow-up assessments at 2 weeks (N = 231), 3 months (N = 330), and 6 months (N = 107). Subsets of both patients (N = 389) and healthy controls (N = 172) completed a long-term follow-up assessment (mean age at follow-up = 20.21 years, SD = 3.75). Results The API demonstrated good concurrent, discriminant, and construct validity, as well as good internal consistency. Conclusion We conclude that the API, using the revised scoring method, is a useful, reliable, and valid measure of abdominal pain severity. PMID:25617048

Unbiased feature selection in learning random forests for high-dimensional data.

PubMed

Nguyen, Thanh-Tung; Huang, Joshua Zhexue; Nguyen, Thuy Thi

2015-01-01

Random forests (RFs) have been widely used as a powerful classification method. However, with the randomization in both bagging samples and feature selection, the trees in the forest tend to select uninformative features for node splitting. This makes RFs have poor accuracy when working with high-dimensional data. Besides that, RFs have bias in the feature selection process where multivalued features are favored. Aiming at debiasing feature selection in RFs, we propose a new RF algorithm, called xRF, to select good features in learning RFs for high-dimensional data. We first remove the uninformative features using p-value assessment, and the subset of unbiased features is then selected based on some statistical measures. This feature subset is then partitioned into two subsets. A feature weighting sampling technique is used to sample features from these two subsets for building trees. This approach enables one to generate more accurate trees, while allowing one to reduce dimensionality and the amount of data needed for learning RFs. An extensive set of experiments has been conducted on 47 high-dimensional real-world datasets including image datasets. The experimental results have shown that RFs with the proposed approach outperformed the existing random forests in increasing the accuracy and the AUC measures.

A Novel Study Paradigm for Long-term Prevention Trials in Alzheimer Disease: The Placebo Group Simulation Approach (PGSA): Application to MCI data from the NACC database.

PubMed

Berres, M; Kukull, W A; Miserez, A R; Monsch, A U; Monsell, S E; Spiegel, R

2014-01-01

The PGSA (Placebo Group Simulation Approach) aims at avoiding problems of sample representativeness and ethical issues typical of placebo-controlled secondary prevention trials with MCI patients. The PGSA uses mathematical modeling to forecast the distribution of quantified outcomes of MCI patient groups based on their own baseline data established at the outset of clinical trials. These forecasted distributions are then compared with the distribution of actual outcomes observed on candidate treatments, thus substituting for a concomitant placebo group. Here we investigate whether a PGSA algorithm that was developed from the MCI population of ADNI 1*, can reliably simulate the distribution of composite neuropsychological outcomes from a larger, independently selected MCI subject sample. Data available from the National Alzheimer's Coordinating Center (NACC) were used. We included 1523 patients with single or multiple domain amnestic mild cognitive impairment (aMCI) and at least two follow-ups after baseline. In order to strengthen the analysis and to verify whether there was a drift over time in the neuropsychological outcomes, the NACC subject sample was split into 3 subsamples of similar size. The previously described PGSA algorithm for the trajectory of a composite neuropsychological test battery (NTB) score was adapted to the test battery used in NACC. Nine demographic, clinical, biological and neuropsychological candidate predictors were included in a mixed model; this model and its error terms were used to simulate trajectories of the adapted NTB. The distributions of empirically observed and simulated data after 1, 2 and 3 years were very similar, with some over-estimation of decline in all 3 subgroups. The by far most important predictor of the NTB trajectories is the baseline NTB score. Other significant predictors are the MMSE baseline score and the interactions of time with ApoE4 and FAQ (functional abilities). These are essentially the same predictors as determined for the original NTB score. An algorithm comprising a small number of baseline variables, notably cognitive performance at baseline, forecasts the group trajectory of cognitive decline in subsequent years with high accuracy. The current analysis of 3 independent subgroups of aMCI patients from the NACC database supports the validity of the PGSA longitudinal algorithm for a NTB. Use of the PGSA in long-term secondary AD prevention trials deserves consideration.

Out-of-Home Placement Decision-Making and Outcomes in Child Welfare: A Longitudinal Study

PubMed Central

McClelland, Gary M.; Weiner, Dana A.; Jordan, Neil; Lyons, John S.

2015-01-01

After children enter the child welfare system, subsequent out-of-home placement decisions and their impact on children’s well-being are complex and under-researched. This study examined two placement decision-making models: a multidisciplinary team approach, and a decision support algorithm using a standardized assessment. Based on 3,911 placement records in the Illinois child welfare system over 4 years, concordant (agreement) and discordant (disagreement) decisions between the two models were compared. Concordant decisions consistently predicted improvement in children’s well-being regardless of placement type. Discordant decisions showed greater variability. In general, placing children in settings less restrictive than the algorithm suggested (“under-placing”) was associated with less severe baseline functioning but also less improvement over time than placing children according to the algorithm. “Over-placing” children in settings more restrictive than the algorithm recommended was associated with more severe baseline functioning but fewer significant results in rate of improvement than predicted by concordant decisions. The importance of placement decision-making on policy, restrictiveness of placement, and delivery of treatments and services in child welfare are discussed. PMID:24677172

Autonomous Navigation by a Mobile Robot

NASA Technical Reports Server (NTRS)

Huntsberger, Terrance; Aghazarian, Hrand

2005-01-01

ROAMAN is a computer program for autonomous navigation of a mobile robot on a long (as much as hundreds of meters) traversal of terrain. Developed for use aboard a robotic vehicle (rover) exploring the surface of a remote planet, ROAMAN could also be adapted to similar use on terrestrial mobile robots. ROAMAN implements a combination of algorithms for (1) long-range path planning based on images acquired by mast-mounted, wide-baseline stereoscopic cameras, and (2) local path planning based on images acquired by body-mounted, narrow-baseline stereoscopic cameras. The long-range path-planning algorithm autonomously generates a series of waypoints that are passed to the local path-planning algorithm, which plans obstacle-avoiding legs between the waypoints. Both the long- and short-range algorithms use an occupancy-grid representation in computations to detect obstacles and plan paths. Maps that are maintained by the long- and short-range portions of the software are not shared because substantial localization errors can accumulate during any long traverse. ROAMAN is not guaranteed to generate an optimal shortest path, but does maintain the safety of the rover.

Right unilateral electroconvulsive therapy does not cause more cognitive impairment than pharmacologic treatment in treatment-resistant bipolar depression: A 6-month randomized controlled trial follow-up study.

PubMed

Bjoerke-Bertheussen, Jeanette; Schoeyen, Helle; Andreassen, Ole A; Malt, Ulrik F; Oedegaard, Ketil J; Morken, Gunnar; Sundet, Kjetil; Vaaler, Arne E; Auestad, Bjoern; Kessler, Ute

2017-12-21

Electroconvulsive therapy is an effective treatment for bipolar depression, but there are concerns about whether it causes long-term neurocognitive impairment. In this multicenter randomized controlled trial, in-patients with treatment-resistant bipolar depression were randomized to either algorithm-based pharmacologic treatment or right unilateral electroconvulsive therapy. After the 6-week treatment period, all of the patients received maintenance pharmacotherapy as recommended by their clinician guided by a relevant treatment algorithm. Patients were assessed at baseline and at 6 months. Neurocognitive functions were assessed using the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery, and autobiographical memory consistency was assessed using the Autobiographical Memory Interview-Short Form. Seventy-three patients entered the trial, of whom 51 and 26 completed neurocognitive assessments at baseline and 6 months, respectively. The MATRICS Consensus Cognitive Battery composite score improved by 4.1 points in both groups (P = .042) from baseline to 6 months (from 40.8 to 44.9 and from 41.9 to 46.0 in the algorithm-based pharmacologic treatment and electroconvulsive therapy groups, respectively). The Autobiographical Memory Interview-Short Form consistency scores were reduced in both groups (72.3% vs 64.3% in the algorithm-based pharmacologic treatment and electroconvulsive therapy groups, respectively; P = .085). This study did not find that right unilateral electroconvulsive therapy caused long-term impairment in neurocognitive functions compared to algorithm-based pharmacologic treatment in bipolar depression as measured using standard neuropsychological tests, but due to the low number of patients in the study the results should be interpreted with caution. ClinicalTrials.gov: NCT00664976. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Effect of bromocriptine-QR therapy on glycemic control in subjects with type 2 diabetes mellitus whose dysglycemia is inadequately controlled on insulin.

PubMed

Chamarthi, Bindu; Cincotta, Anthony H

2017-05-01

The concurrent use of an insulin sensitizer in type 2 diabetes mellitus (T2DM) patients with inadequate glycemic control on basal-bolus insulin may help improve glycemic control while limiting further insulin requirement. Bromocriptine-QR (B-QR), a quick release, sympatholytic, dopamine D2 receptor agonist therapy for T2DM, is a postprandial insulin sensitizer. This study evaluated the effect of B-QR on dysglycemia in T2DM subjects with suboptimal glycemic control on basal-bolus insulin plus metformin. The effect of once-daily morning administration of B-QR on dysglycemia was evaluated in 60 T2DM subjects derived from the Cycloset Safety Trial, with HbA1c >7% on basal-bolus insulin plus metformin at baseline, randomized to B-QR (N = 44) versus placebo (N = 16) and completed 12 weeks of study drug treatment. The analyses also included a subset of subjects on high-dose insulin (total daily insulin dose (TDID) ≥70 units; N = 36: 27 B-QR; 9 placebo). Subjects were well matched at baseline. After 12 weeks of B-QR treatment, mean % HbA1c decreased by -0.73% relative to baseline (p < 0.001) and by -1.13 relative to placebo (p < 0.001). In the high-dose insulin subset, B-QR therapy resulted in % HbA1c reductions of -0.95 and -1.49 relative to baseline (p < 0.001) and placebo (p = 0.001) respectively. Secondary analyses of treatment effect at 24 and 52 weeks demonstrated similar influences of B-QR on HbA1c. The fasting plasma glucose (FPG) and TDID changes within each treatment group were not significant. More subjects achieved HbA1c ≤7 at 12 weeks with B-QR relative to placebo (36.4% B-QR vs 0% placebo, Fisher's exact 2-sided p = 0.003 in the entire cohort and 37% vs 0%, 2-sided p = 0.039 in the high-dose insulin subset). B-QR therapy improves glycemic control in T2DM subjects whose glycemia is poorly controlled on metformin plus basal-bolus insulin, including individuals on high-dose basal-bolus insulin. This glycemic impact occurred without significant change in FPG, suggesting a postprandial glucose lowering mechanism of action. Cycloset Safety Trial registration: ClinicalTrials.gov Identifier: NCT00377676.

An Elegant Sufficiency: Load-Aware Differentiated Scheduling of Data Transfers

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

Kettimuthu, Rajkumar; Vardoyan, Gayane; Agrawal, Gagan

2015-11-15

We investigate the file transfer scheduling problem, where transfers among different endpoints must be scheduled to maximize pertinent metrics. We propose two new algorithms that exploit the fact that the aggregate bandwidth obtained over a network or at a storage system tends to increase with the number of concurrent transfers—but only up to a certain limit. The first algorithm, SEAL, uses runtime information and data-driven models to approximate system load and adapt transfer schedules and concurrency so as to maximize performance while avoiding saturation. We implement this algorithm using GridFTP as the transfer protocol and evaluate it using real transfermore » logs in a production WAN environment. Results show that SEAL can improve average slowdowns and turnaround times by up to 25% and worst-case slowdown and turnaround times by up to 50%, compared with the best-performing baseline scheme. Our second algorithm, STEAL, further leverages user-supplied categorization of transfers as either “interactive” (requiring immediate processing) or “batch” (less time-critical). Results show that STEAL reduces the average slowdown of interactive transfers by 63% compared to the best-performing baseline and by 21% compared to SEAL. For batch transfers, compared to the best-performing baseline, STEAL improves by 18% the utilization of the bandwidth unused by interactive transfers. By elegantly ensuring a sufficient, but not excessive, allocation of concurrency to the right transfers, we significantly improve overall performance despite constraints.« less

Matching CCD images to a stellar catalog using locality-sensitive hashing

NASA Astrophysics Data System (ADS)

Liu, Bo; Yu, Jia-Zong; Peng, Qing-Yu

2018-02-01

The usage of a subset of observed stars in a CCD image to find their corresponding matched stars in a stellar catalog is an important issue in astronomical research. Subgraph isomorphic-based algorithms are the most widely used methods in star catalog matching. When more subgraph features are provided, the CCD images are recognized better. However, when the navigation feature database is large, the method requires more time to match the observing model. To solve this problem, this study investigates further and improves subgraph isomorphic matching algorithms. We present an algorithm based on a locality-sensitive hashing technique, which allocates quadrilateral models in the navigation feature database into different hash buckets and reduces the search range to the bucket in which the observed quadrilateral model is located. Experimental results indicate the effectivity of our method.

Multi-layer service function chaining scheduling based on auxiliary graph in IP over optical network

NASA Astrophysics Data System (ADS)

Li, Yixuan; Li, Hui; Liu, Yuze; Ji, Yuefeng

2017-10-01

Software Defined Optical Network (SDON) can be considered as extension of Software Defined Network (SDN) in optical networks. SDON offers a unified control plane and makes optical network an intelligent transport network with dynamic flexibility and service adaptability. For this reason, a comprehensive optical transmission service, able to achieve service differentiation all the way down to the optical transport layer, can be provided to service function chaining (SFC). IP over optical network, as a promising networking architecture to interconnect data centers, is the most widely used scenarios of SFC. In this paper, we offer a flexible and dynamic resource allocation method for diverse SFC service requests in the IP over optical network. To do so, we firstly propose the concept of optical service function (OSF) and a multi-layer SFC model. OSF represents the comprehensive optical transmission service (e.g., multicast, low latency, quality of service, etc.), which can be achieved in multi-layer SFC model. OSF can also be considered as a special SF. Secondly, we design a resource allocation algorithm, which we call OSF-oriented optical service scheduling algorithm. It is able to address multi-layer SFC optical service scheduling and provide comprehensive optical transmission service, while meeting multiple optical transmission requirements (e.g., bandwidth, latency, availability). Moreover, the algorithm exploits the concept of Auxiliary Graph. Finally, we compare our algorithm with the Baseline algorithm in simulation. And simulation results show that our algorithm achieves superior performance than Baseline algorithm in low traffic load condition.

Signal processing using sparse derivatives with applications to chromatograms and ECG

NASA Astrophysics Data System (ADS)

Ning, Xiaoran

In this thesis, we investigate the sparsity exist in the derivative domain. Particularly, we focus on the type of signals which posses up to Mth (M > 0) order sparse derivatives. Efforts are put on formulating proper penalty functions and optimization problems to capture properties related to sparse derivatives, searching for fast, computationally efficient solvers. Also the effectiveness of these algorithms are applied to two real world applications. In the first application, we provide an algorithm which jointly addresses the problems of chromatogram baseline correction and noise reduction. The series of chromatogram peaks are modeled as sparse with sparse derivatives, and the baseline is modeled as a low-pass signal. A convex optimization problem is formulated so as to encapsulate these non-parametric models. To account for the positivity of chromatogram peaks, an asymmetric penalty function is also utilized with symmetric penalty functions. A robust, computationally efficient, iterative algorithm is developed that is guaranteed to converge to the unique optimal solution. The approach, termed Baseline Estimation And Denoising with Sparsity (BEADS), is evaluated and compared with two state-of-the-art methods using both simulated and real chromatogram data. Promising result is obtained. In the second application, a novel Electrocardiography (ECG) enhancement algorithm is designed also based on sparse derivatives. In the real medical environment, ECG signals are often contaminated by various kinds of noise or artifacts, for example, morphological changes due to motion artifact, non-stationary noise due to muscular contraction (EMG), etc. Some of these contaminations severely affect the usefulness of ECG signals, especially when computer aided algorithms are utilized. By solving the proposed convex l1 optimization problem, artifacts are reduced by modeling the clean ECG signal as a sum of two signals whose second and third-order derivatives (differences) are sparse respectively. At the end, the algorithm is applied to a QRS detection system and validated using the MIT-BIH Arrhythmia database (109452 anotations), resulting a sensitivity of Se = 99.87%$ and a positive prediction of +P = 99.88%.

Delayed effects of rhG-CSF mobilization treatment and apheresis on circulating CD34+ and CD34+ Thy-1dim CD38- progenitor cells, and lymphoid subsets in normal stem cell donors for allogeneic transplantation.

PubMed

Körbling, M; Anderlini, P; Durett, A; Maadani, F; Bojko, P; Seong, D; Giralt, S; Khouri, I; Andersson, B; Mehra, R; vanBesien, K; Mirza, N; Przepiorka, D; Champlin, R

1996-12-01

Allogeneic transplantation of peripheral blood progenitor cells (PBPC) is emerging as a new stem cell transplant modality. Rather than undergoing general anesthesia for bone marrow harvest, normal blood stem cell donors are subjected to rhG-CSF mobilization treatment followed by single or multiple apheresis. Whereas the effects of cytokine treatment and apheresis on stem cell peripheralization and collection have been described, little is known about delayed effects of rhG-CSF treatment and apheresis on a normal hematopoietic system, and there are no long-term data that address safety issues. Ten normal, patient-related donors underwent a 3 or 4 day rhG-CSF (filgrastim) treatment (12 micrograms/kg/day) followed by single or tandem apheresis. We monitored peripheral blood (PB) cellularity including CD34+ and lymphoid subsets at baseline, during cytokine treatment, prior to apheresis, and at days 2, 4, 7, 30 and 100 post-apheresis. The PB progenitor cell concentration peak prior to apheresis was followed by a nadir by day 7 and normalized by day 30, with the exception of the most primitive CD34+ Thy-1dim CD38- progenitor subset that reached a nadir by day 30. Lymphoid subsets such as CD3, 4, 8, suppressor cells (CD3+ 4- 8- TCR+ alpha beta), and B cells (CD19+) showed a similar pattern with a nadir concentration by day 7, followed, except for B cells, by a rebound by day 30 and subnormal counts at day 100. The PB concentrations of hemoglobin and platelets dropped mainly due to the apheresis procedure itself, and normalized by day 30. With cytokine treatment, the PB alkaline phosphatase and lactate dehydrogenase concentrations increased 2.2- and 2.8-fold, respectively, over baseline, and returned to normal range by day 30. Based on the preliminary nature of this study, the clinical relevance of these findings is still unclear.

Evaluation of a Faith-Based Culturally Relevant Program for African American Substance Users at Risk for HIV in the Southern United States

ERIC Educational Resources Information Center

MacMaster, Samuel A.; Jones, Jenny L.; Rasch, Randolph F. R.; Crawford, Sharon L.; Thompson, Stephanie; Sanders, Edwin C., II

2007-01-01

Objective: This article provides an evaluation of a federally funded faith-based program that serves African Americans who use heroin and cocaine and are at risk for HIV/AIDS in Nashville, Tennessee. Methods: Data were collected from 163 individuals at baseline and 6- and 12-month follow-up interviews. A subset of participants (n = 51) completed…

Effect of Physical Therapy Students' Clinical Experiences on Clinician Productivity.

PubMed

Pivko, Susan E; Abbruzzese, Laurel D; Duttaroy, Pragati; Hansen, Ruth L; Ryans, Kathryn

2016-01-01

Physical therapy clinical education experiences (CEEs) are difficult to secure, particularly first-level CEEs. Our purpose was to determine 1) what impact student full-time CEEs have on PT clinician productivity and 2) whether there is a productivity difference between first vs final CEEs. Productivity logs, including possible factors impacting productivity, were distributed to clinician-student pairings on first and final CEEs. Two-week baseline data (without a student) were compared to weeks 1 and 6 (with a student) for 31 logs using a 2x4 repeated-measures ANOVA. In a subset of 17 logs for CEEs 8 weeks or longer, a 2x5 repeated-measures ANOVA was performed. There was a significant increase in the number of patients seen and CPT units billed by both levels of CEEs comparing weeks 1 and 6. In the subset of CEEs, 8 weeks or longer, there was a significant increase in the number of patients treated per hour at week 6 and a trend toward a change at week 8 when compared to baseline week A. The factors selected as impacting productivity were census (59%) and staffing (32%). Physical therapy clinician-student pairings showed an overall increase in productivity during both full-time first and final level CEEs.

Training attentional control in older adults

PubMed Central

MacKay-Brandt, Anna

2013-01-01

Recent research has demonstrated benefits for older adults from training attentional control using a variable priority strategy, but the construct validity of the training task and the degree to which benefits of training transfer to other contexts are unclear. The goal of this study was to characterize baseline performance on the training task in a sample of 105 healthy older adults and to test for transfer of training in a subset (n = 21). Training gains after 5 days and extent of transfer was compared to another subset (n = 20) that served as a control group. Baseline performance on the training task was characterized by a two-factor model of working memory and processing speed. Processing speed correlated with the training task. Training gains in speed and accuracy were reliable and robust (ps <.001, η2 = .57 to .90). Transfer to an analogous task was observed (ps <.05, η2 = .10 to .17). The beneficial effect of training did not translate to improved performance on related measures of processing speed. This study highlights the robust effect of training and transfer to a similar context using a variable priority training task. Although processing speed is an important aspect of the training task, training benefit is either related to an untested aspect of the training task or transfer of training is limited to the training context. PMID:21728889

Integration of MODIS data and Short Baseline Subset (SBAS) technique for land subsidence monitoring in Datong, China

NASA Astrophysics Data System (ADS)

Zhao, Chao-ying; Zhang, Qin; Yang, Chengsheng; Zou, Weibao

2011-07-01

Datong is located in the north of Shanxi Province, which is famous for its old-fashioned coal-mining preservation in China. Some serious issues such as land subsidence, ground fissures, mining collapse, and earthquake hazards have occurred over this area for a long time resulting in significant damages to buildings and roads. In order to monitor and mitigate these natural man-made hazards, Short Baseline Subsets (SBAS) InSAR technique with ten Envisat ASAR data is applied to detect the surface deformation over an area of thousands of square kilometers. Then, five MODIS data are used to check the atmospheric effects on InSAR interferograms. Finally, nine nonlinear land subsidence cumulative results during September 2004 and February 2008 are obtained. Based on the deformation data, three kinds of land subsidence are clearly detected, caused by mine extraction, underground water withdrawal and construction of new economic zones, respectively. The annual mean velocity of subsidence can reach 1 to 4 cm/year in different subsidence areas. A newly designed high-speed railway (HSR) with speeds of 350 km/h will cross through the Datong hi-tech zone. Special measures should be taken for the long run of this project. In addition, another two subsidence regions need further investigation to mitigate such hazards.

A multinational, open-label, phase 2 study of ruxolitinib in Asian patients with myelofibrosis: Japanese subset analysis.

PubMed

Oritani, Kenji; Okamoto, Shinichiro; Tauchi, Tetsuzo; Saito, Shigeki; Ohishi, Kohshi; Handa, Hiroshi; Takenaka, Katsuto; Gopalakrishna, Prashanth; Amagasaki, Taro; Ito, Kazuo; Akashi, Koichi

2015-03-01

Ruxolitinib is a potent Janus kinase (JAK) 1/JAK2 inhibitor that has demonstrated rapid and durable improvements in splenomegaly and symptoms and a survival benefit in 2 phase 3 trials in patients with myelofibrosis. Ruxolitinib was well tolerated and effectively reduced splenomegaly and symptom burden in Asian patients with myelofibrosis in the Asian multinational, phase 2 Study A2202. We present a subset analysis of Japanese patients (n = 30) in Study A2202. At data cutoff, 22 patients were ongoing; 8 discontinued, mainly due to adverse events (n = 4). At week 24, 33 % of patients achieved ≥35 % reduction from baseline in spleen volume; 56.0 % achieved ≥50 % reduction from baseline in total symptom score, as measured by the 7-day Myelofibrosis Symptom Assessment Form v2.0. The most common adverse events were anemia (63 %), thrombocytopenia (40 %), nasopharyngitis (37 %), decreased platelet counts (30 %), and diarrhea (30 %). Dose reductions or interruptions due to hemoglobin decreases were more frequent in Japanese patients; no loss of efficacy and no discontinuations due to hematologic abnormalities were observed. Ruxolitinib was well tolerated in Japanese patients and provided substantial reductions in splenomegaly and myelofibrosis-related symptoms similar to those observed in the overall Asian population and phase 3 COMFORT studies.

Post-Seismic Deformation from the 2009 Mw 6.3 Dachaidan Earthquake in the Northern Qaidam Basin Detected by Small Baseline Subset InSAR Technique

PubMed Central

Liu, Yang; Xu, Caijun; Wen, Yangmao; Li, Zhicai

2016-01-01

On 28 August 2009, one thrust-faulting Mw 6.3 earthquake struck the northern Qaidam basin, China. Due to the lack of ground observations in this remote region, this study presents high-precision and high spatio-temporal resolution post-seismic deformation series with a small baseline subset InSAR technique. At the temporal scale, this changes from fast to slow with time, with a maximum uplift up to 7.4 cm along the line of sight 334 days after the event. At the spatial scale, this is more obvious at the hanging wall than that at the footwall, and decreases from the middle to both sides at the hanging wall. We then propose a method to calculate the correlation coefficient between co-seismic and post-seismic deformation by normalizing them. The correlation coefficient is found to be 0.73, indicating a similar subsurface process occurring during both phases. The results indicate that afterslip may dominate the post-seismic deformation during 19–334 days after the event, which mainly occurs with the fault geometry and depth similar to those of the c-seismic rupturing, and partly extends to the shallower and deeper depths. PMID:26861330

Post-Seismic Deformation from the 2009 Mw 6.3 Dachaidan Earthquake in the Northern Qaidam Basin Detected by Small Baseline Subset InSAR Technique.

PubMed

Liu, Yang; Xu, Caijun; Wen, Yangmao; Li, Zhicai

2016-02-05

On 28 August 2009, one thrust-faulting Mw 6.3 earthquake struck the northern Qaidam basin, China. Due to the lack of ground observations in this remote region, this study presents high-precision and high spatio-temporal resolution post-seismic deformation series with a small baseline subset InSAR technique. At the temporal scale, this changes from fast to slow with time, with a maximum uplift up to 7.4 cm along the line of sight 334 days after the event. At the spatial scale, this is more obvious at the hanging wall than that at the footwall, and decreases from the middle to both sides at the hanging wall. We then propose a method to calculate the correlation coefficient between co-seismic and post-seismic deformation by normalizing them. The correlation coefficient is found to be 0.73, indicating a similar subsurface process occurring during both phases. The results indicate that afterslip may dominate the post-seismic deformation during 19-334 days after the event, which mainly occurs with the fault geometry and depth similar to those of the c-seismic rupturing, and partly extends to the shallower and deeper depths.

New baseline correction algorithm for text-line recognition with bidirectional recurrent neural networks

NASA Astrophysics Data System (ADS)

Morillot, Olivier; Likforman-Sulem, Laurence; Grosicki, Emmanuèle

2013-04-01

Many preprocessing techniques have been proposed for isolated word recognition. However, recently, recognition systems have dealt with text blocks and their compound text lines. In this paper, we propose a new preprocessing approach to efficiently correct baseline skew and fluctuations. Our approach is based on a sliding window within which the vertical position of the baseline is estimated. Segmentation of text lines into subparts is, thus, avoided. Experiments conducted on a large publicly available database (Rimes), with a BLSTM (bidirectional long short-term memory) recurrent neural network recognition system, show that our baseline correction approach highly improves performance.

Simulating large atmospheric phase screens using a woofer-tweeter algorithm.

PubMed

Buscher, David F

2016-10-03

We describe an algorithm for simulating atmospheric wavefront perturbations over ranges of spatial and temporal scales spanning more than 4 orders of magnitude. An open-source implementation of the algorithm written in Python can simulate the evolution of the perturbations more than an order-of-magnitude faster than real time. Testing of the implementation using metrics appropriate to adaptive optics systems and long-baseline interferometers show accuracies at the few percent level or better.

A dynamic data source selection system for smartwatch platform.

PubMed

Nemati, Ebrahim; Sideris, Konstantinos; Kalantarian, Haik; Sarrafzadeh, Majid

2016-08-01

A novel data source selection algorithm is proposed for ambulatory activity tracking of elderly people. The algorithm introduces the concept of dynamic switching between the data collection modules (a smartwatch and a smartphone) to improve accuracy and battery life using contextual information. We show that by making offloading decisions as a function of activity, the proposed algorithm improves power consumption and accuracy of the previous work by 7 hours and 5% respectively compared to the baseline.

Traveling Salesman Problem for Surveillance Mission Using Particle Swarm Optimization

DTIC Science & Technology

2001-03-20

design of experiments, results of the experiments, and qualitative and quantitative analysis . Conclusions and recommendations based on the qualitative and...characterize the algorithm. Such analysis and comparison between LK and a non-deterministic algorithm produces claims such as "Lin-Kernighan algorithm takes... based on experiments 5 and 6. All other parameters are the same as the baseline (see 4.2.1.2). 4.2.2.6 Experiment 10 - Fine Tuning PSO AS: 85,95% Global

Effect of Taxane-Based Neoadjuvant Chemotherapy on Fibroglandular Tissue Volume and Percent Breast Density in the Contralateral Normal Breast: Evaluated at 3T MR

PubMed Central

Chen, Jeon-Hor; Pan, Wei-Fan; Kao, Julian; Lu, Jocelyn; Chen, Li-Kuang; Kuo, Chih-Chen; Chang, Chih-Kai; Chen, Wen-Pin; McLaren, Christine E.; Bahri, Shadfar; Mehta, Rita S.; Su, Min-Ying

2013-01-01

The aim of this study was to evaluate the change of breast density in the normal breast of patients receiving neoadjuvant chemotherapy (NAC). Forty-four breast cancer patients were studied. MRI acquisition was performed before treatment (baseline), and 4 and 12 weeks after treatment. A computer algorithm-based program was used to segment breast tissue and calculate breast volume (BV), fibroglandular tissue volume (FV) and percent density (PD) (the ratio of FV over BV x100%). The reduction of FV and PD after treatment was compared to baseline using paired t-tests with a Bonferroni-Holm correction. The association of density reduction with age was analyzed. FV and PD after NAC showed significant decreases compared to the baseline. FV was 110.0ml (67.2, 189.8) (geometric mean (interquartile range)) at baseline, 104.3ml (66.6, 164.4) after 4 weeks (p< 0.0001), and 94.7ml (60.2, 144.4) after 12 weeks (comparison to baseline, p<0.0001; comparison to 4 weeks, p=0.016). PD was 11.2% (6.4, 22.4) at baseline, 10.6% (6.6, 20.3) after 4 weeks (p< 0.0001), and 9.7% (6.2, 17.9) after 12 weeks (comparison to baseline, p=0.0001; comparison to 4 weeks, p =0.018). Younger patients tended to show a higher density reduction, but overall correlation with age was only moderate (r=0.28 for FV, p=0.07 and r=0.52 for PD, p=0.0003). Our study showed that breast density measured from MR images acquired at 3T MR can be accurately quantified using a robust computer-aided algorithm based on nonparametric nonuniformity normalization (N3) and an adaptive fuzzy C-means algorithm. Similar to doxorubicin and cyclophosphamide regimens, the taxane-based NAC regimen also caused density atrophy in the normal breast and showed reduction in FV and PD. The effect of breast density reduction was age-related and duration-related. PMID:23940080

Inferring duplications, losses, transfers and incomplete lineage sorting with nonbinary species trees.

PubMed

Stolzer, Maureen; Lai, Han; Xu, Minli; Sathaye, Deepa; Vernot, Benjamin; Durand, Dannie

2012-09-15

Gene duplication (D), transfer (T), loss (L) and incomplete lineage sorting (I) are crucial to the evolution of gene families and the emergence of novel functions. The history of these events can be inferred via comparison of gene and species trees, a process called reconciliation, yet current reconciliation algorithms model only a subset of these evolutionary processes. We present an algorithm to reconcile a binary gene tree with a nonbinary species tree under a DTLI parsimony criterion. This is the first reconciliation algorithm to capture all four evolutionary processes driving tree incongruence and the first to reconcile non-binary species trees with a transfer model. Our algorithm infers all optimal solutions and reports complete, temporally feasible event histories, giving the gene and species lineages in which each event occurred. It is fixed-parameter tractable, with polytime complexity when the maximum species outdegree is fixed. Application of our algorithms to prokaryotic and eukaryotic data show that use of an incomplete event model has substantial impact on the events inferred and resulting biological conclusions. Our algorithms have been implemented in Notung, a freely available phylogenetic reconciliation software package, available at http://www.cs.cmu.edu/~durand/Notung. mstolzer@andrew.cmu.edu.

Studies of the DIII-D disruption database using Machine Learning algorithms

NASA Astrophysics Data System (ADS)

Rea, Cristina; Granetz, Robert; Meneghini, Orso

2017-10-01

A Random Forests Machine Learning algorithm, trained on a large database of both disruptive and non-disruptive DIII-D discharges, predicts disruptive behavior in DIII-D with about 90% of accuracy. Several algorithms have been tested and Random Forests was found superior in performances for this particular task. Over 40 plasma parameters are included in the database, with data for each of the parameters taken from 500k time slices. We focused on a subset of non-dimensional plasma parameters, deemed to be good predictors based on physics considerations. Both binary (disruptive/non-disruptive) and multi-label (label based on the elapsed time before disruption) classification problems are investigated. The Random Forests algorithm provides insight on the available dataset by ranking the relative importance of the input features. It is found that q95 and Greenwald density fraction (n/nG) are the most relevant parameters for discriminating between DIII-D disruptive and non-disruptive discharges. A comparison with the Gradient Boosted Trees algorithm is shown and the first results coming from the application of regression algorithms are presented. Work supported by the US Department of Energy under DE-FC02-04ER54698, DE-SC0014264 and DE-FG02-95ER54309.

Development of the Landsat Data Continuity Mission Cloud Cover Assessment Algorithms

USGS Publications Warehouse

Scaramuzza, Pat; Bouchard, M.A.; Dwyer, John L.

2012-01-01

The upcoming launch of the Operational Land Imager (OLI) will start the next era of the Landsat program. However, the Automated Cloud-Cover Assessment (CCA) (ACCA) algorithm used on Landsat 7 requires a thermal band and is thus not suited for OLI. There will be a thermal instrument on the Landsat Data Continuity Mission (LDCM)-the Thermal Infrared Sensor-which may not be available during all OLI collections. This illustrates a need for CCA for LDCM in the absence of thermal data. To research possibilities for full-resolution OLI cloud assessment, a global data set of 207 Landsat 7 scenes with manually generated cloud masks was created. It was used to evaluate the ACCA algorithm, showing that the algorithm correctly classified 79.9% of a standard test subset of 3.95 109 pixels. The data set was also used to develop and validate two successor algorithms for use with OLI data-one derived from an off-the-shelf machine learning package and one based on ACCA but enhanced by a simple neural network. These comprehensive CCA algorithms were shown to correctly classify pixels as cloudy or clear 88.5% and 89.7% of the time, respectively.

Mining Co-Location Patterns with Clustering Items from Spatial Data Sets

NASA Astrophysics Data System (ADS)

Zhou, G.; Li, Q.; Deng, G.; Yue, T.; Zhou, X.

2018-05-01

The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the spatial data mining. Co-location patterns discovery is an important branch in spatial data mining. Spatial co-locations represent the subsets of features which are frequently located together in geographic space. However, the appearance of a spatial feature C is often not determined by a single spatial feature A or B but by the two spatial features A and B, that is to say where A and B appear together, C often appears. We note that this co-location pattern is different from the traditional co-location pattern. Thus, this paper presents a new concept called clustering terms, and this co-location pattern is called co-location patterns with clustering items. And the traditional algorithm cannot mine this co-location pattern, so we introduce the related concept in detail and propose a novel algorithm. This algorithm is extended by join-based approach proposed by Huang. Finally, we evaluate the performance of this algorithm.

Distributed weighted least-squares estimation with fast convergence for large-scale systems.

PubMed

Marelli, Damián Edgardo; Fu, Minyue

2015-01-01

In this paper we study a distributed weighted least-squares estimation problem for a large-scale system consisting of a network of interconnected sub-systems. Each sub-system is concerned with a subset of the unknown parameters and has a measurement linear in the unknown parameters with additive noise. The distributed estimation task is for each sub-system to compute the globally optimal estimate of its own parameters using its own measurement and information shared with the network through neighborhood communication. We first provide a fully distributed iterative algorithm to asymptotically compute the global optimal estimate. The convergence rate of the algorithm will be maximized using a scaling parameter and a preconditioning method. This algorithm works for a general network. For a network without loops, we also provide a different iterative algorithm to compute the global optimal estimate which converges in a finite number of steps. We include numerical experiments to illustrate the performances of the proposed methods.

Distributed weighted least-squares estimation with fast convergence for large-scale systems☆

PubMed Central

Marelli, Damián Edgardo; Fu, Minyue

2015-01-01

In this paper we study a distributed weighted least-squares estimation problem for a large-scale system consisting of a network of interconnected sub-systems. Each sub-system is concerned with a subset of the unknown parameters and has a measurement linear in the unknown parameters with additive noise. The distributed estimation task is for each sub-system to compute the globally optimal estimate of its own parameters using its own measurement and information shared with the network through neighborhood communication. We first provide a fully distributed iterative algorithm to asymptotically compute the global optimal estimate. The convergence rate of the algorithm will be maximized using a scaling parameter and a preconditioning method. This algorithm works for a general network. For a network without loops, we also provide a different iterative algorithm to compute the global optimal estimate which converges in a finite number of steps. We include numerical experiments to illustrate the performances of the proposed methods. PMID:25641976

Constraint programming based biomarker optimization.

PubMed

Zhou, Manli; Luo, Youxi; Sun, Guoquan; Mai, Guoqin; Zhou, Fengfeng

2015-01-01

Efficient and intuitive characterization of biological big data is becoming a major challenge for modern bio-OMIC based scientists. Interactive visualization and exploration of big data is proven to be one of the successful solutions. Most of the existing feature selection algorithms do not allow the interactive inputs from users in the optimizing process of feature selection. This study investigates this question as fixing a few user-input features in the finally selected feature subset and formulates these user-input features as constraints for a programming model. The proposed algorithm, fsCoP (feature selection based on constrained programming), performs well similar to or much better than the existing feature selection algorithms, even with the constraints from both literature and the existing algorithms. An fsCoP biomarker may be intriguing for further wet lab validation, since it satisfies both the classification optimization function and the biomedical knowledge. fsCoP may also be used for the interactive exploration of bio-OMIC big data by interactively adding user-defined constraints for modeling.

Digital Terrain from a Two-Step Segmentation and Outlier-Based Algorithm

NASA Astrophysics Data System (ADS)

Hingee, Kassel; Caccetta, Peter; Caccetta, Louis; Wu, Xiaoliang; Devereaux, Drew

2016-06-01

We present a novel ground filter for remotely sensed height data. Our filter has two phases: the first phase segments the DSM with a slope threshold and uses gradient direction to identify candidate ground segments; the second phase fits surfaces to the candidate ground points and removes outliers. Digital terrain is obtained by a surface fit to the final set of ground points. We tested the new algorithm on digital surface models (DSMs) for a 9600km2 region around Perth, Australia. This region contains a large mix of land uses (urban, grassland, native forest and plantation forest) and includes both a sandy coastal plain and a hillier region (elevations up to 0.5km). The DSMs are captured annually at 0.2m resolution using aerial stereo photography, resulting in 1.2TB of input data per annum. Overall accuracy of the filter was estimated to be 89.6% and on a small semi-rural subset our algorithm was found to have 40% fewer errors compared to Inpho's Match-T algorithm.

Reproducibility and Variability of I/O Performance on BG/Q: Lessons Learned from a Data Aggregation Algorithm

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

Tessier, Francois; Vishwanath, Venkatram

2017-11-28

Reading and writing data efficiently from different tiers of storage is necessary for most scientific simulations to achieve good performance at scale. Many software solutions have been developed to decrease the I/O bottleneck. One wellknown strategy, in the context of collective I/O operations, is the two-phase I/O scheme. This strategy consists of selecting a subset of processes to aggregate contiguous pieces of data before performing reads/writes. In our previous work, we implemented the two-phase I/O scheme with a MPI-based topology-aware algorithm. Our algorithm showed very good performance at scale compared to the standard I/O libraries such as POSIX I/O andmore » MPI I/O. However, the algorithm had several limitations hindering a satisfying reproducibility of our experiments. In this paper, we extend our work by 1) identifying the obstacles we face to reproduce our experiments and 2) discovering solutions that reduce the unpredictability of our results.« less

Elevated temperatures are associated with stress in rooftop-nesting Common Nighthawk (Chordeiles minor) chicks.

PubMed

Newberry, Gretchen N; Swanson, David L

2018-01-01

Grasslands and riparian forests in southeastern South Dakota have been greatly reduced since historical times, primarily due to conversion to row-crop agriculture. Common Nighthawk ( Chordeiles minor ) nesting habitat includes grasslands, open woodlands and urban rooftops, but nesting sites in southeastern South Dakota are confined to rooftops, as natural nesting habitat is limited. Nighthawks nesting on exposed rooftop habitats may encounter thermal conditions that increase operative temperatures relative to vegetated land cover types. Mean humidity has increased and mean wind speed and cloud cover have decreased during the nighthawk breeding season from 1948 to 2016 in southeastern South Dakota. These changes might contribute to increasing operative temperatures at exposed rooftop nest sites and this could influence chick condition. We studied nest micro-climate and the plasma stress response for 24 rooftop-nesting nighthawk chicks from 17 nests during 2015 and 2016. High humidity prior to blood collection reduced both baseline and stress-induced plasma corticosterone (CORT). In contrast, high maximum temperatures during the day before sampling increased stress-induced CORT. The magnitude of the chick stress response was significantly negatively related to maximum wind speed for the week prior to CORT measurement. Other weather and micro-climate variables were not significant effectors of CORT metrics. Most chicks had low baseline CORT and were able to mount a stress response, but a subset of chicks ( n = 4) showed elevated baseline CORT and a negative association between the magnitude of stress response and ambient temperature. For this subset, mean ambient temperature for the day before sampling was significantly higher (2.3°C) than for chicks with typical baseline CORT levels. These data suggest that regional climate change trends could affect the ability of nighthawk chicks to mount a stress response, which, in turn, might influence the susceptibility of nighthawk chicks to climate change in the Northern Prairie region.

Effect of high-dose pitavastatin on glucose homeostasis in patients at elevated risk of new-onset diabetes: insights from the CAPITAIN and PREVAIL-US studies.

PubMed

Chapman, M J; Orsoni, A; Robillard, P; Hounslow, N; Sponseller, C A; Giral, P

2014-05-01

Statin treatment may impair glucose homeostasis and increase the risk of new-onset diabetes mellitus, although this may depend on the statin, dose and patient population. We evaluated the effects of pitavastatin 4 mg/day on glucose homeostasis in patients with metabolic syndrome in the CAPITAIN trial. Findings were validated in a subset of patients enrolled in PREVAIL-US. Participants with a well defined metabolic syndrome phenotype were recruited to CAPITAIN to reduce the influence of confounding factors. Validation and comparison datasets were selected comprising phenotypically similar subsets of individuals enrolled in PREVAIL-US and treated with pitavastatin or pravastatin, respectively. Mean change from baseline in parameters of glucose homeostasis (fasting plasma glucose [FPG], glycated hemoglobin [HbA1c], insulin, quantitative insulin-sensitivity check index [QUICKI] and homeostasis model of assessment-insulin resistance [HOMA-IR]) and plasma lipid profile were assessed at 6 months (CAPITAIN) and 3 months (PREVAIL-US) after initiating treatment. In CAPITAIN (n = 12), no significant differences from baseline in HbA1c, insulin, HOMA-IR and QUICKI were observed at day 180 in patients treated with pitavastatin. A small (4%) increase in FPG from baseline to day 180 (P < 0.05), was observed. In the validation dataset (n = 9), no significant differences from baseline in glycemic parameters were observed at day 84 (all comparisons P > 0.05). Similar results were observed for pravastatin in the comparison dataset (n = 14). Other than a small change in FPG in the CAPITAIN study, neutral effects of pitavastatin on glucose homeostasis were observed in two cohorts of patients with metabolic syndrome, independent of its efficacy in reducing levels of atherogenic lipoproteins. The small number of patients and relatively short follow-up period represent limitations of the study. Nevertheless, these data suggest that statin-induced diabetogenesis may not represent a class effect.

Data Prospecting Framework - a new approach to explore "big data" in Earth Science

NASA Astrophysics Data System (ADS)

Ramachandran, R.; Rushing, J.; Lin, A.; Kuo, K.

2012-12-01

Due to advances in sensors, computation and storage, cost and effort required to produce large datasets have been significantly reduced. As a result, we are seeing a proliferation of large-scale data sets being assembled in almost every science field, especially in geosciences. Opportunities to exploit the "big data" are enormous as new hypotheses can be generated by combining and analyzing large amounts of data. However, such a data-driven approach to science discovery assumes that scientists can find and isolate relevant subsets from vast amounts of available data. Current Earth Science data systems only provide data discovery through simple metadata and keyword-based searches and are not designed to support data exploration capabilities based on the actual content. Consequently, scientists often find themselves downloading large volumes of data, struggling with large amounts of storage and learning new analysis technologies that will help them separate the wheat from the chaff. New mechanisms of data exploration are needed to help scientists discover the relevant subsets We present data prospecting, a new content-based data analysis paradigm to support data-intensive science. Data prospecting allows the researchers to explore big data in determining and isolating data subsets for further analysis. This is akin to geo-prospecting in which mineral sites of interest are determined over the landscape through screening methods. The resulting "data prospects" only provide an interaction with and feel for the data through first-look analytics; the researchers would still have to download the relevant datasets and analyze them deeply using their favorite analytical tools to determine if the datasets will yield new hypotheses. Data prospecting combines two traditional categories of data analysis, data exploration and data mining within the discovery step. Data exploration utilizes manual/interactive methods for data analysis such as standard statistical analysis and visualization, usually on small datasets. On the other hand, data mining utilizes automated algorithms to extract useful information. Humans guide these automated algorithms and specify algorithm parameters (training samples, clustering size, etc.). Data Prospecting combines these two approaches using high performance computing and the new techniques for efficient distributed file access.

Immune cell response to strenuous resistive breathing: comparison with whole body exercise and the effects of antioxidants

PubMed Central

Karatza, Maria-Helena; Vasileiou, Spyridoula; Katsaounou, Paraskevi; Mastora, Zafeiria

2018-01-01

Background/hypothesis Whole body exercise (WBE) changes lymphocyte subset percentages in peripheral blood. Resistive breathing, a hallmark of diseases of airway obstruction, is a form of exercise for the inspiratory muscles. Strenuous muscle contractions induce oxidative stress that may mediate immune alterations following exercise. We hypothesized that inspiratory resistive breathing (IRB) alters peripheral blood lymphocyte subsets and that oxidative stress mediates lymphocyte subpopulation alterations following both WBE and IRB. Patients and methods Six healthy nonathletes performed two WBE and two IRB sessions for 45 minutes at 70% of VO2 maximum and 70% of maximum inspiratory pressure (Pimax), respectively, before and after the administration of antioxidants (vitamins E, A, and C for 75 days, allopurinol for 30 days, and N-acetylcysteine for 3 days). Blood was drawn at baseline, at the end of each session, and 2 hours into recovery. Lymphocyte subsets were determined by flow cytometry. Results Before antioxidant supplementation at both WBE end and IRB end, the natural killer cell percentage increased, the T helper cell (CD3+ CD4+) percentage was reduced, and the CD4/CD8 ratio was depressed, a response which was abolished by antioxidants only after IRB. Furthermore, at IRB end, antioxidants promoted CD8+ CD38+ and blunted cytotoxic T-cell percentage increase. CD8+ CD45RA+ cell percentage changes were blunted after antioxidant supplementation in both WBE and IRB. Conclusion We conclude that IRB produces (as WBE) changes in peripheral blood lymphocyte subsets and that oxidative stress is a major stimulus predominantly for IRB-induced lymphocyte subset alterations. PMID:29445271

Immune cell response to strenuous resistive breathing: comparison with whole body exercise and the effects of antioxidants.

PubMed

Asimakos, Andreas; Toumpanakis, Dimitrios; Karatza, Maria-Helena; Vasileiou, Spyridoula; Katsaounou, Paraskevi; Mastora, Zafeiria; Vassilakopoulos, Theodoros

2018-01-01

Whole body exercise (WBE) changes lymphocyte subset percentages in peripheral blood. Resistive breathing, a hallmark of diseases of airway obstruction, is a form of exercise for the inspiratory muscles. Strenuous muscle contractions induce oxidative stress that may mediate immune alterations following exercise. We hypothesized that inspiratory resistive breathing (IRB) alters peripheral blood lymphocyte subsets and that oxidative stress mediates lymphocyte subpopulation alterations following both WBE and IRB. Six healthy nonathletes performed two WBE and two IRB sessions for 45 minutes at 70% of VO 2 maximum and 70% of maximum inspiratory pressure (Pi max ), respectively, before and after the administration of antioxidants (vitamins E, A, and C for 75 days, allopurinol for 30 days, and N-acetylcysteine for 3 days). Blood was drawn at baseline, at the end of each session, and 2 hours into recovery. Lymphocyte subsets were determined by flow cytometry. Before antioxidant supplementation at both WBE end and IRB end, the natural killer cell percentage increased, the T helper cell (CD3+ CD4+) percentage was reduced, and the CD4/CD8 ratio was depressed, a response which was abolished by antioxidants only after IRB. Furthermore, at IRB end, antioxidants promoted CD8+ CD38+ and blunted cytotoxic T-cell percentage increase. CD8+ CD45RA+ cell percentage changes were blunted after antioxidant supplementation in both WBE and IRB. We conclude that IRB produces (as WBE) changes in peripheral blood lymphocyte subsets and that oxidative stress is a major stimulus predominantly for IRB-induced lymphocyte subset alterations.

Effectiveness of a novel and scalable clinical decision support intervention to improve venous thromboembolism prophylaxis: a quasi-experimental study.

PubMed

Umscheid, Craig A; Hanish, Asaf; Chittams, Jesse; Weiner, Mark G; Hecht, Todd E H

2012-08-31

Venous thromboembolism (VTE) causes morbidity and mortality in hospitalized patients, and regulators and payors are encouraging the use of systems to prevent them. Here, we examine the effect of a computerized clinical decision support (CDS) intervention implemented across a multi-hospital academic health system on VTE prophylaxis and events. The study included 223,062 inpatients admitted between April 2007 and May 2010, and used administrative and clinical data. The intervention was integrated into a commercial electronic health record (EHR) in an admission orderset used for all admissions. Three time periods were examined: baseline (period 1), and the time after implementation of the first CDS intervention (period 2) and a second iteration (period 3). Providers were prompted to accept or decline prophylaxis based on patient risk. Time series analyses examined the impact of the intervention on VTE prophylaxis during time periods two and three compared to baseline, and a simple pre-post design examined impact on VTE events and bleeds secondary to anticoagulation. VTE prophylaxis and events were also examined in a prespecified surgical subset of our population meeting the public reporting criteria defined by the Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator (PSI). Unadjusted analyses suggested that "recommended", "any", and "pharmacologic" prophylaxis increased from baseline to the last study period (27.1% to 51.9%, 56.7% to 78.1%, and 42.0% to 54.4% respectively; p < 0.01 for all comparisons). Results were significant across all hospitals and the health system overall. Interrupted time series analyses suggested that our intervention increased the use of "recommended" and "any" prophylaxis by 7.9% and 9.6% respectively from baseline to time period 2 (p < 0.01 for both comparisons); and 6.6% and 9.6% respectively from baseline to the combined time periods 2 and 3 (p < 0.01 for both comparisons). There were no significant changes in "pharmacologic" prophylaxis in the adjusted model. The overall percent of patients with VTE increased from baseline to the last study period (2.0% to 2.2%; p = 0.03), but an analysis excluding patients with VTE "present on admission" (POA) demonstrated no difference in events (1.3% to 1.3%; p = 0.80). Overall bleeds did not significantly change. An analysis examining VTE prophylaxis and events in a surgical subset of patients defined by the AHRQ PSI demonstrated increased "recommended", "any", and "pharmacologic" prophylaxis from baseline to the last study period (32.3% to 60.0%, 62.8% to 85.7%, and 47.9% to 63.3% respectively; p < 0.01 for all comparisons) as well as reduced VTE events (2.2% to 1.7%; p < 0.01). The CDS intervention was associated with an increase in "recommended" and "any" VTE prophylaxis across the multi-hospital academic health system. The intervention was also associated with increased VTE rates in the overall study population, but a subanalysis using only admissions with appropriate POA documentation suggested no change in VTE rates, and a prespecified analysis of a surgical subset of our sample as defined by the AHRQ PSI for public reporting purposes suggested reduced VTE. This intervention was created in a commonly used commercial EHR and is scalable across institutions with similar systems.

Effectiveness of a novel and scalable clinical decision support intervention to improve venous thromboembolism prophylaxis: a quasi-experimental study

PubMed Central

2012-01-01

Background Venous thromboembolism (VTE) causes morbidity and mortality in hospitalized patients, and regulators and payors are encouraging the use of systems to prevent them. Here, we examine the effect of a computerized clinical decision support (CDS) intervention implemented across a multi-hospital academic health system on VTE prophylaxis and events. Methods The study included 223,062 inpatients admitted between April 2007 and May 2010, and used administrative and clinical data. The intervention was integrated into a commercial electronic health record (EHR) in an admission orderset used for all admissions. Three time periods were examined: baseline (period 1), and the time after implementation of the first CDS intervention (period 2) and a second iteration (period 3). Providers were prompted to accept or decline prophylaxis based on patient risk. Time series analyses examined the impact of the intervention on VTE prophylaxis during time periods two and three compared to baseline, and a simple pre-post design examined impact on VTE events and bleeds secondary to anticoagulation. VTE prophylaxis and events were also examined in a prespecified surgical subset of our population meeting the public reporting criteria defined by the Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator (PSI). Results Unadjusted analyses suggested that “recommended”, “any”, and “pharmacologic” prophylaxis increased from baseline to the last study period (27.1% to 51.9%, 56.7% to 78.1%, and 42.0% to 54.4% respectively; p < 0.01 for all comparisons). Results were significant across all hospitals and the health system overall. Interrupted time series analyses suggested that our intervention increased the use of “recommended” and “any” prophylaxis by 7.9% and 9.6% respectively from baseline to time period 2 (p < 0.01 for both comparisons); and 6.6% and 9.6% respectively from baseline to the combined time periods 2 and 3 (p < 0.01 for both comparisons). There were no significant changes in “pharmacologic” prophylaxis in the adjusted model. The overall percent of patients with VTE increased from baseline to the last study period (2.0% to 2.2%; p = 0.03), but an analysis excluding patients with VTE “present on admission” (POA) demonstrated no difference in events (1.3% to 1.3%; p = 0.80). Overall bleeds did not significantly change. An analysis examining VTE prophylaxis and events in a surgical subset of patients defined by the AHRQ PSI demonstrated increased “recommended”, “any”, and “pharmacologic” prophylaxis from baseline to the last study period (32.3% to 60.0%, 62.8% to 85.7%, and 47.9% to 63.3% respectively; p < 0.01 for all comparisons) as well as reduced VTE events (2.2% to 1.7%; p < 0.01). Conclusions The CDS intervention was associated with an increase in “recommended” and “any” VTE prophylaxis across the multi-hospital academic health system. The intervention was also associated with increased VTE rates in the overall study population, but a subanalysis using only admissions with appropriate POA documentation suggested no change in VTE rates, and a prespecified analysis of a surgical subset of our sample as defined by the AHRQ PSI for public reporting purposes suggested reduced VTE. This intervention was created in a commonly used commercial EHR and is scalable across institutions with similar systems. PMID:22938083

The edge artifact in the point-spread function-based PET reconstruction at different sphere-to-background ratios of radioactivity.

PubMed

Kidera, Daisuke; Kihara, Ken; Akamatsu, Go; Mikasa, Shohei; Taniguchi, Takafumi; Tsutsui, Yuji; Takeshita, Toshiki; Maebatake, Akira; Miwa, Kenta; Sasaki, Masayuki

2016-02-01

The aim of this study was to quantitatively evaluate the edge artifacts in PET images reconstructed using the point-spread function (PSF) algorithm at different sphere-to-background ratios of radioactivity (SBRs). We used a NEMA IEC body phantom consisting of six spheres with 37, 28, 22, 17, 13 and 10 mm in inner diameter. The background was filled with (18)F solution with a radioactivity concentration of 2.65 kBq/mL. We prepared three sets of phantoms with SBRs of 16, 8, 4 and 2. The PET data were acquired for 20 min using a Biograph mCT scanner. The images were reconstructed with the baseline ordered subsets expectation maximization (OSEM) algorithm, and with the OSEM + PSF correction model (PSF). For the image reconstruction, the number of iterations ranged from one to 10. The phantom PET image analyses were performed by a visual assessment of the PET images and profiles, a contrast recovery coefficient (CRC), which is the ratio of SBR in the images to the true SBR, and the percent change in the maximum count between the OSEM and PSF images (Δ % counts). In the PSF images, the spheres with a diameter of 17 mm or larger were surrounded by a dense edge in comparison with the OSEM images. In the spheres with a diameter of 22 mm or smaller, an overshoot appeared in the center of the spheres as a sharp peak in the PSF images in low SBR. These edge artifacts were clearly observed in relation to the increase of the SBR. The overestimation of the CRC was observed in 13 mm spheres in the PSF images. In the spheres with a diameter of 17 mm or smaller, the Δ % counts increased with an increasing SBR. The Δ % counts increased to 91 % in the 10-mm sphere at the SBR of 16. The edge artifacts in the PET images reconstructed using the PSF algorithm increased with an increasing SBR. In the small spheres, the edge artifact was observed as a sharp peak at the center of spheres and could result in overestimation.

Better physical activity classification using smartphone acceleration sensor.

PubMed

Arif, Muhammad; Bilal, Mohsin; Kattan, Ahmed; Ahamed, S Iqbal

2014-09-01

Obesity is becoming one of the serious problems for the health of worldwide population. Social interactions on mobile phones and computers via internet through social e-networks are one of the major causes of lack of physical activities. For the health specialist, it is important to track the record of physical activities of the obese or overweight patients to supervise weight loss control. In this study, acceleration sensor present in the smartphone is used to monitor the physical activity of the user. Physical activities including Walking, Jogging, Sitting, Standing, Walking upstairs and Walking downstairs are classified. Time domain features are extracted from the acceleration data recorded by smartphone during different physical activities. Time and space complexity of the whole framework is done by optimal feature subset selection and pruning of instances. Classification results of six physical activities are reported in this paper. Using simple time domain features, 99 % classification accuracy is achieved. Furthermore, attributes subset selection is used to remove the redundant features and to minimize the time complexity of the algorithm. A subset of 30 features produced more than 98 % classification accuracy for the six physical activities.

A hybrid feature selection method using multiclass SVM for diagnosis of erythemato-squamous disease

NASA Astrophysics Data System (ADS)

Maryam, Setiawan, Noor Akhmad; Wahyunggoro, Oyas

2017-08-01

The diagnosis of erythemato-squamous disease is a complex problem and difficult to detect in dermatology. Besides that, it is a major cause of skin cancer. Data mining implementation in the medical field helps expert to diagnose precisely, accurately, and inexpensively. In this research, we use data mining technique to developed a diagnosis model based on multiclass SVM with a novel hybrid feature selection method to diagnose erythemato-squamous disease. Our hybrid feature selection method, named ChiGA (Chi Square and Genetic Algorithm), uses the advantages from filter and wrapper methods to select the optimal feature subset from original feature. Chi square used as filter method to remove redundant features and GA as wrapper method to select the ideal feature subset with SVM used as classifier. Experiment performed with 10 fold cross validation on erythemato-squamous diseases dataset taken from University of California Irvine (UCI) machine learning database. The experimental result shows that the proposed model based multiclass SVM with Chi Square and GA can give an optimum feature subset. There are 18 optimum features with 99.18% accuracy.

Identifying Physician-Recognized Depression from Administrative Data: Consequences for Quality Measurement

PubMed Central

Spettell, Claire M; Wall, Terry C; Allison, Jeroan; Calhoun, Jaimee; Kobylinski, Richard; Fargason, Rachel; Kiefe, Catarina I

2003-01-01

Background Multiple factors limit identification of patients with depression from administrative data. However, administrative data drives many quality measurement systems, including the Health Plan Employer Data and Information Set (HEDIS®). Methods We investigated two algorithms for identification of physician-recognized depression. The study sample was drawn from primary care physician member panels of a large managed care organization. All members were continuously enrolled between January 1 and December 31, 1997. Algorithm 1 required at least two criteria in any combination: (1) an outpatient diagnosis of depression or (2) a pharmacy claim for an antidepressant. Algorithm 2 included the same criteria as algorithm 1, but required a diagnosis of depression for all patients. With algorithm 1, we identified the medical records of a stratified, random subset of patients with and without depression (n=465). We also identified patients of primary care physicians with a minimum of 10 depressed members by algorithm 1 (n=32,819) and algorithm 2 (n=6,837). Results The sensitivity, specificity, and positive predictive values were: Algorithm 1: 95 percent, 65 percent, 49 percent; Algorithm 2: 52 percent, 88 percent, 60 percent. Compared to algorithm 1, profiles from algorithm 2 revealed higher rates of follow-up visits (43 percent, 55 percent) and appropriate antidepressant dosage acutely (82 percent, 90 percent) and chronically (83 percent, 91 percent) (p<0.05 for all). Conclusions Both algorithms had high false positive rates. Denominator construction (algorithm 1 versus 2) contributed significantly to variability in measured quality. Our findings raise concern about interpreting depression quality reports based upon administrative data. PMID:12968818

Image Reconstruction in Radio Astronomy with Non-Coplanar Synthesis Arrays

NASA Astrophysics Data System (ADS)

Goodrick, L.

2015-03-01

Traditional radio astronomy imaging techniques assume that the interferometric array is coplanar, with a small field of view, and that the two-dimensional Fourier relationship between brightness and visibility remains valid, allowing the Fast Fourier Transform to be used. In practice, to acquire more accurate data, the non-coplanar baseline effects need to be incorporated, as small height variations in the array plane introduces the w spatial frequency component. This component adds an additional phase shift to the incoming signals. There are two approaches to account for the non-coplanar baseline effects: either the full three-dimensional brightness and visibility model can be used to reconstruct an image, or the non-coplanar effects can be removed, reducing the three dimensional relationship to that of the two-dimensional one. This thesis describes and implements the w-projection and w-stacking algorithms. The aim of these algorithms is to account for the phase error introduced by non-coplanar synthesis arrays configurations, making the recovered visibilities more true to the actual brightness distribution model. This is done by reducing the 3D visibilities to a 2D visibility model. The algorithms also have the added benefit of wide-field imaging, although w-stacking supports a wider field of view at the cost of more FFT bin support. For w-projection, the w-term is accounted for in the visibility domain by convolving it out of the problem with a convolution kernel, allowing the use of the two-dimensional Fast Fourier Transform. Similarly, the w-Stacking algorithm applies a phase correction in the image domain to image layers to produce an intensity model that accounts for the non-coplanar baseline effects. This project considers the KAT7 array for simulation and analysis of the limitations and advantages of both the algorithms. Additionally, a variant of the Högbom CLEAN algorithm was used which employs contour trimming for extended source emission flagging. The CLEAN algorithm is an iterative two-dimensional deconvolution method that can further improve image fidelity by removing the effects of the point spread function which can obscure source data.

Clinical and dermoscopic stability and volatility of melanocytic nevi in a population-based cohort of children in Framingham school system

PubMed Central

Scope, Alon; Dusza, Stephen W.; Marghoob, Ashfaq A.; Satagopan, Jaya M.; Braga, Casagrande Tavoloni Juliana; Psaty, Estee L.; Weinstock, Martin A.; Oliveria, Susan A.; Bishop, Marilyn; Geller, Alan C.; Halpern, Allan C.

2011-01-01

Nevi are important risk markers of melanoma. The study aim was to describe changes in nevi of children using longitudinal data from a population-based cohort. Overview back photography and dermoscopic imaging of up to 4 index back nevi was performed at age 11 (baseline) and repeated at age 14 (follow-up). Of 443 children (39% females) imaged at baseline, 366 children (39% females) had repeated imaging three year later. At age 14, median back nevus counts increased by 2; 75% of students (n=274) had at least one new back nevus and 28% (n=103) had at least one nevus that disappeared. Of 936 index nevi imaged dermoscopically at baseline and follow-up, 69% (645 nevi) had retained the same dermoscopic classification from baseline evaluation. Only 4% (n=13) of nevi assessed as globular at baseline were classified as reticular at follow-up, and just 3% (n=3) of baseline reticular nevi were classified as globular at follow-up. Of 9 (1%) index nevi that disappeared at follow-up, none showed halo or regression at baseline. In conclusion, the relative stability of dermoscopic pattern of individual nevi in the face of the overall volatility of nevi during adolescence suggests that specific dermoscopic patterns may represent distinct biologic nevus subsets. PMID:21562569

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

NASA Astrophysics Data System (ADS)

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.

Validation of a Milk Consumption Stage of Change Algorithm among Adolescent Survivors of Childhood Cancer

ERIC Educational Resources Information Center

Mays, Darren; Gerfen, Elissa; Mosher, Revonda B.; Shad, Aziza T.; Tercyak, Kenneth P.

2012-01-01

Objective: To assess the construct validity of a milk consumption Stages of Change (SOC) algorithm among adolescent survivors of childhood cancer ages 11 to 21 years (n = 75). Methods: Baseline data from a randomized controlled trial designed to evaluate a health behavior intervention were analyzed. Assessments included a milk consumption SOC…

A General Algorithm for Reusing Krylov Subspace Information. I. Unsteady Navier-Stokes

NASA Technical Reports Server (NTRS)

Carpenter, Mark H.; Vuik, C.; Lucas, Peter; vanGijzen, Martin; Bijl, Hester

2010-01-01

A general algorithm is developed that reuses available information to accelerate the iterative convergence of linear systems with multiple right-hand sides A x = b (sup i), which are commonly encountered in steady or unsteady simulations of nonlinear equations. The algorithm is based on the classical GMRES algorithm with eigenvector enrichment but also includes a Galerkin projection preprocessing step and several novel Krylov subspace reuse strategies. The new approach is applied to a set of test problems, including an unsteady turbulent airfoil, and is shown in some cases to provide significant improvement in computational efficiency relative to baseline approaches.

Item response theory analysis of the mechanics baseline test

NASA Astrophysics Data System (ADS)

Cardamone, Caroline N.; Abbott, Jonathan E.; Rayyan, Saif; Seaton, Daniel T.; Pawl, Andrew; Pritchard, David E.

2012-02-01

Item response theory is useful in both the development and evaluation of assessments and in computing standardized measures of student performance. In item response theory, individual parameters (difficulty, discrimination) for each item or question are fit by item response models. These parameters provide a means for evaluating a test and offer a better measure of student skill than a raw test score, because each skill calculation considers not only the number of questions answered correctly, but the individual properties of all questions answered. Here, we present the results from an analysis of the Mechanics Baseline Test given at MIT during 2005-2010. Using the item parameters, we identify questions on the Mechanics Baseline Test that are not effective in discriminating between MIT students of different abilities. We show that a limited subset of the highest quality questions on the Mechanics Baseline Test returns accurate measures of student skill. We compare student skills as determined by item response theory to the more traditional measurement of the raw score and show that a comparable measure of learning gain can be computed.

Spatial cluster detection using dynamic programming.

PubMed

Sverchkov, Yuriy; Jiang, Xia; Cooper, Gregory F

2012-03-25

The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP) estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data. When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging. We conclude that the dynamic programming algorithm performs on-par with other available methods for spatial cluster detection and point to its low computational cost and extendability as advantages in favor of further research and use of the algorithm.

Spatial cluster detection using dynamic programming

PubMed Central

2012-01-01

Background The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. Methods We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP) estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data. Results When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging. Conclusions We conclude that the dynamic programming algorithm performs on-par with other available methods for spatial cluster detection and point to its low computational cost and extendability as advantages in favor of further research and use of the algorithm. PMID:22443103

Estimating error statistics for Chambon-la-Forêt observatory definitive data

NASA Astrophysics Data System (ADS)

Lesur, Vincent; Heumez, Benoît; Telali, Abdelkader; Lalanne, Xavier; Soloviev, Anatoly

2017-08-01

We propose a new algorithm for calibrating definitive observatory data with the goal of providing users with estimates of the data error standard deviations (SDs). The algorithm has been implemented and tested using Chambon-la-Forêt observatory (CLF) data. The calibration process uses all available data. It is set as a large, weakly non-linear, inverse problem that ultimately provides estimates of baseline values in three orthogonal directions, together with their expected standard deviations. For this inverse problem, absolute data error statistics are estimated from two series of absolute measurements made within a day. Similarly, variometer data error statistics are derived by comparing variometer data time series between different pairs of instruments over few years. The comparisons of these time series led us to use an autoregressive process of order 1 (AR1 process) as a prior for the baselines. Therefore the obtained baselines do not vary smoothly in time. They have relatively small SDs, well below 300 pT when absolute data are recorded twice a week - i.e. within the daily to weekly measures recommended by INTERMAGNET. The algorithm was tested against the process traditionally used to derive baselines at CLF observatory, suggesting that statistics are less favourable when this latter process is used. Finally, two sets of definitive data were calibrated using the new algorithm. Their comparison shows that the definitive data SDs are less than 400 pT and may be slightly overestimated by our process: an indication that more work is required to have proper estimates of absolute data error statistics. For magnetic field modelling, the results show that even on isolated sites like CLF observatory, there are very localised signals over a large span of temporal frequencies that can be as large as 1 nT. The SDs reported here encompass signals of a few hundred metres and less than a day wavelengths.

Performance characterization of a combined material identification and screening algorithm

NASA Astrophysics Data System (ADS)

Green, Robert L.; Hargreaves, Michael D.; Gardner, Craig M.

2013-05-01

Portable analytical devices based on a gamut of technologies (Infrared, Raman, X-Ray Fluorescence, Mass Spectrometry, etc.) are now widely available. These tools have seen increasing adoption for field-based assessment by diverse users including military, emergency response, and law enforcement. Frequently, end-users of portable devices are non-scientists who rely on embedded software and the associated algorithms to convert collected data into actionable information. Two classes of problems commonly encountered in field applications are identification and screening. Identification algorithms are designed to scour a library of known materials and determine whether the unknown measurement is consistent with a stored response (or combination of stored responses). Such algorithms can be used to identify a material from many thousands of possible candidates. Screening algorithms evaluate whether at least a subset of features in an unknown measurement correspond to one or more specific substances of interest and are typically configured to detect from a small list potential target analytes. Thus, screening algorithms are much less broadly applicable than identification algorithms; however, they typically provide higher detection rates which makes them attractive for specific applications such as chemical warfare agent or narcotics detection. This paper will present an overview and performance characterization of a combined identification/screening algorithm that has recently been developed. It will be shown that the combined algorithm provides enhanced detection capability more typical of screening algorithms while maintaining a broad identification capability. Additionally, we will highlight how this approach can enable users to incorporate situational awareness during a response.

High-confidence assessment of functional impact of human mitochondrial non-synonymous genome variations by APOGEE.

PubMed

Castellana, Stefano; Fusilli, Caterina; Mazzoccoli, Gianluigi; Biagini, Tommaso; Capocefalo, Daniele; Carella, Massimo; Vescovi, Angelo Luigi; Mazza, Tommaso

2017-06-01

24,189 are all the possible non-synonymous amino acid changes potentially affecting the human mitochondrial DNA. Only a tiny subset was functionally evaluated with certainty so far, while the pathogenicity of the vast majority was only assessed in-silico by software predictors. Since these tools proved to be rather incongruent, we have designed and implemented APOGEE, a machine-learning algorithm that outperforms all existing prediction methods in estimating the harmfulness of mitochondrial non-synonymous genome variations. We provide a detailed description of the underlying algorithm, of the selected and manually curated training and test sets of variants, as well as of its classification ability.

Hybrid stochastic simulation of reaction-diffusion systems with slow and fast dynamics.

PubMed

Strehl, Robert; Ilie, Silvana

2015-12-21

In this paper, we present a novel hybrid method to simulate discrete stochastic reaction-diffusion models arising in biochemical signaling pathways. We study moderately stiff systems, for which we can partition each reaction or diffusion channel into either a slow or fast subset, based on its propensity. Numerical approaches missing this distinction are often limited with respect to computational run time or approximation quality. We design an approximate scheme that remedies these pitfalls by using a new blending strategy of the well-established inhomogeneous stochastic simulation algorithm and the tau-leaping simulation method. The advantages of our hybrid simulation algorithm are demonstrated on three benchmarking systems, with special focus on approximation accuracy and efficiency.

Constrained Surface-Level Gateway Placement for Underwater Acoustic Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Li, Deying; Li, Zheng; Ma, Wenkai; Chen, Hong

One approach to guarantee the performance of underwater acoustic sensor networks is to deploy multiple Surface-level Gateways (SGs) at the surface. This paper addresses the connected (or survivable) Constrained Surface-level Gateway Placement (C-SGP) problem for 3-D underwater acoustic sensor networks. Given a set of candidate locations where SGs can be placed, our objective is to place minimum number of SGs at a subset of candidate locations such that it is connected (or 2-connected) from any USN to the base station. We propose a polynomial time approximation algorithm for the connected C-SGP problem and survivable C-SGP problem, respectively. Simulations are conducted to verify our algorithms' efficiency.

Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming

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

Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh

Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. Tomore » alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results: Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs.« less

Self-balancing dynamic scheduling of electrical energy for energy-intensive enterprises

NASA Astrophysics Data System (ADS)

Gao, Yunlong; Gao, Feng; Zhai, Qiaozhu; Guan, Xiaohong

2013-06-01

Balancing production and consumption with self-generation capacity in energy-intensive enterprises has huge economic and environmental benefits. However, balancing production and consumption with self-generation capacity is a challenging task since the energy production and consumption must be balanced in real time with the criteria specified by power grid. In this article, a mathematical model for minimising the production cost with exactly realisable energy delivery schedule is formulated. And a dynamic programming (DP)-based self-balancing dynamic scheduling algorithm is developed to obtain the complete solution set for such a multiple optimal solutions problem. For each stage, a set of conditions are established to determine whether a feasible control trajectory exists. The state space under these conditions is partitioned into subsets and each subset is viewed as an aggregate state, the cost-to-go function is then expressed as a function of initial and terminal generation levels of each stage and is proved to be a staircase function with finite steps. This avoids the calculation of the cost-to-go of every state to resolve the issue of dimensionality in DP algorithm. In the backward sweep process of the algorithm, an optimal policy is determined to maximise the realisability of energy delivery schedule across the entire time horizon. And then in the forward sweep process, the feasible region of the optimal policy with the initial and terminal state at each stage is identified. Different feasible control trajectories can be identified based on the region; therefore, optimising for the feasible control trajectory is performed based on the region with economic and reliability objectives taken into account.

GPU-accelerated regularized iterative reconstruction for few-view cone beam CT

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

Matenine, Dmitri, E-mail: dmitri.matenine.1@ulaval.ca; Goussard, Yves, E-mail: yves.goussard@polymtl.ca; Després, Philippe, E-mail: philippe.despres@phy.ulaval.ca

2015-04-15

Purpose: The present work proposes an iterative reconstruction technique designed for x-ray transmission computed tomography (CT). The main objective is to provide a model-based solution to the cone-beam CT reconstruction problem, yielding accurate low-dose images via few-views acquisitions in clinically acceptable time frames. Methods: The proposed technique combines a modified ordered subsets convex (OSC) algorithm and the total variation minimization (TV) regularization technique and is called OSC-TV. The number of subsets of each OSC iteration follows a reduction pattern in order to ensure the best performance of the regularization method. Considering the high computational cost of the algorithm, it ismore » implemented on a graphics processing unit, using parallelization to accelerate computations. Results: The reconstructions were performed on computer-simulated as well as human pelvic cone-beam CT projection data and image quality was assessed. In terms of convergence and image quality, OSC-TV performs well in reconstruction of low-dose cone-beam CT data obtained via a few-view acquisition protocol. It compares favorably to the few-view TV-regularized projections onto convex sets (POCS-TV) algorithm. It also appears to be a viable alternative to full-dataset filtered backprojection. Execution times are of 1–2 min and are compatible with the typical clinical workflow for nonreal-time applications. Conclusions: Considering the image quality and execution times, this method may be useful for reconstruction of low-dose clinical acquisitions. It may be of particular benefit to patients who undergo multiple acquisitions by reducing the overall imaging radiation dose and associated risks.« less

Personality and Career Success: Concurrent and Longitudinal Relations

PubMed Central

Sutin, Angelina R.; Costa, Paul T.; Miech, Richard; Eaton, William W.

2009-01-01

The present research addresses the dynamic transaction between extrinsic (occupational prestige, income) and intrinsic (job satisfaction) career success and the Five-Factor Model of personality. Participants (N = 731) completed a comprehensive measure of personality and reported their job title, annual income, and job satisfaction; a subset of these participants (n = 302) provided the same information approximately 10 years later. Measured concurrently, emotionally stable and conscientious participants reported higher incomes and job satisfaction. Longitudinal analyses revealed that, among younger participants, higher income at baseline predicted decreases in Neuroticism and baseline Extraversion predicted increases in income across the 10 years. Results suggest that the mutual influence of career success and personality is limited to income and occurs early in the career. PMID:19774106

Sensor failure detection for jet engines

NASA Technical Reports Server (NTRS)

Beattie, E. C.; Laprad, R. F.; Akhter, M. M.; Rock, S. M.

1983-01-01

Revisions to the advanced sensor failure detection, isolation, and accommodation (DIA) algorithm, developed under the sensor failure detection system program were studied to eliminate the steady state errors due to estimation filter biases. Three algorithm revisions were formulated and one revision for detailed evaluation was chosen. The selected version modifies the DIA algorithm to feedback the actual sensor outputs to the integral portion of the control for the nofailure case. In case of a failure, the estimates of the failed sensor output is fed back to the integral portion. The estimator outputs are fed back to the linear regulator portion of the control all the time. The revised algorithm is evaluated and compared to the baseline algorithm developed previously.

McTwo: a two-step feature selection algorithm based on maximal information coefficient.

PubMed

Ge, Ruiquan; Zhou, Manli; Luo, Youxi; Meng, Qinghan; Mai, Guoqin; Ma, Dongli; Wang, Guoqing; Zhou, Fengfeng

2016-03-23

High-throughput bio-OMIC technologies are producing high-dimension data from bio-samples at an ever increasing rate, whereas the training sample number in a traditional experiment remains small due to various difficulties. This "large p, small n" paradigm in the area of biomedical "big data" may be at least partly solved by feature selection algorithms, which select only features significantly associated with phenotypes. Feature selection is an NP-hard problem. Due to the exponentially increased time requirement for finding the globally optimal solution, all the existing feature selection algorithms employ heuristic rules to find locally optimal solutions, and their solutions achieve different performances on different datasets. This work describes a feature selection algorithm based on a recently published correlation measurement, Maximal Information Coefficient (MIC). The proposed algorithm, McTwo, aims to select features associated with phenotypes, independently of each other, and achieving high classification performance of the nearest neighbor algorithm. Based on the comparative study of 17 datasets, McTwo performs about as well as or better than existing algorithms, with significantly reduced numbers of selected features. The features selected by McTwo also appear to have particular biomedical relevance to the phenotypes from the literature. McTwo selects a feature subset with very good classification performance, as well as a small feature number. So McTwo may represent a complementary feature selection algorithm for the high-dimensional biomedical datasets.

Design and Application of Drought Indexes in Highly Regulated Mediterranean Water Systems

NASA Astrophysics Data System (ADS)

Castelletti, A.; Zaniolo, M.; Giuliani, M.

2017-12-01

Costs of drought are progressively increasing due to the undergoing alteration of hydro-meteorological regimes induced by climate change. Although drought management is largely studied in the literature, most of the traditional drought indexes fail in detecting critical events in highly regulated systems, which generally rely on ad-hoc formulations and cannot be generalized to different context. In this study, we contribute a novel framework for the design of a basin-customized drought index. This index represents a surrogate of the state of the basin and is computed by combining the available information about the water available in the system to reproduce a representative target variable for the drought condition of the basin (e.g., water deficit). To select the relevant variables and combinatione thereof, we use an advanced feature extraction algorithm called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS). W-QEISS relies on a multi-objective evolutionary algorithm to find Pareto-efficient subsets of variables by maximizing the wrapper accuracy, minimizing the number of selected variables, and optimizing relevance and redundancy of the subset. The accuracy objective is evaluated trough the calibration of an extreme learning machine of the water deficit for each candidate subset of variables, with the index selected from the resulting solutions identifying a suitable compromise between accuracy, cardinality, relevance, and redundancy. The approach is tested on Lake Como, Italy, a regulated lake mainly operated for irrigation supply. In the absence of an institutional drought monitoring system, we constructed the combined index using all the hydrological variables from the existing monitoring system as well as common drought indicators at multiple time aggregations. The soil moisture deficit in the root zone computed by a distributed-parameter water balance model of the agricultural districts is used as target variable. Numerical results show that our combined drought index succesfully reproduces the deficit. The index represents a valuable information for supporting appropriate drought management strategies, including the possibility of directly informing the lake operations about the drought conditions and improve the overall reliability of the irrigation supply system.

Evaluation of reconstruction techniques in regional cerebral blood flow SPECT using trade-off plots: a Monte Carlo study.

PubMed

Olsson, Anna; Arlig, Asa; Carlsson, Gudrun Alm; Gustafsson, Agnetha

2007-09-01

The image quality of single photon emission computed tomography (SPECT) depends on the reconstruction algorithm used. The purpose of the present study was to evaluate parameters in ordered subset expectation maximization (OSEM) and to compare systematically with filtered back-projection (FBP) for reconstruction of regional cerebral blood flow (rCBF) SPECT, incorporating attenuation and scatter correction. The evaluation was based on the trade-off between contrast recovery and statistical noise using different sizes of subsets, number of iterations and filter parameters. Monte Carlo simulated SPECT studies of a digital human brain phantom were used. The contrast recovery was calculated as measured contrast divided by true contrast. Statistical noise in the reconstructed images was calculated as the coefficient of variation in pixel values. A constant contrast level was reached above 195 equivalent maximum likelihood expectation maximization iterations. The choice of subset size was not crucial as long as there were > or = 2 projections per subset. The OSEM reconstruction was found to give 5-14% higher contrast recovery than FBP for all clinically relevant noise levels in rCBF SPECT. The Butterworth filter, power 6, achieved the highest stable contrast recovery level at all clinically relevant noise levels. The cut-off frequency should be chosen according to the noise level accepted in the image. Trade-off plots are shown to be a practical way of deciding the number of iterations and subset size for the OSEM reconstruction and can be used for other examination types in nuclear medicine.

Analysis of objects in binary images. M.S. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Leonard, Desiree M.

1991-01-01

Digital image processing techniques are typically used to produce improved digital images through the application of successive enhancement techniques to a given image or to generate quantitative data about the objects within that image. In support of and to assist researchers in a wide range of disciplines, e.g., interferometry, heavy rain effects on aerodynamics, and structure recognition research, it is often desirable to count objects in an image and compute their geometric properties. Therefore, an image analysis application package, focusing on a subset of image analysis techniques used for object recognition in binary images, was developed. This report describes the techniques and algorithms utilized in three main phases of the application and are categorized as: image segmentation, object recognition, and quantitative analysis. Appendices provide supplemental formulas for the algorithms employed as well as examples and results from the various image segmentation techniques and the object recognition algorithm implemented.

Accelerated computer generated holography using sparse bases in the STFT domain.

PubMed

Blinder, David; Schelkens, Peter

2018-01-22

Computer-generated holography at high resolutions is a computationally intensive task. Efficient algorithms are needed to generate holograms at acceptable speeds, especially for real-time and interactive applications such as holographic displays. We propose a novel technique to generate holograms using a sparse basis representation in the short-time Fourier space combined with a wavefront-recording plane placed in the middle of the 3D object. By computing the point spread functions in the transform domain, we update only a small subset of the precomputed largest-magnitude coefficients to significantly accelerate the algorithm over conventional look-up table methods. We implement the algorithm on a GPU, and report a speedup factor of over 30. We show that this transform is superior over wavelet-based approaches, and show quantitative and qualitative improvements over the state-of-the-art WASABI method; we report accuracy gains of 2dB PSNR, as well improved view preservation.

Advanced scatter search approach and its application in a sequencing problem of mixed-model assembly lines in a case company

NASA Astrophysics Data System (ADS)

Liu, Qiong; Wang, Wen-xi; Zhu, Ke-ren; Zhang, Chao-yong; Rao, Yun-qing

2014-11-01

Mixed-model assembly line sequencing is significant in reducing the production time and overall cost of production. To improve production efficiency, a mathematical model aiming simultaneously to minimize overtime, idle time and total set-up costs is developed. To obtain high-quality and stable solutions, an advanced scatter search approach is proposed. In the proposed algorithm, a new diversification generation method based on a genetic algorithm is presented to generate a set of potentially diverse and high-quality initial solutions. Many methods, including reference set update, subset generation, solution combination and improvement methods, are designed to maintain the diversification of populations and to obtain high-quality ideal solutions. The proposed model and algorithm are applied and validated in a case company. The results indicate that the proposed advanced scatter search approach is significant for mixed-model assembly line sequencing in this company.

Land Subsidence Monitoring by InSAR Time Series Technique Derived From ALOS-2 PALSAR-2 over Surabaya City, Indonesia

NASA Astrophysics Data System (ADS)

Aditiya, A.; Takeuchi, W.; Aoki, Y.

2017-12-01

Surabaya is the second largest city in Indonesia and the capital of East Java Province with rapid population and industrialization. The impact of urbanization in the big city can suffer potential disasters either nature or anthropogenic such as land subsidence and flood. The pattern of land subsidence need to be mapped for the purposes of planning and structuring the city as well as taking appropriate policy in anticipating and mitigating the impact. This research has used interferometric Synthetic Aperture Radar (InSAR) Small Baseline Subset (SBAS) technique and applied time series analysis to investigate land subsidence occured. The technique includes the process of focusing the SAR data, incorporating the precise orbit, generating interferogram and phase unwrapping using SNAPHU algorithms. The results showed land subsidence has been detected during 2014-2017 over Surabaya city area using ALOS-2/PALSAR-2 images data. These results reveal the subsidence has observed in several area in Surabaya in particular northern part reach up to ∼2 cm/year. The fastest subsidence occurs in highly populated areas suffer vulnerable to flooding and sea level rise impact. In urban areas we found a correlation between land subsidence with residential or industrial land use. It concludes that land subsidence is mainly caused by ground water consumption for industrial and residential use respectively.

Infrared Imaging of Capella with the IOTA Closure Phase Interferometer

NASA Astrophysics Data System (ADS)

Kraus, S.; Schloerb, F. P.; Traub, W. A.; Carleton, N. P.; Lacasse, M.; Pearlman, M.; Monnier, J. D.; Millan-Gabet, R.; Berger, J.-P.; Haguenauer, P.; Perraut, K.; Kern, P.; Malbet, F.; Labeye, P.

2005-07-01

We present infrared aperture synthesis maps produced with the upgraded Infrared Optical Telescope Array interferometer. Michelson interferograms on the close binary system Capella (α Aur) were obtained in the H band between 2002 November 12 and 16 using the IONIC3 beam combiner. With baselines of 15m<=B<=38 m, we were able to determine the relative position of the binary components with milliarcsecond precision and to track their movement along the ~14° arc covered by our observation run. We briefly describe the algorithms used for visibility and closure phase estimation. Three different hybrid mapping and bispectrum fitting techniques were implemented within one software framework and used to reconstruct the source brightness distribution. By dividing our data into subsets, the system could be mapped at three epochs, revealing the motion of the stars. The precise position of the binary components was also determined with model fits, which in addition revealed IAa/IAb=1.49+/-0.10 and apparent stellar uniform-disk diameters of ΘAa=8.9+/-0.6 mas and ΘAb=5.8+/-0.8 mas. To improve the (u,v)-plane coverage, we compensated this orbital motion by applying a rotation-compensating coordinate transformation. The resulting model-independent map with a beam size of 5.4mas×2.6 mas allows the resolution of the stellar surfaces of the Capella giants themselves.

Evaluation of two-fold fully conditional specification multiple imputation for longitudinal electronic health record data

PubMed Central

Welch, Catherine A; Petersen, Irene; Bartlett, Jonathan W; White, Ian R; Marston, Louise; Morris, Richard W; Nazareth, Irwin; Walters, Kate; Carpenter, James

2014-01-01

Most implementations of multiple imputation (MI) of missing data are designed for simple rectangular data structures ignoring temporal ordering of data. Therefore, when applying MI to longitudinal data with intermittent patterns of missing data, some alternative strategies must be considered. One approach is to divide data into time blocks and implement MI independently at each block. An alternative approach is to include all time blocks in the same MI model. With increasing numbers of time blocks, this approach is likely to break down because of co-linearity and over-fitting. The new two-fold fully conditional specification (FCS) MI algorithm addresses these issues, by only conditioning on measurements, which are local in time. We describe and report the results of a novel simulation study to critically evaluate the two-fold FCS algorithm and its suitability for imputation of longitudinal electronic health records. After generating a full data set, approximately 70% of selected continuous and categorical variables were made missing completely at random in each of ten time blocks. Subsequently, we applied a simple time-to-event model. We compared efficiency of estimated coefficients from a complete records analysis, MI of data in the baseline time block and the two-fold FCS algorithm. The results show that the two-fold FCS algorithm maximises the use of data available, with the gain relative to baseline MI depending on the strength of correlations within and between variables. Using this approach also increases plausibility of the missing at random assumption by using repeated measures over time of variables whose baseline values may be missing. PMID:24782349

Denosumab and Bone Metastasis–Free Survival in Men With Nonmetastatic Castration-Resistant Prostate Cancer: Exploratory Analyses by Baseline Prostate-Specific Antigen Doubling Time

PubMed Central

Smith, Matthew R.; Saad, Fred; Oudard, Stephane; Shore, Neal; Fizazi, Karim; Sieber, Paul; Tombal, Bertrand; Damiao, Ronaldo; Marx, Gavin; Miller, Kurt; Van Veldhuizen, Peter; Morote, Juan; Ye, Zhishen; Dansey, Roger; Goessl, Carsten

2013-01-01

Purpose Denosumab, an anti–RANK ligand monoclonal antibody, significantly increases bone metastasis–free survival (BMFS; hazard ratio [HR], 0.85; P = .028) and delays time to first bone metastasis in men with nonmetastatic castration-resistant prostate cancer (CRPC) and baseline prostate-specific antigen (PSA) ≥ 8.0 ng/mL and/or PSA doubling time (PSADT) ≤ 10.0 months. To identify men at greatest risk for bone metastasis or death, we evaluated relationships between PSA and PSADT with BMFS in the placebo group and the efficacy and safety of denosumab in men with PSADT ≤ 10, ≤ 6, and ≤ 4 months. Patients and Methods A total of 1,432 men with nonmetastatic CRPC were randomly assigned 1:1 to monthly subcutaneous denosumab 120 mg or placebo. Enrollment began February 2006; primary analysis cutoff was July 2010, when approximately 660 men were anticipated to have developed bone metastases or died. Results In the placebo group, shorter BMFS was observed as PSADT decreased below 8 months. In analyses by shorter baseline PSADT, denosumab consistently increased BMFS by a median of 6.0, 7.2, and 7.5 months among men with PSADT ≤ 10 (HR, 0.84; P = .042), ≤ 6 (HR, 0.77; P = .006), and ≤ 4 months (HR, 0.71; P = .004), respectively. Denosumab also consistently increased time to bone metastasis by PSADT subset. No difference in survival was observed between treatment groups for the overall study population or PSADT subsets. Conclusion Patients with shorter PSADT are at greater risk for bone metastasis or death. Denosumab consistently improves BMFS in men with shorter PSADT and seems to have the greatest treatment effects in men at high risk for progression. PMID:24043751

Denosumab and bone metastasis-free survival in men with nonmetastatic castration-resistant prostate cancer: exploratory analyses by baseline prostate-specific antigen doubling time.

PubMed

Smith, Matthew R; Saad, Fred; Oudard, Stephane; Shore, Neal; Fizazi, Karim; Sieber, Paul; Tombal, Bertrand; Damiao, Ronaldo; Marx, Gavin; Miller, Kurt; Van Veldhuizen, Peter; Morote, Juan; Ye, Zhishen; Dansey, Roger; Goessl, Carsten

2013-10-20

Denosumab, an anti-RANK ligand monoclonal antibody, significantly increases bone metastasis-free survival (BMFS; hazard ratio [HR], 0.85; P = .028) and delays time to first bone metastasis in men with nonmetastatic castration-resistant prostate cancer (CRPC) and baseline prostate-specific antigen (PSA) ≥ 8.0 ng/mL and/or PSA doubling time (PSADT) ≤ 10.0 months. To identify men at greatest risk for bone metastasis or death, we evaluated relationships between PSA and PSADT with BMFS in the placebo group and the efficacy and safety of denosumab in men with PSADT ≤ 10, ≤ 6, and ≤ 4 months. A total of 1,432 men with nonmetastatic CRPC were randomly assigned 1:1 to monthly subcutaneous denosumab 120 mg or placebo. Enrollment began February 2006; primary analysis cutoff was July 2010, when approximately 660 men were anticipated to have developed bone metastases or died. In the placebo group, shorter BMFS was observed as PSADT decreased below 8 months. In analyses by shorter baseline PSADT, denosumab consistently increased BMFS by a median of 6.0, 7.2, and 7.5 months among men with PSADT ≤ 10 (HR, 0.84; P = .042), ≤ 6 (HR, 0.77; P = .006), and ≤ 4 months (HR, 0.71; P = .004), respectively. Denosumab also consistently increased time to bone metastasis by PSADT subset. No difference in survival was observed between treatment groups for the overall study population or PSADT subsets. Patients with shorter PSADT are at greater risk for bone metastasis or death. Denosumab consistently improves BMFS in men with shorter PSADT and seems to have the greatest treatment effects in men at high risk for progression.

Long-term outcomes from the National Drug Abuse Treatment Clinical Trials Network Prescription Opioid Addiction Treatment Study.

PubMed

Weiss, Roger D; Potter, Jennifer Sharpe; Griffin, Margaret L; Provost, Scott E; Fitzmaurice, Garrett M; McDermott, Katherine A; Srisarajivakul, Emily N; Dodd, Dorian R; Dreifuss, Jessica A; McHugh, R Kathryn; Carroll, Kathleen M

2015-05-01

Despite the growing prevalence of prescription opioid dependence, longitudinal studies have not examined long-term treatment response. The current study examined outcomes over 42 months in the Prescription Opioid Addiction Treatment Study (POATS). POATS was a multi-site clinical trial lasting up to 9 months, examining different durations of buprenorphine-naloxone plus standard medical management for prescription opioid dependence, with participants randomized to receive or not receive additional opioid drug counseling. A subset of participants (N=375 of 653) enrolled in a follow-up study. Telephone interviews were administered approximately 18, 30, and 42 months after main-trial enrollment. Comparison of baseline characteristics by follow-up participation suggested few differences. At Month 42, much improvement was seen: 31.7% were abstinent from opioids and not on agonist therapy; 29.4% were receiving opioid agonist therapy, but met no symptom criteria for current opioid dependence; 7.5% were using illicit opioids while on agonist therapy; and the remaining 31.4% were using opioids without agonist therapy. Participants reporting a lifetime history of heroin use at baseline were more likely to meet DSM-IV criteria for opioid dependence at Month 42 (OR=4.56, 95% CI=1.29-16.04, p<.05). Engagement in agonist therapy was associated with a greater likelihood of illicit-opioid abstinence. Eight percent (n=27/338) used heroin for the first time during follow-up; 10.1% reported first-time injection heroin use. Long-term outcomes for those dependent on prescription opioids demonstrated clear improvement from baseline. However, a subset exhibited a worsening course, by initiating heroin use and/or injection opioid use. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Incorporating High-Frequency Physiologic Data Using Computational Dictionary Learning Improves Prediction of Delayed Cerebral Ischemia Compared to Existing Methods.

PubMed

Megjhani, Murad; Terilli, Kalijah; Frey, Hans-Peter; Velazquez, Angela G; Doyle, Kevin William; Connolly, Edward Sander; Roh, David Jinou; Agarwal, Sachin; Claassen, Jan; Elhadad, Noemie; Park, Soojin

2018-01-01

Accurate prediction of delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) can be critical for planning interventions to prevent poor neurological outcome. This paper presents a model using convolution dictionary learning to extract features from physiological data available from bedside monitors. We develop and validate a prediction model for DCI after SAH, demonstrating improved precision over standard methods alone. 488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Modified Fisher Scale was considered the standard grading scale in clinical use; baseline features also analyzed included age, sex, Hunt-Hess, and Glasgow Coma Scales. An unsupervised approach using convolution dictionary learning was used to extract features from physiological time series (systolic blood pressure and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation). Classifiers (partial least squares and linear and kernel support vector machines) were trained on feature subsets of the derivation dataset. Models were applied to the validation dataset. The performances of the best classifiers on the validation dataset are reported by feature subset. Standard grading scale (mFS): AUC 0.54. Combined demographics and grading scales (baseline features): AUC 0.63. Kernel derived physiologic features: AUC 0.66. Combined baseline and physiologic features with redundant feature reduction: AUC 0.71 on derivation dataset and 0.78 on validation dataset. Current DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that we could incorporate individual physiologic data to achieve higher classification accuracy.

Postprandial Monocyte Activation in Individuals With Metabolic Syndrome

PubMed Central

Khan, Ilvira M.; Pokharel, Yashashwi; Dadu, Razvan T.; Lewis, Dorothy E.; Hoogeveen, Ron C.; Wu, Huaizhu

2016-01-01

Context: Postprandial hyperlipidemia has been suggested to contribute to atherogenesis by inducing proinflammatory changes in monocytes. Individuals with metabolic syndrome (MS), shown to have higher blood triglyceride concentration and delayed triglyceride clearance, may thus have increased risk for development of atherosclerosis. Objective: Our objective was to examine fasting levels and effects of a high-fat meal on phenotypes of monocyte subsets in individuals with obesity and MS and in healthy controls. Design, Setting, Participants, Intervention: Individuals with obesity and MS and gender- and age-matched healthy controls were recruited. Blood was collected from participants after an overnight fast (baseline) and at 3 and 5 hours after ingestion of a high-fat meal. At each time point, monocyte phenotypes were examined by multiparameter flow cytometry. Main Outcome Measures: Baseline levels of activation markers and postprandial inflammatory response in each of the three monocyte subsets were measured. Results: At baseline, individuals with obesity and MS had higher proportions of circulating lipid-laden foamy monocytes than controls, which were positively correlated with fasting triglyceride levels. Additionally, the MS group had increased counts of nonclassical monocytes, higher CD11c, CX3CR1, and human leukocyte antigen-DR levels on intermediate monocytes, and higher CCR5 and tumor necrosis factor-α levels on classical monocytes in the circulation. Postprandial triglyceride increases in both groups were paralleled by upregulation of lipid-laden foamy monocytes. MS, but not control, subjects had significant postprandial increases of CD11c and percentages of IL-1β+ and tumor necrosis factor-α+ cells in nonclassical monocytes. Conclusions: Compared to controls, individuals with obesity and MS had increased fasting and postprandial monocyte lipid accumulation and activation. PMID:27575945

Fall 2014 SEI Research Review Probabilistic Analysis of Time Sensitive Systems

DTIC Science & Technology

2014-10-28

Osmosis SMC Tool Osmosis is a tool for Statistical Model Checking (SMC) with Semantic Importance Sampling. • Input model is written in subset of C...ASSERT() statements in model indicate conditions that must hold. • Input probability distributions defined by the user. • Osmosis returns the...on: – Target relative error, or – Set number of simulations Osmosis Main Algorithm 1 http://dreal.cs.cmu.edu/ (?⃑?): Indicator

Clinical Evaluation of 68Ga-PSMA-II and 68Ga-RM2 PET Images Reconstructed With an Improved Scatter Correction Algorithm.

PubMed

Wangerin, Kristen A; Baratto, Lucia; Khalighi, Mohammad Mehdi; Hope, Thomas A; Gulaka, Praveen K; Deller, Timothy W; Iagaru, Andrei H

2018-06-06

Gallium-68-labeled radiopharmaceuticals pose a challenge for scatter estimation because their targeted nature can produce high contrast in these regions of the kidneys and bladder. Even small errors in the scatter estimate can result in washout artifacts. Administration of diuretics can reduce these artifacts, but they may result in adverse events. Here, we investigated the ability of algorithmic modifications to mitigate washout artifacts and eliminate the need for diuretics or other interventions. The model-based scatter algorithm was modified to account for PET/MRI scanner geometry and challenges of non-FDG tracers. Fifty-three clinical 68 Ga-RM2 and 68 Ga-PSMA-11 whole-body images were reconstructed using the baseline scatter algorithm. For comparison, reconstruction was also processed with modified sampling in the single-scatter estimation and with an offset in the scatter tail-scaling process. None of the patients received furosemide to attempt to decrease the accumulation of radiopharmaceuticals in the bladder. The images were scored independently by three blinded reviewers using the 5-point Likert scale. The scatter algorithm improvements significantly decreased or completely eliminated the washout artifacts. When comparing the baseline and most improved algorithm, the image quality increased and image artifacts were reduced for both 68 Ga-RM2 and for 68 Ga-PSMA-11 in the kidneys and bladder regions. Image reconstruction with the improved scatter correction algorithm mitigated washout artifacts and recovered diagnostic image quality in 68 Ga PET, indicating that the use of diuretics may be avoided.

Guided color consistency optimization for image mosaicking

NASA Astrophysics Data System (ADS)

Xie, Renping; Xia, Menghan; Yao, Jian; Li, Li

2018-01-01

This paper studies the problem of color consistency correction for sequential images with diverse color characteristics. Existing algorithms try to adjust all images to minimize color differences among images under a unified energy framework, however, the results are prone to presenting a consistent but unnatural appearance when the color difference between images is large and diverse. In our approach, this problem is addressed effectively by providing a guided initial solution for the global consistency optimization, which avoids converging to a meaningless integrated solution. First of all, to obtain the reliable intensity correspondences in overlapping regions between image pairs, we creatively propose the histogram extreme point matching algorithm which is robust to image geometrical misalignment to some extents. In the absence of the extra reference information, the guided initial solution is learned from the major tone of the original images by searching some image subset as the reference, whose color characteristics will be transferred to the others via the paths of graph analysis. Thus, the final results via global adjustment will take on a consistent color similar to the appearance of the reference image subset. Several groups of convincing experiments on both the synthetic dataset and the challenging real ones sufficiently demonstrate that the proposed approach can achieve as good or even better results compared with the state-of-the-art approaches.

3D tomographic imaging with the γ-eye planar scintigraphic gamma camera

NASA Astrophysics Data System (ADS)

Tunnicliffe, H.; Georgiou, M.; Loudos, G. K.; Simcox, A.; Tsoumpas, C.

2017-11-01

γ-eye is a desktop planar scintigraphic gamma camera (100 mm × 50 mm field of view) designed by BET Solutions as an affordable tool for dynamic, whole body, small-animal imaging. This investigation tests the viability of using γ-eye for the collection of tomographic data for 3D SPECT reconstruction. Two software packages, QSPECT and STIR (software for tomographic image reconstruction), have been compared. Reconstructions have been performed using QSPECT’s implementation of the OSEM algorithm and STIR’s OSMAPOSL (Ordered Subset Maximum A Posteriori One Step Late) and OSSPS (Ordered Subsets Separable Paraboloidal Surrogate) algorithms. Reconstructed images of phantom and mouse data have been assessed in terms of spatial resolution, sensitivity to varying activity levels and uniformity. The effect of varying the number of iterations, the voxel size (1.25 mm default voxel size reduced to 0.625 mm and 0.3125 mm), the point spread function correction and the weight of prior terms were explored. While QSPECT demonstrated faster reconstructions, STIR outperformed it in terms of resolution (as low as 1 mm versus 3 mm), particularly when smaller voxel sizes were used, and in terms of uniformity, particularly when prior terms were used. Little difference in terms of sensitivity was seen throughout.

A multi-model fusion strategy for multivariate calibration using near and mid-infrared spectra of samples from brewing industry

NASA Astrophysics Data System (ADS)

Tan, Chao; Chen, Hui; Wang, Chao; Zhu, Wanping; Wu, Tong; Diao, Yuanbo

2013-03-01

Near and mid-infrared (NIR/MIR) spectroscopy techniques have gained great acceptance in the industry due to their multiple applications and versatility. However, a success of application often depends heavily on the construction of accurate and stable calibration models. For this purpose, a simple multi-model fusion strategy is proposed. It is actually the combination of Kohonen self-organizing map (KSOM), mutual information (MI) and partial least squares (PLSs) and therefore named as KMICPLS. It works as follows: First, the original training set is fed into a KSOM for unsupervised clustering of samples, on which a series of training subsets are constructed. Thereafter, on each of the training subsets, a MI spectrum is calculated and only the variables with higher MI values than the mean value are retained, based on which a candidate PLS model is constructed. Finally, a fixed number of PLS models are selected to produce a consensus model. Two NIR/MIR spectral datasets from brewing industry are used for experiments. The results confirms its superior performance to two reference algorithms, i.e., the conventional PLS and genetic algorithm-PLS (GAPLS). It can build more accurate and stable calibration models without increasing the complexity, and can be generalized to other NIR/MIR applications.

A hybrid genetic algorithm-extreme learning machine approach for accurate significant wave height reconstruction

NASA Astrophysics Data System (ADS)

Alexandre, E.; Cuadra, L.; Nieto-Borge, J. C.; Candil-García, G.; del Pino, M.; Salcedo-Sanz, S.

2015-08-01

Wave parameters computed from time series measured by buoys (significant wave height Hs, mean wave period, etc.) play a key role in coastal engineering and in the design and operation of wave energy converters. Storms or navigation accidents can make measuring buoys break down, leading to missing data gaps. In this paper we tackle the problem of locally reconstructing Hs at out-of-operation buoys by using wave parameters from nearby buoys, based on the spatial correlation among values at neighboring buoy locations. The novelty of our approach for its potential application to problems in coastal engineering is twofold. On one hand, we propose a genetic algorithm hybridized with an extreme learning machine that selects, among the available wave parameters from the nearby buoys, a subset FnSP with nSP parameters that minimizes the Hs reconstruction error. On the other hand, we evaluate to what extent the selected parameters in subset FnSP are good enough in assisting other machine learning (ML) regressors (extreme learning machines, support vector machines and gaussian process regression) to reconstruct Hs. The results show that all the ML method explored achieve a good Hs reconstruction in the two different locations studied (Caribbean Sea and West Atlantic).

Focusing high-squint and large-baseline one-stationary bistatic SAR data using keystone transform and enhanced nonlinear chirp scaling based on an ellipse model

NASA Astrophysics Data System (ADS)

Zhong, Hua; Zhang, Song; Hu, Jian; Sun, Minhong

2017-12-01

This paper deals with the imaging problem for one-stationary bistatic synthetic aperture radar (BiSAR) with high-squint, large-baseline configuration. In this bistatic configuration, accurate focusing of BiSAR data is a difficult issue due to the relatively large range cell migration (RCM), severe range-azimuth coupling, and inherent azimuth-geometric variance. To circumvent these issues, an enhanced azimuth nonlinear chirp scaling (NLCS) algorithm based on an ellipse model is investigated in this paper. In the range processing, a method combining deramp operation and keystone transform (KT) is adopted to remove linear RCM completely and mitigate range-azimuth cross-coupling. In the azimuth focusing, an ellipse model is established to analyze and depict the characteristic of azimuth-variant Doppler phase. Based on the new model, an enhanced azimuth NLCS algorithm is derived to focus one-stationary BiSAR data. Simulating results exhibited at the end of this paper validate the effectiveness of the proposed algorithm.

Enhancing Time-Series Detection Algorithms for Automated Biosurveillance

PubMed Central

Burkom, Howard; Xing, Jian; English, Roseanne; Bloom, Steven; Cox, Kenneth; Pavlin, Julie A.

2009-01-01

BioSense is a US national system that uses data from health information systems for automated disease surveillance. We studied 4 time-series algorithm modifications designed to improve sensitivity for detecting artificially added data. To test these modified algorithms, we used reports of daily syndrome visits from 308 Department of Defense (DoD) facilities and 340 hospital emergency departments (EDs). At a constant alert rate of 1%, sensitivity was improved for both datasets by using a minimum standard deviation (SD) of 1.0, a 14–28 day baseline duration for calculating mean and SD, and an adjustment for total clinic visits as a surrogate denominator. Stratifying baseline days into weekdays versus weekends to account for day-of-week effects increased sensitivity for the DoD data but not for the ED data. These enhanced methods may increase sensitivity without increasing the alert rate and may improve the ability to detect outbreaks by using automated surveillance system data. PMID:19331728

Choosing non-redundant representative subsets of protein sequence data sets using submodular optimization.

PubMed

Libbrecht, Maxwell W; Bilmes, Jeffrey A; Noble, William Stafford

2018-04-01

Selecting a non-redundant representative subset of sequences is a common step in many bioinformatics workflows, such as the creation of non-redundant training sets for sequence and structural models or selection of "operational taxonomic units" from metagenomics data. Previous methods for this task, such as CD-HIT, PISCES, and UCLUST, apply a heuristic threshold-based algorithm that has no theoretical guarantees. We propose a new approach based on submodular optimization. Submodular optimization, a discrete analogue to continuous convex optimization, has been used with great success for other representative set selection problems. We demonstrate that the submodular optimization approach results in representative protein sequence subsets with greater structural diversity than sets chosen by existing methods, using as a gold standard the SCOPe library of protein domain structures. In this setting, submodular optimization consistently yields protein sequence subsets that include more SCOPe domain families than sets of the same size selected by competing approaches. We also show how the optimization framework allows us to design a mixture objective function that performs well for both large and small representative sets. The framework we describe is the best possible in polynomial time (under some assumptions), and it is flexible and intuitive because it applies a suite of generic methods to optimize one of a variety of objective functions. © 2018 Wiley Periodicals, Inc.

Application of phase-diverse phase retrieval to wavefront sensing in non-connected complicated pupil optics

NASA Astrophysics Data System (ADS)

Mao, Heng; Wang, Xiao; Zhao, Dazun

2007-07-01

Baseline algorithm, as a tool in wavefront sensing (WFS), incorporates the phase-diverse phase retrieval (PDPR) method with hybrid-unwrapping approach to ensure a unique pupil phase estimate with high WFS accuracy even in the case of high dynamic range aberration, as long as the pupil shape is of a convex set. However, for a complicated pupil, such as that in obstructed pupil optics, the said unwrapping approach would fail owing to the fake values at points located in obstructed areas of the pupil. Thus a modified unwrapping approach that can minimize the negative effects of the obstructed areas is proposed. Simulations have shown the validity of this unwrapping approach when it is embedded in Baseline algorithm.

Quantum load balancing in ad hoc networks

NASA Astrophysics Data System (ADS)

Hasanpour, M.; Shariat, S.; Barnaghi, P.; Hoseinitabatabaei, S. A.; Vahid, S.; Tafazolli, R.

2017-06-01

This paper presents a novel approach in targeting load balancing in ad hoc networks utilizing the properties of quantum game theory. This approach benefits from the instantaneous and information-less capability of entangled particles to synchronize the load balancing strategies in ad hoc networks. The quantum load balancing (QLB) algorithm proposed by this work is implemented on top of OLSR as the baseline routing protocol; its performance is analyzed against the baseline OLSR, and considerable gain is reported regarding some of the main QoS metrics such as delay and jitter. Furthermore, it is shown that QLB algorithm supports a solid stability gain in terms of throughput which stands a proof of concept for the load balancing properties of the proposed theory.

State estimation of spatio-temporal phenomena

NASA Astrophysics Data System (ADS)

Yu, Dan

This dissertation addresses the state estimation problem of spatio-temporal phenomena which can be modeled by partial differential equations (PDEs), such as pollutant dispersion in the atmosphere. After discretizing the PDE, the dynamical system has a large number of degrees of freedom (DOF). State estimation using Kalman Filter (KF) is computationally intractable, and hence, a reduced order model (ROM) needs to be constructed first. Moreover, the nonlinear terms, external disturbances or unknown boundary conditions can be modeled as unknown inputs, which leads to an unknown input filtering problem. Furthermore, the performance of KF could be improved by placing sensors at feasible locations. Therefore, the sensor scheduling problem to place multiple mobile sensors is of interest. The first part of the dissertation focuses on model reduction for large scale systems with a large number of inputs/outputs. A commonly used model reduction algorithm, the balanced proper orthogonal decomposition (BPOD) algorithm, is not computationally tractable for large systems with a large number of inputs/outputs. Inspired by the BPOD and randomized algorithms, we propose a randomized proper orthogonal decomposition (RPOD) algorithm and a computationally optimal RPOD (RPOD*) algorithm, which construct an ROM to capture the input-output behaviour of the full order model, while reducing the computational cost of BPOD by orders of magnitude. It is demonstrated that the proposed RPOD* algorithm could construct the ROM in real-time, and the performance of the proposed algorithms on different advection-diffusion equations. Next, we consider the state estimation problem of linear discrete-time systems with unknown inputs which can be treated as a wide-sense stationary process with rational power spectral density, while no other prior information needs to be known. We propose an autoregressive (AR) model based unknown input realization technique which allows us to recover the input statistics from the output data by solving an appropriate least squares problem, then fit an AR model to the recovered input statistics and construct an innovations model of the unknown inputs using the eigensystem realization algorithm. The proposed algorithm outperforms the augmented two-stage Kalman Filter (ASKF) and the unbiased minimum-variance (UMV) algorithm are shown in several examples. Finally, we propose a framework to place multiple mobile sensors to optimize the long-term performance of KF in the estimation of the state of a PDE. The major challenges are that placing multiple sensors is an NP-hard problem, and the optimization problem is non-convex in general. In this dissertation, first, we construct an ROM using RPOD* algorithm, and then reduce the feasible sensor locations into a subset using the ROM. The Information Space Receding Horizon Control (I-RHC) approach and a modified Monte Carlo Tree Search (MCTS) approach are applied to solve the sensor scheduling problem using the subset. Various applications have been provided to demonstrate the performance of the proposed approach.

Initiation of insulin glargine therapy in type 2 diabetes subjects suboptimally controlled on oral antidiabetic agents: results from the AT.LANTUS trial.

PubMed

Davies, M; Lavalle-González, F; Storms, F; Gomis, R

2008-05-01

For many patients with type 2 diabetes, oral antidiabetic agents (OADs) do not provide optimal glycaemic control, necessitating insulin therapy. Fear of hypoglycaemia is a major barrier to initiating insulin therapy. The AT.LANTUS study investigated optimal methods to initiate and maintain insulin glargine (LANTUS, glargine, Sanofi-aventis, Paris, France) therapy using two treatment algorithms. This subgroup analysis investigated the initiation of once-daily glargine therapy in patients suboptimally controlled on multiple OADs. This study was a 24-week, multinational (59 countries), multicenter (611), randomized study. Algorithm 1 was a clinic-driven titration and algorithm 2 was a patient-driven titration. Titration was based on target fasting blood glucose < or =100 mg/dl (< or =5.5 mmol/l). Algorithms were compared for incidence of severe hypoglycaemia [requiring assistance and blood glucose <50 mg/dl (<2.8 mmol/l)] and baseline to end-point change in haemoglobin A(1c) (HbA(1c)). Of the 4961 patients enrolled in the study, 865 were included in this subgroup analysis: 340 received glargine plus 1 OAD and 525 received glargine plus >1 OAD. Incidence of severe hypoglycaemia was <1%. HbA(1c) decreased significantly between baseline and end-point for patients receiving glargine plus 1 OAD (-1.4%, p < 0.001; algorithm 1 -1.3% vs. algorithm 2 -1.5%; p = 0.03) and glargine plus >1 OAD (-1.7%, p < 0.001; algorithm 1 -1.5% vs. algorithm 2 -1.8%; p = 0.001). This study shows that initiation of once-daily glargine with OADs results in significant reduction of HbA(1c) with a low risk of hypoglycaemia. The greater reduction in HbA(1c) was seen in patients randomized to the patient-driven algorithm (algorithm 2) on 1 or >1 OAD.

Validation of the Abdominal Pain Index using a revised scoring method.

PubMed

Laird, Kelsey T; Sherman, Amanda L; Smith, Craig A; Walker, Lynn S

2015-06-01

Evaluate the psychometric properties of child- and parent-report versions of the four-item Abdominal Pain Index (API) in children with functional abdominal pain (FAP) and healthy controls, using a revised scoring method that facilitates comparisons of scores across samples and time. Pediatric patients aged 8-18 years with FAP and controls completed the API at baseline (N = 1,967); a subset of their parents (N = 290) completed the API regarding the child's pain. Subsets of patients completed follow-up assessments at 2 weeks (N = 231), 3 months (N = 330), and 6 months (N = 107). Subsets of both patients (N = 389) and healthy controls (N = 172) completed a long-term follow-up assessment (mean age at follow-up = 20.21 years, SD = 3.75). The API demonstrated good concurrent, discriminant, and construct validity, as well as good internal consistency. We conclude that the API, using the revised scoring method, is a useful, reliable, and valid measure of abdominal pain severity. © The Author 2015. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Introducing TreeCollapse: a novel greedy algorithm to solve the cophylogeny reconstruction problem.

PubMed

Drinkwater, Benjamin; Charleston, Michael A

2014-01-01

Cophylogeny mapping is used to uncover deep coevolutionary associations between two or more phylogenetic histories at a macro coevolutionary scale. As cophylogeny mapping is NP-Hard, this technique relies heavily on heuristics to solve all but the most trivial cases. One notable approach utilises a metaheuristic to search only a subset of the exponential number of fixed node orderings possible for the phylogenetic histories in question. This is of particular interest as it is the only known heuristic that guarantees biologically feasible solutions. This has enabled research to focus on larger coevolutionary systems, such as coevolutionary associations between figs and their pollinator wasps, including over 200 taxa. Although able to converge on solutions for problem instances of this size, a reduction from the current cubic running time is required to handle larger systems, such as Wolbachia and their insect hosts. Rather than solving this underlying problem optimally this work presents a greedy algorithm called TreeCollapse, which uses common topological patterns to recover an approximation of the coevolutionary history where the internal node ordering is fixed. This approach offers a significant speed-up compared to previous methods, running in linear time. This algorithm has been applied to over 100 well-known coevolutionary systems converging on Pareto optimal solutions in over 68% of test cases, even where in some cases the Pareto optimal solution has not previously been recoverable. Further, while TreeCollapse applies a local search technique, it can guarantee solutions are biologically feasible, making this the fastest method that can provide such a guarantee. As a result, we argue that the newly proposed algorithm is a valuable addition to the field of coevolutionary research. Not only does it offer a significantly faster method to estimate the cost of cophylogeny mappings but by using this approach, in conjunction with existing heuristics, it can assist in recovering a larger subset of the Pareto front than has previously been possible.

Network Design in Close-Range Photogrammetry with Short Baseline Images

NASA Astrophysics Data System (ADS)

Barazzetti, L.

2017-08-01

The avaibility of automated software for image-based 3D modelling has changed the way people acquire images for photogrammetric applications. Short baseline images are required to match image points with SIFT-like algorithms, obtaining more images than those necessary for "old fashioned" photogrammetric projects based on manual measurements. This paper describes some considerations on network design for short baseline image sequences, especially on precision and reliability of bundle adjustment. Simulated results reveal that the large number of 3D points used for image orientation has very limited impact on network precision.

Probabilistic Open Set Recognition

NASA Astrophysics Data System (ADS)

Jain, Lalit Prithviraj

Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary support vector machines. Building from the success of statistical EVT based recognition methods such as PI-SVM and W-SVM on the open set problem, we present a new general supervised learning algorithm for multi-class classification and multi-class open set recognition called the Extreme Value Local Basis (EVLB). The design of this algorithm is motivated by the observation that extrema from known negative class distributions are the closest negative points to any positive sample during training, and thus should be used to define the parameters of a probabilistic decision model. In the EVLB, the kernel distribution for each positive training sample is estimated via an EVT distribution fit over the distances to the separating hyperplane between positive training sample and closest negative samples, with a subset of the overall positive training data retained to form a probabilistic decision boundary. Using this subset as a frame of reference, the probability of a sample at test time decreases as it moves away from the positive class. Possessing this property, the EVLB is well-suited to open set recognition problems where samples from unknown or novel classes are encountered at test. Our experimental evaluation shows that the EVLB provides a substantial improvement in scalability compared to standard radial basis function kernel machines, as well as P I-SVM and W-SVM, with improved accuracy in many cases. We evaluate our algorithm on open set variations of the standard visual learning benchmarks, as well as with an open subset of classes from Caltech 256 and ImageNet. Our experiments show that PI-SVM, WSVM and EVLB provide significant advances over the previous state-of-the-art solutions for the same tasks.

Structure theorems for game trees

PubMed Central

Govindan, Srihari; Wilson, Robert

2002-01-01

Kohlberg and Mertens [Kohlberg, E. & Mertens, J. (1986) Econometrica 54, 1003–1039] proved that the graph of the Nash equilibrium correspondence is homeomorphic to its domain when the domain is the space of payoffs in normal-form games. A counterexample disproves the analog for the equilibrium outcome correspondence over the space of payoffs in extensive-form games, but we prove an analog when the space of behavior strategies is perturbed so that every path in the game tree has nonzero probability. Without such perturbations, the graph is the closure of the union of a finite collection of its subsets, each diffeomorphic to a corresponding path-connected open subset of the space of payoffs. As an application, we construct an algorithm for computing equilibria of an extensive-form game with a perturbed strategy space, and thus approximate equilibria of the unperturbed game. PMID:12060702

Structure theorems for game trees.

PubMed

Govindan, Srihari; Wilson, Robert

2002-06-25

Kohlberg and Mertens [Kohlberg, E. & Mertens, J. (1986) Econometrica 54, 1003-1039] proved that the graph of the Nash equilibrium correspondence is homeomorphic to its domain when the domain is the space of payoffs in normal-form games. A counterexample disproves the analog for the equilibrium outcome correspondence over the space of payoffs in extensive-form games, but we prove an analog when the space of behavior strategies is perturbed so that every path in the game tree has nonzero probability. Without such perturbations, the graph is the closure of the union of a finite collection of its subsets, each diffeomorphic to a corresponding path-connected open subset of the space of payoffs. As an application, we construct an algorithm for computing equilibria of an extensive-form game with a perturbed strategy space, and thus approximate equilibria of the unperturbed game.

Atmospheric turbulence and sensor system effects on biometric algorithm performance

NASA Astrophysics Data System (ADS)

Espinola, Richard L.; Leonard, Kevin R.; Byrd, Kenneth A.; Potvin, Guy

2015-05-01

Biometric technologies composed of electro-optical/infrared (EO/IR) sensor systems and advanced matching algorithms are being used in various force protection/security and tactical surveillance applications. To date, most of these sensor systems have been widely used in controlled conditions with varying success (e.g., short range, uniform illumination, cooperative subjects). However the limiting conditions of such systems have yet to be fully studied for long range applications and degraded imaging environments. Biometric technologies used for long range applications will invariably suffer from the effects of atmospheric turbulence degradation. Atmospheric turbulence causes blur, distortion and intensity fluctuations that can severely degrade image quality of electro-optic and thermal imaging systems and, for the case of biometrics technology, translate to poor matching algorithm performance. In this paper, we evaluate the effects of atmospheric turbulence and sensor resolution on biometric matching algorithm performance. We use a subset of the Facial Recognition Technology (FERET) database and a commercial algorithm to analyze facial recognition performance on turbulence degraded facial images. The goal of this work is to understand the feasibility of long-range facial recognition in degraded imaging conditions, and the utility of camera parameter trade studies to enable the design of the next generation biometrics sensor systems.

Efficient least angle regression for identification of linear-in-the-parameters models

PubMed Central

Beach, Thomas H.; Rezgui, Yacine

2017-01-01

Least angle regression, as a promising model selection method, differentiates itself from conventional stepwise and stagewise methods, in that it is neither too greedy nor too slow. It is closely related to L1 norm optimization, which has the advantage of low prediction variance through sacrificing part of model bias property in order to enhance model generalization capability. In this paper, we propose an efficient least angle regression algorithm for model selection for a large class of linear-in-the-parameters models with the purpose of accelerating the model selection process. The entire algorithm works completely in a recursive manner, where the correlations between model terms and residuals, the evolving directions and other pertinent variables are derived explicitly and updated successively at every subset selection step. The model coefficients are only computed when the algorithm finishes. The direct involvement of matrix inversions is thereby relieved. A detailed computational complexity analysis indicates that the proposed algorithm possesses significant computational efficiency, compared with the original approach where the well-known efficient Cholesky decomposition is involved in solving least angle regression. Three artificial and real-world examples are employed to demonstrate the effectiveness, efficiency and numerical stability of the proposed algorithm. PMID:28293140

Seeking out SARI: an automated search of electronic health records.

PubMed

O'Horo, John C; Dziadzko, Mikhail; Sakusic, Amra; Ali, Rashid; Sohail, M Rizwan; Kor, Daryl J; Gajic, Ognjen

2018-06-01

The definition of severe acute respiratory infection (SARI) - a respiratory illness with fever and cough, occurring within the past 10 days and requiring hospital admission - has not been evaluated for critically ill patients. Using integrated electronic health records data, we developed an automated search algorithm to identify SARI cases in a large cohort of critical care patients and evaluate patient outcomes. We conducted a retrospective cohort study of all admissions to a medical intensive care unit from August 2009 through March 2016. Subsets were randomly selected for deriving and validating a search algorithm, which was compared with temporal trends in laboratory-confirmed influenza to ensure that SARI was correlated with influenza. The algorithm was applied to the cohort to identify clinical differences for patients with and without SARI. For identifying SARI, the algorithm (sensitivity, 86.9%; specificity, 95.6%) outperformed billing-based searching (sensitivity, 73.8%; specificity, 78.8%). Automated searching correlated with peaks in laboratory-confirmed influenza. Adjusted for severity of illness, SARI was associated with more hospital, intensive care unit and ventilator days but not with death or dismissal to home. The search algorithm accurately identified SARI for epidemiologic study and surveillance.

Fast, Safe, Propellant-Efficient Spacecraft Motion Planning Under Clohessy-Wiltshire-Hill Dynamics

NASA Technical Reports Server (NTRS)

Starek, Joseph A.; Schmerling, Edward; Maher, Gabriel D.; Barbee, Brent W.; Pavone, Marco

2016-01-01

This paper presents a sampling-based motion planning algorithm for real-time and propellant-optimized autonomous spacecraft trajectory generation in near-circular orbits. Specifically, this paper leverages recent algorithmic advances in the field of robot motion planning to the problem of impulsively actuated, propellant- optimized rendezvous and proximity operations under the Clohessy-Wiltshire-Hill dynamics model. The approach calls upon a modified version of the FMT* algorithm to grow a set of feasible trajectories over a deterministic, low-dispersion set of sample points covering the free state space. To enforce safety, the tree is only grown over the subset of actively safe samples, from which there exists a feasible one-burn collision-avoidance maneuver that can safely circularize the spacecraft orbit along its coasting arc under a given set of potential thruster failures. Key features of the proposed algorithm include 1) theoretical guarantees in terms of trajectory safety and performance, 2) amenability to real-time implementation, and 3) generality, in the sense that a large class of constraints can be handled directly. As a result, the proposed algorithm offers the potential for widespread application, ranging from on-orbit satellite servicing to orbital debris removal and autonomous inspection missions.

Identification of Disease Critical Genes Using Collective Meta-heuristic Approaches: An Application to Preeclampsia.

PubMed

Biswas, Surama; Dutta, Subarna; Acharyya, Sriyankar

2017-12-01

Identifying a small subset of disease critical genes out of a large size of microarray gene expression data is a challenge in computational life sciences. This paper has applied four meta-heuristic algorithms, namely, honey bee mating optimization (HBMO), harmony search (HS), differential evolution (DE) and genetic algorithm (basic version GA) to find disease critical genes of preeclampsia which affects women during gestation. Two hybrid algorithms, namely, HBMO-kNN and HS-kNN have been newly proposed here where kNN (k nearest neighbor classifier) is used for sample classification. Performances of these new approaches have been compared with other two hybrid algorithms, namely, DE-kNN and SGA-kNN. Three datasets of different sizes have been used. In a dataset, the set of genes found common in the output of each algorithm is considered here as disease critical genes. In different datasets, the percentage of classification or classification accuracy of meta-heuristic algorithms varied between 92.46 and 100%. HBMO-kNN has the best performance (99.64-100%) in almost all data sets. DE-kNN secures the second position (99.42-100%). Disease critical genes obtained here match with clinically revealed preeclampsia genes to a large extent.

A fast 4D cone beam CT reconstruction method based on the OSC-TV algorithm.

PubMed

Mascolo-Fortin, Julia; Matenine, Dmitri; Archambault, Louis; Després, Philippe

2018-01-01

Four-dimensional cone beam computed tomography allows for temporally resolved imaging with useful applications in radiotherapy, but raises particular challenges in terms of image quality and computation time. The purpose of this work is to develop a fast and accurate 4D algorithm by adapting a GPU-accelerated ordered subsets convex algorithm (OSC), combined with the total variation minimization regularization technique (TV). Different initialization schemes were studied to adapt the OSC-TV algorithm to 4D reconstruction: each respiratory phase was initialized either with a 3D reconstruction or a blank image. Reconstruction algorithms were tested on a dynamic numerical phantom and on a clinical dataset. 4D iterations were implemented for a cluster of 8 GPUs. All developed methods allowed for an adequate visualization of the respiratory movement and compared favorably to the McKinnon-Bates and adaptive steepest descent projection onto convex sets algorithms, while the 4D reconstructions initialized from a prior 3D reconstruction led to better overall image quality. The most suitable adaptation of OSC-TV to 4D CBCT was found to be a combination of a prior FDK reconstruction and a 4D OSC-TV reconstruction with a reconstruction time of 4.5 minutes. This relatively short reconstruction time could facilitate a clinical use.

Algorithm of composing the schedule of construction and installation works

NASA Astrophysics Data System (ADS)

Nehaj, Rustam; Molotkov, Georgij; Rudchenko, Ivan; Grinev, Anatolij; Sekisov, Aleksandr

2017-10-01

An algorithm for scheduling works is developed, in which the priority of the work corresponds to the total weight of the subordinate works, the vertices of the graph, and it is proved that for graphs of the tree type the algorithm is optimal. An algorithm is synthesized to reduce the search for solutions when drawing up schedules of construction and installation works, allocating a subset with the optimal solution of the problem of the minimum power, which is determined by the structure of its initial data and numerical values. An algorithm for scheduling construction and installation work is developed, taking into account the schedule for the movement of brigades, which is characterized by the possibility to efficiently calculate the values of minimizing the time of work performance by the parameters of organizational and technological reliability through the use of the branch and boundary method. The program of the computational algorithm was compiled in the MatLAB-2008 program. For the initial data of the matrix, random numbers were taken, uniformly distributed in the range from 1 to 100. It takes 0.5; 2.5; 7.5; 27 minutes to solve the problem. Thus, the proposed method for estimating the lower boundary of the solution is sufficiently accurate and allows efficient solution of the minimax task of scheduling construction and installation works.

Accurate wavelength measurements of a putative standard for near-infrared diffuse reflection spectrometry.

PubMed

Isaksson, Tomas; Yang, Husheng; Kemeny, Gabor J; Jackson, Richard S; Wang, Qian; Alam, M Kathleen; Griffiths, Peter R

2003-02-01

The diffuse reflection (DR) spectrum of a sample consisting of a mixture of rare earth oxides and talc was measured at 2 cm-1 resolution, using five different accessories installed on five different Fourier transform near-infrared (FT-NIR) spectrometers from four manufacturers. Peak positions for 37 peaks were determined using two peak-picking algorithms: center-of-mass and polynomial fitting. The wavenumber of the band center reported by either of these techniques was sensitive to the slope of the baseline, and so the baseline of the spectra was corrected using either a polynomial fit or conversion to the second derivative. Significantly different results were obtained with one combination of spectrometer and accessory than the others. Apparently, the beam path through the interferometer and DR accessory was different for this accessory than for any of the other measurements, causing a severe degradation of the resolution. Spectra measured on this instrument were removed as outliers. For measurements made on FT-NIR spectrometers, it is shown that it is important to check the resolution at which the spectrum has been measured using lines in the vibration-rotation spectrum of atmospheric water vapor and to specify the peak-picking and baseline-correction algorithms that are used to process the measured spectra. The variance between the results given by the four different methods of peak-picking and baseline correction was substantially larger than the variance between the remaining five measurements. Certain bands were found to be more suitable than others for use as wavelength standards. A band at 5943.13 cm-1 (1682.62 nm) was found to be the most stable band between the four methods and the six measurements. A band at 5177.04 cm-1 (1931.61 nm) has the highest precision between different measurements when polynomial baseline correction and polynomial peak-picking algorithms are used.

Linear MALDI-ToF simultaneous spectrum deconvolution and baseline removal.

PubMed

Picaud, Vincent; Giovannelli, Jean-Francois; Truntzer, Caroline; Charrier, Jean-Philippe; Giremus, Audrey; Grangeat, Pierre; Mercier, Catherine

2018-04-05

Thanks to a reasonable cost and simple sample preparation procedure, linear MALDI-ToF spectrometry is a growing technology for clinical microbiology. With appropriate spectrum databases, this technology can be used for early identification of pathogens in body fluids. However, due to the low resolution of linear MALDI-ToF instruments, robust and accurate peak picking remains a challenging task. In this context we propose a new peak extraction algorithm from raw spectrum. With this method the spectrum baseline and spectrum peaks are processed jointly. The approach relies on an additive model constituted by a smooth baseline part plus a sparse peak list convolved with a known peak shape. The model is then fitted under a Gaussian noise model. The proposed method is well suited to process low resolution spectra with important baseline and unresolved peaks. We developed a new peak deconvolution procedure. The paper describes the method derivation and discusses some of its interpretations. The algorithm is then described in a pseudo-code form where the required optimization procedure is detailed. For synthetic data the method is compared to a more conventional approach. The new method reduces artifacts caused by the usual two-steps procedure, baseline removal then peak extraction. Finally some results on real linear MALDI-ToF spectra are provided. We introduced a new method for peak picking, where peak deconvolution and baseline computation are performed jointly. On simulated data we showed that this global approach performs better than a classical one where baseline and peaks are processed sequentially. A dedicated experiment has been conducted on real spectra. In this study a collection of spectra of spiked proteins were acquired and then analyzed. Better performances of the proposed method, in term of accuracy and reproductibility, have been observed and validated by an extended statistical analysis.

Effect of vitamin D replacement on immunological biomarkers in patients with multiple sclerosis.

PubMed

Mrad, May F; El Ayoubi, Nabil K; Esmerian, Maria O; Kazan, Jalal M; Khoury, Samia J

2017-08-01

We aimed to investigate the immunologic effects of vitamin D replacement in RRMS patients. In a controlled single center study, patients deficient in 25-hydroxyvitamin D (serum level<25ng/ml) received 10,000IU/week cholecalciferol for 3months. Sufficient vitamin D patients (serum level>35ng/ml) were followed for the same period. Assessments were performed at baseline and at 3months. 25-hydroxyvitamin D levels increased significantly from baseline to month-3 in the deficient group after treatment and remained stable in the sufficient group. We observed a decreased interferon-γ (IFNγ) secretion by CD4 + T cells in vitamin D deficient group but not in the sufficient group, and a negative correlation between baseline serum vitamin D and IFNγ production. There was no change in the frequency of T helper or regulatory T cell subsets in either group. Increasing serum levels of 25-hydroxyvitamin D are associated with decreased production of IFNγ by CD4 + T cells. Copyright © 2017 Elsevier Inc. All rights reserved.

Effectiveness of a childhood obesity prevention programme delivered through schools, targeting 6 and 7 year olds: cluster randomised controlled trial (WAVES study)

PubMed Central

Pallan, Miranda J; Lancashire, Emma R; Hemming, Karla; Frew, Emma; Barrett, Tim; Bhopal, Raj; Cade, Janet E; Canaway, Alastair; Clarke, Joanne L; Daley, Amanda; Deeks, Jonathan J; Duda, Joan L; Ekelund, Ulf; Gill, Paramjit; Griffin, Tania; McGee, Eleanor; Hurley, Kiya; Martin, James; Parry, Jayne; Passmore, Sandra; Cheng, K K

2018-01-01

Abstract Objective To assess the effectiveness of a school and family based healthy lifestyle programme (WAVES intervention) compared with usual practice, in preventing childhood obesity. Design Cluster randomised controlled trial. Setting UK primary schools from the West Midlands. Participants 200 schools were randomly selected from all state run primary schools within 35 miles of the study centre (n=980), oversampling those with high minority ethnic populations. These schools were randomly ordered and sequentially invited to participate. 144 eligible schools were approached to achieve the target recruitment of 54 schools. After baseline measurements 1467 year 1 pupils aged 5 and 6 years (control: 28 schools, 778 pupils) were randomised, using a blocked balancing algorithm. 53 schools remained in the trial and data on 1287 (87.7%) and 1169 (79.7%) pupils were available at first follow-up (15 month) and second follow-up (30 month), respectively. Interventions The 12 month intervention encouraged healthy eating and physical activity, including a daily additional 30 minute school time physical activity opportunity, a six week interactive skill based programme in conjunction with Aston Villa football club, signposting of local family physical activity opportunities through mail-outs every six months, and termly school led family workshops on healthy cooking skills. Main outcome measures The protocol defined primary outcomes, assessed blind to allocation, were between arm difference in body mass index (BMI) z score at 15 and 30 months. Secondary outcomes were further anthropometric, dietary, physical activity, and psychological measurements, and difference in BMI z score at 39 months in a subset. Results Data for primary outcome analyses were: baseline, 54 schools: 1392 pupils (732 controls); first follow-up (15 months post-baseline), 53 schools: 1249 pupils (675 controls); second follow-up (30 months post-baseline), 53 schools: 1145 pupils (621 controls). The mean BMI z score was non-significantly lower in the intervention arm compared with the control arm at 15 months (mean difference −0.075 (95% confidence interval −0.183 to 0.033, P=0.18) in the baseline adjusted models. At 30 months the mean difference was −0.027 (−0.137 to 0.083, P=0.63). There was no statistically significant difference between groups for other anthropometric, dietary, physical activity, or psychological measurements (including assessment of harm). Conclusions The primary analyses suggest that this experiential focused intervention had no statistically significant effect on BMI z score or on preventing childhood obesity. Schools are unlikely to impact on the childhood obesity epidemic by incorporating such interventions without wider support across multiple sectors and environments. Trial registration Current Controlled Trials ISRCTN97000586. PMID:29437667

Effectiveness of a childhood obesity prevention programme delivered through schools, targeting 6 and 7 year olds: cluster randomised controlled trial (WAVES study).

PubMed

Adab, Peymane; Pallan, Miranda J; Lancashire, Emma R; Hemming, Karla; Frew, Emma; Barrett, Tim; Bhopal, Raj; Cade, Janet E; Canaway, Alastair; Clarke, Joanne L; Daley, Amanda; Deeks, Jonathan J; Duda, Joan L; Ekelund, Ulf; Gill, Paramjit; Griffin, Tania; McGee, Eleanor; Hurley, Kiya; Martin, James; Parry, Jayne; Passmore, Sandra; Cheng, K K

2018-02-07

To assess the effectiveness of a school and family based healthy lifestyle programme (WAVES intervention) compared with usual practice, in preventing childhood obesity. Cluster randomised controlled trial. UK primary schools from the West Midlands. 200 schools were randomly selected from all state run primary schools within 35 miles of the study centre (n=980), oversampling those with high minority ethnic populations. These schools were randomly ordered and sequentially invited to participate. 144 eligible schools were approached to achieve the target recruitment of 54 schools. After baseline measurements 1467 year 1 pupils aged 5 and 6 years (control: 28 schools, 778 pupils) were randomised, using a blocked balancing algorithm. 53 schools remained in the trial and data on 1287 (87.7%) and 1169 (79.7%) pupils were available at first follow-up (15 month) and second follow-up (30 month), respectively. The 12 month intervention encouraged healthy eating and physical activity, including a daily additional 30 minute school time physical activity opportunity, a six week interactive skill based programme in conjunction with Aston Villa football club, signposting of local family physical activity opportunities through mail-outs every six months, and termly school led family workshops on healthy cooking skills. The protocol defined primary outcomes, assessed blind to allocation, were between arm difference in body mass index (BMI) z score at 15 and 30 months. Secondary outcomes were further anthropometric, dietary, physical activity, and psychological measurements, and difference in BMI z score at 39 months in a subset. Data for primary outcome analyses were: baseline, 54 schools: 1392 pupils (732 controls); first follow-up (15 months post-baseline), 53 schools: 1249 pupils (675 controls); second follow-up (30 months post-baseline), 53 schools: 1145 pupils (621 controls). The mean BMI z score was non-significantly lower in the intervention arm compared with the control arm at 15 months (mean difference -0.075 (95% confidence interval -0.183 to 0.033, P=0.18) in the baseline adjusted models. At 30 months the mean difference was -0.027 (-0.137 to 0.083, P=0.63). There was no statistically significant difference between groups for other anthropometric, dietary, physical activity, or psychological measurements (including assessment of harm). The primary analyses suggest that this experiential focused intervention had no statistically significant effect on BMI z score or on preventing childhood obesity. Schools are unlikely to impact on the childhood obesity epidemic by incorporating such interventions without wider support across multiple sectors and environments. Current Controlled Trials ISRCTN97000586. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Circulating Branched-chain Amino Acid Concentrations Are Associated with Obesity and Future Insulin Resistance in Children and Adolescents

PubMed Central

McCormack, Shana E.; Shaham, Oded; McCarthy, Meaghan A.; Deik, Amy A.; Wang, Thomas J.; Gerszten, Robert E.; Clish, Clary B.; Mootha, Vamsi K.; Grinspoon, Steven K.; Fleischman, Amy

2012-01-01

Background Branched-chain amino acid (BCAA) concentrations are elevated in response to overnutrition, and can affect both insulin sensitivity and secretion. Alterations in their metabolism may therefore play a role in the early pathogenesis of type 2 diabetes in overweight children. Objective To determine whether pediatric obesity is associated with elevations in fasting circulating concentrations of branched-chain amino acids (isoleucine, leucine, and valine), and whether these elevations predict future insulin resistance. Research Design and Methods Sixty-nine healthy subjects, ages 8 to18 years, were enrolled as a cross-sectional cohort. A subset who were pre- or early-pubertal, ages 8 to 13 years, were enrolled in a prospective longitudinal cohort for 18 months (n=17 with complete data). Results Elevations in the concentrations of BCAA’s were significantly associated with BMI Z-score (Spearman’s Rho 0.27, p=0.03) in the cross-sectional cohort. In the subset of subjects followed longitudinally, baseline BCAA concentrations were positively associated with HOMA-IR measured 18 months later after controlling for baseline clinical factors including BMI Z-score, sex, and pubertal stage (p=0.046). Conclusions Elevations in the concentrations of circulating branched-chain amino acids are significantly associated with obesity in children and adolescents, and may independently predict future insulin resistance. PMID:22961720

JAK inhibition induces silencing of T Helper cytokine secretion and a profound reduction in T regulatory cells.

PubMed

Keohane, Clodagh; Kordasti, Shahram; Seidl, Thomas; Perez Abellan, Pilar; Thomas, Nicholas S B; Harrison, Claire N; McLornan, Donal P; Mufti, Ghulam J

2015-10-01

CD4(+) T cells maintain cancer surveillance and immune tolerance. Chronic inflammation has been proposed as a driver of clonal evolution in myeloproliferative neoplasms (MPN), suggesting that T cells play an important role in their pathogenesis. Treatment with JAK inhibitors (JAKi) results in improvements in MPN-associated constitutional symptoms as well as reductions in splenomegaly. However, effects of JAKi on T cells in MPN are not well established and the baseline immune signature remains unclear. We investigated the frequency and function of CD4(+) T cell subsets in 50 MPN patients at baseline as well as during treatment with either ruxolitinib or fedratinib in a subset. We show that CD4(+)  CD127(low)  CD25(high)  FOXP3(+) T regulatory cells are reduced in MPN patients compared to healthy controls and that this decrease is even more pronounced following JAKi therapy. Moreover, we show that after 6 months of treatment the number of T helper (Th)-17 cells increased. We also describe a functional 'silencing' of T helper cells both in vivo and in vitro and a blockade of pro-inflammatory cytokines from these cells. This profound effect of JAKi on T cell function may underlay augmented rates of atypical infections that have been reported with use of these drugs. © 2015 John Wiley & Sons Ltd.

Training attentional control in older adults.

PubMed

Mackay-Brandt, Anna

2011-07-01

Recent research has demonstrated benefits for older adults from training attentional control using a variable priority strategy, but the construct validity of the training task and the degree to which benefits of training transfer to other contexts are unclear. The goal of this study was to characterize baseline performance on the training task in a sample of 105 healthy older adults and to test for transfer of training in a subset (n = 21). Training gains after 5 days and extent of transfer was compared to another subset (n = 20) that served as a control group. Baseline performance on the training task was characterized by a two-factor model of working memory and processing speed. Processing speed correlated with the training task. Training gains in speed and accuracy were reliable and robust (ps <.001, η(2) = .57 to .90). Transfer to an analogous task was observed (ps <.05, η(2) = .10 to .17). The beneficial effect of training did not translate to improved performance on related measures of processing speed. This study highlights the robust effect of training and transfer to a similar context using a variable priority training task. Although processing speed is an important aspect of the training task, training benefit is either related to an untested aspect of the training task or transfer of training is limited to the training context.

A new prior for bayesian anomaly detection: application to biosurveillance.

PubMed

Shen, Y; Cooper, G F

2010-01-01

Bayesian anomaly detection computes posterior probabilities of anomalous events by combining prior beliefs and evidence from data. However, the specification of prior probabilities can be challenging. This paper describes a Bayesian prior in the context of disease outbreak detection. The goal is to provide a meaningful, easy-to-use prior that yields a posterior probability of an outbreak that performs at least as well as a standard frequentist approach. If this goal is achieved, the resulting posterior could be usefully incorporated into a decision analysis about how to act in light of a possible disease outbreak. This paper describes a Bayesian method for anomaly detection that combines learning from data with a semi-informative prior probability over patterns of anomalous events. A univariate version of the algorithm is presented here for ease of illustration of the essential ideas. The paper describes the algorithm in the context of disease-outbreak detection, but it is general and can be used in other anomaly detection applications. For this application, the semi-informative prior specifies that an increased count over baseline is expected for the variable being monitored, such as the number of respiratory chief complaints per day at a given emergency department. The semi-informative prior is derived based on the baseline prior, which is estimated from using historical data. The evaluation reported here used semi-synthetic data to evaluate the detection performance of the proposed Bayesian method and a control chart method, which is a standard frequentist algorithm that is closest to the Bayesian method in terms of the type of data it uses. The disease-outbreak detection performance of the Bayesian method was statistically significantly better than that of the control chart method when proper baseline periods were used to estimate the baseline behavior to avoid seasonal effects. When using longer baseline periods, the Bayesian method performed as well as the control chart method. The time complexity of the Bayesian algorithm is linear in the number of the observed events being monitored, due to a novel, closed-form derivation that is introduced in the paper. This paper introduces a novel prior probability for Bayesian outbreak detection that is expressive, easy-to-apply, computationally efficient, and performs as well or better than a standard frequentist method.

Baseline mathematics and geodetics for tracking operations

NASA Technical Reports Server (NTRS)

James, R.

1981-01-01

Various geodetic and mapping algorithms are analyzed as they apply to radar tracking systems and tested in extended BASIC computer language for real time computer applications. Closed-form approaches to the solution of converting Earth centered coordinates to latitude, longitude, and altitude are compared with classical approximations. A simplified approach to atmospheric refractivity called gradient refraction is compared with conventional ray tracing processes. An extremely detailed set of documentation which provides the theory, derivations, and application of algorithms used in the programs is included. Validation methods are also presented for testing the accuracy of the algorithms.

Detection of QT prolongation using a novel electrocardiographic analysis algorithm applying intelligent automation: prospective blinded evaluation using the Cardiac Safety Research Consortium electrocardiographic database.

PubMed

Green, Cynthia L; Kligfield, Paul; George, Samuel; Gussak, Ihor; Vajdic, Branislav; Sager, Philip; Krucoff, Mitchell W

2012-03-01

The Cardiac Safety Research Consortium (CSRC) provides both "learning" and blinded "testing" digital electrocardiographic (ECG) data sets from thorough QT (TQT) studies annotated for submission to the US Food and Drug Administration (FDA) to developers of ECG analysis technologies. This article reports the first results from a blinded testing data set that examines developer reanalysis of original sponsor-reported core laboratory data. A total of 11,925 anonymized ECGs including both moxifloxacin and placebo arms of a parallel-group TQT in 181 subjects were blindly analyzed using a novel ECG analysis algorithm applying intelligent automation. Developer-measured ECG intervals were submitted to CSRC for unblinding, temporal reconstruction of the TQT exposures, and statistical comparison to core laboratory findings previously submitted to FDA by the pharmaceutical sponsor. Primary comparisons included baseline-adjusted interval measurements, baseline- and placebo-adjusted moxifloxacin QTcF changes (ddQTcF), and associated variability measures. Developer and sponsor-reported baseline-adjusted data were similar with average differences <1 ms for all intervals. Both developer- and sponsor-reported data demonstrated assay sensitivity with similar ddQTcF changes. Average within-subject SD for triplicate QTcF measurements was significantly lower for developer- than sponsor-reported data (5.4 and 7.2 ms, respectively; P < .001). The virtually automated ECG algorithm used for this analysis produced similar yet less variable TQT results compared with the sponsor-reported study, without the use of a manual core laboratory. These findings indicate that CSRC ECG data sets can be useful for evaluating novel methods and algorithms for determining drug-induced QT/QTc prolongation. Although the results should not constitute endorsement of specific algorithms by either CSRC or FDA, the value of a public domain digital ECG warehouse to provide prospective, blinded comparisons of ECG technologies applied for QT/QTc measurement is illustrated. Copyright © 2012 Mosby, Inc. All rights reserved.

Detection of QT prolongation using a novel ECG analysis algorithm applying intelligent automation: Prospective blinded evaluation using the Cardiac Safety Research Consortium ECG database

PubMed Central

Green, Cynthia L.; Kligfield, Paul; George, Samuel; Gussak, Ihor; Vajdic, Branislav; Sager, Philip; Krucoff, Mitchell W.

2013-01-01

Background The Cardiac Safety Research Consortium (CSRC) provides both “learning” and blinded “testing” digital ECG datasets from thorough QT (TQT) studies annotated for submission to the US Food and Drug Administration (FDA) to developers of ECG analysis technologies. This manuscript reports the first results from a blinded “testing” dataset that examines Developer re-analysis of original Sponsor-reported core laboratory data. Methods 11,925 anonymized ECGs including both moxifloxacin and placebo arms of a parallel-group TQT in 191 subjects were blindly analyzed using a novel ECG analysis algorithm applying intelligent automation. Developer measured ECG intervals were submitted to CSRC for unblinding, temporal reconstruction of the TQT exposures, and statistical comparison to core laboratory findings previously submitted to FDA by the pharmaceutical sponsor. Primary comparisons included baseline-adjusted interval measurements, baseline- and placebo-adjusted moxifloxacin QTcF changes (ddQTcF), and associated variability measures. Results Developer and Sponsor-reported baseline-adjusted data were similar with average differences less than 1 millisecond (ms) for all intervals. Both Developer and Sponsor-reported data demonstrated assay sensitivity with similar ddQTcF changes. Average within-subject standard deviation for triplicate QTcF measurements was significantly lower for Developer than Sponsor-reported data (5.4 ms and 7.2 ms, respectively; p<0.001). Conclusion The virtually automated ECG algorithm used for this analysis produced similar yet less variable TQT results compared to the Sponsor-reported study, without the use of a manual core laboratory. These findings indicate CSRC ECG datasets can be useful for evaluating novel methods and algorithms for determining QT/QTc prolongation by drugs. While the results should not constitute endorsement of specific algorithms by either CSRC or FDA, the value of a public domain digital ECG warehouse to provide prospective, blinded comparisons of ECG technologies applied for QT/QTc measurement is illustrated. PMID:22424006

Potential for Integrating Entry Guidance into the Multi-Disciplinary Entry Vehicle Optimization Environment

NASA Technical Reports Server (NTRS)

D'souza, Sarah N.; Kinney, David J.; Garcia, Joseph A.; Sarigul-Klijn, Nesrin

2014-01-01

The state-of-the-art in vehicle design decouples flight feasible trajectory generation from the optimization process of an entry spacecraft shape. The disadvantage to this decoupled process is seen when a particular aeroshell does not meet in-flight requirements when integrated into Guidance, Navigation, and Control simulations. It is postulated that the integration of a guidance algorithm into the design process will provide a real-time, rapid trajectory generation technique to enhance the robustness of vehicle design solutions. The potential benefit of this integration is a reduction in design cycles (possible cost savings) and increased accuracy in the aerothermal environment (possible mass savings). This work examines two aspects: 1) the performance of a reference tracking guidance algorithm for five different geometries with the same reference trajectory and 2) the potential of mass savings from improved aerothermal predictions. An Apollo Derived Guidance (ADG) algorithm is used in this study. The baseline geometry and five test case geometries were flown using the same baseline trajectory. The guided trajectory results are compared to separate trajectories determined in a vehicle optimization study conducted for NASA's Mars Entry, Descent, and Landing System Analysis. This study revealed several aspects regarding the potential gains and required developments for integrating a guidance algorithm into the vehicle optimization environment. First, the generation of flight feasible trajectories is only as good as the robustness of the guidance algorithm. The set of dispersed geometries modelled aerodynamic dispersions that ranged from +/-1% to +/-17% and a single extreme case was modelled where the aerodynamics were approximately 80% less than the baseline geometry. The ADG, as expected, was able to guide the vehicle into the aeroshell separation box at the target location for dispersions up to 17%, but failed for the 80% dispersion cases. Finally, the results revealed that including flight feasible trajectories for a set of dispersed geometries has the potential to save mass up to 430 kg.

Interseismic Deformation along the Red River Fault from InSAR Measurements

NASA Astrophysics Data System (ADS)

Chen, J.; Li, Z.; Clarke, P. J.

2017-12-01

The Red River Fault (RRF) zone is a profound geological discontinuity separating South China from Indochina. Right lateral movements along this >900 km fault are considered to accommodate the extrusion of SE China. Crustal deformation monitoring at high resolution is the key to understand the present-day mode of deformation in this zone and its interaction with the adjacent regions. This is the first study to measure the interseismic deformation of the entire fault with ALOS-1/2 and Sentinel-1 observations. Nine ascending tracks of ALOS-1 data between 2007 and 2011 are collected from the Alaska Satellite Facility (ASF), four descending tracks of Sentinel-1 data are acquired every 24 days since October 2014, and ALOS-2 data are being systematically acquired since 2014. The long wavelength (L-band) of ALOS-1/2 and short temporal baseline of Sentinel-1 ensure good coherence to overcome the limitations of heavy vegetation and variable climate in the region. Stacks of interferograms are generated by our automatic processing chain based on the InSAR Scientific Computing Environment (ISCE) software, ionospheric errors are estimated and corrected using the split-spectrum method (Fattahi et al., IEEE Trans. Geosci. Remote Sens., 2017) and the tropospheric delays are calibrated using the Generic Atmospheric Correction Online Service for InSAR (GACOS: http://ceg-research.ncl.ac.uk/v2/gacos) with high-resolution ECMWF products (Yu et al., J. Geophys. Res., 2017). Time series analysis is performed to determine the interseismic deformation rate of the RRF using the in-house InSAR time series with atmospheric estimation model (InSAR TS + AEM) package based on the Small Baseline Subset (SBAS) algorithm. Our results reveal the decrease of slip rate from north to south. We map the interseismic strain rate field to characterize the deformation patterns and seismic hazard throughout the RRF zone.

Code-based Diagnostic Algorithms for Idiopathic Pulmonary Fibrosis. Case Validation and Improvement.

PubMed

Ley, Brett; Urbania, Thomas; Husson, Gail; Vittinghoff, Eric; Brush, David R; Eisner, Mark D; Iribarren, Carlos; Collard, Harold R

2017-06-01

Population-based studies of idiopathic pulmonary fibrosis (IPF) in the United States have been limited by reliance on diagnostic code-based algorithms that lack clinical validation. To validate a well-accepted International Classification of Diseases, Ninth Revision, code-based algorithm for IPF using patient-level information and to develop a modified algorithm for IPF with enhanced predictive value. The traditional IPF algorithm was used to identify potential cases of IPF in the Kaiser Permanente Northern California adult population from 2000 to 2014. Incidence and prevalence were determined overall and by age, sex, and race/ethnicity. A validation subset of cases (n = 150) underwent expert medical record and chest computed tomography review. A modified IPF algorithm was then derived and validated to optimize positive predictive value. From 2000 to 2014, the traditional IPF algorithm identified 2,608 cases among 5,389,627 at-risk adults in the Kaiser Permanente Northern California population. Annual incidence was 6.8/100,000 person-years (95% confidence interval [CI], 6.1-7.7) and was higher in patients with older age, male sex, and white race. The positive predictive value of the IPF algorithm was only 42.2% (95% CI, 30.6 to 54.6%); sensitivity was 55.6% (95% CI, 21.2 to 86.3%). The corrected incidence was estimated at 5.6/100,000 person-years (95% CI, 2.6-10.3). A modified IPF algorithm had improved positive predictive value but reduced sensitivity compared with the traditional algorithm. A well-accepted International Classification of Diseases, Ninth Revision, code-based IPF algorithm performs poorly, falsely classifying many non-IPF cases as IPF and missing a substantial proportion of IPF cases. A modification of the IPF algorithm may be useful for future population-based studies of IPF.

Center for Quantum Algorithms and Complexity

DTIC Science & Technology

2014-05-12

precisely, it asserts that for any subset L of particles, the entanglement entropy between L and L̄ is bounded by the surface area of L (the area is...ground states of gapped local Hamiltonians. Roughly, it says that the entanglement in such states is very local, and the entanglement entropy scales...the theorem states that the entanglement entropy is bounded by exp(X), where X = log(d/?). Hastingss result implies that ground states of gapped 1D

Reconfiguration Schemes for Fault-Tolerant Processor Arrays

DTIC Science & Technology

1992-10-15

partially notion of linear schedule are easily related to similar ordered subset of a multidimensional integer lattice models and concepts used in [11-[131...and several other (called indec set). The points of this lattice correspond works. to (i.e.. are the indices of) computations, and the partial There are...These data dependencies are represented as vectors that of all computations of the algorithm is to be minimized. connect points of the lattice . If a

A Fault Recognition System for Gearboxes of Wind Turbines

NASA Astrophysics Data System (ADS)

Yang, Zhiling; Huang, Haiyue; Yin, Zidong

2017-12-01

Costs of maintenance and loss of power generation caused by the faults of wind turbines gearboxes are the main components of operation costs for a wind farm. Therefore, the technology of condition monitoring and fault recognition for wind turbines gearboxes is becoming a hot topic. A condition monitoring and fault recognition system (CMFRS) is presented for CBM of wind turbines gearboxes in this paper. The vibration signals from acceleration sensors at different locations of gearbox and the data from supervisory control and data acquisition (SCADA) system are collected to CMFRS. Then the feature extraction and optimization algorithm is applied to these operational data. Furthermore, to recognize the fault of gearboxes, the GSO-LSSVR algorithm is proposed, combining the least squares support vector regression machine (LSSVR) with the Glowworm Swarm Optimization (GSO) algorithm. Finally, the results show that the fault recognition system used in this paper has a high rate for identifying three states of wind turbines’ gears; besides, the combination of date features can affect the identifying rate and the selection optimization algorithm presented in this paper can get a pretty good date feature subset for the fault recognition.

A model of proto-object based saliency

PubMed Central

Russell, Alexander F.; Mihalaş, Stefan; von der Heydt, Rudiger; Niebur, Ernst; Etienne-Cummings, Ralph

2013-01-01

Organisms use the process of selective attention to optimally allocate their computational resources to the instantaneously most relevant subsets of a visual scene, ensuring that they can parse the scene in real time. Many models of bottom-up attentional selection assume that elementary image features, like intensity, color and orientation, attract attention. Gestalt psychologists, how-ever, argue that humans perceive whole objects before they analyze individual features. This is supported by recent psychophysical studies that show that objects predict eye-fixations better than features. In this report we present a neurally inspired algorithm of object based, bottom-up attention. The model rivals the performance of state of the art non-biologically plausible feature based algorithms (and outperforms biologically plausible feature based algorithms) in its ability to predict perceptual saliency (eye fixations and subjective interest points) in natural scenes. The model achieves this by computing saliency as a function of proto-objects that establish the perceptual organization of the scene. All computational mechanisms of the algorithm have direct neural correlates, and our results provide evidence for the interface theory of attention. PMID:24184601

Development of a two-stage gene selection method that incorporates a novel hybrid approach using the cuckoo optimization algorithm and harmony search for cancer classification.

PubMed

Elyasigomari, V; Lee, D A; Screen, H R C; Shaheed, M H

2017-03-01

For each cancer type, only a few genes are informative. Due to the so-called 'curse of dimensionality' problem, the gene selection task remains a challenge. To overcome this problem, we propose a two-stage gene selection method called MRMR-COA-HS. In the first stage, the minimum redundancy and maximum relevance (MRMR) feature selection is used to select a subset of relevant genes. The selected genes are then fed into a wrapper setup that combines a new algorithm, COA-HS, using the support vector machine as a classifier. The method was applied to four microarray datasets, and the performance was assessed by the leave one out cross-validation method. Comparative performance assessment of the proposed method with other evolutionary algorithms suggested that the proposed algorithm significantly outperforms other methods in selecting a fewer number of genes while maintaining the highest classification accuracy. The functions of the selected genes were further investigated, and it was confirmed that the selected genes are biologically relevant to each cancer type. Copyright © 2017. Published by Elsevier Inc.

Evaluation of Origin Ensemble algorithm for image reconstruction for pixelated solid-state detectors with large number of channels

NASA Astrophysics Data System (ADS)

Kolstein, M.; De Lorenzo, G.; Mikhaylova, E.; Chmeissani, M.; Ariño, G.; Calderón, Y.; Ozsahin, I.; Uzun, D.

2013-04-01

The Voxel Imaging PET (VIP) Pathfinder project intends to show the advantages of using pixelated solid-state technology for nuclear medicine applications. It proposes designs for Positron Emission Tomography (PET), Positron Emission Mammography (PEM) and Compton gamma camera detectors with a large number of signal channels (of the order of 106). For PET scanners, conventional algorithms like Filtered Back-Projection (FBP) and Ordered Subset Expectation Maximization (OSEM) are straightforward to use and give good results. However, FBP presents difficulties for detectors with limited angular coverage like PEM and Compton gamma cameras, whereas OSEM has an impractically large time and memory consumption for a Compton gamma camera with a large number of channels. In this article, the Origin Ensemble (OE) algorithm is evaluated as an alternative algorithm for image reconstruction. Monte Carlo simulations of the PET design are used to compare the performance of OE, FBP and OSEM in terms of the bias, variance and average mean squared error (MSE) image quality metrics. For the PEM and Compton camera designs, results obtained with OE are presented.

Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5-14 μm) to discriminate vegetation species.

PubMed

Ullah, Saleem; Groen, Thomas A; Schlerf, Martin; Skidmore, Andrew K; Nieuwenhuis, Willem; Vaiphasa, Chaichoke

2012-01-01

Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities. The performance was evaluated using an overall classification accuracy and Jeffries Matusita distance. For the multiple plant species, the targeted bands based on genetic algorithms resulted in a high overall classification accuracy (90%). Concentrating on the pairwise comparison results, the selected wavebands based on genetic algorithms resulted in higher Jeffries Matusita (J-M) distances than randomly selected wavebands did. This study concludes that targeted wavebands from leaf emissivity spectra are able to discriminate vegetation species.

Monochromatic-beam-based dynamic X-ray microtomography based on OSEM-TV algorithm.

PubMed

Xu, Liang; Chen, Rongchang; Yang, Yiming; Deng, Biao; Du, Guohao; Xie, Honglan; Xiao, Tiqiao

2017-01-01

Monochromatic-beam-based dynamic X-ray computed microtomography (CT) was developed to observe evolution of microstructure inside samples. However, the low flux density results in low efficiency in data collection. To increase efficiency, reducing the number of projections should be a practical solution. However, it has disadvantages of low image reconstruction quality using the traditional filtered back projection (FBP) algorithm. In this study, an iterative reconstruction method using an ordered subset expectation maximization-total variation (OSEM-TV) algorithm was employed to address and solve this problem. The simulated results demonstrated that normalized mean square error of the image slices reconstructed by the OSEM-TV algorithm was about 1/4 of that by FBP. Experimental results also demonstrated that the density resolution of OSEM-TV was high enough to resolve different materials with the number of projections less than 100. As a result, with the introduction of OSEM-TV, the monochromatic-beam-based dynamic X-ray microtomography is potentially practicable for the quantitative and non-destructive analysis to the evolution of microstructure with acceptable efficiency in data collection and reconstructed image quality.

Hybrid stochastic simulation of reaction-diffusion systems with slow and fast dynamics

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

Strehl, Robert; Ilie, Silvana, E-mail: silvana@ryerson.ca

2015-12-21

In this paper, we present a novel hybrid method to simulate discrete stochastic reaction-diffusion models arising in biochemical signaling pathways. We study moderately stiff systems, for which we can partition each reaction or diffusion channel into either a slow or fast subset, based on its propensity. Numerical approaches missing this distinction are often limited with respect to computational run time or approximation quality. We design an approximate scheme that remedies these pitfalls by using a new blending strategy of the well-established inhomogeneous stochastic simulation algorithm and the tau-leaping simulation method. The advantages of our hybrid simulation algorithm are demonstrated onmore » three benchmarking systems, with special focus on approximation accuracy and efficiency.« less

Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition

PubMed Central

Mala, S.; Latha, K.

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition. PMID:25574185

Feature selection in classification of eye movements using electrooculography for activity recognition.

PubMed

Mala, S; Latha, K

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.

Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images

PubMed Central

Wang, Yu; Zhang, Yaonan; Yao, Zhaomin; Zhao, Ruixue; Zhou, Fengfeng

2016-01-01

Non-lethal macular diseases greatly impact patients’ life quality, and will cause vision loss at the late stages. Visual inspection of the optical coherence tomography (OCT) images by the experienced clinicians is the main diagnosis technique. We proposed a computer-aided diagnosis (CAD) model to discriminate age-related macular degeneration (AMD), diabetic macular edema (DME) and healthy macula. The linear configuration pattern (LCP) based features of the OCT images were screened by the Correlation-based Feature Subset (CFS) selection algorithm. And the best model based on the sequential minimal optimization (SMO) algorithm achieved 99.3% in the overall accuracy for the three classes of samples. PMID:28018716

Very Long Baseline Interferometry Applied to Polar Motion, Relativity and Geodesy. Ph.D. Thesis - Maryland Univ.

NASA Technical Reports Server (NTRS)

Ma, C.

1978-01-01

The causes and effects of diurnal polar motion are described. An algorithm is developed for modeling the effects on very long baseline interferometry observables. Five years of radio-frequency very long baseline interferometry data from stations in Massachusetts, California, and Sweden are analyzed for diurnal polar motion. It is found that the effect is larger than predicted by McClure. Corrections to the standard nutation series caused by the deformability of the earth have a significant effect on the estimated diurnal polar motion scaling factor and the post-fit residual scatter. Simulations of high precision very long baseline interferometry experiments taking into account both measurement uncertainty and modeled errors are described.

A new model for approximating RNA folding trajectories and population kinetics

NASA Astrophysics Data System (ADS)

Kirkpatrick, Bonnie; Hajiaghayi, Monir; Condon, Anne

2013-01-01

RNA participates both in functional aspects of the cell and in gene regulation. The interactions of these molecules are mediated by their secondary structure which can be viewed as a planar circle graph with arcs for all the chemical bonds between pairs of bases in the RNA sequence. The problem of predicting RNA secondary structure, specifically the chemically most probable structure, has many useful and efficient algorithms. This leaves RNA folding, the problem of predicting the dynamic behavior of RNA structure over time, as the main open problem. RNA folding is important for functional understanding because some RNA molecules change secondary structure in response to interactions with the environment. The full RNA folding model on at most O(3n) secondary structures is the gold standard. We present a new subset approximation model for the full model, give methods to analyze its accuracy and discuss the relative merits of our model as compared with a pre-existing subset approximation. The main advantage of our model is that it generates Monte Carlo folding pathways with the same probabilities with which they are generated under the full model. The pre-existing subset approximation does not have this property.

Optimization of camera exposure durations for multi-exposure speckle imaging of the microcirculation

PubMed Central

Kazmi, S. M. Shams; Balial, Satyajit; Dunn, Andrew K.

2014-01-01

Improved Laser Speckle Contrast Imaging (LSCI) blood flow analyses that incorporate inverse models of the underlying laser-tissue interaction have been used to develop more quantitative implementations of speckle flowmetry such as Multi-Exposure Speckle Imaging (MESI). In this paper, we determine the optimal camera exposure durations required for obtaining flow information with comparable accuracy with the prevailing MESI implementation utilized in recent in vivo rodent studies. A looping leave-one-out (LOO) algorithm was used to identify exposure subsets which were analyzed for accuracy against flows obtained from analysis with the original full exposure set over 9 animals comprising n = 314 regional flow measurements. From the 15 original exposures, 6 exposures were found using the LOO process to provide comparable accuracy, defined as being no more than 10% deviant, with the original flow measurements. The optimal subset of exposures provides a basis set of camera durations for speckle flowmetry studies of the microcirculation and confers a two-fold faster acquisition rate and a 28% reduction in processing time without sacrificing accuracy. Additionally, the optimization process can be used to identify further reductions in the exposure subsets for tailoring imaging over less expansive flow distributions to enable even faster imaging. PMID:25071956

An improved wrapper-based feature selection method for machinery fault diagnosis

PubMed Central

2017-01-01

A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most representative feature subset for the machine learning algorithm. In contrast, the trade-off relationship between capability when selecting the best feature subset and computational effort is inevitable in the wrapper-based feature selection (WFS) method. This paper proposes an improved WFS technique before integration with a support vector machine (SVM) model classifier as a complete fault diagnosis system for a rolling element bearing case study. The bearing vibration dataset made available by the Case Western Reserve University Bearing Data Centre was executed using the proposed WFS and its performance has been analysed and discussed. The results reveal that the proposed WFS secures the best feature subset with a lower computational effort by eliminating the redundancy of re-evaluation. The proposed WFS has therefore been found to be capable and efficient to carry out feature selection tasks. PMID:29261689

Embedded algorithms within an FPGA-based system to process nonlinear time series data

NASA Astrophysics Data System (ADS)

Jones, Jonathan D.; Pei, Jin-Song; Tull, Monte P.

2008-03-01

This paper presents some preliminary results of an ongoing project. A pattern classification algorithm is being developed and embedded into a Field-Programmable Gate Array (FPGA) and microprocessor-based data processing core in this project. The goal is to enable and optimize the functionality of onboard data processing of nonlinear, nonstationary data for smart wireless sensing in structural health monitoring. Compared with traditional microprocessor-based systems, fast growing FPGA technology offers a more powerful, efficient, and flexible hardware platform including on-site (field-programmable) reconfiguration capability of hardware. An existing nonlinear identification algorithm is used as the baseline in this study. The implementation within a hardware-based system is presented in this paper, detailing the design requirements, validation, tradeoffs, optimization, and challenges in embedding this algorithm. An off-the-shelf high-level abstraction tool along with the Matlab/Simulink environment is utilized to program the FPGA, rather than coding the hardware description language (HDL) manually. The implementation is validated by comparing the simulation results with those from Matlab. In particular, the Hilbert Transform is embedded into the FPGA hardware and applied to the baseline algorithm as the centerpiece in processing nonlinear time histories and extracting instantaneous features of nonstationary dynamic data. The selection of proper numerical methods for the hardware execution of the selected identification algorithm and consideration of the fixed-point representation are elaborated. Other challenges include the issues of the timing in the hardware execution cycle of the design, resource consumption, approximation accuracy, and user flexibility of input data types limited by the simplicity of this preliminary design. Future work includes making an FPGA and microprocessor operate together to embed a further developed algorithm that yields better computational and power efficiency.

Development and Validation of an Algorithm to Identify Patients with Multiple Myeloma Using Administrative Claims Data.

PubMed

Princic, Nicole; Gregory, Chris; Willson, Tina; Mahue, Maya; Felici, Diana; Werther, Winifred; Lenhart, Gregory; Foley, Kathleen A

2016-01-01

The objective was to expand on prior work by developing and validating a new algorithm to identify multiple myeloma (MM) patients in administrative claims. Two files were constructed to select MM cases from MarketScan Oncology Electronic Medical Records (EMR) and controls from the MarketScan Primary Care EMR during January 1, 2000-March 31, 2014. Patients were linked to MarketScan claims databases, and files were merged. Eligible cases were age ≥18, had a diagnosis and visit for MM in the Oncology EMR, and were continuously enrolled in claims for ≥90 days preceding and ≥30 days after diagnosis. Controls were age ≥18, had ≥12 months of overlap in claims enrollment (observation period) in the Primary Care EMR and ≥1 claim with an ICD-9-CM diagnosis code of MM (203.0×) during that time. Controls were excluded if they had chemotherapy; stem cell transplant; or text documentation of MM in the EMR during the observation period. A split sample was used to develop and validate algorithms. A maximum of 180 days prior to and following each MM diagnosis was used to identify events in the diagnostic process. Of 20 algorithms explored, the baseline algorithm of 2 MM diagnoses and the 3 best performing were validated. Values for sensitivity, specificity, and positive predictive value (PPV) were calculated. Three claims-based algorithms were validated with ~10% improvement in PPV (87-94%) over prior work (81%) and the baseline algorithm (76%) and can be considered for future research. Consistent with prior work, it was found that MM diagnoses before and after tests were needed.

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

PubMed

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.

Feature Selection for Wearable Smartphone-Based Human Activity Recognition with Able bodied, Elderly, and Stroke Patients

PubMed Central

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. PMID:25885272

Assessment of DInSAR Potential in Simulating Geological Subsurface Structure

NASA Astrophysics Data System (ADS)

Fouladi Moghaddam, N.; Rudiger, C.; Samsonov, S. V.; Hall, M.; Walker, J. P.; Camporese, M.

2013-12-01

High resolution geophysical surveys, including seismic, gravity, magnetic, etc., provide valuable information about subsurface structuring but they are very costly and time consuming with non-unique and sometimes conflicting interpretations. Several recent studies have examined the application of DInSAR to estimate surface deformation, monitor possible fault reactivation and constrain reservoir dynamic behaviour in geothermal and groundwater fields. The main focus of these studies was to generate an elevation map, which represents the reservoir extraction induced deformation. This research study, however, will focus on developing methods to simulate subsurface structuring and identify hidden faults/hydraulic barriers using DInSAR surface observations, as an innovative and cost-effective reconnaissance exploration tool for planning of seismic acquisition surveys in geothermal and Carbon Capture and Sequestration regions. By direct integration of various DInSAR datasets with overlapping temporal and spatial coverage we produce multi-temporal ground deformation maps with high resolution and precision to evaluate the potential of a new multidimensional MSBAS technique (Samsonov & d'Oreye, 2012). The technique is based on the Small Baseline Subset Algorithm (SBAS) that is modified to account for variation in sensor parameters. It allows integration of data from sensors with different wave-band, azimuth and incidence angles, different spatial and temporal sampling and resolutions. These deformation maps then will be used as an input for inverse modelling to simulate strain history and shallow depth structure. To achieve the main objective of our research, i.e. developing a method for coupled InSAR and geophysical observations and better understanding of subsurface structuring, comparing DInSAR inverse modelling results with previously provided static structural model will result in iteratively modified DInSAR structural model for adequate match with in situ observations. The newly developed and modified algorithm will then be applied in another part of the region where subsurface information is limited.

Progression of Patterns (POP): A Machine Classifier Algorithm to Identify Glaucoma Progression in Visual Fields

PubMed Central

Goldbaum, Michael H.; Lee, Intae; Jang, Giljin; Balasubramanian, Madhusudhanan; Sample, Pamela A.; Weinreb, Robert N.; Liebmann, Jeffrey M.; Girkin, Christopher A.; Anderson, Douglas R.; Zangwill, Linda M.; Fredette, Marie-Josee; Jung, Tzyy-Ping; Medeiros, Felipe A.; Bowd, Christopher

2012-01-01

Purpose. We evaluated Progression of Patterns (POP) for its ability to identify progression of glaucomatous visual field (VF) defects. Methods. POP uses variational Bayesian independent component mixture model (VIM), a machine learning classifier (MLC) developed previously. VIM separated Swedish Interactive Thresholding Algorithm (SITA) VFs from a set of 2,085 normal and glaucomatous eyes into nine axes (VF patterns): seven glaucomatous. Stable glaucoma was simulated in a second set of 55 patient eyes with five VFs each, collected within four weeks. A third set of 628 eyes with 4,186 VFs (mean ± SD of 6.7 ± 1.7 VFs over 4.0 ± 1.4 years) was tested for progression. Tested eyes were placed into suspect and glaucoma categories at baseline, based on VFs and disk stereoscopic photographs; a subset of eyes had stereophotographic evidence of progressive glaucomatous optic neuropathy (PGON). Each sequence of fields was projected along seven VIM glaucoma axes. Linear regression (LR) slopes generated from projections onto each axis yielded a degree of confidence (DOC) that there was progression. At 95% specificity, progression cutoffs were established for POP, visual field index (VFI), and mean deviation (MD). Guided progression analysis (GPA) was also compared. Results. POP identified a statistically similar number of eyes (P > 0.05) as progressing compared with VFI, MD, and GPA in suspects (3.8%, 2.7%, 5.6%, and 2.9%, respectively), and more eyes than GPA (P = 0.01) in glaucoma (16.0%, 15.3%, 12.0%, and 7.3%, respectively), and more eyes than GPA (P = 0.05) in PGON eyes (26.3%, 23.7%, 27.6%, and 14.5%, respectively). Conclusions. POP, with its display of DOC of progression and its identification of progressing VF defect pattern, adds to the information available to the clinician for detecting VF progression. PMID:22786913

Improvement of Forest Height Retrieval By Integration of Dual-Baseline PolInSAR Data And External DEM Data

NASA Astrophysics Data System (ADS)

Xie, Q.; Wang, C.; Zhu, J.; Fu, H.; Wang, C.

2015-06-01

In recent years, a lot of studies have shown that polarimetric synthetic aperture radar interferometry (PolInSAR) is a powerful technique for forest height mapping and monitoring. However, few researches address the problem of terrain slope effect, which will be one of the major limitations for forest height inversion in mountain forest area. In this paper, we present a novel forest height retrieval algorithm by integration of dual-baseline PolInSAR data and external DEM data. For the first time, we successfully expand the S-RVoG (Sloped-Random Volume over Ground) model for forest parameters inversion into the case of dual-baseline PolInSAR configuration. In this case, the proposed method not only corrects terrain slope variation effect efficiently, but also involves more observations to improve the accuracy of parameters inversion. In order to demonstrate the performance of the inversion algorithm, a set of quad-pol images acquired at the P-band in interferometric repeat-pass mode by the German Aerospace Center (DLR) with the Experimental SAR (E-SAR) system, in the frame of the BioSAR2008 campaign, has been used for the retrieval of forest height over Krycklan boreal forest in northern Sweden. At the same time, a high accuracy external DEM in the experimental area has been collected for computing terrain slope information, which subsequently is used as an inputting parameter in the S-RVoG model. Finally, in-situ ground truth heights in stand-level have been collected to validate the inversion result. The preliminary results show that the proposed inversion algorithm promises to provide much more accurate estimation of forest height than traditional dualbaseline inversion algorithms.

Canopy Height and Vertical Structure from Multibaseline Polarimetric InSAR: First Results of the 2016 NASA/ESA AfriSAR Campaign

NASA Astrophysics Data System (ADS)

Lavalle, M.; Hensley, S.; Lou, Y.; Saatchi, S. S.; Pinto, N.; Simard, M.; Fatoyinbo, T. E.; Duncanson, L.; Dubayah, R.; Hofton, M. A.; Blair, J. B.; Armston, J.

2016-12-01

In this paper we explore the derivation of canopy height and vertical structure from polarimetric-interferometric SAR (PolInSAR) data collected during the 2016 AfriSAR campaign in Gabon. AfriSAR is a joint effort between NASA and ESA to acquire multi-baseline L- and P-band radar data, lidar data and field data over tropical forests and savannah sites to support calibration, validation and algorithm development in preparation for the NISAR, GEDI and BIOMASS missions. Here we focus on the L-band UAVSAR dataset acquired over the Lope National Park in Central Gabon to demonstrate mapping of canopy height and vertical structure using PolInSAR and tomographic techniques. The Lope site features a natural gradient of forest biomass from the forest-savanna boundary (< 100 Mg/ha) to dense undisturbed humid tropical forests (> 400 Mg/ha). Our dataset includes 9 long-baseline, full-polarimetric UAVSAR acquisitions along with field and lidar data from the Laser Vegetation Ice Sensor (LVIS). We first present a brief theoretical background of the PolInSAR and tomographic techniques. We then show the results of our PolInSAR algorithms to create maps of canopy height generated via inversion of the random-volume-over-ground (RVOG) and random-motion-over-ground (RVoG) models. In our approach multiple interferometric baselines are merged incoherently to maximize the interferometric sensitivity over a broad range of tree heights. Finally we show how traditional tomographic algorithms are used for the retrieval of the full vertical canopy profile. We compare our results from the different PolInSAR/tomographic algorithms to validation data derived from lidar and field data.

On-Demand Associative Cross-Language Information Retrieval

NASA Astrophysics Data System (ADS)

Geraldo, André Pinto; Moreira, Viviane P.; Gonçalves, Marcos A.

This paper proposes the use of algorithms for mining association rules as an approach for Cross-Language Information Retrieval. These algorithms have been widely used to analyse market basket data. The idea is to map the problem of finding associations between sales items to the problem of finding term translations over a parallel corpus. The proposal was validated by means of experiments using queries in two distinct languages: Portuguese and Finnish to retrieve documents in English. The results show that the performance of our proposed approach is comparable to the performance of the monolingual baseline and to query translation via machine translation, even though these systems employ more complex Natural Language Processing techniques. The combination between machine translation and our approach yielded the best results, even outperforming the monolingual baseline.

Estimation of longitudinal stability and control derivatives for an icing research aircraft from flight data

NASA Technical Reports Server (NTRS)

Batterson, James G.; Omara, Thomas M.

1989-01-01

The results of applying a modified stepwise regression algorithm and a maximum likelihood algorithm to flight data from a twin-engine commuter-class icing research aircraft are presented. The results are in the form of body-axis stability and control derivatives related to the short-period, longitudinal motion of the aircraft. Data were analyzed for the baseline (uniced) and for the airplane with an artificial glaze ice shape attached to the leading edge of the horizontal tail. The results are discussed as to the accuracy of the derivative estimates and the difference between the derivative values found for the baseline and the iced airplane. Additional comparisons were made between the maximum likelihood results and the modified stepwise regression results with causes for any discrepancies postulated.

Adaptive intercolor error prediction coder for lossless color (rgb) picutre compression

NASA Astrophysics Data System (ADS)

Mann, Y.; Peretz, Y.; Mitchell, Harvey B.

2001-09-01

Most of the current lossless compression algorithms, including the new international baseline JPEG-LS algorithm, do not exploit the interspectral correlations that exist between the color planes in an input color picture. To improve the compression performance (i.e., lower the bit rate) it is necessary to exploit these correlations. A major concern is to find efficient methods for exploiting the correlations that, at the same time, are compatible with and can be incorporated into the JPEG-LS algorithm. One such algorithm is the method of intercolor error prediction (IEP), which when used with the JPEG-LS algorithm, results on average in a reduction of 8% in the overall bit rate. We show how the IEP algorithm can be simply modified and that it nearly doubles the size of the reduction in bit rate to 15%.

Efficacy and safety of etanercept in patients from Latin America, Central Europe and Asia with early non-radiographic axial spondyloarthritis.

PubMed

Wei, James Cheng-Chung; Tsai, Wen-Chan; Citera, Gustavo; Kotak, Sameer; Llamado, Lyndon

2016-11-11

To evaluate etanercept in patients from Latin America, Central/Eastern Europe, and Asia with non-radiographic axial spondyloarthritis (nr-axSpA). A subset analysis was performed on nr-axSpA patients from Argentina, Colombia, the Czech Republic, Hungary, Russia and Taiwan who were enrolled in EMBARK (NCT01258738). Patients received either etanercept 50 mg or placebo once weekly. The primary endpoint was proportion of patients achieving 40% improvement from baseline based on Assessment of SpondyloArthritis International Society (ASAS) criteria. Secondary endpoints included other efficacy assessments, health-related quality of life (HRQoL) and safety. Of the 117 patients in this subset, 59 were treated with etanercept and 58 received placebo. At week 12, numerically greater improvements from baseline were observed for all efficacy endpoints in etanercept-treated patients compared with those receiving placebo. Statistically significant differences between the two treatment groups were observed for proportion of patients achieving ASAS40 (P = 0.0413, at week 8), ASAS5/6 (P = 0.0126), Ankylosing Spondylitis Disease Activity Score - C-reactive protein (CRP) inactive disease (P = 0.0093), Spondyloarthritis Research Consortium of Canada magnetic resonance imaging of sacroiliac joint scores (P = 0.0014), high-sensitivity CRP (P=0.032), and erythrocyte sedimentation rate (P = 0.0082). Statistically significant improvements in the etanercept-treated group compared with placebo group were observed for nocturnal back pain (P = 0.040), total back pain (P = 0.025), physician global assessment of disease (P = 0.023), and Work Productivity and Activity Impairment Questionnaire percent impairment while working (P = 0.047). Adverse events were similar between the two treatment groups. In this subset of patients with nr-axSpA from Latin America, Central/Eastern Europe, and Asia, treatment with etanercept, compared with placebo, resulted in improved disease symptoms and patient HRQoL. Etanercept was well tolerated. © 2016 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

Urinary Bother as a Predictor of Postsurgical Changes in Urinary Function After Robotic Radical Prostatectomy.

PubMed

Murphy, Gregory; Haddock, Peter; Doak, Hoyt; Jackson, Max; Dorin, Ryan; Meraney, Anoop; Kesler, Stuart; Staff, Ilene; Wagner, Joseph R

2015-10-01

To characterize changes in indices of urinary function in prostatectomy patients with presurgical voiding symptoms. A retrospective analysis of our prostate cancer database identified robot-assisted radical prostatectomy patients between April 2007 and December 2011 who completed pre- and postsurgical (24 months) Expanded Prostate Cancer Index Composite-26 surveys. Gleason score, margins, D'Amico risk, prostate-specific antigen, radiotherapy, and nerve-sparing status were tabulated. Survey questions addressed urinary irritation/obstruction, incontinence, and overall bother. Responses were averaged to calculate a urinary sum (US) score. Patients were stratified according to the severity of their baseline urinary bother (UB), and changes in urinary indices determined at 24 months. A total of 737 patients were included. Postsurgical improvement in urinary obstruction, bother, and sum score was related to baseline UB (P <.001). Men with severe baseline bother had the greatest improvement in US (+9.3), whereas those with asymptomatic baseline UB experienced a decline in US (-2.8). All patients experienced a decline in urinary incontinence of 6.3-8.3 that was independent of baseline bother (P = .507). Patients with severe UB experienced positive outcomes, whereas those at asymptomatic baseline experienced negative US outcomes. Negative urinary incontinence outcomes were unrelated to baseline UB. Age, radiotherapy, and nerve-sparing status were not associated with improved UB (P = .029). However, baseline UB was significantly associated with improvement in postsurgical UB (P = .001). Baseline UB is a predictor of postsurgical improvement in urinary function. These data are helpful when counseling a subset of robot-assisted laparoscopic radical prostatectomy patients with severe preoperative urinary symptoms. Copyright © 2015 Elsevier Inc. All rights reserved.

Children's binge eating and development of metabolic syndrome.

PubMed

Tanofsky-Kraff, M; Shomaker, L B; Stern, E A; Miller, R; Sebring, N; Dellavalle, D; Yanovski, S Z; Hubbard, V S; Yanovski, J A

2012-07-01

Binge eating predisposes children to excessive weight gain. However, it is unknown if pediatric binge eating predicts other obesity-associated adverse health outcomes. The objective of this study was to investigate the relationship between binge eating and metabolic syndrome (MetS) in children. Children aged 5-12 years at high risk for adult obesity, either because they were overweight/obese when first examined or because their parents were overweight/obese, were recruited from Washington, DC and its suburbs. Children completed a questionnaire assessment of binge eating at baseline and underwent measurements of MetS components at baseline and at a follow-up visit approximately 5 years later. Magnetic resonance imaging was used to measure the visceral adipose tissue (VAT) in a subset. In all, 180 children were studied between July 1996 and August 2010. Baseline self-reported binge eating presence was associated with a 5.33 greater odds of having MetS at follow-up (95% confidence interval (CI): 1.47, 19.27, P=0.01). The association between binge eating and body mass index (BMI) only partially explained changes in MetS components: baseline binge eating predicted higher follow-up triglycerides, even after accounting for baseline triglycerides, baseline BMI, BMI change, sex, race, baseline age and time in study (P = 0.05). Also, adjusting for baseline VAT and demographics, baseline binge eating predicted greater follow-up L(2-3) VAT (P = 0.01). Children's reports of binge eating predicted development of MetS, worsening triglycerides and increased VAT. The excessive weight gain associated with children's binge eating partly explained its adverse metabolic health outcomes. Reported binge eating may represent an early behavioral marker upon which to focus interventions for obesity and MetS.

A developmental approach to learning causal models for cyber security

NASA Astrophysics Data System (ADS)

Mugan, Jonathan

2013-05-01

To keep pace with our adversaries, we must expand the scope of machine learning and reasoning to address the breadth of possible attacks. One approach is to employ an algorithm to learn a set of causal models that describes the entire cyber network and each host end node. Such a learning algorithm would run continuously on the system and monitor activity in real time. With a set of causal models, the algorithm could anticipate novel attacks, take actions to thwart them, and predict the second-order effects flood of information, and the algorithm would have to determine which streams of that flood were relevant in which situations. This paper will present the results of efforts toward the application of a developmental learning algorithm to the problem of cyber security. The algorithm is modeled on the principles of human developmental learning and is designed to allow an agent to learn about the computer system in which it resides through active exploration. Children are flexible learners who acquire knowledge by actively exploring their environment and making predictions about what they will find,1, 2 and our algorithm is inspired by the work of the developmental psychologist Jean Piaget.3 Piaget described how children construct knowledge in stages and learn new concepts on top of those they already know. Developmental learning allows our algorithm to focus on subsets of the environment that are most helpful for learning given its current knowledge. In experiments, the algorithm was able to learn the conditions for file exfiltration and use that knowledge to protect sensitive files.

Iterative Stable Alignment and Clustering of 2D Transmission Electron Microscope Images

PubMed Central

Yang, Zhengfan; Fang, Jia; Chittuluru, Johnathan; Asturias, Francisco J.; Penczek, Pawel A.

2012-01-01

SUMMARY Identification of homogeneous subsets of images in a macromolecular electron microscopy (EM) image data set is a critical step in single-particle analysis. The task is handled by iterative algorithms, whose performance is compromised by the compounded limitations of image alignment and K-means clustering. Here we describe an approach, iterative stable alignment and clustering (ISAC) that, relying on a new clustering method and on the concepts of stability and reproducibility, can extract validated, homogeneous subsets of images. ISAC requires only a small number of simple parameters and, with minimal human intervention, can eliminate bias from two-dimensional image clustering and maximize the quality of group averages that can be used for ab initio three-dimensional structural determination and analysis of macromolecular conformational variability. Repeated testing of the stability and reproducibility of a solution within ISAC eliminates heterogeneous or incorrect classes and introduces critical validation to the process of EM image clustering. PMID:22325773

A Novel Collection of snRNA-Like Promoters with Tissue-Specific Transcription Properties

PubMed Central

Garritano, Sonia; Gigoni, Arianna; Costa, Delfina; Malatesta, Paolo; Florio, Tullio; Cancedda, Ranieri; Pagano, Aldo

2012-01-01

We recently identified a novel dataset of snRNA-like trascriptional units in the human genome. The investigation of a subset of these elements showed that they play relevant roles in physiology and/or pathology. In this work we expand our collection of small RNAs taking advantage of a newly developed algorithm able to identify genome sequence stretches with RNA polymerase (pol) III type 3 promoter features thus constituting putative pol III binding sites. The bioinformatic analysis of a subset of these elements that map in introns of protein-coding genes in antisense configuration suggest their association with alternative splicing, similarly to other recently characterized small RNAs. Interestingly, the analysis of the transcriptional activity of these novel promoters shows that they are active in a cell-type specific manner, in accordance with the emerging body of evidence of a tissue/cell-specific activity of pol III. PMID:23109855

A novel collection of snRNA-like promoters with tissue-specific transcription properties.

PubMed

Garritano, Sonia; Gigoni, Arianna; Costa, Delfina; Malatesta, Paolo; Florio, Tullio; Cancedda, Ranieri; Pagano, Aldo

2012-01-01

We recently identified a novel dataset of snRNA-like trascriptional units in the human genome. The investigation of a subset of these elements showed that they play relevant roles in physiology and/or pathology. In this work we expand our collection of small RNAs taking advantage of a newly developed algorithm able to identify genome sequence stretches with RNA polymerase (pol) III type 3 promoter features thus constituting putative pol III binding sites. The bioinformatic analysis of a subset of these elements that map in introns of protein-coding genes in antisense configuration suggest their association with alternative splicing, similarly to other recently characterized small RNAs. Interestingly, the analysis of the transcriptional activity of these novel promoters shows that they are active in a cell-type specific manner, in accordance with the emerging body of evidence of a tissue/cell-specific activity of pol III.

Superiorization with level control

NASA Astrophysics Data System (ADS)

Cegielski, Andrzej; Al-Musallam, Fadhel

2017-04-01

The convex feasibility problem is to find a common point of a finite family of closed convex subsets. In many applications one requires something more, namely finding a common point of closed convex subsets which minimizes a continuous convex function. The latter requirement leads to an application of the superiorization methodology which is actually settled between methods for convex feasibility problem and the convex constrained minimization. Inspired by the superiorization idea we introduce a method which sequentially applies a long-step algorithm for a sequence of convex feasibility problems; the method employs quasi-nonexpansive operators as well as subgradient projections with level control and does not require evaluation of the metric projection. We replace a perturbation of the iterations (applied in the superiorization methodology) by a perturbation of the current level in minimizing the objective function. We consider the method in the Euclidean space in order to guarantee the strong convergence, although the method is well defined in a Hilbert space.

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

NASA Astrophysics Data System (ADS)

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.

HIV-1 protease cleavage site prediction based on two-stage feature selection method.

PubMed

Niu, Bing; Yuan, Xiao-Cheng; Roeper, Preston; Su, Qiang; Peng, Chun-Rong; Yin, Jing-Yuan; Ding, Juan; Li, HaiPeng; Lu, Wen-Cong

2013-03-01

Knowledge of the mechanism of HIV protease cleavage specificity is critical to the design of specific and effective HIV inhibitors. Searching for an accurate, robust, and rapid method to correctly predict the cleavage sites in proteins is crucial when searching for possible HIV inhibitors. In this article, HIV-1 protease specificity was studied using the correlation-based feature subset (CfsSubset) selection method combined with Genetic Algorithms method. Thirty important biochemical features were found based on a jackknife test from the original data set containing 4,248 features. By using the AdaBoost method with the thirty selected features the prediction model yields an accuracy of 96.7% for the jackknife test and 92.1% for an independent set test, with increased accuracy over the original dataset by 6.7% and 77.4%, respectively. Our feature selection scheme could be a useful technique for finding effective competitive inhibitors of HIV protease.

GenoCore: A simple and fast algorithm for core subset selection from large genotype datasets.

PubMed

Jeong, Seongmun; Kim, Jae-Yoon; Jeong, Soon-Chun; Kang, Sung-Taeg; Moon, Jung-Kyung; Kim, Namshin

2017-01-01

Selecting core subsets from plant genotype datasets is important for enhancing cost-effectiveness and to shorten the time required for analyses of genome-wide association studies (GWAS), and genomics-assisted breeding of crop species, etc. Recently, a large number of genetic markers (>100,000 single nucleotide polymorphisms) have been identified from high-density single nucleotide polymorphism (SNP) arrays and next-generation sequencing (NGS) data. However, there is no software available for picking out the efficient and consistent core subset from such a huge dataset. It is necessary to develop software that can extract genetically important samples in a population with coherence. We here present a new program, GenoCore, which can find quickly and efficiently the core subset representing the entire population. We introduce simple measures of coverage and diversity scores, which reflect genotype errors and genetic variations, and can help to select a sample rapidly and accurately for crop genotype dataset. Comparison of our method to other core collection software using example datasets are performed to validate the performance according to genetic distance, diversity, coverage, required system resources, and the number of selected samples. GenoCore selects the smallest, most consistent, and most representative core collection from all samples, using less memory with more efficient scores, and shows greater genetic coverage compared to the other software tested. GenoCore was written in R language, and can be accessed online with an example dataset and test results at https://github.com/lovemun/Genocore.

Identification of Incident CKD Stage 3 in Research Studies

PubMed Central

Grams, Morgan E.; Rebholz, Casey; MacMahon, Blaithin; Whelton, Seamus; Ballew, Shoshana H.; Selvin, Elizabeth; Wruck, Lisa; Coresh, Josef

2014-01-01

Background In epidemiologic research, incident chronic kidney disease (CKD) is commonly determined by laboratory tests performed at planned study visits. Given the morbidity and mortality associated with CKD, persons with incident disease may be less likely to attend scheduled visits, affecting observed associations. The objective of this study was to quantify loss-to-follow-up by CKD status, and to determine whether supplementation with diagnostic code data improves capture of incident CKD. Study Design Prospective cohort study. Setting & Participants 11,560 participants in the Atherosclerosis Risk in Communities (ARIC) Study underwent continuous surveillance for hospitalizations and death from baseline visit (1996-1999) to follow-up visit (2011-2013). A subset of hospitalizations in Washington County, MD, was used in diagnostic code validation (n=2,540). Predictor Baseline demographics and comorbid conditions. Outcomes Incident CKD stage 3 ascertained by follow-up visit (visit-based definition), or by hospitalization surveillance (hospitalization-based definition). Measurements Visit-based definition: ≥25% decline from baseline estimated glomerular filtration rate to <60 ml/min/1.73 m2 at follow-up visit; hospitalization-based definition: hospitalization CKD diagnostic code. Results Among 11,560 participants, 5,951 attended the follow-up visit, and 9,264 were hospitalized. Never-hospitalized participants were younger, more often female, and had fewer comorbid conditions; 73.5% attended the follow-up visit. Incident CKD stage 3 occurred in 1,172 participants by the visit-based definition (251 were never-hospitalized) and 1,078 participants by the hospitalization-based definition (237 attended the follow-up study visit). The sensitivity of the hospitalization-based CKD definition was 35.5% (95% CI, 31.6%-39.7%); specificity was 95.7% (95% CI, 94.2%-96.8%). Sensitivity was higher with later time period, older participant age, and baseline prevalent diabetes and CKD. Limitations A subset of hospitalizations were used for validation; 15-year gap between study visits. Conclusions The sensitivity of diagnostic code–identified CKD is low and varies by certain factors; however, supplementing a visit-based definition with hospitalization information can increase disease identification during periods of follow-up without study visits. PMID:24726628

PANDA: Protein function prediction using domain architecture and affinity propagation.

PubMed

Wang, Zheng; Zhao, Chenguang; Wang, Yiheng; Sun, Zheng; Wang, Nan

2018-02-22

We developed PANDA (Propagation of Affinity and Domain Architecture) to predict protein functions in the format of Gene Ontology (GO) terms. PANDA at first executes profile-profile alignment algorithm to search against PfamA, KOG, COG, and SwissProt databases, and then launches PSI-BLAST against UniProt for homologue search. PANDA integrates a domain architecture inference algorithm based on the Bayesian statistics that calculates the probability of having a GO term. All the candidate GO terms are pooled and filtered based on Z-score. After that, the remaining GO terms are clustered using an affinity propagation algorithm based on the GO directed acyclic graph, followed by a second round of filtering on the clusters of GO terms. We benchmarked the performance of all the baseline predictors PANDA integrates and also for every pooling and filtering step of PANDA. It can be found that PANDA achieves better performances in terms of area under the curve for precision and recall compared to the baseline predictors. PANDA can be accessed from http://dna.cs.miami.edu/PANDA/ .

The ALMA Science Pipeline: Current Status

NASA Astrophysics Data System (ADS)

Humphreys, Elizabeth; Miura, Rie; Brogan, Crystal L.; Hibbard, John; Hunter, Todd R.; Indebetouw, Remy

2016-09-01

The ALMA Science Pipeline is being developed for the automated calibration and imaging of ALMA interferometric and single-dish data. The calibration Pipeline for interferometric data was accepted for use by ALMA Science Operations in 2014, and for single-dish data end-to-end processing in 2015. However, work is ongoing to expand the use cases for which the Pipeline can be used e.g. for higher frequency and lower signal-to-noise datasets, and for new observing modes. A current focus includes the commissioning of science target imaging for interferometric data. For the Single Dish Pipeline, the line finding algorithm used in baseline subtraction and baseline flagging heuristics have been greately improved since the prototype used for data from the previous cycle. These algorithms, unique to the Pipeline, produce better results than standard manual processing in many cases. In this poster, we report on the current status of the Pipeline capabilities, present initial results from the Imaging Pipeline, and the smart line finding and flagging algorithm used in the Single Dish Pipeline. The Pipeline is released as part of CASA (the Common Astronomy Software Applications package).

Fast, accurate semiempirical molecular orbital calculations for macromolecules

NASA Astrophysics Data System (ADS)

Dixon, Steven L.; Merz, Kenneth M., Jr.

1997-07-01

A detailed review of the semiempirical divide-and-conquer (D&C) method is given, including a new approach to subsetting, which involves dual buffer regions. Comparisons are drawn between this method and other semiempirical macromolecular schemes. D&C calculations are carried out using a basic 32 Mbyte memory workstation on a variety of peptide systems, including proteins containing up to 1960 atoms. Aspects of storage and SCF convergence are addressed, and parallelization of the D&C algorithm is discussed.

Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data

NASA Astrophysics Data System (ADS)

Hamedianfar, Alireza; Shafri, Helmi Zulhaidi Mohd

2016-04-01

This paper integrates decision tree-based data mining (DM) and object-based image analysis (OBIA) to provide a transferable model for the detailed characterization of urban land-cover classes using WorldView-2 (WV-2) satellite images. Many articles have been published on OBIA in recent years based on DM for different applications. However, less attention has been paid to the generation of a transferable model for characterizing detailed urban land cover features. Three subsets of WV-2 images were used in this paper to generate transferable OBIA rule-sets. Many features were explored by using a DM algorithm, which created the classification rules as a decision tree (DT) structure from the first study area. The developed DT algorithm was applied to object-based classifications in the first study area. After this process, we validated the capability and transferability of the classification rules into second and third subsets. Detailed ground truth samples were collected to assess the classification results. The first, second, and third study areas achieved 88%, 85%, and 85% overall accuracies, respectively. Results from the investigation indicate that DM was an efficient method to provide the optimal and transferable classification rules for OBIA, which accelerates the rule-sets creation stage in the OBIA classification domain.

A multi-model fusion strategy for multivariate calibration using near and mid-infrared spectra of samples from brewing industry.

PubMed

Tan, Chao; Chen, Hui; Wang, Chao; Zhu, Wanping; Wu, Tong; Diao, Yuanbo

2013-03-15

Near and mid-infrared (NIR/MIR) spectroscopy techniques have gained great acceptance in the industry due to their multiple applications and versatility. However, a success of application often depends heavily on the construction of accurate and stable calibration models. For this purpose, a simple multi-model fusion strategy is proposed. It is actually the combination of Kohonen self-organizing map (KSOM), mutual information (MI) and partial least squares (PLSs) and therefore named as KMICPLS. It works as follows: First, the original training set is fed into a KSOM for unsupervised clustering of samples, on which a series of training subsets are constructed. Thereafter, on each of the training subsets, a MI spectrum is calculated and only the variables with higher MI values than the mean value are retained, based on which a candidate PLS model is constructed. Finally, a fixed number of PLS models are selected to produce a consensus model. Two NIR/MIR spectral datasets from brewing industry are used for experiments. The results confirms its superior performance to two reference algorithms, i.e., the conventional PLS and genetic algorithm-PLS (GAPLS). It can build more accurate and stable calibration models without increasing the complexity, and can be generalized to other NIR/MIR applications. Copyright © 2012 Elsevier B.V. All rights reserved.