Hydrocarbon source potential and maturation in eocene New Zealand vitrinite-rich coals: Insights from traditional coal analyses, and Rock-Eval and biomarker studies

USGS Publications Warehouse

Newman, J.; Price, L.C.; Johnston, J.H.

1997-01-01

The results of traditional methods of coal characterisation (proximate, specific energy, and ultimate analyses) for 28 Eocene coal samples from the West Coast of New Zealand correspond well with biomarker ratios and Rock-Eval analyses. Isorank variations in vitrinite fluorescence and reflectance recorded for these samples are closely related to their volatile-matter content, and therefore indicate that the original vitrinite chemistry is a key controlling factor. By contrast, the mineral-matter content and the proportion of coal macerals present appear to have had only a minor influence on the coal samples' properties. Our analyses indicate that a number of triterpane biomarker ratios show peak maturities by high volatile bituminous A rank; apparent maturities are then reversed and decline at the higher medium volatile bituminous rank. The Rock-Eval S1 +S2 yield also maximizes by high volatile bituminous A rank, and then declines; however, this decline is retarded in samples with the most hydrogen-rich (perhydrous) vitrinites. These Rock-Eval and biomarker trends, as well as trends in traditional coal analyses, are used to define the rank at which expulsion of gas and oil occurs from the majority of the coals. This expulsion commences at high volatile A bituminous rank, and persists up to the threshold of medium volatile bituminous rank (c. 1.1% Ro ran. or 1.2% Ro max in this sample set), where marked hydrocarbon expulsion from perhydrous vitrinites begins to take place.

The prediction of airborne and structure-borne noise potential for a tire

NASA Astrophysics Data System (ADS)

Sakamoto, Nicholas Y.

Tire/pavement interaction noise is a major component of both exterior pass-by noise and vehicle interior noise. The current testing methods for ranking tires from loud to quiet require expensive equipment, multiple tires, and/or long experimental set-up and run times. If a laboratory based off-vehicle test could be used to identify the airborne and structure-borne potential of a tire from its dynamic characteristics, a relative ranking of a large group of tires could be performed at relatively modest expense. This would provide a smaller sample set of tires for follow-up testing and thus save expense for automobile OEMs. The focus of this research was identifying key noise features from a tire/pavement experiment. These results were compared against a stationary tire test in which the natural response of the tire to a forced input was measured. Since speed was identified as having some effect on the noise, an input function was also developed to allow the tires to be ranked at an appropriate speed. A relative noise model was used on a second sample set of tires to verify if the ranking could be used against interior vehicle measurements. While overall level analysis of the specified spectrum had mixed success, important noise generating features were identified, and the methods used could be improved to develop a standard off-vehicle test to predict a tire's noise potential.

Robust Covariate-Adjusted Log-Rank Statistics and Corresponding Sample Size Formula for Recurrent Events Data

PubMed Central

Song, Rui; Kosorok, Michael R.; Cai, Jianwen

2009-01-01

Summary Recurrent events data are frequently encountered in clinical trials. This article develops robust covariate-adjusted log-rank statistics applied to recurrent events data with arbitrary numbers of events under independent censoring and the corresponding sample size formula. The proposed log-rank tests are robust with respect to different data-generating processes and are adjusted for predictive covariates. It reduces to the Kong and Slud (1997, Biometrika 84, 847–862) setting in the case of a single event. The sample size formula is derived based on the asymptotic normality of the covariate-adjusted log-rank statistics under certain local alternatives and a working model for baseline covariates in the recurrent event data context. When the effect size is small and the baseline covariates do not contain significant information about event times, it reduces to the same form as that of Schoenfeld (1983, Biometrics 39, 499–503) for cases of a single event or independent event times within a subject. We carry out simulations to study the control of type I error and the comparison of powers between several methods in finite samples. The proposed sample size formula is illustrated using data from an rhDNase study. PMID:18162107

Evaluating Gene Set Enrichment Analysis Via a Hybrid Data Model

PubMed Central

Hua, Jianping; Bittner, Michael L.; Dougherty, Edward R.

2014-01-01

Gene set enrichment analysis (GSA) methods have been widely adopted by biological labs to analyze data and generate hypotheses for validation. Most of the existing comparison studies focus on whether the existing GSA methods can produce accurate P-values; however, practitioners are often more concerned with the correct gene-set ranking generated by the methods. The ranking performance is closely related to two critical goals associated with GSA methods: the ability to reveal biological themes and ensuring reproducibility, especially for small-sample studies. We have conducted a comprehensive simulation study focusing on the ranking performance of seven representative GSA methods. We overcome the limitation on the availability of real data sets by creating hybrid data models from existing large data sets. To build the data model, we pick a master gene from the data set to form the ground truth and artificially generate the phenotype labels. Multiple hybrid data models can be constructed from one data set and multiple data sets of smaller sizes can be generated by resampling the original data set. This approach enables us to generate a large batch of data sets to check the ranking performance of GSA methods. Our simulation study reveals that for the proposed data model, the Q2 type GSA methods have in general better performance than other GSA methods and the global test has the most robust results. The properties of a data set play a critical role in the performance. For the data sets with highly connected genes, all GSA methods suffer significantly in performance. PMID:24558298

A sampling-based method for ranking protein structural models by integrating multiple scores and features.

PubMed

Shi, Xiaohu; Zhang, Jingfen; He, Zhiquan; Shang, Yi; Xu, Dong

2011-09-01

One of the major challenges in protein tertiary structure prediction is structure quality assessment. In many cases, protein structure prediction tools generate good structural models, but fail to select the best models from a huge number of candidates as the final output. In this study, we developed a sampling-based machine-learning method to rank protein structural models by integrating multiple scores and features. First, features such as predicted secondary structure, solvent accessibility and residue-residue contact information are integrated by two Radial Basis Function (RBF) models trained from different datasets. Then, the two RBF scores and five selected scoring functions developed by others, i.e., Opus-CA, Opus-PSP, DFIRE, RAPDF, and Cheng Score are synthesized by a sampling method. At last, another integrated RBF model ranks the structural models according to the features of sampling distribution. We tested the proposed method by using two different datasets, including the CASP server prediction models of all CASP8 targets and a set of models generated by our in-house software MUFOLD. The test result shows that our method outperforms any individual scoring function on both best model selection, and overall correlation between the predicted ranking and the actual ranking of structural quality.

Quantile rank maps: a new tool for understanding individual brain development.

PubMed

Chen, Huaihou; Kelly, Clare; Castellanos, F Xavier; He, Ye; Zuo, Xi-Nian; Reiss, Philip T

2015-05-01

We propose a novel method for neurodevelopmental brain mapping that displays how an individual's values for a quantity of interest compare with age-specific norms. By estimating smoothly age-varying distributions at a set of brain regions of interest, we derive age-dependent region-wise quantile ranks for a given individual, which can be presented in the form of a brain map. Such quantile rank maps could potentially be used for clinical screening. Bootstrap-based confidence intervals are proposed for the quantile rank estimates. We also propose a recalibrated Kolmogorov-Smirnov test for detecting group differences in the age-varying distribution. This test is shown to be more robust to model misspecification than a linear regression-based test. The proposed methods are applied to brain imaging data from the Nathan Kline Institute Rockland Sample and from the Autism Brain Imaging Data Exchange (ABIDE) sample. Copyright © 2015 Elsevier Inc. All rights reserved.

Assessment and improvement of statistical tools for comparative proteomics analysis of sparse data sets with few experimental replicates.

PubMed

Schwämmle, Veit; León, Ileana Rodríguez; Jensen, Ole Nørregaard

2013-09-06

Large-scale quantitative analyses of biological systems are often performed with few replicate experiments, leading to multiple nonidentical data sets due to missing values. For example, mass spectrometry driven proteomics experiments are frequently performed with few biological or technical replicates due to sample-scarcity or due to duty-cycle or sensitivity constraints, or limited capacity of the available instrumentation, leading to incomplete results where detection of significant feature changes becomes a challenge. This problem is further exacerbated for the detection of significant changes on the peptide level, for example, in phospho-proteomics experiments. In order to assess the extent of this problem and the implications for large-scale proteome analysis, we investigated and optimized the performance of three statistical approaches by using simulated and experimental data sets with varying numbers of missing values. We applied three tools, including standard t test, moderated t test, also known as limma, and rank products for the detection of significantly changing features in simulated and experimental proteomics data sets with missing values. The rank product method was improved to work with data sets containing missing values. Extensive analysis of simulated and experimental data sets revealed that the performance of the statistical analysis tools depended on simple properties of the data sets. High-confidence results were obtained by using the limma and rank products methods for analyses of triplicate data sets that exhibited more than 1000 features and more than 50% missing values. The maximum number of differentially represented features was identified by using limma and rank products methods in a complementary manner. We therefore recommend combined usage of these methods as a novel and optimal way to detect significantly changing features in these data sets. This approach is suitable for large quantitative data sets from stable isotope labeling and mass spectrometry experiments and should be applicable to large data sets of any type. An R script that implements the improved rank products algorithm and the combined analysis is available.

Gene selection with multiple ordering criteria.

PubMed

Chen, James J; Tsai, Chen-An; Tzeng, Shengli; Chen, Chun-Houh

2007-03-05

A microarray study may select different differentially expressed gene sets because of different selection criteria. For example, the fold-change and p-value are two commonly known criteria to select differentially expressed genes under two experimental conditions. These two selection criteria often result in incompatible selected gene sets. Also, in a two-factor, say, treatment by time experiment, the investigator may be interested in one gene list that responds to both treatment and time effects. We propose three layer ranking algorithms, point-admissible, line-admissible (convex), and Pareto, to provide a preference gene list from multiple gene lists generated by different ranking criteria. Using the public colon data as an example, the layer ranking algorithms are applied to the three univariate ranking criteria, fold-change, p-value, and frequency of selections by the SVM-RFE classifier. A simulation experiment shows that for experiments with small or moderate sample sizes (less than 20 per group) and detecting a 4-fold change or less, the two-dimensional (p-value and fold-change) convex layer ranking selects differentially expressed genes with generally lower FDR and higher power than the standard p-value ranking. Three applications are presented. The first application illustrates a use of the layer rankings to potentially improve predictive accuracy. The second application illustrates an application to a two-factor experiment involving two dose levels and two time points. The layer rankings are applied to selecting differentially expressed genes relating to the dose and time effects. In the third application, the layer rankings are applied to a benchmark data set consisting of three dilution concentrations to provide a ranking system from a long list of differentially expressed genes generated from the three dilution concentrations. The layer ranking algorithms are useful to help investigators in selecting the most promising genes from multiple gene lists generated by different filter, normalization, or analysis methods for various objectives.

A test for patterns of modularity in sequences of developmental events.

PubMed

Poe, Steven

2004-08-01

This study presents a statistical test for modularity in the context of relative timing of developmental events. The test assesses whether sets of developmental events show special phylogenetic conservation of rank order. The test statistic is the correlation coefficient of developmental ranks of the N events of the hypothesized module across taxa. The null distribution is obtained by taking correlation coefficients for randomly sampled sets of N events. This test was applied to two datasets, including one where phylogenetic information was taken into account. The events of limb development in two frog species were found to behave as a module.

A human fecal contamination index for ranking impaired ...

EPA Pesticide Factsheets

Human fecal pollution of surface water remains a public health concern worldwide. As a result, there is a growing interest in the application of human-associated fecal source identification quantitative real-time PCR (qPCR) technologies for recreational water quality risk management. The transition from a research subject to a management tool requires the integration of standardized water sampling, laboratory, and data analysis procedures. In this study, a standardized HF183/BacR287 qPCR method was combined with a water sampling strategy and Bayesian data algorithm to establish a human fecal contamination index that can be used to rank impaired recreational water sites polluted with human waste. Stability and bias of index predictions were investigated under various parameters including siteswith different pollution levels, sampling period time range (1-15 weeks), and number of qPCR replicates per sample (2-14 replicates). Sensitivity analyses were conducted with simulated data sets (100 iterations) seeded with HF183/BacR287 qPCR laboratory measurements from water samples collected from three Southern California sites (588 qPCR measurements). Findings suggest that site ranking is feasible and that all parameters tested influence stability and bias in human fecal contamination indexscoring. Trends identified by sensitivity analyses will provide managers with the information needed to design and conduct field studies to rank impaired recreational water sites based

The Model-Based Study of the Effectiveness of Reporting Lists of Small Feature Sets Using RNA-Seq Data.

PubMed

Kim, Eunji; Ivanov, Ivan; Hua, Jianping; Lampe, Johanna W; Hullar, Meredith Aj; Chapkin, Robert S; Dougherty, Edward R

2017-01-01

Ranking feature sets for phenotype classification based on gene expression is a challenging issue in cancer bioinformatics. When the number of samples is small, all feature selection algorithms are known to be unreliable, producing significant error, and error estimators suffer from different degrees of imprecision. The problem is compounded by the fact that the accuracy of classification depends on the manner in which the phenomena are transformed into data by the measurement technology. Because next-generation sequencing technologies amount to a nonlinear transformation of the actual gene or RNA concentrations, they can potentially produce less discriminative data relative to the actual gene expression levels. In this study, we compare the performance of ranking feature sets derived from a model of RNA-Seq data with that of a multivariate normal model of gene concentrations using 3 measures: (1) ranking power, (2) length of extensions, and (3) Bayes features. This is the model-based study to examine the effectiveness of reporting lists of small feature sets using RNA-Seq data and the effects of different model parameters and error estimators. The results demonstrate that the general trends of the parameter effects on the ranking power of the underlying gene concentrations are preserved in the RNA-Seq data, whereas the power of finding a good feature set becomes weaker when gene concentrations are transformed by the sequencing machine.

Object Classification With Joint Projection and Low-Rank Dictionary Learning.

PubMed

Foroughi, Homa; Ray, Nilanjan; Hong Zhang

2018-02-01

For an object classification system, the most critical obstacles toward real-world applications are often caused by large intra-class variability, arising from different lightings, occlusion, and corruption, in limited sample sets. Most methods in the literature would fail when the training samples are heavily occluded, corrupted or have significant illumination or viewpoint variations. Besides, most of the existing methods and especially deep learning-based methods, need large training sets to achieve a satisfactory recognition performance. Although using the pre-trained network on a generic large-scale data set and fine-tune it to the small-sized target data set is a widely used technique, this would not help when the content of base and target data sets are very different. To address these issues simultaneously, we propose a joint projection and low-rank dictionary learning method using dual graph constraints. Specifically, a structured class-specific dictionary is learned in the low-dimensional space, and the discrimination is further improved by imposing a graph constraint on the coding coefficients, that maximizes the intra-class compactness and inter-class separability. We enforce structural incoherence and low-rank constraints on sub-dictionaries to reduce the redundancy among them, and also make them robust to variations and outliers. To preserve the intrinsic structure of data, we introduce a supervised neighborhood graph into the framework to make the proposed method robust to small-sized and high-dimensional data sets. Experimental results on several benchmark data sets verify the superior performance of our method for object classification of small-sized data sets, which include a considerable amount of different kinds of variation, and may have high-dimensional feature vectors.

RANKED SET SAMPLING FOR ECOLOGICAL RESEARCH: ACCOUNTING FOR THE TOTAL COSTS OF SAMPLING, BY MODE, CONQUEST, AND MARKER. (R825173)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

Estimates of the Sampling Distribution of Scalability Coefficient H

ERIC Educational Resources Information Center

Van Onna, Marieke J. H.

2004-01-01

Coefficient "H" is used as an index of scalability in nonparametric item response theory (NIRT). It indicates the degree to which a set of items rank orders examinees. Theoretical sampling distributions, however, have only been derived asymptotically and only under restrictive conditions. Bootstrap methods offer an alternative possibility to…

Efficient marginalization to compute protein posterior probabilities from shotgun mass spectrometry data

PubMed Central

Serang, Oliver; MacCoss, Michael J.; Noble, William Stafford

2010-01-01

The problem of identifying proteins from a shotgun proteomics experiment has not been definitively solved. Identifying the proteins in a sample requires ranking them, ideally with interpretable scores. In particular, “degenerate” peptides, which map to multiple proteins, have made such a ranking difficult to compute. The problem of computing posterior probabilities for the proteins, which can be interpreted as confidence in a protein’s presence, has been especially daunting. Previous approaches have either ignored the peptide degeneracy problem completely, addressed it by computing a heuristic set of proteins or heuristic posterior probabilities, or by estimating the posterior probabilities with sampling methods. We present a probabilistic model for protein identification in tandem mass spectrometry that recognizes peptide degeneracy. We then introduce graph-transforming algorithms that facilitate efficient computation of protein probabilities, even for large data sets. We evaluate our identification procedure on five different well-characterized data sets and demonstrate our ability to efficiently compute high-quality protein posteriors. PMID:20712337

MRM-Lasso: A Sparse Multiview Feature Selection Method via Low-Rank Analysis.

PubMed

Yang, Wanqi; Gao, Yang; Shi, Yinghuan; Cao, Longbing

2015-11-01

Learning about multiview data involves many applications, such as video understanding, image classification, and social media. However, when the data dimension increases dramatically, it is important but very challenging to remove redundant features in multiview feature selection. In this paper, we propose a novel feature selection algorithm, multiview rank minimization-based Lasso (MRM-Lasso), which jointly utilizes Lasso for sparse feature selection and rank minimization for learning relevant patterns across views. Instead of simply integrating multiple Lasso from view level, we focus on the performance of sample-level (sample significance) and introduce pattern-specific weights into MRM-Lasso. The weights are utilized to measure the contribution of each sample to the labels in the current view. In addition, the latent correlation across different views is successfully captured by learning a low-rank matrix consisting of pattern-specific weights. The alternating direction method of multipliers is applied to optimize the proposed MRM-Lasso. Experiments on four real-life data sets show that features selected by MRM-Lasso have better multiview classification performance than the baselines. Moreover, pattern-specific weights are demonstrated to be significant for learning about multiview data, compared with view-specific weights.

Pearson's chi-square test and rank correlation inferences for clustered data.

PubMed

Shih, Joanna H; Fay, Michael P

2017-09-01

Pearson's chi-square test has been widely used in testing for association between two categorical responses. Spearman rank correlation and Kendall's tau are often used for measuring and testing association between two continuous or ordered categorical responses. However, the established statistical properties of these tests are only valid when each pair of responses are independent, where each sampling unit has only one pair of responses. When each sampling unit consists of a cluster of paired responses, the assumption of independent pairs is violated. In this article, we apply the within-cluster resampling technique to U-statistics to form new tests and rank-based correlation estimators for possibly tied clustered data. We develop large sample properties of the new proposed tests and estimators and evaluate their performance by simulations. The proposed methods are applied to a data set collected from a PET/CT imaging study for illustration. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Ranking Specific Sets of Objects.

PubMed

Maly, Jan; Woltran, Stefan

2017-01-01

Ranking sets of objects based on an order between the single elements has been thoroughly studied in the literature. In particular, it has been shown that it is in general impossible to find a total ranking - jointly satisfying properties as dominance and independence - on the whole power set of objects. However, in many applications certain elements from the entire power set might not be required and can be neglected in the ranking process. For instance, certain sets might be ruled out due to hard constraints or are not satisfying some background theory. In this paper, we treat the computational problem whether an order on a given subset of the power set of elements satisfying different variants of dominance and independence can be found, given a ranking on the elements. We show that this problem is tractable for partial rankings and NP-complete for total rankings.

Analysis of high-throughput biological data using their rank values.

PubMed

Dembélé, Doulaye

2018-01-01

High-throughput biological technologies are routinely used to generate gene expression profiling or cytogenetics data. To achieve high performance, methods available in the literature become more specialized and often require high computational resources. Here, we propose a new versatile method based on the data-ordering rank values. We use linear algebra, the Perron-Frobenius theorem and also extend a method presented earlier for searching differentially expressed genes for the detection of recurrent copy number aberration. A result derived from the proposed method is a one-sample Student's t-test based on rank values. The proposed method is to our knowledge the only that applies to gene expression profiling and to cytogenetics data sets. This new method is fast, deterministic, and requires a low computational load. Probabilities are associated with genes to allow a statistically significant subset selection in the data set. Stability scores are also introduced as quality parameters. The performance and comparative analyses were carried out using real data sets. The proposed method can be accessed through an R package available from the CRAN (Comprehensive R Archive Network) website: https://cran.r-project.org/web/packages/fcros .

Low-Rank Discriminant Embedding for Multiview Learning.

PubMed

Li, Jingjing; Wu, Yue; Zhao, Jidong; Lu, Ke

2017-11-01

This paper focuses on the specific problem of multiview learning where samples have the same feature set but different probability distributions, e.g., different viewpoints or different modalities. Since samples lying in different distributions cannot be compared directly, this paper aims to learn a latent subspace shared by multiple views assuming that the input views are generated from this latent subspace. Previous approaches usually learn the common subspace by either maximizing the empirical likelihood, or preserving the geometric structure. However, considering the complementarity between the two objectives, this paper proposes a novel approach, named low-rank discriminant embedding (LRDE), for multiview learning by taking full advantage of both sides. By further considering the duality between data points and features of multiview scene, i.e., data points can be grouped based on their distribution on features, while features can be grouped based on their distribution on the data points, LRDE not only deploys low-rank constraints on both sample level and feature level to dig out the shared factors across different views, but also preserves geometric information in both the ambient sample space and the embedding feature space by designing a novel graph structure under the framework of graph embedding. Finally, LRDE jointly optimizes low-rank representation and graph embedding in a unified framework. Comprehensive experiments in both multiview manner and pairwise manner demonstrate that LRDE performs much better than previous approaches proposed in recent literatures.

Do diseases have a prestige hierarchy? A survey among physicians and medical students.

PubMed

Album, Dag; Westin, Steinar

2008-01-01

Surveys have shown that the prestige of medical specialities is ordered hierarchically. We investigate whether similar tacit agreement in the medical community also applies to diseases, since such rankings can affect priority settings in medical practice. A cross-sectional survey was performed in three samples of physicians and medical students in Norway in 2002. A questionnaire was sent to 305 senior doctors (response rate, 79%), 500 general practitioners (response rate, 65%) and 490 final-year medical students (response rate, 64%). Outcome measures were ratings on a 1-9 scale of the prestige these respondents believed most health personnel would accord to a sample set of 38 different diseases as well as 23 medical specialities. Both diseases and specialities were clearly and consistently ranked according to prestige. Myocardial infarction, leukaemia and brain tumour were among the highest ranked, and fibromyalgia and anxiety neurosis were among the lowest. Among specialities, neurosurgery and thoracic surgery were accorded the highest rank, and geriatrics and dermatovenerology the lowest. Our interpretation of the data is that diseases and specialities associated with technologically sophisticated, immediate and invasive procedures in vital organs located in the upper parts of the body are given high prestige scores, especially where the typical patient is young or middle-aged. At the other end, low prestige scores are given to diseases and specialities associated with chronic conditions located in the lower parts of the body or having no specific bodily location, with less visible treatment procedures, and with elderly patients.

Self-Taught Low-Rank Coding for Visual Learning.

PubMed

Li, Sheng; Li, Kang; Fu, Yun

2018-03-01

The lack of labeled data presents a common challenge in many computer vision and machine learning tasks. Semisupervised learning and transfer learning methods have been developed to tackle this challenge by utilizing auxiliary samples from the same domain or from a different domain, respectively. Self-taught learning, which is a special type of transfer learning, has fewer restrictions on the choice of auxiliary data. It has shown promising performance in visual learning. However, existing self-taught learning methods usually ignore the structure information in data. In this paper, we focus on building a self-taught coding framework, which can effectively utilize the rich low-level pattern information abstracted from the auxiliary domain, in order to characterize the high-level structural information in the target domain. By leveraging a high quality dictionary learned across auxiliary and target domains, the proposed approach learns expressive codings for the samples in the target domain. Since many types of visual data have been proven to contain subspace structures, a low-rank constraint is introduced into the coding objective to better characterize the structure of the given target set. The proposed representation learning framework is called self-taught low-rank (S-Low) coding, which can be formulated as a nonconvex rank-minimization and dictionary learning problem. We devise an efficient majorization-minimization augmented Lagrange multiplier algorithm to solve it. Based on the proposed S-Low coding mechanism, both unsupervised and supervised visual learning algorithms are derived. Extensive experiments on five benchmark data sets demonstrate the effectiveness of our approach.

Combining evidence and values in priority setting: testing the balance sheet method in a low-income country.

PubMed

Makundi, Emmanuel; Kapiriri, Lydia; Norheim, Ole Frithjof

2007-09-24

Procedures for priority setting need to incorporate both scientific evidence and public values. The aim of this study was to test out a model for priority setting which incorporates both scientific evidence and public values, and to explore use of evidence by a selection of stakeholders and to study reasons for the relative ranking of health care interventions in a setting of extreme resource scarcity. Systematic search for and assessment of relevant evidence for priority setting in a low-income country. Development of a balance sheet according to Eddy's explicit method. Eight group interviews (n-85), using a modified nominal group technique for eliciting individual and group rankings of a given set of health interventions. The study procedure made it possible to compare the groups' ranking before and after all the evidence was provided to participants. A rank deviation is significant if the rank order of the same intervention differed by two or more points on the ordinal scale. A comparison between the initial rank and the final rank (before deliberation) showed a rank deviation of 67%. The difference between the initial rank and the final rank after discussion and voting gave a rank deviation of 78%. Evidence-based and deliberative decision-making does change priorities significantly in an experimental setting. Our use of the balance sheet method was meant as a demonstration project, but could if properly developed be feasible for health planners, experts and health workers, although more work is needed before it can be used for laypersons.

FORTRAN implementation of Friedman's test for several related samples

NASA Technical Reports Server (NTRS)

Davidson, S. A.

1982-01-01

The FRIEDMAN program is a FORTRAN-coded implementation of Friedman's nonparametric test for several related samples with one observation per treatment/-block combination, or as it is sometimes called, the two-way analysis of variance by ranks. The FRIEDMAN program is described and a test data set and its results are presented to aid potential users of this program.

Ranking network of a captive rhesus macaque society: a sophisticated corporative kingdom.

PubMed

Fushing, Hsieh; McAssey, Michael P; Beisner, Brianne; McCowan, Brenda

2011-03-15

We develop a three-step computing approach to explore a hierarchical ranking network for a society of captive rhesus macaques. The computed network is sufficiently informative to address the question: Is the ranking network for a rhesus macaque society more like a kingdom or a corporation? Our computations are based on a three-step approach. These steps are devised to deal with the tremendous challenges stemming from the transitivity of dominance as a necessary constraint on the ranking relations among all individual macaques, and the very high sampling heterogeneity in the behavioral conflict data. The first step simultaneously infers the ranking potentials among all network members, which requires accommodation of heterogeneous measurement error inherent in behavioral data. Our second step estimates the social rank for all individuals by minimizing the network-wide errors in the ranking potentials. The third step provides a way to compute confidence bounds for selected empirical features in the social ranking. We apply this approach to two sets of conflict data pertaining to two captive societies of adult rhesus macaques. The resultant ranking network for each society is found to be a sophisticated mixture of both a kingdom and a corporation. Also, for validation purposes, we reanalyze conflict data from twenty longhorn sheep and demonstrate that our three-step approach is capable of correctly computing a ranking network by eliminating all ranking error.

Predicting cyclohexane/water distribution coefficients for the SAMPL5 challenge using MOSCED and the SMD solvation model.

PubMed

Diaz-Rodriguez, Sebastian; Bozada, Samantha M; Phifer, Jeremy R; Paluch, Andrew S

2016-11-01

We present blind predictions using the solubility parameter based method MOSCED submitted for the SAMPL5 challenge on calculating cyclohexane/water distribution coefficients at 298 K. Reference data to parameterize MOSCED was generated with knowledge only of chemical structure by performing solvation free energy calculations using electronic structure calculations in the SMD continuum solvent. To maintain simplicity and use only a single method, we approximate the distribution coefficient with the partition coefficient of the neutral species. Over the final SAMPL5 set of 53 compounds, we achieved an average unsigned error of [Formula: see text] log units (ranking 15 out of 62 entries), the correlation coefficient (R) was [Formula: see text] (ranking 35), and [Formula: see text] of the predictions had the correct sign (ranking 30). While used here to predict cyclohexane/water distribution coefficients at 298 K, MOSCED is broadly applicable, allowing one to predict temperature dependent infinite dilution activity coefficients in any solvent for which parameters exist, and provides a means by which an excess Gibbs free energy model may be parameterized to predict composition dependent phase-equilibrium.

Unified method of knowledge representation in the evolutionary artificial intelligence systems

NASA Astrophysics Data System (ADS)

Bykov, Nickolay M.; Bykova, Katherina N.

2003-03-01

The evolution of artificial intelligence systems called by complicating of their operation topics and science perfecting has resulted in a diversification of the methods both the algorithms of knowledge representation and usage in these systems. Often by this reason it is very difficult to design the effective methods of knowledge discovering and operation for such systems. In the given activity the authors offer a method of unitized representation of the systems knowledge about objects of an external world by rank transformation of their descriptions, made in the different features spaces: deterministic, probabilistic, fuzzy and other. The proof of a sufficiency of the information about the rank configuration of the object states in the features space for decision making is presented. It is shown that the geometrical and combinatorial model of the rank configurations set introduce their by group of some system of incidence, that allows to store the information on them in a convolute kind. The method of the rank configuration description by the DRP - code (distance rank preserving code) is offered. The problems of its completeness, information capacity, noise immunity and privacy are reviewed. It is shown, that the capacity of a transmission channel for such submission of the information is more than unit, as the code words contain the information both about the object states, and about the distance ranks between them. The effective algorithm of the data clustering for the object states identification, founded on the given code usage, is described. The knowledge representation with the help of the rank configurations allows to unitize and to simplify algorithms of the decision making by fulfillment of logic operations above the DRP - code words. Examples of the proposed clustering techniques operation on the given samples set, the rank configuration of resulted clusters and its DRP-codes are presented.

A framework for list representation, enabling list stabilization through incorporation of gene exchangeabilities.

PubMed

Soneson, Charlotte; Fontes, Magnus

2012-01-01

Analysis of multivariate data sets from, for example, microarray studies frequently results in lists of genes which are associated with some response of interest. The biological interpretation is often complicated by the statistical instability of the obtained gene lists, which may partly be due to the functional redundancy among genes, implying that multiple genes can play exchangeable roles in the cell. In this paper, we use the concept of exchangeability of random variables to model this functional redundancy and thereby account for the instability. We present a flexible framework to incorporate the exchangeability into the representation of lists. The proposed framework supports straightforward comparison between any 2 lists. It can also be used to generate new more stable gene rankings incorporating more information from the experimental data. Using 2 microarray data sets, we show that the proposed method provides more robust gene rankings than existing methods with respect to sampling variations, without compromising the biological significance of the rankings.

A Psychometric Assessment of the "Businessweek," "U.S. News & World Report," and "Financial Times" Rankings of Business Schools' MBA Programs

ERIC Educational Resources Information Center

Iacobucci, Dawn

2013-01-01

This research investigates the reliability and validity of three major publications' rankings of MBA programs. Each set of rankings showed reasonable consistency over time, both at the level of the overall rankings and for most of the facets from which the rankings are derived. Each set of rankings also showed some levels of convergent and…

SKATE: a docking program that decouples systematic sampling from scoring.

PubMed

Feng, Jianwen A; Marshall, Garland R

2010-11-15

SKATE is a docking prototype that decouples systematic sampling from scoring. This novel approach removes any interdependence between sampling and scoring functions to achieve better sampling and, thus, improves docking accuracy. SKATE systematically samples a ligand's conformational, rotational and translational degrees of freedom, as constrained by a receptor pocket, to find sterically allowed poses. Efficient systematic sampling is achieved by pruning the combinatorial tree using aggregate assembly, discriminant analysis, adaptive sampling, radial sampling, and clustering. Because systematic sampling is decoupled from scoring, the poses generated by SKATE can be ranked by any published, or in-house, scoring function. To test the performance of SKATE, ligands from the Asetex/CDCC set, the Surflex set, and the Vertex set, a total of 266 complexes, were redocked to their respective receptors. The results show that SKATE was able to sample poses within 2 A RMSD of the native structure for 98, 95, and 98% of the cases in the Astex/CDCC, Surflex, and Vertex sets, respectively. Cross-docking accuracy of SKATE was also assessed by docking 10 ligands to thymidine kinase and 73 ligands to cyclin-dependent kinase. 2010 Wiley Periodicals, Inc.

Discriminative Dictionary Learning With Two-Level Low Rank and Group Sparse Decomposition for Image Classification.

PubMed

Wen, Zaidao; Hou, Zaidao; Jiao, Licheng

2017-11-01

Discriminative dictionary learning (DDL) framework has been widely used in image classification which aims to learn some class-specific feature vectors as well as a representative dictionary according to a set of labeled training samples. However, interclass similarities and intraclass variances among input samples and learned features will generally weaken the representability of dictionary and the discrimination of feature vectors so as to degrade the classification performance. Therefore, how to explicitly represent them becomes an important issue. In this paper, we present a novel DDL framework with two-level low rank and group sparse decomposition model. In the first level, we learn a class-shared and several class-specific dictionaries, where a low rank and a group sparse regularization are, respectively, imposed on the corresponding feature matrices. In the second level, the class-specific feature matrix will be further decomposed into a low rank and a sparse matrix so that intraclass variances can be separated to concentrate the corresponding feature vectors. Extensive experimental results demonstrate the effectiveness of our model. Compared with the other state-of-the-arts on several popular image databases, our model can achieve a competitive or better performance in terms of the classification accuracy.

Using rank-order geostatistics for spatial interpolation of highly skewed data in a heavy-metal contaminated site.

PubMed

Juang, K W; Lee, D Y; Ellsworth, T R

2001-01-01

The spatial distribution of a pollutant in contaminated soils is usually highly skewed. As a result, the sample variogram often differs considerably from its regional counterpart and the geostatistical interpolation is hindered. In this study, rank-order geostatistics with standardized rank transformation was used for the spatial interpolation of pollutants with a highly skewed distribution in contaminated soils when commonly used nonlinear methods, such as logarithmic and normal-scored transformations, are not suitable. A real data set of soil Cd concentrations with great variation and high skewness in a contaminated site of Taiwan was used for illustration. The spatial dependence of ranks transformed from Cd concentrations was identified and kriging estimation was readily performed in the standardized-rank space. The estimated standardized rank was back-transformed into the concentration space using the middle point model within a standardized-rank interval of the empirical distribution function (EDF). The spatial distribution of Cd concentrations was then obtained. The probability of Cd concentration being higher than a given cutoff value also can be estimated by using the estimated distribution of standardized ranks. The contour maps of Cd concentrations and the probabilities of Cd concentrations being higher than the cutoff value can be simultaneously used for delineation of hazardous areas of contaminated soils.

Dimension independence in exterior algebra.

PubMed Central

Hawrylycz, M

1995-01-01

The identities between homogeneous expressions in rank 1 vectors and rank n - 1 covectors in a Grassmann-Cayley algebra of rank n, in which one set occurs multilinearly, are shown to represent a set of dimension-independent identities. The theorem yields an infinite set of nontrivial geometric identities from a given identity. PMID:11607520

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

Clark, Darin P.; Badea, Cristian T., E-mail: cristian.badea@duke.edu; Lee, Chang-Lung

Purpose: X-ray computed tomography (CT) is widely used, both clinically and preclinically, for fast, high-resolution anatomic imaging; however, compelling opportunities exist to expand its use in functional imaging applications. For instance, spectral information combined with nanoparticle contrast agents enables quantification of tissue perfusion levels, while temporal information details cardiac and respiratory dynamics. The authors propose and demonstrate a projection acquisition and reconstruction strategy for 5D CT (3D + dual energy + time) which recovers spectral and temporal information without substantially increasing radiation dose or sampling time relative to anatomic imaging protocols. Methods: The authors approach the 5D reconstruction problem withinmore » the framework of low-rank and sparse matrix decomposition. Unlike previous work on rank-sparsity constrained CT reconstruction, the authors establish an explicit rank-sparse signal model to describe the spectral and temporal dimensions. The spectral dimension is represented as a well-sampled time and energy averaged image plus regularly undersampled principal components describing the spectral contrast. The temporal dimension is represented as the same time and energy averaged reconstruction plus contiguous, spatially sparse, and irregularly sampled temporal contrast images. Using a nonlinear, image domain filtration approach, the authors refer to as rank-sparse kernel regression, the authors transfer image structure from the well-sampled time and energy averaged reconstruction to the spectral and temporal contrast images. This regularization strategy strictly constrains the reconstruction problem while approximately separating the temporal and spectral dimensions. Separability results in a highly compressed representation for the 5D data in which projections are shared between the temporal and spectral reconstruction subproblems, enabling substantial undersampling. The authors solved the 5D reconstruction problem using the split Bregman method and GPU-based implementations of backprojection, reprojection, and kernel regression. Using a preclinical mouse model, the authors apply the proposed algorithm to study myocardial injury following radiation treatment of breast cancer. Results: Quantitative 5D simulations are performed using the MOBY mouse phantom. Twenty data sets (ten cardiac phases, two energies) are reconstructed with 88 μm, isotropic voxels from 450 total projections acquired over a single 360° rotation. In vivo 5D myocardial injury data sets acquired in two mice injected with gold and iodine nanoparticles are also reconstructed with 20 data sets per mouse using the same acquisition parameters (dose: ∼60 mGy). For both the simulations and the in vivo data, the reconstruction quality is sufficient to perform material decomposition into gold and iodine maps to localize the extent of myocardial injury (gold accumulation) and to measure cardiac functional metrics (vascular iodine). Their 5D CT imaging protocol represents a 95% reduction in radiation dose per cardiac phase and energy and a 40-fold decrease in projection sampling time relative to their standard imaging protocol. Conclusions: Their 5D CT data acquisition and reconstruction protocol efficiently exploits the rank-sparse nature of spectral and temporal CT data to provide high-fidelity reconstruction results without increased radiation dose or sampling time.« less

Spectrotemporal CT data acquisition and reconstruction at low dose

PubMed Central

Clark, Darin P.; Lee, Chang-Lung; Kirsch, David G.; Badea, Cristian T.

2015-01-01

Purpose: X-ray computed tomography (CT) is widely used, both clinically and preclinically, for fast, high-resolution anatomic imaging; however, compelling opportunities exist to expand its use in functional imaging applications. For instance, spectral information combined with nanoparticle contrast agents enables quantification of tissue perfusion levels, while temporal information details cardiac and respiratory dynamics. The authors propose and demonstrate a projection acquisition and reconstruction strategy for 5D CT (3D + dual energy + time) which recovers spectral and temporal information without substantially increasing radiation dose or sampling time relative to anatomic imaging protocols. Methods: The authors approach the 5D reconstruction problem within the framework of low-rank and sparse matrix decomposition. Unlike previous work on rank-sparsity constrained CT reconstruction, the authors establish an explicit rank-sparse signal model to describe the spectral and temporal dimensions. The spectral dimension is represented as a well-sampled time and energy averaged image plus regularly undersampled principal components describing the spectral contrast. The temporal dimension is represented as the same time and energy averaged reconstruction plus contiguous, spatially sparse, and irregularly sampled temporal contrast images. Using a nonlinear, image domain filtration approach, the authors refer to as rank-sparse kernel regression, the authors transfer image structure from the well-sampled time and energy averaged reconstruction to the spectral and temporal contrast images. This regularization strategy strictly constrains the reconstruction problem while approximately separating the temporal and spectral dimensions. Separability results in a highly compressed representation for the 5D data in which projections are shared between the temporal and spectral reconstruction subproblems, enabling substantial undersampling. The authors solved the 5D reconstruction problem using the split Bregman method and GPU-based implementations of backprojection, reprojection, and kernel regression. Using a preclinical mouse model, the authors apply the proposed algorithm to study myocardial injury following radiation treatment of breast cancer. Results: Quantitative 5D simulations are performed using the MOBY mouse phantom. Twenty data sets (ten cardiac phases, two energies) are reconstructed with 88 μm, isotropic voxels from 450 total projections acquired over a single 360° rotation. In vivo 5D myocardial injury data sets acquired in two mice injected with gold and iodine nanoparticles are also reconstructed with 20 data sets per mouse using the same acquisition parameters (dose: ∼60 mGy). For both the simulations and the in vivo data, the reconstruction quality is sufficient to perform material decomposition into gold and iodine maps to localize the extent of myocardial injury (gold accumulation) and to measure cardiac functional metrics (vascular iodine). Their 5D CT imaging protocol represents a 95% reduction in radiation dose per cardiac phase and energy and a 40-fold decrease in projection sampling time relative to their standard imaging protocol. Conclusions: Their 5D CT data acquisition and reconstruction protocol efficiently exploits the rank-sparse nature of spectral and temporal CT data to provide high-fidelity reconstruction results without increased radiation dose or sampling time. PMID:26520724

A Ranking Method for Evaluating Constructed Responses

ERIC Educational Resources Information Center

Attali, Yigal

2014-01-01

This article presents a comparative judgment approach for holistically scored constructed response tasks. In this approach, the grader rank orders (rather than rate) the quality of a small set of responses. A prior automated evaluation of responses guides both set formation and scaling of rankings. Sets are formed to have similar prior scores and…

Predicting cyclohexane/water distribution coefficients for the SAMPL5 challenge using MOSCED and the SMD solvation model

NASA Astrophysics Data System (ADS)

Diaz-Rodriguez, Sebastian; Bozada, Samantha M.; Phifer, Jeremy R.; Paluch, Andrew S.

2016-11-01

We present blind predictions using the solubility parameter based method MOSCED submitted for the SAMPL5 challenge on calculating cyclohexane/water distribution coefficients at 298 K. Reference data to parameterize MOSCED was generated with knowledge only of chemical structure by performing solvation free energy calculations using electronic structure calculations in the SMD continuum solvent. To maintain simplicity and use only a single method, we approximate the distribution coefficient with the partition coefficient of the neutral species. Over the final SAMPL5 set of 53 compounds, we achieved an average unsigned error of 2.2± 0.2 log units (ranking 15 out of 62 entries), the correlation coefficient ( R) was 0.6± 0.1 (ranking 35), and 72± 6 % of the predictions had the correct sign (ranking 30). While used here to predict cyclohexane/water distribution coefficients at 298 K, MOSCED is broadly applicable, allowing one to predict temperature dependent infinite dilution activity coefficients in any solvent for which parameters exist, and provides a means by which an excess Gibbs free energy model may be parameterized to predict composition dependent phase-equilibrium.

Rank Order Entropy: why one metric is not enough

PubMed Central

McLellan, Margaret R.; Ryan, M. Dominic; Breneman, Curt M.

2011-01-01

The use of Quantitative Structure-Activity Relationship models to address problems in drug discovery has a mixed history, generally resulting from the mis-application of QSAR models that were either poorly constructed or used outside of their domains of applicability. This situation has motivated the development of a variety of model performance metrics (r2, PRESS r2, F-tests, etc) designed to increase user confidence in the validity of QSAR predictions. In a typical workflow scenario, QSAR models are created and validated on training sets of molecules using metrics such as Leave-One-Out or many-fold cross-validation methods that attempt to assess their internal consistency. However, few current validation methods are designed to directly address the stability of QSAR predictions in response to changes in the information content of the training set. Since the main purpose of QSAR is to quickly and accurately estimate a property of interest for an untested set of molecules, it makes sense to have a means at hand to correctly set user expectations of model performance. In fact, the numerical value of a molecular prediction is often less important to the end user than knowing the rank order of that set of molecules according to their predicted endpoint values. Consequently, a means for characterizing the stability of predicted rank order is an important component of predictive QSAR. Unfortunately, none of the many validation metrics currently available directly measure the stability of rank order prediction, making the development of an additional metric that can quantify model stability a high priority. To address this need, this work examines the stabilities of QSAR rank order models created from representative data sets, descriptor sets, and modeling methods that were then assessed using Kendall Tau as a rank order metric, upon which the Shannon Entropy was evaluated as a means of quantifying rank-order stability. Random removal of data from the training set, also known as Data Truncation Analysis (DTA), was used as a means for systematically reducing the information content of each training set while examining both rank order performance and rank order stability in the face of training set data loss. The premise for DTA ROE model evaluation is that the response of a model to incremental loss of training information will be indicative of the quality and sufficiency of its training set, learning method, and descriptor types to cover a particular domain of applicability. This process is termed a “rank order entropy” evaluation, or ROE. By analogy with information theory, an unstable rank order model displays a high level of implicit entropy, while a QSAR rank order model which remains nearly unchanged during training set reductions would show low entropy. In this work, the ROE metric was applied to 71 data sets of different sizes, and was found to reveal more information about the behavior of the models than traditional metrics alone. Stable, or consistently performing models, did not necessarily predict rank order well. Models that performed well in rank order did not necessarily perform well in traditional metrics. In the end, it was shown that ROE metrics suggested that some QSAR models that are typically used should be discarded. ROE evaluation helps to discern which combinations of data set, descriptor set, and modeling methods lead to usable models in prioritization schemes, and provides confidence in the use of a particular model within a specific domain of applicability. PMID:21875058

SparRec: An effective matrix completion framework of missing data imputation for GWAS

NASA Astrophysics Data System (ADS)

Jiang, Bo; Ma, Shiqian; Causey, Jason; Qiao, Linbo; Hardin, Matthew Price; Bitts, Ian; Johnson, Daniel; Zhang, Shuzhong; Huang, Xiuzhen

2016-10-01

Genome-wide association studies present computational challenges for missing data imputation, while the advances of genotype technologies are generating datasets of large sample sizes with sample sets genotyped on multiple SNP chips. We present a new framework SparRec (Sparse Recovery) for imputation, with the following properties: (1) The optimization models of SparRec, based on low-rank and low number of co-clusters of matrices, are different from current statistics methods. While our low-rank matrix completion (LRMC) model is similar to Mendel-Impute, our matrix co-clustering factorization (MCCF) model is completely new. (2) SparRec, as other matrix completion methods, is flexible to be applied to missing data imputation for large meta-analysis with different cohorts genotyped on different sets of SNPs, even when there is no reference panel. This kind of meta-analysis is very challenging for current statistics based methods. (3) SparRec has consistent performance and achieves high recovery accuracy even when the missing data rate is as high as 90%. Compared with Mendel-Impute, our low-rank based method achieves similar accuracy and efficiency, while the co-clustering based method has advantages in running time. The testing results show that SparRec has significant advantages and competitive performance over other state-of-the-art existing statistics methods including Beagle and fastPhase.

SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure.

PubMed

Wang, Jinling; Belatreche, Ammar; Maguire, Liam P; McGinnity, Thomas Martin

2017-01-01

This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

Online ranking by projecting.

PubMed

Crammer, Koby; Singer, Yoram

2005-01-01

We discuss the problem of ranking instances. In our framework, each instance is associated with a rank or a rating, which is an integer in 1 to k. Our goal is to find a rank-prediction rule that assigns each instance a rank that is as close as possible to the instance's true rank. We discuss a group of closely related online algorithms, analyze their performance in the mistake-bound model, and prove their correctness. We describe two sets of experiments, with synthetic data and with the EachMovie data set for collaborative filtering. In the experiments we performed, our algorithms outperform online algorithms for regression and classification applied to ranking.

High-resolution dynamic 31 P-MRSI using a low-rank tensor model.

PubMed

Ma, Chao; Clifford, Bryan; Liu, Yuchi; Gu, Yuning; Lam, Fan; Yu, Xin; Liang, Zhi-Pei

2017-08-01

To develop a rapid 31 P-MRSI method with high spatiospectral resolution using low-rank tensor-based data acquisition and image reconstruction. The multidimensional image function of 31 P-MRSI is represented by a low-rank tensor to capture the spatial-spectral-temporal correlations of data. A hybrid data acquisition scheme is used for sparse sampling, which consists of a set of "training" data with limited k-space coverage to capture the subspace structure of the image function, and a set of sparsely sampled "imaging" data for high-resolution image reconstruction. An explicit subspace pursuit approach is used for image reconstruction, which estimates the bases of the subspace from the "training" data and then reconstructs a high-resolution image function from the "imaging" data. We have validated the feasibility of the proposed method using phantom and in vivo studies on a 3T whole-body scanner and a 9.4T preclinical scanner. The proposed method produced high-resolution static 31 P-MRSI images (i.e., 6.9 × 6.9 × 10 mm 3 nominal resolution in a 15-min acquisition at 3T) and high-resolution, high-frame-rate dynamic 31 P-MRSI images (i.e., 1.5 × 1.5 × 1.6 mm 3 nominal resolution, 30 s/frame at 9.4T). Dynamic spatiospectral variations of 31 P-MRSI signals can be efficiently represented by a low-rank tensor. Exploiting this mathematical structure for data acquisition and image reconstruction can lead to fast 31 P-MRSI with high resolution, frame-rate, and SNR. Magn Reson Med 78:419-428, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Identifying a set of influential spreaders in complex networks

NASA Astrophysics Data System (ADS)

Zhang, Jian-Xiong; Chen, Duan-Bing; Dong, Qiang; Zhao, Zhi-Dan

2016-06-01

Identifying a set of influential spreaders in complex networks plays a crucial role in effective information spreading. A simple strategy is to choose top-r ranked nodes as spreaders according to influence ranking method such as PageRank, ClusterRank and k-shell decomposition. Besides, some heuristic methods such as hill-climbing, SPIN, degree discount and independent set based are also proposed. However, these approaches suffer from a possibility that some spreaders are so close together that they overlap sphere of influence or time consuming. In this report, we present a simply yet effectively iterative method named VoteRank to identify a set of decentralized spreaders with the best spreading ability. In this approach, all nodes vote in a spreader in each turn, and the voting ability of neighbors of elected spreader will be decreased in subsequent turn. Experimental results on four real networks show that under Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models, VoteRank outperforms the traditional benchmark methods on both spreading rate and final affected scale. What’s more, VoteRank has superior computational efficiency.

High-dimensional statistical inference: From vector to matrix

NASA Astrophysics Data System (ADS)

Zhang, Anru

Statistical inference for sparse signals or low-rank matrices in high-dimensional settings is of significant interest in a range of contemporary applications. It has attracted significant recent attention in many fields including statistics, applied mathematics and electrical engineering. In this thesis, we consider several problems in including sparse signal recovery (compressed sensing under restricted isometry) and low-rank matrix recovery (matrix recovery via rank-one projections and structured matrix completion). The first part of the thesis discusses compressed sensing and affine rank minimization in both noiseless and noisy cases and establishes sharp restricted isometry conditions for sparse signal and low-rank matrix recovery. The analysis relies on a key technical tool which represents points in a polytope by convex combinations of sparse vectors. The technique is elementary while leads to sharp results. It is shown that, in compressed sensing, delta kA < 1/3, deltak A+ thetak,kA < 1, or deltatkA < √( t - 1)/t for any given constant t ≥ 4/3 guarantee the exact recovery of all k sparse signals in the noiseless case through the constrained ℓ1 minimization, and similarly in affine rank minimization delta rM < 1/3, deltar M + thetar, rM < 1, or deltatrM< √( t - 1)/t ensure the exact reconstruction of all matrices with rank at most r in the noiseless case via the constrained nuclear norm minimization. Moreover, for any epsilon > 0, delta kA < 1/3 + epsilon, deltak A + thetak,kA < 1 + epsilon, or deltatkA< √(t - 1) / t + epsilon are not sufficient to guarantee the exact recovery of all k-sparse signals for large k. Similar result also holds for matrix recovery. In addition, the conditions delta kA<1/3, deltak A+ thetak,kA<1, delta tkA < √(t - 1)/t and deltarM<1/3, delta rM+ thetar,rM<1, delta trM< √(t - 1)/ t are also shown to be sufficient respectively for stable recovery of approximately sparse signals and low-rank matrices in the noisy case. For the second part of the thesis, we introduce a rank-one projection model for low-rank matrix recovery and propose a constrained nuclear norm minimization method for stable recovery of low-rank matrices in the noisy case. The procedure is adaptive to the rank and robust against small perturbations. Both upper and lower bounds for the estimation accuracy under the Frobenius norm loss are obtained. The proposed estimator is shown to be rate-optimal under certain conditions. The estimator is easy to implement via convex programming and performs well numerically. The techniques and main results developed in the chapter also have implications to other related statistical problems. An application to estimation of spiked covariance matrices from one-dimensional random projections is considered. The results demonstrate that it is still possible to accurately estimate the covariance matrix of a high-dimensional distribution based only on one-dimensional projections. For the third part of the thesis, we consider another setting of low-rank matrix completion. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. We provide theoretical justification for the proposed SMC method and derive lower bound for the estimation errors, which together establish the optimal rate of recovery over certain classes of approximately low-rank matrices. Simulation studies show that the method performs well in finite sample under a variety of configurations. The method is applied to integrate several ovarian cancer genomic studies with different extent of genomic measurements, which enables us to construct more accurate prediction rules for ovarian cancer survival.

Improving predicted protein loop structure ranking using a Pareto-optimality consensus method.

PubMed

Li, Yaohang; Rata, Ionel; Chiu, See-wing; Jakobsson, Eric

2010-07-20

Accurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction. We have developed a Pareto Optimal Consensus (POC) method, which is a consensus model ranking approach to integrate multiple knowledge- or physics-based scoring functions. The procedure of identifying the models of best quality in a model set includes: 1) identifying the models at the Pareto optimal front with respect to a set of scoring functions, and 2) ranking them based on the fuzzy dominance relationship to the rest of the models. We apply the POC method to a large number of decoy sets for loops of 4- to 12-residue in length using a functional space composed of several carefully-selected scoring functions: Rosetta, DOPE, DDFIRE, OPLS-AA, and a triplet backbone dihedral potential developed in our lab. Our computational results show that the sets of Pareto-optimal decoys, which are typically composed of approximately 20% or less of the overall decoys in a set, have a good coverage of the best or near-best decoys in more than 99% of the loop targets. Compared to the individual scoring function yielding best selection accuracy in the decoy sets, the POC method yields 23%, 37%, and 64% less false positives in distinguishing the native conformation, indentifying a near-native model (RMSD < 0.5A from the native) as top-ranked, and selecting at least one near-native model in the top-5-ranked models, respectively. Similar effectiveness of the POC method is also found in the decoy sets from membrane protein loops. Furthermore, the POC method outperforms the other popularly-used consensus strategies in model ranking, such as rank-by-number, rank-by-rank, rank-by-vote, and regression-based methods. By integrating multiple knowledge- and physics-based scoring functions based on Pareto optimality and fuzzy dominance, the POC method is effective in distinguishing the best loop models from the other ones within a loop model set.

Improving predicted protein loop structure ranking using a Pareto-optimality consensus method

PubMed Central

2010-01-01

Background Accurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction. Results We have developed a Pareto Optimal Consensus (POC) method, which is a consensus model ranking approach to integrate multiple knowledge- or physics-based scoring functions. The procedure of identifying the models of best quality in a model set includes: 1) identifying the models at the Pareto optimal front with respect to a set of scoring functions, and 2) ranking them based on the fuzzy dominance relationship to the rest of the models. We apply the POC method to a large number of decoy sets for loops of 4- to 12-residue in length using a functional space composed of several carefully-selected scoring functions: Rosetta, DOPE, DDFIRE, OPLS-AA, and a triplet backbone dihedral potential developed in our lab. Our computational results show that the sets of Pareto-optimal decoys, which are typically composed of ~20% or less of the overall decoys in a set, have a good coverage of the best or near-best decoys in more than 99% of the loop targets. Compared to the individual scoring function yielding best selection accuracy in the decoy sets, the POC method yields 23%, 37%, and 64% less false positives in distinguishing the native conformation, indentifying a near-native model (RMSD < 0.5A from the native) as top-ranked, and selecting at least one near-native model in the top-5-ranked models, respectively. Similar effectiveness of the POC method is also found in the decoy sets from membrane protein loops. Furthermore, the POC method outperforms the other popularly-used consensus strategies in model ranking, such as rank-by-number, rank-by-rank, rank-by-vote, and regression-based methods. Conclusions By integrating multiple knowledge- and physics-based scoring functions based on Pareto optimality and fuzzy dominance, the POC method is effective in distinguishing the best loop models from the other ones within a loop model set. PMID:20642859

Implication of correlations among some common stability statistics - a Monte Carlo simulations.

PubMed

Piepho, H P

1995-03-01

Stability analysis of multilocation trials is often based on a mixed two-way model. Two stability measures in frequent use are the environmental variance (S i (2) )and the ecovalence (W i). Under the two-way model the rank orders of the expected values of these two statistics are identical for a given set of genotypes. By contrast, empirical rank correlations among these measures are consistently low. This suggests that the two-way mixed model may not be appropriate for describing real data. To check this hypothesis, a Monte Carlo simulation was conducted. It revealed that the low empirical rank correlation amongS i (2) and W i is most likely due to sampling errors. It is concluded that the observed low rank correlation does not invalidate the two-way model. The paper also discusses tests for homogeneity of S i (2) as well as implications of the two-way model for the classification of stability statistics.

Normalization Approaches for Removing Systematic Biases Associated with Mass Spectrometry and Label-Free Proteomics

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

Callister, Stephen J.; Barry, Richard C.; Adkins, Joshua N.

2006-02-01

Central tendency, linear regression, locally weighted regression, and quantile techniques were investigated for normalization of peptide abundance measurements obtained from high-throughput liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR MS). Arbitrary abundances of peptides were obtained from three sample sets, including a standard protein sample, two Deinococcus radiodurans samples taken from different growth phases, and two mouse striatum samples from control and methamphetamine-stressed mice (strain C57BL/6). The selected normalization techniques were evaluated in both the absence and presence of biological variability by estimating extraneous variability prior to and following normalization. Prior to normalization, replicate runs from each sample setmore » were observed to be statistically different, while following normalization replicate runs were no longer statistically different. Although all techniques reduced systematic bias, assigned ranks among the techniques revealed significant trends. For most LC-FTICR MS analyses, linear regression normalization ranked either first or second among the four techniques, suggesting that this technique was more generally suitable for reducing systematic biases.« less

Value-of-information analysis within a stakeholder-driven research prioritization process in a US setting: an application in cancer genomics.

PubMed

Carlson, Josh J; Thariani, Rahber; Roth, Josh; Gralow, Julie; Henry, N Lynn; Esmail, Laura; Deverka, Pat; Ramsey, Scott D; Baker, Laurence; Veenstra, David L

2013-05-01

The objective of this study was to evaluate the feasibility and outcomes of incorporating value-of-information (VOI) analysis into a stakeholder-driven research prioritization process in a US-based setting. . Within a program to prioritize comparative effectiveness research areas in cancer genomics, over a period of 7 months, we developed decision-analytic models and calculated upper-bound VOI estimates for 3 previously selected genomic tests. Thirteen stakeholders representing patient advocates, payers, test developers, regulators, policy makers, and community-based oncologists ranked the tests before and after receiving VOI results. The stakeholders were surveyed about the usefulness and impact of the VOI findings. The estimated upper-bound VOI ranged from $33 million to $2.8 billion for the 3 research areas. Seven stakeholders indicated the results modified their rankings, 9 stated VOI data were useful, and all indicated they would support its use in future prioritization processes. Some stakeholders indicated expected value of sampled information might be the preferred choice when evaluating specific Limitations. Our study was limited by the size and the potential for selection bias in the composition of the external stakeholder group, lack of a randomized design to assess effect of VOI data on rankings, and the use of expected value of perfect information v. expected value of sample information methods. Value of information analyses may have a meaningful role in research topic prioritization for comparative effectiveness research in the United States, particularly when large differences in VOI across topic areas are identified. Additional research is needed to facilitate the use of more complex value of information analyses in this setting.

Learning Robust and Discriminative Subspace With Low-Rank Constraints.

PubMed

Li, Sheng; Fu, Yun

2016-11-01

In this paper, we aim at learning robust and discriminative subspaces from noisy data. Subspace learning is widely used in extracting discriminative features for classification. However, when data are contaminated with severe noise, the performance of most existing subspace learning methods would be limited. Recent advances in low-rank modeling provide effective solutions for removing noise or outliers contained in sample sets, which motivates us to take advantage of low-rank constraints in order to exploit robust and discriminative subspace for classification. In particular, we present a discriminative subspace learning method called the supervised regularization-based robust subspace (SRRS) approach, by incorporating the low-rank constraint. SRRS seeks low-rank representations from the noisy data, and learns a discriminative subspace from the recovered clean data jointly. A supervised regularization function is designed to make use of the class label information, and therefore to enhance the discriminability of subspace. Our approach is formulated as a constrained rank-minimization problem. We design an inexact augmented Lagrange multiplier optimization algorithm to solve it. Unlike the existing sparse representation and low-rank learning methods, our approach learns a low-dimensional subspace from recovered data, and explicitly incorporates the supervised information. Our approach and some baselines are evaluated on the COIL-100, ALOI, Extended YaleB, FERET, AR, and KinFace databases. The experimental results demonstrate the effectiveness of our approach, especially when the data contain considerable noise or variations.

Implementation of Chaotic Gaussian Particle Swarm Optimization for Optimize Learning-to-Rank Software Defect Prediction Model Construction

NASA Astrophysics Data System (ADS)

Buchari, M. A.; Mardiyanto, S.; Hendradjaya, B.

2018-03-01

Finding the existence of software defect as early as possible is the purpose of research about software defect prediction. Software defect prediction activity is required to not only state the existence of defects, but also to be able to give a list of priorities which modules require a more intensive test. Therefore, the allocation of test resources can be managed efficiently. Learning to rank is one of the approach that can provide defect module ranking data for the purposes of software testing. In this study, we propose a meta-heuristic chaotic Gaussian particle swarm optimization to improve the accuracy of learning to rank software defect prediction approach. We have used 11 public benchmark data sets as experimental data. Our overall results has demonstrated that the prediction models construct using Chaotic Gaussian Particle Swarm Optimization gets better accuracy on 5 data sets, ties in 5 data sets and gets worse in 1 data sets. Thus, we conclude that the application of Chaotic Gaussian Particle Swarm Optimization in Learning-to-Rank approach can improve the accuracy of the defect module ranking in data sets that have high-dimensional features.

Phase II Trials for Heterogeneous Patient Populations with a Time-to-Event Endpoint.

PubMed

Jung, Sin-Ho

2017-07-01

In this paper, we consider a single-arm phase II trial with a time-to-event end-point. We assume that the study population has multiple subpopulations with different prognosis, but the study treatment is expected to be similarly efficacious across the subpopulations. We review a stratified one-sample log-rank test and present its sample size calculation method under some practical design settings. Our sample size method requires specification of the prevalence of subpopulations. We observe that the power of the resulting sample size is not very sensitive to misspecification of the prevalence.

Ranking Information in Networks

NASA Astrophysics Data System (ADS)

Eliassi-Rad, Tina; Henderson, Keith

Given a network, we are interested in ranking sets of nodes that score highest on user-specified criteria. For instance in graphs from bibliographic data (e.g. PubMed), we would like to discover sets of authors with expertise in a wide range of disciplines. We present this ranking task as a Top-K problem; utilize fixed-memory heuristic search; and present performance of both the serial and distributed search algorithms on synthetic and real-world data sets.

[Phenotypic and genotypic spectra of patients with glucose-6-phosphate dehydrogenase deficiency gene known pathogenic variants: a single-center study].

PubMed

Chen, X; Yang, L; Wang, H J; Wu, B B; Lu, Y L; Dong, X R; Zhou, W H

2018-05-02

Objective: To analyze the hotspots of known pathogenic disease-causing variants of glucose-6-phosphate dehydrogenase (G6PD) and the phenotype spectrum of neonatal patients with known pathogenic disease-causing variants of G6PD. Methods: The known pathogenic disease-causing variants of G6PD were collected from Human Gene Mutation Database. Screening was performed for these variants among the 7 966 cases (2 357 neonatal, 5 609 non-neonatal) in the database of sequencing at Molecular Diagnosis Center, Children's Hospital of Fudan University. All these samples were from patients suspected with genetic disorder. The database contained Whole Exon Sequencing data and Clinical Exon Sequencing data. We screened out the patients with known pathogenic disease-causing variants of G6PD, analyzed the hotspot of G6PD and the phenotype spectrum of neonatal patients with known pathogenic disease-causing variants of G6PD. Results: (1) Among the next generation sequencing data of the 7 966 samples, 86 samples (1.1%) were detected as positive for the known pathogenic disease-causing variants of G6PD (positive samples set). In the positive sample set, 51 patients (33 males, 18 females) were newborn babies. Forty-three patients (26 males, 17 females) had the enzyme activity data of G6PD. (2) Among the 86 samples, Arg463His, Arg459Leu, Leu342Phe, Val291Met were the leading 4 disease-causing variants found in 72 samples (84%). (3) Male neonatal patients with the same variants had the statistically significant differences in enzyme activity: among 13 patients with Arg463His, enzyme activity of 9 patients was ranked as grade Ⅲ, 1 case ranked as Ⅳ, 3 cases had no activity data;among 10 patients with Arg459Leu, enzyme activity of 4 patients was ranked as Ⅱ, 4 cases ranked as Ⅲ, 2 cases had no activity data;among 2 patients with His32Arg, enzyme activity of one patient was ranked as Ⅱ, another was Ⅲ. Male neonatal patients with the same mutation and enzyme activity also had the statistically significant differences in phenotype spectrum: among 9 patients with Arg463His and level Ⅲ enzyme activity, 6 presented hyperbilirubinemia, 2 met the criteria for exchange transfusion therapy, 2 showed hemolysis;among 4 patients with Arg459Leu and level Ⅱ enzyme activity, 3 presented hyperbilirubinemia;among 4 patients with Arg459Leu and level Ⅲ enzyme activity, 2 presented hyperbilirubinemia, 1 met the standard of exchange transfusion therapy;among 3 patients with Val291Met and level Ⅲ enzyme activity, 1 presented hyperbilirubinemia. Conclusions: Arg463His, Arg459Leu, Leu342Phe, Val291Met were the hotspots variants for the G6PD. Patients with the same G6PD variants and sex present different phenotype, patients with the same G6PD variants, sex and enzyme activity also present different phenotype .

Web document ranking via active learning and kernel principal component analysis

NASA Astrophysics Data System (ADS)

Cai, Fei; Chen, Honghui; Shu, Zhen

2015-09-01

Web document ranking arises in many information retrieval (IR) applications, such as the search engine, recommendation system and online advertising. A challenging issue is how to select the representative query-document pairs and informative features as well for better learning and exploring new ranking models to produce an acceptable ranking list of candidate documents of each query. In this study, we propose an active sampling (AS) plus kernel principal component analysis (KPCA) based ranking model, viz. AS-KPCA Regression, to study the document ranking for a retrieval system, i.e. how to choose the representative query-document pairs and features for learning. More precisely, we fill those documents gradually into the training set by AS such that each of which will incur the highest expected DCG loss if unselected. Then, the KPCA is performed via projecting the selected query-document pairs onto p-principal components in the feature space to complete the regression. Hence, we can cut down the computational overhead and depress the impact incurred by noise simultaneously. To the best of our knowledge, we are the first to perform the document ranking via dimension reductions in two dimensions, namely, the number of documents and features simultaneously. Our experiments demonstrate that the performance of our approach is better than that of the baseline methods on the public LETOR 4.0 datasets. Our approach brings an improvement against RankBoost as well as other baselines near 20% in terms of MAP metric and less improvements using P@K and NDCG@K, respectively. Moreover, our approach is particularly suitable for document ranking on the noisy dataset in practice.

On the degrees of freedom of reduced-rank estimators in multivariate regression

PubMed Central

Mukherjee, A.; Chen, K.; Wang, N.; Zhu, J.

2015-01-01

Summary We study the effective degrees of freedom of a general class of reduced-rank estimators for multivariate regression in the framework of Stein's unbiased risk estimation. A finite-sample exact unbiased estimator is derived that admits a closed-form expression in terms of the thresholded singular values of the least-squares solution and hence is readily computable. The results continue to hold in the high-dimensional setting where both the predictor and the response dimensions may be larger than the sample size. The derived analytical form facilitates the investigation of theoretical properties and provides new insights into the empirical behaviour of the degrees of freedom. In particular, we examine the differences and connections between the proposed estimator and a commonly-used naive estimator. The use of the proposed estimator leads to efficient and accurate prediction risk estimation and model selection, as demonstrated by simulation studies and a data example. PMID:26702155

Targeted MS Assay Predicting Tamoxifen Resistance in Estrogen-Receptor-Positive Breast Cancer Tissues and Sera.

PubMed

De Marchi, Tommaso; Kuhn, Erik; Dekker, Lennard J; Stingl, Christoph; Braakman, Rene B H; Opdam, Mark; Linn, Sabine C; Sweep, Fred C G J; Span, Paul N; Luider, Theo M; Foekens, John A; Martens, John W M; Carr, Steven A; Umar, Arzu

2016-04-01

We recently reported on the development of a 4-protein-based classifier (PDCD4, CGN, G3BP2, and OCIAD1) capable of predicting outcome to tamoxifen treatment in recurrent, estrogen-receptor-positive breast cancer based on high-resolution MS data. A precise and high-throughput assay to measure these proteins in a multiplexed, targeted fashion would be favorable to measure large numbers of patient samples to move these findings toward a clinical setting. By coupling immunoprecipitation to multiple reaction monitoring (MRM) MS and stable isotope dilution, we developed a high-precision assay to measure the 4-protein signature in 38 primary breast cancer whole tissue lysates (WTLs). Furthermore, we evaluated the presence and patient stratification capabilities of our signature in an independent set of 24 matched (pre- and post-therapy) sera. We compared the performance of immuno-MRM (iMRM) with direct MRM in the absence of fractionation and shotgun proteomics in combination with label-free quantification (LFQ) on both WTL and laser capture microdissected (LCM) tissues. Measurement of the 4-proteins by iMRM showed not only higher accuracy in measuring proteotypic peptides (Spearman r: 0.74 to 0.93) when compared with MRM (Spearman r: 0.0 to 0.76) but also significantly discriminated patient groups based on treatment outcome (hazard ratio [HR]: 10.96; 95% confidence interval [CI]: 4.33 to 27.76; Log-rank P < 0.001) when compared with LCM (HR: 2.85; 95% CI: 1.24 to 6.54; Log-rank P = 0.013) and WTL (HR: 1.16; 95% CI: 0.57 to 2.33; Log-rank P = 0.680) LFQ-based predictors. Serum sample analysis by iMRM confirmed the detection of the four proteins in these samples. We hereby report that iMRM outperformed regular MRM, confirmed our previous high-resolution MS results in tumor tissues, and has shown that the 4-protein signature is measurable in serum samples.

Dominance-based ranking functions for interval-valued intuitionistic fuzzy sets.

PubMed

Chen, Liang-Hsuan; Tu, Chien-Cheng

2014-08-01

The ranking of interval-valued intuitionistic fuzzy sets (IvIFSs) is difficult since they include the interval values of membership and nonmembership. This paper proposes ranking functions for IvIFSs based on the dominance concept. The proposed ranking functions consider the degree to which an IvIFS dominates and is not dominated by other IvIFSs. Based on the bivariate framework and the dominance concept, the functions incorporate not only the boundary values of membership and nonmembership, but also the relative relations among IvIFSs in comparisons. The dominance-based ranking functions include bipolar evaluations with a parameter that allows the decision-maker to reflect his actual attitude in allocating the various kinds of dominance. The relationship for two IvIFSs that satisfy the dual couple is defined based on four proposed ranking functions. Importantly, the proposed ranking functions can achieve a full ranking for all IvIFSs. Two examples are used to demonstrate the applicability and distinctiveness of the proposed ranking functions.

Simpson's Paradox and Confounding Factors in University Rankings: A Demonstration Using QS 2011-12 Data

ERIC Educational Resources Information Center

Soh, Kay Cheng

2012-01-01

University ranking has become ritualistic in higher education. Ranking results are taken as bona fide by rank users. Ranking systems usually use large data sets from highly heterogeneous universities of varied backgrounds. This poses the problem of Simpson's Paradox and the lurking variables causing it. Using QS 2011-2012 Ranking data, the dual…

Max-margin multiattribute learning with low-rank constraint.

PubMed

Zhang, Qiang; Chen, Lin; Li, Baoxin

2014-07-01

Attribute learning has attracted a lot of interests in recent years for its advantage of being able to model high-level concepts with a compact set of midlevel attributes. Real-world objects often demand multiple attributes for effective modeling. Most existing methods learn attributes independently without explicitly considering their intrinsic relatedness. In this paper, we propose max margin multiattribute learning with low-rank constraint, which learns a set of attributes simultaneously, using only relative ranking of the attributes for the data. By learning all the attributes simultaneously through low-rank constraint, the proposed method is able to capture their intrinsic correlation for improved learning; by requiring only relative ranking, the method avoids restrictive binary labels of attributes that are often assumed by many existing techniques. The proposed method is evaluated on both synthetic data and real visual data including a challenging video data set. Experimental results demonstrate the effectiveness of the proposed method.

Gene integrated set profile analysis: a context-based approach for inferring biological endpoints

PubMed Central

Kowalski, Jeanne; Dwivedi, Bhakti; Newman, Scott; Switchenko, Jeffery M.; Pauly, Rini; Gutman, David A.; Arora, Jyoti; Gandhi, Khanjan; Ainslie, Kylie; Doho, Gregory; Qin, Zhaohui; Moreno, Carlos S.; Rossi, Michael R.; Vertino, Paula M.; Lonial, Sagar; Bernal-Mizrachi, Leon; Boise, Lawrence H.

2016-01-01

The identification of genes with specific patterns of change (e.g. down-regulated and methylated) as phenotype drivers or samples with similar profiles for a given gene set as drivers of clinical outcome, requires the integration of several genomic data types for which an ‘integrate by intersection’ (IBI) approach is often applied. In this approach, results from separate analyses of each data type are intersected, which has the limitation of a smaller intersection with more data types. We introduce a new method, GISPA (Gene Integrated Set Profile Analysis) for integrated genomic analysis and its variation, SISPA (Sample Integrated Set Profile Analysis) for defining respective genes and samples with the context of similar, a priori specified molecular profiles. With GISPA, the user defines a molecular profile that is compared among several classes and obtains ranked gene sets that satisfy the profile as drivers of each class. With SISPA, the user defines a gene set that satisfies a profile and obtains sample groups of profile activity. Our results from applying GISPA to human multiple myeloma (MM) cell lines contained genes of known profiles and importance, along with several novel targets, and their further SISPA application to MM coMMpass trial data showed clinical relevance. PMID:26826710

Multiple sclerosis and birth order.

PubMed Central

James, W H

1984-01-01

Studies on the birth order of patients with multiple sclerosis have yielded contradictory conclusions. Most of the sets of data, however, have been tested by biased tests. Data that have been submitted to unbiased tests seem to suggest that cases are more likely to occur in early birth ranks. This should be tested on further samples and some comments are offered on how this should be done. PMID:6707558

Bridging the gap: perceived educational needs in the inpatient to home care setting for the person with a new ostomy.

PubMed

Werth, Sherry Lynn; Schutte, Debra L; Stommel, Manfred

2014-01-01

The purpose of this study was to investigate what specific ostomy self-care educational content is considered the most useful by the new ostomy patient after discharge. A cross-sectional, correlational design was used to address study aims. The sample comprised 33 men and 27 women with a mean age of 55.58 ± 15.56 (mean ± SD) years, range 27 to 79 years old. The study setting was a 587-bed teaching hospital, level 1 trauma center in the Midwest, with Magnet designation. Demographic data were collected during the patients' hospital stay as part of routine care. This information is used for follow-up with all ostomy patients who have surgery in this hospital. All of the participants in this study completed an interview administered by phone or in person. A semistructured interview guide was used to elicit participant perceptions of the usefulness of 4 categories of ostomy care, including (1) ostomy information (ostomy function), (2) activities of daily living (strategies to manage travel, bathing, intimacy, odor), (3) ostomy care (strategies for managing the ostomy), and (4) other informational needs (social support resources). Participants were asked to rate these 4 areas from most useful to least useful, using a 4-point scale. At the end of the interview, participants were asked, "Has there been anything that has happened or event related to your ostomy that your ostomy teaching did not prepare you for?" The interview took place several weeks after surgery or during their readmission visit for surgical ostomy takedown. Sixty-two patients were enrolled into the study, and 60 participants completed the data collection. The sample included 26 (43%) patients with ileostomies, 18 (30%) with colostomies, and 16 (27%) with urostomies. Ninety percent ranked the education category of ostomy self-care as the most useful content, 55% ranked information on resuming activities of daily living as the second most useful category, and 55% ranked general information as third most useful content area. Fifty respondents (83%) ranked ostomy support as the least useful. Nine (15%) of the participants felt unprepared for their first pouch leak, and 2 (3%) reported difficulty adjusting to the feel of the stool entering the pouch. Two patients would have liked more photos of peristomal skin conditions and how to treat them. Since the category of ostomy self-care ranked highest, patient teaching for the new ostomy patient should focus on this skill set, including stoma care, how, and when to empty and change the pouch.

Classifying short genomic fragments from novel lineages using composition and homology

PubMed Central

2011-01-01

Background The assignment of taxonomic attributions to DNA fragments recovered directly from the environment is a vital step in metagenomic data analysis. Assignments can be made using rank-specific classifiers, which assign reads to taxonomic labels from a predetermined level such as named species or strain, or rank-flexible classifiers, which choose an appropriate taxonomic rank for each sequence in a data set. The choice of rank typically depends on the optimal model for a given sequence and on the breadth of taxonomic groups seen in a set of close-to-optimal models. Homology-based (e.g., LCA) and composition-based (e.g., PhyloPythia, TACOA) rank-flexible classifiers have been proposed, but there is at present no hybrid approach that utilizes both homology and composition. Results We first develop a hybrid, rank-specific classifier based on BLAST and Naïve Bayes (NB) that has comparable accuracy and a faster running time than the current best approach, PhymmBL. By substituting LCA for BLAST or allowing the inclusion of suboptimal NB models, we obtain a rank-flexible classifier. This hybrid classifier outperforms established rank-flexible approaches on simulated metagenomic fragments of length 200 bp to 1000 bp and is able to assign taxonomic attributions to a subset of sequences with few misclassifications. We then demonstrate the performance of different classifiers on an enhanced biological phosphorous removal metagenome, illustrating the advantages of rank-flexible classifiers when representative genomes are absent from the set of reference genomes. Application to a glacier ice metagenome demonstrates that similar taxonomic profiles are obtained across a set of classifiers which are increasingly conservative in their classification. Conclusions Our NB-based classification scheme is faster than the current best composition-based algorithm, Phymm, while providing equally accurate predictions. The rank-flexible variant of NB, which we term ε-NB, is complementary to LCA and can be combined with it to yield conservative prediction sets of very high confidence. The simple parameterization of LCA and ε-NB allows for tuning of the balance between more predictions and increased precision, allowing the user to account for the sensitivity of downstream analyses to misclassified or unclassified sequences. PMID:21827705

Power and sample size evaluation for the Cochran-Mantel-Haenszel mean score (Wilcoxon rank sum) test and the Cochran-Armitage test for trend.

PubMed

Lachin, John M

2011-11-10

The power of a chi-square test, and thus the required sample size, are a function of the noncentrality parameter that can be obtained as the limiting expectation of the test statistic under an alternative hypothesis specification. Herein, we apply this principle to derive simple expressions for two tests that are commonly applied to discrete ordinal data. The Wilcoxon rank sum test for the equality of distributions in two groups is algebraically equivalent to the Mann-Whitney test. The Kruskal-Wallis test applies to multiple groups. These tests are equivalent to a Cochran-Mantel-Haenszel mean score test using rank scores for a set of C-discrete categories. Although various authors have assessed the power function of the Wilcoxon and Mann-Whitney tests, herein it is shown that the power of these tests with discrete observations, that is, with tied ranks, is readily provided by the power function of the corresponding Cochran-Mantel-Haenszel mean scores test for two and R > 2 groups. These expressions yield results virtually identical to those derived previously for rank scores and also apply to other score functions. The Cochran-Armitage test for trend assesses whether there is an monotonically increasing or decreasing trend in the proportions with a positive outcome or response over the C-ordered categories of an ordinal independent variable, for example, dose. Herein, it is shown that the power of the test is a function of the slope of the response probabilities over the ordinal scores assigned to the groups that yields simple expressions for the power of the test. Copyright © 2011 John Wiley & Sons, Ltd.

Second-order advantage obtained from standard addition first-order instrumental data and multivariate curve resolution-alternating least squares. Calculation of the feasible bands of results.

PubMed

Mohseni, Naimeh; Bahram, Morteza; Olivieri, Alejandro C

2014-03-25

In order to achieve the second-order advantage, second-order data per sample is usually required, e.g., kinetic-spectrophotometric data. In this study, instead of monitoring the time evolution of spectra (and collecting the kinetic-spectrophotometric data) replicate spectra are used to build a virtual second order data. This data matrix (replicate mode×λ) is rank deficient. Augmentation of these data with standard addition data [or standard sample(s)] will break the rank deficiency, making the quantification of the analyte of interest possible. The MCR-ALS algorithm was applied for the resolution and quantitation of the analyte in both simulated and experimental data sets. In order to evaluate the rotational ambiguity in the retrieved solutions, the MCR-BANDS algorithm was employed. It has been shown that the reliability of the quantitative results significantly depends on the amount of spectral overlap in the spectral region of occurrence of the compound of interest and the remaining constituent(s). Copyright © 2013 Elsevier B.V. All rights reserved.

Correlations between cephalometric and photographic measurements of facial attractiveness in Chinese and US patients after orthodontic treatment.

PubMed

Oh, Hee Soo; Korn, Edward L; Zhang, Xiaoyun; Liu, Yan; Xu, Tianmin; Boyd, Robert; Baumrind, Sheldon

2009-12-01

Orthodontists rely on esthetic judgments from facial photographs. Concordance between estimates of facial attractiveness made from lateral cephalograms and those made from clinical photographs has not been determined. We conducted a preliminary examination to correlate clinicians' rankings of facial attractiveness from standardized end-of-treatment facial photographs (Photo Attractiveness Rank) with cephalometric measurements of facial attractiveness made for the same subjects at the same time. Forty-five Chinese and US orthodontic clinicians ranked end-of-treatment photographs of separate samples of 45 US and 48 Chinese adolescent patients for facial attractiveness. Separately for each sample, the photographic rankings were correlated with the values of 21 conventional hard- and soft-tissue measures from lateral cephalograms taken at the same visits as the photographs. Among US patients, higher rank for facial attractiveness on the photographs was strongly associated with higher values for profile angle, chin prominence, lower lip prominence, and Z-angle, and also with lower values for angle of convexity, H-angle, and ANB. Among Chinese patients, higher rank for facial attractiveness on the photographs was strongly associated with higher values for Z-angle and chin prominence, and also with lower values for angle of convexity, H-angle, B-line to upper lip, and mandibular plane angle. Chinese patients whose %lower face height values approximated the ethnic "ideal" (54%) tended to rank higher for facial attractiveness than patients with either higher or lower values for %lower face height. The absolute values of the correlations for the 7 US measures noted above ranged from 0.41 to 0.59; those of the 7 Chinese measures ranged from 0.39 to 0.49.The P value of the least statistically significant of these 14 correlations was 0.006, unadjusted for multiple comparisons. On the other hand, many cephalometric measures believed by clinicians to be indicators of facial attractiveness failed to correlate with facial attractiveness rank for either ethnicity at even the P <0.05 level, including SN-pogonion angle, lower incisor to mandibular plane angle, and Wits appraisal. In general, there was less association than expected or desired between objective measurements on the lateral cephalograms and clinicians' rankings of facial attractiveness on sets of clinical photographs.

Rank-based permutation approaches for non-parametric factorial designs.

PubMed

Umlauft, Maria; Konietschke, Frank; Pauly, Markus

2017-11-01

Inference methods for null hypotheses formulated in terms of distribution functions in general non-parametric factorial designs are studied. The methods can be applied to continuous, ordinal or even ordered categorical data in a unified way, and are based only on ranks. In this set-up Wald-type statistics and ANOVA-type statistics are the current state of the art. The first method is asymptotically exact but a rather liberal statistical testing procedure for small to moderate sample size, while the latter is only an approximation which does not possess the correct asymptotic α level under the null. To bridge these gaps, a novel permutation approach is proposed which can be seen as a flexible generalization of the Kruskal-Wallis test to all kinds of factorial designs with independent observations. It is proven that the permutation principle is asymptotically correct while keeping its finite exactness property when data are exchangeable. The results of extensive simulation studies foster these theoretical findings. A real data set exemplifies its applicability. © 2017 The British Psychological Society.

Interval-Valued Rank in Finite Ordered Sets

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

Joslyn, Cliff; Pogel, Alex; Purvine, Emilie

We consider the concept of rank as a measure of the vertical levels and positions of elements of partially ordered sets (posets). We are motivated by the need for algorithmic measures on large, real-world hierarchically-structured data objects like the semantic hierarchies of ontolog- ical databases. These rarely satisfy the strong property of gradedness, which is required for traditional rank functions to exist. Representing such semantic hierarchies as finite, bounded posets, we recognize the duality of ordered structures to motivate rank functions which respect verticality both from the bottom and from the top. Our rank functions are thus interval-valued, and alwaysmore » exist, even for non-graded posets, providing order homomorphisms to an interval order on the interval-valued ranks. The concept of rank width arises naturally, allowing us to identify the poset region with point-valued width as its longest graded portion (which we call the “spindle”). A standard interval rank function is naturally motivated both in terms of its extremality and on pragmatic grounds. Its properties are examined, including the relation- ship to traditional grading and rank functions, and methods to assess comparisons of standard interval-valued ranks.« less

RankExplorer: Visualization of Ranking Changes in Large Time Series Data.

PubMed

Shi, Conglei; Cui, Weiwei; Liu, Shixia; Xu, Panpan; Chen, Wei; Qu, Huamin

2012-12-01

For many applications involving time series data, people are often interested in the changes of item values over time as well as their ranking changes. For example, people search many words via search engines like Google and Bing every day. Analysts are interested in both the absolute searching number for each word as well as their relative rankings. Both sets of statistics may change over time. For very large time series data with thousands of items, how to visually present ranking changes is an interesting challenge. In this paper, we propose RankExplorer, a novel visualization method based on ThemeRiver to reveal the ranking changes. Our method consists of four major components: 1) a segmentation method which partitions a large set of time series curves into a manageable number of ranking categories; 2) an extended ThemeRiver view with embedded color bars and changing glyphs to show the evolution of aggregation values related to each ranking category over time as well as the content changes in each ranking category; 3) a trend curve to show the degree of ranking changes over time; 4) rich user interactions to support interactive exploration of ranking changes. We have applied our method to some real time series data and the case studies demonstrate that our method can reveal the underlying patterns related to ranking changes which might otherwise be obscured in traditional visualizations.

[Index Copernicus: The Central and Eastern European Journals Ranking System. Why indexing needed in the region?] .

PubMed

Graczynski, M R

2000-09-10

Index Copernicus is ranking system set up by members of the medical community in the Region. There were created five groups of parameters like scientific, editorial and technical quality, circulation and frequency-market stability, which allow for the generation of such a ranking system. The Authors of the Ranking System are aware of the deficiencies of parametrical analysis of science, however they believe the numbers at least set up clear, objective and just rules for all. Index Copernicus could be said the primary objectives of the system for which it has been created for.

The Privilege of Ranking: Google Plays Ball.

ERIC Educational Resources Information Center

Wiggins, Richard

2003-01-01

Discussion of ranking systems used in various settings, including college football and academic admissions, focuses on the Google search engine. Explains the PageRank mathematical formula that scores Web pages by connecting the number of links; limitations, including authenticity and accuracy of ranked Web pages; relevancy; adjusting algorithms;…

SortNet: learning to rank by a neural preference function.

PubMed

Rigutini, Leonardo; Papini, Tiziano; Maggini, Marco; Scarselli, Franco

2011-09-01

Relevance ranking consists in sorting a set of objects with respect to a given criterion. However, in personalized retrieval systems, the relevance criteria may usually vary among different users and may not be predefined. In this case, ranking algorithms that adapt their behavior from users' feedbacks must be devised. Two main approaches are proposed in the literature for learning to rank: the use of a scoring function, learned by examples, that evaluates a feature-based representation of each object yielding an absolute relevance score, a pairwise approach, where a preference function is learned to determine the object that has to be ranked first in a given pair. In this paper, we present a preference learning method for learning to rank. A neural network, the comparative neural network (CmpNN), is trained from examples to approximate the comparison function for a pair of objects. The CmpNN adopts a particular architecture designed to implement the symmetries naturally present in a preference function. The learned preference function can be embedded as the comparator into a classical sorting algorithm to provide a global ranking of a set of objects. To improve the ranking performances, an active-learning procedure is devised, that aims at selecting the most informative patterns in the training set. The proposed algorithm is evaluated on the LETOR dataset showing promising performances in comparison with other state-of-the-art algorithms.

Relationships between genotype x environment interactions and rank orders for a set of genotypes tested in different environments.

PubMed

Hühn, M; Lotito, S; Piepho, H P

1993-09-01

Multilocation trials in plant breeding lead to cross-classified data sets with rows=genotypes and columns=environments, where the breeder is particularly interested in the rank orders of the genotypes in the different environments. Non-identical rank orders are the result of genotype x environment interactions. Not every interaction, however, causes rank changes among the genotypes (rank-interaction). From a breeder's point of view, interaction is tolerable only as long as it does not affect the rank orders. Therefore, the question arises of under which circumstances does interaction become rank-interaction. This paper contributes to our understanding of this topic. In our study we emphasized the detection of relationships between the similarity of the rank orders (measured by Kendall's coefficient of concordance W) and the functions of the diverse variance components (genotypes, environments, interaction, error). On the basis of extensive data sets on different agricultural crops (faba bean, fodder beet, sugar beet, oats, winter rape) obtained from registration trials (1985-1989) carried out in the Federal Republic of Germany, we obtained the following as main result: W ≅ σ 2 (g) /(σ 2 (g) + σ 2 (v) ) where σ 2 (g) =genotypic variance and σ 2 (v) = σ 2 (ge) + σ 2 (o) /L with σ 2 (ge) =interaction variance, σ 2 (o) =error variance and L=number of replications.

Using ground and intact coal Samples to evaluate hydrocarbon fate during supercritical CO2 injection into coal beds: effects of particle size and coal moisture

USGS Publications Warehouse

Kolak, Jon; Hackley, Paul C.; Ruppert, Leslie F.; Warwick, Peter D.; Burruss, Robert

2015-01-01

To investigate the potential for mobilizing organic compounds from coal beds during geologic carbon dioxide (CO2) storage (sequestration), a series of solvent extractions using dichloromethane (DCM) and using supercritical CO2 (40 °C and 10 MPa) were conducted on a set of coal samples collected from Louisiana and Ohio. The coal samples studied range in rank from lignite A to high volatile A bituminous, and were characterized using proximate, ultimate, organic petrography, and sorption isotherm analyses. Sorption isotherm analyses of gaseous CO2 and methane show a general increase in gas storage capacity with coal rank, consistent with findings from previous studies. In the solvent extractions, both dry, ground coal samples and moist, intact core plug samples were used to evaluate effects of variations in particle size and moisture content. Samples were spiked with perdeuterated surrogate compounds prior to extraction, and extracts were analyzed via gas chromatography–mass spectrometry. The DCM extracts generally contained the highest concentrations of organic compounds, indicating the existence of additional hydrocarbons within the coal matrix that were not mobilized during supercritical CO2 extractions. Concentrations of aliphatic and aromatic compounds measured in supercritical CO2 extracts of core plug samples generally are lower than concentrations in corresponding extracts of dry, ground coal samples, due to differences in particle size and moisture content. Changes in the amount of extracted compounds and in surrogate recovery measured during consecutive supercritical CO2extractions of core plug samples appear to reflect the transition from a water-wet to a CO2-wet system. Changes in coal core plug mass during supercritical CO2 extraction range from 3.4% to 14%, indicating that a substantial portion of coal moisture is retained in the low-rank coal samples. Moisture retention within core plug samples, especially in low-rank coals, appears to inhibit accessibility of supercritical CO2 to coal matrix porosity, limiting the extent to which hydrocarbons are mobilized. Conversely, the enhanced recovery of some surrogates from core plugs relative to dry, ground coal samples might indicate that, once mobilized, supercritical CO2 is capable of transporting these constituents through coal beds. These results underscore the need for using intact coal samples, and for better characterization of forms of water in coal, to understand fate and transport of organic compounds during supercritical CO2 injection into coal beds.

Learning of Rule Ensembles for Multiple Attribute Ranking Problems

NASA Astrophysics Data System (ADS)

Dembczyński, Krzysztof; Kotłowski, Wojciech; Słowiński, Roman; Szeląg, Marcin

In this paper, we consider the multiple attribute ranking problem from a Machine Learning perspective. We propose two approaches to statistical learning of an ensemble of decision rules from decision examples provided by the Decision Maker in terms of pairwise comparisons of some objects. The first approach consists in learning a preference function defining a binary preference relation for a pair of objects. The result of application of this function on all pairs of objects to be ranked is then exploited using the Net Flow Score procedure, giving a linear ranking of objects. The second approach consists in learning a utility function for single objects. The utility function also gives a linear ranking of objects. In both approaches, the learning is based on the boosting technique. The presented approaches to Preference Learning share good properties of the decision rule preference model and have good performance in the massive-data learning problems. As Preference Learning and Multiple Attribute Decision Aiding share many concepts and methodological issues, in the introduction, we review some aspects bridging these two fields. To illustrate the two approaches proposed in this paper, we solve with them a toy example concerning the ranking of a set of cars evaluated by multiple attributes. Then, we perform a large data experiment on real data sets. The first data set concerns credit rating. Since recent research in the field of Preference Learning is motivated by the increasing role of modeling preferences in recommender systems and information retrieval, we chose two other massive data sets from this area - one comes from movie recommender system MovieLens, and the other concerns ranking of text documents from 20 Newsgroups data set.

Dynamic security contingency screening and ranking using neural networks.

PubMed

Mansour, Y; Vaahedi, E; El-Sharkawi, M A

1997-01-01

This paper summarizes BC Hydro's experience in applying neural networks to dynamic security contingency screening and ranking. The idea is to use the information on the prevailing operating condition and directly provide contingency screening and ranking using a trained neural network. To train the two neural networks for the large scale systems of BC Hydro and Hydro Quebec, in total 1691 detailed transient stability simulation were conducted, 1158 for BC Hydro system and 533 for the Hydro Quebec system. The simulation program was equipped with the energy margin calculation module (second kick) to measure the energy margin in each run. The first set of results showed poor performance for the neural networks in assessing the dynamic security. However a number of corrective measures improved the results significantly. These corrective measures included: 1) the effectiveness of output; 2) the number of outputs; 3) the type of features (static versus dynamic); 4) the number of features; 5) system partitioning; and 6) the ratio of training samples to features. The final results obtained using the large scale systems of BC Hydro and Hydro Quebec demonstrates a good potential for neural network in dynamic security assessment contingency screening and ranking.

Identification of genes and gene pathways associated with major depressive disorder by integrative brain analysis of rat and human prefrontal cortex transcriptomes

PubMed Central

Malki, K; Pain, O; Tosto, M G; Du Rietz, E; Carboni, L; Schalkwyk, L C

2015-01-01

Despite moderate heritability estimates, progress in uncovering the molecular substrate underpinning major depressive disorder (MDD) has been slow. In this study, we used prefrontal cortex (PFC) gene expression from a genetic rat model of MDD to inform probe set prioritization in PFC in a human post-mortem study to uncover genes and gene pathways associated with MDD. Gene expression differences between Flinders sensitive (FSL) and Flinders resistant (FRL) rat lines were statistically evaluated using the RankProd, non-parametric algorithm. Top ranking probe sets in the rat study were subsequently used to prioritize orthologous selection in a human PFC in a case–control post-mortem study on MDD from the Stanley Brain Consortium. Candidate genes in the human post-mortem study were then tested against a matched control sample using the RankProd method. A total of 1767 probe sets were differentially expressed in the PFC between FSL and FRL rat lines at (q⩽0.001). A total of 898 orthologous probe sets was found on Affymetrix's HG-U95A chip used in the human study. Correcting for the number of multiple, non-independent tests, 20 probe sets were found to be significantly dysregulated between human cases and controls at q⩽0.05. These probe sets tagged the expression profile of 18 human genes (11 upregulated and seven downregulated). Using an integrative rat–human study, a number of convergent genes that may have a role in pathogenesis of MDD were uncovered. Eighty percent of these genes were functionally associated with a key stress response signalling cascade, involving NF-κB (nuclear factor kappa-light-chain-enhancer of activated B cells), AP-1 (activator protein 1) and ERK/MAPK, which has been systematically associated with MDD, neuroplasticity and neurogenesis. PMID:25734512

Class Attendance and Students' Evaluations of Teaching: Do No-Shows Bias Course Ratings and Rankings?

ERIC Educational Resources Information Center

Wolbring, Tobias

2012-01-01

Background: Many university departments use students' evaluations of teaching (SET) to compare and rank courses. However, absenteeism from class is often nonrandom and, therefore, SET for different courses might not be comparable. Objective: The present study aims to answer two questions. Are SET positively biased due to absenteeism? Do…

Perceptions About Competing Psychosocial Problems and Treatment Priorities Among Older Adults With Depression

PubMed Central

Proctor, Enola K.; Hasche, Leslie; Morrow-Howell, Nancy; Shumway, Martha; Snell, Grace

2009-01-01

Objective Depression often co-occurs with other conditions that may pose competing demands to depression care, particularly in later life. This study examined older adults’ perceptions of depression among cooccurring social, medical, and functional problems and compared the priority of depression with that of other problems. Methods The study’s purposeful sample comprised 49 adults age 60 or older with a history of depression and in publicly funded community long-term care. Fourpart, mixed-methods interviews sought to capture participants’ perceptions of life problems as well as the priority they placed on depression. Methods included standardized depression screening, semistructured qualitative interviews, listing of problems, and qualitative and quantitative analysis of problem rankings. Results Most participants identified health, functional, and psychosocial problems co-occurring with depressive symptoms. Depression was ranked low among the co-occurring conditions; 6% ranked depression as the most important of their problems, whereas 45% ranked it last. Relative rank scores for problems were remarkably similar, with the notable exception of depression, which was ranked lowest of all problems. Participants did not see depression as a high priority compared with co-occurring problems, particularly psychosocial ones. Conclusions Effective and durable improvements to mental health care must be shaped by an understanding of client perceptions and priorities. Motivational interviewing, health education, and assessment of treatment priorities may be necessary in helping older adults value and accept depression care. Nonspecialty settings of care may effectively link depression treatment to other services, thereby increasing receptivity to mental health services. PMID:18511588

Minimizing the semantic gap in biomedical content-based image retrieval

NASA Astrophysics Data System (ADS)

Guan, Haiying; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2010-03-01

A major challenge in biomedical Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings that minimize the semantic gap between the high-level biomedical semantic concepts and the low-level visual features in images. This paper presents a comprehensive learning-based scheme toward meeting this challenge and improving retrieval quality. The article presents two algorithms: a learning-based feature selection and fusion algorithm and the Ranking Support Vector Machine (Ranking SVM) algorithm. The feature selection algorithm aims to select 'good' features and fuse them using different similarity measurements to provide a better representation of the high-level concepts with the low-level image features. Ranking SVM is applied to learn the retrieval rank function and associate the selected low-level features with query concepts, given the ground-truth ranking of the training samples. The proposed scheme addresses four major issues in CBIR to improve the retrieval accuracy: image feature extraction, selection and fusion, similarity measurements, the association of the low-level features with high-level concepts, and the generation of the rank function to support high-level semantic image retrieval. It models the relationship between semantic concepts and image features, and enables retrieval at the semantic level. We apply it to the problem of vertebra shape retrieval from a digitized spine x-ray image set collected by the second National Health and Nutrition Examination Survey (NHANES II). The experimental results show an improvement of up to 41.92% in the mean average precision (MAP) over conventional image similarity computation methods.

Everything under Control? The Effects of Age, Gender, and Education on Trajectories of Perceived Control in a Nationally Representative German Sample

ERIC Educational Resources Information Center

Specht, Jule; Egloff, Boris; Schmukle, Stefan C.

2013-01-01

Perceived control is an important variable for various demands involved in successful aging. However, perceived control is not set in stone but rather changes throughout the life course. The aim of this study was to identify cross-sectional age differences and longitudinal mean-level changes as well as rank-order changes in perceived control with…

Trends and anomalies in gas evolution from coal samples

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

Vorres, K.S.

1993-09-01

As part of the stability studies on these sealed samples a number of the samples were given to the Analytical Chemistry Laboratory at ANL for periodic gas analysis. 1. Higher rank coals evolve methane, and lower rank coals evolve carbon dioxide with some evolution of both gases for the intermediate ranks. 2. The evolution proceeds over times of years for pulverized coals in sealed ampules. 3. Gas concentrations are higher above -20 mesh samples than above -100 mesh material. 4. Carbon monoxide is not evolved.

MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis.

PubMed

Wang, QuanQiu; Xu, Rong

2017-07-01

Human metabolomics has great potential in disease mechanism understanding, early diagnosis, and therapy. Existing metabolomics studies are often based on profiling patient biofluids and tissue samples and are difficult owing to the challenges of sample collection and data processing. Here, we report an alternative approach and developed a computation-based prediction system, MetabolitePredict, for disease metabolomics biomarker prediction. We applied MetabolitePredict to identify metabolite biomarkers and metabolite targeting therapies for rheumatoid arthritis (RA), a last-lasting complex disease with multiple genetic and environmental factors involved. MetabolitePredict is a de novo prediction system. It first constructs a disease-specific genetic profile using genes and pathways data associated with an input disease. It then constructs genetic profiles for a total of 259,170 chemicals/metabolites using known chemical genetics and human metabolomic data. MetabolitePredict prioritizes metabolites for a given disease based on the genetic profile similarities between disease and metabolites. We evaluated MetabolitePredict using 63 known RA-associated metabolites. MetabolitePredict found 24 of the 63 metabolites (recall: 0.38) and ranked them highly (mean ranking: top 4.13%, median ranking: top 1.10%, P-value: 5.08E-19). MetabolitePredict performed better than an existing metabolite prediction system, PROFANCY, in predicting RA-associated metabolites (PROFANCY: recall: 0.31, mean ranking: 20.91%, median ranking: 16.47%, P-value: 3.78E-7). Short-chain fatty acids (SCFAs), the abundant metabolites of gut microbiota in the fermentation of fiber, ranked highly (butyrate, 0.03%; acetate, 0.05%; propionate, 0.38%). Finally, we established MetabolitePredict's potential in novel metabolite targeting for disease treatment: MetabolitePredict ranked highly three known metabolite inhibitors for RA treatments (methotrexate:0.25%; leflunomide: 0.56%; sulfasalazine: 0.92%). MetabolitePredict is a generalizable disease metabolite prediction system. The only required input to the system is a disease name or a set of disease-associated genes. The web-based MetabolitePredict is available at:http://xulab. edu/MetabolitePredict. Copyright © 2017 Elsevier Inc. All rights reserved.

Spotting words in handwritten Arabic documents

NASA Astrophysics Data System (ADS)

Srihari, Sargur; Srinivasan, Harish; Babu, Pavithra; Bhole, Chetan

2006-01-01

The design and performance of a system for spotting handwritten Arabic words in scanned document images is presented. Three main components of the system are a word segmenter, a shape based matcher for words and a search interface. The user types in a query in English within a search window, the system finds the equivalent Arabic word, e.g., by dictionary look-up, locates word images in an indexed (segmented) set of documents. A two-step approach is employed in performing the search: (1) prototype selection: the query is used to obtain a set of handwritten samples of that word from a known set of writers (these are the prototypes), and (2) word matching: the prototypes are used to spot each occurrence of those words in the indexed document database. A ranking is performed on the entire set of test word images-- where the ranking criterion is a similarity score between each prototype word and the candidate words based on global word shape features. A database of 20,000 word images contained in 100 scanned handwritten Arabic documents written by 10 different writers was used to study retrieval performance. Using five writers for providing prototypes and the other five for testing, using manually segmented documents, 55% precision is obtained at 50% recall. Performance increases as more writers are used for training.

5 CFR 451.301 - Ranks for the Senior Executive Service.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 5 Administrative Personnel 1 2010-01-01 2010-01-01 false Ranks for the Senior Executive Service... REGULATIONS AWARDS Presidential Rank Awards § 451.301 Ranks for the Senior Executive Service. (a) The... to a Senior Executive Service (SES) career appointee are set forth in 5 U.S.C. 4507. (b) To be...

Relationship between Journal-Ranking Metrics for a Multidisciplinary Set of Journals

ERIC Educational Resources Information Center

Perera, Upeksha; Wijewickrema, Manjula

2018-01-01

Ranking of scholarly journals is important to many parties. Studying the relationships among various ranking metrics is key to understanding the significance of one metric based on another. This research investigates the relationship among four major journal-ranking indicators: the impact factor (IF), the Eigenfactor score (ES), the "h."…

Benchmarking Jiangsu University to Improve Its Academic Ranking

ERIC Educational Resources Information Center

Li, Xinchao; Thige, Joseph Muiruri

2017-01-01

This paper collates research on global ranking through U.S.News.com in relation to Jiangsu University's nonappearance in global ranking of higher education institutions. The author critiques the Academic set up of the University in comparison with universities Ranked as World Class. The author navigates the study largely through descriptive and…

It's all relative: ranking the diversity of aquatic bacterial communities.

PubMed

Shaw, Allison K; Halpern, Aaron L; Beeson, Karen; Tran, Bao; Venter, J Craig; Martiny, Jennifer B H

2008-09-01

The study of microbial diversity patterns is hampered by the enormous diversity of microbial communities and the lack of resources to sample them exhaustively. For many questions about richness and evenness, however, one only needs to know the relative order of diversity among samples rather than total diversity. We used 16S libraries from the Global Ocean Survey to investigate the ability of 10 diversity statistics (including rarefaction, non-parametric, parametric, curve extrapolation and diversity indices) to assess the relative diversity of six aquatic bacterial communities. Overall, we found that the statistics yielded remarkably similar rankings of the samples for a given sequence similarity cut-off. This correspondence, despite the different underlying assumptions of the statistics, suggests that diversity statistics are a useful tool for ranking samples of microbial diversity. In addition, sequence similarity cut-off influenced the diversity ranking of the samples, demonstrating that diversity statistics can also be used to detect differences in phylogenetic structure among microbial communities. Finally, a subsampling analysis suggests that further sequencing from these particular clone libraries would not have substantially changed the richness rankings of the samples.

Sparse Contextual Activation for Efficient Visual Re-Ranking.

PubMed

Bai, Song; Bai, Xiang

2016-03-01

In this paper, we propose an extremely efficient algorithm for visual re-ranking. By considering the original pairwise distance in the contextual space, we develop a feature vector called sparse contextual activation (SCA) that encodes the local distribution of an image. Hence, re-ranking task can be simply accomplished by vector comparison under the generalized Jaccard metric, which has its theoretical meaning in the fuzzy set theory. In order to improve the time efficiency of re-ranking procedure, inverted index is successfully introduced to speed up the computation of generalized Jaccard metric. As a result, the average time cost of re-ranking for a certain query can be controlled within 1 ms. Furthermore, inspired by query expansion, we also develop an additional method called local consistency enhancement on the proposed SCA to improve the retrieval performance in an unsupervised manner. On the other hand, the retrieval performance using a single feature may not be satisfactory enough, which inspires us to fuse multiple complementary features for accurate retrieval. Based on SCA, a robust feature fusion algorithm is exploited that also preserves the characteristic of high time efficiency. We assess our proposed method in various visual re-ranking tasks. Experimental results on Princeton shape benchmark (3D object), WM-SRHEC07 (3D competition), YAEL data set B (face), MPEG-7 data set (shape), and Ukbench data set (image) manifest the effectiveness and efficiency of SCA.

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

Tutorial: Calculating Percentile Rank and Percentile Norms Using SPSS

ERIC Educational Resources Information Center

Baumgartner, Ted A.

2009-01-01

Practitioners can benefit from using norms, but they often have to develop their own percentile rank and percentile norms. This article is a tutorial on how to quickly and easily calculate percentile rank and percentile norms using SPSS, and this information is presented for a data set. Some issues in calculating percentile rank and percentile…

The hierarchy of work pursuits of public health managers.

PubMed

Braithwaite, Jeffrey; Luft, Sabine; Bender, Wolfgang; Callen, Joanne; Westbrook, Johanna I; Westbrook, Mary T; Mallock, Nadine A; Iedema, Rick; Hindle, Donald; Jochelson, Tanya

2007-05-01

How public health is managed in various settings is an important but under-examined issue. We examine themes in the management literature, contextualize issues facing public health managers and investigate the relative importance placed on their various work pursuits using a 14-activity management model empirically derived from studies of clinician-managers in hospitals. Ethnographic case studies of 10 managers in nine diverse public health settings were conducted. The case study accounts of managers' activities were content analysed, and substantive words encapsulating their work were categorized using the model. Managerial activities of the nine public health managers were ranked according to the number of words describing each activity. Kendall's coefficient of concordance yielded W = 0.710, P < 0.000, revealing significant similarity between the activity patterns of the public health managers. A rank order correlation between the activity patterns of the average ranks for the public health sample and for the hospital clinician-managers (n = 52) was R = 0.420, P = 0.131, indicating no significant relationship between relative activity priorities of the two groups. Public health managers put less emphasis on pursuits associated with structure, hierarchy and education, and more on external relations and decision-making. The model of hospital clinician-managers' managerial activities is applicable to public health managers while identifying differences in the way the two groups manage. The findings suggest that public health management work is more managerialist than previously thought.

Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters.

PubMed

Rácz, A; Bajusz, D; Héberger, K

2015-01-01

Recent implementations of QSAR modelling software provide the user with numerous models and a wealth of information. In this work, we provide some guidance on how one should interpret the results of QSAR modelling, compare and assess the resulting models, and select the best and most consistent ones. Two QSAR datasets are applied as case studies for the comparison of model performance parameters and model selection methods. We demonstrate the capabilities of sum of ranking differences (SRD) in model selection and ranking, and identify the best performance indicators and models. While the exchange of the original training and (external) test sets does not affect the ranking of performance parameters, it provides improved models in certain cases (despite the lower number of molecules in the training set). Performance parameters for external validation are substantially separated from the other merits in SRD analyses, highlighting their value in data fusion.

Promoted combustion of nine structural metals in high-pressure gaseous oxygen - A comparison of ranking methods

NASA Technical Reports Server (NTRS)

Steinberg, Theodore A.; Rucker, Michelle A.; Beeson, Harold D.

1989-01-01

The 316, 321, 440C, and 17-4 PH stainless steels, as well as Inconel 600, Inconel 718, Waspaloy, Monel 400, and Al 2219, have been evaluated for relative nonflammability in a high-pressure oxygen environment with a view to the comparative advantages of four different flammability-ranking methods. The effects of changes in test pressure, sample diameter, promoter type, and sample configuration on ranking method results are evaluated; ranking methods employing velocity as the primary ranking criterion are limited by diameter effects, while those which use extinguishing pressure are nonselective for metals with similar flammabilities.

Experimental congruence of interval scale production from paired comparisons and ranking for image evaluation

NASA Astrophysics Data System (ADS)

Handley, John C.; Babcock, Jason S.; Pelz, Jeff B.

2003-12-01

Image evaluation tasks are often conducted using paired comparisons or ranking. To elicit interval scales, both methods rely on Thurstone's Law of Comparative Judgment in which objects closer in psychological space are more often confused in preference comparisons by a putative discriminal random process. It is often debated whether paired comparisons and ranking yield the same interval scales. An experiment was conducted to assess scale production using paired comparisons and ranking. For this experiment a Pioneer Plasma Display and Apple Cinema Display were used for stimulus presentation. Observers performed rank order and paired comparisons tasks on both displays. For each of five scenes, six images were created by manipulating attributes such as lightness, chroma, and hue using six different settings. The intention was to simulate the variability from a set of digital cameras or scanners. Nineteen subjects, (5 females, 14 males) ranging from 19-51 years of age participated in this experiment. Using a paired comparison model and a ranking model, scales were estimated for each display and image combination yielding ten scale pairs, ostensibly measuring the same psychological scale. The Bradley-Terry model was used for the paired comparisons data and the Bradley-Terry-Mallows model was used for the ranking data. Each model was fit using maximum likelihood estimation and assessed using likelihood ratio tests. Approximate 95% confidence intervals were also constructed using likelihood ratios. Model fits for paired comparisons were satisfactory for all scales except those from two image/display pairs; the ranking model fit uniformly well on all data sets. Arguing from overlapping confidence intervals, we conclude that paired comparisons and ranking produce no conflicting decisions regarding ultimate ordering of treatment preferences, but paired comparisons yield greater precision at the expense of lack-of-fit.

A Trade Study and Metric for Penetration and Sampling Devices for Possible Use on the NASA 2003 and 2005 Mars Sample Return Missions

NASA Technical Reports Server (NTRS)

McConnell, Joshua B.

2000-01-01

The scientific exploration of Mars will require the collection and return of subterranean samples to Earth for examination. This necessitates the use of some type of device or devices that possesses the ability to effectively penetrate the Martian surface, collect suitable samples and return them to the surface in a manner consistent with imposed scientific constraints. The first opportunity for such a device will occur on the 2003 and 2005 Mars Sample Return missions, being performed by NASA. This paper reviews the work completed on the compilation of a database containing viable penetrating and sampling devices, the performance of a system level trade study comparing selected devices to a set of prescribed parameters and the employment of a metric for the evaluation and ranking of the traded penetration and sampling devices, with respect to possible usage on the 03 and 05 sample return missions. The trade study performed is based on a select set of scientific, engineering, programmatic and socio-political criterion. The use of a metric for the various penetration and sampling devices will act to expedite current and future device selection.

SWIFT-Review: a text-mining workbench for systematic review.

PubMed

Howard, Brian E; Phillips, Jason; Miller, Kyle; Tandon, Arpit; Mav, Deepak; Shah, Mihir R; Holmgren, Stephanie; Pelch, Katherine E; Walker, Vickie; Rooney, Andrew A; Macleod, Malcolm; Shah, Ruchir R; Thayer, Kristina

2016-05-23

There is growing interest in using machine learning approaches to priority rank studies and reduce human burden in screening literature when conducting systematic reviews. In addition, identifying addressable questions during the problem formulation phase of systematic review can be challenging, especially for topics having a large literature base. Here, we assess the performance of the SWIFT-Review priority ranking algorithm for identifying studies relevant to a given research question. We also explore the use of SWIFT-Review during problem formulation to identify, categorize, and visualize research areas that are data rich/data poor within a large literature corpus. Twenty case studies, including 15 public data sets, representing a range of complexity and size, were used to assess the priority ranking performance of SWIFT-Review. For each study, seed sets of manually annotated included and excluded titles and abstracts were used for machine training. The remaining references were then ranked for relevance using an algorithm that considers term frequency and latent Dirichlet allocation (LDA) topic modeling. This ranking was evaluated with respect to (1) the number of studies screened in order to identify 95 % of known relevant studies and (2) the "Work Saved over Sampling" (WSS) performance metric. To assess SWIFT-Review for use in problem formulation, PubMed literature search results for 171 chemicals implicated as EDCs were uploaded into SWIFT-Review (264,588 studies) and categorized based on evidence stream and health outcome. Patterns of search results were surveyed and visualized using a variety of interactive graphics. Compared with the reported performance of other tools using the same datasets, the SWIFT-Review ranking procedure obtained the highest scores on 11 out of 15 of the public datasets. Overall, these results suggest that using machine learning to triage documents for screening has the potential to save, on average, more than 50 % of the screening effort ordinarily required when using un-ordered document lists. In addition, the tagging and annotation capabilities of SWIFT-Review can be useful during the activities of scoping and problem formulation. Text-mining and machine learning software such as SWIFT-Review can be valuable tools to reduce the human screening burden and assist in problem formulation.

Ranking influential spreaders is an ill-defined problem

NASA Astrophysics Data System (ADS)

Gu, Jain; Lee, Sungmin; Saramäki, Jari; Holme, Petter

2017-06-01

Finding influential spreaders of information and disease in networks is an important theoretical problem, and one of considerable recent interest. It has been almost exclusively formulated as a node-ranking problem —methods for identifying influential spreaders output a ranking of the nodes. In this work, we show that such a greedy heuristic does not necessarily work: the set of most influential nodes depends on the number of nodes in the set. Therefore, the set of n most important nodes to vaccinate does not need to have any node in common with the set of n + 1 most important nodes. We propose a method for quantifying the extent and impact of this phenomenon. By this method, we show that it is a common phenomenon in both empirical and model networks.

Comparison of health care professionals' self-assessments of standards of care and patients' opinions on the care they received in hospital: observational study

PubMed Central

Durieux, P; Bissery, A; Dubois, S; Gasquet, I; Coste, J

2004-01-01

Objectives: To compare the views of healthcare professionals and patients regarding compliance with standards of care concerning patient information. Design: Self-rated questionnaire survey. Setting: Nine wards in short stay French hospitals. Participants: 939 patients and 359 healthcare professionals (physicians, nurses, assistants and other professionals). Main outcome measure: Patients' and healthcare professionals' views of compliance with 20 standards of patient care described in the French accreditation manual. Comparison of the rank order of the standards within the two samples. Results: The response rate was 61.5% in the patient group and 85.8% in the healthcare professionals. The rank orders for the 20 items were similar in both groups (Spearman rank order correlation 0.6, p = 0.004). The two items ranked highest by healthcare professionals ("consent request for a surgical procedure" and "the doctors ask the visitors to leave the room before examining a patient") were also the two ranked highest by the patients. Three items were ranked low by both groups: "consent request for students to be present", "health education given to patients", and "possibility to express satisfaction during discharge". Patients were more satisfied with their pain management than were healthcare providers. Professionals were more satisfied with the social services than the patients. Conclusion: There are both similarities and differences between patients' and healthcare professionals' views of care. Accurate assessments of quality performed during the accreditation procedure require that both patients' and professionals' views be taken into account. PMID:15175490

Visualizing Rank Time Series of Wikipedia Top-Viewed Pages.

PubMed

Xia, Jing; Hou, Yumeng; Chen, Yingjie Victor; Qian, Zhenyu Cheryl; Ebert, David S; Chen, Wei

2017-01-01

Visual clutter is a common challenge when visualizing large rank time series data. WikiTopReader, a reader of Wikipedia page rank, lets users explore connections among top-viewed pages by connecting page-rank behaviors with page-link relations. Such a combination enhances the unweighted Wikipedia page-link network and focuses attention on the page of interest. A set of user evaluations shows that the system effectively represents evolving ranking patterns and page-wise correlation.

Econophysics of a ranked demand and supply resource allocation problem

NASA Astrophysics Data System (ADS)

Priel, Avner; Tamir, Boaz

2018-01-01

We present a two sided resource allocation problem, between demands and supplies, where both parties are ranked. For example, in Big Data problems where a set of different computational tasks is divided between a set of computers each with its own resources, or between employees and employers where both parties are ranked, the employees by their fitness and the employers by their package benefits. The allocation process can be viewed as a repeated game where in each iteration the strategy is decided by a meta-rule, based on the ranks of both parties and the results of the previous games. We show the existence of a phase transition between an absorbing state, where all demands are satisfied, and an active one where part of the demands are always left unsatisfied. The phase transition is governed by the ratio between supplies and demand. In a job allocation problem we find positive correlation between the rank of the workers and the rank of the factories; higher rank workers are usually allocated to higher ranked factories. These all suggest global emergent properties stemming from local variables. To demonstrate the global versus local relations, we introduce a local inertial force that increases the rank of employees in proportion to their persistence time in the same factory. We show that such a local force induces non trivial global effects, mostly to benefit the lower ranked employees.

Understanding scaling through history-dependent processes with collapsing sample space.

PubMed

Corominas-Murtra, Bernat; Hanel, Rudolf; Thurner, Stefan

2015-04-28

History-dependent processes are ubiquitous in natural and social systems. Many such stochastic processes, especially those that are associated with complex systems, become more constrained as they unfold, meaning that their sample space, or their set of possible outcomes, reduces as they age. We demonstrate that these sample-space-reducing (SSR) processes necessarily lead to Zipf's law in the rank distributions of their outcomes. We show that by adding noise to SSR processes the corresponding rank distributions remain exact power laws, p(x) ~ x(-λ), where the exponent directly corresponds to the mixing ratio of the SSR process and noise. This allows us to give a precise meaning to the scaling exponent in terms of the degree to which a given process reduces its sample space as it unfolds. Noisy SSR processes further allow us to explain a wide range of scaling exponents in frequency distributions ranging from α = 2 to ∞. We discuss several applications showing how SSR processes can be used to understand Zipf's law in word frequencies, and how they are related to diffusion processes in directed networks, or aging processes such as in fragmentation processes. SSR processes provide a new alternative to understand the origin of scaling in complex systems without the recourse to multiplicative, preferential, or self-organized critical processes.

Diversity rankings among bacterial lineages in soil.

PubMed

Youssef, Noha H; Elshahed, Mostafa S

2009-03-01

We used rarefaction curve analysis and diversity ordering-based approaches to rank the 11 most frequently encountered bacterial lineages in soil according to diversity in 5 previously reported 16S rRNA gene clone libraries derived from agricultural, undisturbed tall grass prairie and forest soils (n=26,140, 28 328, 31 818, 13 001 and 53 533). The Planctomycetes, Firmicutes and the delta-Proteobacteria were consistently ranked among the most diverse lineages in all data sets, whereas the Verrucomicrobia, Gemmatimonadetes and beta-Proteobacteria were consistently ranked among the least diverse. On the other hand, the rankings of alpha-Proteobacteria, Acidobacteria, Actinobacteria, Bacteroidetes and Chloroflexi varied widely in different soil clone libraries. In general, lineages exhibiting largest differences in diversity rankings also exhibited the largest difference in relative abundance in the data sets examined. Within these lineages, a positive correlation between relative abundance and diversity was observed within the Acidobacteria, Actinobacteria and Chloroflexi, and a negative diversity-abundance correlation was observed within the Bacteroidetes. The ecological and evolutionary implications of these results are discussed.

Sparsity-weighted outlier FLOODing (OFLOOD) method: Efficient rare event sampling method using sparsity of distribution.

PubMed

Harada, Ryuhei; Nakamura, Tomotake; Shigeta, Yasuteru

2016-03-30

As an extension of the Outlier FLOODing (OFLOOD) method [Harada et al., J. Comput. Chem. 2015, 36, 763], the sparsity of the outliers defined by a hierarchical clustering algorithm, FlexDice, was considered to achieve an efficient conformational search as sparsity-weighted "OFLOOD." In OFLOOD, FlexDice detects areas of sparse distribution as outliers. The outliers are regarded as candidates that have high potential to promote conformational transitions and are employed as initial structures for conformational resampling by restarting molecular dynamics simulations. When detecting outliers, FlexDice defines a rank in the hierarchy for each outlier, which relates to sparsity in the distribution. In this study, we define a lower rank (first ranked), a medium rank (second ranked), and the highest rank (third ranked) outliers, respectively. For instance, the first-ranked outliers are located in a given conformational space away from the clusters (highly sparse distribution), whereas those with the third-ranked outliers are nearby the clusters (a moderately sparse distribution). To achieve the conformational search efficiently, resampling from the outliers with a given rank is performed. As demonstrations, this method was applied to several model systems: Alanine dipeptide, Met-enkephalin, Trp-cage, T4 lysozyme, and glutamine binding protein. In each demonstration, the present method successfully reproduced transitions among metastable states. In particular, the first-ranked OFLOOD highly accelerated the exploration of conformational space by expanding the edges. In contrast, the third-ranked OFLOOD reproduced local transitions among neighboring metastable states intensively. For quantitatively evaluations of sampled snapshots, free energy calculations were performed with a combination of umbrella samplings, providing rigorous landscapes of the biomolecules. © 2015 Wiley Periodicals, Inc.

Sequoia Messaging Rate Benchmark

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

Friedley, Andrew

2008-01-22

The purpose of this benchmark is to measure the maximal message rate of a single compute node. The first num_cores ranks are expected to reside on the 'core' compute node for which message rate is being tested. After that, the next num_nbors ranks are neighbors for the first core rank, the next set of num_nbors ranks are neighbors for the second core rank, and so on. For example, testing an 8-core node (num_cores = 8) with 4 neighbors (num_nbors = 4) requires 8 + 8 * 4 - 40 ranks. The first 8 of those 40 ranks are expected tomore » be on the 'core' node being benchmarked, while the rest of the ranks are on separate nodes.« less

Intrusion detection using rough set classification.

PubMed

Zhang, Lian-hua; Zhang, Guan-hua; Zhang, Jie; Bai, Ying-cai

2004-09-01

Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of "IF-THEN" rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).

SEDIMENT-HOSTED PRECIOUS METAL DEPOSITS.

USGS Publications Warehouse

Bagby, W.C.; Pickthorn, W.J.; Goldfarb, R.; Hill, R.A.

1984-01-01

The Dee mine is a sediment-hosted, disseminated gold deposit in the Roberts Mountains allochthon of north central Nevada. Soil samples were collected from the C-horizon in undisturbed areas over the deposit in order to investigate the usefulness of soil geochemistry in identifying this type of deposit. Each sample was sieved to minus 80 mesh and analyzed quantitatively for Au, Ag, As, Sb, Hg, Tl and semi-quantitative data for an additional 31 elements. Rank sum analysis is successful for the Au, Ag, As, Sb, Hg, Tl suite, even though bedrock geology is disregarded. This method involves data transformation into a total element signature by ranking the data in ascending order and summing the element ranks for each sample. The rank sums are then divided into percentile groups and plotted. The rank sum plot for the Dee soils unequivocally identifies three of four known ore zones.

What matters in the patients' decision to revisit the same primary care physician?

PubMed

Antoun, Jumana M; Hamadeh, Ghassan N; Adib, Salim M

2014-01-01

To assess the priority of various aspects of the patient-primary care physician relationship in the decision to visit again that same physician. STUDY SETTINGS: A total of 400 community residents in Ras Beirut, Lebanon. A cross-sectional community based study sampled by a nonrandom sex-education quota-based procedure. Participants were asked to fill a survey where they indicated the ranking of nine items by importance in their decision to revisit the same physician. The nine items were chosen from three categories of factors: professional expertise of the physician; characteristics of the patient-physician relationship, office organization. Having a physician that gives the patient adequate time for discussion prevailed as rank 1 and luxurious clinic ranked as 9th. Affordability was one of the main concerns among men, those with poor health and those of lower socioeconomic status. Accessibility of the physician's phone was considered highly important among women and those of lesser education status. This study emphasizes the importance of adequate time with the patient, accessibility and affordability of the physician in maintaining continuity of care and patient satisfaction, beyond mere medical expertise.

Statistical methods of fracture characterization using acoustic borehole televiewer log interpretation

NASA Astrophysics Data System (ADS)

Massiot, Cécile; Townend, John; Nicol, Andrew; McNamara, David D.

2017-08-01

Acoustic borehole televiewer (BHTV) logs provide measurements of fracture attributes (orientations, thickness, and spacing) at depth. Orientation, censoring, and truncation sampling biases similar to those described for one-dimensional outcrop scanlines, and other logging or drilling artifacts specific to BHTV logs, can affect the interpretation of fracture attributes from BHTV logs. K-means, fuzzy K-means, and agglomerative clustering methods provide transparent means of separating fracture groups on the basis of their orientation. Fracture spacing is calculated for each of these fracture sets. Maximum likelihood estimation using truncated distributions permits the fitting of several probability distributions to the fracture attribute data sets within truncation limits, which can then be extrapolated over the entire range where they naturally occur. Akaike Information Criterion (AIC) and Schwartz Bayesian Criterion (SBC) statistical information criteria rank the distributions by how well they fit the data. We demonstrate these attribute analysis methods with a data set derived from three BHTV logs acquired from the high-temperature Rotokawa geothermal field, New Zealand. Varying BHTV log quality reduces the number of input data points, but careful selection of the quality levels where fractures are deemed fully sampled increases the reliability of the analysis. Spacing data analysis comprising up to 300 data points and spanning three orders of magnitude can be approximated similarly well (similar AIC rankings) with several distributions. Several clustering configurations and probability distributions can often characterize the data at similar levels of statistical criteria. Thus, several scenarios should be considered when using BHTV log data to constrain numerical fracture models.

Tripartite-to-Bipartite Entanglement Transformation by Stochastic Local Operations and Classical Communication and the Structure of Matrix Spaces

NASA Astrophysics Data System (ADS)

Li, Yinan; Qiao, Youming; Wang, Xin; Duan, Runyao

2018-03-01

We study the problem of transforming a tripartite pure state to a bipartite one using stochastic local operations and classical communication (SLOCC). It is known that the tripartite-to-bipartite SLOCC convertibility is characterized by the maximal Schmidt rank of the given tripartite state, i.e. the largest Schmidt rank over those bipartite states lying in the support of the reduced density operator. In this paper, we further study this problem and exhibit novel results in both multi-copy and asymptotic settings, utilizing powerful results from the structure of matrix spaces. In the multi-copy regime, we observe that the maximal Schmidt rank is strictly super-multiplicative, i.e. the maximal Schmidt rank of the tensor product of two tripartite pure states can be strictly larger than the product of their maximal Schmidt ranks. We then provide a full characterization of those tripartite states whose maximal Schmidt rank is strictly super-multiplicative when taking tensor product with itself. Notice that such tripartite states admit strict advantages in tripartite-to-bipartite SLOCC transformation when multiple copies are provided. In the asymptotic setting, we focus on determining the tripartite-to-bipartite SLOCC entanglement transformation rate. Computing this rate turns out to be equivalent to computing the asymptotic maximal Schmidt rank of the tripartite state, defined as the regularization of its maximal Schmidt rank. Despite the difficulty caused by the super-multiplicative property, we provide explicit formulas for evaluating the asymptotic maximal Schmidt ranks of two important families of tripartite pure states by resorting to certain results of the structure of matrix spaces, including the study of matrix semi-invariants. These formulas turn out to be powerful enough to give a sufficient and necessary condition to determine whether a given tripartite pure state can be transformed to the bipartite maximally entangled state under SLOCC, in the asymptotic setting. Applying the recent progress on the non-commutative rank problem, we can verify this condition in deterministic polynomial time.

An ensemble rank learning approach for gene prioritization.

PubMed

Lee, Po-Feng; Soo, Von-Wun

2013-01-01

Several different computational approaches have been developed to solve the gene prioritization problem. We intend to use the ensemble boosting learning techniques to combine variant computational approaches for gene prioritization in order to improve the overall performance. In particular we add a heuristic weighting function to the Rankboost algorithm according to: 1) the absolute ranks generated by the adopted methods for a certain gene, and 2) the ranking relationship between all gene-pairs from each prioritization result. We select 13 known prostate cancer genes in OMIM database as training set and protein coding gene data in HGNC database as test set. We adopt the leave-one-out strategy for the ensemble rank boosting learning. The experimental results show that our ensemble learning approach outperforms the four gene-prioritization methods in ToppGene suite in the ranking results of the 13 known genes in terms of mean average precision, ROC and AUC measures.

Risk assessment and risk management at the Canadian Food Inspection Agency (CFIA): a perspective on the monitoring of foods for chemical residues.

PubMed

Bietlot, Henri P; Kolakowski, Beata

2012-08-01

The Canadian Food Inspection Agency (CFIA) uses 'Ranked Risk Assessment' (RRA) to prioritize chemical hazards for inclusion in monitoring programmes or method development projects based on their relative risk. The relative risk is calculated for a chemical by scoring toxicity and exposure in the 'risk model scoring system' of the Risk Priority Compound List (RPCL). The relative ranking and the risk management options are maintained and updated in the RPCL. The ranking may be refined by the data generated by the sampling and testing programs. The two principal sampling and testing programmes are the National Chemical Residue Monitoring Program (NCRMP) and the Food Safety Action Plan (FSAP). The NCRMP sampling plans focus on the analysis of federally registered products (dairy, eggs, honey, meat and poultry, fresh and processed fruit and vegetable commodities, and maple syrup) for residues of veterinary drugs, pesticides, environmental contaminants, mycotoxins, and metals. The NCRMP is complemented by the Food Safety Action Plan (FSAP) targeted surveys. These surveys focus on emerging chemical hazards associated with specific foods or geographical regions for which applicable maximum residue limits (MRLs) are not set. The data from the NCRMP and FSAP also influence the risk management (follow-up) options. Follow-up actions vary according to the magnitude of the health risk, all with the objective of preventing any repeat occurrence to minimize consumer exposure to a product representing a potential risk to human health. © Her Majesty the Queen in Right of Canada 2012. Drug Testing and Analysis © 2012 John Wiley & Sons, Ltd.

The Steady State Great Ape? Long Term Isotopic Records Reveal the Effects of Season, Social Rank and Reproductive Status on Bonobo Feeding Behavior

PubMed Central

Oelze, Vicky M.; Douglas, Pamela Heidi; Stephens, Colleen R.; Surbeck, Martin; Behringer, Verena; Richards, Michael P.; Fruth, Barbara; Hohmann, Gottfried

2016-01-01

Dietary ecology of extant great apes is known to respond to environmental conditions such as climate and food availability, but also to vary depending on social status and life history characteristics. Bonobos (Pan paniscus) live under comparatively steady ecological conditions in the evergreen rainforests of the Congo Basin. Bonobos are an ideal species for investigating influences of sociodemographic and physiological factors, such as female reproductive status, on diet. We investigate the long term dietary pattern in wild but fully habituated bonobos by stable isotope analysis in hair and integrating a variety of long-term sociodemographic information obtained through observations. We analyzed carbon and nitrogen stable isotopes in 432 hair sections obtained from 101 non-invasively collected hair samples. These samples represented the dietary behavior of 23 adult bonobos from 2008 through 2010. By including isotope and crude protein data from plants we could establish an isotope baseline and interpret the results of several general linear mixed models using the predictors climate, sex, social rank, reproductive state of females, adult age and age of infants. We found that low canopy foliage is a useful isotopic tracer for tropical rainforest settings, and consumption of terrestrial herbs best explains the temporal isotope patterns we found in carbon isotope values of bonobo hair. Only the diet of male bonobos was affected by social rank, with lower nitrogen isotope values in low-ranking young males. Female isotope values mainly differed between different stages of reproduction (cycling, pregnancy, lactation). These isotopic differences appear to be related to changes in dietary preference during pregnancy (high protein diet) and lactation (high energy diet), which allow to compensate for different nutritional needs during maternal investment. PMID:27626279

Harnessing data structure for recovery of randomly missing structural vibration responses time history: Sparse representation versus low-rank structure

NASA Astrophysics Data System (ADS)

Yang, Yongchao; Nagarajaiah, Satish

2016-06-01

Randomly missing data of structural vibration responses time history often occurs in structural dynamics and health monitoring. For example, structural vibration responses are often corrupted by outliers or erroneous measurements due to sensor malfunction; in wireless sensing platforms, data loss during wireless communication is a common issue. Besides, to alleviate the wireless data sampling or communication burden, certain accounts of data are often discarded during sampling or before transmission. In these and other applications, recovery of the randomly missing structural vibration responses from the available, incomplete data, is essential for system identification and structural health monitoring; it is an ill-posed inverse problem, however. This paper explicitly harnesses the data structure itself-of the structural vibration responses-to address this (inverse) problem. What is relevant is an empirical, but often practically true, observation, that is, typically there are only few modes active in the structural vibration responses; hence a sparse representation (in frequency domain) of the single-channel data vector, or, a low-rank structure (by singular value decomposition) of the multi-channel data matrix. Exploiting such prior knowledge of data structure (intra-channel sparse or inter-channel low-rank), the new theories of ℓ1-minimization sparse recovery and nuclear-norm-minimization low-rank matrix completion enable recovery of the randomly missing or corrupted structural vibration response data. The performance of these two alternatives, in terms of recovery accuracy and computational time under different data missing rates, is investigated on a few structural vibration response data sets-the seismic responses of the super high-rise Canton Tower and the structural health monitoring accelerations of a real large-scale cable-stayed bridge. Encouraging results are obtained and the applicability and limitation of the presented methods are discussed.

JOINT AND INDIVIDUAL VARIATION EXPLAINED (JIVE) FOR INTEGRATED ANALYSIS OF MULTIPLE DATA TYPES.

PubMed

Lock, Eric F; Hoadley, Katherine A; Marron, J S; Nobel, Andrew B

2013-03-01

Research in several fields now requires the analysis of datasets in which multiple high-dimensional types of data are available for a common set of objects. In particular, The Cancer Genome Atlas (TCGA) includes data from several diverse genomic technologies on the same cancerous tumor samples. In this paper we introduce Joint and Individual Variation Explained (JIVE), a general decomposition of variation for the integrated analysis of such datasets. The decomposition consists of three terms: a low-rank approximation capturing joint variation across data types, low-rank approximations for structured variation individual to each data type, and residual noise. JIVE quantifies the amount of joint variation between data types, reduces the dimensionality of the data, and provides new directions for the visual exploration of joint and individual structure. The proposed method represents an extension of Principal Component Analysis and has clear advantages over popular two-block methods such as Canonical Correlation Analysis and Partial Least Squares. A JIVE analysis of gene expression and miRNA data on Glioblastoma Multiforme tumor samples reveals gene-miRNA associations and provides better characterization of tumor types.

Label Information Guided Graph Construction for Semi-Supervised Learning.

PubMed

Zhuang, Liansheng; Zhou, Zihan; Gao, Shenghua; Yin, Jingwen; Lin, Zhouchen; Ma, Yi

2017-09-01

In the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation. This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.

UQlust: combining profile hashing with linear-time ranking for efficient clustering and analysis of big macromolecular data.

PubMed

Adamczak, Rafal; Meller, Jarek

2016-12-28

Advances in computing have enabled current protein and RNA structure prediction and molecular simulation methods to dramatically increase their sampling of conformational spaces. The quickly growing number of experimentally resolved structures, and databases such as the Protein Data Bank, also implies large scale structural similarity analyses to retrieve and classify macromolecular data. Consequently, the computational cost of structure comparison and clustering for large sets of macromolecular structures has become a bottleneck that necessitates further algorithmic improvements and development of efficient software solutions. uQlust is a versatile and easy-to-use tool for ultrafast ranking and clustering of macromolecular structures. uQlust makes use of structural profiles of proteins and nucleic acids, while combining a linear-time algorithm for implicit comparison of all pairs of models with profile hashing to enable efficient clustering of large data sets with a low memory footprint. In addition to ranking and clustering of large sets of models of the same protein or RNA molecule, uQlust can also be used in conjunction with fragment-based profiles in order to cluster structures of arbitrary length. For example, hierarchical clustering of the entire PDB using profile hashing can be performed on a typical laptop, thus opening an avenue for structural explorations previously limited to dedicated resources. The uQlust package is freely available under the GNU General Public License at https://github.com/uQlust . uQlust represents a drastic reduction in the computational complexity and memory requirements with respect to existing clustering and model quality assessment methods for macromolecular structure analysis, while yielding results on par with traditional approaches for both proteins and RNAs.

Ranking independent timber investments by alternative investment criteria

Treesearch

Thomas J. Mills; Gary E. Dixon

1982-01-01

A sample of 231 independent timber investments were ranked by internal rate of return, present net worth per acre and the benefit cost ratio—the last two discounted by 3, 6.4. 7.5. and 10 percent—to determine if the different criteria had a practical influence on timber investment ranking. The samples in this study were drawn from a group of timber investments...

Complex sources of variance in female dominance rank in a nepotistic society

PubMed Central

Lea, Amanda J.; Learn, Niki H.; Theus, Marcus J.; Altmann, Jeanne; Alberts, Susan C.

2016-01-01

Many mammalian societies are structured by dominance hierarchies, and an individual’s position within this hierarchy can influence reproduction, behaviour, physiology and health. In nepotistic hierarchies, which are common in cercopithecine primates and also seen in spotted hyaenas, Crocuta crocuta, adult daughters are expected to rank immediately below their mother, and in reverse age order (a phenomenon known as ‘youngest ascendancy’). This pattern is well described, but few studies have systematically examined the frequency or causes of departures from the expected pattern. Using a longitudinal data set from a natural population of yellow baboons, Papio cynocephalus, we measured the influence of maternal kin, paternal kin and group size on female rank positions at two life history milestones, menarche and first live birth. At menarche, most females (73%) ranked adjacent to their family members (i.e. the female held an ordinal rank in consecutive order with other members of her maternal family); however, only 33% of females showed youngest ascendancy within their matriline at menarche. By the time they experienced their first live birth, many females had improved their dominance rank: 78% ranked adjacent to their family members and 49% showed youngest ascendancy within their matriline. The presence of mothers and maternal sisters exerted a powerful influence on rank outcomes. However, the presence of fathers, brothers and paternal siblings did not produce a clear effect on female dominance rank in our analyses, perhaps because females in our data set co-resided with variable numbers and types of paternal and male relatives. Our results also raise the possibility that female body size or competitive ability may influence dominance rank, even in this classically nepotistic species. In total, our analyses reveal that the predictors of dominance rank in nepotistic rank systems are much more complex than previously thought. PMID:26997663

Multi-wavelength HPLC fingerprints from complex substances: An exploratory chemometrics study of the Cassia seed example.

PubMed

Ni, Yongnian; Lai, Yanhua; Brandes, Sarina; Kokot, Serge

2009-08-11

Multi-wavelength fingerprints of Cassia seed, a traditional Chinese medicine (TCM), were collected by high-performance liquid chromatography (HPLC) at two wavelengths with the use of diode array detection. The two data sets of chromatograms were combined by the data fusion-based method. This data set of fingerprints was compared separately with the two data sets collected at each of the two wavelengths. It was demonstrated with the use of principal component analysis (PCA), that multi-wavelength fingerprints provided a much improved representation of the differences in the samples. Thereafter, the multi-wavelength fingerprint data set was submitted for classification to a suite of chemometrics methods viz. fuzzy clustering (FC), SIMCA and the rank ordering MCDM PROMETHEE and GAIA. Each method highlighted different properties of the data matrix according to the fingerprints from different types of Cassia seeds. In general, the PROMETHEE and GAIA MCDM methods provided the most comprehensive information for matching and discrimination of the fingerprints, and appeared to be best suited for quality assurance purposes for these and similar types of sample.

Rank Weighting in Multiattribute Utility Decision Making: Avoiding the Pitfalls of Equal Weights.

DTIC Science & Technology

1979-09-01

set change are discussed in relation to the conditions of Wainer’s (Wainer, 1976) ’ equal weights theorem’ and the resulting sensitivity to weighting of...as equal weights. Rank weighting of importance dimensions demonstrate marked improvement of approximation as reflected in both Pearson and rank order

Ranking Accounting Authors and Departments in Accounting Education: Different Methodologies--Significantly Different Results

ERIC Educational Resources Information Center

Bernardi, Richard A.; Zamojcin, Kimberly A.; Delande, Taylor L.

2016-01-01

This research tests whether Holderness Jr., D. K., Myers, N., Summers, S. L., & Wood, D. A. [(2014). "Accounting education research: Ranking institutions and individual scholars." "Issues in Accounting Education," 29(1), 87-115] accounting-education rankings are sensitive to a change in the set of journals used. It provides…

Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network.

PubMed

Chin, Wei-Chien-Benny; Wen, Tzai-Hung

2015-01-01

A network approach, which simplifies geographic settings as a form of nodes and links, emphasizes the connectivity and relationships of spatial features. Topological networks of spatial features are used to explore geographical connectivity and structures. The PageRank algorithm, a network metric, is often used to help identify important locations where people or automobiles concentrate in the geographical literature. However, geographic considerations, including proximity and location attractiveness, are ignored in most network metrics. The objective of the present study is to propose two geographically modified PageRank algorithms-Distance-Decay PageRank (DDPR) and Geographical PageRank (GPR)-that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network. Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics. In comparing location attractiveness and distance decay, we conclude that the concentration of human movement is largely determined by the distance decay. This implies that geographic proximity remains a key factor in human mobility.

Image Re-Ranking Based on Topic Diversity.

PubMed

Qian, Xueming; Lu, Dan; Wang, Yaxiong; Zhu, Li; Tang, Yuan Yan; Wang, Meng

2017-08-01

Social media sharing Websites allow users to annotate images with free tags, which significantly contribute to the development of the web image retrieval. Tag-based image search is an important method to find images shared by users in social networks. However, how to make the top ranked result relevant and with diversity is challenging. In this paper, we propose a topic diverse ranking approach for tag-based image retrieval with the consideration of promoting the topic coverage performance. First, we construct a tag graph based on the similarity between each tag. Then, the community detection method is conducted to mine the topic community of each tag. After that, inter-community and intra-community ranking are introduced to obtain the final retrieved results. In the inter-community ranking process, an adaptive random walk model is employed to rank the community based on the multi-information of each topic community. Besides, we build an inverted index structure for images to accelerate the searching process. Experimental results on Flickr data set and NUS-Wide data sets show the effectiveness of the proposed approach.

The singularity structure of scale-invariant rank-2 Coulomb branches

NASA Astrophysics Data System (ADS)

Argyres, Philip C.; Long, Cody; Martone, Mario

2018-05-01

We compute the spectrum of scaling dimensions of Coulomb branch operators in 4d rank-2 N=2 superconformal field theories. Only a finite rational set of scaling dimensions is allowed. It is determined by using information about the global topology of the locus of metric singularities on the Coulomb branch, the special Kähler geometry near those singularities, and electric-magnetic duality monodromies along orbits of the U(1) R symmetry. A set of novel topological and geometric results are developed which promise to be useful for the study and classification of Coulomb branch geometries at all ranks.

Role of RANK and Akt1 activation in human osteosarcoma progression: A clinicopathological study.

PubMed

Zhu, Jianxi; Liu, Yuwei; Zhu, Yong; Zeng, Min; Xie, Jie; Lei, Pengfei; Li, Kanghua; Hu, Yihe

2017-06-01

The receptor activator of nuclear factor κB (RANK) axis is the fundamental signaling pathway in bone formation as well as bone tumor pathophysiology. The aim of the present study was to evaluate the impact of the expression of RANK and its downstream signaling molecule Akt1 on tumor progression in patients with osteosarcoma. Expression of RANK and Akt1 was examined in 78 human osteosarcoma samples by immunohistochemistry using formalin-fixed samples. Following this, each graded immunohistochemistry result was correlated with clinicopathological parameters and patient survival. In total, 60 osteosarcomas (76.9%) expressed RANK and 58 cases (74.4%) showed expression of Akt1. In addition, expression of RANK was negatively correlated with disease-free survival by Kaplan-Meier analysis. A resistance was observed to chemotherapy in RANK-expressing cases, which was statistically significant (P<0.05). In addition, chemotherapy and staging of the tumor were found to independent factors that have an effect on patient survival (P<0.05). Thus, RANK was identified as a negative prognostic factor of osteosarcoma survival.

Nongeneric positive partial transpose states of rank five in 3×3 dimensions

NASA Astrophysics Data System (ADS)

Hansen, Leif Ove; Myrheim, Jan

In 3×3 dimensions, entangled mixed states that are positive under partial transposition (PPT states) must have rank at least four. These rank four states are completely understood. We say that they have rank (4,4) since both a state ρ and its partial transpose ρP have rank four. The next problem is to understand the extremal PPT states of rank (5,5). We call two states SL⊗SL-equivalent if they are related by a product transformation. A generic rank (5,5) PPT state ρ is extremal, and both ρ and ρP have six product vectors in their ranges, and no product vectors in their kernels. The three numbers {6,6;0} are SL⊗SL-invariants that help us classify the state. There is no analytical understanding of such states. We have studied numerically a few types of nongeneric rank five PPT states, in particular, states with one or more product vectors in their kernels. We find an interesting new analytical construction of all rank four extremal PPT states, up to SL⊗SL-equivalence, where they appear as boundary states on one single five-dimensional face on the set of normalized PPT states. The interior of the face consists of rank (5,5) states with four common product vectors in their kernels, it is a simplex of separable states surrounded by entangled PPT states. We say that a state ρ is SL⊗SL-symmetric if ρ and ρP are SL⊗SL-equivalent, and is genuinely SL⊗SL-symmetric if it is SL⊗SL-equivalent to a state τ with τ=τP. Genuine SL⊗SL-symmetry implies a special form of SL⊗SL-symmetry. We have produced numerically, by a special method, a random sample of rank (5,5) SL⊗SL-symmetric states. About 50 of these are of type {6,6;0}, among those all are extremal and about half are genuinely SL⊗SL-symmetric. All these genuinely SL⊗SL-symmetric states can be transformed to have a circulant form. We find however that this is not a generic property of genuinely SL⊗SL-symmetric states. The remaining SL⊗SL-symmetric states found in the search have product vectors in their kernels, and they inspired us to study such states without regard to SL⊗SL-symmetry.

Comparison of Chinese and US orthodontists' averaged evaluations of "facial attractiveness" from end-of-treatment facial photographs.

PubMed

Liu, Yan; Korn, Edward L; Oh, Hee Soo; Pearson, Helmer; Xu, Tian-Min; Baumrind, Sheldon

2009-05-01

This study continues our assessment of agreement and disagreement among 25 Chinese and 20 US orthodontists in the ranking for facial attractiveness of end-of-treatment photographs of randomly sampled growing Chinese and white orthodontic patients. The main aims of this article were to (1) measure the overall pattern of agreement between the mean rankings of US and Chinese orthodontists, and (2) measure the strength of agreement between the rankings of the US and Chinese orthodontists for each patient. Each judge independently ranked standard clinical sets of profile, frontal, and frontal-smiling photographs of 43 US patients and 48 Chinese patients. For each patient, a separate mean rank was computed from the responses of each group of judges. Pearson correlations between the mean ranks of the 2 groups of judges were used to measure their overall agreement. Paired and unpaired t tests were used to measure the agreement between the judges of the 2 groups for each patient. The overall agreement between the mean rankings of the US and Chinese judges was very high. For the US patients, the correlation between the Chinese and US judges means was r = 0.92, P <0.0001. For the Chinese patients, the analogous value was r = 0.86, P <0.0001. Agreement between the 2 groups of judges concerning each patient was also generally strong. For two thirds of the patients, the mean ranks of the US and Chinese judges differed by less than 1 unit in a scale of 12. However, for 6 patients considered individually (5 Chinese and 1 US), the assessment of the 2 groups of judges was statistically significantly different at P values ranging from 0.02 to less than 0.0001, even after the Bonferroni correction. These findings demonstrate that orthodontic clinicians can reliably identify and rank subtle differences between patients, and that differences between judges and between patients can be distinguished at a high level of statistical significance, given appropriate study designs. However, the reasons clinicians give for the differences in their judgments are more difficult to investigate and will require further study.

Effects of parceling on model selection: Parcel-allocation variability in model ranking.

PubMed

Sterba, Sonya K; Rights, Jason D

2017-03-01

Research interest often lies in comparing structural model specifications implying different relationships among latent factors. In this context parceling is commonly accepted, assuming the item-level measurement structure is well known and, conservatively, assuming items are unidimensional in the population. Under these assumptions, researchers compare competing structural models, each specified using the same parcel-level measurement model. However, little is known about consequences of parceling for model selection in this context-including whether and when model ranking could vary across alternative item-to-parcel allocations within-sample. This article first provides a theoretical framework that predicts the occurrence of parcel-allocation variability (PAV) in model selection index values and its consequences for PAV in ranking of competing structural models. These predictions are then investigated via simulation. We show that conditions known to manifest PAV in absolute fit of a single model may or may not manifest PAV in model ranking. Thus, one cannot assume that low PAV in absolute fit implies a lack of PAV in ranking, and vice versa. PAV in ranking is shown to occur under a variety of conditions, including large samples. To provide an empirically supported strategy for selecting a model when PAV in ranking exists, we draw on relationships between structural model rankings in parcel- versus item-solutions. This strategy employs the across-allocation modal ranking. We developed software tools for implementing this strategy in practice, and illustrate them with an example. Even if a researcher has substantive reason to prefer one particular allocation, investigating PAV in ranking within-sample still provides an informative sensitivity analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

The Wilcoxon signed rank test for paired comparisons of clustered data.

PubMed

Rosner, Bernard; Glynn, Robert J; Lee, Mei-Ling T

2006-03-01

The Wilcoxon signed rank test is a frequently used nonparametric test for paired data (e.g., consisting of pre- and posttreatment measurements) based on independent units of analysis. This test cannot be used for paired comparisons arising from clustered data (e.g., if paired comparisons are available for each of two eyes of an individual). To incorporate clustering, a generalization of the randomization test formulation for the signed rank test is proposed, where the unit of randomization is at the cluster level (e.g., person), while the individual paired units of analysis are at the subunit within cluster level (e.g., eye within person). An adjusted variance estimate of the signed rank test statistic is then derived, which can be used for either balanced (same number of subunits per cluster) or unbalanced (different number of subunits per cluster) data, with an exchangeable correlation structure, with or without tied values. The resulting test statistic is shown to be asymptotically normal as the number of clusters becomes large, if the cluster size is bounded. Simulation studies are performed based on simulating correlated ranked data from a signed log-normal distribution. These studies indicate appropriate type I error for data sets with > or =20 clusters and a superior power profile compared with either the ordinary signed rank test based on the average cluster difference score or the multivariate signed rank test of Puri and Sen. Finally, the methods are illustrated with two data sets, (i) an ophthalmologic data set involving a comparison of electroretinogram (ERG) data in retinitis pigmentosa (RP) patients before and after undergoing an experimental surgical procedure, and (ii) a nutritional data set based on a randomized prospective study of nutritional supplements in RP patients where vitamin E intake outside of study capsules is compared before and after randomization to monitor compliance with nutritional protocols.

Comprehensive evaluation of long-term trends in occupational exposure: Part 1. Description of the database

PubMed Central

Symanski, E.; Kupper, L. L.; Rappaport, S. M.

1998-01-01

OBJECTIVES: To conduct a comprehensive evaluation of long term changes in occupational exposure among a broad cross section of industries worldwide. METHODS: A review of the scientific literature identified studies that reported historical changes in exposure. About 700 sets of data from 119 published and several unpublished sources were compiled. Data were published over a 30 year period in 25 journals that spanned a range of disciplines. For each data set, the average exposure level was compiled for each period and details on the contaminant, the industry and location, changes in the threshold limit value (TLV), as well as the type of sampling method were recorded. Spearman rank correlation coefficients were used to identify monotonic changes in exposure over time and simple linear regression analyses were used to characterise trends in exposure. RESULTS: About 78% of the natural log transformed data showed linear trends towards lower exposure levels whereas 22% indicated increasing trends. (The Spearman rank correlation analyses produced a similar breakdown between exposures monotonically increasing or decreasing over time.) Although the rates of reduction for the data showing downward trends ranged from -1% to -62% per year, most exposures declined at rates between -4% and -14% per year (the interquartile range), with a median value of -8% per year. Exposures seemed to increase at rates that were slightly lower than those of exposures which have declined over time. Data sets that showed downward (versus upward) trends were influenced by several factors including type and carcinogenicity of the contaminant, type of monitoring, historical changes in the threshold limit values (TLVs), and period of sampling. CONCLUSIONS: This review supports the notion that occupational exposures are generally lower today than they were years or decades ago. However, such trends seem to have been affected by factors related to the contaminant, as well as to the period and type of sampling.   PMID:9764107

Teaching Gateways and Bridges To Rank Broadcast Messages for Educational Networks.

ERIC Educational Resources Information Center

Losee, Robert M., Jr.

Messages entering an educational information distribution network may be ranked for an ordered introduction into the network to maximize the timeliness of message arrivals over the set of users. Electronic mail, EDI documents, and broadcast news may be ranked by the users who choose to examine those messages of interest or benefit to themselves.…

A new Weyl-like tensor of geometric origin

NASA Astrophysics Data System (ADS)

Vishwakarma, Ram Gopal

2018-04-01

A set of new tensors of purely geometric origin have been investigated, which form a hierarchy. A tensor of a lower rank plays the role of the potential for the tensor of one rank higher. The tensors have interesting mathematical and physical properties. The highest rank tensor of the hierarchy possesses all the geometrical properties of the Weyl tensor.

Evaluation of the osteoclastogenic process associated with RANK / RANK-L / OPG in odontogenic myxomas

PubMed Central

González-Galván, María del Carmen; Mosqueda-Taylor, Adalberto; Bologna-Molina, Ronell; Setien-Olarra, Amaia; Marichalar-Mendia, Xabier; Aguirre-Urizar, José-Manuel

2018-01-01

Background Odontogenic myxoma (OM) is a benign intraosseous neoplasm that exhibits local aggressiveness and high recurrence rates. Osteoclastogenesis is an important phenomenon in the tumor growth of maxillary neoplasms. RANK (Receptor Activator of Nuclear Factor κappa B) is the signaling receptor of RANK-L (Receptor activator of nuclear factor kappa-Β ligand) that activates the osteoclasts. OPG (osteoprotegerin) is a decoy receptor for RANK-L that inhibits pro-osteoclastogenesis. The RANK / RANKL / OPG system participates in the regulation of osteolytic activity under normal conditions, and its alteration has been associated with greater bone destruction, and also with tumor growth. Objectives To analyze the immunohistochemical expression of OPG, RANK and RANK-L proteins in odontogenic myxomas (OMs) and their relationship with the tumor size. Material and Methods Eighteen OMs, 4 small (<3 cm) and 14 large (> 3cm) and 18 dental follicles (DF) that were included as control were studied by means of standard immunohistochemical procedure with RANK, RANKL and OPG antibodies. For the evaluation, 5 fields (40x) of representative areas of OM and DF were selected where the expression of each antibody was determined. Descriptive and comparative statistical analyses were performed with the obtained data. Results There are significant differences in the expression of RANK in OM samples as compared to DF (p = 0.022) and among the OMSs and OMLs (p = 0.032). Also a strong association is recognized in the expression of RANK-L and OPG in OM samples. Conclusions Activation of the RANK / RANK-L / OPG triad seems to be involved in the mechanisms of bone balance and destruction, as well as associated with tumor growth in odontogenic myxomas. Key words:Odontogenic myxoma, dental follicle, RANK, RANK-L, OPG, osteoclastogenesis. PMID:29680857

Extension of latin hypercube samples with correlated variables.

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

Hora, Stephen Curtis; Helton, Jon Craig; Sallaberry, Cedric J. PhD.

2006-11-01

A procedure for extending the size of a Latin hypercube sample (LHS) with rank correlated variables is described and illustrated. The extension procedure starts with an LHS of size m and associated rank correlation matrix C and constructs a new LHS of size 2m that contains the elements of the original LHS and has a rank correlation matrix that is close to the original rank correlation matrix C. The procedure is intended for use in conjunction with uncertainty and sensitivity analysis of computationally demanding models in which it is important to make efficient use of a necessarily limited number ofmore » model evaluations.« less

Kinematic evidence of satellite galaxy populations in the potential wells of first-ranked cluster galaxies

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

Cowie, L.L.; Hu, E.M.

1986-06-01

The velocities of 38 centrally positioned galaxies (r much less than 100 kpc) were measured relative to the velocity of the first-ranked galaxy in 14 rich clusters. Analysis of the velocity distribution function of this sample and of previous data shows that the population cannot be fit by a single Gaussian. An adequate fit is obtained if 60 percent of the objects lie in a Gaussian with sigma = 250 km/s and the remainder in a population with sigma = 1400 km/s. All previous data sets are individually consistent with this conclusion. This suggests that there is a bound populationmore » of galaxies in the potential well of the central galaxy in addition to the normal population of the cluster core. This is taken as supporting evidence for the galactic cannibalism model of cD galaxy formation. 14 references.« less

Kinematic evidence of satellite galaxy populations in the potential wells of first-ranked cluster galaxies

NASA Technical Reports Server (NTRS)

Cowie, L. L.; Hu, E. M.

1986-01-01

The velocities of 38 centrally positioned galaxies (r much less than 100 kpc) were measured relative to the velocity of the first-ranked galaxy in 14 rich clusters. Analysis of the velocity distribution function of this sample and of previous data shows that the population cannot be fit by a single Gaussian. An adequate fit is obtained if 60 percent of the objects lie in a Gaussian with sigma = 250 km/s and the remainder in a population with sigma = 1400 km/s. All previous data sets are individually consistent with this conclusion. This suggests that there is a bound population of galaxies in the potential well of the central galaxy in addition to the normal population of the cluster core. This is taken as supporting evidence for the galactic cannibalism model of cD galaxy formation.

Informed public preferences for electricity portfolios with CCS and other low-carbon technologies.

PubMed

Fleishman, Lauren A; De Bruin, Wändi Bruine; Morgan, M Granger

2010-09-01

Public perceptions of carbon capture and sequestration (CCS) and other low-carbon electricity-generating technologies may affect the feasibility of their widespread deployment. We asked a diverse sample of 60 participants recruited from community groups in Pittsburgh, Pennsylvania to rank 10 technologies (e.g., coal with CCS, natural gas, nuclear, various renewables, and energy efficiency), and seven realistic low-carbon portfolios composed of these technologies, after receiving comprehensive and carefully balanced materials that explained the costs and benefits of each technology. Rankings were obtained in small group settings as well as individually before and after the group discussions. The ranking exercise asked participants to assume that the U.S. Congress had mandated a reduction in carbon dioxide emissions from power plants to be built in the future. Overall, rankings suggest that participants favored energy efficiency, followed by nuclear power, integrated gasification combined-cycle coal with CCS and wind. The most preferred portfolio also included these technologies. We find that these informed members of the general public preferred diverse portfolios that contained CCS and nuclear over alternatives once they fully understood the benefits, cost, and limitations of each. The materials and approach developed for this study may also have value in educating members of the general public about the challenges of achieving a low-carbon energy future. © 2010 Society for Risk Analysis.

Strong smoker interest in 'setting an example to children' by quitting: national survey data.

PubMed

Thomson, George; Wilson, Nick; Weerasekera, Deepa; Edwards, Richard

2011-02-01

To further explore smoker views on reasons to quit. As part of the multi-country ITC Project, a national sample of 1,376 New Zealand adult (18+ years) smokers was surveyed in 2007/08. This sample included boosted sampling of Māori, Pacific and Asian New Zealanders. 'Setting an example to children' was given as 'very much' a reason to quit by 51%, compared to 45% giving personal health concerns. However, the 'very much' and 'somewhat' responses (combined) were greater for personal health (81%) than 'setting an example to children' (74%). Price was the third ranked reason (67%). In a multivariate analysis, women were significantly more likely to state that 'setting an example to children' was 'very much' or 'somewhat' a reason to quit; as were Māori, or Pacific compared to European; and those suffering financial stress. The relatively high importance of 'example to children' as a reason to quit is an unusual finding, and may have arisen as a result of social marketing campaigns encouraging cessation to protect families in New Zealand. The policy implications could include a need for a greater emphasis on social reasons (e.g. 'example to children'), in pack warnings, and in social marketing for smoking cessation. © 2011 The Authors. ANZJPH © 2010 Public Health Association of Australia.

Comparison of rapid descriptive sensory methodologies: Free-Choice Profiling, Flash Profile and modified Flash Profile.

PubMed

Liu, Jing; Bredie, Wender L P; Sherman, Emma; Harbertson, James F; Heymann, Hildegarde

2018-04-01

Rapid sensory methods have been developed as alternatives to traditional sensory descriptive analysis methods. Among them, Free-Choice Profiling (FCP) and Flash Profile (FP) are two that have been known for many years. The objectives of this work were to compare the rating-based FCP and ranking-based FP method; to evaluate the impact of adding adjustments to FP approach; to investigate the influence of the number of assessors on the outcome of modified FP. To achieve these aims, a conventional descriptive analysis (DA), FCP, FP and a modified version of FP were carried out. Red wines made by different grape maturity and ethanol concentration were used for sensory testing. This study showed that DA provided a more detailed and accurate information on products through a quantitative measure of the intensity of sensory attributes than FCP and FP. However, the panel hours for conducting DA were higher than that for rapid methods, and FP was even able to separate the samples to a higher degree than DA. When comparing FCP and FP, this study showed that the ranking-based FP provided a clearer separation of samples than rating-based FCP, but the latter was an easier task for most assessors. When restricting assessors on their use of attributes in FP, the sample space became clearer and the ranking task was simplified. The FP protocol with restricted attribute sets seems to be a promising approach for efficient screening of sensory properties in wine. When increasing the number of assessors from 10 to 20 for conducting the modified FP, the outcome tended to be slightly more stable, however, one should consider the degree of panel training when deciding the optimal number of assessors for conducting FP. Copyright © 2018 Elsevier Ltd. All rights reserved.

The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data

DOE PAGES

Liu, S.; Bremer, P. -T; Jayaraman, J. J.; ...

2016-06-04

Linear projections are one of the most common approaches to visualize high-dimensional data. Since the space of possible projections is large, existing systems usually select a small set of interesting projections by ranking a large set of candidate projections based on a chosen quality measure. However, while highly ranked projections can be informative, some lower ranked ones could offer important complementary information. Therefore, selection based on ranking may miss projections that are important to provide a global picture of the data. Here, the proposed work fills this gap by presenting the Grassmannian Atlas, a framework that captures the global structuresmore » of quality measures in the space of all projections, which enables a systematic exploration of many complementary projections and provides new insights into the properties of existing quality measures.« less

Stratification of co-evolving genomic groups using ranked phylogenetic profiles

PubMed Central

Freilich, Shiri; Goldovsky, Leon; Gottlieb, Assaf; Blanc, Eric; Tsoka, Sophia; Ouzounis, Christos A

2009-01-01

Background Previous methods of detecting the taxonomic origins of arbitrary sequence collections, with a significant impact to genome analysis and in particular metagenomics, have primarily focused on compositional features of genomes. The evolutionary patterns of phylogenetic distribution of genes or proteins, represented by phylogenetic profiles, provide an alternative approach for the detection of taxonomic origins, but typically suffer from low accuracy. Herein, we present rank-BLAST, a novel approach for the assignment of protein sequences into genomic groups of the same taxonomic origin, based on the ranking order of phylogenetic profiles of target genes or proteins across the reference database. Results The rank-BLAST approach is validated by computing the phylogenetic profiles of all sequences for five distinct microbial species of varying degrees of phylogenetic proximity, against a reference database of 243 fully sequenced genomes. The approach - a combination of sequence searches, statistical estimation and clustering - analyses the degree of sequence divergence between sets of protein sequences and allows the classification of protein sequences according to the species of origin with high accuracy, allowing taxonomic classification of 64% of the proteins studied. In most cases, a main cluster is detected, representing the corresponding species. Secondary, functionally distinct and species-specific clusters exhibit different patterns of phylogenetic distribution, thus flagging gene groups of interest. Detailed analyses of such cases are provided as examples. Conclusion Our results indicate that the rank-BLAST approach can capture the taxonomic origins of sequence collections in an accurate and efficient manner. The approach can be useful both for the analysis of genome evolution and the detection of species groups in metagenomics samples. PMID:19860884

Air- and Dustborne Mycoflora in Houses Free of Water Damage and Fungal Growth

PubMed Central

Horner, W. Elliott; Worthan, Anthony G.; Morey, Philip R.

2004-01-01

Typically, studies on indoor fungal growth in buildings focus on structures with known or suspected water damage, moisture, and/or indoor fungal growth problems. Reference information on types of culturable fungi and total fungal levels are generally not available for buildings without these problems. This study assessed 50 detached single-family homes in metropolitan Atlanta, Ga., to establish a baseline of “normal and typical” types and concentrations of airborne and dustborne fungi in urban homes which were predetermined not to have noteworthy moisture problems or indoor fungal growth. Each home was visually examined, and samples of indoor and outdoor air and of indoor settled dust were taken in winter and summer. The results showed that rankings by prevalence and abundance of the types of airborne and dustborne fungi did not differ from winter to summer, nor did these rankings differ when air samples taken indoors were compared with those taken outdoors. Water indicator fungi were essentially absent from both air and dust samples. The air and dust data sets were also examined specifically for the proportions of colonies from ecological groupings such as leaf surface fungi and soil fungi. In the analysis of dust for culturable fungal colonies, leaf surface fungi constituted a considerable portion (>20%) of the total colonies in at least 85% of the samples. Thus, replicate dust samples with less than 20% of colonies from leaf surface fungi are unlikely to be from buildings free of moisture or mold growth problems. PMID:15528497

Characteristics of good quality pharmaceutical services common to community pharmacies and dispensing general practices.

PubMed

Grey, Elisabeth; Harris, Michael; Rodham, Karen; Weiss, Marjorie C

2016-10-01

In the United Kingdom, pharmaceutical services can be delivered by both community pharmacies (CPs) and dispensing doctor practices (DPs). Both must adhere to minimum standards set out in NHS regulations; however, no common framework exists to guide quality improvement. Previous phases of this research had developed a set of characteristics indicative of good pharmaceutical service provision. To ask key stakeholders to confirm, and rank the importance of, a set of characteristics of good pharmaceutical service provision. A two-round Delphi-type survey was conducted in south-west England and was sent to participants representing three stakeholder groups: DPs, CPs and patients/lay members. Participants were asked to confirm, and rank, the importance of these characteristics as representing good quality pharmaceutical services. Thirty people were sent the first round survey; 22 participants completed both rounds. Median ratings for the 23 characteristics showed that all were seen to represent important aspects of pharmaceutical service provision. Participants' comments highlighted potential problems with the practicality of the characteristics. Characteristics relating to patient safety were deemed to be the most important and those relating to public health the least important. A set of 23 characteristics for providing good pharmaceutical services in CPs and DPs was developed and attained approval from a sample of stakeholders. With further testing and wider discussion, it is hoped that the characteristics will form the basis of a quality improvement tool for CPs and DPs. © 2016 Royal Pharmaceutical Society.

An Organic Geochemical Assessment of CO2-Coal Interactions During Sequestration

USGS Publications Warehouse

Kolak, Jonathan J.; Burruss, Robert A.

2003-01-01

Three well-characterized coal samples of varying rank were extracted with supercritical CO2 to determine the amount of polycyclic aromatic hydrocarbons (PAHs) that could be mobilized during simulated CO2 injection/sequestration in deep coal beds. The supercritical CO2 extractions were conducted at 40?C and 100 bars, roughly corresponding to a depth of 1 km. The greatest amount of PAHs was extracted from the high-volatile C bituminous coal sample. Extracts from the subbituminous C and anthracite coal samples contained lower concentrations of these compounds. The effectiveness of supercritical CO2 in liberating PAHs from the coal sample was evaluated in a comparison with a parallel series of Soxhlet extractions using 100% dichloromethane. More PAHs were extracted from the lower rank coal samples with dichloromethane than with supercritical CO2. The results from this investigation indicate that, regardless of coal rank, CO2 injection into deep coal beds may mobilize PAHs from the coal matrix. However, more PAHs could be mobilized during CO2 sequestration in a high-volatile C bituminous coal bed than in either of the other two coal ranks studied.

A novel feature extraction approach for microarray data based on multi-algorithm fusion

PubMed Central

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions. PMID:25780277

A novel feature extraction approach for microarray data based on multi-algorithm fusion.

PubMed

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions.

Low rank magnetic resonance fingerprinting.

PubMed

Mazor, Gal; Weizman, Lior; Tal, Assaf; Eldar, Yonina C

2016-08-01

Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI using randomized acquisition. Extraction of physical quantitative tissue values is preformed off-line, based on acquisition with varying parameters and a dictionary generated according to the Bloch equations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore high under-sampling ratio in the sampling domain (k-space) is required. This under-sampling causes spatial artifacts that hamper the ability to accurately estimate the quantitative tissue values. In this work, we introduce a new approach for quantitative MRI using MRF, called Low Rank MRF. We exploit the low rank property of the temporal domain, on top of the well-known sparsity of the MRF signal in the generated dictionary domain. We present an iterative scheme that consists of a gradient step followed by a low rank projection using the singular value decomposition. Experiments on real MRI data demonstrate superior results compared to conventional implementation of compressed sensing for MRF at 15% sampling ratio.

Recurrent fuzzy ranking methods

NASA Astrophysics Data System (ADS)

Hajjari, Tayebeh

2012-11-01

With the increasing development of fuzzy set theory in various scientific fields and the need to compare fuzzy numbers in different areas. Therefore, Ranking of fuzzy numbers plays a very important role in linguistic decision-making, engineering, business and some other fuzzy application systems. Several strategies have been proposed for ranking of fuzzy numbers. Each of these techniques has been shown to produce non-intuitive results in certain case. In this paper, we reviewed some recent ranking methods, which will be useful for the researchers who are interested in this area.

What Does Professional Rank Mean to Teachers? A Survey of the Multiple Impacts of Professional Rank on Urban and Rural Compulsory Education Teachers

ERIC Educational Resources Information Center

Yuyou, Qin; Wenjing, Zeng

2018-01-01

Professional rank is an important indicator of the professional capacity of compulsory education teachers. A rational professional rank evaluation system plays an important role in mobilizing the enthusiasm of teachers, improving the overall quality of teachers, and promoting the development of education. Based on stratified random sample data…

TopKube: A Rank-Aware Data Cube for Real-Time Exploration of Spatiotemporal Data.

PubMed

Miranda, Fabio; Lins, Lauro; Klosowski, James; Silva, Claudio

2017-02-17

From economics to sports to entertainment and social media, ranking objects according to some notion of importance is a fundamental tool we humans use all the time to better understand our world. With the ever-increasing amount of user-generated content found online, "what's trending" is now a commonplace phrase that tries to capture the zeitgeist of the world by ranking the most popular microblogging hashtags in a given region and time. However, before we can understand what these rankings tell us about the world, we need to be able to more easily create and explore them, given the significant scale of today's data. In this paper, we describe the computational challenges in building a real-time visual exploratory tool for finding top-ranked objects; build on the recent work involving in-memory and rank-aware data cubes to propose TOPKUBE: a data structure that answers top-k queries up to one order of magnitude faster than the previous state of the art; demonstrate the usefulness of our methods using a set of real-world, publicly available datasets; and provide a new set of benchmarks for other researchers to validate their methods and compare to our own.

Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network

PubMed Central

2015-01-01

A network approach, which simplifies geographic settings as a form of nodes and links, emphasizes the connectivity and relationships of spatial features. Topological networks of spatial features are used to explore geographical connectivity and structures. The PageRank algorithm, a network metric, is often used to help identify important locations where people or automobiles concentrate in the geographical literature. However, geographic considerations, including proximity and location attractiveness, are ignored in most network metrics. The objective of the present study is to propose two geographically modified PageRank algorithms—Distance-Decay PageRank (DDPR) and Geographical PageRank (GPR)—that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network. Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics. In comparing location attractiveness and distance decay, we conclude that the concentration of human movement is largely determined by the distance decay. This implies that geographic proximity remains a key factor in human mobility. PMID:26437000

Optimizing Estimated Loss Reduction for Active Sampling in Rank Learning

DTIC Science & Technology

2008-01-01

active learning framework for SVM-based and boosting-based rank learning. Our approach suggests sampling based on maximizing the estimated loss differential over unlabeled data. Experimental results on two benchmark corpora show that the proposed model substantially reduces the labeling effort, and achieves superior performance rapidly with as much as 30% relative improvement over the margin-based sampling

Joint analysis of multiple high-dimensional data types using sparse matrix approximations of rank-1 with applications to ovarian and liver cancer.

PubMed

Okimoto, Gordon; Zeinalzadeh, Ashkan; Wenska, Tom; Loomis, Michael; Nation, James B; Fabre, Tiphaine; Tiirikainen, Maarit; Hernandez, Brenda; Chan, Owen; Wong, Linda; Kwee, Sandi

2016-01-01

Technological advances enable the cost-effective acquisition of Multi-Modal Data Sets (MMDS) composed of measurements for multiple, high-dimensional data types obtained from a common set of bio-samples. The joint analysis of the data matrices associated with the different data types of a MMDS should provide a more focused view of the biology underlying complex diseases such as cancer that would not be apparent from the analysis of a single data type alone. As multi-modal data rapidly accumulate in research laboratories and public databases such as The Cancer Genome Atlas (TCGA), the translation of such data into clinically actionable knowledge has been slowed by the lack of computational tools capable of analyzing MMDSs. Here, we describe the Joint Analysis of Many Matrices by ITeration (JAMMIT) algorithm that jointly analyzes the data matrices of a MMDS using sparse matrix approximations of rank-1. The JAMMIT algorithm jointly approximates an arbitrary number of data matrices by rank-1 outer-products composed of "sparse" left-singular vectors (eigen-arrays) that are unique to each matrix and a right-singular vector (eigen-signal) that is common to all the matrices. The non-zero coefficients of the eigen-arrays identify small subsets of variables for each data type (i.e., signatures) that in aggregate, or individually, best explain a dominant eigen-signal defined on the columns of the data matrices. The approximation is specified by a single "sparsity" parameter that is selected based on false discovery rate estimated by permutation testing. Multiple signals of interest in a given MDDS are sequentially detected and modeled by iterating JAMMIT on "residual" data matrices that result from a given sparse approximation. We show that JAMMIT outperforms other joint analysis algorithms in the detection of multiple signatures embedded in simulated MDDS. On real multimodal data for ovarian and liver cancer we show that JAMMIT identified multi-modal signatures that were clinically informative and enriched for cancer-related biology. Sparse matrix approximations of rank-1 provide a simple yet effective means of jointly reducing multiple, big data types to a small subset of variables that characterize important clinical and/or biological attributes of the bio-samples from which the data were acquired.

Can a Simple Dietary Index Derived from a Sub-Set of Questionnaire Items Assess Diet Quality in a Sample of Australian Adults?

PubMed Central

Trapp, Georgina S. A.; Knuiman, Matthew; Hooper, Paula; Ambrosini, Gina L.

2018-01-01

Large, longitudinal surveys often lack consistent dietary data, limiting the use of existing tools and methods that are available to measure diet quality. This study describes a method that was used to develop a simple index for ranking individuals according to their diet quality in a longitudinal study. The RESIDential Environments (RESIDE) project (2004–2011) collected dietary data in varying detail, across four time points. The most detailed dietary data were collected using a 24-item questionnaire at the final time point (n = 555; age ≥ 25 years). At preceding time points, sub-sets of the 24 items were collected. A RESIDE dietary guideline index (RDGI) that was based on the 24-items was developed to assess diet quality in relation to the Australian Dietary Guidelines. The RDGI scores were regressed on the longitudinal sub-sets of six and nine questionnaire items at T4, from which two simple index scores (S-RDGI1 and S-RDGI2) were predicted. The S-RDGI1 and S-RDGI2 showed reasonable agreement with the RDGI (Spearman’s rho = 0.78 and 0.84; gross misclassification = 1.8%; correct classification = 64.9% and 69.7%; and, Cohen’s weighted kappa = 0.58 and 0.64, respectively). For all of the indices, higher diet quality was associated with being female, undertaking moderate to high amounts of physical activity, not smoking, and self-reported health. The S-RDGI1 and S-RDGI2 explained 62% and 73% of the variation in RDGI scores, demonstrating that a large proportion of the variability in diet quality scores can be captured using a relatively small sub-set of questionnaire items. The methods described in this study can be applied elsewhere, in situations where limited dietary data are available, to generate a sample-specific score for ranking individuals according to diet quality. PMID:29652828

Can a Simple Dietary Index Derived from a Sub-Set of Questionnaire Items Assess Diet Quality in a Sample of Australian Adults?

PubMed

Bivoltsis, Alexia; Trapp, Georgina S A; Knuiman, Matthew; Hooper, Paula; Ambrosini, Gina L

2018-04-13

Large, longitudinal surveys often lack consistent dietary data, limiting the use of existing tools and methods that are available to measure diet quality. This study describes a method that was used to develop a simple index for ranking individuals according to their diet quality in a longitudinal study. The RESIDential Environments (RESIDE) project (2004-2011) collected dietary data in varying detail, across four time points. The most detailed dietary data were collected using a 24-item questionnaire at the final time point ( n = 555; age ≥ 25 years). At preceding time points, sub-sets of the 24 items were collected. A RESIDE dietary guideline index (RDGI) that was based on the 24-items was developed to assess diet quality in relation to the Australian Dietary Guidelines. The RDGI scores were regressed on the longitudinal sub-sets of six and nine questionnaire items at T4, from which two simple index scores (S-RDGI1 and S-RDGI2) were predicted. The S-RDGI1 and S-RDGI2 showed reasonable agreement with the RDGI (Spearman's rho = 0.78 and 0.84; gross misclassification = 1.8%; correct classification = 64.9% and 69.7%; and, Cohen's weighted kappa = 0.58 and 0.64, respectively). For all of the indices, higher diet quality was associated with being female, undertaking moderate to high amounts of physical activity, not smoking, and self-reported health. The S-RDGI1 and S-RDGI2 explained 62% and 73% of the variation in RDGI scores, demonstrating that a large proportion of the variability in diet quality scores can be captured using a relatively small sub-set of questionnaire items. The methods described in this study can be applied elsewhere, in situations where limited dietary data are available, to generate a sample-specific score for ranking individuals according to diet quality.

Population models and simulation methods: The case of the Spearman rank correlation.

PubMed

Astivia, Oscar L Olvera; Zumbo, Bruno D

2017-11-01

The purpose of this paper is to highlight the importance of a population model in guiding the design and interpretation of simulation studies used to investigate the Spearman rank correlation. The Spearman rank correlation has been known for over a hundred years to applied researchers and methodologists alike and is one of the most widely used non-parametric statistics. Still, certain misconceptions can be found, either explicitly or implicitly, in the published literature because a population definition for this statistic is rarely discussed within the social and behavioural sciences. By relying on copula distribution theory, a population model is presented for the Spearman rank correlation, and its properties are explored both theoretically and in a simulation study. Through the use of the Iman-Conover algorithm (which allows the user to specify the rank correlation as a population parameter), simulation studies from previously published articles are explored, and it is found that many of the conclusions purported in them regarding the nature of the Spearman correlation would change if the data-generation mechanism better matched the simulation design. More specifically, issues such as small sample bias and lack of power of the t-test and r-to-z Fisher transformation disappear when the rank correlation is calculated from data sampled where the rank correlation is the population parameter. A proof for the consistency of the sample estimate of the rank correlation is shown as well as the flexibility of the copula model to encompass results previously published in the mathematical literature. © 2017 The British Psychological Society.

Inheritance of Properties of Normal and Non-Normal Distributions after Transformation of Scores to Ranks

ERIC Educational Resources Information Center

Zimmerman, Donald W.

2011-01-01

This study investigated how population parameters representing heterogeneity of variance, skewness, kurtosis, bimodality, and outlier-proneness, drawn from normal and eleven non-normal distributions, also characterized the ranks corresponding to independent samples of scores. When the parameters of population distributions from which samples were…

Quality of Selected Hungarian Coals

USGS Publications Warehouse

Landis, E.R.; Rohrbacher, T.J.; Gluskoter, H.J.; Fodor, B.; Gombar, G.

2007-01-01

As part of a program conducted jointly by the U.S. Geological Survey and the Hungarian Geological Survey under the auspices of the United States-Hungarian Science and Technology Fund, a total of 39 samples from five coal mines in Hungary were selected for analysis. The mine areas sampled represent most of the coal mined recently in Hungary. Almost all the coal is used to generate electricity. Coals from the five mines (four underground, one surface) reflect differences in age, depositional setting, organic and inorganic components of the original sediments, and deformational history. Classified according to the ranking system of the American Society for Testing and Materials, the coals range in rank from lignite B (Pliocene[?] coals) to high volatile A bituminous (Jurassic coals). With respect to grade classification, based on seam-weighted averages of moisture, ash, and sulfur contents: (1) all contain high moisture (more than 10 percent), (2) all except the Eocene coals are high (more than 15 percent) in ash yield, and (3) two (Jurassic and Eocene coals) are high in sulfur (more than 3 percent) and three (Cretaceous, Miocene, and Pliocene coals) have medium sulfur contents (1 to 3 percent). Average heat values range from 4,000 to 8,650 British thermal units per pound.

Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction.

PubMed

Sadowski, Peter; Fooshee, David; Subrahmanya, Niranjan; Baldi, Pierre

2016-11-28

Machine learning (ML) and quantum mechanical (QM) methods can be used in two-way synergy to build chemical reaction expert systems. The proposed ML approach identifies electron sources and sinks among reactants and then ranks all source-sink pairs. This addresses a bottleneck of QM calculations by providing a prioritized list of mechanistic reaction steps. QM modeling can then be used to compute the transition states and activation energies of the top-ranked reactions, providing additional or improved examples of ranked source-sink pairs. Retraining the ML model closes the loop, producing more accurate predictions from a larger training set. The approach is demonstrated in detail using a small set of organic radical reactions.

The Cantor-Bendixson Rank of Certain Bridgeland-Smith Stability Conditions

NASA Astrophysics Data System (ADS)

Aulicino, David

2018-01-01

We provide a novel proof that the set of directions that admit a saddle connection on a meromorphic quadratic differential with at least one pole of order at least two is closed, which generalizes a result of Bridgeland and Smith, and Gaiotto, Moore, and Neitzke. Secondly, we show that this set has finite Cantor-Bendixson rank and give a tight bound. Finally, we present a family of surfaces realizing all possible Cantor-Bendixson ranks. The techniques in the proof of this result exclusively concern Abelian differentials on Riemann surfaces, also known as translation surfaces. The concept of a "slit translation surface" is introduced as the primary tool for studying meromorphic quadratic differentials with higher order poles.

Optimization of the two-sample rank Neyman-Pearson detector

NASA Astrophysics Data System (ADS)

Akimov, P. S.; Barashkov, V. M.

1984-10-01

The development of optimal algorithms concerned with rank considerations in the case of finite sample sizes involves considerable mathematical difficulties. The present investigation provides results related to the design and the analysis of an optimal rank detector based on a utilization of the Neyman-Pearson criteria. The detection of a signal in the presence of background noise is considered, taking into account n observations (readings) x1, x2, ... xn in the experimental communications channel. The computation of the value of the rank of an observation is calculated on the basis of relations between x and the variable y, representing interference. Attention is given to conditions in the absence of a signal, the probability of the detection of an arriving signal, details regarding the utilization of the Neyman-Pearson criteria, the scheme of an optimal rank, multichannel, incoherent detector, and an analysis of the detector.

Jackknife Variance Estimator for Two Sample Linear Rank Statistics

DTIC Science & Technology

1988-11-01

Accesion For - - ,NTIS GPA&I "TIC TAB Unann c, nc .. [d Keywords: strong consistency; linear rank test’ influence function . i , at L By S- )Distribut...reverse if necessary and identify by block number) FIELD IGROUP SUB-GROUP Strong consistency; linear rank test; influence function . 19. ABSTRACT

Iterative random vs. Kennard-Stone sampling for IR spectrum-based classification task using PLS2-DA

NASA Astrophysics Data System (ADS)

Lee, Loong Chuen; Liong, Choong-Yeun; Jemain, Abdul Aziz

2018-04-01

External testing (ET) is preferred over auto-prediction (AP) or k-fold-cross-validation in estimating more realistic predictive ability of a statistical model. With IR spectra, Kennard-stone (KS) sampling algorithm is often used to split the data into training and test sets, i.e. respectively for model construction and for model testing. On the other hand, iterative random sampling (IRS) has not been the favored choice though it is theoretically more likely to produce reliable estimation. The aim of this preliminary work is to compare performances of KS and IRS in sampling a representative training set from an attenuated total reflectance - Fourier transform infrared spectral dataset (of four varieties of blue gel pen inks) for PLS2-DA modeling. The `best' performance achievable from the dataset is estimated with AP on the full dataset (APF, error). Both IRS (n = 200) and KS were used to split the dataset in the ratio of 7:3. The classic decision rule (i.e. maximum value-based) is employed for new sample prediction via partial least squares - discriminant analysis (PLS2-DA). Error rate of each model was estimated repeatedly via: (a) AP on full data (APF, error); (b) AP on training set (APS, error); and (c) ET on the respective test set (ETS, error). A good PLS2-DA model is expected to produce APS, error and EVS, error that is similar to the APF, error. Bearing that in mind, the similarities between (a) APS, error vs. APF, error; (b) ETS, error vs. APF, error and; (c) APS, error vs. ETS, error were evaluated using correlation tests (i.e. Pearson and Spearman's rank test), using series of PLS2-DA models computed from KS-set and IRS-set, respectively. Overall, models constructed from IRS-set exhibits more similarities between the internal and external error rates than the respective KS-set, i.e. less risk of overfitting. In conclusion, IRS is more reliable than KS in sampling representative training set.

Comparison and ranking of superelasticity of different austenite active nickel-titanium orthodontic archwires using mechanical tensile testing and correlating with its electrical resistivity

PubMed Central

Nagarajan, D.; Baskaranarayanan, Balashanmugam; Usha, K.; Jayanthi, M. S.; Vijjaykanth, M.

2016-01-01

Introduction: The application of light and continuous forces for optimum physiological response and the least damage to the tooth supporting structures should be the primary aim of an orthodontist. Nickel-titanium (NiTi) alloys with their desirable properties are one of the natural choices of the clinicians. Aim: This study was aimed to compare and rank them based on its tensile strength and electrical resistivity. Materials and Methods: The sample consisted of eight groups of 0.017 inch × 0.025 inch rectangular archwires from eight different manufacturers, and five samples from each group for tensile testing and nine samples for electrical resistivity tests were used. Data for stress at 10% strain and the initial slope were statistically analyzed with an analysis of variance and Scheffe tests with P < 0.05. The stress/strain plots of each product were ranked for superelastic behavior. The rankings of the wires tested were based primarily on the unloading curve's slope which is indicative of the magnitude of the deactivation force and secondarily on the length of the horizontal segment which is indicative of continuous forces during deactivation. For calculating the electric resistivity, the change in resistance after inducing strain in the wires was taken into account for the calculation of degree of martensite transformation and for ranking. Results: In tensile testing Ortho Organizers wires ranked first and GAC Lowland NiTi wires ranked last. For resistivity tests Ormco A wires were found superior and Morelli remained last. Conclusion: these rankings should be correlated clinically and need further studies. PMID:27829751

Geochemical investigation of the potential for mobilizing non-methane hydrocarbons during carbon dioxide storage in deep coal beds

USGS Publications Warehouse

Kolak, J.J.; Burruss, R.C.

2006-01-01

Coal samples of different rank (lignite to anthracite) were extracted in the laboratory with supercritical CO2 (40 ??C; 10 MPa) to evaluate the potential for mobilizing non-methane hydrocarbons during CO2 storage (sequestration) or enhanced coal bed methane recovery from deep (???1-km depth) coal beds. The total measured alkane concentrations mobilized from the coal samples ranged from 3.0 to 64 g tonne-1 of dry coal. The highest alkane concentration was measured in the lignite sample extract; the lowest was measured in the anthracite sample extract. Substantial concentrations of polycyclic aromatic hydrocarbons (PAHs) were also mobilized from these samples: 3.1 - 91 g tonne-1 of dry coal. The greatest amounts of PAHs were mobilized from the high-volatile bituminous coal samples. The distributions of aliphatic and aromatic hydrocarbons mobilized from the coal samples also varied with rank. In general, these variations mimicked the chemical changes that occur with increasing degrees of coalification and thermal maturation. For example, the amount of PAHs mobilized from coal samples paralleled the general trend of bitumen formation with increasing coal rank. The coal samples yielded hydrocarbons during consecutive extractions with supercritical CO2, although the amount of hydrocarbons mobilized declined with each successive extraction. These results demonstrate that the potential for supercritical CO2 to mobilize non-methane hydrocarbons from coal beds, and the effect of coal rank on this process, are important to consider when evaluating deep coal beds for CO2 storage.

Improving the Rank Precision of Population Health Measures for Small Areas with Longitudinal and Joint Outcome Models

PubMed Central

Athens, Jessica K.; Remington, Patrick L.; Gangnon, Ronald E.

2015-01-01

Objectives The University of Wisconsin Population Health Institute has published the County Health Rankings since 2010. These rankings use population-based data to highlight health outcomes and the multiple determinants of these outcomes and to encourage in-depth health assessment for all United States counties. A significant methodological limitation, however, is the uncertainty of rank estimates, particularly for small counties. To address this challenge, we explore the use of longitudinal and pooled outcome data in hierarchical Bayesian models to generate county ranks with greater precision. Methods In our models we used pooled outcome data for three measure groups: (1) Poor physical and poor mental health days; (2) percent of births with low birth weight and fair or poor health prevalence; and (3) age-specific mortality rates for nine age groups. We used the fixed and random effects components of these models to generate posterior samples of rates for each measure. We also used time-series data in longitudinal random effects models for age-specific mortality. Based on the posterior samples from these models, we estimate ranks and rank quartiles for each measure, as well as the probability of a county ranking in its assigned quartile. Rank quartile probabilities for univariate, joint outcome, and/or longitudinal models were compared to assess improvements in rank precision. Results The joint outcome model for poor physical and poor mental health days resulted in improved rank precision, as did the longitudinal model for age-specific mortality rates. Rank precision for low birth weight births and fair/poor health prevalence based on the univariate and joint outcome models were equivalent. Conclusion Incorporating longitudinal or pooled outcome data may improve rank certainty, depending on characteristics of the measures selected. For measures with different determinants, joint modeling neither improved nor degraded rank precision. This approach suggests a simple way to use existing information to improve the precision of small-area measures of population health. PMID:26098858

Expanding the landscape of $$ \\mathcal{N} $$ = 2 rank 1 SCFTs

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

Argyres, Philip C.; Lotito, Matteo; Lu, Yongchao

Here, we refine our previous proposal [1-3] for systematically classifying 4d rank-1 N = 2 SCFTs by constructing their possible Coulomb branch geometries. Four new recently discussed rank-1 theories [4, 5], including novel N = 3 SCFTs, sit beautifully in our refined classification framework. By arguing for the consistency of their RG flows we can make a strong case for the existence of at least four additional rank-1 SCFTs, nearly doubling the number of known rank-1 SCFTs. The refinement consists of relaxing the assumption that the flavor symmetries of the SCFTs have no discrete factors. This results in an enlargedmore » (but finite) set of possible rank-1 SCFTs. Their existence can be further constrained using consistency of their central charges and RG flows.« less

Expanding the landscape of $$ \\mathcal{N} $$ = 2 rank 1 SCFTs

DOE PAGES

Argyres, Philip C.; Lotito, Matteo; Lu, Yongchao; ...

2016-05-16

Here, we refine our previous proposal [1-3] for systematically classifying 4d rank-1 N = 2 SCFTs by constructing their possible Coulomb branch geometries. Four new recently discussed rank-1 theories [4, 5], including novel N = 3 SCFTs, sit beautifully in our refined classification framework. By arguing for the consistency of their RG flows we can make a strong case for the existence of at least four additional rank-1 SCFTs, nearly doubling the number of known rank-1 SCFTs. The refinement consists of relaxing the assumption that the flavor symmetries of the SCFTs have no discrete factors. This results in an enlargedmore » (but finite) set of possible rank-1 SCFTs. Their existence can be further constrained using consistency of their central charges and RG flows.« less

Measurement of empathy among Argentine cardiologists: Psychometrics and differences by age, gender, and subspecialty.

PubMed

Borracci, Raúl Alfredo; Doval, Hernán C; Nuñez, Carmen; Samarelli, Marisa; Tamini, Susana; Tanus, Eduardo

2015-01-01

Cardiologists are involved in the management of patients with multiple cardiovascular risk factors and chronic heart diseases, so empathy is a necessary feature to deal with them. The aim of the study was to evaluate the validity and reliability of the Spanish version of the Jefferson Scale of Physician Empathy (JSPE) among Argentine cardiologists and to explore the potential differences by age, gender, and subspecialty. Between August and September 2012, we performed a survey in a non-randomized sample of 566 Spanish-speaking cardiologists of Argentina. A Principle Component Analysis (PCA) was used to explore the link between observed variables and latent variables in order to identify the factor structure. The PCA criteria for identifying the factor structure were examined with the Kaiser-Meyer-Olkin (KMO) analysis. The KMO measure of sampling adequacy was 0.86 and Bartlett's test of sphericity was highly significant (p = 0.000), determining the suitability of the data set for factor analysis. The PCA of 20 items yielded a three factor model that accounted for 40.6% of the variance. The JSPE mean rank score for women was 307.9 vs. 275.0 for men (p = 0.017). The comparison of mean rank score according to age (quartiles) showed a significant relation between older age and empathy. No difference was found when the mean rank scores were compared by respondent subspecialty. JSPE provides a valid and reliable scale to measure Argentine cardiologists' attitudes towards empathy. Female cardiologists seem to be more empathic than their male colleagues, and a positive relationship between age and empathy was found.

Distributional fold change test – a statistical approach for detecting differential expression in microarray experiments

PubMed Central

2012-01-01

Background Because of the large volume of data and the intrinsic variation of data intensity observed in microarray experiments, different statistical methods have been used to systematically extract biological information and to quantify the associated uncertainty. The simplest method to identify differentially expressed genes is to evaluate the ratio of average intensities in two different conditions and consider all genes that differ by more than an arbitrary cut-off value to be differentially expressed. This filtering approach is not a statistical test and there is no associated value that can indicate the level of confidence in the designation of genes as differentially expressed or not differentially expressed. At the same time the fold change by itself provide valuable information and it is important to find unambiguous ways of using this information in expression data treatment. Results A new method of finding differentially expressed genes, called distributional fold change (DFC) test is introduced. The method is based on an analysis of the intensity distribution of all microarray probe sets mapped to a three dimensional feature space composed of average expression level, average difference of gene expression and total variance. The proposed method allows one to rank each feature based on the signal-to-noise ratio and to ascertain for each feature the confidence level and power for being differentially expressed. The performance of the new method was evaluated using the total and partial area under receiver operating curves and tested on 11 data sets from Gene Omnibus Database with independently verified differentially expressed genes and compared with the t-test and shrinkage t-test. Overall the DFC test performed the best – on average it had higher sensitivity and partial AUC and its elevation was most prominent in the low range of differentially expressed features, typical for formalin-fixed paraffin-embedded sample sets. Conclusions The distributional fold change test is an effective method for finding and ranking differentially expressed probesets on microarrays. The application of this test is advantageous to data sets using formalin-fixed paraffin-embedded samples or other systems where degradation effects diminish the applicability of correlation adjusted methods to the whole feature set. PMID:23122055

Rank-based testing of equal survivorship based on cross-sectional survival data with or without prospective follow-up.

PubMed

Chan, Kwun Chuen Gary; Qin, Jing

2015-10-01

Existing linear rank statistics cannot be applied to cross-sectional survival data without follow-up since all subjects are essentially censored. However, partial survival information are available from backward recurrence times and are frequently collected from health surveys without prospective follow-up. Under length-biased sampling, a class of linear rank statistics is proposed based only on backward recurrence times without any prospective follow-up. When follow-up data are available, the proposed rank statistic and a conventional rank statistic that utilizes follow-up information from the same sample are shown to be asymptotically independent. We discuss four ways to combine these two statistics when follow-up is present. Simulations show that all combined statistics have substantially improved power compared with conventional rank statistics, and a Mantel-Haenszel test performed the best among the proposal statistics. The method is applied to a cross-sectional health survey without follow-up and a study of Alzheimer's disease with prospective follow-up. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Influences for Gender Disparity in Academic Psychiatry in the United States.

PubMed

Sheikh, Muhammad H; Chaudhary, Amna Mohyud Din; Khan, Anum S; Tahir, Muhammad A; Yahya, Hafiz A; Naveed, Sadiq; Khosa, Faisal

2018-04-22

Introduction Academic undertakings, including research, lead to career progression. However, the career paths of female psychiatrists appear to diverge significantly from that of their male counterparts. This article reviews the pervasiveness of the trend of women being less likely to pursue active research in psychiatry. In addition, we examine the correlation between academic rank and research productivity. Methods We searched the American Medical Association's (AMA) Fellowship and Residency Electronic Interactive Database (FREIDA) to identify training programs for psychiatry. A total of 5234 psychiatrists met our inclusion criteria. The gender, academic rank, research work, and h-index of faculty members were compared. The ratio of women reaching senior ranks as compared to men was also calculated. The Scopus database was used to determine the h-index of the individuals included in this study. Data analysis was done with SPSS 22.0 Release 2013 (IBM SPSS Statistics for Windows, IBM, Armonk, NY, USA). Kruskal-Wallis and Mann-Whitney U tests were used where required, with the P-value set at less than 0.05. Results In our study sample, 2181 (42%) of the psychiatrists were women. However, according to the information obtained from the websites of 23 programs, few women reached higher ranks, full professorship, or positions such as the chairperson of a program, and only 9% of women achieved the designation of chairperson of the psychiatry department, with men representing the other 91%. Higher academic rank correlated with higher h-index. A statistically-significant difference between the genders in terms of h-index was found for the assistant professor rank as well. However, this difference was not observed at the level of an associate professor. Conclusions Despite adequate representation of women in the academic workforce in psychiatry, there appears to be a discrepancy in the research productivity of the two genders. This study highlights the need for targeted interventions to address gender disparities in academic psychiatry.

The structure of first-ranked cluster galaxies and the radius-magnitude relation

NASA Astrophysics Data System (ADS)

Lugger, P. M.

1984-11-01

To investigate theoretical predictions for the dynamical evolution of first-ranked galaxies, a quantitative study of their properties, as a function of cluster morphology, has been carried out using photographic plates obtained with the Palomar 48 inch (1.2 m) Schmidt telescope. Surface brightness profiles to radii of several hundred kpc for 35 first-ranked cluster galaxies have been analyzed. The dispersion in the metric magnitudes of first-ranked galaxies is quite small (about 0.4 mag), which is consistent with the results of Kristian, Sandage, and Westphal (1978) as well as those of Hoessel, Gunn, and Thuan (1980) and the recent work of Schneider, Gunn, and Hoessel (1983). For the cD (supergiant elliptical) galaxy sample, the mean metric magnitude is about 0.5 mag brighter than for the non-cD galaxies. The mean de Vaucouleurs effective radius for the cD galaxy sample is 80 percent larger than that of the non-cD sample. The relation between de Vaucouleurs effective radius and magnitude determined in the present study for first-ranked galaxies, log r(e) equal to about -0.26 M + constant is consistent with the relations found for fainter galaxies by Strom and Strom (1978) as well as Wirth (1982). The residuals in radius from the mean radius-magnitude relation for first-ranked galaxies do not correlate with the Bautz-Morgan (1970) type of the cluster.

Adaptive protection algorithm and system

DOEpatents

Hedrick, Paul [Pittsburgh, PA; Toms, Helen L [Irwin, PA; Miller, Roger M [Mars, PA

2009-04-28

An adaptive protection algorithm and system for protecting electrical distribution systems traces the flow of power through a distribution system, assigns a value (or rank) to each circuit breaker in the system and then determines the appropriate trip set points based on the assigned rank.

Physiology of Pseudomonas aeruginosa in biofilms as revealed by transcriptome analysis

PubMed Central

2010-01-01

Background Transcriptome analysis was applied to characterize the physiological activities of Pseudomonas aeruginosa grown for three days in drip-flow biofilm reactors. Conventional applications of transcriptional profiling often compare two paired data sets that differ in a single experimentally controlled variable. In contrast this study obtained the transcriptome of a single biofilm state, ranked transcript signals to make the priorities of the population manifest, and compared ranki ngs for a priori identified physiological marker genes between the biofilm and published data sets. Results Biofilms tolerated exposure to antibiotics, harbored steep oxygen concentration gradients, and exhibited stratified and heterogeneous spatial patterns of protein synthetic activity. Transcriptional profiling was performed and the signal intensity of each transcript was ranked to gain insight into the physiological state of the biofilm population. Similar rankings were obtained from data sets published in the GEO database http://www.ncbi.nlm.nih.gov/geo. By comparing the rank of genes selected as markers for particular physiological activities between the biofilm and comparator data sets, it was possible to infer qualitative features of the physiological state of the biofilm bacteria. These biofilms appeared, from their transcriptome, to be glucose nourished, iron replete, oxygen limited, and growing slowly or exhibiting stationary phase character. Genes associated with elaboration of type IV pili were strongly expressed in the biofilm. The biofilm population did not indicate oxidative stress, homoserine lactone mediated quorum sensing, or activation of efflux pumps. Using correlations with transcript ranks, the average specific growth rate of biofilm cells was estimated to be 0.08 h-1. Conclusions Collectively these data underscore the oxygen-limited, slow-growing nature of the biofilm population and are consistent with antimicrobial tolerance due to low metabolic activity. PMID:21083928

Conservation threats and the phylogenetic utility of IUCN Red List rankings in Incilius toads.

PubMed

Schachat, Sandra R; Mulcahy, Daniel G; Mendelson, Joseph R

2016-02-01

Phylogenetic analysis of extinction threat is an emerging tool in the field of conservation. However, there are problems with the methods and data as commonly used. Phylogenetic sampling usually extends to the level of family or genus, but International Union for Conservation of Nature (IUCN) rankings are available only for individual species, and, although different species within a taxonomic group may have the same IUCN rank, the species may have been ranked as such for different reasons. Therefore, IUCN rank may not reflect evolutionary history and thus may not be appropriate for use in a phylogenetic context. To be used appropriately, threat-risk data should reflect the cause of extinction threat rather than the IUCN threat ranking. In a case study of the toad genus Incilius, with phylogenetic sampling at the species level (so that the resolution of the phylogeny matches character data from the IUCN Red List), we analyzed causes of decline and IUCN threat rankings by calculating metrics of phylogenetic signal (such as Fritz and Purvis' D). We also analyzed the extent to which cause of decline and threat ranking overlap by calculating phylogenetic correlation between these 2 types of character data. Incilius species varied greatly in both threat ranking and cause of decline; this variability would be lost at a coarser taxonomic resolution. We found far more phylogenetic signal, likely correlated with evolutionary history, for causes of decline than for IUCN threat ranking. Individual causes of decline and IUCN threat rankings were largely uncorrelated on the phylogeny. Our results demonstrate the importance of character selection and taxonomic resolution when extinction threat is analyzed in a phylogenetic context. © 2015 Society for Conservation Biology.

Effects of normalization on quantitative traits in association test

PubMed Central

2009-01-01

Background Quantitative trait loci analysis assumes that the trait is normally distributed. In reality, this is often not observed and one strategy is to transform the trait. However, it is not clear how much normality is required and which transformation works best in association studies. Results We performed simulations on four types of common quantitative traits to evaluate the effects of normalization using the logarithm, Box-Cox, and rank-based transformations. The impact of sample size and genetic effects on normalization is also investigated. Our results show that rank-based transformation gives generally the best and consistent performance in identifying the causal polymorphism and ranking it highly in association tests, with a slight increase in false positive rate. Conclusion For small sample size or genetic effects, the improvement in sensitivity for rank transformation outweighs the slight increase in false positive rate. However, for large sample size and genetic effects, normalization may not be necessary since the increase in sensitivity is relatively modest. PMID:20003414

Item Calibration Samples and the Stability of Achievement Estimates and System Rankings: Another Look at the PISA Model

ERIC Educational Resources Information Center

Rutkowski, Leslie; Rutkowski, David; Zhou, Yan

2016-01-01

Using an empirically-based simulation study, we show that typically used methods of choosing an item calibration sample have significant impacts on achievement bias and system rankings. We examine whether recent PISA accommodations, especially for lower performing participants, can mitigate some of this bias. Our findings indicate that standard…

Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images.

PubMed

Møllersen, Kajsa; Zortea, Maciel; Schopf, Thomas R; Kirchesch, Herbert; Godtliebsen, Fred

2017-01-01

Melanoma is the deadliest form of skin cancer, and early detection is crucial for patient survival. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images, of which 75 were of melanomas, was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to participate in the challenge, and investigate the impact of segmentation and classifier. The diagnostic accuracy when using an automatic versus the semi-automatic/manual segmentation is investigated. The unexpected small impact of segmentation method suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have clinical impact, their performance should be ranked by a high-sensitivity measure.

A cautionary note on the rank product statistic.

PubMed

Koziol, James A

2016-06-01

The rank product method introduced by Breitling R et al. [2004, FEBS Letters 573, 83-92] has rapidly generated popularity in practical settings, in particular, detecting differential expression of genes in microarray experiments. The purpose of this note is to point out a particular property of the rank product method, namely, its differential sensitivity to over- and underexpression. It turns out that overexpression is less likely to be detected than underexpression with the rank product statistic. We have conducted both empirical and exact power studies that demonstrate this phenomenon, and summarize these findings in this note. © 2016 Federation of European Biochemical Societies.

Factors influencing career success in nursing.

PubMed

Zimmerman, L; Yeaworth, R

1986-06-01

The purpose of this descriptive study was to examine educational preparation, personal characteristics, and significant others in the career success of women in nursing. The sample was a random selection of 194 doctorally prepared female nurses who reported some degree of career success. The factor, personal characteristics, was ranked as the most important in facilitating career success, educational preparations was ranked second, and significant others was ranked third. Among significant others ranked as influential were teachers, peer/colleagues, and supervisors.

Relationships between nurse- and physician-to-population ratios and state health rankings.

PubMed

Bigbee, Jeri L

2008-01-01

To evaluate the relationship between nurse-to-population ratios and population health, as indicated by state health ranking, and to compare the findings with physician-to-population ratios. Secondary analysis correlational design. The sample consisted of all 50 states in the United States. Data sources included the United Health Foundation's 2006 state health rankings, the 2004 National Sample Survey for Registered Nurses, and the U.S. Health Workforce Profile from the New York Center for Health Workforce Studies. Significant relationships between nurse-to-population ratio and overall state health ranking (rho=-.446, p tf?>=.001) and 11 of the 18 components of that ranking were found. Significant components included motor vehicle death rate, high school graduation rate, violent crime rate, infectious disease rate, percentage of children in poverty, percentage of uninsured residents, immunization rate, adequacy of prenatal care, number of poor mental health days, number of poor physical health days, and premature death rate, with higher nurse-to-population ratios associated with higher health rankings. Specialty (public health and school) nurse-to-population ratios were not as strongly related to state health ranking. Physician-to-population ratios were also significantly related to state health ranking, but were associated with different components than nurses. These findings suggest that greater nurses per capita may be uniquely associated with healthier communities; however, further multivariate research is needed.

EXAMINING SOCIOECONOMIC HEALTH DISPARITIES USING A RANK-DEPENDENT RÉNYI INDEX.

PubMed

Talih, Makram

2015-06-01

The Rényi index (RI) is a one-parameter class of indices that summarize health disparities among population groups by measuring divergence between the distributions of disease burden and population shares of these groups. The rank-dependent RI introduced in this paper is a two-parameter class of health disparity indices that also accounts for the association between socioeconomic rank and health; it may be derived from a rank-dependent social welfare function. Two competing classes are discussed and the rank-dependent RI is shown to be more robust to changes in the distribution of either socioeconomic rank or health. The standard error and sampling distribution of the rank-dependent RI are evaluated using linearization and re-sampling techniques, and the methodology is illustrated using health survey data from the U.S. National Health and Nutrition Examination Survey and registry data from the U.S. Surveillance, Epidemiology and End Results Program. Such data underlie many population-based objectives within the U.S. Healthy People 2020 initiative. The rank-dependent RI provides a unified mathematical framework for eliciting various societal positions with regards to the policies that are tied to such wide-reaching public health initiatives. For example, if population groups with lower socioeconomic position were ascertained to be more likely to utilize costly public programs, then the parameters of the RI could be selected to reflect prioritizing those population groups for intervention or treatment.

EXAMINING SOCIOECONOMIC HEALTH DISPARITIES USING A RANK-DEPENDENT RÉNYI INDEX

PubMed Central

Talih, Makram

2015-01-01

The Rényi index (RI) is a one-parameter class of indices that summarize health disparities among population groups by measuring divergence between the distributions of disease burden and population shares of these groups. The rank-dependent RI introduced in this paper is a two-parameter class of health disparity indices that also accounts for the association between socioeconomic rank and health; it may be derived from a rank-dependent social welfare function. Two competing classes are discussed and the rank-dependent RI is shown to be more robust to changes in the distribution of either socioeconomic rank or health. The standard error and sampling distribution of the rank-dependent RI are evaluated using linearization and re-sampling techniques, and the methodology is illustrated using health survey data from the U.S. National Health and Nutrition Examination Survey and registry data from the U.S. Surveillance, Epidemiology and End Results Program. Such data underlie many population-based objectives within the U.S. Healthy People 2020 initiative. The rank-dependent RI provides a unified mathematical framework for eliciting various societal positions with regards to the policies that are tied to such wide-reaching public health initiatives. For example, if population groups with lower socioeconomic position were ascertained to be more likely to utilize costly public programs, then the parameters of the RI could be selected to reflect prioritizing those population groups for intervention or treatment. PMID:26566419

Classification of large-scale fundus image data sets: a cloud-computing framework.

PubMed

Roychowdhury, Sohini

2016-08-01

Large medical image data sets with high dimensionality require substantial amount of computation time for data creation and data processing. This paper presents a novel generalized method that finds optimal image-based feature sets that reduce computational time complexity while maximizing overall classification accuracy for detection of diabetic retinopathy (DR). First, region-based and pixel-based features are extracted from fundus images for classification of DR lesions and vessel-like structures. Next, feature ranking strategies are used to distinguish the optimal classification feature sets. DR lesion and vessel classification accuracies are computed using the boosted decision tree and decision forest classifiers in the Microsoft Azure Machine Learning Studio platform, respectively. For images from the DIARETDB1 data set, 40 of its highest-ranked features are used to classify four DR lesion types with an average classification accuracy of 90.1% in 792 seconds. Also, for classification of red lesion regions and hemorrhages from microaneurysms, accuracies of 85% and 72% are observed, respectively. For images from STARE data set, 40 high-ranked features can classify minor blood vessels with an accuracy of 83.5% in 326 seconds. Such cloud-based fundus image analysis systems can significantly enhance the borderline classification performances in automated screening systems.

Rank estimation and the multivariate analysis of in vivo fast-scan cyclic voltammetric data

PubMed Central

Keithley, Richard B.; Carelli, Regina M.; Wightman, R. Mark

2010-01-01

Principal component regression has been used in the past to separate current contributions from different neuromodulators measured with in vivo fast-scan cyclic voltammetry. Traditionally, a percent cumulative variance approach has been used to determine the rank of the training set voltammetric matrix during model development, however this approach suffers from several disadvantages including the use of arbitrary percentages and the requirement of extreme precision of training sets. Here we propose that Malinowski’s F-test, a method based on a statistical analysis of the variance contained within the training set, can be used to improve factor selection for the analysis of in vivo fast-scan cyclic voltammetric data. These two methods of rank estimation were compared at all steps in the calibration protocol including the number of principal components retained, overall noise levels, model validation as determined using a residual analysis procedure, and predicted concentration information. By analyzing 119 training sets from two different laboratories amassed over several years, we were able to gain insight into the heterogeneity of in vivo fast-scan cyclic voltammetric data and study how differences in factor selection propagate throughout the entire principal component regression analysis procedure. Visualizing cyclic voltammetric representations of the data contained in the retained and discarded principal components showed that using Malinowski’s F-test for rank estimation of in vivo training sets allowed for noise to be more accurately removed. Malinowski’s F-test also improved the robustness of our criterion for judging multivariate model validity, even though signal-to-noise ratios of the data varied. In addition, pH change was the majority noise carrier of in vivo training sets while dopamine prediction was more sensitive to noise. PMID:20527815

There is More than a Power Law in Zipf

PubMed Central

Cristelli, Matthieu; Batty, Michael; Pietronero, Luciano

2012-01-01

The largest cities, the most frequently used words, the income of the richest countries, and the most wealthy billionaires, can be all described in terms of Zipf’s Law, a rank-size rule capturing the relation between the frequency of a set of objects or events and their size. It is assumed to be one of many manifestations of an underlying power law like Pareto’s or Benford’s, but contrary to popular belief, from a distribution of, say, city sizes and a simple random sampling, one does not obtain Zipf’s law for the largest cities. This pathology is reflected in the fact that Zipf’s Law has a functional form depending on the number of events N. This requires a fundamental property of the sample distribution which we call ‘coherence’ and it corresponds to a ‘screening’ between various elements of the set. We show how it should be accounted for when fitting Zipf’s Law. PMID:23139862

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.

Ranking procedure for partial discriminant analysis

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

Beckman, R.J.; Johnson, M.E.

1981-09-01

A rank procedure developed by Broffitt, Randles, and Hogg (1976) is modified to control the conditional probability of misclassification given that classification has been attempted. This modification leads to a useful solution to the two-population partial discriminant analysis problem for even moderately sized training sets.

Construction of Nutrition Literacy Indicators for College Students in Taiwan: A Delphi Consensus Study.

PubMed

Liao, Li-Ling; Lai, I-Ju

2017-10-01

To use the Delphi process to select nutrition literacy (NL) indicators for Taiwan college students. Initial formulation of 8 principal indicators and 77 subindicators, followed by a 2-round Delphi survey and final selection of indicators. A total of 28 nutrition experts selected through snowball sampling; 100% response rate. An expert panel scored and ranked NL themes and indicators for relevance, representativeness, and importance. Quantitative analysis. For principal indicators, the defined cutoff was mean (relevance and representativeness) > 4 and SD < 1. For subindicators, screening criteria were: (1) >20 experts ranked the nutrition theme's importance in the top 50% of the 12 themes; (2) mean (relevance and representativeness) > 4 and SD < 1 and >20 experts ranked the indicator's importance in the top 50% of all indicators within a domain. Consensus was reached on 8 principal indicators and 28 subindicators in 8 themes, including 10 in understand, 8 in analyze, 5 in appraise, and 5 in apply. An initial set of NL indicators was developed for Taiwan college students, serving as a basis to develop Taiwan College's Nutrition Literacy Scale and providing information on nutrition education. Copyright © 2017 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

A ranking method for the concurrent learning of compounds with various activity profiles.

PubMed

Dörr, Alexander; Rosenbaum, Lars; Zell, Andreas

2015-01-01

In this study, we present a SVM-based ranking algorithm for the concurrent learning of compounds with different activity profiles and their varying prioritization. To this end, a specific labeling of each compound was elaborated in order to infer virtual screening models against multiple targets. We compared the method with several state-of-the-art SVM classification techniques that are capable of inferring multi-target screening models on three chemical data sets (cytochrome P450s, dehydrogenases, and a trypsin-like protease data set) containing three different biological targets each. The experiments show that ranking-based algorithms show an increased performance for single- and multi-target virtual screening. Moreover, compounds that do not completely fulfill the desired activity profile are still ranked higher than decoys or compounds with an entirely undesired profile, compared to other multi-target SVM methods. SVM-based ranking methods constitute a valuable approach for virtual screening in multi-target drug design. The utilization of such methods is most helpful when dealing with compounds with various activity profiles and the finding of many ligands with an already perfectly matching activity profile is not to be expected.

A Case-Based Reasoning Method with Rank Aggregation

NASA Astrophysics Data System (ADS)

Sun, Jinhua; Du, Jiao; Hu, Jian

2018-03-01

In order to improve the accuracy of case-based reasoning (CBR), this paper addresses a new CBR framework with the basic principle of rank aggregation. First, the ranking methods are put forward in each attribute subspace of case. The ordering relation between cases on each attribute is got between cases. Then, a sorting matrix is got. Second, the similar case retrieval process from ranking matrix is transformed into a rank aggregation optimal problem, which uses the Kemeny optimal. On the basis, a rank aggregation case-based reasoning algorithm, named RA-CBR, is designed. The experiment result on UCI data sets shows that case retrieval accuracy of RA-CBR algorithm is higher than euclidean distance CBR and mahalanobis distance CBR testing.So we can get the conclusion that RA-CBR method can increase the performance and efficiency of CBR.

Multiple Ordinal Regression by Maximizing the Sum of Margins

PubMed Central

Hamsici, Onur C.; Martinez, Aleix M.

2016-01-01

Human preferences are usually measured using ordinal variables. A system whose goal is to estimate the preferences of humans and their underlying decision mechanisms requires to learn the ordering of any given sample set. We consider the solution of this ordinal regression problem using a Support Vector Machine algorithm. Specifically, the goal is to learn a set of classifiers with common direction vectors and different biases correctly separating the ordered classes. Current algorithms are either required to solve a quadratic optimization problem, which is computationally expensive, or are based on maximizing the minimum margin (i.e., a fixed margin strategy) between a set of hyperplanes, which biases the solution to the closest margin. Another drawback of these strategies is that they are limited to order the classes using a single ranking variable (e.g., perceived length). In this paper, we define a multiple ordinal regression algorithm based on maximizing the sum of the margins between every consecutive class with respect to one or more rankings (e.g., perceived length and weight). We provide derivations of an efficient, easy-to-implement iterative solution using a Sequential Minimal Optimization procedure. We demonstrate the accuracy of our solutions in several datasets. In addition, we provide a key application of our algorithms in estimating human subjects’ ordinal classification of attribute associations to object categories. We show that these ordinal associations perform better than the binary one typically employed in the literature. PMID:26529784

Quantum probability ranking principle for ligand-based virtual screening.

PubMed

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal

2017-04-01

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.

Quantum probability ranking principle for ligand-based virtual screening

NASA Astrophysics Data System (ADS)

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal

2017-04-01

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.

Error analysis of stochastic gradient descent ranking.

PubMed

Chen, Hong; Tang, Yi; Li, Luoqing; Yuan, Yuan; Li, Xuelong; Tang, Yuanyan

2013-06-01

Ranking is always an important task in machine learning and information retrieval, e.g., collaborative filtering, recommender systems, drug discovery, etc. A kernel-based stochastic gradient descent algorithm with the least squares loss is proposed for ranking in this paper. The implementation of this algorithm is simple, and an expression of the solution is derived via a sampling operator and an integral operator. An explicit convergence rate for leaning a ranking function is given in terms of the suitable choices of the step size and the regularization parameter. The analysis technique used here is capacity independent and is novel in error analysis of ranking learning. Experimental results on real-world data have shown the effectiveness of the proposed algorithm in ranking tasks, which verifies the theoretical analysis in ranking error.

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

PubMed Central

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

2017-01-01

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

Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking.

PubMed

Yu, Jun; Yang, Xiaokang; Gao, Fei; Tao, Dacheng

2017-12-01

How do we retrieve images accurately? Also, how do we rank a group of images precisely and efficiently for specific queries? These problems are critical for researchers and engineers to generate a novel image searching engine. First, it is important to obtain an appropriate description that effectively represent the images. In this paper, multimodal features are considered for describing images. The images unique properties are reflected by visual features, which are correlated to each other. However, semantic gaps always exist between images visual features and semantics. Therefore, we utilize click feature to reduce the semantic gap. The second key issue is learning an appropriate distance metric to combine these multimodal features. This paper develops a novel deep multimodal distance metric learning (Deep-MDML) method. A structured ranking model is adopted to utilize both visual and click features in distance metric learning (DML). Specifically, images and their related ranking results are first collected to form the training set. Multimodal features, including click and visual features, are collected with these images. Next, a group of autoencoders is applied to obtain initially a distance metric in different visual spaces, and an MDML method is used to assign optimal weights for different modalities. Next, we conduct alternating optimization to train the ranking model, which is used for the ranking of new queries with click features. Compared with existing image ranking methods, the proposed method adopts a new ranking model to use multimodal features, including click features and visual features in DML. We operated experiments to analyze the proposed Deep-MDML in two benchmark data sets, and the results validate the effects of the method.

Can streamlined multi-criteria decision analysis be used to implement shared decision making for colorectal cancer screening?

PubMed Central

Dolan, James G.; Boohaker, Emily; Allison, Jeroan; Imperiale, Thomas F.

2013-01-01

Background Current US colorectal cancer screening guidelines that call for shared decision making regarding the choice among several recommended screening options are difficult to implement. Multi-criteria decision analysis (MCDA) is an established methodology well suited for supporting shared decision making. Our study goal was to determine if a streamlined form of MCDA using rank order based judgments can accurately assess patients’ colorectal cancer screening priorities. Methods We converted priorities for four decision criteria and three sub-criteria regarding colorectal cancer screening obtained from 484 average risk patients using the Analytic Hierarchy Process (AHP) in a prior study into rank order-based priorities using rank order centroids. We compared the two sets of priorities using Spearman rank correlation and non-parametric Bland-Altman limits of agreement analysis. We assessed the differential impact of using the rank order-based versus the AHP-based priorities on the results of a full MCDA comparing three currently recommended colorectal cancer screening strategies. Generalizability of the results was assessed using Monte Carlo simulation. Results Correlations between the two sets of priorities for the seven criteria ranged from 0.55 to 0.92. The proportions of absolute differences between rank order-based and AHP-based priorities that were more than ± 0.15 ranged from 1% to 16%. Differences in the full MCDA results were minimal and the relative rankings of the three screening options were identical more than 88% of the time. The Monte Carlo simulation results were similar. Conclusion Rank order-based MCDA could be a simple, practical way to guide individual decisions and assess population decision priorities regarding colorectal cancer screening strategies. Additional research is warranted to further explore the use of these methods for promoting shared decision making. PMID:24300851

First International Diagnosis Competition - DXC'09

NASA Technical Reports Server (NTRS)

Kurtoglu, tolga; Narasimhan, Sriram; Poll, Scott; Garcia, David; Kuhn, Lukas; deKleer, Johan; vanGemund, Arjan; Feldman, Alexander

2009-01-01

A framework to compare and evaluate diagnosis algorithms (DAs) has been created jointly by NASA Ames Research Center and PARC. In this paper, we present the first concrete implementation of this framework as a competition called DXC 09. The goal of this competition was to evaluate and compare DAs in a common platform and to determine a winner based on diagnosis results. 12 DAs (model-based and otherwise) competed in this first year of the competition in 3 tracks that included industrial and synthetic systems. Specifically, the participants provided algorithms that communicated with the run-time architecture to receive scenario data and return diagnostic results. These algorithms were run on extended scenario data sets (different from sample set) to compute a set of pre-defined metrics. A ranking scheme based on weighted metrics was used to declare winners. This paper presents the systems used in DXC 09, description of faults and data sets, a listing of participating DAs, the metrics and results computed from running the DAs, and a superficial analysis of the results.

Quantum annealing versus classical machine learning applied to a simplified computational biology problem

NASA Astrophysics Data System (ADS)

Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

2018-03-01

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to classify and rank binding affinities. Using simplified data sets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified data sets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems.

Classification of singularities in the problem of motion of the Kovalevskaya top in a double force field

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

Ryabov, Pavel E; Kharlamov, Mikhail P

2012-02-28

The problem of motion of the Kovalevskaya top in a double force field is investigated (the integrable case of A.G. Reyman and M.A. Semenov-Tian-Shansky without a gyrostatic momentum). It is a completely integrable Hamiltonian system with three degrees of freedom not reducible to a family of systems with two degrees of freedom. The critical set of the integral map is studied. The critical subsystems and bifurcation diagrams are described. The classification of all nondegenerate critical points is given. The set of these points consists of equilibria (nondegenerate singularities of rank 0), of singular periodic motions (nondegenerate singularities of rank 1),more » and also of critical two-frequency motions (nondegenerate singularities of rank 2). Bibliography: 32 titles.« less

An exploratory study of a text classification framework for Internet-based surveillance of emerging epidemics

PubMed Central

Torii, Manabu; Yin, Lanlan; Nguyen, Thang; Mazumdar, Chand T.; Liu, Hongfang; Hartley, David M.; Nelson, Noele P.

2014-01-01

Purpose Early detection of infectious disease outbreaks is crucial to protecting the public health of a society. Online news articles provide timely information on disease outbreaks worldwide. In this study, we investigated automated detection of articles relevant to disease outbreaks using machine learning classifiers. In a real-life setting, it is expensive to prepare a training data set for classifiers, which usually consists of manually labeled relevant and irrelevant articles. To mitigate this challenge, we examined the use of randomly sampled unlabeled articles as well as labeled relevant articles. Methods Naïve Bayes and Support Vector Machine (SVM) classifiers were trained on 149 relevant and 149 or more randomly sampled unlabeled articles. Diverse classifiers were trained by varying the number of sampled unlabeled articles and also the number of word features. The trained classifiers were applied to 15 thousand articles published over 15 days. Top-ranked articles from each classifier were pooled and the resulting set of 1337 articles was reviewed by an expert analyst to evaluate the classifiers. Results Daily averages of areas under ROC curves (AUCs) over the 15-day evaluation period were 0.841 and 0.836, respectively, for the naïve Bayes and SVM classifier. We referenced a database of disease outbreak reports to confirm that this evaluation data set resulted from the pooling method indeed covered incidents recorded in the database during the evaluation period. Conclusions The proposed text classification framework utilizing randomly sampled unlabeled articles can facilitate a cost-effective approach to training machine learning classifiers in a real-life Internet-based biosurveillance project. We plan to examine this framework further using larger data sets and using articles in non-English languages. PMID:21134784

Malthus in the Bedroom: Birth Spacing as Birth Control in Pre-Transition England.

PubMed

Cinnirella, Francesco; Klemp, Marc; Weisdorf, Jacob

2017-04-01

We use duration models on a well-known historical data set of more than 15,000 families and 60,000 births in England for the period 1540-1850 to show that the sampled families adjusted the timing of their births in accordance with the economic conditions as well as their stock of dependent children. The effects were larger among the lower socioeconomic ranks. Our findings on the existence of parity-dependent as well as parity-independent birth spacing in England are consistent with the growing evidence that marital birth control was present in pre-transitional populations.

Validation of reference genes aiming accurate normalization of qRT-PCR data in Dendrocalamus latiflorus Munro.

PubMed

Liu, Mingying; Jiang, Jing; Han, Xiaojiao; Qiao, Guirong; Zhuo, Renying

2014-01-01

Dendrocalamus latiflorus Munro distributes widely in subtropical areas and plays vital roles as valuable natural resources. The transcriptome sequencing for D. latiflorus Munro has been performed and numerous genes especially those predicted to be unique to D. latiflorus Munro were revealed. qRT-PCR has become a feasible approach to uncover gene expression profiling, and the accuracy and reliability of the results obtained depends upon the proper selection of stable reference genes for accurate normalization. Therefore, a set of suitable internal controls should be validated for D. latiflorus Munro. In this report, twelve candidate reference genes were selected and the assessment of gene expression stability was performed in ten tissue samples and four leaf samples from seedlings and anther-regenerated plants of different ploidy. The PCR amplification efficiency was estimated, and the candidate genes were ranked according to their expression stability using three software packages: geNorm, NormFinder and Bestkeeper. GAPDH and EF1α were characterized to be the most stable genes among different tissues or in all the sample pools, while CYP showed low expression stability. RPL3 had the optimal performance among four leaf samples. The application of verified reference genes was illustrated by analyzing ferritin and laccase expression profiles among different experimental sets. The analysis revealed the biological variation in ferritin and laccase transcript expression among the tissues studied and the individual plants. geNorm, NormFinder, and BestKeeper analyses recommended different suitable reference gene(s) for normalization according to the experimental sets. GAPDH and EF1α had the highest expression stability across different tissues and RPL3 for the other sample set. This study emphasizes the importance of validating superior reference genes for qRT-PCR analysis to accurately normalize gene expression of D. latiflorus Munro.

Getting to the top: an analysis of 25 years of career rankings trajectories for professional women's tennis.

PubMed

Kovalchik, Stephanie A; Bane, Michael K; Reid, Machar

2017-10-01

Official rankings are the most common measure of success in professional women's tennis. Despite their importance for earning potential and tournament seeding, little is known about ranking trajectories of female players and their influence on career success. Our objective was to conduct a comprehensive study of the career progression of elite female tennis talent. The study examined the ranking trajectories of the top 250 female professionals between 1990 and 2015. Using regression modelling of yearly peak rankings, we found a strong association between the shape of the ranking trajectory and the highest career ranking earned. Players with the highest career peak ranking were the youngest when first ranked. For example, top 10 players were first ranked at age 15.5 years (99% CI = 14.8-15.9), 1.2 years (99% CI = 0.8-1.5) earlier than top 51-100 players. Top 10 players were also ranked in the top 100 longer than other players, holding a top 100 ranking until a mean age of 29.0 years (99% CI = 27.8-30.3) compared with age 24.4 years (99% CI = 23.7-25.2) for top 51-100 players. Ranking trajectories were more distinct with respect to player age than years from first ranking. The present study's findings will be instructive for players, coaches, and administrators in setting goals and assessing athlete development in women's tennis.

Observations on the Invalid Scoring Algorithm of "NASA" and Similar Consensus Tasks.

ERIC Educational Resources Information Center

Slevin, Dennis P.

1978-01-01

The NASA ranking task and similar ranking activities used to demonstrate the superiority of group thinking are examined. It is argued that the current scores cannot be used to prove the superiority of group-consensus decision making in either training or research settings. (Author)

Efficiently Ranking Hyphotheses in Machine Learning

NASA Technical Reports Server (NTRS)

Chien, Steve

1997-01-01

This paper considers the problem of learning the ranking of a set of alternatives based upon incomplete information (e.g. a limited number of observations). At each decision cycle, the system can output a complete ordering on the hypotheses or decide to gather additional information (e.g. observation) at some cost.

38 CFR 61.32 - Ranking non-capital grant recipients for per diem.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Availability will be reviewed and grouped in categories according to the funding priorities set forth in the... available, within highest priority funding category if applicable, will be conditionally selected for eligibility to receive per diem payments in accordance with their ranked order. If funding priorities have...

38 CFR 61.54 - Awarding technical assistance grants.

Code of Federal Regulations, 2010 CFR

2010-07-01

...) Applicants will first be grouped in categories according to the funding priorities set forth in the NOFA, if... highest-ranked applications for which funding is available, within highest priority funding category if... ranked order, as determined under § 61.53 of this part. If funding priorities have been established and...

38 CFR 61.14 - Selecting applications for capital grants.

Code of Federal Regulations, 2010 CFR

2010-07-01

... capital grants. (a) Applicants will first be grouped in categories according to the funding priorities set... applicable. The highest-ranked applications for which funding is available, within highest priority funding... ranked order, as determined under § 61.13 of this part. If funding priorities have been established and...

12 CFR 1806.203 - Selection Process, actual award amounts.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Community Financing Activities, ranked in the order set forth in the applicable NOFA. (3) Third Priority. If... amounts based on the process described in this section. (c) Priority of Awards. The Fund will rank Applicants in each category of Qualified Activity according to the priorities described in this paragraph (c...

Human rights violations among sexual and gender minorities in Kathmandu, Nepal: a qualitative investigation.

PubMed

Singh, Sonal; Pant, Sunil Babu; Dhakal, Suben; Pokhrel, Subash; Mullany, Luke C

2012-05-16

Nepal has experienced sporadic reports of human rights violations among sexual and gender minorities. Our objective was to identify a range of human rights that are enshrined in international law and/or are commonly reported by sexual and gender minority participants in Kathmandu, to be nonprotected or violated. In September 2009 three focus group discussions were conducted by trained interviewers among a convenience sample of sexual and gender minority participants in Kathmandu Nepal. The modified Delphi technique was utilized to elicit and rank participant-generated definitions of human rights and their subsequent violations. Data was analyzed independently and cross checked by another investigator. Participants (n = 29) reported experiencing a range of human rights violations at home, work, educational, health care settings and in public places. Lack of adequate legal protection, physical and mental abuse and torture were commonly reported. Access to adequate legal protection and improvements in the family and healthcare environment were ranked as the most important priority areas. Sexual and gender minorities in Nepal experienced a range of human rights violations. Future efforts should enroll a larger and more systematic sample of participants to determine frequency, timing, and/or intensity of exposure to rights violations, and estimate the population-based impact of these rights violations on specific health outcomes.

A collaborative filtering approach for protein-protein docking scoring functions.

PubMed

Bourquard, Thomas; Bernauer, Julie; Azé, Jérôme; Poupon, Anne

2011-04-22

A protein-protein docking procedure traditionally consists in two successive tasks: a search algorithm generates a large number of candidate conformations mimicking the complex existing in vivo between two proteins, and a scoring function is used to rank them in order to extract a native-like one. We have already shown that using Voronoi constructions and a well chosen set of parameters, an accurate scoring function could be designed and optimized. However to be able to perform large-scale in silico exploration of the interactome, a near-native solution has to be found in the ten best-ranked solutions. This cannot yet be guaranteed by any of the existing scoring functions. In this work, we introduce a new procedure for conformation ranking. We previously developed a set of scoring functions where learning was performed using a genetic algorithm. These functions were used to assign a rank to each possible conformation. We now have a refined rank using different classifiers (decision trees, rules and support vector machines) in a collaborative filtering scheme. The scoring function newly obtained is evaluated using 10 fold cross-validation, and compared to the functions obtained using either genetic algorithms or collaborative filtering taken separately. This new approach was successfully applied to the CAPRI scoring ensembles. We show that for 10 targets out of 12, we are able to find a near-native conformation in the 10 best ranked solutions. Moreover, for 6 of them, the near-native conformation selected is of high accuracy. Finally, we show that this function dramatically enriches the 100 best-ranking conformations in near-native structures.

A Collaborative Filtering Approach for Protein-Protein Docking Scoring Functions

PubMed Central

Bourquard, Thomas; Bernauer, Julie; Azé, Jérôme; Poupon, Anne

2011-01-01

A protein-protein docking procedure traditionally consists in two successive tasks: a search algorithm generates a large number of candidate conformations mimicking the complex existing in vivo between two proteins, and a scoring function is used to rank them in order to extract a native-like one. We have already shown that using Voronoi constructions and a well chosen set of parameters, an accurate scoring function could be designed and optimized. However to be able to perform large-scale in silico exploration of the interactome, a near-native solution has to be found in the ten best-ranked solutions. This cannot yet be guaranteed by any of the existing scoring functions. In this work, we introduce a new procedure for conformation ranking. We previously developed a set of scoring functions where learning was performed using a genetic algorithm. These functions were used to assign a rank to each possible conformation. We now have a refined rank using different classifiers (decision trees, rules and support vector machines) in a collaborative filtering scheme. The scoring function newly obtained is evaluated using 10 fold cross-validation, and compared to the functions obtained using either genetic algorithms or collaborative filtering taken separately. This new approach was successfully applied to the CAPRI scoring ensembles. We show that for 10 targets out of 12, we are able to find a near-native conformation in the 10 best ranked solutions. Moreover, for 6 of them, the near-native conformation selected is of high accuracy. Finally, we show that this function dramatically enriches the 100 best-ranking conformations in near-native structures. PMID:21526112

Reduction from cost-sensitive ordinal ranking to weighted binary classification.

PubMed

Lin, Hsuan-Tien; Li, Ling

2012-05-01

We present a reduction framework from ordinal ranking to binary classification. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranker from the binary classifier. Based on the framework, we show that a weighted 0/1 loss of the binary classifier upper-bounds the mislabeling cost of the ranker, both error-wise and regret-wise. Our framework allows not only the design of good ordinal ranking algorithms based on well-tuned binary classification approaches, but also the derivation of new generalization bounds for ordinal ranking from known bounds for binary classification. In addition, our framework unifies many existing ordinal ranking algorithms, such as perceptron ranking and support vector ordinal regression. When compared empirically on benchmark data sets, some of our newly designed algorithms enjoy advantages in terms of both training speed and generalization performance over existing algorithms. In addition, the newly designed algorithms lead to better cost-sensitive ordinal ranking performance, as well as improved listwise ranking performance.

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

PubMed Central

Roy, Janine; Aust, Daniela; Knösel, Thomas; Rümmele, Petra; Jahnke, Beatrix; Hentrich, Vera; Rückert, Felix; Niedergethmann, Marco; Weichert, Wilko; Bahra, Marcus; Schlitt, Hans J.; Settmacher, Utz; Friess, Helmut; Büchler, Markus; Saeger, Hans-Detlev; Schroeder, Michael; Pilarsky, Christian; Grützmann, Robert

2012-01-01

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice. PMID:22615549

Ensemble Feature Learning of Genomic Data Using Support Vector Machine

PubMed Central

Anaissi, Ali; Goyal, Madhu; Catchpoole, Daniel R.; Braytee, Ali; Kennedy, Paul J.

2016-01-01

The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification. Testament to that is random forest which combines random decision trees with bagging to improve overall feature selection and classification accuracy. Surprisingly, the adoption of these methods in support vector machines has only recently received attention but mostly on classification not gene selection. This paper introduces an ensemble SVM-Recursive Feature Elimination (ESVM-RFE) for gene selection that follows the concepts of ensemble and bagging used in random forest but adopts the backward elimination strategy which is the rationale of RFE algorithm. The rationale behind this is, building ensemble SVM models using randomly drawn bootstrap samples from the training set, will produce different feature rankings which will be subsequently aggregated as one feature ranking. As a result, the decision for elimination of features is based upon the ranking of multiple SVM models instead of choosing one particular model. Moreover, this approach will address the problem of imbalanced datasets by constructing a nearly balanced bootstrap sample. Our experiments show that ESVM-RFE for gene selection substantially increased the classification performance on five microarray datasets compared to state-of-the-art methods. Experiments on the childhood leukaemia dataset show that an average 9% better accuracy is achieved by ESVM-RFE over SVM-RFE, and 5% over random forest based approach. The selected genes by the ESVM-RFE algorithm were further explored with Singular Value Decomposition (SVD) which reveals significant clusters with the selected data. PMID:27304923

Using Trained Pixel Classifiers to Select Images of Interest

NASA Technical Reports Server (NTRS)

Mazzoni, D.; Wagstaff, K.; Castano, R.

2004-01-01

We present a machine-learning-based approach to ranking images based on learned priorities. Unlike previous methods for image evaluation, which typically assess the value of each image based on the presence of predetermined specific features, this method involves using two levels of machine-learning classifiers: one level is used to classify each pixel as belonging to one of a group of rather generic classes, and another level is used to rank the images based on these pixel classifications, given some example rankings from a scientist as a guide. Initial results indicate that the technique works well, producing new rankings that match the scientist's rankings significantly better than would be expected by chance. The method is demonstrated for a set of images collected by a Mars field-test rover.

Identification of Reference Genes for Quantitative Gene Expression Studies in a Non-Model Tree Pistachio (Pistacia vera L.)

PubMed Central

Moazzam Jazi, Maryam; Ghadirzadeh Khorzoghi, Effat; Botanga, Christopher; Seyedi, Seyed Mahdi

2016-01-01

The tree species, Pistacia vera (P. vera) is an important commercial product that is salt-tolerant and long-lived, with a possible lifespan of over one thousand years. Gene expression analysis is an efficient method to explore the possible regulatory mechanisms underlying these characteristics. Therefore, having the most suitable set of reference genes is required for transcript level normalization under different conditions in P. vera. In the present study, we selected eight widely used reference genes, ACT, EF1α, α-TUB, β-TUB, GAPDH, CYP2, UBQ10, and 18S rRNA. Using qRT-PCR their expression was assessed in 54 different samples of three cultivars of P. vera. The samples were collected from different organs under various abiotic treatments (cold, drought, and salt) across three time points. Several statistical programs (geNorm, NormFinder, and BestKeeper) were applied to estimate the expression stability of candidate reference genes. Results obtained from the statistical analysis were then exposed to Rank aggregation package to generate a consensus gene rank. Based on our results, EF1α was found to be the superior reference gene in all samples under all abiotic treatments. In addition to EF1α, ACT and β-TUB were the second best reference genes for gene expression analysis in leaf and root. We recommended β-TUB as the second most stable gene for samples under the cold and drought treatments, while ACT holds the same position in samples analyzed under salt treatment. This report will benefit future research on the expression profiling of P. vera and other members of the Anacardiaceae family. PMID:27308855

Identification of Reference Genes for Quantitative Gene Expression Studies in a Non-Model Tree Pistachio (Pistacia vera L.).

PubMed

Moazzam Jazi, Maryam; Ghadirzadeh Khorzoghi, Effat; Botanga, Christopher; Seyedi, Seyed Mahdi

2016-01-01

The tree species, Pistacia vera (P. vera) is an important commercial product that is salt-tolerant and long-lived, with a possible lifespan of over one thousand years. Gene expression analysis is an efficient method to explore the possible regulatory mechanisms underlying these characteristics. Therefore, having the most suitable set of reference genes is required for transcript level normalization under different conditions in P. vera. In the present study, we selected eight widely used reference genes, ACT, EF1α, α-TUB, β-TUB, GAPDH, CYP2, UBQ10, and 18S rRNA. Using qRT-PCR their expression was assessed in 54 different samples of three cultivars of P. vera. The samples were collected from different organs under various abiotic treatments (cold, drought, and salt) across three time points. Several statistical programs (geNorm, NormFinder, and BestKeeper) were applied to estimate the expression stability of candidate reference genes. Results obtained from the statistical analysis were then exposed to Rank aggregation package to generate a consensus gene rank. Based on our results, EF1α was found to be the superior reference gene in all samples under all abiotic treatments. In addition to EF1α, ACT and β-TUB were the second best reference genes for gene expression analysis in leaf and root. We recommended β-TUB as the second most stable gene for samples under the cold and drought treatments, while ACT holds the same position in samples analyzed under salt treatment. This report will benefit future research on the expression profiling of P. vera and other members of the Anacardiaceae family.

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

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

Nee, K.; Bryan, S.; Levitskaia, T.

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

DOE PAGES

Nee, K.; Bryan, S.; Levitskaia, T.; ...

2017-12-28

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less

Do Portuguese and UK health state values differ across valuation methods?

PubMed

Ferreira, Lara N; Ferreira, Pedro L; Rowen, Donna; Brazier, John E

2011-05-01

There has been an increasing interest in developing country-specific preference weights for widely used measures of health-related quality of life. The valuation of health states has usually been done using cardinal preference elicitation techniques of standard gamble (SG) or time trade-off (TTO). Yet there is increasing interest in the use of ordinal methods to elicit health state utility values as an alternative to the more conventional cardinal techniques.This raises the issue of firstly whether ordinal and cardinal methods of preference elicitation provide similar results and secondly whether this relationship is robust across different valuation studies and different populations. This study examines SG and rank preference weights for the SF-6D derived from samples of the UK and Portuguese general population. The preference weights for the Portuguese sample (n = 140) using rank data are estimated here with 810 health state valuations. The study further examines whether the use of these different preference weights has an impact when comparing the health of different age and severity groups in the Portuguese working population (n = 2,459). The rank model performed well across the majority of measures of goodness of fit used. The preference weights for the Portuguese sample using rank data are systematically lower than the UK weights for physical functioning and pain. Yet our results suggest higher similarity between preference weights derived using rank data than using standard gamble across the UK and Portuguese samples. Our results further suggest that the SF-6D values for a sample of the Portuguese working-age population and differences across groups are affected by the use of different preference weights. We suggest that the use of a Portuguese SF-6D weighting system is preferred for studies aiming to reflect the health state preferences of the Portuguese population.

[Establishment of simultaneous measurement method of 8 salivary components using urinary test paper and clinical evaluation of oral environment].

PubMed

Yuuki, Kenji; Tsukasaki, Hiroaki; Kawawa, Tadaharu; Shiba, Akihiko; Shiba, Kiyoko

2008-07-01

Clinical findings were compared with glucose, protein, albumin, bilirubin, creatinine, pH, occult blood, ketone body, nitrite, and white blood cells contained in whole saliva to investigate the components that most markedly reflect the periodontal condition. The subjects were staff of the Prosthodontics Department, Showa University, and patients who visited for dental treatments (57 subjects in total). At the first time, saliva samples were gargled with 1.5 ml of distilled water for 15 seconds and collected by spitting out into a paper cup. At the second time, saliva samples were collected by the same method. At the third time, saliva samples after chewing paraffin gum for 60 seconds were collected by spitting out into a paper cup. Thus whole saliva collecting that was divided on three times. After sampling, 8 mul of the saliva sample was dripped in reagent sticks for the 10 items of urinary test paper and the reflectance was measured using a specific reflectometer. In the periodontal tissue evaluation, the degree of alveolar bone resorption, probing value, and tooth mobility and the presence or absence of lesions in the root furcation were examined and classified into 4 ranks. The mean values in each periodontal disease rank and correlation between the periodontal disease ranks and the components were statistically analyzed. Bilirubin and ketone body were not measurable. The components density of the 8 items was increased as the periodontal disease rank increased. Regarding the correlation between the periodontal disease ranks and the components, high correlations were noted for protein, albumin, creatinine, pH, and white blood cells. The simultaneous measurement method of 8 salivary components using test paper may be very useful for the diagnosis of periodontal disease of abutment teeth.

Predictors and long-term reproducibility of urinary phthalate metabolites in middle-aged men and women living in urban Shanghai

PubMed Central

Starling, Anne P.; Engel, Lawrence S.; Calafat, Antonia M.; Koutros, Stella; Satagopan, Jaya M.; Yang, Gong; Matthews, Charles E.; Cai, Qiuyin; Buckley, Jessie P.; Ji, Bu-Tian; Cai, Hui; Chow, Wong-Ho; Zheng, Wei; Gao, Yu-Tang; Rothman, Nathaniel; Xiang, Yong-Bing; Shu, Xiao-Ou

2015-01-01

Phthalate esters are man-made chemicals commonly used as plasticizers and solvents, and humans may be exposed through ingestion, inhalation, and dermal absorption. Little is known about predictors of phthalate exposure, particularly in Asian countries. Because phthalates are rapidly metabolized and excreted from the body following exposure, it is important to evaluate whether phthalate metabolites measured at a single point in time can reliably rank exposures to phthalates over a period of time. We examined the concentrations and predictors of phthalate metabolite concentrations among 50 middle-aged women and 50 men from two Shanghai cohorts, enrolled in 1997-2000 and 2002-2006, respectively. We assessed the reproducibility of urinary concentrations of phthalate metabolites in three spot samples per participant taken several years apart (mean interval between first and third sample was 7.5 years [women] or 2.9 years [men]), using Spearman's rank correlation coefficients and intra-class correlation coefficients. We detected ten phthalate metabolites in at least 50% of individuals for two or more samples. Participant sex, age, menopausal status, education, income, body mass index, consumption of bottled water, recent intake of medication, and time of day of collection of the urine sample were associated with concentrations of certain phthalate metabolites. The reproducibility of an individual's urinary concentration of phthalate metabolites across several years was low, with all intra-class correlation coefficients and most Spearman rank correlation coefficients ≤ 0.3. Only mono(2-ethylhexyl) phthalate, a metabolite of di(2-ethylhexyl)phthalate, had a Spearman rank correlation coefficient ≥ 0.4 among men, suggesting moderate reproducibility. These findings suggest that a single spot urine sample is not sufficient to rank exposures to phthalates over several years in an adult urban Chinese population. PMID:26255822

Public engagement in setting healthcare priorities: a ranking exercise in Cyprus.

PubMed

Farmakas, Antonis; Theodorou, Mamas; Galanis, Petros; Karayiannis, Georgios; Ghobrial, Stefanos; Polyzos, Nikos; Papastavrou, Evridiki; Agapidaki, Eirini; Souliotis, Kyriakos

2017-01-01

In countries such as Cyprus the financial crisis and the recession have severely affected the funding and priority setting of the health care system. There is evidence highlighting the importance of population' preferences in designing priorities for health care settings. Although public preferences have been thorough analysed in many countries, there is a research gap in terms of simultaneously investigating the relative importance and the weight of differing and competing criteria for determining healthcare priority settings. The main objective of the study was tο investigate public preferences for the relative utility and weight of differing and competing criteria for health care priority setting in Cyprus. The 'conjoint analysis' technique was applied to develop a ranking exercise. The aim of the study was to identify the preferences of the participants for alternative options. Participants were asked to grade in a priority order 16 hypothetical case scenarios of patients with different disease and of diverse socio-economic characteristics awaiting treatment. The sample was purposive and consisted of 100 Cypriots, selected from public locations all over the country. It was revealed that the "severity of the disease" and the " age of the patient" were the key prioritization criteria. Participants assigned the smallest relative value to the criterion " healthy lifestyle" . More precisely, participants older than 35 years old assigned higher relative importance to " age" , while younger participants to the " severity of the disease". The " healthy lifestyle" criterion was assigned to the lowest relative importance to by all participants. In Cyprus, public participation in health care priority setting is almost inexistent. Nonetheless, it seems that the public's participation in this process could lead to a wider acceptance of the healthcare system especially as a result of the financial crisis and the upcoming reforms implemented such as the establishment of the General System of Health Insurance.

Research Productivity in Top-Ranked Schools in Psychology and Social Work: Research Cultures Do Matter!

ERIC Educational Resources Information Center

Holosko, Michael J.; Barner, John R.

2016-01-01

Objectives: We sought the answer to one major research question--Does psychology have a more defined culture of research than social work? Methods: Using "U.S. News and World Report" 2012 and 2013 rankings, we compared psychology faculty (N = 969) from their 25 top ranked programs with a controlled sample of social work faculty (N = 970)…

A geochemical investigation into the effect of coal rank on the potential environmental effects of CO2 sequestration in deep coal beds

USGS Publications Warehouse

Kolak, Jonathan J.; Burruss, Robert A.

2005-01-01

Coal samples of different rank were extracted in the laboratory with supercritical CO2 to evaluate the potential for mobilizing hydrocarbons during CO2 sequestration or enhanced coal bed methane recovery from deep coal beds. The concentrations of aliphatic hydrocarbons mobilized from the subbituminous C, high-volatile C bituminous, and anthracite coal samples were 41.2, 43.1, and 3.11 ?g g-1 dry coal, respectively. Substantial, but lower, concentrations of polycyclic aromatic hydrocarbons (PAHs) were mobilized from these samples: 2.19, 10.1, and 1.44 ?g g-1 dry coal, respectively. The hydrocarbon distributions within the aliphatic and aromatic fractions obtained from each coal sample also varied with coal rank and reflected changes to the coal matrix associated with increasing degree of coalification. Bitumen present within the coal matrix may affect hydrocarbon partitioning between coal and supercritical CO2. The coal samples continued to yield hydrocarbons during consecutive extractions with supercritical CO2. The amount of hydrocarbons mobilized declined with each successive extraction, and the relative proportion of higher molecular weight hydrocarbons increased during successive extractions. These results demonstrate that the potential for mobilizing hydrocarbons from coal beds, and the effect of coal rank on this process, are important to consider when evaluating coal beds for CO2 storage.

Time-Aware Service Ranking Prediction in the Internet of Things Environment

PubMed Central

Huang, Yuze; Huang, Jiwei; Cheng, Bo; He, Shuqing; Chen, Junliang

2017-01-01

With the rapid development of the Internet of things (IoT), building IoT systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedures of building IoT systems, QoS-aware service selection is an important concern, which requires the ranking of a set of functionally similar services according to their QoS values. In reality, however, it is quite expensive and even impractical to evaluate all geographically-dispersed IoT services at a single client to obtain such a ranking. Nevertheless, distributed measurement and ranking aggregation have to deal with the high dynamics of QoS values and the inconsistency of partial rankings. To address these challenges, we propose a time-aware service ranking prediction approach named TSRPred for obtaining the global ranking from the collection of partial rankings. Specifically, a pairwise comparison model is constructed to describe the relationships between different services, where the partial rankings are obtained by time series forecasting on QoS values. The comparisons of IoT services are formulated by random walks, and thus, the global ranking can be obtained by sorting the steady-state probabilities of the underlying Markov chain. Finally, the efficacy of TSRPred is validated by simulation experiments based on large-scale real-world datasets. PMID:28448451

Time-Aware Service Ranking Prediction in the Internet of Things Environment.

PubMed

Huang, Yuze; Huang, Jiwei; Cheng, Bo; He, Shuqing; Chen, Junliang

2017-04-27

With the rapid development of the Internet of things (IoT), building IoT systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedures of building IoT systems, QoS-aware service selection is an important concern, which requires the ranking of a set of functionally similar services according to their QoS values. In reality, however, it is quite expensive and even impractical to evaluate all geographically-dispersed IoT services at a single client to obtain such a ranking. Nevertheless, distributed measurement and ranking aggregation have to deal with the high dynamics of QoS values and the inconsistency of partial rankings. To address these challenges, we propose a time-aware service ranking prediction approach named TSRPred for obtaining the global ranking from the collection of partial rankings. Specifically, a pairwise comparison model is constructed to describe the relationships between different services, where the partial rankings are obtained by time series forecasting on QoS values. The comparisons of IoT services are formulated by random walks, and thus, the global ranking can be obtained by sorting the steady-state probabilities of the underlying Markov chain. Finally, the efficacy of TSRPred is validated by simulation experiments based on large-scale real-world datasets.

Patients' self-interested preferences: empirical evidence from a priority setting experiment.

PubMed

Alvarez, Begoña; Rodríguez-Míguez, Eva

2011-04-01

This paper explores whether patients act according to self-interest in priority setting experiments. The analysis is based on a ranking experiment, conducted in Galicia (Spain), to elicit preferences regarding the prioritization of patients on a waiting list for an elective surgical intervention (prostatectomy for benign prostatic hyperplasia). Participants were patients awaiting a similar intervention and members of the general populations. All of them were asked to rank hypothetical patients on a waiting list. A rank-ordered logit was then applied to their responses in order to obtain a prioritization scoring system. Using these estimations, we first test for differences in preferences between patients and general population. Second, we implement a procedure based on the similarity between respondents (true patients) and the hypothetical scenarios they evaluate (hypothetical patients) to analyze whether patients provide self-interested rankings. Our results show that patient preferences differ significantly from general population preferences. The findings also indicate that, when patients rank the hypothetical scenarios on the waiting list, they consider not only the explicit attributes but also the similarity of each scenario to their own. In particular, they assign a higher priority to scenarios that more closely match their own states. We also find that such a preference structure increases their likelihood of reporting "irrational" answers. Copyright © 2011 Elsevier Ltd. All rights reserved.

: A Scalable and Transparent System for Simulating MPI Programs

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

Perumalla, Kalyan S

2010-01-01

is a scalable, transparent system for experimenting with the execution of parallel programs on simulated computing platforms. The level of simulated detail can be varied for application behavior as well as for machine characteristics. Unique features of are repeatability of execution, scalability to millions of simulated (virtual) MPI ranks, scalability to hundreds of thousands of host (real) MPI ranks, portability of the system to a variety of host supercomputing platforms, and the ability to experiment with scientific applications whose source-code is available. The set of source-code interfaces supported by is being expanded to support a wider set of applications, andmore » MPI-based scientific computing benchmarks are being ported. In proof-of-concept experiments, has been successfully exercised to spawn and sustain very large-scale executions of an MPI test program given in source code form. Low slowdowns are observed, due to its use of purely discrete event style of execution, and due to the scalability and efficiency of the underlying parallel discrete event simulation engine, sik. In the largest runs, has been executed on up to 216,000 cores of a Cray XT5 supercomputer, successfully simulating over 27 million virtual MPI ranks, each virtual rank containing its own thread context, and all ranks fully synchronized by virtual time.« less

Non-coding cancer driver candidates identified with a sample- and position-specific model of the somatic mutation rate

PubMed Central

Juul, Malene; Bertl, Johanna; Guo, Qianyun; Nielsen, Morten Muhlig; Świtnicki, Michał; Hornshøj, Henrik; Madsen, Tobias; Hobolth, Asger; Pedersen, Jakob Skou

2017-01-01

Non-coding mutations may drive cancer development. Statistical detection of non-coding driver regions is challenged by a varying mutation rate and uncertainty of functional impact. Here, we develop a statistically founded non-coding driver-detection method, ncdDetect, which includes sample-specific mutational signatures, long-range mutation rate variation, and position-specific impact measures. Using ncdDetect, we screened non-coding regulatory regions of protein-coding genes across a pan-cancer set of whole-genomes (n = 505), which top-ranked known drivers and identified new candidates. For individual candidates, presence of non-coding mutations associates with altered expression or decreased patient survival across an independent pan-cancer sample set (n = 5454). This includes an antigen-presenting gene (CD1A), where 5’UTR mutations correlate significantly with decreased survival in melanoma. Additionally, mutations in a base-excision-repair gene (SMUG1) correlate with a C-to-T mutational-signature. Overall, we find that a rich model of mutational heterogeneity facilitates non-coding driver identification and integrative analysis points to candidates of potential clinical relevance. DOI: http://dx.doi.org/10.7554/eLife.21778.001 PMID:28362259

Recursive algorithms for phylogenetic tree counting.

PubMed

Gavryushkina, Alexandra; Welch, David; Drummond, Alexei J

2013-10-28

In Bayesian phylogenetic inference we are interested in distributions over a space of trees. The number of trees in a tree space is an important characteristic of the space and is useful for specifying prior distributions. When all samples come from the same time point and no prior information available on divergence times, the tree counting problem is easy. However, when fossil evidence is used in the inference to constrain the tree or data are sampled serially, new tree spaces arise and counting the number of trees is more difficult. We describe an algorithm that is polynomial in the number of sampled individuals for counting of resolutions of a constraint tree assuming that the number of constraints is fixed. We generalise this algorithm to counting resolutions of a fully ranked constraint tree. We describe a quadratic algorithm for counting the number of possible fully ranked trees on n sampled individuals. We introduce a new type of tree, called a fully ranked tree with sampled ancestors, and describe a cubic time algorithm for counting the number of such trees on n sampled individuals. These algorithms should be employed for Bayesian Markov chain Monte Carlo inference when fossil data are included or data are serially sampled.

Ranking Institutional Settings Based on Publications in Community Psychology Journals

ERIC Educational Resources Information Center

Jason, Leonard A.; Pokorny, Steven B.; Patka, Mazna; Adams, Monica; Morello, Taylor

2007-01-01

Two primary outlets for community psychology research, the "American Journal of Community Psychology" and the "Journal of Community Psychology", were assessed to rank institutions based on publication frequency and scientific influence of publications over a 32-year period. Three specific periods were assessed (1973-1983, 1984-1994, 1995-2004).…

Keypress-Based Musical Preference Is Both Individual and Lawful.

PubMed

Livengood, Sherri L; Sheppard, John P; Kim, Byoung W; Malthouse, Edward C; Bourne, Janet E; Barlow, Anne E; Lee, Myung J; Marin, Veronica; O'Connor, Kailyn P; Csernansky, John G; Block, Martin P; Blood, Anne J; Breiter, Hans C

2017-01-01

Musical preference is highly individualized and is an area of active study to develop methods for its quantification. Recently, preference-based behavior, associated with activity in brain reward circuitry, has been shown to follow lawful, quantifiable patterns, despite broad variation across individuals. These patterns, observed using a keypress paradigm with visual stimuli, form the basis for relative preference theory (RPT). Here, we sought to determine if such patterns extend to non-visual domains (i.e., audition) and dynamic stimuli, potentially providing a method to supplement psychometric, physiological, and neuroimaging approaches to preference quantification. For this study, we adapted our keypress paradigm to two sets of stimuli consisting of seventeenth to twenty-first century western art music (Classical) and twentieth to twenty-first century jazz and popular music (Popular). We studied a pilot sample and then a separate primary experimental sample with this paradigm, and used iterative mathematical modeling to determine if RPT relationships were observed with high R 2 fits. We further assessed the extent of heterogeneity in the rank ordering of keypress-based responses across subjects. As expected, individual rank orderings of preferences were quite heterogeneous, yet we observed mathematical patterns fitting these data similar to those observed previously with visual stimuli. These patterns in music preference were recurrent across two cohorts and two stimulus sets, and scaled between individual and group data, adhering to the requirements for lawfulness. Our findings suggest a general neuroscience framework that predicts human approach/avoidance behavior, while also allowing for individual differences and the broad diversity of human choices; the resulting framework may offer novel approaches to advancing music neuroscience, or its applications to medicine and recommendation systems.

Keypress-Based Musical Preference Is Both Individual and Lawful

PubMed Central

Livengood, Sherri L.; Sheppard, John P.; Kim, Byoung W.; Malthouse, Edward C.; Bourne, Janet E.; Barlow, Anne E.; Lee, Myung J.; Marin, Veronica; O'Connor, Kailyn P.; Csernansky, John G.; Block, Martin P.; Blood, Anne J.; Breiter, Hans C.

2017-01-01

Musical preference is highly individualized and is an area of active study to develop methods for its quantification. Recently, preference-based behavior, associated with activity in brain reward circuitry, has been shown to follow lawful, quantifiable patterns, despite broad variation across individuals. These patterns, observed using a keypress paradigm with visual stimuli, form the basis for relative preference theory (RPT). Here, we sought to determine if such patterns extend to non-visual domains (i.e., audition) and dynamic stimuli, potentially providing a method to supplement psychometric, physiological, and neuroimaging approaches to preference quantification. For this study, we adapted our keypress paradigm to two sets of stimuli consisting of seventeenth to twenty-first century western art music (Classical) and twentieth to twenty-first century jazz and popular music (Popular). We studied a pilot sample and then a separate primary experimental sample with this paradigm, and used iterative mathematical modeling to determine if RPT relationships were observed with high R2 fits. We further assessed the extent of heterogeneity in the rank ordering of keypress-based responses across subjects. As expected, individual rank orderings of preferences were quite heterogeneous, yet we observed mathematical patterns fitting these data similar to those observed previously with visual stimuli. These patterns in music preference were recurrent across two cohorts and two stimulus sets, and scaled between individual and group data, adhering to the requirements for lawfulness. Our findings suggest a general neuroscience framework that predicts human approach/avoidance behavior, while also allowing for individual differences and the broad diversity of human choices; the resulting framework may offer novel approaches to advancing music neuroscience, or its applications to medicine and recommendation systems. PMID:28512395

Coal Rank and Stratigraphy of Pennsylvanian Coal and Coaly Shale Samples, Young County, North-Central Texas

USGS Publications Warehouse

Guevara, Edgar H.; Breton, Caroline; Hackley, Paul C.

2007-01-01

Vitrinite reflectance measurements were made to determine the rank of selected subsurface coal and coaly shale samples from Young County, north-central Texas, for the National Coal Resources Database System State Cooperative Program conducted by the Bureau of Economic Geology at The University of Texas at Austin. This research is the continuation of a pilot study that began in adjacent Archer County, and forms part of a larger investigation of the coalbed methane resource potential of Pennsylvanian coals in north-central Texas. A total of 57 samples of coal and coaly shale fragments were hand-picked from drill cuttings from depths of about 2,000 ft in five wells, and Ro determinations were made on an initial 10-sample subset. Electric-log correlation of the sampled wells indicates that the collected samples represent coal and coaly shale layers in the Strawn (Pennsylvanian), Canyon (Pennsylvanian), and Cisco (Pennsylvanian-Permian) Groups. Coal rank in the initial sample subset ranges from lignite (Ro=0.39), in a sample from the Cisco Group at a depth of 310 to 320 ft, to high volatile bituminous A coal (Ro=0.91) in a sample from the lower part of the Canyon Group at a depth of 2,030 to 2,040 ft.

Do Standard Bibliometric Measures Correlate with Academic Rank of Full-Time Pediatric Dentistry Faculty Members?

PubMed

Susarla, Harlyn K; Dhar, Vineet; Karimbux, Nadeem Y; Tinanoff, Norman

2017-04-01

The aim of this cross-sectional study was to assess the relationship between quantitative measures of research productivity and academic rank for full-time pediatric dentistry faculty members in accredited U.S. and Canadian residency programs. For each pediatric dentist in the study group, academic rank and bibliometric factors derived from publicly available databases were recorded. Academic ranks were lecturer/instructor, assistant professor, associate professor, and professor. Bibliometric factors were mean total number of publications, mean total number of citations, maximum number of citations for a single work, and h-index (a measure of the impact of publications, determined by total number of publications h that had at least h citations each). The study sample was comprised of 267 pediatric dentists: 4% were lecturers/instructors, 44% were assistant professors, 30% were associate professors, and 22% were professors. The mean number of publications for the sample was 15.4±27.8. The mean number of citations was 218.4±482.0. The mean h-index was 4.9±6.6. The h-index was strongly correlated with academic rank (r=0.60, p=0.001). For this sample, an h-index of ≥3 was identified as a threshold for promotion to associate professor, and an h-index of ≥6 was identified as a threshold for promotion to professor. The h-index was strongly correlated with the academic rank of these pediatric dental faculty members, suggesting that this index may be considered a measure for promotion, along with a faculty member's quality and quantity of research, teaching, service, and clinical activities.

Contextual effects on the perceived health benefits of exercise: the exercise rank hypothesis.

PubMed

Maltby, John; Wood, Alex M; Vlaev, Ivo; Taylor, Michael J; Brown, Gordon D A

2012-12-01

Many accounts of social influences on exercise participation describe how people compare their behaviors to those of others. We develop and test a novel hypothesis, the exercise rank hypothesis, of how this comparison can occur. The exercise rank hypothesis, derived from evolutionary theory and the decision by sampling model of judgment, suggests that individuals' perceptions of the health benefits of exercise are influenced by how individuals believe the amount of exercise ranks in comparison with other people's amounts of exercise. Study 1 demonstrated that individuals' perceptions of the health benefits of their own current exercise amounts were as predicted by the exercise rank hypothesis. Study 2 demonstrated that the perceptions of the health benefits of an amount of exercise can be manipulated by experimentally changing the ranked position of the amount within a comparison context. The discussion focuses on how social norm-based interventions could benefit from using rank information.

Systems and methods for maintaining multiple objects within a camera field-of-view

DOEpatents

Gans, Nicholas R.; Dixon, Warren

2016-03-15

In one embodiment, a system and method for maintaining objects within a camera field of view include identifying constraints to be enforced, each constraint relating to an attribute of the viewed objects, identifying a priority rank for the constraints such that more important constraints have a higher priority that less important constraints, and determining the set of solutions that satisfy the constraints relative to the order of their priority rank such that solutions that satisfy lower ranking constraints are only considered viable if they also satisfy any higher ranking constraints, each solution providing an indication as to how to control the camera to maintain the objects within the camera field of view.

Use of cost-effectiveness data in priority setting decisions: experiences from the national guidelines for heart diseases in Sweden

PubMed Central

Eckard, Nathalie; Janzon, Magnus; Levin, Lars-Åke

2014-01-01

Background: The inclusion of cost-effectiveness data, as a basis for priority setting rankings, is a distinguishing feature in the formulation of the Swedish national guidelines. Guidelines are generated with the direct intent to influence health policy and support decisions about the efficient allocation of scarce healthcare resources. Certain medical conditions may be given higher priority rankings i.e. given more resources than others, depending on how serious the medical condition is. This study investigated how a decision-making group, the Priority Setting Group (PSG), used cost-effectiveness data in ranking priority setting decisions in the national guidelines for heart diseases. Methods: A qualitative case study methodology was used to explore the use of such data in ranking priority setting healthcare decisions. The study addressed availability of cost-effectiveness data, evidence understanding, interpretation difficulties, and the reliance on evidence. We were also interested in the explicit use of data in ranking decisions, especially in situations where economic arguments impacted the reasoning behind the decisions. Results: This study showed that cost-effectiveness data was an important and integrated part of the decision-making process. Involvement of a health economist and reliance on the data facilitated the use of cost-effectiveness data. Economic arguments were used both as a fine-tuning instrument and a counterweight for dichotomization. Cost-effectiveness data were used when the overall evidence base was weak and the decision-makers had trouble making decisions due to lack of clinical evidence and in times of uncertainty. Cost-effectiveness data were also used for decisions on the introduction of new expensive medical technologies. Conclusion: Cost-effectiveness data matters in decision-making processes and the results of this study could be applicable to other jurisdictions where health economics is implemented in decision-making. This study contributes to knowledge on how cost-effectiveness data is used in actual decision-making, to ensure that the decisions are offered on equal terms and that patients receive medical care according their needs in order achieve maximum benefit. PMID:25396208

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

Townsend, M., Prothro, L. B., Obi, C.

A test bed for a series of chemical explosives tests known as Source Physics Experiments (SPE) was constructed in granitic rock of the Climax stock, in northern Yucca Flat at the Nevada National Security Site in 2010-2011. These tests are sponsored by the U.S. Department of Energy, National Nuclear Security Administration's National Center for Nuclear Security. The test series is designed to study the generation and propagation of seismic waves, and will provide data that will improve the predictive capability of calculational models for detecting and characterizing underground explosions. Abundant geologic data are available for the area, primarily as amore » result of studies performed in conjunction with the three underground nuclear tests conducted in the Climax granite in the 1960s and a few later studies of various types. The SPE test bed was constructed at an elevation of approximately 1,524 meters (m), and consists of a 91.4-centimeter (cm) diameter source hole at its center, surrounded by two rings of three 20.3-cm diameter instrument holes. The inner ring of holes is positioned 10 m away from the source hole, and the outer ring of holes is positioned 20 m from the source hole. An initial 160-m deep core hole was drilled at the location of the source hole that provided information on the geology of the site and rock samples for later laboratory testing. A suite of geophysical logs was run in the core hole and all six instruments holes to obtain matrix and fracture properties. Detailed information on the character and density of fractures encountered was obtained from the borehole image logs run in the holes. A total of 2,488 fractures were identified in the seven boreholes, and these were ranked into six categories (0 through 5) on the basis of their degree of openness and continuity. The analysis presented here considered only the higher-ranked fractures (ranks 2 through 5), of which there were 1,215 (approximately 49 percent of all fractures identified from borehole image logs). The fractures were grouped into sets based on their orientation. The most ubiquitous fracture set (50 percent of all higher-ranked fractures) is a group of low-angle fractures (dips 0 to 30 degrees). Fractures with dips of 60 to 90 degrees account for 38 percent of high-ranked fractures, and the remaining 12 percent are fractures with moderate dips (30 to 60 degrees). The higher-angle fractures are further subdivided into three sets based on their dip direction: fractures of Set 1 dip to the north-northeast, fractures of Set 2 dip to the south-southwest, and Set 3 consists of high-angle fractures that dip to the southeast and strike northeast. The low-angle fractures (Set 4) dip eastward. Fracture frequency does not appear to change substantially with depth. True fracture spacing averages 0.9 to 1.2 m for high-angle Sets 1, 2, and 3, and 0.6 m for Set 4. Two significant faults were observed in the core, centered at the depths of 25.3 and 32.3 m. The upper of these two faults dips 80 degrees to the north-northeast and, thus, is related to the Set-1 fractures. The lower fault dips 79 degrees to the south-southwest and is related to SPE Set-2 fractures. Neither fault has an identifiable surface trace. Groundwater was encountered in all holes drilled on the SPE test bed, and the fluid level averaged about 15.2 to 18.3 m below ground surface. An informal study of variations in the fluid level in the holes conducted during various phases of construction of the test bed concluded that groundwater flow through the fractured granitic rocks is not uniform, and appears to be controlled by variations in the orientation and degree of interconnectedness of the fractures. It may also be possible that an aplite dike or quartz vein may be present in the test bed, which could act as a barrier to groundwater flow and, thus, could account for anisotropy seen in the groundwater recovery measurements.« less

Constant size descriptors for accurate machine learning models of molecular properties

NASA Astrophysics Data System (ADS)

Collins, Christopher R.; Gordon, Geoffrey J.; von Lilienfeld, O. Anatole; Yaron, David J.

2018-06-01

Two different classes of molecular representations for use in machine learning of thermodynamic and electronic properties are studied. The representations are evaluated by monitoring the performance of linear and kernel ridge regression models on well-studied data sets of small organic molecules. One class of representations studied here counts the occurrence of bonding patterns in the molecule. These require only the connectivity of atoms in the molecule as may be obtained from a line diagram or a SMILES string. The second class utilizes the three-dimensional structure of the molecule. These include the Coulomb matrix and Bag of Bonds, which list the inter-atomic distances present in the molecule, and Encoded Bonds, which encode such lists into a feature vector whose length is independent of molecular size. Encoded Bonds' features introduced here have the advantage of leading to models that may be trained on smaller molecules and then used successfully on larger molecules. A wide range of feature sets are constructed by selecting, at each rank, either a graph or geometry-based feature. Here, rank refers to the number of atoms involved in the feature, e.g., atom counts are rank 1, while Encoded Bonds are rank 2. For atomization energies in the QM7 data set, the best graph-based feature set gives a mean absolute error of 3.4 kcal/mol. Inclusion of 3D geometry substantially enhances the performance, with Encoded Bonds giving 2.4 kcal/mol, when used alone, and 1.19 kcal/mol, when combined with graph features.

The Augmented Lagrange Multipliers Method for Matrix Completion from Corrupted Samplings with Application to Mixed Gaussian-Impulse Noise Removal

PubMed Central

Meng, Fan; Yang, Xiaomei; Zhou, Chenghu

2014-01-01

This paper studies the problem of the restoration of images corrupted by mixed Gaussian-impulse noise. In recent years, low-rank matrix reconstruction has become a research hotspot in many scientific and engineering domains such as machine learning, image processing, computer vision and bioinformatics, which mainly involves the problem of matrix completion and robust principal component analysis, namely recovering a low-rank matrix from an incomplete but accurate sampling subset of its entries and from an observed data matrix with an unknown fraction of its entries being arbitrarily corrupted, respectively. Inspired by these ideas, we consider the problem of recovering a low-rank matrix from an incomplete sampling subset of its entries with an unknown fraction of the samplings contaminated by arbitrary errors, which is defined as the problem of matrix completion from corrupted samplings and modeled as a convex optimization problem that minimizes a combination of the nuclear norm and the -norm in this paper. Meanwhile, we put forward a novel and effective algorithm called augmented Lagrange multipliers to exactly solve the problem. For mixed Gaussian-impulse noise removal, we regard it as the problem of matrix completion from corrupted samplings, and restore the noisy image following an impulse-detecting procedure. Compared with some existing methods for mixed noise removal, the recovery quality performance of our method is dominant if images possess low-rank features such as geometrically regular textures and similar structured contents; especially when the density of impulse noise is relatively high and the variance of Gaussian noise is small, our method can outperform the traditional methods significantly not only in the simultaneous removal of Gaussian noise and impulse noise, and the restoration ability for a low-rank image matrix, but also in the preservation of textures and details in the image. PMID:25248103

Comparing Recruitment and Retention Strategies for Rehabilitation Professionals among Hospital and Home Care Employers

PubMed Central

Tran, Diem; Davis, Aileen; McGillis Hall, Linda

2012-01-01

ABSTRACT Purpose: The objective of this study was to compare hospital and home care employers' rankings of both the importance and the feasibility of workforce strategies for recruiting and retaining rehabilitation professionals. Methods: An online self-administered questionnaire was distributed to all employers of rehabilitation professionals in Ontario hospitals (n=144) and Community Care Access Centre home care providers (n=34). Importance and feasibility rankings were based on the percentage of high ratings; 95% CIs were used to determine significant differences between hospital and home care rankings of recruitment and retention strategies. Results: The response rate was 50% (72/144) from hospitals and 73.5% (25/34) from home-care settings. The recruitment and retention strategies considered most important and feasible for rehabilitation therapists, regardless of setting, were communication between employer and worker, compensation packages, access to research, and professional development in budget planning. Tangible resources, support personnel, work safety, and marketing rehabilitation careers to high school students were ranked significantly higher by hospitals than by home care providers. Conclusions: Similarities exist between hospital and home care employers in terms of the importance and feasibility of recruitment and retention strategies for rehabilitation professionals. However, when developing a rehabilitation health human resources plan, the strategies identified as different between hospital and home care settings should be taken into account. PMID:23277683

Integrating habitat status, human population pressure, and protection status into biodiversity conservation priority setting

USGS Publications Warehouse

Shi, Hua; Singh, Ashbindu; Kant, S.; Zhu, Zhiliang; Waller, E.

2005-01-01

Priority setting is an essential component of biodiversity conservation. Existing methods to identify priority areas for conservation have focused almost entirely on biological factors. We suggest a new relative ranking method for identifying priority conservation areas that integrates both biological and social aspects. It is based on the following criteria: the habitat's status, human population pressure, human efforts to protect habitat, and number of endemic plant and vertebrate species. We used this method to rank 25 hotspots, 17 megadiverse countries, and the hotspots within each megadiverse country. We used consistent, comprehensive, georeferenced, and multiband data sets and analytical remote sensing and geographic information system tools to quantify habitat status, human population pressure, and protection status. The ranking suggests that the Philippines, Atlantic Forest, Mediterranean Basin, Caribbean Islands, Caucasus, and Indo-Burma are the hottest hotspots and that China, the Philippines, and India are the hottest megadiverse countries. The great variation in terms of habitat, protected areas, and population pressure among the hotspots, the megadiverse countries, and the hotspots within the same country suggests the need for hotspot- and country-specific conservation policies.

A molecular identification system for grasses: a novel technology for forensic botany.

PubMed

Ward, J; Peakall, R; Gilmore, S R; Robertson, J

2005-09-10

Our present inability to rapidly, accurately and cost-effectively identify trace botanical evidence remains the major impediment to the routine application of forensic botany. Grasses are amongst the most likely plant species encountered as forensic trace evidence and have the potential to provide links between crime scenes and individuals or other vital crime scene information. We are designing a molecular DNA-based identification system for grasses consisting of several PCR assays that, like a traditional morphological taxonomic key, provide criteria that progressively identify an unknown grass sample to a given taxonomic rank. In a prior study of DNA sequences across 20 phylogenetically representative grass species, we identified a series of potentially informative indels in the grass mitochondrial genome. In this study we designed and tested five PCR assays spanning these indels and assessed the feasibility of these assays to aid identification of unknown grass samples. We confirmed that for our control set of 20 samples, on which the design of the PCR assays was based, the five primer combinations produced the expected results. Using these PCR assays in a 'blind test', we were able to identify 25 unknown grass samples with some restrictions. Species belonging to genera represented in our control set were all correctly identified to genus with one exception. Similarly, genera belonging to tribes in the control set were correctly identified to the tribal level. Finally, for those samples for which neither the tribal or genus specific PCR assays were designed, we could confidently exclude these samples from belonging to certain tribes and genera. The results confirmed the utility of the PCR assays and the feasibility of developing a robust full-scale usable grass identification system for forensic purposes.

Genome Scan Meta-Analysis of Schizophrenia and Bipolar Disorder, Part II: Schizophrenia

PubMed Central

Lewis, Cathryn M.; Levinson, Douglas F.; Wise, Lesley H.; DeLisi, Lynn E.; Straub, Richard E.; Hovatta, Iiris; Williams, Nigel M.; Schwab, Sibylle G.; Pulver, Ann E.; Faraone, Stephen V.; Brzustowicz, Linda M.; Kaufmann, Charles A.; Garver, David L.; Gurling, Hugh M. D.; Lindholm, Eva; Coon, Hilary; Moises, Hans W.; Byerley, William; Shaw, Sarah H.; Mesen, Andrea; Sherrington, Robin; O’Neill, F. Anthony; Walsh, Dermot; Kendler, Kenneth S.; Ekelund, Jesper; Paunio, Tiina; Lönnqvist, Jouko; Peltonen, Leena; O’Donovan, Michael C.; Owen, Michael J.; Wildenauer, Dieter B.; Maier, Wolfgang; Nestadt, Gerald; Blouin, Jean-Louis; Antonarakis, Stylianos E.; Mowry, Bryan J.; Silverman, Jeremy M.; Crowe, Raymond R.; Cloninger, C. Robert; Tsuang, Ming T.; Malaspina, Dolores; Harkavy-Friedman, Jill M.; Svrakic, Dragan M.; Bassett, Anne S.; Holcomb, Jennifer; Kalsi, Gursharan; McQuillin, Andrew; Brynjolfson, Jon; Sigmundsson, Thordur; Petursson, Hannes; Jazin, Elena; Zoëga, Tomas; Helgason, Tomas

2003-01-01

Schizophrenia is a common disorder with high heritability and a 10-fold increase in risk to siblings of probands. Replication has been inconsistent for reports of significant genetic linkage. To assess evidence for linkage across studies, rank-based genome scan meta-analysis (GSMA) was applied to data from 20 schizophrenia genome scans. Each marker for each scan was assigned to 1 of 120 30-cM bins, with the bins ranked by linkage scores (1 = most significant) and the ranks averaged across studies (Ravg) and then weighted for sample size (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} \\begin{equation*}\\sqrt{N[affected cases]}\\end{equation*}\\end{document}). A permutation test was used to compute the probability of observing, by chance, each bin’s average rank (PAvgRnk) or of observing it for a bin with the same place (first, second, etc.) in the order of average ranks in each permutation (Pord). The GSMA produced significant genomewide evidence for linkage on chromosome 2q (PAvgRnk<.000417). Two aggregate criteria for linkage were also met (clusters of nominally significant P values that did not occur in 1,000 replicates of the entire data set with no linkage present): 12 consecutive bins with both PAvgRnk and Pord<.05, including regions of chromosomes 5q, 3p, 11q, 6p, 1q, 22q, 8p, 20q, and 14p, and 19 consecutive bins with Pord<.05, additionally including regions of chromosomes 16q, 18q, 10p, 15q, 6q, and 17q. There is greater consistency of linkage results across studies than has been previously recognized. The results suggest that some or all of these regions contain loci that increase susceptibility to schizophrenia in diverse populations. PMID:12802786

MiRNA-TF-gene network analysis through ranking of biomolecules for multi-informative uterine leiomyoma dataset.

PubMed

Mallik, Saurav; Maulik, Ujjwal

2015-10-01

Gene ranking is an important problem in bioinformatics. Here, we propose a new framework for ranking biomolecules (viz., miRNAs, transcription-factors/TFs and genes) in a multi-informative uterine leiomyoma dataset having both gene expression and methylation data using (statistical) eigenvector centrality based approach. At first, genes that are both differentially expressed and methylated, are identified using Limma statistical test. A network, comprising these genes, corresponding TFs from TRANSFAC and ITFP databases, and targeter miRNAs from miRWalk database, is then built. The biomolecules are then ranked based on eigenvector centrality. Our proposed method provides better average accuracy in hub gene and non-hub gene classifications than other methods. Furthermore, pre-ranked Gene set enrichment analysis is applied on the pathway database as well as GO-term databases of Molecular Signatures Database with providing a pre-ranked gene-list based on different centrality values for comparing among the ranking methods. Finally, top novel potential gene-markers for the uterine leiomyoma are provided. Copyright © 2015 Elsevier Inc. All rights reserved.

Extremes in ecology: Avoiding the misleading effects of sampling variation in summary analyses

USGS Publications Warehouse

Link, W.A.; Sauer, J.R.

1996-01-01

Surveys such as the North American Breeding Bird Survey (BBS) produce large collections of parameter estimates. One's natural inclination when confronted with lists of parameter estimates is to look for the extreme values: in the BBS, these correspond to the species that appear to have the greatest changes in population size through time. Unfortunately, extreme estimates are liable to correspond to the most poorly estimated parameters. Consequently, the most extreme parameters may not match up with the most extreme parameter estimates. The ranking of parameter values on the basis of their estimates are a difficult statistical problem. We use data from the BBS and simulations to illustrate the potential misleading effects of sampling variation in rankings of parameters. We describe empirical Bayes and constrained empirical Bayes procedures which provide partial solutions to the problem of ranking in the presence of sampling variation.

Therapeutic Drug Monitoring of Asparaginase Activity-Method Comparison of MAAT and AHA Test Used in the International AIEOP-BFM ALL 2009 Trial.

PubMed

Lanvers-Kaminsky, Claudia; Rüffer, Andrea; Würthwein, Gudrun; Gerss, Joachim; Zucchetti, Massimo; Ballerini, Andrea; Attarbaschi, Andishe; Smisek, Petr; Nath, Christa; Lee, Samiuela; Elitzur, Sara; Zimmermann, Martin; Möricke, Anja; Schrappe, Martin; Rizzari, Carmelo; Boos, Joachim

2018-02-01

In the international AIEOP-BFM ALL 2009 trial, asparaginase (ASE) activity was monitored after each dose of pegylated Escherichia coli ASE (PEG-ASE). Two methods were used: the aspartic acid β-hydroxamate (AHA) test and medac asparaginase activity test (MAAT). As the latter method overestimates PEG-ASE activity because it calibrates using E. coli ASE, method comparison was performed using samples from the AIEOP-BFM ALL 2009 trial. PEG-ASE activities were determined using MAAT and AHA test in 2 sets of samples (first set: 630 samples and second set: 91 samples). Bland-Altman analysis was performed on ratios between MAAT and AHA tests. The mean difference between both methods, limits of agreement, and 95% confidence intervals were calculated and compared for all samples and samples grouped according to the calibration ranges of the MAAT and the AHA test. PEG-ASE activity determined using the MAAT was significantly higher than when determined using the AHA test (P < 0.001; Wilcoxon signed-rank test). Within the calibration range of the MAAT (30-600 U/L), PEG-ASE activities determined using the MAAT were on average 23% higher than PEG-ASE activities determined using the AHA test. This complies with the mean difference reported in the MAAT manual. With PEG-ASE activities >600 U/L, the discrepancies between MAAT and AHA test increased. Above the calibration range of the MAAT (>600 U/L) and the AHA test (>1000 U/L), a mean difference of 42% was determined. Because more than 70% of samples had PEG-ASE activities >600 U/L and required additional sample dilution, an overall mean difference of 37% was calculated for all samples (37% for the first and 34% for the second set). Comparison of the MAAT and AHA test for PEG-ASE activity confirmed a mean difference of 23% between MAAT and AHA test for PEG-ASE activities between 30 and 600 U/L. The discrepancy increased in samples with >600 U/L PEG-ASE activity, which will be especially relevant when evaluating high PEG-ASE activities in relation to toxicity, efficacy, and population pharmacokinetics.

Composite multi-parameter ranking of real and virtual compounds for design of MC4R agonists: renaissance of the Free-Wilson methodology.

PubMed

Nilsson, Ingemar; Polla, Magnus O

2012-10-01

Drug design is a multi-parameter task present in the analysis of experimental data for synthesized compounds and in the prediction of new compounds with desired properties. This article describes the implementation of a binned scoring and composite ranking scheme for 11 experimental parameters that were identified as key drivers in the MC4R project. The composite ranking scheme was implemented in an AstraZeneca tool for analysis of project data, thereby providing an immediate re-ranking as new experimental data was added. The automated ranking also highlighted compounds overlooked by the project team. The successful implementation of a composite ranking on experimental data led to the development of an equivalent virtual score, which was based on Free-Wilson models of the parameters from the experimental ranking. The individual Free-Wilson models showed good to high predictive power with a correlation coefficient between 0.45 and 0.97 based on the external test set. The virtual ranking adds value to the selection of compounds for synthesis but error propagation must be controlled. The experimental ranking approach adds significant value, is parameter independent and can be tuned and applied to any drug discovery project.

SU-E-T-654: Quantifying Plan Quality Can Effectively Distinguish Between Competing Equivocal IMRT Prostate Plans

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

Price, A; Lo, J; Department of Radiology, Duke University Medical Center, Durham, NC

2015-06-15

Purpose: The purpose of this study was to create a prostate IMRT plan quality index (PQI) that may be used to quantitatively compare competing plans using a methodology that mimics physician preference. This methodology allows planners to choose between plans with equivocal characteristics, prior to physician scrutiny. Methods: An observer study was conducted to gather data from 3 radiation oncology physicians who ranked a set of 20 patients (each with 5 plans). The rankings were used to optimize a PQI that combined weighted portions of the rectum, bladder, and planning target volume DVHs, such that the relative PQI mimicked physicianmore » rankings as best as possible. Once optimized, a test study assessed the PQI by comparison to physician rankings in a new set of 25 patients (each with 4 plans). The physician group in the test study included 6 physicians, 5 of whom were not included in the modeling study. PQI scores were evaluated against the physicians’ rank list using Spearman rank correlation. Results: The optimized plan quality index combined the following DVH features: high dose regions above 40Gy/60Gy (rectum/bladder), organ weightings, and PTV shoulder coverage. Mean correlation of the PQI vs. physicians’ rankings in the modeling study was 0.507 (range: 0.345–0.706). By comparison, the mean correlation between physicians was 0.301 (range: 0.242–0.334). The mean correlation of the PQI vs. physician rankings in test study was 0.726 (range: 0.416–0.936), indicating robustness of the PQI by virtue of producing similar results to the modeling study. Intra-physician correlation was 0.564 (range: 0.398–0.689). Conclusion: The correlation coefficients of the PQI vs. physicians were similar to the correlation coefficients of the physicians with each other, implying that the PQI developed in this work shows promise in reflecting physician clinical preference when selecting between competing, dosimetrically equivocal plans.« less

An interactive database for setting conservation priorities for western neotropical migrants

Treesearch

Michael F. Carter; Keith Barker

1993-01-01

We develop and explain a species ranking system for the states and physiographic regions of the Neotropical Migratory Bird Conservation Program's West Working Group. The ranking criteria attempt to measure characteristics of species which make them vulnerable to extirpation, as well as assess the relative importance of different geographic and/or political areas...

The origins of deference: when do people prefer lower status?

PubMed

Anderson, Cameron; Willer, Robb; Kilduff, Gavin J; Brown, Courtney E

2012-05-01

Although the desire for high status is considered universal, prior research suggests individuals often opt for lower status positions. Why would anyone favor a position of apparent disadvantage? In 5 studies, we found that the broad construct of status striving can be broken up into two conceptions: one based on rank, the other on respect. While individuals might universally desire high levels of respect, we find that they vary widely in the extent to which they strive for high-status rank, with many individuals opting for middle- or low-status rank. The status rank that individuals preferred depended on their self-perceived value to the group: when they believed they provided less value, they preferred lower status rank. Mediation and moderation analyses suggest that beliefs about others' expectations were the primary driver of these effects. Individuals who believed they provided little value to their group inferred that others expected them to occupy a lower status position. Individuals in turn conformed to these perceived expectations, accepting lower status rank in such settings.

A Recursive Partitioning Method for the Prediction of Preference Rankings Based Upon Kemeny Distances.

PubMed

D'Ambrosio, Antonio; Heiser, Willem J

2016-09-01

Preference rankings usually depend on the characteristics of both the individuals judging a set of objects and the objects being judged. This topic has been handled in the literature with log-linear representations of the generalized Bradley-Terry model and, recently, with distance-based tree models for rankings. A limitation of these approaches is that they only work with full rankings or with a pre-specified pattern governing the presence of ties, and/or they are based on quite strict distributional assumptions. To overcome these limitations, we propose a new prediction tree method for ranking data that is totally distribution-free. It combines Kemeny's axiomatic approach to define a unique distance between rankings with the CART approach to find a stable prediction tree. Furthermore, our method is not limited by any particular design of the pattern of ties. The method is evaluated in an extensive full-factorial Monte Carlo study with a new simulation design.

Geothermal Potential of the Cascade and Aleutian Arcs, with Ranking of Individual Volcanic Centers for their Potential to Host Electricity-Grade Reservoirs

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

Shevenell, Lisa; Coolbaugh, Mark; Hinz, Nick

This project brings a global perspective to volcanic arc geothermal play fairway analysis by developing statistics for the occurrence of geothermal reservoirs and their geoscience context worldwide in order to rank U.S. prospects. The focus of the work was to develop play fairways for the Cascade and Aleutian arcs to rank the individual volcanic centers in these arcs by their potential to host electricity grade geothermal systems. The Fairway models were developed by describing key geologic factors expected to be indicative of productive geothermal systems in a global training set, which includes 74 volcanic centers world-wide with current power production.more » To our knowledge, this is the most robust geothermal benchmark training set for magmatic systems to date that will be made public.« less

A collaborative filtering recommendation algorithm based on weighted SimRank and social trust

NASA Astrophysics Data System (ADS)

Su, Chang; Zhang, Butao

2017-05-01

Collaborative filtering is one of the most widely used recommendation technologies, but the data sparsity and cold start problem of collaborative filtering algorithms are difficult to solve effectively. In order to alleviate the problem of data sparsity in collaborative filtering algorithm, firstly, a weighted improved SimRank algorithm is proposed to compute the rating similarity between users in rating data set. The improved SimRank can find more nearest neighbors for target users according to the transmissibility of rating similarity. Then, we build trust network and introduce the calculation of trust degree in the trust relationship data set. Finally, we combine rating similarity and trust to build a comprehensive similarity in order to find more appropriate nearest neighbors for target user. Experimental results show that the algorithm proposed in this paper improves the recommendation precision of the Collaborative algorithm effectively.

Limited Rank Matrix Learning, discriminative dimension reduction and visualization.

PubMed

Bunte, Kerstin; Schneider, Petra; Hammer, Barbara; Schleif, Frank-Michael; Villmann, Thomas; Biehl, Michael

2012-02-01

We present an extension of the recently introduced Generalized Matrix Learning Vector Quantization algorithm. In the original scheme, adaptive square matrices of relevance factors parameterize a discriminative distance measure. We extend the scheme to matrices of limited rank corresponding to low-dimensional representations of the data. This allows to incorporate prior knowledge of the intrinsic dimension and to reduce the number of adaptive parameters efficiently. In particular, for very large dimensional data, the limitation of the rank can reduce computation time and memory requirements significantly. Furthermore, two- or three-dimensional representations constitute an efficient visualization method for labeled data sets. The identification of a suitable projection is not treated as a pre-processing step but as an integral part of the supervised training. Several real world data sets serve as an illustration and demonstrate the usefulness of the suggested method. Copyright © 2011 Elsevier Ltd. All rights reserved.

An Evaluation of Techniques for Ranking Academic Information Systems Journals

DTIC Science & Technology

1993-09-23

business schools . The instructions were to rank the top ten journals in order of decreasing importance. An importance/prestige index was 4 created...in the samples taken by each study could account for the variation in ranking. Doke and Luke (1987) surveyed deans of business schools (with...is unknown. Doke and Luke (1987) sent 45 their surveys primarily to business schools , whereas the surveys in our study went directly to MIS faculty

Reinforce: An Ensemble Approach for Inferring PPI Network from AP-MS Data.

PubMed

Tian, Bo; Duan, Qiong; Zhao, Can; Teng, Ben; He, Zengyou

2017-05-17

Affinity Purification-Mass Spectrometry (AP-MS) is one of the most important technologies for constructing protein-protein interaction (PPI) networks. In this paper, we propose an ensemble method, Reinforce, for inferring PPI network from AP-MS data set. The new algorithm named Reinforce is based on rank aggregation and false discovery rate control. Under the null hypothesis that the interaction scores from different scoring methods are randomly generated, Reinforce follows three steps to integrate multiple ranking results from different algorithms or different data sets. The experimental results show that Reinforce can get more stable and accurate inference results than existing algorithms. The source codes of Reinforce and data sets used in the experiments are available at: https://sourceforge.net/projects/reinforce/.

RANK Expression and Osteoclastogenesis in Human Monocytes in Peripheral Blood from Rheumatoid Arthritis Patients.

PubMed

Nanke, Yuki; Kobashigawa, Tsuyoshi; Yago, Toru; Kawamoto, Manabu; Yamanaka, Hisashi; Kotake, Shigeru

2016-01-01

Rheumatoid arthritis (RA) appears as inflammation of synovial tissue and joint destruction. Receptor activator of NF- κ B (RANK) is a member of the TNF receptor superfamily and a receptor for the RANK ligand (RANKL). In this study, we examined the expression of RANK high and CCR6 on CD14 + monocytes from patients with RA and healthy volunteers. Peripheral blood samples were obtained from both the RA patients and the healthy volunteers. Osteoclastogenesis from monocytes was induced by RANKL and M-CSF in vitro . To study the expression of RANK high and CCR6 on CD14 + monocytes, two-color flow cytometry was performed. Levels of expression of RANK on monocytes were significantly correlated with the level of osteoclastogenesis in the healthy volunteers. The expression of RANK high on CD14 + monocyte in RA patients without treatment was elevated and that in those receiving treatment was decreased. In addition, the high-level expression of RANK on CD14 + monocytes was correlated with the high-level expression of CCR6 in healthy volunteers. Monocytes expressing both RANK and CCR6 differentiate into osteoclasts. The expression of CD14 + RANK high in untreated RA patients was elevated. RANK and CCR6 expressed on monocytes may be novel targets for the regulation of bone resorption in RA and osteoporosis.

Empirical prediction intervals improve energy forecasting

PubMed Central

Kaack, Lynn H.; Apt, Jay; Morgan, M. Granger; McSharry, Patrick

2017-01-01

Hundreds of organizations and analysts use energy projections, such as those contained in the US Energy Information Administration (EIA)’s Annual Energy Outlook (AEO), for investment and policy decisions. Retrospective analyses of past AEO projections have shown that observed values can differ from the projection by several hundred percent, and thus a thorough treatment of uncertainty is essential. We evaluate the out-of-sample forecasting performance of several empirical density forecasting methods, using the continuous ranked probability score (CRPS). The analysis confirms that a Gaussian density, estimated on past forecasting errors, gives comparatively accurate uncertainty estimates over a variety of energy quantities in the AEO, in particular outperforming scenario projections provided in the AEO. We report probabilistic uncertainties for 18 core quantities of the AEO 2016 projections. Our work frames how to produce, evaluate, and rank probabilistic forecasts in this setting. We propose a log transformation of forecast errors for price projections and a modified nonparametric empirical density forecasting method. Our findings give guidance on how to evaluate and communicate uncertainty in future energy outlooks. PMID:28760997

Application of machine vision to pup loaf bread evaluation

NASA Astrophysics Data System (ADS)

Zayas, Inna Y.; Chung, O. K.

1996-12-01

Intrinsic end-use quality of hard winter wheat breeding lines is routinely evaluated at the USDA, ARS, USGMRL, Hard Winter Wheat Quality Laboratory. Experimental baking test of pup loaves is the ultimate test for evaluating hard wheat quality. Computer vision was applied to developing an objective methodology for bread quality evaluation for the 1994 and 1995 crop wheat breeding line samples. Computer extracted features for bread crumb grain were studied, using subimages (32 by 32 pixel) and features computed for the slices with different threshold settings. A subsampling grid was located with respect to the axis of symmetry of a slice to provide identical topological subimage information. Different ranking techniques were applied to the databases. Statistical analysis was run on the database with digital image and breadmaking features. Several ranking algorithms and data visualization techniques were employed to create a sensitive scale for porosity patterns of bread crumb. There were significant linear correlations between machine vision extracted features and breadmaking parameters. Crumb grain scores by human experts were correlated more highly with some image features than with breadmaking parameters.

Ranking Practice Variability in the Medical Student Performance Evaluation: So Bad, It's "Good".

PubMed

Boysen Osborn, Megan; Mattson, James; Yanuck, Justin; Anderson, Craig; Tekian, Ara; Fox, John Christian; Harris, Ilene B

2016-11-01

To examine the variability among medical schools in ranking systems used in medical student performance evaluations (MSPEs). The authors reviewed MSPEs from U.S. MD-granting medical schools received by the University of California, Irvine emergency medicine and internal medicine residency programs during 2012-2013 and 2014-2015. They recorded whether the school used a ranking system, the type of ranking system used, the size and description of student categories, the location of the ranking statement and category legend, and whether nonranking schools used language suggestive of rank. Of the 134 medical schools in the study sample, the majority (n = 101; 75%) provided ranks for students in the MSPE. Most of the ranking schools (n = 63; 62%) placed students into named category groups, but the number and size of groups varied. The most common descriptors used for these 63 schools' top, second, third, and lowest groups were "outstanding," "excellent," "very good," and "good," respectively, but each of these terms was used across a broad range of percentile ranks. Student ranks and school category legends were found in various locations. Many of the 33 schools that did not rank students included language suggestive of rank. There is extensive variation in ranking systems used in MSPEs. Program directors may find it difficult to use MSPEs to compare applicants, which may diminish the MSPE's value in the residency application process and negatively affect high-achieving students. A consistent approach to ranking students would benefit program directors, students, and student affairs officers.

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

PubMed Central

Liu, Jiangang; Jolly, Robert A.; Smith, Aaron T.; Searfoss, George H.; Goldstein, Keith M.; Uversky, Vladimir N.; Dunker, Keith; Li, Shuyu; Thomas, Craig E.; Wei, Tao

2011-01-01

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses. PMID:21935387

Permutation tests for goodness-of-fit testing of mathematical models to experimental data.

PubMed

Fişek, M Hamit; Barlas, Zeynep

2013-03-01

This paper presents statistical procedures for improving the goodness-of-fit testing of theoretical models to data obtained from laboratory experiments. We use an experimental study in the expectation states research tradition which has been carried out in the "standardized experimental situation" associated with the program to illustrate the application of our procedures. We briefly review the expectation states research program and the fundamentals of resampling statistics as we develop our procedures in the resampling context. The first procedure we develop is a modification of the chi-square test which has been the primary statistical tool for assessing goodness of fit in the EST research program, but has problems associated with its use. We discuss these problems and suggest a procedure to overcome them. The second procedure we present, the "Average Absolute Deviation" test, is a new test and is proposed as an alternative to the chi square test, as being simpler and more informative. The third and fourth procedures are permutation versions of Jonckheere's test for ordered alternatives, and Kendall's tau(b), a rank order correlation coefficient. The fifth procedure is a new rank order goodness-of-fit test, which we call the "Deviation from Ideal Ranking" index, which we believe may be more useful than other rank order tests for assessing goodness-of-fit of models to experimental data. The application of these procedures to the sample data is illustrated in detail. We then present another laboratory study from an experimental paradigm different from the expectation states paradigm - the "network exchange" paradigm, and describe how our procedures may be applied to this data set. Copyright © 2012 Elsevier Inc. All rights reserved.

Incentives and Barriers That Influence Clinical Computerization in Hong Kong: A Population-based Physician Survey

PubMed Central

Leung, Gabriel M.; Yu, Philip L. H.; Wong, Irene O. L.; Johnston, Janice M.; Tin, Keith Y. K.

2003-01-01

Objective: Given the slow adoption of medical informatics in Hong Kong and Asia, we sought to understand the contributory barriers and potential incentives associated with information technology implementation. Design and Measurements: A representative sample of 949 doctors (response rate = 77.0%) was asked through a postal survey to rank a list of nine barriers associated with clinical computerization according to self-perceived importance. They ranked seven incentives or catalysts that may influence computerization. We generated mean rank scores and used multidimensional preference analysis to explore key explanatory dimensions of these variables. A hierarchical cluster analysis was performed to identify homogenous subgroups of respondents. We further determined the relationships between the sets of barriers and incentives/catalysts collectively using canonical correlation. Results: Time costs, lack of technical support and large capital investments were the biggest barriers to computerization, whereas improved office efficiency and better-quality care were ranked highest as potential incentives to computerize. Cost vs. noncost, physician-related vs. patient-related, and monetary vs. nonmonetary factors were the key dimensions explaining the barrier variables. Similarly, within-practice vs external and “push” vs “pull” factors accounted for the incentive variables. Four clusters were identified for barriers and three for incentives/catalysts. Canonical correlation revealed that respondents who were concerned with the costs of computerization also perceived financial incentives and government regulation to be important incentives/catalysts toward computerization. Those who found the potential interference with communication important also believed that the promise of improved care from computerization to be a significant incentive. Conclusion: This study provided evidence regarding common barriers associated with clinical computerization. Our findings also identified possible incentive strategies that may be employed to accelerate uptake of computer systems. PMID:12595409

Improved Hip-Based Individual Recognition Using Wearable Motion Recording Sensor

NASA Astrophysics Data System (ADS)

Gafurov, Davrondzhon; Bours, Patrick

In todays society the demand for reliable verification of a user identity is increasing. Although biometric technologies based on fingerprint or iris can provide accurate and reliable recognition performance, they are inconvenient for periodic or frequent re-verification. In this paper we propose a hip-based user recognition method which can be suitable for implicit and periodic re-verification of the identity. In our approach we use a wearable accelerometer sensor attached to the hip of the person, and then the measured hip motion signal is analysed for identity verification purposes. The main analyses steps consists of detecting gait cycles in the signal and matching two sets of detected gait cycles. Evaluating the approach on a hip data set consisting of 400 gait sequences (samples) from 100 subjects, we obtained equal error rate (EER) of 7.5% and identification rate at rank 1 was 81.4%. These numbers are improvements by 37.5% and 11.2% respectively of the previous study using the same data set.

An Optimization-Based Method for Feature Ranking in Nonlinear Regression Problems.

PubMed

Bravi, Luca; Piccialli, Veronica; Sciandrone, Marco

2017-04-01

In this paper, we consider the feature ranking problem, where, given a set of training instances, the task is to associate a score with the features in order to assess their relevance. Feature ranking is a very important tool for decision support systems, and may be used as an auxiliary step of feature selection to reduce the high dimensionality of real-world data. We focus on regression problems by assuming that the process underlying the generated data can be approximated by a continuous function (for instance, a feedforward neural network). We formally state the notion of relevance of a feature by introducing a minimum zero-norm inversion problem of a neural network, which is a nonsmooth, constrained optimization problem. We employ a concave approximation of the zero-norm function, and we define a smooth, global optimization problem to be solved in order to assess the relevance of the features. We present the new feature ranking method based on the solution of instances of the global optimization problem depending on the available training data. Computational experiments on both artificial and real data sets are performed, and point out that the proposed feature ranking method is a valid alternative to existing methods in terms of effectiveness. The obtained results also show that the method is costly in terms of CPU time, and this may be a limitation in the solution of large-dimensional problems.

CAFÉ-Map: Context Aware Feature Mapping for mining high dimensional biomedical data.

PubMed

Minhas, Fayyaz Ul Amir Afsar; Asif, Amina; Arif, Muhammad

2016-12-01

Feature selection and ranking is of great importance in the analysis of biomedical data. In addition to reducing the number of features used in classification or other machine learning tasks, it allows us to extract meaningful biological and medical information from a machine learning model. Most existing approaches in this domain do not directly model the fact that the relative importance of features can be different in different regions of the feature space. In this work, we present a context aware feature ranking algorithm called CAFÉ-Map. CAFÉ-Map is a locally linear feature ranking framework that allows recognition of important features in any given region of the feature space or for any individual example. This allows for simultaneous classification and feature ranking in an interpretable manner. We have benchmarked CAFÉ-Map on a number of toy and real world biomedical data sets. Our comparative study with a number of published methods shows that CAFÉ-Map achieves better accuracies on these data sets. The top ranking features obtained through CAFÉ-Map in a gene profiling study correlate very well with the importance of different genes reported in the literature. Furthermore, CAFÉ-Map provides a more in-depth analysis of feature ranking at the level of individual examples. CAFÉ-Map Python code is available at: http://faculty.pieas.edu.pk/fayyaz/software.html#cafemap . The CAFÉ-Map package supports parallelization and sparse data and provides example scripts for classification. This code can be used to reconstruct the results given in this paper. Copyright © 2016 Elsevier Ltd. All rights reserved.

Ranking docking poses by graph matching of protein-ligand interactions: lessons learned from the D3R Grand Challenge 2

NASA Astrophysics Data System (ADS)

da Silva Figueiredo Celestino Gomes, Priscila; Da Silva, Franck; Bret, Guillaume; Rognan, Didier

2018-01-01

A novel docking challenge has been set by the Drug Design Data Resource (D3R) in order to predict the pose and affinity ranking of a set of Farnesoid X receptor (FXR) agonists, prior to the public release of their bound X-ray structures and potencies. In a first phase, 36 agonists were docked to 26 Protein Data Bank (PDB) structures of the FXR receptor, and next rescored using the in-house developed GRIM method. GRIM aligns protein-ligand interaction patterns of docked poses to those of available PDB templates for the target protein, and rescore poses by a graph matching method. In agreement with results obtained during the previous 2015 docking challenge, we clearly show that GRIM rescoring improves the overall quality of top-ranked poses by prioritizing interaction patterns already visited in the PDB. Importantly, this challenge enables us to refine the applicability domain of the method by better defining the conditions of its success. We notably show that rescoring apolar ligands in hydrophobic pockets leads to frequent GRIM failures. In the second phase, 102 FXR agonists were ranked by decreasing affinity according to the Gibbs free energy of the corresponding GRIM-selected poses, computed by the HYDE scoring function. Interestingly, this fast and simple rescoring scheme provided the third most accurate ranking method among 57 contributions. Although the obtained ranking is still unsuitable for hit to lead optimization, the GRIM-HYDE scoring scheme is accurate and fast enough to post-process virtual screening data.

Comparison of multianalyte proficiency test results by sum of ranking differences, principal component analysis, and hierarchical cluster analysis.

PubMed

Škrbić, Biljana; Héberger, Károly; Durišić-Mladenović, Nataša

2013-10-01

Sum of ranking differences (SRD) was applied for comparing multianalyte results obtained by several analytical methods used in one or in different laboratories, i.e., for ranking the overall performances of the methods (or laboratories) in simultaneous determination of the same set of analytes. The data sets for testing of the SRD applicability contained the results reported during one of the proficiency tests (PTs) organized by EU Reference Laboratory for Polycyclic Aromatic Hydrocarbons (EU-RL-PAH). In this way, the SRD was also tested as a discriminant method alternative to existing average performance scores used to compare mutlianalyte PT results. SRD should be used along with the z scores--the most commonly used PT performance statistics. SRD was further developed to handle the same rankings (ties) among laboratories. Two benchmark concentration series were selected as reference: (a) the assigned PAH concentrations (determined precisely beforehand by the EU-RL-PAH) and (b) the averages of all individual PAH concentrations determined by each laboratory. Ranking relative to the assigned values and also to the average (or median) values pointed to the laboratories with the most extreme results, as well as revealed groups of laboratories with similar overall performances. SRD reveals differences between methods or laboratories even if classical test(s) cannot. The ranking was validated using comparison of ranks by random numbers (a randomization test) and using seven folds cross-validation, which highlighted the similarities among the (methods used in) laboratories. Principal component analysis and hierarchical cluster analysis justified the findings based on SRD ranking/grouping. If the PAH-concentrations are row-scaled, (i.e., z scores are analyzed as input for ranking) SRD can still be used for checking the normality of errors. Moreover, cross-validation of SRD on z scores groups the laboratories similarly. The SRD technique is general in nature, i.e., it can be applied to any experimental problem in which multianalyte results obtained either by several analytical procedures, analysts, instruments, or laboratories need to be compared.

Mental Health Research in Correctional Settings: Perceptions of Risk and Vulnerabilities

PubMed Central

Johnson, Mark E.; Kondo, Karli K.; Brems, Christiane; Ironside, Erica F.; Eldridge, Gloria D.

2015-01-01

With over half of individuals incarcerated having serious mental health concerns, correctional settings offer excellent opportunities for epidemiological, prevention, and intervention research. However, due to unique ethical and structural challenges, these settings create risks and vulnerabilities for participants not typically encountered in research populations. We surveyed 1,224 researchers, IRB members, and IRB prisoner representatives to assess their perceptions of risks associated with mental health research conducted in correctional settings. Highest-ranked risks were related to privacy, stigma, and confidentiality; lowest-ranked risks were related to prisoners’ loss of privileges or becoming targets of violence due to having participated in research. Cognitive impairment, mental illness, lack of autonomy, and limited access to services emerged as the greatest sources of vulnerability; being male, being female, being over age of 60, being a minority, and being pregnant were the lowest-ranked sources of vulnerability. Researchers with corrections experience perceived lower risks and vulnerabilities than all other groups, raising the question whether these researchers accurately appraise risk and vulnerability based on experience, or if their lower risk and vulnerability perceptions reflect potential bias due to their vested interests. By identifying areas of particular risk and vulnerability, this study provides important information for researchers and research reviewers alike. PMID:27092025

A network-based dynamical ranking system for competitive sports

NASA Astrophysics Data System (ADS)

Motegi, Shun; Masuda, Naoki

2012-12-01

From the viewpoint of networks, a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score of a player (or team) fluctuates over time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. We derive a set of linear online update equations for the score of each player. The proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts.

Ranking online quality and reputation via the user activity

NASA Astrophysics Data System (ADS)

Liu, Xiao-Lu; Guo, Qiang; Hou, Lei; Cheng, Can; Liu, Jian-Guo

2015-10-01

How to design an accurate algorithm for ranking the object quality and user reputation is of importance for online rating systems. In this paper we present an improved iterative algorithm for online ranking object quality and user reputation in terms of the user degree (IRUA), where the user's reputation is measured by his/her rating vector, the corresponding objects' quality vector and the user degree. The experimental results for the empirical networks show that the AUC values of the IRUA algorithm can reach 0.9065 and 0.8705 in Movielens and Netflix data sets, respectively, which is better than the results generated by the traditional iterative ranking methods. Meanwhile, the results for the synthetic networks indicate that user degree should be considered in real rating systems due to users' rating behaviors. Moreover, we find that enhancing or reducing the influences of the large-degree users could produce more accurate reputation ranking lists.

Scalable Faceted Ranking in Tagging Systems

NASA Astrophysics Data System (ADS)

Orlicki, José I.; Alvarez-Hamelin, J. Ignacio; Fierens, Pablo I.

Nowadays, web collaborative tagging systems which allow users to upload, comment on and recommend contents, are growing. Such systems can be represented as graphs where nodes correspond to users and tagged-links to recommendations. In this paper we analyze the problem of computing a ranking of users with respect to a facet described as a set of tags. A straightforward solution is to compute a PageRank-like algorithm on a facet-related graph, but it is not feasible for online computation. We propose an alternative: (i) a ranking for each tag is computed offline on the basis of tag-related subgraphs; (ii) a faceted order is generated online by merging rankings corresponding to all the tags in the facet. Based on the graph analysis of YouTube and Flickr, we show that step (i) is scalable. We also present efficient algorithms for step (ii), which are evaluated by comparing their results with two gold standards.

Designing a two-rank acceptance sampling plan for quality inspection of geospatial data products

NASA Astrophysics Data System (ADS)

Tong, Xiaohua; Wang, Zhenhua; Xie, Huan; Liang, Dan; Jiang, Zuoqin; Li, Jinchao; Li, Jun

2011-10-01

To address the disadvantages of classical sampling plans designed for traditional industrial products, we originally propose a two-rank acceptance sampling plan (TRASP) for the inspection of geospatial data outputs based on the acceptance quality level (AQL). The first rank sampling plan is to inspect the lot consisting of map sheets, and the second is to inspect the lot consisting of features in an individual map sheet. The TRASP design is formulated as an optimization problem with respect to sample size and acceptance number, which covers two lot size cases. The first case is for a small lot size with nonconformities being modeled by a hypergeometric distribution function, and the second is for a larger lot size with nonconformities being modeled by a Poisson distribution function. The proposed TRASP is illustrated through two empirical case studies. Our analysis demonstrates that: (1) the proposed TRASP provides a general approach for quality inspection of geospatial data outputs consisting of non-uniform items and (2) the proposed acceptance sampling plan based on TRASP performs better than other classical sampling plans. It overcomes the drawbacks of percent sampling, i.e., "strictness for large lot size, toleration for small lot size," and those of a national standard used specifically for industrial outputs, i.e., "lots with different sizes corresponding to the same sampling plan."

A study of performance assessment task organization in high school optics

NASA Astrophysics Data System (ADS)

Zawicki, Joseph Leo

2002-01-01

This investigation was undertaken to validate three performance assessment tasks in high school physics. The tasks that were studied were developed around three organizational models of performance assessments: integrated, independent and surrogate. The integrated model required students to answer questions, make observations and demonstrate skills related to the index of refraction of a particular material. All of the questions and activities the students completed were related to a sample of a particular plastic sample that was the focus of this task. The independent model is analogous to the station model that is currently used on three New York State assessments: the Grade 4 - Elementary Science Program Evaluation Test, the Intermediate Level Science (ILS) Test, and the Physical Setting: Earth Science Regents Exam. Students took measurements related to the index of refraction of a plastic sample that was the focus of the initial portion of this task; the remaining questions on the assessment were generally related to the concept of the index of refraction but did not refer back to the initial sample. The final task organization followed the surrogate model. In this model, students reviewed data that was collected and analyzed by other (fictitious) students. The students completing this task were asked to review the work presented on this assessment for errors; they evaluated the conclusions and statements presented on the assessment. Students were also asked to determine if the student work was acceptable or if this investigation should be repeated. Approximately 300 students from urban, suburban and rural districts across Western New York State participated in the study. The tasks were administered during the spring semester of the 2000--2001 school year. The participating schools had at least covered the topic of refraction, both in classroom lectures and in laboratory activities. Each student completed only one form of the task---either the integrated, the independent or the surrogate form. A set of ten questions, compiled from past New York State Regents Examinations in Physics, was used as an additional measurement of student conceptual understanding. This question set was identified as the "Optics Baseline Test" (OBT). Additionally, classroom teachers ranked the academic performance of each of the students in their classroom on the outcomes of the physics course; these rankings were compared with student scores on the performance assessment tasks. The process skills incorporated within the individual questions on each task were reviewed by a panel of expert teachers. Student scores on the tasks themselves were examined using a principal component analysis. This analysis provided support for the process skill subtests organized around the general process skills of planning, performing, and reasoning. Scoring guides and inter-rater reliabilities were established for each task. The reliabilities for tasks, subtests and questions were fairly high, indicting adequate task reliability. Correlations between student performance on the individual tasks and the OBT were not significant. Teacher ranking of student achievement in individual classrooms also failed to correlate significantly with student performance on tasks. The lack of correlation could be attributed to several factors, including (among others) a wide range of student opportunities to learn from the seven schools in the sample. As has been reported in the performance assessment literature, there were no significant differences between the performance of male and female students. (Abstract shortened by UMI.)

PubMed Phrases, an open set of coherent phrases for searching biomedical literature

PubMed Central

Kim, Sun; Yeganova, Lana; Comeau, Donald C.; Wilbur, W. John; Lu, Zhiyong

2018-01-01

In biomedicine, key concepts are often expressed by multiple words (e.g., ‘zinc finger protein’). Previous work has shown treating a sequence of words as a meaningful unit, where applicable, is not only important for human understanding but also beneficial for automatic information seeking. Here we present a collection of PubMed® Phrases that are beneficial for information retrieval and human comprehension. We define these phrases as coherent chunks that are logically connected. To collect the phrase set, we apply the hypergeometric test to detect segments of consecutive terms that are likely to appear together in PubMed. These text segments are then filtered using the BM25 ranking function to ensure that they are beneficial from an information retrieval perspective. Thus, we obtain a set of 705,915 PubMed Phrases. We evaluate the quality of the set by investigating PubMed user click data and manually annotating a sample of 500 randomly selected noun phrases. We also analyze and discuss the usage of these PubMed Phrases in literature search. PMID:29893755

A mixture model-based approach to the clustering of microarray expression data.

PubMed

McLachlan, G J; Bean, R W; Peel, D

2002-03-01

This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets. EMMIX-GENE is available at http://www.maths.uq.edu.au/~gjm/emmix-gene/

The contribution of social rank and attachment theory to depression in a non clinical sample of adolescents.

PubMed

Puissant, Sylvia Pinna; Gauthier, Jean-Marie; Van Oirbeek, Robin

2011-11-01

This study explores the relative contribution of the overall quality of attachment to the mother, to the father and to peers (Inventory of Parent and Peer Attachment scales), the style of attachment towards peers (Attachment Questionnaire for Children scale), the social rank variables (submissive behavior and social comparison), and sex and age variables in predicting the depression score (Center of Epidemiological Studies Depression Scale) on a non-psychiatric sample of 13-18 year old adolescents (n = 225). Results of our integrated model (adjusted R-Square of .50) show that attachment variables (overall quality of attachment to the father and to the mother), social rank variables (social comparison and submissive behavior), age and sex are important in predicting depressive symptoms during adolescence. Moreover, the attachment to peers variables (quality of attachment to peers, secure and ambivalent style of attachment) and sex are mediated by the social rank variables (social comparison and submissive behavior).

Effect of different mixing methods on the bacterial microleakage of calcium-enriched mixture cement.

PubMed

Shahi, Shahriar; Jeddi Khajeh, Soniya; Rahimi, Saeed; Yavari, Hamid R; Jafari, Farnaz; Samiei, Mohammad; Ghasemi, Negin; Milani, Amin S

2016-10-01

Calcium-enriched mixture (CEM) cement is used in the field of endodontics. It is similar to mineral trioxide aggregate in its main ingredients. The present study investigated the effect of different mixing methods on the bacterial microleakage of CEM cement. A total of 55 human single-rooted human permanent teeth were decoronated so that 14-mm-long samples were obtained and obturated with AH26 sealer and gutta-percha using lateral condensation technique. Three millimeters of the root end were cut off and randomly divided into 3 groups of 15 each (3 mixing methods of amalgamator, ultrasonic and conventional) and 2 negative and positive control groups (each containing 5 samples). BHI (brain-heart infusion agar) suspension containing Enterococcus faecalis was used for bacterial leakage assessment. Statistical analysis was carried out using descriptive statistics, Kaplan-Meier survival analysis with censored data and log rank test. Statistical significance was set at P<0.05. The survival means for conventional, amalgamator and ultrasonic methods were 62.13±12.44, 68.87±12.79 and 77.53±12.52 days, respectively. The log rank test showed no significant differences between the groups. Based on the results of the present study it can be concluded that different mixing methods had no significant effect on the bacterial microleakage of CEM cement.

Influence of altitude concerning the contamination of humus soils in the German Alps: a data evaluation approach using PyHasse.

PubMed

Voigt, Kristina; Brüggemann, Rainer; Kirchner, Manfred; Schramm, Karl-Werner

2010-02-01

In an international project named MONARPOP (Monitoring Network in the Alpine Region for Persistent and other Organic Pollutants), selected chemicals in different environmental media were analysed in the years 2004 and 2005. Seventeen pesticides were chosen and analysed in humus and mineral soil in the German Alps. The samples were taken at different altitudes. In such a rather complex environmental datasets, it is often necessary to compare different sets of criteria and their influence on rankings. In the similarity analysis which is part of the theory of the Hasse diagram technique, we intend to calculate the similarity of different rankings. Furthermore, we perform a so-called dominance-dominance/dominance-separability method, followed by a sensitivity analysis, both subroutines in the newly developed PyHasse programme in order to find out if the concentration of the chemicals can be related to the altitudes at which the samples were taken. It can be demonstrated that the altitude has a considerable influence on the concentration of some organic chemicals in humus: The concentrations of some chemicals increase with the altitude. This increase shows certain irregularities for which several explication attempts including possible effects of atmospheric stratification phenomena in valleys have been made. These results should be complemented in further studies with MONARPOP monitoring data from other Alpine countries, e.g. Austria, Switzerland, Italy and Slovenia.

Human rights violations among sexual and gender minorities in Kathmandu, Nepal: a qualitative investigation

PubMed Central

2012-01-01

Background Nepal has experienced sporadic reports of human rights violations among sexual and gender minorities. Our objective was to identify a range of human rights that are enshrined in international law and/or are commonly reported by sexual and gender minority participants in Kathmandu, to be nonprotected or violated. Methods In September 2009 three focus group discussions were conducted by trained interviewers among a convenience sample of sexual and gender minority participants in Kathmandu Nepal. The modified Delphi technique was utilized to elicit and rank participant-generated definitions of human rights and their subsequent violations. Data was analyzed independently and cross checked by another investigator. Results Participants (n = 29) reported experiencing a range of human rights violations at home, work, educational, health care settings and in public places. Lack of adequate legal protection, physical and mental abuse and torture were commonly reported. Access to adequate legal protection and improvements in the family and healthcare environment were ranked as the most important priority areas. Conclusions Sexual and gender minorities in Nepal experienced a range of human rights violations. Future efforts should enroll a larger and more systematic sample of participants to determine frequency, timing, and/or intensity of exposure to rights violations, and estimate the population-based impact of these rights violations on specific health outcomes PMID:22591775

Rise to power: a case study of male fecal androgen and cortisol levels before and after a non-aggressive rank change in a group of wild white-faced capuchins (Cebus capucinus).

PubMed

Schoof, Valérie A M; Jack, Katharine M; Carnegie, Sarah D

2011-01-01

We examined fecal androgen and cortisol levels in three adult male white-faced capuchin monkeys (Cebus capucinus) before and after a non-aggressive rank increase in one habituated group residing in the Santa Rosa Sector of the Área de Conservación Guanacaste, Costa Rica. Fecal samples (n = 116) were collected opportunistically between July 2006 and July 2007. Alpha males had higher mean androgen levels than subordinates, and acquisition of the alpha position was linked to an immediate increase in mean androgens. Cortisol levels also increased in the alpha male after acquisition of his new rank, though this increase was delayed relative to the change in rank. These results indicate that, during a non-aggressive rank change, androgen and cortisol levels in male white-faced capuchins are physiological responses to dominance rank, rather than precursors that facilitate rank acquisition. Copyright © 2012 S. Karger AG, Basel.

New Ratings of Humanities Journals Do More than Rank--They Rankle

ERIC Educational Resources Information Center

Howard, Jennifer

2008-01-01

This paper reports that a large-scale, multinational attempt in Europe to rank humanities journals has set off a revolt. In a protest letter, some journal editors have called it "a dangerous and misguided exercise." The project has also started a drumbeat of alarm in this country, as U.S.-based scholars begin to grasp the implications…

Estimating botanical composition by the dry-weight-rank method in California's annual grasslands

Treesearch

Raymond D. Ratliff; William E. Frost

1990-01-01

The dry-weight-rank method of estimating botanical composition on California's annual grasslands is a viable alternative to harvesting and sorting or methods using points. Two data sets of sorted species weights were available. One spanned nine years with quadrats harvested at peak of production. The second spanned one growing season with 20 harvest dates. Two...

Email Opens up a World of Possibilities; The Great Turning Point; Rankings for Sale: Payola on the Information Highway? Or Payments for Good Shelf Space; Beware the Grey Flannel Trojan Horse.

ERIC Educational Resources Information Center

Morton, Emily; McKenzie, Jamie

2001-01-01

Includes four articles that discuss issues relating to use of the Internet in classroom settings. Topics include the use of email; curriculum rich strategies that require professional and program development; ranking search engines; and beneficial business partnerships with schools. (LRW)

Human preferences for colorful birds: Vivid colors or pattern?

PubMed

Lišková, Silvie; Landová, Eva; Frynta, Daniel

2015-04-29

In a previous study, we found that the shape of a bird, rather than its color, plays a major role in the determination of human preferences. Thus, in the present study, we asked whether the preferences of human respondents towards uniformly shaped, colorful birds are determined by pattern rather than color. The experimental stimuli were pictures of small passerine birds of the family Pittidae possessing uniform shape but vivid coloration. We asked 200 participants to rank 43 colored and 43 identical, but grayscaled, pictures of birds. To find the traits determining human preferences, we performed GLM analysis in which we tried to explain the mean preference ranks and PC axes by the following explanatory variables: the overall lightness and saturation, edges (pattern), and the portion of each of the basic color hues. The results showed that the mean preference ranks of the grayscale set is explained mostly by the birds' pattern, whereas the colored set ranking is mostly determined by the overall lightness. The effect of colors was weaker, but still significant, and revealed that people liked blue and green birds. We found no significant role of the color red, the perception of which was acquired relatively recently in evolution.

Low-rank matrix fitting based on subspace perturbation analysis with applications to structure from motion.

PubMed

Jia, Hongjun; Martinez, Aleix M

2009-05-01

The task of finding a low-rank (r) matrix that best fits an original data matrix of higher rank is a recurring problem in science and engineering. The problem becomes especially difficult when the original data matrix has some missing entries and contains an unknown additive noise term in the remaining elements. The former problem can be solved by concatenating a set of r-column matrices that share a common single r-dimensional solution space. Unfortunately, the number of possible submatrices is generally very large and, hence, the results obtained with one set of r-column matrices will generally be different from that captured by a different set. Ideally, we would like to find that solution that is least affected by noise. This requires that we determine which of the r-column matrices (i.e., which of the original feature points) are less influenced by the unknown noise term. This paper presents a criterion to successfully carry out such a selection. Our key result is to formally prove that the more distinct the r vectors of the r-column matrices are, the less they are swayed by noise. This key result is then combined with the use of a noise model to derive an upper bound for the effect that noise and occlusions have on each of the r-column matrices. It is shown how this criterion can be effectively used to recover the noise-free matrix of rank r. Finally, we derive the affine and projective structure-from-motion (SFM) algorithms using the proposed criterion. Extensive validation on synthetic and real data sets shows the superiority of the proposed approach over the state of the art.

Assessing Low-Intensity Relationships in Complex Networks

PubMed Central

Spitz, Andreas; Gimmler, Anna; Stoeck, Thorsten; Zweig, Katharina Anna; Horvát, Emőke-Ágnes

2016-01-01

Many large network data sets are noisy and contain links representing low-intensity relationships that are difficult to differentiate from random interactions. This is especially relevant for high-throughput data from systems biology, large-scale ecological data, but also for Web 2.0 data on human interactions. In these networks with missing and spurious links, it is possible to refine the data based on the principle of structural similarity, which assesses the shared neighborhood of two nodes. By using similarity measures to globally rank all possible links and choosing the top-ranked pairs, true links can be validated, missing links inferred, and spurious observations removed. While many similarity measures have been proposed to this end, there is no general consensus on which one to use. In this article, we first contribute a set of benchmarks for complex networks from three different settings (e-commerce, systems biology, and social networks) and thus enable a quantitative performance analysis of classic node similarity measures. Based on this, we then propose a new methodology for link assessment called z* that assesses the statistical significance of the number of their common neighbors by comparison with the expected value in a suitably chosen random graph model and which is a consistently top-performing algorithm for all benchmarks. In addition to a global ranking of links, we also use this method to identify the most similar neighbors of each single node in a local ranking, thereby showing the versatility of the method in two distinct scenarios and augmenting its applicability. Finally, we perform an exploratory analysis on an oceanographic plankton data set and find that the distribution of microbes follows similar biogeographic rules as those of macroorganisms, a result that rejects the global dispersal hypothesis for microbes. PMID:27096435

Assessing Low-Intensity Relationships in Complex Networks.

PubMed

Spitz, Andreas; Gimmler, Anna; Stoeck, Thorsten; Zweig, Katharina Anna; Horvát, Emőke-Ágnes

2016-01-01

Many large network data sets are noisy and contain links representing low-intensity relationships that are difficult to differentiate from random interactions. This is especially relevant for high-throughput data from systems biology, large-scale ecological data, but also for Web 2.0 data on human interactions. In these networks with missing and spurious links, it is possible to refine the data based on the principle of structural similarity, which assesses the shared neighborhood of two nodes. By using similarity measures to globally rank all possible links and choosing the top-ranked pairs, true links can be validated, missing links inferred, and spurious observations removed. While many similarity measures have been proposed to this end, there is no general consensus on which one to use. In this article, we first contribute a set of benchmarks for complex networks from three different settings (e-commerce, systems biology, and social networks) and thus enable a quantitative performance analysis of classic node similarity measures. Based on this, we then propose a new methodology for link assessment called z* that assesses the statistical significance of the number of their common neighbors by comparison with the expected value in a suitably chosen random graph model and which is a consistently top-performing algorithm for all benchmarks. In addition to a global ranking of links, we also use this method to identify the most similar neighbors of each single node in a local ranking, thereby showing the versatility of the method in two distinct scenarios and augmenting its applicability. Finally, we perform an exploratory analysis on an oceanographic plankton data set and find that the distribution of microbes follows similar biogeographic rules as those of macroorganisms, a result that rejects the global dispersal hypothesis for microbes.

Setting stroke research priorities: The consumer perspective.

PubMed

Sangvatanakul, Pukkaporn; Hillege, Sharon; Lalor, Erin; Levi, Christopher; Hill, Kelvin; Middleton, Sandy

2010-12-01

To test a method of engaging consumers in research priority-setting using a quantitative approach and to determine consumer views on stroke research priorities for clinical practice recommendations with lower levels of evidence (Level III and Level IV) and expert consensus opinion as published in the Australian stroke clinical practice guidelines. Survey Urban community Eighteen stroke survivors (n = 12) and carers (n = 6) who were members of the "Working Aged Group - Stroke" (WAGS) consumer support group. Phase I: Participants were asked whether recommendations were "worth" researching ("yes" or "no"); and, if researched, what potential impact they likely would have on patient outcomes. Phase II: Participants were asked to rank recommendations rated by more than 75% of participants in Phase I as "worth" researching and "highly likely" or "likely" to generate research with a significant effect on patient outcomes (n = 13) in order of priority for future stroke research. All recommendations were rated by at least half (n = 9, 50%) of participants as "worth" researching. The majority (67% to 100%) rated all recommendations as "highly likely" or "likely" that research would have a significant effect on patient outcomes. Thirteen out of 20 recommendations were ranked for their research priorities. Recommendations under the topic heading Getting to hospital were ranked highest and Organization of care and Living with stroke were ranked as a lower priority for research. This study provided an example of how to involve consumers in research priority setting successfully using a quantitative approach. Stroke research priorities from the consumer perspective were different from those of health professionals, as published in the literature; thus, consumer opinion should be considered when setting research priorities. Copyright © 2010 Society for Vascular Nursing, Inc. Published by Mosby, Inc. All rights reserved.

The Roles of Socioeconomic Status, Occupational Health and Job Rank on the Epidemiology of Different Psychiatric Symptoms in a Sample of UK Workers.

PubMed

Lopes, B; Kamau, C; Jaspal, R

2018-03-06

There is a considerable gap in epidemiological literature about community mental health showing how psychiatric symptoms are associated with job rank, socioeconomic status, and occupational health. We examine data from 4596 employees collected in the United Kingdom's Psychiatric Morbidity among Adults Living in Private Households Survey. There were 939 workers in managerial jobs, 739 in supervisory jobs and 2918 employees in lower ranking jobs. Of the 4596 workers, 2463 had depressive symptoms and 2133 no depressive symptoms. Job rank, household gross income, social class, personal gross income and socio-economic group were significantly associated with general health, occupational health and depressive and avoidant symptoms. Job rank, occupational and physical health also explained the variance in paranoid and avoidant symptoms among the employees. This study shows that severe psychopathology is related to workers' job rank.

Virtual screening with AutoDock Vina and the common pharmacophore engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: participation in the SAMPL4 protein-ligand binding challenge

NASA Astrophysics Data System (ADS)

Perryman, Alexander L.; Santiago, Daniel N.; Forli, Stefano; Santos-Martins, Diogo; Olson, Arthur J.

2014-04-01

To rigorously assess the tools and protocols that can be used to understand and predict macromolecular recognition, and to gain more structural insight into three newly discovered allosteric binding sites on a critical drug target involved in the treatment of HIV infections, the Olson and Levy labs collaborated on the SAMPL4 challenge. This computational blind challenge involved predicting protein-ligand binding against the three allosteric sites of HIV integrase (IN), a viral enzyme for which two drugs (that target the active site) have been approved by the FDA. Positive control cross-docking experiments were utilized to select 13 receptor models out of an initial ensemble of 41 different crystal structures of HIV IN. These 13 models of the targets were selected using our new "Rank Difference Ratio" metric. The first stage of SAMPL4 involved using virtual screens to identify 62 active, allosteric IN inhibitors out of a set of 321 compounds. The second stage involved predicting the binding site(s) and crystallographic binding mode(s) for 57 of these inhibitors. Our team submitted four entries for the first stage that utilized: (1) AutoDock Vina (AD Vina) plus visual inspection; (2) a new common pharmacophore engine; (3) BEDAM replica exchange free energy simulations, and a Consensus approach that combined the predictions of all three strategies. Even with the SAMPL4's very challenging compound library that displayed a significantly lower amount of structural diversity than most libraries that are conventionally employed in prospective virtual screens, these approaches produced hit rates of 24, 25, 34, and 27 %, respectively, on a set with 19 % declared binders. Our only entry for the second stage challenge was based on the results of AD Vina plus visual inspection, and it ranked third place overall according to several different metrics provided by the SAMPL4 organizers. The successful results displayed by these approaches highlight the utility of the computational structure-based drug discovery tools and strategies that are being developed to advance the goals of the newly created, multi-institution, NIH-funded center called the "HIV Interaction and Viral Evolution Center".

Virtual screening with AutoDock Vina and the common pharmacophore engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: participation in the SAMPL4 protein-ligand binding challenge.

PubMed

Perryman, Alexander L; Santiago, Daniel N; Forli, Stefano; Martins, Diogo Santos; Olson, Arthur J

2014-04-01

To rigorously assess the tools and protocols that can be used to understand and predict macromolecular recognition, and to gain more structural insight into three newly discovered allosteric binding sites on a critical drug target involved in the treatment of HIV infections, the Olson and Levy labs collaborated on the SAMPL4 challenge. This computational blind challenge involved predicting protein-ligand binding against the three allosteric sites of HIV integrase (IN), a viral enzyme for which two drugs (that target the active site) have been approved by the FDA. Positive control cross-docking experiments were utilized to select 13 receptor models out of an initial ensemble of 41 different crystal structures of HIV IN. These 13 models of the targets were selected using our new "Rank Difference Ratio" metric. The first stage of SAMPL4 involved using virtual screens to identify 62 active, allosteric IN inhibitors out of a set of 321 compounds. The second stage involved predicting the binding site(s) and crystallographic binding mode(s) for 57 of these inhibitors. Our team submitted four entries for the first stage that utilized: (1) AutoDock Vina (AD Vina) plus visual inspection; (2) a new common pharmacophore engine; (3) BEDAM replica exchange free energy simulations, and a Consensus approach that combined the predictions of all three strategies. Even with the SAMPL4's very challenging compound library that displayed a significantly lower amount of structural diversity than most libraries that are conventionally employed in prospective virtual screens, these approaches produced hit rates of 24, 25, 34, and 27 %, respectively, on a set with 19 % declared binders. Our only entry for the second stage challenge was based on the results of AD Vina plus visual inspection, and it ranked third place overall according to several different metrics provided by the SAMPL4 organizers. The successful results displayed by these approaches highlight the utility of the computational structure-based drug discovery tools and strategies that are being developed to advance the goals of the newly created, multi-institution, NIH-funded center called the "HIV Interaction and Viral Evolution Center".

The exact probability distribution of the rank product statistics for replicated experiments.

PubMed

Eisinga, Rob; Breitling, Rainer; Heskes, Tom

2013-03-18

The rank product method is a widely accepted technique for detecting differentially regulated genes in replicated microarray experiments. To approximate the sampling distribution of the rank product statistic, the original publication proposed a permutation approach, whereas recently an alternative approximation based on the continuous gamma distribution was suggested. However, both approximations are imperfect for estimating small tail probabilities. In this paper we relate the rank product statistic to number theory and provide a derivation of its exact probability distribution and the true tail probabilities. Copyright © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Exposure Assessment of Livestock Carcass Management ...

EPA Pesticide Factsheets

Report This report describes relative exposures and hazards for different livestock carcass management options in the event of a natural disaster. A quantitative exposure assessment by which livestock carcass management options are ranked relative to one another for a hypothetical site setting, a standardized set of environmental conditions (e.g., meteorology), and following a single set of assumptions about how the carcass management options are designed and implemented. These settings, conditions, and assumptions are not necessarily representative of site-specific carcass management efforts. Therefore, the exposure assessment should not be interpreted as estimating levels of chemical and microbial exposure that can be expected to result from the management options evaluated. The intent of the relative rankings is to support scientifically-based livestock carcass management decisions that consider potential hazards to human health, livestock, and the environment. This exposure assessment also provides information to support choices about mitigation measures to minimize or eliminate specific exposure pathways.

Hazard-Ranking of Agricultural Pesticides for Chronic Health Effects in Yuma County, Arizona

PubMed Central

Sugeng, Anastasia J.; Beamer, Paloma I.; Lutz, Eric A.; Rosales, Cecilia B.

2013-01-01

With thousands of pesticides registered by the United States Environmental Protection Agency, it not feasible to sample for all pesticides applied in agricultural communities. Hazard-ranking pesticides based on use, toxicity, and exposure potential can help prioritize community-specific pesticide hazards. This study applied hazard-ranking schemes for cancer, endocrine disruption, and reproductive/developmental toxicity in Yuma County, Arizona. An existing cancer hazard-ranking scheme was modified, and novel schemes for endocrine disruption and reproductive/developmental toxicity were developed to rank pesticide hazards. The hazard-ranking schemes accounted for pesticide use, toxicity, and exposure potential based on chemical properties of each pesticide. Pesticides were ranked as hazards with respect to each health effect, as well as overall chronic health effects. The highest hazard-ranked pesticides for overall chronic health effects were maneb, metam sodium, trifluralin, pronamide, and bifenthrin. The relative pesticide rankings were unique for each health effect. The highest hazard-ranked pesticides differed from those most heavily applied, as well as from those previously detected in Yuma homes over a decade ago. The most hazardous pesticides for cancer in Yuma County, Arizona were also different from a previous hazard-ranking applied in California. Hazard-ranking schemes that take into account pesticide use, toxicity, and exposure potential can help prioritize pesticides of greatest health risk in agricultural communities. This study is the first to provide pesticide hazard-rankings for endocrine disruption and reproductive/developmental toxicity based on use, toxicity, and exposure potential. These hazard-ranking schemes can be applied to other agricultural communities for prioritizing community-specific pesticide hazards to target decreasing health risk. PMID:23783270

Peering is not a formal indicator of subordination in bonobos (Pan paniscus).

PubMed

Stevens, Jeroen M G; Vervaecke, Hilde; De Vries, Han; Van Elsacker, Linda

2005-03-01

It has been suggested that peering behavior in bonobos is a formal signal acknowledging social dominance status. We investigated whether peering meets the published criteria for a formal signal of subordination in five captive groups of bonobos. The degree of linearity in the set of peering relationships was significantly high in all study groups, and a linear rank order was found. However, unidirectionality was low, and there was little correspondence between the peering order and the agonistic dominance rank. Therefore, peering does not satisfy the criteria of a formal subordination indicator. We also studied the relation between peering and agonistic dominance rank, age, and sex. Animals directed peering significantly more often at high-ranking animals in four of the groups. We suggest that peering is indirectly related to dominance rank by the resource-holding potential of individuals. In contexts where dominant individuals can monopolize resources, peerers may direct their attention at those high-ranking animals. When resources are distributed more evenly, high-ranking animals may peer down the hierarchy. We speculate on the reasons why a formal dominance or subordination signal appears to be absent in bonobos. Copyright (c) 2005 Wiley-Liss, Inc.

Recognition of Risk Information - Adaptation of J. Bertin's Orderable Matrix for social communication

NASA Astrophysics Data System (ADS)

Ishida, Keiichi

2018-05-01

This paper aims to show capability of the Orderable Matrix of Jacques Bertin which is a visualization method of data analyze and/or a method to recognize data. That matrix can show the data by replacing numbers to visual element. As an example, using a set of data regarding natural hazard rankings for certain metropolitan cities in the world, this paper describes how the Orderable Matrix handles the data set and show characteristic factors of this data to understand it. Not only to see a kind of risk ranking of cities, the Orderable Matrix shows how differently danger concerned cities ones and others are. Furthermore, we will see that the visualized data by Orderable Matrix allows us to see the characteristics of the data set comprehensively and instantaneously.

The valuation of the EQ-5D in Portugal.

PubMed

Ferreira, Lara N; Ferreira, Pedro L; Pereira, Luis N; Oppe, Mark

2014-03-01

The EQ-5D is a preference-based measure widely used in cost-utility analysis (CUA). Several countries have conducted surveys to derive value sets, but this was not the case for Portugal. The purpose of this study was to estimate a value set for the EQ-5D for Portugal using the time trade-off (TTO). A representative sample of the Portuguese general population (n = 450) stratified by age and gender valued 24 health states. Face-to-face interviews were conducted by trained interviewers. Each respondent ranked and valued seven health states using the TTO. Several models were estimated at both the individual and aggregated levels to predict health state valuations. Alternative functional forms were considered to account for the skewed distribution of these valuations. The models were analyzed in terms of their coefficients, overall fit and the ability for predicting the TTO values. Random effects models were estimated using generalized least squares and were robust across model specification. The results are generally consistent with other value sets. This research provides the Portuguese EQ-5D value set based on the preferences of the Portuguese general population as measured by the TTO. This value set is recommended for use in CUA conducted in Portugal.

Influence of dominance rank and affiliation relationships on self-directed behavior in female Tibetan macaques (Macaca thibetana).

PubMed

Zhang, Qi-Xin; Li, Jin-Hua; Xia, Dong-Po; Zhu, Yong; Wang, Xi; Zhang, Dao

2014-05-01

Self-directed behavior (SDB) is characterized as an indicator of anxiety, frustration and stress in nonhuman primates. In this study, we collected self-directed behavior data from one group of free-ranging Tibetan macaques (Macaca thibetana) at Mt. Huangshan, China (September 2012-May 2013) using a combination of behavioral sampling methods including focal animal sampling, behavioral sampling, continuous sampling and instantaneous sampling. Our results showed that females engaged in significantly higher rates of self-directed behavior when they were in proximity to dominant individuals compared to subordinate ones. Conflict losers significantly increased their SDB rates after agonistic episodes, indicating that SDB might also serve as an index of anxiety in M. thibetana. We further found that females significantly increased their SDB rates when focal individual was proximity to weakly affiliation relationship higher rank members than to strongly affiliation relationship higher rank members. If conflicts were not reconciled, the postconflict SDB rates of losers were higher when they stayed with strongly affiliation opponents; if conflicts were reconciled, victims of strongly affiliation relationships opponents engaged in more SDB rates before reconciliation than after reconciliation, while victims of moderately affiliation relationships opponents did not engaged in more SDB rates before reconciliation than after reconciliation. We conclude that both of dominance rank and affiliation relationships might both influence the SDB rates of female Tibetan macaques significantly, suggesting that SDB is not only an index of anxiety in Tibetan macaques, but also can provide a new insight into evaluation of social relationships between individuals.

DockRank: Ranking docked conformations using partner-specific sequence homology-based protein interface prediction

PubMed Central

Xue, Li C.; Jordan, Rafael A.; EL-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

2015-01-01

Selecting near-native conformations from the immense number of conformations generated by docking programs remains a major challenge in molecular docking. We introduce DockRank, a novel approach to scoring docked conformations based on the degree to which the interface residues of the docked conformation match a set of predicted interface residues. Dock-Rank uses interface residues predicted by partner-specific sequence homology-based protein–protein interface predictor (PS-HomPPI), which predicts the interface residues of a query protein with a specific interaction partner. We compared the performance of DockRank with several state-of-the-art docking scoring functions using Success Rate (the percentage of cases that have at least one near-native conformation among the top m conformations) and Hit Rate (the percentage of near-native conformations that are included among the top m conformations). In cases where it is possible to obtain partner-specific (PS) interface predictions from PS-HomPPI, DockRank consistently outperforms both (i) ZRank and IRAD, two state-of-the-art energy-based scoring functions (improving Success Rate by up to 4-fold); and (ii) Variants of DockRank that use predicted interface residues obtained from several protein interface predictors that do not take into account the binding partner in making interface predictions (improving success rate by up to 39-fold). The latter result underscores the importance of using partner-specific interface residues in scoring docked conformations. We show that DockRank, when used to re-rank the conformations returned by ClusPro, improves upon the original ClusPro rankings in terms of both Success Rate and Hit Rate. DockRank is available as a server at http://einstein.cs.iastate.edu/DockRank/. PMID:23873600

DockRank: ranking docked conformations using partner-specific sequence homology-based protein interface prediction.

PubMed

Xue, Li C; Jordan, Rafael A; El-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

2014-02-01

Selecting near-native conformations from the immense number of conformations generated by docking programs remains a major challenge in molecular docking. We introduce DockRank, a novel approach to scoring docked conformations based on the degree to which the interface residues of the docked conformation match a set of predicted interface residues. DockRank uses interface residues predicted by partner-specific sequence homology-based protein-protein interface predictor (PS-HomPPI), which predicts the interface residues of a query protein with a specific interaction partner. We compared the performance of DockRank with several state-of-the-art docking scoring functions using Success Rate (the percentage of cases that have at least one near-native conformation among the top m conformations) and Hit Rate (the percentage of near-native conformations that are included among the top m conformations). In cases where it is possible to obtain partner-specific (PS) interface predictions from PS-HomPPI, DockRank consistently outperforms both (i) ZRank and IRAD, two state-of-the-art energy-based scoring functions (improving Success Rate by up to 4-fold); and (ii) Variants of DockRank that use predicted interface residues obtained from several protein interface predictors that do not take into account the binding partner in making interface predictions (improving success rate by up to 39-fold). The latter result underscores the importance of using partner-specific interface residues in scoring docked conformations. We show that DockRank, when used to re-rank the conformations returned by ClusPro, improves upon the original ClusPro rankings in terms of both Success Rate and Hit Rate. DockRank is available as a server at http://einstein.cs.iastate.edu/DockRank/. Copyright © 2013 Wiley Periodicals, Inc.

A Bayesian hierarchical model for discrete choice data in health care.

PubMed

Antonio, Anna Liza M; Weiss, Robert E; Saigal, Christopher S; Dahan, Ely; Crespi, Catherine M

2017-01-01

In discrete choice experiments, patients are presented with sets of health states described by various attributes and asked to make choices from among them. Discrete choice experiments allow health care researchers to study the preferences of individual patients by eliciting trade-offs between different aspects of health-related quality of life. However, many discrete choice experiments yield data with incomplete ranking information and sparsity due to the limited number of choice sets presented to each patient, making it challenging to estimate patient preferences. Moreover, methods to identify outliers in discrete choice data are lacking. We develop a Bayesian hierarchical random effects rank-ordered multinomial logit model for discrete choice data. Missing ranks are accounted for by marginalizing over all possible permutations of unranked alternatives to estimate individual patient preferences, which are modeled as a function of patient covariates. We provide a Bayesian version of relative attribute importance, and adapt the use of the conditional predictive ordinate to identify outlying choice sets and outlying individuals with unusual preferences compared to the population. The model is applied to data from a study using a discrete choice experiment to estimate individual patient preferences for health states related to prostate cancer treatment.

ArrayVigil: a methodology for statistical comparison of gene signatures using segregated-one-tailed (SOT) Wilcoxon's signed-rank test.

PubMed

Khan, Haseeb Ahmad

2005-01-28

Due to versatile diagnostic and prognostic fidelity molecular signatures or fingerprints are anticipated as the most powerful tools for cancer management in the near future. Notwithstanding the experimental advancements in microarray technology, methods for analyzing either whole arrays or gene signatures have not been firmly established. Recently, an algorithm, ArraySolver has been reported by Khan for two-group comparison of microarray gene expression data using two-tailed Wilcoxon signed-rank test. Most of the molecular signatures are composed of two sets of genes (hybrid signatures) wherein up-regulation of one set and down-regulation of the other set collectively define the purpose of a gene signature. Since the direction of a selected gene's expression (positive or negative) with respect to a particular disease condition is known, application of one-tailed statistics could be a more relevant choice. A novel method, ArrayVigil, is described for comparing hybrid signatures using segregated-one-tailed (SOT) Wilcoxon signed-rank test and the results compared with integrated-two-tailed (ITT) procedures (SPSS and ArraySolver). ArrayVigil resulted in lower P values than those obtained from ITT statistics while comparing real data from four signatures.

Initial review of Electronic Residency Application Service charts by orthopaedic residency faculty members. Does applicant gender matter?

PubMed

Scherl, S A; Lively, N; Simon, M A

2001-01-01

Orthopaedic surgery is a male-dominated field. As of 1998, women accounted for 42% of medical school graduates, yet only 6.9% of the total number of orthopaedic residents were female. The purpose of our study was to determine whether the Electronic Residency Application Service charts of female candidates for orthopaedic residencies are ranked lower by faculty reviewers than are those of male candidates with similar qualifications. After we obtained permission from the applicants, the Electronic Residency Application Service applications submitted by ninety male and ten female candidates for admission to a university orthopaedic residency program for the 1998 National Residency Matching Program were randomly divided into ten groups, consisting of the charts of nine male candidates and one female candidate. Each chart from a female candidate was altered into a "male" version, in which all names and personal pronouns were changed but which was otherwise identical to the original female version. Therefore, each group of ten charts existed as a paired set: one containing the true female chart and one, the altered "male" chart. The paired sets acted as their own control. One hundred and twenty-one faculty reviewers from fourteen orthopaedic residency programs around the United States each reviewed either the "male" or the female version of one set, without knowledge of the goals of the study, and ranked the ten charts in the order in which they would like to have the candidates as residents in their own programs. Each version of the sets was reviewed by at least five separate reviewers. Reviewers at a given institution were randomized to review different sets, so that there was no overlap among them. The rankings of the female-"male" pairs were compared with use of a standard paired t test. No significant difference was detected in the rankings of the female and "male" charts (p = 0.5). The mean difference in rankings was -0.33, with a 95% confidence interval ranging from -1.41 (favoring females) to 0.74 (favoring "males"). The low percentage of female residents is not due to bias against female applicants in the initial chart-review phase of the orthopaedic residency selection process. It is possible that bias is introduced in other stages of the selection process, such as the interview.

Developing a Scorecard to Assess Global Progress in Scaling Up Diarrhea Control Tools: A Qualitative Study of Academic Leaders and Implementers

PubMed Central

Rosinski, Alexander Anthony; Narine, Steven; Yamey, Gavin

2013-01-01

Background In 2010, diarrhea caused 0.75 million child deaths, accounting for nearly 12% of all under-five mortality worldwide. Many evidence-based interventions can reduce diarrhea mortality, including oral rehydration solution (ORS), zinc, and improved sanitation. Yet global coverage levels of such interventions remain low. A new scorecard of diarrhea control, showing how different countries are performing in their control efforts, could draw greater attention to the low coverage levels of proven interventions. Methods We conducted in-depth qualitative interviews with 21 experts, purposively sampled for their relevant academic or implementation expertise, to explore their views on (a) the value of a scorecard of global diarrhea control and (b) which indicators should be included in such a scorecard. We then conducted a ranking exercise in which we compiled a list of all 49 indicators suggested by the experts, sent the list to the 21 experts, and asked them to choose 10 indicators that they would include and 10 that they would exclude from such a scorecard. Finally, we created a “prototype” scorecard based on the 9 highest-ranked indicators. Results Key themes that emerged from coding the interview transcripts were: a scorecard could facilitate country comparisons; it could help to identify best practices, set priorities, and spur donor action; and it could help with goal-setting and accountability in diarrhea control. The nine highest ranking indicators, in descending order, were ORS coverage, rotavirus vaccine coverage, zinc coverage, diarrhea-specific mortality rate, diarrhea prevalence, proportion of population with access to improved sanitation, proportion with access to improved drinking water, exclusive breastfeeding coverage, and measles vaccine coverage. Conclusion A new scorecard of global diarrhea control could help track progress, focus prevention and treatment efforts on the most effective interventions, establish transparency and accountability, and alert donors and ministries of health to inadequacies in diarrhea control efforts. PMID:23874412

Automated confidence ranked classification of randomized controlled trial articles: an aid to evidence-based medicine

PubMed Central

Smalheiser, Neil R; McDonagh, Marian S; Yu, Clement; Adams, Clive E; Davis, John M; Yu, Philip S

2015-01-01

Objective: For many literature review tasks, including systematic review (SR) and other aspects of evidence-based medicine, it is important to know whether an article describes a randomized controlled trial (RCT). Current manual annotation is not complete or flexible enough for the SR process. In this work, highly accurate machine learning predictive models were built that include confidence predictions of whether an article is an RCT. Materials and Methods: The LibSVM classifier was used with forward selection of potential feature sets on a large human-related subset of MEDLINE to create a classification model requiring only the citation, abstract, and MeSH terms for each article. Results: The model achieved an area under the receiver operating characteristic curve of 0.973 and mean squared error of 0.013 on the held out year 2011 data. Accurate confidence estimates were confirmed on a manually reviewed set of test articles. A second model not requiring MeSH terms was also created, and performs almost as well. Discussion: Both models accurately rank and predict article RCT confidence. Using the model and the manually reviewed samples, it is estimated that about 8000 (3%) additional RCTs can be identified in MEDLINE, and that 5% of articles tagged as RCTs in Medline may not be identified. Conclusion: Retagging human-related studies with a continuously valued RCT confidence is potentially more useful for article ranking and review than a simple yes/no prediction. The automated RCT tagging tool should offer significant savings of time and effort during the process of writing SRs, and is a key component of a multistep text mining pipeline that we are building to streamline SR workflow. In addition, the model may be useful for identifying errors in MEDLINE publication types. The RCT confidence predictions described here have been made available to users as a web service with a user query form front end at: http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/RCT_Tagger.cgi. PMID:25656516

A Comparison of Conjoint Analysis Response Formats

Treesearch

Kevin J. Boyle; Thomas P. Holmes; Mario F. Teisl; Brian Roe

2001-01-01

A split-sample design is used to evaluate the convergent validity of three response formats used in conjoint analysis experiments. WC investigate whether recoding rating data to rankings and choose-one formats, and recoding ranking data to choose one. result in structural models and welfare estimates that are statistically indistinguishable from...

Selecting Pesticides and Nonchemical Alternatives: Green Thumbs' Rules of Thumb Decision Tools.

ERIC Educational Resources Information Center

Grieshop, James I.; And Others

1992-01-01

A sample of 78 (of 320) home gardeners use rules of thumb (heuristics) to choose between chemical pesticides and nonchemical alternatives. Pesticides rank low in 24 choice attributes where alternatives rank high, and vice versa. Gender, age, and years of pesticide use correlate with pesticide selection. (SK)

How Many Alternatives Can Be Ranked? A Comparison of the Paired Comparison and Ranking Methods.

PubMed

Ock, Minsu; Yi, Nari; Ahn, Jeonghoon; Jo, Min-Woo

2016-01-01

To determine the feasibility of converting ranking data into paired comparison (PC) data and suggest the number of alternatives that can be ranked by comparing a PC and a ranking method. Using a total of 222 health states, a household survey was conducted in a sample of 300 individuals from the general population. Each respondent performed a PC 15 times and a ranking method 6 times (two attempts of ranking three, four, and five health states, respectively). The health states of the PC and the ranking method were constructed to overlap each other. We converted the ranked data into PC data and examined the consistency of the response rate. Applying probit regression, we obtained the predicted probability of each method. Pearson correlation coefficients were determined between the predicted probabilities of those methods. The mean absolute error was also assessed between the observed and the predicted values. The overall consistency of the response rate was 82.8%. The Pearson correlation coefficients were 0.789, 0.852, and 0.893 for ranking three, four, and five health states, respectively. The lowest mean absolute error was 0.082 (95% confidence interval [CI] 0.074-0.090) in ranking five health states, followed by 0.123 (95% CI 0.111-0.135) in ranking four health states and 0.126 (95% CI 0.113-0.138) in ranking three health states. After empirically examining the consistency of the response rate between a PC and a ranking method, we suggest that using five alternatives in the ranking method may be superior to using three or four alternatives. Copyright © 2016 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

A scoring mechanism for the rank aggregation of network robustness

NASA Astrophysics Data System (ADS)

Yazdani, Alireza; Dueñas-Osorio, Leonardo; Li, Qilin

2013-10-01

To date, a number of metrics have been proposed to quantify inherent robustness of network topology against failures. However, each single metric usually only offers a limited view of network vulnerability to different types of random failures and targeted attacks. When applied to certain network configurations, different metrics rank network topology robustness in different orders which is rather inconsistent, and no single metric fully characterizes network robustness against different modes of failure. To overcome such inconsistency, this work proposes a multi-metric approach as the basis of evaluating aggregate ranking of network topology robustness. This is based on simultaneous utilization of a minimal set of distinct robustness metrics that are standardized so to give way to a direct comparison of vulnerability across networks with different sizes and configurations, hence leading to an initial scoring of inherent topology robustness. Subsequently, based on the inputs of initial scoring a rank aggregation method is employed to allocate an overall ranking of robustness to each network topology. A discussion is presented in support of the presented multi-metric approach and its applications to more realistically assess and rank network topology robustness.

Sandstone detrital modes and basinal setting of the Trinity Peninsula Group, northern Graham Land, Antarctic Peninsula: A preliminary survey

NASA Astrophysics Data System (ADS)

Smellie, J. L.

Sandstone detrital modes for a representative sample of the Trinity Peninsula Group in northern Graham Land are described and assessed. Whereas the volumetrically dominant quartz and feldspar were derived principally from erosion of a plutonic and high-rank metamorphic terrane, the lithic population was derived mainly from a volcanic cover. The data clearly indicate the presence of two major sandstone suites (petro-facies I and II) with distinctive and probably separate provenances. Further scope for subdivision is limited by the small sample set, but four petrofacies (Ia, Ib, IIa, and IIb) may be present, three of which correspond with previously described lithostratigraphical units (Legoupil, Hope Bay, and View Point formations). The sample distribution and detrital modes enable approximate geographical limits to be assigned to each petrofacies for the first time, although the nature of the boundaries (stratigraphical or structural) is unknown. Petrofacies II could have been derived from an active magmatic arc and deposited in a forearc basin (sensu lato) or series of basins at a major consuming margin. Petrofacies I is a much more quartzose suite, although otherwise petrographically very similar to petrofacies II. Its depositional setting is ambiguous on the basis of the data presently available, and deposition can only be said to have occurred at either an active or a passive continental margin. Finally, there is the possibility that strike-slip faulting has structurally shuffled the Trinity Peninsula Group, causing the pronounced age and compositional contrasts observed.

A simplified risk-ranking system for prioritizing toxic pollution sites in low- and middle-income countries.

PubMed

Caravanos, Jack; Gualtero, Sandra; Dowling, Russell; Ericson, Bret; Keith, John; Hanrahan, David; Fuller, Richard

2014-01-01

In low- and middle-income countries (LMICs), chemical exposures in the environment due to hazardous waste sites and toxic pollutants are typically poorly documented and their health impacts insufficiently quantified. Furthermore, there often is only limited understanding of the health and environmental consequences of point source pollution problems, and little consensus on how to assess and rank them. The contributions of toxic environmental exposures to the global burden of disease are not well characterized. The aim of this study was to describe the simple but effective approach taken by Blacksmith Institute's Toxic Sites Identification Program to quantify and rank toxic exposures in LMICs. This system is already in use at more than 3000 sites in 48 countries such as India, Indonesia, China, Ghana, Kenya, Tanzania, Peru, Bolivia, Argentina, Uruguay, Armenia, Azerbaijan, and Ukraine. A hazard ranking system formula, the Blacksmith Index (BI), takes into account important factors such as the scale of the pollution source, the size of the population possibly affected, and the exposure pathways, and is designed for use reliably in low-resource settings by local personnel provided with limited training. Four representative case studies are presented, with varying locations, populations, pollutants, and exposure pathways. The BI was successfully applied to assess the extent and severity of environmental pollution problems at these sites. The BI is a risk-ranking tool that provides direct and straightforward characterization, quantification, and prioritization of toxic pollution sites in settings where time, money, or resources are limited. It will be an important and useful tool for addressing toxic pollution problems in LMICs. Although the BI does not have the sophistication of the US Environmental Protection Agency's Hazard Ranking System, the case studies presented here document the effectiveness of the BI in the field, especially in low-resource settings. Understanding of the risks posed by toxic pollution sites helps assure better use of resources to manage sites and mitigate risks to public health. Quantification of these hazards is an important input to assessments of the global burden of disease. Copyright © 2014 Icahn School of Medicine at Mount Sinai. Published by Elsevier Inc. All rights reserved.

Expert opinion on landslide susceptibility elicted by probabilistic inversion from scenario rankings

NASA Astrophysics Data System (ADS)

Lee, Katy; Dashwood, Claire; Lark, Murray

2016-04-01

For many natural hazards the opinion of experts, with experience in assessing susceptibility under different circumstances, is a valuable source of information on which to base risk assessments. This is particularly important where incomplete process understanding, and limited data, limit the scope to predict susceptibility by mechanistic or statistical modelling. The expert has a tacit model of a system, based on their understanding of processes and their field experience. This model may vary in quality, depending on the experience of the expert. There is considerable interest in how one may elicit expert understanding by a process which is transparent and robust, to provide a basis for decision support. One approach is to provide experts with a set of scenarios, and then to ask them to rank small overlapping subsets of these with respect to susceptibility. Methods of probabilistic inversion have been used to compute susceptibility scores for each scenario, implicit in the expert ranking. It is also possible to model these scores as functions of measurable properties of the scenarios. This approach has been used to assess susceptibility of animal populations to invasive diseases, to assess risk to vulnerable marine environments and to assess the risk in hypothetical novel technologies for food production. We will present the results of a study in which a group of geologists with varying degrees of expertise in assessing landslide hazards were asked to rank sets of hypothetical simplified scenarios with respect to land slide susceptibility. We examine the consistency of their rankings and the importance of different properties of the scenarios in the tacit susceptibility model that their rankings implied. Our results suggest that this is a promising approach to the problem of how experts can communicate their tacit model of uncertain systems to those who want to make use of their expertise.

Candidate genes for obesity-susceptibility show enriched association within a large genome-wide association study for BMI.

PubMed

Vimaleswaran, Karani S; Tachmazidou, Ioanna; Zhao, Jing Hua; Hirschhorn, Joel N; Dudbridge, Frank; Loos, Ruth J F

2012-10-15

Before the advent of genome-wide association studies (GWASs), hundreds of candidate genes for obesity-susceptibility had been identified through a variety of approaches. We examined whether those obesity candidate genes are enriched for associations with body mass index (BMI) compared with non-candidate genes by using data from a large-scale GWAS. A thorough literature search identified 547 candidate genes for obesity-susceptibility based on evidence from animal studies, Mendelian syndromes, linkage studies, genetic association studies and expression studies. Genomic regions were defined to include the genes ±10 kb of flanking sequence around candidate and non-candidate genes. We used summary statistics publicly available from the discovery stage of the genome-wide meta-analysis for BMI performed by the genetic investigation of anthropometric traits consortium in 123 564 individuals. Hypergeometric, rank tail-strength and gene-set enrichment analysis tests were used to test for the enrichment of association in candidate compared with non-candidate genes. The hypergeometric test of enrichment was not significant at the 5% P-value quantile (P = 0.35), but was nominally significant at the 25% quantile (P = 0.015). The rank tail-strength and gene-set enrichment tests were nominally significant for the full set of genes and borderline significant for the subset without SNPs at P < 10(-7). Taken together, the observed evidence for enrichment suggests that the candidate gene approach retains some value. However, the degree of enrichment is small despite the extensive number of candidate genes and the large sample size. Studies that focus on candidate genes have only slightly increased chances of detecting associations, and are likely to miss many true effects in non-candidate genes, at least for obesity-related traits.

Testing the robustness of management decisions to uncertainty: Everglades restoration scenarios.

PubMed

Fuller, Michael M; Gross, Louis J; Duke-Sylvester, Scott M; Palmer, Mark

2008-04-01

To effectively manage large natural reserves, resource managers must prepare for future contingencies while balancing the often conflicting priorities of different stakeholders. To deal with these issues, managers routinely employ models to project the response of ecosystems to different scenarios that represent alternative management plans or environmental forecasts. Scenario analysis is often used to rank such alternatives to aid the decision making process. However, model projections are subject to uncertainty in assumptions about model structure, parameter values, environmental inputs, and subcomponent interactions. We introduce an approach for testing the robustness of model-based management decisions to the uncertainty inherent in complex ecological models and their inputs. We use relative assessment to quantify the relative impacts of uncertainty on scenario ranking. To illustrate our approach we consider uncertainty in parameter values and uncertainty in input data, with specific examples drawn from the Florida Everglades restoration project. Our examples focus on two alternative 30-year hydrologic management plans that were ranked according to their overall impacts on wildlife habitat potential. We tested the assumption that varying the parameter settings and inputs of habitat index models does not change the rank order of the hydrologic plans. We compared the average projected index of habitat potential for four endemic species and two wading-bird guilds to rank the plans, accounting for variations in parameter settings and water level inputs associated with hypothetical future climates. Indices of habitat potential were based on projections from spatially explicit models that are closely tied to hydrology. For the American alligator, the rank order of the hydrologic plans was unaffected by substantial variation in model parameters. By contrast, simulated major shifts in water levels led to reversals in the ranks of the hydrologic plans in 24.1-30.6% of the projections for the wading bird guilds and several individual species. By exposing the differential effects of uncertainty, relative assessment can help resource managers assess the robustness of scenario choice in model-based policy decisions.

Identification of symptom and functional domains that fibromyalgia patients would like to see improved: a cluster analysis.

PubMed

Bennett, Robert M; Russell, Jon; Cappelleri, Joseph C; Bushmakin, Andrew G; Zlateva, Gergana; Sadosky, Alesia

2010-06-28

The purpose of this study was to determine whether some of the clinical features of fibromyalgia (FM) that patients would like to see improved aggregate into definable clusters. Seven hundred and eighty-eight patients with clinically confirmed FM and baseline pain > or =40 mm on a 100 mm visual analogue scale ranked 5 FM clinical features that the subjects would most like to see improved after treatment (one for each priority quintile) from a list of 20 developed during focus groups. For each subject, clinical features were transformed into vectors with rankings assigned values 1-5 (lowest to highest ranking). Logistic analysis was used to create a distance matrix and hierarchical cluster analysis was applied to identify cluster structure. The frequency of cluster selection was determined, and cluster importance was ranked using cluster scores derived from rankings of the clinical features. Multidimensional scaling was used to visualize and conceptualize cluster relationships. Six clinical features clusters were identified and named based on their key characteristics. In order of selection frequency, the clusters were Pain (90%; 4 clinical features), Fatigue (89%; 4 clinical features), Domestic (42%; 4 clinical features), Impairment (29%; 3 functions), Affective (21%; 3 clinical features), and Social (9%; 2 functional). The "Pain Cluster" was ranked of greatest importance by 54% of subjects, followed by Fatigue, which was given the highest ranking by 28% of subjects. Multidimensional scaling mapped these clusters to two dimensions: Status (bounded by Physical and Emotional domains), and Setting (bounded by Individual and Group interactions). Common clinical features of FM could be grouped into 6 clusters (Pain, Fatigue, Domestic, Impairment, Affective, and Social) based on patient perception of relevance to treatment. Furthermore, these 6 clusters could be charted in the 2 dimensions of Status and Setting, thus providing a unique perspective for interpretation of FM symptomatology.

Robust estimation of microbial diversity in theory and in practice

PubMed Central

Haegeman, Bart; Hamelin, Jérôme; Moriarty, John; Neal, Peter; Dushoff, Jonathan; Weitz, Joshua S

2013-01-01

Quantifying diversity is of central importance for the study of structure, function and evolution of microbial communities. The estimation of microbial diversity has received renewed attention with the advent of large-scale metagenomic studies. Here, we consider what the diversity observed in a sample tells us about the diversity of the community being sampled. First, we argue that one cannot reliably estimate the absolute and relative number of microbial species present in a community without making unsupported assumptions about species abundance distributions. The reason for this is that sample data do not contain information about the number of rare species in the tail of species abundance distributions. We illustrate the difficulty in comparing species richness estimates by applying Chao's estimator of species richness to a set of in silico communities: they are ranked incorrectly in the presence of large numbers of rare species. Next, we extend our analysis to a general family of diversity metrics (‘Hill diversities'), and construct lower and upper estimates of diversity values consistent with the sample data. The theory generalizes Chao's estimator, which we retrieve as the lower estimate of species richness. We show that Shannon and Simpson diversity can be robustly estimated for the in silico communities. We analyze nine metagenomic data sets from a wide range of environments, and show that our findings are relevant for empirically-sampled communities. Hence, we recommend the use of Shannon and Simpson diversity rather than species richness in efforts to quantify and compare microbial diversity. PMID:23407313

Professional, generational, and gender differences in perception of organisational values among Israeli physicians and nurses: Implications for retention.

PubMed

Warshawski, Sigalit; Barnoy, Sivia; Kagan, Ilya

2017-11-01

The global health workforce today is more age diverse than ever before and spans three generations: baby boomers, X and Y generations. Each generation has a distinct set of characteristics, values, and beliefs. This diversity can lead to increased creativity and a greater richness of values and skills, but at the same time it can also lead to value clashes, disrespect, and conflicts. This study aimed to examine professional, generational, and gender differences in the perception of the importance of organisational values among nurses and physicians working in both hospitals and outpatient clinics in Israel. Data were collected from a large sample of nurses and physicians (N = 603) from 11 hospitals and community services across Israel. The participants completed a self-administered questionnaire rating the perceived importance of 20 organisational values, such as leadership, risk-taking, competition, power, and collaboration. The five values ranked most important were performance quality, cooperation, commitment, effectiveness, and efficiency. The five values ranked least important were competition, marketing, power, risk-taking, and assertiveness. Significant value differences were found by profession, generation, and gender. Nurses scored efficiency, assertiveness, risk-taking, power, and marketing higher than physicians did. The Y generation scored power higher and marketing lower than the two older generations. Women ranked the values of cooperation, commitment, innovativeness, vision, and marketing significantly higher than men did. Understanding differences between professions, generations, and gender is a useful first step in improving employees' job satisfaction, productivity, and retention.

Stability and change in disease prestige: A comparative analysis of three surveys spanning a quarter of a century.

PubMed

Album, Dag; Johannessen, Lars E F; Rasmussen, Erik B

2017-05-01

In this paper, we present a comparative analysis of three survey studies of disease prestige in medical culture. The studies were conducted in 1990, 2002 and 2014 using the same research design. In each of the three rounds, a sample of Norwegian physicians was asked to rate a set of 38 diseases on a scale from 1 to 9 according to the prestige they believed health personnel in general would award them. The results show a remarkable stability in the prestige rank order over 25 years. The top three diseases in all three surveys were leukaemia, brain tumour and myocardial infarction. The four lowest ranked were fibromyalgia, depressive neurosis, anxiety neurosis and hepatocirrhosis. The most notable change concerns apoplexy (brain stroke), which moved from a rank of 33 to 29 and then to 23 over the three rounds. We argue that the stable pattern, as well as this change, substantiate the interpretation of previous research, i.e. that the prestige of a disease is affected by the localization of the affected organ or body part, the effect and style of its typical treatment, and the social attributes of the typical patient. Analysing physicians' shared evaluations of different diseases, the paper contributes to the cultural understanding of disease conceptions in medicine. Understanding these conceptions is important because disease prestige may influence decision-making in the healthcare sector. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effect of Industry Sponsorship on Dental Restorative Trials.

PubMed

Schwendicke, F; Tu, Y-K; Blunck, U; Paris, S; Göstemeyer, G

2016-01-01

Industry sponsorship was found to potentially introduce bias into clinical trials. We assessed the effects of industry sponsorship on the design, comparator choice, and findings of randomized controlled trials on dental restorative materials. A systematic review was performed via MEDLINE, CENTRAL, and EMBASE. Randomized trials on dental restorative and adhesive materials published 2005 to 2015 were included. The design of sponsored and nonsponsored trials was compared statistically (risk of bias, treatment indication, setting, transferability, sample size). Comparator choice and network geometry of sponsored and nonsponsored trials were assessed via network analysis. Material performance rankings in different trial types were estimated via Bayesian network meta-analysis. Overall, 114 studies were included (15,321 restorations in 5,232 patients). We found 21 and 41 (18% and 36%) trials being clearly or possibly industry sponsored, respectively. Trial design of sponsored and nonsponsored trials did not significantly differ for most assessed items. Sponsored trials evaluated restorations of load-bearing cavities significantly more often than nonsponsored trials, had longer follow-up periods, and showed significantly increased risk of detection bias. Regardless of sponsorship status, comparisons were mainly performed within material classes. The proportion of trials comparing against gold standard restorative or adhesive materials did not differ between trial types. If ranked for performance according to the need to re-treat (best: least re-treatments), most material combinations were ranked similarly in sponsored and nonsponsored trials. The effect of industry sponsorship on dental restorative trials seems limited. © International & American Associations for Dental Research 2015.

Geogenic organic contaminants in the low-rank coal-bearing Carrizo-Wilcox aquifer of East Texas, USA

NASA Astrophysics Data System (ADS)

Chakraborty, Jayeeta; Varonka, Matthew; Orem, William; Finkelman, Robert B.; Manton, William

2017-06-01

The organic composition of groundwater along the Carrizo-Wilcox aquifer in East Texas (USA), sampled from rural wells in May and September 2015, was examined as part of a larger study of the potential health and environmental effects of organic compounds derived from low-rank coals. The quality of water from the low-rank coal-bearing Carrizo-Wilcox aquifer is a potential environmental concern and no detailed studies of the organic compounds in this aquifer have been published. Organic compounds identified in the water samples included: aliphatics and their fatty acid derivatives, phenols, biphenyls, N-, O-, and S-containing heterocyclic compounds, polycyclic aromatic hydrocarbons (PAHs), aromatic amines, and phthalates. Many of the identified organic compounds (aliphatics, phenols, heterocyclic compounds, PAHs) are geogenic and originated from groundwater leaching of young and unmetamorphosed low-rank coals. Estimated concentrations of individual compounds ranged from about 3.9 to 0.01 μg/L. In many rural areas in East Texas, coal strata provide aquifers for drinking water wells. Organic compounds observed in groundwater are likely to be present in drinking water supplied from wells that penetrate the coal. Some of the organic compounds identified in the water samples are potentially toxic to humans, but at the estimated levels in these samples, the compounds are unlikely to cause acute health problems. The human health effects of low-level chronic exposure to coal-derived organic compounds in drinking water in East Texas are currently unknown, and continuing studies will evaluate possible toxicity.

Virulotyping of Shigella spp. isolated from pediatric patients in Tehran, Iran.

PubMed

Ranjbar, Reza; Bolandian, Masomeh; Behzadi, Payam

2017-03-01

Shigellosis is a considerable infectious disease with high morbidity and mortality among children worldwide. In this survey the prevalence of four important virulence genes including ial, ipaH, set1A, and set1B were investigated among Shigella strains and the related gene profiles identified in the present investigation, stool specimens were collected from children who were referred to two hospitals in Tehran, Iran. The samples were collected during 3 years (2008-2010) from children who were suspected to shigellosis. Shigella spp. were identified throughout microbiological and serological tests and then subjected to PCR for virulotyping. Shigella sonnei was ranking first (65.5%) followed by Shigella flexneri (25.9%), Shigella boydii (6.9%), and Shigella dysenteriae (1.7%). The ial gene was the most frequent virulence gene among isolated bacterial strains and was followed by ipaH, set1B, and set1A. S. flexneri possessed all of the studied virulence genes (ial 65.51%, ipaH 58.62%, set1A 12.07%, and set1B 22.41%). Moreover, the pattern of virulence gene profiles including ial, ial-ipaH, ial-ipaH-set1B, and ial-ipaH-set1B-set1A was identified for isolated Shigella spp. strains. The pattern of virulence genes is changed in isolated strains of Shigella in this study. So, the ial gene is placed first and the ipaH in second.

Development of a questionnaire to assess sedentary time in older persons – a comparative study using accelerometry

PubMed Central

2013-01-01

Background There is currently no validated questionnaire available to assess total sedentary time in older adults. Most studies only used TV viewing time as an indicator of sedentary time. The first aim of our study was to investigate the self-reported time spent by older persons on a set of sedentary activities, and to compare this with objective sedentary time measured by accelerometry. The second aim was to determine what set of self-reported sedentary activities should be used to validly rank people’s total sedentary time. Finally we tested the reliability of our newly developed questionnaire using the best performing set of sedentary activities. Methods The study sample included 83 men and women aged 65–92 y, a random sample of Longitudinal Aging Study Amsterdam participants, who completed a questionnaire including ten sedentary activities and wore an Actigraph GT3X accelerometer for 8 days. Spearman correlation coefficients were calculated to examine the association between self-reported time and objective sedentary time. The test-retest reliability was calculated using the intraclass correlation coefficient (ICC). Results Mean total self-reported sedentary time was 10.4 (SD 3.5) h/d and was not significantly different from mean total objective sedentary time (10.2 (1.2) h/d, p = 0.63). Total self-reported sedentary time on an average day (sum of ten activities) correlated moderately (Spearman’s r = 0.35, p < 0.01) with total objective sedentary time. The correlation improved when using the sum of six activities (r = 0.46, p < 0.01), and was much higher than when using TV watching only (r = 0.22, p = 0.05). The test-retest reliability of the sum of six sedentary activities was 0.71 (95% CI 0.57-0.81). Conclusions A questionnaire including six sedentary activities was moderately associated with accelerometry-derived sedentary time and can be used to reliably rank sedentary time in older persons. PMID:23899190

Assessing significance in a Markov chain without mixing.

PubMed

Chikina, Maria; Frieze, Alan; Pegden, Wesley

2017-03-14

We present a statistical test to detect that a presented state of a reversible Markov chain was not chosen from a stationary distribution. In particular, given a value function for the states of the Markov chain, we would like to show rigorously that the presented state is an outlier with respect to the values, by establishing a [Formula: see text] value under the null hypothesis that it was chosen from a stationary distribution of the chain. A simple heuristic used in practice is to sample ranks of states from long random trajectories on the Markov chain and compare these with the rank of the presented state; if the presented state is a [Formula: see text] outlier compared with the sampled ranks (its rank is in the bottom [Formula: see text] of sampled ranks), then this observation should correspond to a [Formula: see text] value of [Formula: see text] This significance is not rigorous, however, without good bounds on the mixing time of the Markov chain. Our test is the following: Given the presented state in the Markov chain, take a random walk from the presented state for any number of steps. We prove that observing that the presented state is an [Formula: see text]-outlier on the walk is significant at [Formula: see text] under the null hypothesis that the state was chosen from a stationary distribution. We assume nothing about the Markov chain beyond reversibility and show that significance at [Formula: see text] is best possible in general. We illustrate the use of our test with a potential application to the rigorous detection of gerrymandering in Congressional districting.

Assessing significance in a Markov chain without mixing

PubMed Central

Chikina, Maria; Frieze, Alan; Pegden, Wesley

2017-01-01

We present a statistical test to detect that a presented state of a reversible Markov chain was not chosen from a stationary distribution. In particular, given a value function for the states of the Markov chain, we would like to show rigorously that the presented state is an outlier with respect to the values, by establishing a p value under the null hypothesis that it was chosen from a stationary distribution of the chain. A simple heuristic used in practice is to sample ranks of states from long random trajectories on the Markov chain and compare these with the rank of the presented state; if the presented state is a 0.1% outlier compared with the sampled ranks (its rank is in the bottom 0.1% of sampled ranks), then this observation should correspond to a p value of 0.001. This significance is not rigorous, however, without good bounds on the mixing time of the Markov chain. Our test is the following: Given the presented state in the Markov chain, take a random walk from the presented state for any number of steps. We prove that observing that the presented state is an ε-outlier on the walk is significant at p=2ε under the null hypothesis that the state was chosen from a stationary distribution. We assume nothing about the Markov chain beyond reversibility and show that significance at p≈ε is best possible in general. We illustrate the use of our test with a potential application to the rigorous detection of gerrymandering in Congressional districting. PMID:28246331

Influence of protonation, tautomeric, and stereoisomeric states on protein-ligand docking results.

PubMed

ten Brink, Tim; Exner, Thomas E

2009-06-01

In this work, we present a systematical investigation of the influence of ligand protonation states, stereoisomers, and tautomers on results obtained with the two protein-ligand docking programs GOLD and PLANTS. These different states were generated with a fully automated tool, called SPORES (Structure PrOtonation and Recognition System). First, the most probable protonations, as defined by this rule based system, were compared to the ones stored in the well-known, manually revised CCDC/ASTEX data set. Then, to investigate the influence of the ligand protonation state on the docking results, different protonation states were created. Redocking and virtual screening experiments were conducted demonstrating that both docking programs have problems in identifying the correct protomer for each complex. Therefore, a preselection of plausible protomers or the improvement of the scoring functions concerning their ability to rank different molecules/states is needed. Additionally, ligand stereoisomers were tested for a subset of the CCDC/ASTEX set, showing similar problems regarding the ranking of these stereoisomers as the ranking of the protomers.

Strong Similarity Measures for Ordered Sets of Documents in Information Retrieval.

ERIC Educational Resources Information Center

Egghe, L.; Michel, Christine

2002-01-01

Presents a general method to construct ordered similarity measures in information retrieval based on classical similarity measures for ordinary sets. Describes a test of some of these measures in an information retrieval system that extracted ranked document sets and discuses the practical usability of the ordered similarity measures. (Author/LRW)

Ranking Practice Variability in the Medical Student Performance Evaluation: So Bad, It’s “Good”

PubMed Central

Boysen Osborn, Megan; Mattson, James; Yanuck, Justin; Anderson, Craig; Tekian, Ara; Fox, Christian John; Harris, Ilene B.

2018-01-01

Purpose To examine the variability among medical schools in ranking systems used in medical student performance evaluations (MSPEs). Method The authors reviewed MSPEs from U.S. MD-granting medical schools received by the University of California, Irvine emergency medicine and internal medicine residency programs during 2012–2013 and 2014–2015. They recorded whether the school used a ranking system, the type of ranking system used, the size and description of student categories, the location of the ranking statement and category legend, and whether nonranking schools used language suggestive of rank. Results Of the 134 medical schools in the study sample, the majority (n = 101; 75%) provided ranks for students in the MSPE. Most of the ranking schools (n = 63; 62%) placed students into named category groups, but the number and size of groups varied. The most common descriptors used for these 63 schools’ top, second, third, and lowest groups were “outstanding,” “excellent,” “very good,” and “good,” respectively, but each of these terms was used across a broad range of percentile ranks. Student ranks and school category legends were found in various locations. Many of the 33 schools that did not rank students included language suggestive of rank. Conclusions There is extensive variation in ranking systems used in MSPEs. Program directors may find it difficult to use MSPEs to compare applicants, which may diminish the MSPE’s value in the residency application process and negatively affect high-achieving students. A consistent approach to ranking students would benefit program directors, students, and student affairs officers. PMID:27075499

Hazard-ranking of agricultural pesticides for chronic health effects in Yuma County, Arizona.

PubMed

Sugeng, Anastasia J; Beamer, Paloma I; Lutz, Eric A; Rosales, Cecilia B

2013-10-01

With thousands of pesticides registered by the United States Environmental Protection Agency, it not feasible to sample for all pesticides applied in agricultural communities. Hazard-ranking pesticides based on use, toxicity, and exposure potential can help prioritize community-specific pesticide hazards. This study applied hazard-ranking schemes for cancer, endocrine disruption, and reproductive/developmental toxicity in Yuma County, Arizona. An existing cancer hazard-ranking scheme was modified, and novel schemes for endocrine disruption and reproductive/developmental toxicity were developed to rank pesticide hazards. The hazard-ranking schemes accounted for pesticide use, toxicity, and exposure potential based on chemical properties of each pesticide. Pesticides were ranked as hazards with respect to each health effect, as well as overall chronic health effects. The highest hazard-ranked pesticides for overall chronic health effects were maneb, metam-sodium, trifluralin, pronamide, and bifenthrin. The relative pesticide rankings were unique for each health effect. The highest hazard-ranked pesticides differed from those most heavily applied, as well as from those previously detected in Yuma homes over a decade ago. The most hazardous pesticides for cancer in Yuma County, Arizona were also different from a previous hazard-ranking applied in California. Hazard-ranking schemes that take into account pesticide use, toxicity, and exposure potential can help prioritize pesticides of greatest health risk in agricultural communities. This study is the first to provide pesticide hazard-rankings for endocrine disruption and reproductive/developmental toxicity based on use, toxicity, and exposure potential. These hazard-ranking schemes can be applied to other agricultural communities for prioritizing community-specific pesticide hazards to target decreasing health risk. Copyright © 2013 Elsevier B.V. All rights reserved.

Evaluating the accuracy of sampling to estimate central line-days: simplification of the National Healthcare Safety Network surveillance methods.

PubMed

Thompson, Nicola D; Edwards, Jonathan R; Bamberg, Wendy; Beldavs, Zintars G; Dumyati, Ghinwa; Godine, Deborah; Maloney, Meghan; Kainer, Marion; Ray, Susan; Thompson, Deborah; Wilson, Lucy; Magill, Shelley S

2013-03-01

To evaluate the accuracy of weekly sampling of central line-associated bloodstream infection (CLABSI) denominator data to estimate central line-days (CLDs). Obtained CLABSI denominator logs showing daily counts of patient-days and CLD for 6-12 consecutive months from participants and CLABSI numerators and facility and location characteristics from the National Healthcare Safety Network (NHSN). Convenience sample of 119 inpatient locations in 63 acute care facilities within 9 states participating in the Emerging Infections Program. Actual CLD and estimated CLD obtained from sampling denominator data on all single-day and 2-day (day-pair) samples were compared by assessing the distributions of the CLD percentage error. Facility and location characteristics associated with increased precision of estimated CLD were assessed. The impact of using estimated CLD to calculate CLABSI rates was evaluated by measuring the change in CLABSI decile ranking. The distribution of CLD percentage error varied by the day and number of days sampled. On average, day-pair samples provided more accurate estimates than did single-day samples. For several day-pair samples, approximately 90% of locations had CLD percentage error of less than or equal to ±5%. A lower number of CLD per month was most significantly associated with poor precision in estimated CLD. Most locations experienced no change in CLABSI decile ranking, and no location's CLABSI ranking changed by more than 2 deciles. Sampling to obtain estimated CLD is a valid alternative to daily data collection for a large proportion of locations. Development of a sampling guideline for NHSN users is underway.

Receptor Activator of Nuclear Factor Kappa B (RANK) and Clinicopathological Variables in Endometrial Cancer: A Study at Protein and Gene Level.

PubMed

Gómez, Raúl; Castro, Ana; Martínez, Jessica; Rodríguez-García, Víctor; Burgués, Octavio; Tarín, Juan J; Cano, Antonio

2018-06-22

The system integrated by the receptor activator of nuclear factor kappa B (RANK) and its ligand, RANKL, modulates the role of hormones in the genesis and progression of breast tumors. We investigated whether the expression of RANK was related with clinicopathological features of primary endometrial tumors. Immunohistochemistry was used in an endometrial cancer tissue array containing samples from 36 tumors. The amount of RANK mRNA was examined in a tissue scan cDNA array containing cDNA from 40 tumors. Normal endometrium was examined for comparison. Immunohistochemical analyses showed that RANK expression was higher in malignant than in normal endometrium ( p < 0.05). RANK expression was related to histological grade (Pearson correlation index = 0.484, p < 0.001), but not to tumor stage or to age of the women. The gene expression was similar in malignant and normal endometrium. The study of RANK isoforms confirmed that the overall relative abundance of the three clearly identified transcripts was similar in normal and pathological endometrium. RANK protein expression increased from normal to malignant endometrium, and the expression level was related with tumor grade but not with stage or the age of subjects in endometrial cancer. In contrast, similar comparisons showed no change in RANK gene expression.

GAtor: A First-Principles Genetic Algorithm for Molecular Crystal Structure Prediction.

PubMed

Curtis, Farren; Li, Xiayue; Rose, Timothy; Vázquez-Mayagoitia, Álvaro; Bhattacharya, Saswata; Ghiringhelli, Luca M; Marom, Noa

2018-04-10

We present the implementation of GAtor, a massively parallel, first-principles genetic algorithm (GA) for molecular crystal structure prediction. GAtor is written in Python and currently interfaces with the FHI-aims code to perform local optimizations and energy evaluations using dispersion-inclusive density functional theory (DFT). GAtor offers a variety of fitness evaluation, selection, crossover, and mutation schemes. Breeding operators designed specifically for molecular crystals provide a balance between exploration and exploitation. Evolutionary niching is implemented in GAtor by using machine learning to cluster the dynamically updated population by structural similarity and then employing a cluster-based fitness function. Evolutionary niching promotes uniform sampling of the potential energy surface by evolving several subpopulations, which helps overcome initial pool biases and selection biases (genetic drift). The various settings offered by GAtor increase the likelihood of locating numerous low-energy minima, including those located in disconnected, hard to reach regions of the potential energy landscape. The best structures generated are re-relaxed and re-ranked using a hierarchy of increasingly accurate DFT functionals and dispersion methods. GAtor is applied to a chemically diverse set of four past blind test targets, characterized by different types of intermolecular interactions. The experimentally observed structures and other low-energy structures are found for all four targets. In particular, for Target II, 5-cyano-3-hydroxythiophene, the top ranked putative crystal structure is a Z' = 2 structure with P1̅ symmetry and a scaffold packing motif, which has not been reported previously.

Reconstruction of interatomic vectors by principle component analysis of nuclear magnetic resonance data in multiple alignments

NASA Astrophysics Data System (ADS)

Hus, Jean-Christophe; Bruschweiler, Rafael

2002-07-01

A general method is presented for the reconstruction of interatomic vector orientations from nuclear magnetic resonance (NMR) spectroscopic data of tensor interactions of rank 2, such as dipolar coupling and chemical shielding anisotropy interactions, in solids and partially aligned liquid-state systems. The method, called PRIMA, is based on a principal component analysis of the covariance matrix of the NMR parameters collected for multiple alignments. The five nonzero eigenvalues and their eigenvectors efficiently allow the approximate reconstruction of the vector orientations of the underlying interactions. The method is demonstrated for an isotropic distribution of sample orientations as well as for finite sets of orientations and internuclear vectors encountered in protein systems.

Rapid acquisition of data dense solid-state CPMG NMR spectral sets using multi-dimensional statistical analysis

DOE PAGES

Mason, H. E.; Uribe, E. C.; Shusterman, J. A.

2018-01-01

Tensor-rank decomposition methods have been applied to variable contact time 29 Si{ 1 H} CP/CPMG NMR data sets to extract NMR dynamics information and dramatically decrease conventional NMR acquisition times.

Rapid acquisition of data dense solid-state CPMG NMR spectral sets using multi-dimensional statistical analysis

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

Mason, H. E.; Uribe, E. C.; Shusterman, J. A.

Tensor-rank decomposition methods have been applied to variable contact time 29 Si{ 1 H} CP/CPMG NMR data sets to extract NMR dynamics information and dramatically decrease conventional NMR acquisition times.

What is a species? A new universal method to measure differentiation and assess the taxonomic rank of allopatric populations, using continuous variables

PubMed Central

Donegan, Thomas M.

2018-01-01

Abstract Existing models for assigning species, subspecies, or no taxonomic rank to populations which are geographically separated from one another were analyzed. This was done by subjecting over 3,000 pairwise comparisons of vocal or biometric data based on birds to a variety of statistical tests that have been proposed as measures of differentiation. One current model which aims to test diagnosability (Isler et al. 1998) is highly conservative, applying a hard cut-off, which excludes from consideration differentiation below diagnosis. It also includes non-overlap as a requirement, a measure which penalizes increases to sample size. The “species scoring” model of Tobias et al. (2010) involves less drastic cut-offs, but unlike Isler et al. (1998), does not control adequately for sample size and attributes scores in many cases to differentiation which is not statistically significant. Four different models of assessing effect sizes were analyzed: using both pooled and unpooled standard deviations and controlling for sample size using t-distributions or omitting to do so. Pooled standard deviations produced more conservative effect sizes when uncontrolled for sample size but less conservative effect sizes when so controlled. Pooled models require assumptions to be made that are typically elusive or unsupported for taxonomic studies. Modifications to improving these frameworks are proposed, including: (i) introducing statistical significance as a gateway to attributing any weighting to findings of differentiation; (ii) abandoning non-overlap as a test; (iii) recalibrating Tobias et al. (2010) scores based on effect sizes controlled for sample size using t-distributions. A new universal method is proposed for measuring differentiation in taxonomy using continuous variables and a formula is proposed for ranking allopatric populations. This is based first on calculating effect sizes using unpooled standard deviations, controlled for sample size using t-distributions, for a series of different variables. All non-significant results are excluded by scoring them as zero. Distance between any two populations is calculated using Euclidian summation of non-zeroed effect size scores. If the score of an allopatric pair exceeds that of a related sympatric pair, then the allopatric population can be ranked as species and, if not, then at most subspecies rank should be assigned. A spreadsheet has been programmed and is being made available which allows this and other tests of differentiation and rank studied in this paper to be rapidly analyzed. PMID:29780266

A Novel Database to Rank and Display Archeomagnetic Intensity Data

NASA Astrophysics Data System (ADS)

Donadini, F.; Korhonen, K.; Riisager, P.; Pesonen, L. J.; Kahma, K.

2005-12-01

To understand the content and the causes of the changes in the Earth's magnetic field beyond the observatory records one has to rely on archeomagnetic and lake sediment paleomagnetic data. The regional archeointensity curves are often of different quality and temporally variable which hampers the global analysis of the data in terms of dipole vs non-dipole field. We have developed a novel archeointensity database application utilizing MySQL, PHP (PHP Hypertext Preprocessor), and the Generic Mapping Tools (GMT) for ranking and displaying geomagnetic intensity data from the last 12000 years. Our application has the advantage that no specific software is required to query the database and view the results. Querying the database is performed using any Web browser; a fill-out form is used to enter the site location and a minimum ranking value to select the data points to be displayed. The form also features the possibility to select plotting of the data as an archeointensity curve with error bars, and a Virtual Axial Dipole Moment (VADM) or ancient field value (Ba) curve calculated using the CALS7K model (Continuous Archaeomagnetic and Lake Sediment geomagnetic model) of (Korte and Constable, 2005). The results of a query are displayed on a Web page containing a table summarizing the query parameters, a table showing the archeointensity values satisfying the query parameters, and a plot of VADM or Ba as a function of sample age. The database consists of eight related tables. The main one, INTENSITIES, stores the 3704 archeointensity measurements collected from 159 publications as VADM (and VDM when available) and Ba values, including their standard deviations and sampling locations. It also contains the number of samples and specimens measured from each site. The REFS table stores the references to a particular study. The names, latitudes, and longitudes of the regions where the samples were collected are stored in the SITES table. The MATERIALS, METHODS, SPECIMEN_TYPES and DATING_METHODS tables store information about the sample materials, intensity determination methods, specimen types and age determination methods. The SIGMA_COUNT table is used indirectly for ranking data according to the number of samples measured and their standard deviations. Each intensity measurement is assigned a score (0--2) depending on the number of specimens measured and their standard deviations, the intensity determination method, the type of specimens measured and materials. The ranking of each data point is calculated as the sum of the four scores and varies between 0 and 8. Additionally, users can select the parameters that will be included in the ranking.

Sex-ratio biasing towards daughters among lower-ranking co-wives in Rwanda.

PubMed

Pollet, Thomas V; Fawcett, Tim W; Buunk, Abraham P; Nettle, Daniel

2009-12-23

There is considerable debate as to whether human females bias the sex ratio of their offspring as a function of their own condition. We apply the Trivers-Willard prediction-that mothers in poor condition will overproduce daughters-to a novel measure of condition, namely wife rank within a polygynous marriage. Using a large-scale sample of over 95 000 Rwandan mothers, we show that lower-ranking polygynous wives do indeed have significantly more daughters than higher-ranking polygynous wives and monogamously married women. This effect remains when controlling for potential confounds such as maternal age. We discuss these results in reference to previous work on sex-ratio adjustment in humans.

A novel sample size formula for the weighted log-rank test under the proportional hazards cure model.

PubMed

Xiong, Xiaoping; Wu, Jianrong

2017-01-01

The treatment of cancer has progressed dramatically in recent decades, such that it is no longer uncommon to see a cure or log-term survival in a significant proportion of patients with various types of cancer. To adequately account for the cure fraction when designing clinical trials, the cure models should be used. In this article, a sample size formula for the weighted log-rank test is derived under the fixed alternative hypothesis for the proportional hazards cure models. Simulation showed that the proposed sample size formula provides an accurate estimation of sample size for designing clinical trials under the proportional hazards cure models. Copyright © 2016 John Wiley & Sons, Ltd.

Does the patient's inherent rating tendency influence reported satisfaction scores and affect division ranking?

PubMed

Francis, Patricia; Agoritsas, Thomas; Chopard, Pierre; Perneger, Thomas

2016-04-01

To determine the impact of adjusting for rating tendency (RT) on patient satisfaction scores in a large teaching hospital and to assess the impact of adjustment on the ranking of divisions. Cross-sectional survey. Large 2200-bed university teaching hospital. All adult patients hospitalized during a 1-month period in one of 20 medical divisions. None. Patient experience of care measured by the Picker Patient Experience questionnaire and RT scores. Problem scores were weakly but significantly associated with RT. Division ranking was slightly modified in RT adjusted models. Division ranking changed substantially in case-mix adjusted models. Adjusting patient self-reported problem scores for RT did impact ranking of divisions, although marginally. Further studies are needed to determine the impact of RT when comparing different institutions, particularly across inter-cultural settings, where the difference in RT may be more substantial. © The Author 2016. Published by Oxford University Press in association with the International Society for Quality in Health Care; all rights reserved.

Performance of low-rank QR approximation of the finite element Biot-Savart law

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

White, D A; Fasenfest, B J

2006-01-12

We are concerned with the computation of magnetic fields from known electric currents in the finite element setting. In finite element eddy current simulations it is necessary to prescribe the magnetic field (or potential, depending upon the formulation) on the conductor boundary. In situations where the magnetic field is due to a distributed current density, the Biot-Savart law can be used, eliminating the need to mesh the nonconducting regions. Computation of the Biot-Savart law can be significantly accelerated using a low-rank QR approximation. We review the low-rank QR method and report performance on selected problems.

A gender-based comparison of academic rank and scholarly productivity in academic neurological surgery.

PubMed

Tomei, Krystal L; Nahass, Meghan M; Husain, Qasim; Agarwal, Nitin; Patel, Smruti K; Svider, Peter F; Eloy, Jean Anderson; Liu, James K

2014-07-01

The number of women pursuing training opportunities in neurological surgery has increased, although they are still underrepresented at senior positions relative to junior academic ranks. Research productivity is an important component of the academic advancement process. We sought to use the h-index, a bibliometric previously analyzed among neurological surgeons, to evaluate whether there are gender differences in academic rank and research productivity among academic neurological surgeons. The h-index was calculated for 1052 academic neurological surgeons from 84 institutions, and organized by gender and academic rank. Overall men had statistically higher research productivity (mean 13.3) than their female colleagues (mean 9.5), as measured by the h-index, in the overall sample (p<0.0007). When separating by academic rank, there were no statistical differences (p>0.05) in h-index at the assistant professor (mean 7.2 male, 6.3 female), associate professor (11.2 male, 10.8 female), and professor (20.0 male, 18.0 female) levels based on gender. There was insufficient data to determine significance at the chairperson rank, as there was only one female chairperson. Although overall gender differences in scholarly productivity were detected, these differences did not reach statistical significance upon controlling for academic rank. Women were grossly underrepresented at the level of chairpersons in this sample of 1052 academic neurological surgeons, likely a result of the low proportion of females in this specialty. Future studies may be needed to investigate gender-specific research trends for neurosurgical residents, a cohort that in recent years has seen increased representation by women. Copyright © 2013 Elsevier Ltd. All rights reserved.

Local constructions of gender-based violence amongst IDPs in northern Uganda: analysis of archival data collected using a gender- and age-segmented participatory ranking methodology.

PubMed

Ager, Alastair; Bancroft, Carolyn; Berger, Elizabeth; Stark, Lindsay

2018-01-01

Gender-based violence (GBV) is a significant problem in conflict-affected settings. Understanding local constructions of such violence is crucial to developing preventive and responsive interventions to address this issue. This study reports on a secondary analysis of archived data collected as part of formative qualitative work - using a group participatory ranking methodology (PRM) - informing research on the prevalence of GBV amongst IDPs in northern Uganda in 2006. Sixty-four PRM group discussions were held with women, with men, with girls (aged 14 to 18 years), and with boys (aged 14 to 18 years) selected on a randomized basis across four internally displaced persons (IDP) camps in Lira District. Discussions elicited problems facing women in the camps, and - through structured participatory methods - consensus ranking of their importance and narrative accounts explaining these judgments. Amongst forms of GBV faced by women, rape was ranked as the greatest concern amongst participants (with a mean problem rank of 3.4), followed by marital rape (mean problem rank of 4.5) and intimate partner violence (mean problem rank of 4.9). Girls ranked all forms of GBV as higher priority concerns than other participants. Discussions indicated that these forms of GBV were generally considered normalized within the camp. Gender roles and power, economic deprivation, and physical and social characteristics of the camp setting emerged as key explanatory factors in accounts of GBV prevalence, although these played out in different ways with respect to differing forms of violence. All groups acknowledged GBV to represent a significant threat - among other major concerns such as transportation, water, shelter, food and security - for women residing in the camps. Given evidence of the significantly higher risk in the camp of intimate partner violence and marital rape, the relative prominence of the issue of rape in all rankings suggests normalization of violence within the home. Programs targeting reduction in GBV need to address community-identified root causes such as economic deprivation and social norms related to gender roles. More generally, PRM appears to offer an efficient means of identifying local constructions of prevailing challenges in a manner that can inform programming.

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

PubMed

Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P; Ekins, Sean

2017-12-04

Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.

Multi-dimensional Rankings, Program Termination, and Complexity Bounds of Flowchart Programs

NASA Astrophysics Data System (ADS)

Alias, Christophe; Darte, Alain; Feautrier, Paul; Gonnord, Laure

Proving the termination of a flowchart program can be done by exhibiting a ranking function, i.e., a function from the program states to a well-founded set, which strictly decreases at each program step. A standard method to automatically generate such a function is to compute invariants for each program point and to search for a ranking in a restricted class of functions that can be handled with linear programming techniques. Previous algorithms based on affine rankings either are applicable only to simple loops (i.e., single-node flowcharts) and rely on enumeration, or are not complete in the sense that they are not guaranteed to find a ranking in the class of functions they consider, if one exists. Our first contribution is to propose an efficient algorithm to compute ranking functions: It can handle flowcharts of arbitrary structure, the class of candidate rankings it explores is larger, and our method, although greedy, is provably complete. Our second contribution is to show how to use the ranking functions we generate to get upper bounds for the computational complexity (number of transitions) of the source program. This estimate is a polynomial, which means that we can handle programs with more than linear complexity. We applied the method on a collection of test cases from the literature. We also show the links and differences with previous techniques based on the insertion of counters.

ReactionPredictor: prediction of complex chemical reactions at the mechanistic level using machine learning.

PubMed

Kayala, Matthew A; Baldi, Pierre

2012-10-22

Proposing reasonable mechanisms and predicting the course of chemical reactions is important to the practice of organic chemistry. Approaches to reaction prediction have historically used obfuscating representations and manually encoded patterns or rules. Here we present ReactionPredictor, a machine learning approach to reaction prediction that models elementary, mechanistic reactions as interactions between approximate molecular orbitals (MOs). A training data set of productive reactions known to occur at reasonable rates and yields and verified by inclusion in the literature or textbooks is derived from an existing rule-based system and expanded upon with manual curation from graduate level textbooks. Using this training data set of complex polar, hypervalent, radical, and pericyclic reactions, a two-stage machine learning prediction framework is trained and validated. In the first stage, filtering models trained at the level of individual MOs are used to reduce the space of possible reactions to consider. In the second stage, ranking models over the filtered space of possible reactions are used to order the reactions such that the productive reactions are the top ranked. The resulting model, ReactionPredictor, perfectly ranks polar reactions 78.1% of the time and recovers all productive reactions 95.7% of the time when allowing for small numbers of errors. Pericyclic and radical reactions are perfectly ranked 85.8% and 77.0% of the time, respectively, rising to >93% recovery for both reaction types with a small number of allowed errors. Decisions about which of the polar, pericyclic, or radical reaction type ranking models to use can be made with >99% accuracy. Finally, for multistep reaction pathways, we implement the first mechanistic pathway predictor using constrained tree-search to discover a set of reasonable mechanistic steps from given reactants to given products. Webserver implementations of both the single step and pathway versions of ReactionPredictor are available via the chemoinformatics portal http://cdb.ics.uci.edu/.

Effect of feed supplementation with live yeast on the intestinal transcriptome profile of weaning pigs orally challenged with Escherichia coli F4.

PubMed

Trevisi, P; Latorre, R; Priori, D; Luise, D; Archetti, I; Mazzoni, M; D'Inca, R; Bosi, P

2017-01-01

The ability of live yeasts to modulate pig intestinal cell signals in response to infection with Escherichia coli F4ac (ETEC) has not been studied in-depth. The aim of this trial was to evaluate the effect of Saccharomyces cerevisiae CNCM I-4407 (Sc), supplied at different times, on the transcriptome profile of the jejunal mucosa of pigs 24 h after infection with ETEC. In total, 20 piglets selected to be ETEC-susceptible were weaned at 24 days of age (day 0) and allotted by litter to one of following groups: control (CO), CO+colistin (AB), CO+5×1010 colony-forming unit (CFU) Sc/kg feed, from day 0 (PR) and CO+5×1010 CFU Sc/kg feed from day 7 (CM). On day 7, the pigs were orally challenged with ETEC and were slaughtered 24 h later after blood sampling for haptoglobin (Hp) and C-reactive protein (CRP) determination. The jejunal mucosa was sampled (1) for morphometry; (2) for quantification of proliferation, apoptosis and zonula occludens (ZO-1); (3) to carry out the microarray analysis. A functional analysis was carried out using Gene Set Enrichment Analysis. The normalized enrichment score (NES) was calculated for each gene set, and statistical significance was defined when the False Discovery Rate % was <25 and P-values of NES were <0.05. The blood concentration of CRP and Hp, and the score for ZO-1 integrity on the jejunal villi did not differ between groups. The intestinal crypts were deeper in the AB (P=0.05) and the yeast groups (P<0.05) than in the CO group. Antibiotic treatment increased the number of mitotic cells in intestinal villi as compared with the control group (P<0.05). The PR group tended to increase the mitotic cells in villi and crypts and tended to reduce the cells in apoptosis as compared with the CM group. The transcriptome profiles of the AB and PR groups were similar. In both groups, the gene sets involved in mitosis and in mitochondria development ranked the highest, whereas in the CO group, the gene sets related to cell junction and anion channels were affected. In the CM group, the gene sets linked to the metabolic process, and transcription ranked the highest; a gene set linked with a negative effect on growth was also affected. In conclusion, the constant supplementation in the feed with the strain of yeast tested was effective in counteracting the detrimental effect of ETEC infection in susceptible pigs limits the early activation of the gene sets related to the impairment of the jejunal mucosa.

Performance Analysis of Hybrid Electric Vehicle over Different Driving Cycles

NASA Astrophysics Data System (ADS)

Panday, Aishwarya; Bansal, Hari Om

2017-02-01

Article aims to find the nature and response of a hybrid vehicle on various standard driving cycles. Road profile parameters play an important role in determining the fuel efficiency. Typical parameters of road profile can be reduced to a useful smaller set using principal component analysis and independent component analysis. Resultant data set obtained after size reduction may result in more appropriate and important parameter cluster. With reduced parameter set fuel economies over various driving cycles, are ranked using TOPSIS and VIKOR multi-criteria decision making methods. The ranking trend is then compared with the fuel economies achieved after driving the vehicle over respective roads. Control strategy responsible for power split is optimized using genetic algorithm. 1RC battery model and modified SOC estimation method are considered for the simulation and improved results compared with the default are obtained.

Daily Interpersonal Experience Partially Explains the Association Between Social Rank and Physical Health.

PubMed

Cundiff, Jenny M; Kamarck, Thomas W; Manuck, Stephen B

2016-12-01

Socioeconomic position is a well-established risk factor for poor physical health. This study examines whether the effects of lower social rank on physical health may be accounted for by differences in daily social experience. In a large community sample (N = 475), we examined whether subjective social rank is associated with self-rated health, in part, through positive and negative perceptions of daily interpersonal interactions, assessed using ecological momentary assessment. Higher social rank was associated with higher average perceived positivity of social interactions in daily life (e.g., B = .18, p < .001), but not with perceived negativity of social interactions. Further, the association between social rank and self-rated physical health was partially accounted for by differences in perceived positivity of social interactions. This effect was independent of well-characterized objective markers of SES and personality traits. Differences in the quality of day-to-day social interactions is a viable pathway linking lower social rank to poorer physical health.

Thalamo-Sensorimotor Functional Connectivity Correlates with World Ranking of Olympic, Elite, and High Performance Athletes.

PubMed

Huang, Zirui; Davis, Henry Hap; Wolff, Annemarie; Northoff, Georg

2017-01-01

Brain plasticity studies have shown functional reorganization in participants with outstanding motor expertise. Little is known about neural plasticity associated with exceptionally long motor training or of its predictive value for motor performance excellence. The present study utilised resting-state functional magnetic resonance imaging (rs-fMRI) in a unique sample of world-class athletes: Olympic, elite, and internationally ranked swimmers ( n = 30). Their world ranking ranged from 1st to 250th: each had prepared for participation in the Olympic Games. Combining rs-fMRI graph-theoretical and seed-based functional connectivity analyses, it was discovered that the thalamus has its strongest connections with the sensorimotor network in elite swimmers with the highest world rankings (career best rank: 1-35). Strikingly, thalamo-sensorimotor functional connections were highly correlated with the swimmers' motor performance excellence, that is, accounting for 41% of the individual variance in best world ranking. Our findings shed light on neural correlates of long-term athletic performance involving thalamo-sensorimotor functional circuits.

Fracturing ranked surfaces

NASA Astrophysics Data System (ADS)

Schrenk, K. J.; Araújo, N. A. M.; Andrade, J. S., Jr.; Herrmann, H. J.

2012-04-01

Discretized landscapes can be mapped onto ranked surfaces, where every element (site or bond) has a unique rank associated with its corresponding relative height. By sequentially allocating these elements according to their ranks and systematically preventing the occupation of bridges, namely elements that, if occupied, would provide global connectivity, we disclose that bridges hide a new tricritical point at an occupation fraction p = pc, where pc is the percolation threshold of random percolation. For any value of p in the interval pc < p <= 1, our results show that the set of bridges has a fractal dimension dBB ~ 1.22 in two dimensions. In the limit p --> 1, a self-similar fracture is revealed as a singly connected line that divides the system in two domains. We then unveil how several seemingly unrelated physical models tumble into the same universality class and also present results for higher dimensions.

Scalable Nonparametric Low-Rank Kernel Learning Using Block Coordinate Descent.

PubMed

Hu, En-Liang; Kwok, James T

2015-09-01

Nonparametric kernel learning (NPKL) is a flexible approach to learn the kernel matrix directly without assuming any parametric form. It can be naturally formulated as a semidefinite program (SDP), which, however, is not very scalable. To address this problem, we propose the combined use of low-rank approximation and block coordinate descent (BCD). Low-rank approximation avoids the expensive positive semidefinite constraint in the SDP by replacing the kernel matrix variable with V(T)V, where V is a low-rank matrix. The resultant nonlinear optimization problem is then solved by BCD, which optimizes each column of V sequentially. It can be shown that the proposed algorithm has nice convergence properties and low computational complexities. Experiments on a number of real-world data sets show that the proposed algorithm outperforms state-of-the-art NPKL solvers.

Headspace-programmed temperature vaporization-mass spectrometry for the rapid determination of possible volatile biomarkers of lung cancer in urine.

PubMed

Pérez Antón, Ana; Ramos, Álvaro García; Del Nogal Sánchez, Miguel; Pavón, José Luis Pérez; Cordero, Bernardo Moreno; Pozas, Ángel Pedro Crisolino

2016-07-01

We propose a new method for the rapid determination of five volatile compounds described in the literature as possible biomarkers of lung cancer in urine samples. The method is based on the coupling of a headspace sampler, a programmed temperature vaporizer in solvent-vent injection mode, and a mass spectrometer (HS-PTV-MS). This configuration is known as an electronic nose based on mass spectrometry. Once the method was developed, it was used for the analysis of urine samples from lung cancer patients and healthy individuals. Multivariate calibration models were employed to quantify the biomarker concentrations in the samples. The detection limits ranged between 0.16 and 21 μg/L. For the assignment of the samples to the patient group or the healthy individuals, the Wilcoxon signed-rank test was used, comparing the concentrations obtained with the median of a reference set of healthy individuals. To date, this is the first time that multivariate calibration and non-parametric methods have been combined to classify biological samples from profile signals obtained with an electronic nose. When significant differences in the concentration of one or more biomarkers were found with respect to the reference set, the sample is considered as a positive one and a new analysis was performed using a chromatographic method (HS-PTV-GC/MS) to confirm the result. The main advantage of the proposed HS-PTV-MS methodology is that no prior chromatographic separation and no sample manipulation are required, which allows an increase of the number of samples analyzed per hour and restricts the use of time-consuming techniques to only when necessary. Graphical abstract Schematic diagram of the developed methodology.

Does the Introduction of the Ranking Task in Valuation Studies Improve Data Quality and Reduce Inconsistencies? The Case of the EQ-5D-5L.

PubMed

Ramos-Goñi, Juan M; Rand-Hendriksen, Kim; Pinto-Prades, Jose Luis

2016-06-01

Time trade-off (TTO)-based valuation studies for the three-level version of the EuroQol five-dimensional questionnaire (EQ-5D) typically started off with a ranking task (ordering the health states by preference). This was not included in the protocol for the five-level EQ-5D (EQ-5D-5L) valuation study. To test whether reintroducing a ranking task before the composite TTO (C-TTO) could help to reduce inconsistencies in C-TTO responses and improve the data quality. Respondents were randomly assigned to three study arms. The control arm was the present EQ-5D-5L study protocol, without ranking. The second arm (ranking without sorting) preceded the present protocol by asking respondents to rank the target health states using physical cards. The states were then valued in random order using C-TTO. In the third arm (ranking and sorting), the ranked states remained visible through the C-TTO tasks and the order of valuation was determined by the ranking. The study used only 10 EQ-5D-5L health states. We compared the C-TTO-based inconsistent pairs of health states and ties. The final sample size was 196 in the control arm, 205 in the ranking without sorting arm, and 199 in the ranking and sorting arm. The percentages of ties by respondents were 15.1%, 12.5%, and 12.6% for the control arm, the ranking without sorting arm, and the ranking and sorting arm, respectively. The extra cost for adding the ranking task was about 15%. The benefit does not justify the effort involved in the ranking task. For this reason, the addition of the ranking task to the present EQ-5D-5L valuation protocol is not an attractive option. Copyright © 2016 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Biostatistics Series Module 3: Comparing Groups: Numerical Variables.

PubMed

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Numerical data that are normally distributed can be analyzed with parametric tests, that is, tests which are based on the parameters that define a normal distribution curve. If the distribution is uncertain, the data can be plotted as a normal probability plot and visually inspected, or tested for normality using one of a number of goodness of fit tests, such as the Kolmogorov-Smirnov test. The widely used Student's t-test has three variants. The one-sample t-test is used to assess if a sample mean (as an estimate of the population mean) differs significantly from a given population mean. The means of two independent samples may be compared for a statistically significant difference by the unpaired or independent samples t-test. If the data sets are related in some way, their means may be compared by the paired or dependent samples t-test. The t-test should not be used to compare the means of more than two groups. Although it is possible to compare groups in pairs, when there are more than two groups, this will increase the probability of a Type I error. The one-way analysis of variance (ANOVA) is employed to compare the means of three or more independent data sets that are normally distributed. Multiple measurements from the same set of subjects cannot be treated as separate, unrelated data sets. Comparison of means in such a situation requires repeated measures ANOVA. It is to be noted that while a multiple group comparison test such as ANOVA can point to a significant difference, it does not identify exactly between which two groups the difference lies. To do this, multiple group comparison needs to be followed up by an appropriate post hoc test. An example is the Tukey's honestly significant difference test following ANOVA. If the assumptions for parametric tests are not met, there are nonparametric alternatives for comparing data sets. These include Mann-Whitney U-test as the nonparametric counterpart of the unpaired Student's t-test, Wilcoxon signed-rank test as the counterpart of the paired Student's t-test, Kruskal-Wallis test as the nonparametric equivalent of ANOVA and the Friedman's test as the counterpart of repeated measures ANOVA.

Learning to rank image tags with limited training examples.

PubMed

Songhe Feng; Zheyun Feng; Rong Jin

2015-04-01

With an increasing number of images that are available in social media, image annotation has emerged as an important research topic due to its application in image matching and retrieval. Most studies cast image annotation into a multilabel classification problem. The main shortcoming of this approach is that it requires a large number of training images with clean and complete annotations in order to learn a reliable model for tag prediction. We address this limitation by developing a novel approach that combines the strength of tag ranking with the power of matrix recovery. Instead of having to make a binary decision for each tag, our approach ranks tags in the descending order of their relevance to the given image, significantly simplifying the problem. In addition, the proposed method aggregates the prediction models for different tags into a matrix, and casts tag ranking into a matrix recovery problem. It introduces the matrix trace norm to explicitly control the model complexity, so that a reliable prediction model can be learned for tag ranking even when the tag space is large and the number of training images is limited. Experiments on multiple well-known image data sets demonstrate the effectiveness of the proposed framework for tag ranking compared with the state-of-the-art approaches for image annotation and tag ranking.

PREDICTING ER BINDING AFFINITY FOR EDC RANKING AND PRIORITIZATION: MODEL I

EPA Science Inventory

A Common Reactivity Pattern (COREPA) model, based on consideration of multiple energetically reasonable conformations of flexible chemicals was developed using a training set of 232 rat estrogen receptor (rER) relative binding affinity (RBA) measurements. The training set include...

Pre-analytical Factors Influence Accuracy of Urine Spot Iodine Assessment in Epidemiological Surveys.

PubMed

Doggui, Radhouene; El Ati-Hellal, Myriam; Traissac, Pierre; El Ati, Jalila

2018-03-26

Urinary iodine concentration (UIC) is commonly used to assess iodine status of subjects in epidemiological surveys. As pre-analytical factors are an important source of measurement error and studies about this phase are scarce, our objective was to assess the influence of urine sampling conditions on UIC, i.e., whether the child ate breakfast or not, urine void rank of the day, and time span between last meal and urine collection. A nationwide, two-stage, stratified, cross-sectional study including 1560 children (6-12 years) was performed in 2012. UIC was determined by the Sandell-Kolthoff method. Pre-analytical factors were assessed from children's mothers by using a questionnaire. Association between iodine status and pre-analytical factors were adjusted for one another and socio-economic characteristics by multivariate linear and multinomial regression models (RPR: relative prevalence ratios). Skipping breakfast prior to morning urine sampling decreased UIC by 40 to 50 μg/L and the proportion of UIC < 100 μg/L was higher among children having those skipped breakfast (RPR = 3.2[1.0-10.4]). In unadjusted analyses, UIC was less among children sampled more than 5 h from their last meal. UIC decreased with rank of urine void (e.g., first vs. second, P < 0.001); also, the proportion of UIC < 100 μg/L was greater among 4th rank samples (vs. second RPR = 2.1[1.1-4.0]). Subjects' breakfast status and urine void rank should be accounted for when assessing iodine status. Providing recommendations to standardize pre-analytical factors is a key step toward improving accuracy and comparability of survey results for assessing iodine status from spot urine samples. These recommendations have to be evaluated by future research.

How to infer relative fitness from a sample of genomic sequences.

PubMed

Dayarian, Adel; Shraiman, Boris I

2014-07-01

Mounting evidence suggests that natural populations can harbor extensive fitness diversity with numerous genomic loci under selection. It is also known that genealogical trees for populations under selection are quantifiably different from those expected under neutral evolution and described statistically by Kingman's coalescent. While differences in the statistical structure of genealogies have long been used as a test for the presence of selection, the full extent of the information that they contain has not been exploited. Here we demonstrate that the shape of the reconstructed genealogical tree for a moderately large number of random genomic samples taken from a fitness diverse, but otherwise unstructured, asexual population can be used to predict the relative fitness of individuals within the sample. To achieve this we define a heuristic algorithm, which we test in silico, using simulations of a Wright-Fisher model for a realistic range of mutation rates and selection strength. Our inferred fitness ranking is based on a linear discriminator that identifies rapidly coalescing lineages in the reconstructed tree. Inferred fitness ranking correlates strongly with actual fitness, with a genome in the top 10% ranked being in the top 20% fittest with false discovery rate of 0.1-0.3, depending on the mutation/selection parameters. The ranking also enables us to predict the genotypes that future populations inherit from the present one. While the inference accuracy increases monotonically with sample size, samples of 200 nearly saturate the performance. We propose that our approach can be used for inferring relative fitness of genomes obtained in single-cell sequencing of tumors and in monitoring viral outbreaks. Copyright © 2014 by the Genetics Society of America.

Predictive and External Validity of a Pre-Market Study to Determine the Most Effective Pictorial Health Warning Label Content for Cigarette Packages

PubMed Central

Thrasher, James F.; Reid, Jessica L.; Hammond, David

2016-01-01

Abstract Introduction: Studies examining cigarette package pictorial health warning label (HWL) content have primarily used designs that do not allow determination of effectiveness after repeated, naturalistic exposure. This research aimed to determine the predictive and external validity of a pre-market evaluation study of pictorial HWLs. Methods: Data were analyzed from: (1) a pre-market convenience sample of 544 adult smokers who participated in field experiments in Mexico City before pictorial HWL implementation (September 2010); and (2) a post-market population-based representative sample of 1765 adult smokers in the Mexican administration of the International Tobacco Control Policy Evaluation Survey after pictorial HWL implementation. Participants in both samples rated six HWLs that appeared on cigarette packs, and also ranked HWLs with four different themes. Mixed effects models were estimated for each sample to assess ratings of relative effectiveness for the six HWLs, and to assess which HWL themes were ranked as the most effective. Results: Pre- and post-market data showed similar relative ratings across the six HWLs, with the least and most effective HWLs consistently differentiated from other HWLs. Models predicting rankings of HWL themes in post-market sample indicated: (1) pictorial HWLs were ranked as more effective than text-only HWLs; (2) HWLs with both graphic and “lived experience” content outperformed symbolic content; and, (3) testimonial content significantly outperformed didactic content. Pre-market data showed a similar pattern of results, but with fewer statistically significant findings. Conclusions: The study suggests well-designed pre-market studies can have predictive and external validity, helping regulators select HWL content. PMID:26377516

Predictive and External Validity of a Pre-Market Study to Determine the Most Effective Pictorial Health Warning Label Content for Cigarette Packages.

PubMed

Huang, Li-Ling; Thrasher, James F; Reid, Jessica L; Hammond, David

2016-05-01

Studies examining cigarette package pictorial health warning label (HWL) content have primarily used designs that do not allow determination of effectiveness after repeated, naturalistic exposure. This research aimed to determine the predictive and external validity of a pre-market evaluation study of pictorial HWLs. Data were analyzed from: (1) a pre-market convenience sample of 544 adult smokers who participated in field experiments in Mexico City before pictorial HWL implementation (September 2010); and (2) a post-market population-based representative sample of 1765 adult smokers in the Mexican administration of the International Tobacco Control Policy Evaluation Survey after pictorial HWL implementation. Participants in both samples rated six HWLs that appeared on cigarette packs, and also ranked HWLs with four different themes. Mixed effects models were estimated for each sample to assess ratings of relative effectiveness for the six HWLs, and to assess which HWL themes were ranked as the most effective. Pre- and post-market data showed similar relative ratings across the six HWLs, with the least and most effective HWLs consistently differentiated from other HWLs. Models predicting rankings of HWL themes in post-market sample indicated: (1) pictorial HWLs were ranked as more effective than text-only HWLs; (2) HWLs with both graphic and "lived experience" content outperformed symbolic content; and, (3) testimonial content significantly outperformed didactic content. Pre-market data showed a similar pattern of results, but with fewer statistically significant findings. The study suggests well-designed pre-market studies can have predictive and external validity, helping regulators select HWL content. © The Author 2015. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data.

PubMed

Qeli, Ermir; Omasits, Ulrich; Goetze, Sandra; Stekhoven, Daniel J; Frey, Juerg E; Basler, Konrad; Wollscheid, Bernd; Brunner, Erich; Ahrens, Christian H

2014-08-28

The in silico prediction of the best-observable "proteotypic" peptides in mass spectrometry-based workflows is a challenging problem. Being able to accurately predict such peptides would enable the informed selection of proteotypic peptides for targeted quantification of previously observed and non-observed proteins for any organism, with a significant impact for clinical proteomics and systems biology studies. Current prediction algorithms rely on physicochemical parameters in combination with positive and negative training sets to identify those peptide properties that most profoundly affect their general detectability. Here we present PeptideRank, an approach that uses learning to rank algorithm for peptide detectability prediction from shotgun proteomics data, and that eliminates the need to select a negative dataset for the training step. A large number of different peptide properties are used to train ranking models in order to predict a ranking of the best-observable peptides within a protein. Empirical evaluation with rank accuracy metrics showed that PeptideRank complements existing prediction algorithms. Our results indicate that the best performance is achieved when it is trained on organism-specific shotgun proteomics data, and that PeptideRank is most accurate for short to medium-sized and abundant proteins, without any loss in prediction accuracy for the important class of membrane proteins. Targeted proteomics approaches have been gaining a lot of momentum and hold immense potential for systems biology studies and clinical proteomics. However, since only very few complete proteomes have been reported to date, for a considerable fraction of a proteome there is no experimental proteomics evidence that would allow to guide the selection of the best-suited proteotypic peptides (PTPs), i.e. peptides that are specific to a given proteoform and that are repeatedly observed in a mass spectrometer. We describe a novel, rank-based approach for the prediction of the best-suited PTPs for targeted proteomics applications. By building on methods developed in the field of information retrieval (e.g. web search engines like Google's PageRank), we circumvent the delicate step of selecting positive and negative training sets and at the same time also more closely reflect the experimentalist´s need for selecting e.g. the 5 most promising peptides for targeting a protein of interest. This approach allows to predict PTPs for not yet observed proteins or for organisms without prior experimental proteomics data such as many non-model organisms. Copyright © 2014 Elsevier B.V. All rights reserved.

A goal attainment pain management program for older adults with arthritis.

PubMed

Davis, Gail C; White, Terri L

2008-12-01

The purpose of this study was to test a pain management intervention that integrates goal setting with older adults (age > or =65) living independently in residential settings. This preliminary testing of the Goal Attainment Pain Management Program (GAPMAP) included a sample of 17 adults (mean age 79.29 years) with self-reported pain related to arthritis. Specific study aims were to: 1) explore the use of individual goal setting; 2) determine participants' levels of goal attainment; 3) determine whether changes occurred in the pain management methods used and found to be helpful by GAPMAP participants; and 4) determine whether changes occurred in selected pain-related variables (i.e., experience of living with persistent pain, the expected outcomes of pain management, pain management barriers, and global ratings of perceived pain intensity and success of pain management). Because of the small sample size, both parametric (t test) and nonparametric (Wilcoxon signed rank test) analyses were used to examine differences from pretest to posttest. Results showed that older individuals could successfully participate in setting and attaining individual goals. Thirteen of the 17 participants (76%) met their goals at the expected level or above. Two management methods (exercise and using a heated pool, tub, or shower) were used significantly more often after the intervention, and two methods (exercise and distraction) were identified as significantly more helpful. Two pain-related variables (experience of living with persistent pain and expected outcomes of pain management) revealed significant change, and all of those tested showed overall improvement.

Investigation of pyrolysis kinetics of humic acids from low rank Anatolian coal by thermal analysis

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

Tonbul, Y.; Erdogan, S.

2007-07-01

Thermogravimetric analysis (TGA) of humic acid samples from low rank Anatolian (east of Turkey, Bingol) coal were investigated under atmospheric pressure. The samples were subjected for the decomposition of organic matter ambient to 800{sup o} C at four different heating rates (5, 10, 15, and 20 degrees C min{sup -1}). The humic acid samples were started at decomposition between 170 - 206{sup o}C and amount of residues varied 55-60% according to heating rate. Each of samples showed a single step mass loss. TG/DTG data of samples were analyzed to determine activation energy values by Coats and Redfern method and Arrheniusmore » method. Activation energy values are similar obtained from Coats and Redfern method and Arrhenius method and varied from 25 to 29 kJ mol{sup -1}.« less

High-dimensional inference with the generalized Hopfield model: principal component analysis and corrections.

PubMed

Cocco, S; Monasson, R; Sessak, V

2011-05-01

We consider the problem of inferring the interactions between a set of N binary variables from the knowledge of their frequencies and pairwise correlations. The inference framework is based on the Hopfield model, a special case of the Ising model where the interaction matrix is defined through a set of patterns in the variable space, and is of rank much smaller than N. We show that maximum likelihood inference is deeply related to principal component analysis when the amplitude of the pattern components ξ is negligible compared to √N. Using techniques from statistical mechanics, we calculate the corrections to the patterns to the first order in ξ/√N. We stress the need to generalize the Hopfield model and include both attractive and repulsive patterns in order to correctly infer networks with sparse and strong interactions. We present a simple geometrical criterion to decide how many attractive and repulsive patterns should be considered as a function of the sampling noise. We moreover discuss how many sampled configurations are required for a good inference, as a function of the system size N and of the amplitude ξ. The inference approach is illustrated on synthetic and biological data.

Strategic 3-hydroxy-2-butanone release in the dominant male lobster cockroach, Nauphoeta cinerea

NASA Astrophysics Data System (ADS)

Chen, Shu-Chun; Yang, Rou-Ling; Ho, Hsiao-Yung; Chou, Szu-Ying; Kou, Rong

2007-11-01

In the lobster cockroach Nauphoete cinerea, the dominant subordinate hierarchy formed via the agonistic interactions is unstable, and changes in rank order are common. Our previous results showed that in the first encounter fight during initial rank formation, microgram levels of 3H-2B are released by the aggressive posture (AP)-adopting dominant male. In the present study, the pattern of daily pheromone (3H-2B) release during the domination period and on the day of rank switch, rank duration, and rank switch frequency were investigated in three-male groups and six-male groups to examine the effect of higher frequency of agonistic encounters. The results showed that, in the three-male groups (50-day observation period), daily 3H-2B release rate was not constant, but fluctuated, the average duration of dominant rank was 16.6 ± 2.0 days, rank switch occurred in 58.8% of groups, and the frequency of rank switching (average number of rank switches/group/50 days) was 1.4 ± 0.2. For the six-male groups (30-day observation period), the daily 3H-2B release rate also fluctuated, but the duration of dominant rank was significantly shorter at 4.2 ± 0.6 days, rank switch occurred in 100% of groups, and the frequency of rank switching (average number of rank switches/group/30 days) was significantly higher at 6.9 ± 0.6. The results for both sets of male groups showed that as a new rank formed (either on the first encounter day or on the day of rank switching), the dominant status was significantly associated with a higher 3H-2B release rate. In the animal kingdom, fighting usually involves communication or the exchange of signals, and the results of this study indicated that the fluctuating daily 3H-2B release rate adopted by the dominants is a kind of strategic release and the 3H-2B release rate is a signal used to determine dominance.

A Delphi study and ranking exercise to support commissioning services: future delivery of Thrombectomy services in England.

PubMed

Halvorsrud, Kristoffer; Flynn, Darren; Ford, Gary A; McMeekin, Peter; Bhalla, Ajay; Balami, Joyce; Craig, Dawn; White, Phil

2018-02-22

Intra-arterial thrombectomy is the gold standard treatment for large artery occlusive stroke. However, the evidence of its benefits is almost entirely based on trials delivered by experienced neurointerventionists working in established teams in neuroscience centres. Those responsible for the design and prospective reconfiguration of services need access to a comprehensive and complementary array of information on which to base their decisions. This will help to ensure the demonstrated effects from trials may be realised in practice and account for regional/local variations in resources and skill-sets. One approach to elucidate the implementation preferences and considerations of key experts is a Delphi survey. In order to support commissioning decisions, we aimed using an electronic Delphi survey to establish consensus on the options for future organisation of thrombectomy services among physicians with clinical experience in managing large artery occlusive stroke. A Delphi survey was developed with 12 options for future organisation of thrombectomy services in England. A purposive sampling strategy established an expert panel of stroke physicians from the British Association of Stroke Physicians (BASP) Clinical Standards and/or Executive Membership that deliver 24/7 intravenous thrombolysis. Options with aggregate scores falling within the lowest quartile were removed from the subsequent Delphi round. Options reaching consensus following the two Delphi rounds were then ranked in a final exercise by both the wider BASP membership and the British Society of Neuroradiologists (BSNR). Eleven stroke physicians from BASP completed the initial two Delphi rounds. Three options achieved consensus, with subsequently wider BASP (97%, n = 43) and BSNR members (86%, n = 21) assigning the highest approval rankings in the final exercise for transferring large artery occlusive stroke patients to nearest neuroscience centre for thrombectomy based on local CT/CT Angiography. The initial Delphi rounds ensured optimal reduction of options by an expert panel of stroke physicians, while subsequent ranking exercises allowed remaining options to be ranked by a wider group of experts within stroke to reach consensus. The preferred implementation option for thrombectomy is investigating suspected acute stroke patients by CT/CT Angiography and secondary transfer of large artery occlusive stroke patients to the nearest neuroscience (thrombectomy) centre.

Healthcare preferences of lesbian, gay, bisexual, transgender and questioning youth.

PubMed

Hoffman, Neal D; Freeman, Katherine; Swann, Stephanie

2009-09-01

Lesbian, gay, bisexual, transgender and questioning (LGBTQ) youth appear to be at higher risk for certain adverse health outcomes, and to have several personal, cultural and structural barriers to accessing healthcare. Little is known, however, about the experiences of LGBTQ youth with healthcare providers and healthcare services. Our goal was to recruit a sample of LGBTQ youth and to determine their preferences regarding healthcare providers, healthcare settings and the health issues that they consider important to discuss with a healthcare provider. We conducted a cross-sectional Internet-based survey. Respondents ages 13-21 years and living in the U.S. or Canada were asked to review three lists of items pertaining to qualities of healthcare providers, qualities of offices or health centers, and concerns or problems to discuss with a healthcare provider, and then to assign for each item a relative importance. Items in each of the three lists were then ranked, and differences among ranks were assessed. Inter-group differences by age, gender, and race/ethnicity were also assessed. 733 youth met eligibility criteria. Youth indicated as most important competence overall and specifically in issues unique to taking care of youth and LGBTQ persons, as well as being respected and treated by providers the same as other youth. Notably, youth ranked as least important the provider's gender and sexual orientation. Youth ranked accessibility issues higher than specific services provided. As health concerns to discuss with a provider, youth ranked preventive healthcare, nutrition, safe sex, and family as important as common morbidities. Youth placed as much importance on provider qualities and interpersonal skills as provider knowledge and experience, and placed little importance on a provider's gender and sexual orientation. Youth indicated the importance of providers addressing not only health risks, but also wellness and health promotion, and to do so within the context of home and family. Subgroup analyses underscore the need for greater sensitivity to both cultural and developmental differences among LGBTQ youth. These results provide a foundation for further research about healthcare services and delivery systems for youth, training initiatives for healthcare providers, and the role of utilizing the Internet for health research purposes to access and recruit hard-to-reach youth.

Everything under control? The effects of age, gender, and education on trajectories of perceived control in a nationally representative German sample.

PubMed

Specht, Jule; Egloff, Boris; Schmukle, Stefan C

2013-02-01

Perceived control is an important variable for various demands involved in successful aging. However, perceived control is not set in stone but rather changes throughout the life course. The aim of this study was to identify cross-sectional age differences and longitudinal mean-level changes as well as rank-order changes in perceived control with respect to gender and education. Furthermore, changes in income and health were analyzed to explain trajectories of perceived control. In a large and representative sample of Germans across all of adulthood, 9,484 individuals gave information about their perceived control twice over a period of 6 years. Using locally weighted smoothing (LOESS) curves and latent structural equation modeling, four main findings were revealed: (a) Perceived control increased until ages 30-40, then decreased until about age 60, and increased slightly afterwards. (b) The rank order of individuals in perceived control was relatively unstable, especially in young adulthood, and reached a plateau at about age 40. (c) Men perceived that they had more control than did women, but there were no gender differences in the development of perceived control. (d) Individuals with more education perceived that they had more control than those with less education, and there were slight differences in the development of perceived control dependent on education. Taken together, these findings offer important insights into the development of perceived control across the life span. (c) 2013 APA, all rights reserved.

Assessing age in the desert tortoise Gopherus agassizii: Testing skeletochronology with individuals of known age

USGS Publications Warehouse

Curtin, A.J.; Zug, G.R.; Medica, P.A.; Spotila, J.R.

2008-01-01

Eight desert tortoises Gopherus agassizii from a long-term mark-recapture study in the Mojave Desert, Nevada, USA, afforded an opportunity to examine the accuracy of skeletochronological age estimation on tortoises from a seasonal, yet environmentally erratic environment. These 8 tortoises were marked as hatchlings or within the first 2 yr of life, and their carcasses were salvaged from predator kills. Using d blind protocol, 2 skeletochronological protocols (correction-factor and ranking) provided age estimates for a set of 4 bony elements (humerus, scapula, femur, ilium) from these tortoises of known age. The age at death of the tortoises ranged from 15 to 50 yr. The most accurate protocol - ranking using the growth layers within each of the 4 elements - provided estimates from 21 to 47 yr, with the highest accuracy from the ilia. The results indicate that skeletochronological age estimation provides a reasonably accurate method for assessing the age at death of desert tortoises and, if used with a large sample of individuals, will provide a valuable tool for examining age-related mortality parameters in desert tortoise and likely in other gopher tortoises (Gopherus). ?? Inter-Research 2008.

Designing experiments on thermal interactions by secondary-school students in a simulated laboratory environment

NASA Astrophysics Data System (ADS)

Lefkos, Ioannis; Psillos, Dimitris; Hatzikraniotis, Euripides

2011-07-01

Background and purpose: The aim of this study was to explore the effect of investigative activities with manipulations in a virtual laboratory on students' ability to design experiments. Sample Fourteen students in a lower secondary school in Greece attended a teaching sequence on thermal phenomena based on the use of information and communication technology, and specifically of the simulated virtual laboratory 'ThermoLab'. Design and methods A pre-post comparison was applied. Students' design of experiments was rated in eight dimensions; namely, hypothesis forming and verification, selection of variables, initial conditions, device settings, materials and devices used, process and phenomena description. A three-level ranking scheme was employed for the evaluation of students' answers in each dimension. Results A Wilcoxon signed-rank test revealed a statistically significant difference between the students' pre- and post-test scores. Additional analysis by comparing the pre- and post-test scores using the Hake gain showed high gains in all but one dimension, which suggests that this improvement was almost inclusive. Conclusions We consider that our findings support the statement that there was an improvement in students' ability to design experiments.

Weighted Discriminative Dictionary Learning based on Low-rank Representation

NASA Astrophysics Data System (ADS)

Chang, Heyou; Zheng, Hao

2017-01-01

Low-rank representation has been widely used in the field of pattern classification, especially when both training and testing images are corrupted with large noise. Dictionary plays an important role in low-rank representation. With respect to the semantic dictionary, the optimal representation matrix should be block-diagonal. However, traditional low-rank representation based dictionary learning methods cannot effectively exploit the discriminative information between data and dictionary. To address this problem, this paper proposed weighted discriminative dictionary learning based on low-rank representation, where a weighted representation regularization term is constructed. The regularization associates label information of both training samples and dictionary atoms, and encourages to generate a discriminative representation with class-wise block-diagonal structure, which can further improve the classification performance where both training and testing images are corrupted with large noise. Experimental results demonstrate advantages of the proposed method over the state-of-the-art methods.

Transformational leadership in the local police in Spain: a leader-follower distance approach.

PubMed

Álvarez, Octavio; Lila, Marisol; Tomás, Inés; Castillo, Isabel

2014-01-01

Based on the transformational leadership theory (Bass, 1985), the aim of the present study was to analyze the differences in leadership styles according to the various leading ranks and the organizational follower-leader distance reported by a representative sample of 975 local police members (828 male and 147 female) from Valencian Community (Spain). Results showed differences by rank (p < .01), and by rank distance (p < .05). The general intendents showed the most optimal profile of leadership in all the variables examined (transformational-leadership behaviors, transactional-leadership behaviors, laissez-faire behaviors, satisfaction with the leader, extra effort by follower, and perceived leadership effectiveness). By contrast, the least optimal profiles were presented by intendents. Finally, the maximum distance (five ranks) generally yielded the most optimal profiles, whereas the 3-rank distance generally produced the least optimal profiles for all variables examined. Outcomes and practical implications for the workforce dimensioning are also discussed.

Wilcoxon's signed-rank statistic: what null hypothesis and why it matters.

PubMed

Li, Heng; Johnson, Terri

2014-01-01

In statistical literature, the term 'signed-rank test' (or 'Wilcoxon signed-rank test') has been used to refer to two distinct tests: a test for symmetry of distribution and a test for the median of a symmetric distribution, sharing a common test statistic. To avoid potential ambiguity, we propose to refer to those two tests by different names, as 'test for symmetry based on signed-rank statistic' and 'test for median based on signed-rank statistic', respectively. The utility of such terminological differentiation should become evident through our discussion of how those tests connect and contrast with sign test and one-sample t-test. Published 2014. This article is a U.S. Government work and is in the public domain in the USA. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

Acoustic emission and sorptive deformation induced in coals of various rank by the sorption-desorption of gas

NASA Astrophysics Data System (ADS)

Majewska, Zofia; Ziętek, Jerzy

2007-09-01

Simultaneous measurements of acoustic emission (AE) and expansion/contraction of coal samples subjected to gas sorption-desorption processes were conducted on high-and medium-rank coal. The aim of this study was to examine the influence of the coal rank and type of sorbate on measured AE and strain characteristics. The experimental equipment employed in this study consisted of a pressure vessel and associated pressurisation and monitoring units. The arrangement of pressure-vacuum valves permitted the coal sample to be pressurised and depressurised. Carbon-dioxide and methane were used as sorbats. Acoustic emission and strains were recorded continuously for a period of 50 hours during sorption and for at least 12 hours during the desorption process. Tests were conducted on cylindrical coal samples at 298 K. The experimental data were presented as plots of AE basic parameters versus time and in strain diagrams. These studies lead to the following conclusions: 1. There are significant differences in AE and strain characteristics for the two systems (coal-CO2 and coal-CH4); 2. There is a direct influence of rank and type of coal on its behaviour during the sorption-desorption of gas. An attempt has been made to interpret the results obtained on the grounds of the copolymer model of coal structure. More research is needed into this topic in order to get a quantitative description of the observed facts.

Effect of the initial configuration for user-object reputation systems

NASA Astrophysics Data System (ADS)

Wu, Ying-Ying; Guo, Qiang; Liu, Jian-Guo; Zhang, Yi-Cheng

2018-07-01

Identifying the user reputation accurately is significant for the online social systems. For different fair rating parameter q, by changing the parameter values α and β of the beta probability distribution (RBPD) for ranking online user reputation, we investigate the effect of the initial configuration of the RBPD method for the online user ranking performance. Experimental results for the Netflix and MovieLens data sets show that when the parameter q equals to 0.8 and 0.9, the accuracy value AUC would increase about 4.5% and 3.5% for the Netflix data set, while the AUC value increases about 1.5% for the MovieLens data set when the parameter q is 0.9. Furthermore, we investigate the evolution characteristics of the AUC value for different α and β, and find that as the rating records increase, the AUC value increases about 0.2 and 0.16 for the Netflix and MovieLens data sets, indicating that online users' reputations will increase as they rate more and more objects.

Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks.

PubMed

Blatti, Charles; Sinha, Saurabh

2016-07-15

Analysis of co-expressed gene sets typically involves testing for enrichment of different annotations or 'properties' such as biological processes, pathways, transcription factor binding sites, etc., one property at a time. This common approach ignores any known relationships among the properties or the genes themselves. It is believed that known biological relationships among genes and their many properties may be exploited to more accurately reveal commonalities of a gene set. Previous work has sought to achieve this by building biological networks that combine multiple types of gene-gene or gene-property relationships, and performing network analysis to identify other genes and properties most relevant to a given gene set. Most existing network-based approaches for recognizing genes or annotations relevant to a given gene set collapse information about different properties to simplify (homogenize) the networks. We present a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types that preserve more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only these relevant properties. We then re-rank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. We demonstrate the effectiveness of this algorithm for ranking genes related to Drosophila embryonic development and aggressive responses in the brains of social animals. DRaWR was implemented as an R package available at veda.cs.illinois.edu/DRaWR. blatti@illinois.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

[Sampling optimization for tropical invertebrates: an example using dung beetles (Coleoptera: Scarabaeinae) in Venezuela].

PubMed

Ferrer-Paris, José Rafael; Sánchez-Mercado, Ada; Rodríguez, Jon Paul

2013-03-01

The development of efficient sampling protocols is an essential prerequisite to evaluate and identify priority conservation areas. There are f ew protocols for fauna inventory and monitoring in wide geographical scales for the tropics, where the complexity of communities and high biodiversity levels, make the implementation of efficient protocols more difficult. We proposed here a simple strategy to optimize the capture of dung beetles, applied to sampling with baited traps and generalizable to other sampling methods. We analyzed data from eight transects sampled between 2006-2008 withthe aim to develop an uniform sampling design, that allows to confidently estimate species richness, abundance and composition at wide geographical scales. We examined four characteristics of any sampling design that affect the effectiveness of the sampling effort: the number of traps, sampling duration, type and proportion of bait, and spatial arrangement of the traps along transects. We used species accumulation curves, rank-abundance plots, indicator species analysis, and multivariate correlograms. We captured 40 337 individuals (115 species/morphospecies of 23 genera). Most species were attracted by both dung and carrion, but two thirds had greater relative abundance in traps baited with human dung. Different aspects of the sampling design influenced each diversity attribute in different ways. To obtain reliable richness estimates, the number of traps was the most important aspect. Accurate abundance estimates were obtained when the sampling period was increased, while the spatial arrangement of traps was determinant to capture the species composition pattern. An optimum sampling strategy for accurate estimates of richness, abundance and diversity should: (1) set 50-70 traps to maximize the number of species detected, (2) get samples during 48-72 hours and set trap groups along the transect to reliably estimate species abundance, (3) set traps in groups of at least 10 traps to suitably record the local species composition, and (4) separate trap groups by a distance greater than 5-10km to avoid spatial autocorrelation. For the evaluation of other sampling protocols we recommend to, first, identify the elements of sampling design that could affect the sampled effort (the number of traps, sampling duration, type and proportion of bait) and their spatial distribution (spatial arrangement of the traps) and then, to evaluate how they affect richness, abundance and species composition estimates.

A novel hazard assessment method for biomass gasification stations based on extended set pair analysis

PubMed Central

Yan, Fang; Xu, Kaili; Li, Deshun; Cui, Zhikai

2017-01-01

Biomass gasification stations are facing many hazard factors, therefore, it is necessary to make hazard assessment for them. In this study, a novel hazard assessment method called extended set pair analysis (ESPA) is proposed based on set pair analysis (SPA). However, the calculation of the connection degree (CD) requires the classification of hazard grades and their corresponding thresholds using SPA for the hazard assessment. In regard to the hazard assessment using ESPA, a novel calculation algorithm of the CD is worked out when hazard grades and their corresponding thresholds are unknown. Then the CD can be converted into Euclidean distance (ED) by a simple and concise calculation, and the hazard of each sample will be ranked based on the value of ED. In this paper, six biomass gasification stations are introduced to make hazard assessment using ESPA and general set pair analysis (GSPA), respectively. By the comparison of hazard assessment results obtained from ESPA and GSPA, the availability and validity of ESPA can be proved in the hazard assessment for biomass gasification stations. Meanwhile, the reasonability of ESPA is also justified by the sensitivity analysis of hazard assessment results obtained by ESPA and GSPA. PMID:28938011

Liquefaction/solubilization of low-rank Turkish coals by white-rot fungus (Phanerochaete chrysosporium)

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

Elbeyli, I.Y.; Palantoken, A.; Piskin, S.

2006-08-15

Microbial coal liquefaction/solubilization of three low-rank Turkish coals (Bursa-Kestelek, Kutahya-Seyitomer and Mugla-Yatagan lignite) was attempted by using a white-rot fungus (Phanerochaete chrysosporium DSM No. 6909); chemical compositions of the products were investigated. The lignite samples were oxidized by nitric acid under moderate conditions and then oxidized samples were placed on the agar medium of Phanerochaete chrysosporium. FTIR spectra of raw lignites, oxidized lignites and liquid products were recorded, and the acetone-soluble fractions of these samples were identified by GC-MS technique. Results show that the fungus affects the nitro and carboxyl/carbonyl groups in oxidized lignite sample, the liquid products obtained bymore » microbial effects are the mixture of water-soluble compounds, and show limited organic solubility.« less

STBase: one million species trees for comparative biology.

PubMed

McMahon, Michelle M; Deepak, Akshay; Fernández-Baca, David; Boss, Darren; Sanderson, Michael J

2015-01-01

Comprehensively sampled phylogenetic trees provide the most compelling foundations for strong inferences in comparative evolutionary biology. Mismatches are common, however, between the taxa for which comparative data are available and the taxa sampled by published phylogenetic analyses. Moreover, many published phylogenies are gene trees, which cannot always be adapted immediately for species level comparisons because of discordance, gene duplication, and other confounding biological processes. A new database, STBase, lets comparative biologists quickly retrieve species level phylogenetic hypotheses in response to a query list of species names. The database consists of 1 million single- and multi-locus data sets, each with a confidence set of 1000 putative species trees, computed from GenBank sequence data for 413,000 eukaryotic taxa. Two bodies of theoretical work are leveraged to aid in the assembly of multi-locus concatenated data sets for species tree construction. First, multiply labeled gene trees are pruned to conflict-free singly-labeled species-level trees that can be combined between loci. Second, impacts of missing data in multi-locus data sets are ameliorated by assembling only decisive data sets. Data sets overlapping with the user's query are ranked using a scheme that depends on user-provided weights for tree quality and for taxonomic overlap of the tree with the query. Retrieval times are independent of the size of the database, typically a few seconds. Tree quality is assessed by a real-time evaluation of bootstrap support on just the overlapping subtree. Associated sequence alignments, tree files and metadata can be downloaded for subsequent analysis. STBase provides a tool for comparative biologists interested in exploiting the most relevant sequence data available for the taxa of interest. It may also serve as a prototype for future species tree oriented databases and as a resource for assembly of larger species phylogenies from precomputed trees.

Maternal rank influences the outcome of aggressive interactions between immature chimpanzees

PubMed Central

Markham, A. Catherine; Lonsdorf, Elizabeth V.; Pusey, Anne E.; Murray, Carson M.

2015-01-01

For many long-lived mammalian species, extended maternal investment has a profound effect on offspring integration in complex social environments. One component of this investment may be aiding young in aggressive interactions, which can set the stage for offspring social position later in life. Here we examined maternal effects on dyadic aggressive interactions between immature (<12 years) chimpanzees. Specifically, we tested whether relative maternal rank predicted the probability of winning an aggressive interaction. We also examined maternal responses to aggressive interactions to determine whether maternal interventions explain interaction outcomes. Using a 12-year behavioural data set (2000–2011) from Gombe National Park, Tanzania, we found that relative maternal rank predicted the probability of winning aggressive interactions in male–male and male–female aggressive interactions: offspring were more likely to win if their mother outranked their opponent’s mother. Female–female aggressive interactions occurred infrequently (two interactions), so could not be analysed. The probability of winning was also higher for relatively older individuals in male–male interactions, and for males in male–female interactions. Maternal interventions were rare (7.3% of 137 interactions), suggesting that direct involvement does not explain the outcome for the vast majority of aggressive interactions. These findings provide important insight into the ontogeny of aggressive behaviour and early dominance relationships in wild apes and highlight a potential social advantage for offspring of higher-ranking mothers. This advantage may be particularly pronounced for sons, given male philopatry in chimpanzees and the potential for social status early in life to translate more directly to adult rank. PMID:25624528

Geogenic organic contaminants in the low-rank coal-bearing Carrizo-Wilcox aquifer of East Texas, USA

USGS Publications Warehouse

Chakraborty, Jayeeta; Varonka, Matthew S.; Orem, William H.; Finkelman, Robert B.; Manton, William

2017-01-01

The organic composition of groundwater along the Carrizo-Wilcox aquifer in East Texas (USA), sampled from rural wells in May and September 2015, was examined as part of a larger study of the potential health and environmental effects of organic compounds derived from low-rank coals. The quality of water from the low-rank coal-bearing Carrizo-Wilcox aquifer is a potential environmental concern and no detailed studies of the organic compounds in this aquifer have been published. Organic compounds identified in the water samples included: aliphatics and their fatty acid derivatives, phenols, biphenyls, N-, O-, and S-containing heterocyclic compounds, polycyclic aromatic hydrocarbons (PAHs), aromatic amines, and phthalates. Many of the identified organic compounds (aliphatics, phenols, heterocyclic compounds, PAHs) are geogenic and originated from groundwater leaching of young and unmetamorphosed low-rank coals. Estimated concentrations of individual compounds ranged from about 3.9 to 0.01 μg/L. In many rural areas in East Texas, coal strata provide aquifers for drinking water wells. Organic compounds observed in groundwater are likely to be present in drinking water supplied from wells that penetrate the coal. Some of the organic compounds identified in the water samples are potentially toxic to humans, but at the estimated levels in these samples, the compounds are unlikely to cause acute health problems. The human health effects of low-level chronic exposure to coal-derived organic compounds in drinking water in East Texas are currently unknown, and continuing studies will evaluate possible toxicity.

Social preferences of future physicians

PubMed Central

Li, Jing; Dow, William H.

2017-01-01

We measure the social preferences of a sample of US medical students and compare their preferences with those of the general population sampled in the American Life Panel (ALP). We also compare the medical students with a subsample of highly educated, wealthy ALP subjects as well as elite law school students and undergraduate students. We further associate the heterogeneity in social preferences within medical students to the tier ranking of their medical schools and their expected specialty choice. Our experimental design allows us to rigorously distinguish altruism from preferences regarding equality–efficiency tradeoffs and accurately measure both at the individual level rather than pooling data or assuming homogeneity across subjects. This is particularly informative, because the subjects in our sample display widely heterogeneous social preferences in terms of both their altruism and equality–efficiency tradeoffs. We find that medical students are substantially less altruistic and more efficiency focused than the average American. Furthermore, medical students attending the top-ranked medical schools are less altruistic than those attending lower-ranked schools. We further show that the social preferences of those attending top-ranked medical schools are statistically indistinguishable from the preferences of a sample of elite law school students. The key limitation of this study is that our experimental measures of social preferences have not yet been externally validated against actual physician practice behaviors. Pending this future research, we probed the predictive validity of our experimental measures of social preferences by showing that the medical students choosing higher-paying medical specialties are less altruistic than those choosing lower-paying specialties. PMID:29146826

Targeting receptor-activator of nuclear kappaB ligand in aneurysmal bone cysts: verification of target and therapeutic response.

PubMed

Pelle, Dominic W; Ringler, Jonathan W; Peacock, Jacqueline D; Kampfschulte, Kevin; Scholten, Donald J; Davis, Mary M; Mitchell, Deanna S; Steensma, Matthew R

2014-08-01

Aneurysmal bone cyst (ABC) is a benign tumor of bone presenting as a cystic, expansile lesion in both the axial and appendicular skeleton. Axial lesions demand special consideration, because treatment-related morbidity can be devastating. In similar lesions, such as giant cell tumor of bone (GCTB), the receptor-activator of nuclear kappaB ligand (RANKL)-receptor-activator of nuclear kappaB (RANK) signaling axis is essential to tumor progression. Although ABC and GCTB are distinct entities, they both contain abundant multinucleated giant cells and are osteolytic characteristically. We hypothesize that ABCs express both RANKL and RANK similarly in a cell-type specific manner, and that targeted RANKL therapy will mitigate ABC tumor progression. Cellular expression of RANKL and RANK was determined in freshly harvested ABC samples using laser confocal microscopy. A consistent cell-type-specific pattern was observed: fibroblastlike stromal cells expressed RANKL strongly whereas monocyte/macrophage precursor and multinucleated giant cells expressed RANK. Relative RANKL expression was determined by quantitative real-time polymerase chain reaction in ABC and GCTB tissue samples; no difference in relative expression was observed (P > 0.05). In addition, we review the case of a 5-year-old boy with a large, aggressive sacral ABC. After 3 months of targeted RANKL inhibition with denosumab, magnetic resonance imaging demonstrated tumor shrinkage, bone reconstitution, and healing of a pathologic fracture. Ambulation, and bowel and bladder function were restored at 6 months. Denosumab treatment was well tolerated. Post hoc analysis demonstrated strong RANKL expression in the pretreatment tumor sample. These findings demonstrate that RANKL-RANK signal activation is essential to ABC tumor progression. RANKL-targeted therapy may be an effective alternative to surgery in select ABC presentations. Copyright © 2014 Mosby, Inc. All rights reserved.

Statistical grand rounds: a review of analysis and sample size calculation considerations for Wilcoxon tests.

PubMed

Divine, George; Norton, H James; Hunt, Ronald; Dienemann, Jacqueline

2013-09-01

When a study uses an ordinal outcome measure with unknown differences in the anchors and a small range such as 4 or 7, use of the Wilcoxon rank sum test or the Wilcoxon signed rank test may be most appropriate. However, because nonparametric methods are at best indirect functions of standard measures of location such as means or medians, the choice of the most appropriate summary measure can be difficult. The issues underlying use of these tests are discussed. The Wilcoxon-Mann-Whitney odds directly reflects the quantity that the rank sum procedure actually tests, and thus it can be a superior summary measure. Unlike the means and medians, its value will have a one-to-one correspondence with the Wilcoxon rank sum test result. The companion article appearing in this issue of Anesthesia & Analgesia ("Aromatherapy as Treatment for Postoperative Nausea: A Randomized Trial") illustrates these issues and provides an example of a situation for which the medians imply no difference between 2 groups, even though the groups are, in fact, quite different. The trial cited also provides an example of a single sample that has a median of zero, yet there is a substantial shift for much of the nonzero data, and the Wilcoxon signed rank test is quite significant. These examples highlight the potential discordance between medians and Wilcoxon test results. Along with the issues surrounding the choice of a summary measure, there are considerations for the computation of sample size and power, confidence intervals, and multiple comparison adjustment. In addition, despite the increased robustness of the Wilcoxon procedures relative to parametric tests, some circumstances in which the Wilcoxon tests may perform poorly are noted, along with alternative versions of the procedures that correct for such limitations. 

Mining User Dwell Time for Personalized Web Search Re-Ranking

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

Xu, Songhua; Jiang, Hao; Lau, Francis

We propose a personalized re-ranking algorithm through mining user dwell times derived from a user's previously online reading or browsing activities. We acquire document level user dwell times via a customized web browser, from which we then infer conceptword level user dwell times in order to understand a user's personal interest. According to the estimated concept word level user dwell times, our algorithm can estimate a user's potential dwell time over a new document, based on which personalized webpage re-ranking can be carried out. We compare the rankings produced by our algorithm with rankings generated by popular commercial search enginesmore » and a recently proposed personalized ranking algorithm. The results clearly show the superiority of our method. In this paper, we propose a new personalized webpage ranking algorithmthrough mining dwell times of a user. We introduce a quantitative model to derive concept word level user dwell times from the observed document level user dwell times. Once we have inferred a user's interest over the set of concept words the user has encountered in previous readings, we can then predict the user's potential dwell time over a new document. Such predicted user dwell time allows us to carry out personalized webpage re-ranking. To explore the effectiveness of our algorithm, we measured the performance of our algorithm under two conditions - one with a relatively limited amount of user dwell time data and the other with a doubled amount. Both evaluation cases put our algorithm for generating personalized webpage rankings to satisfy a user's personal preference ahead of those by Google, Yahoo!, and Bing, as well as a recent personalized webpage ranking algorithm.« less

Sentiment analysis of feature ranking methods for classification accuracy

NASA Astrophysics Data System (ADS)

Joseph, Shashank; Mugauri, Calvin; Sumathy, S.

2017-11-01

Text pre-processing and feature selection are important and critical steps in text mining. Text pre-processing of large volumes of datasets is a difficult task as unstructured raw data is converted into structured format. Traditional methods of processing and weighing took much time and were less accurate. To overcome this challenge, feature ranking techniques have been devised. A feature set from text preprocessing is fed as input for feature selection. Feature selection helps improve text classification accuracy. Of the three feature selection categories available, the filter category will be the focus. Five feature ranking methods namely: document frequency, standard deviation information gain, CHI-SQUARE, and weighted-log likelihood -ratio is analyzed.

Finding differentially expressed genes in high dimensional data: Rank based test statistic via a distance measure.

PubMed

Mathur, Sunil; Sadana, Ajit

2015-12-01

We present a rank-based test statistic for the identification of differentially expressed genes using a distance measure. The proposed test statistic is highly robust against extreme values and does not assume the distribution of parent population. Simulation studies show that the proposed test is more powerful than some of the commonly used methods, such as paired t-test, Wilcoxon signed rank test, and significance analysis of microarray (SAM) under certain non-normal distributions. The asymptotic distribution of the test statistic, and the p-value function are discussed. The application of proposed method is shown using a real-life data set. © The Author(s) 2011.

Target Fishing for Chemical Compounds using Target-Ligand Activity data and Ranking based Methods

PubMed Central

Wale, Nikil; Karypis, George

2009-01-01

In recent years the development of computational techniques that identify all the likely targets for a given chemical compound, also termed as the problem of Target Fishing, has been an active area of research. Identification of likely targets of a chemical compound helps to understand problems such as toxicity, lack of efficacy in humans, and poor physical properties associated with that compound in the early stages of drug discovery. In this paper we present a set of techniques whose goal is to rank or prioritize targets in the context of a given chemical compound such that most targets that this compound may show activity against appear higher in the ranked list. These methods are based on our extensions to the SVM and Ranking Perceptron algorithms for this problem. Our extensive experimental study shows that the methods developed in this work outperform previous approaches by 2% to 60% under different evaluation criterions. PMID:19764745

Learning Low-Rank Decomposition for Pan-Sharpening With Spatial-Spectral Offsets.

PubMed

Yang, Shuyuan; Zhang, Kai; Wang, Min

2017-08-25

Finding accurate injection components is the key issue in pan-sharpening methods. In this paper, a low-rank pan-sharpening (LRP) model is developed from a new perspective of offset learning. Two offsets are defined to represent the spatial and spectral differences between low-resolution multispectral and high-resolution multispectral (HRMS) images, respectively. In order to reduce spatial and spectral distortions, spatial equalization and spectral proportion constraints are designed and cast on the offsets, to develop a spatial and spectral constrained stable low-rank decomposition algorithm via augmented Lagrange multiplier. By fine modeling and heuristic learning, our method can simultaneously reduce spatial and spectral distortions in the fused HRMS images. Moreover, our method can efficiently deal with noises and outliers in source images, for exploring low-rank and sparse characteristics of data. Extensive experiments are taken on several image data sets, and the results demonstrate the efficiency of the proposed LRP.

Concordant integrative gene set enrichment analysis of multiple large-scale two-sample expression data sets.

PubMed

Lai, Yinglei; Zhang, Fanni; Nayak, Tapan K; Modarres, Reza; Lee, Norman H; McCaffrey, Timothy A

2014-01-01

Gene set enrichment analysis (GSEA) is an important approach to the analysis of coordinate expression changes at a pathway level. Although many statistical and computational methods have been proposed for GSEA, the issue of a concordant integrative GSEA of multiple expression data sets has not been well addressed. Among different related data sets collected for the same or similar study purposes, it is important to identify pathways or gene sets with concordant enrichment. We categorize the underlying true states of differential expression into three representative categories: no change, positive change and negative change. Due to data noise, what we observe from experiments may not indicate the underlying truth. Although these categories are not observed in practice, they can be considered in a mixture model framework. Then, we define the mathematical concept of concordant gene set enrichment and calculate its related probability based on a three-component multivariate normal mixture model. The related false discovery rate can be calculated and used to rank different gene sets. We used three published lung cancer microarray gene expression data sets to illustrate our proposed method. One analysis based on the first two data sets was conducted to compare our result with a previous published result based on a GSEA conducted separately for each individual data set. This comparison illustrates the advantage of our proposed concordant integrative gene set enrichment analysis. Then, with a relatively new and larger pathway collection, we used our method to conduct an integrative analysis of the first two data sets and also all three data sets. Both results showed that many gene sets could be identified with low false discovery rates. A consistency between both results was also observed. A further exploration based on the KEGG cancer pathway collection showed that a majority of these pathways could be identified by our proposed method. This study illustrates that we can improve detection power and discovery consistency through a concordant integrative analysis of multiple large-scale two-sample gene expression data sets.

Adaptive Set-Based Methods for Association Testing

PubMed Central

Su, Yu-Chen; Gauderman, W. James; Kiros, Berhane; Lewinger, Juan Pablo

2017-01-01

With a typical sample size of a few thousand subjects, a single genomewide association study (GWAS) using traditional one-SNP-at-a-time methods can only detect genetic variants conferring a sizable effect on disease risk. Set-based methods, which analyze sets of SNPs jointly, can detect variants with smaller effects acting within a gene, a pathway, or other biologically relevant sets. While self-contained set-based methods (those that test sets of variants without regard to variants not in the set) are generally more powerful than competitive set-based approaches (those that rely on comparison of variants in the set of interest with variants not in the set), there is no consensus as to which self-contained methods are best. In particular, several self-contained set tests have been proposed to directly or indirectly ‘adapt’ to the a priori unknown proportion and distribution of effects of the truly associated SNPs in the set, which is a major determinant of their power. A popular adaptive set-based test is the adaptive rank truncated product (ARTP), which seeks the set of SNPs that yields the best-combined evidence of association. We compared the standard ARTP, several ARTP variations we introduced, and other adaptive methods in a comprehensive simulation study to evaluate their performance. We used permutations to assess significance for all the methods and thus provide a level playing field for comparison. We found the standard ARTP test to have the highest power across our simulations followed closely by the global model of random effects (GMRE) and a LASSO based test. PMID:26707371

Visualizing Concordance of Sets

DTIC Science & Technology

2006-01-01

Elements Filtering with Human Muscular Dystrophy Dataset of 21 sets and 163 elements. 4.1.4 Diagram Ordering using the Rank-by-Feature Framework...Proceedings of Advanced Visual Interfaces, pp. 110-119, 2000. [4] R. A. Becker and W. S. Cleveland, "Brushing Scatterplots," Technometrics, vol. 29, pp. 127

Estimation of distributional parameters for censored trace level water quality data: 1. Estimation techniques

USGS Publications Warehouse

Gilliom, Robert J.; Helsel, Dennis R.

1986-01-01

A recurring difficulty encountered in investigations of many metals and organic contaminants in ambient waters is that a substantial portion of water sample concentrations are below limits of detection established by analytical laboratories. Several methods were evaluated for estimating distributional parameters for such censored data sets using only uncensored observations. Their reliabilities were evaluated by a Monte Carlo experiment in which small samples were generated from a wide range of parent distributions and censored at varying levels. Eight methods were used to estimate the mean, standard deviation, median, and interquartile range. Criteria were developed, based on the distribution of uncensored observations, for determining the best performing parameter estimation method for any particular data set. The most robust method for minimizing error in censored-sample estimates of the four distributional parameters over all simulation conditions was the log-probability regression method. With this method, censored observations are assumed to follow the zero-to-censoring level portion of a lognormal distribution obtained by a least squares regression between logarithms of uncensored concentration observations and their z scores. When method performance was separately evaluated for each distributional parameter over all simulation conditions, the log-probability regression method still had the smallest errors for the mean and standard deviation, but the lognormal maximum likelihood method had the smallest errors for the median and interquartile range. When data sets were classified prior to parameter estimation into groups reflecting their probable parent distributions, the ranking of estimation methods was similar, but the accuracy of error estimates was markedly improved over those without classification.

Estimation of distributional parameters for censored trace-level water-quality data

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

Gilliom, R.J.; Helsel, D.R.

1984-01-01

A recurring difficulty encountered in investigations of many metals and organic contaminants in ambient waters is that a substantial portion of water-sample concentrations are below limits of detection established by analytical laboratories. Several methods were evaluated for estimating distributional parameters for such censored data sets using only uncensored observations. Their reliabilities were evaluated by a Monte Carlo experiment in which small samples were generated from a wide range of parent distributions and censored at varying levels. Eight methods were used to estimate the mean, standard deviation, median, and interquartile range. Criteria were developed, based on the distribution of uncensored observations,more » for determining the best-performing parameter estimation method for any particular data set. The most robust method for minimizing error in censored-sample estimates of the four distributional parameters over all simulation conditions was the log-probability regression method. With this method, censored observations are assumed to follow the zero-to-censoring level portion of a lognormal distribution obtained by a least-squares regression between logarithms of uncensored concentration observations and their z scores. When method performance was separately evaluated for each distributional parameter over all simulation conditions, the log-probability regression method still had the smallest errors for the mean and standard deviation, but the lognormal maximum likelihood method had the smallest errors for the median and interquartile range. When data sets were classified prior to parameter estimation into groups reflecting their probable parent distributions, the ranking of estimation methods was similar, but the accuracy of error estimates was markedly improved over those without classification. 6 figs., 6 tabs.« less

Validation and Application of a PCR Primer Set to Quantify Fungal Communities in the Soil Environment by Real-Time Quantitative PCR

PubMed Central

Chemidlin Prévost-Bouré, Nicolas; Christen, Richard; Dequiedt, Samuel; Mougel, Christophe; Lelièvre, Mélanie; Jolivet, Claudy; Shahbazkia, Hamid Reza; Guillou, Laure; Arrouays, Dominique; Ranjard, Lionel

2011-01-01

Fungi constitute an important group in soil biological diversity and functioning. However, characterization and knowledge of fungal communities is hampered because few primer sets are available to quantify fungal abundance by real-time quantitative PCR (real-time Q-PCR). The aim in this study was to quantify fungal abundance in soils by incorporating, into a real-time Q-PCR using the SYBRGreen® method, a primer set already used to study the genetic structure of soil fungal communities. To satisfy the real-time Q-PCR requirements to enhance the accuracy and reproducibility of the detection technique, this study focused on the 18S rRNA gene conserved regions. These regions are little affected by length polymorphism and may provide sufficiently small targets, a crucial criterion for enhancing accuracy and reproducibility of the detection technique. An in silico analysis of 33 primer sets targeting the 18S rRNA gene was performed to select the primer set with the best potential for real-time Q-PCR: short amplicon length; good fungal specificity and coverage. The best consensus between specificity, coverage and amplicon length among the 33 sets tested was the primer set FR1 / FF390. This in silico analysis of the specificity of FR1 / FF390 also provided additional information to the previously published analysis on this primer set. The specificity of the primer set FR1 / FF390 for Fungi was validated in vitro by cloning - sequencing the amplicons obtained from a real time Q-PCR assay performed on five independent soil samples. This assay was also used to evaluate the sensitivity and reproducibility of the method. Finally, fungal abundance in samples from 24 soils with contrasting physico-chemical and environmental characteristics was examined and ranked to determine the importance of soil texture, organic carbon content, C∶N ratio and land use in determining fungal abundance in soils. PMID:21931659

Reproductive state and rank influence patterns of meat consumption in wild female chimpanzees (Pan troglodytes schweinfurthii).

PubMed

O'Malley, Robert C; Stanton, Margaret A; Gilby, Ian C; Lonsdorf, Elizabeth V; Pusey, Anne; Markham, A Catherine; Murray, Carson M

2016-01-01

An increase in faunivory is a consistent component of human evolutionary models. Animal matter is energy- and nutrient-dense and can provide macronutrients, minerals, and vitamins that are limited or absent in plant foods. For female humans and other omnivorous primates, faunivory may be of particular importance during the costly periods of pregnancy and early lactation. Yet, because animal prey is often monopolizable, access to fauna among group-living primates may be mediated by social factors such as rank. Wild chimpanzees (Pan troglodytes) across Africa habitually consume insects and/or vertebrates. However, no published studies have examined patterns of female chimpanzee faunivory during pregnancy and early lactation relative to non-reproductive periods, or by females of different rank. In this study, we assessed the influence of reproductive state and dominance rank on the consumption of fauna (meat and insects) by female chimpanzees of Gombe National Park, Tanzania. Using observational data collected over 38 years, we tested (a) whether faunivory varied by reproductive state, and (b) if high-ranking females spent more time consuming fauna than lower-ranking females. In single-factor models, pregnant females consumed more meat than lactating and baseline (meaning not pregnant and not in early lactation) females, and high-ranking females consumed more meat than lower-ranking females. A two-factor analysis of a subset of well-sampled females identified an interaction between rank and reproductive state: lower-ranking females consumed more meat during pregnancy than lower-ranking lactating and baseline females did. High-ranking females did not significantly differ in meat consumption between reproductive states. We found no relationships between rank or reproductive state with insectivory. We conclude that, unlike insectivory, meat consumption by female chimpanzees is mediated by both reproductive state and social rank. We outline possible mechanisms for these patterns, relate our findings to meat-eating patterns in women from well-studied hunter-gatherer societies, and discuss potential avenues for future research. Copyright © 2015 Elsevier Ltd. All rights reserved.

Reproductive State and Rank Influence Patterns of Meat Consumption in Wild Female Chimpanzees (Pan troglodytes schweinfurthii)

PubMed Central

Stanton, Margaret A.; Gilby, Ian C.; Lonsdorf, Elizabeth V.; Pusey, Anne; Markham, A. Catherine; Murray, Carson M.

2015-01-01

An increase in faunivory is a consistent component of human evolutionary models. Animal matter is energy- and nutrient-dense and can provide macronutrients, minerals, and vitamins that are limited or absent in plant foods. For female humans and other omnivorous primates, faunivory may be of particular importance during the costly periods of pregnancy and early lactation. Yet, because animal prey is often monopolizable, access to fauna among group-living primates may be mediated by social factors such as rank. Wild chimpanzees (Pan troglodytes) across Africa habitually consume insects and/or vertebrates. However, no published studies have examined patterns of female chimpanzee faunivory during pregnancy and early lactation relative to non-reproductive periods, or by females of different rank. In this study, we assessed the influence of reproductive state and dominance rank on the consumption of fauna (meat and insects) by female chimpanzees of Gombe National Park, Tanzania. Using observational data collected over 38 years, we tested (a) whether faunivory varied by reproductive state, and (b) if high-ranking females spent more time consuming fauna than lower-ranking females. In single-factor models, pregnant females consumed more meat than lactating and baseline (meaning not pregnant and not in early lactation) females, and high-ranking females consumed more meat than lower-ranking females. A two-factor analysis of a subset of well-sampled females identified an interaction between rank and reproductive state: lower-ranking females consumed more meat during pregnancy than lower-ranking lactating and baseline females did. High-ranking females did not significantly differ in meat consumption between reproductive states. We found no relationships between rank or reproductive state with insectivory. We conclude that, unlike insectivory, meat consumption by female chimpanzees is mediated by both reproductive state and social rank. We outline several possible mechanisms for these patterns, relate our findings to meat-eating patterns in women from well-studied hunter-gatherer societies, and discuss potential avenues for future research. PMID:26767956

Learning in Australian Early Childhood Education and Care Settings: Changing Professional Practice

ERIC Educational Resources Information Center

Tayler, Collette

2012-01-01

For the first time across Australia, early education and care services are subject to a single, national set of regulations and standards governing the quality of provision. Concurrently, a set of outcomes for all children aged from birth to 5 years and a ranking system to make transparent the performance of programmes have been developed. This…

Solving the influence maximization problem reveals regulatory organization of the yeast cell cycle.

PubMed

Gibbs, David L; Shmulevich, Ilya

2017-06-01

The Influence Maximization Problem (IMP) aims to discover the set of nodes with the greatest influence on network dynamics. The problem has previously been applied in epidemiology and social network analysis. Here, we demonstrate the application to cell cycle regulatory network analysis for Saccharomyces cerevisiae. Fundamentally, gene regulation is linked to the flow of information. Therefore, our implementation of the IMP was framed as an information theoretic problem using network diffusion. Utilizing more than 26,000 regulatory edges from YeastMine, gene expression dynamics were encoded as edge weights using time lagged transfer entropy, a method for quantifying information transfer between variables. By picking a set of source nodes, a diffusion process covers a portion of the network. The size of the network cover relates to the influence of the source nodes. The set of nodes that maximizes influence is the solution to the IMP. By solving the IMP over different numbers of source nodes, an influence ranking on genes was produced. The influence ranking was compared to other metrics of network centrality. Although the top genes from each centrality ranking contained well-known cell cycle regulators, there was little agreement and no clear winner. However, it was found that influential genes tend to directly regulate or sit upstream of genes ranked by other centrality measures. The influential nodes act as critical sources of information flow, potentially having a large impact on the state of the network. Biological events that affect influential nodes and thereby affect information flow could have a strong effect on network dynamics, potentially leading to disease. Code and data can be found at: https://github.com/gibbsdavidl/miergolf.

Setting local rank constraints by orthogonal projections for image resolution analysis: application to the determination of a low dose pharmaceutical compound.

PubMed

Boiret, Mathieu; de Juan, Anna; Gorretta, Nathalie; Ginot, Yves-Michel; Roger, Jean-Michel

2015-09-10

Raman chemical imaging provides chemical and spatial information about pharmaceutical drug product. By using resolution methods on acquired spectra, the objective is to calculate pure spectra and distribution maps of image compounds. With multivariate curve resolution-alternating least squares, constraints are used to improve the performance of the resolution and to decrease the ambiguity linked to the final solution. Non negativity and spatial local rank constraints have been identified as the most powerful constraints to be used. In this work, an alternative method to set local rank constraints is proposed. The method is based on orthogonal projections pretreatment. For each drug product compound, raw Raman spectra are orthogonally projected to a basis including all the variability from the formulation compounds other than the product of interest. Presence or absence of the compound of interest is obtained by observing the correlations between the orthogonal projected spectra and a pure spectrum orthogonally projected to the same basis. By selecting an appropriate threshold, maps of presence/absence of compounds can be set up for all the product compounds. This method appears as a powerful approach to identify a low dose compound within a pharmaceutical drug product. The maps of presence/absence of compounds can be used as local rank constraints in resolution methods, such as multivariate curve resolution-alternating least squares process in order to improve the resolution of the system. The method proposed is particularly suited for pharmaceutical systems, where the identity of all compounds in the formulations is known and, therefore, the space of interferences can be well defined. Copyright © 2015 Elsevier B.V. All rights reserved.

Advancing the Certified in Public Health Examination: A Job Task Analysis.

PubMed

Kurz, Richard S; Yager, Christopher; Yager, James D; Foster, Allison; Breidenbach, Daniel H; Irwin, Zachary

In 2014, the National Board of Public Health Examiners performed a job task analysis (JTA) to revise the Certified in Public Health (CPH) examination. The objectives of this study were to describe the development, administration, and results of the JTA survey; to present an analysis of the survey results; and to review the implications of this first-ever public health JTA. An advisory committee of public health professionals developed a list of 200 public health job tasks categorized into 10 work domains. The list of tasks was incorporated into a web-based survey, and a snowball sample of public health professionals provided 4850 usable responses. Respondents rated job tasks as essential (4), very important (3), important (2), not very important (1), and never performed (0). The mean task importance ratings ranged from 2.61 to 3.01 (important to very important). The highest mean ratings were for tasks in the ethics domain (mean rating, 3.01). Respondents ranked 10 of the 200 tasks as the most important, with mean task rankings ranging from 2.98 to 3.39. We found subtle differences between male and female respondents and between master of public health and doctor of public health respondents in their rankings. The JTA established a set of job tasks in 10 public health work domains, and the results provided a foundation for refining the CPH examination. Additional steps are needed to further modify the content outline of the examination. An empirical assessment of public health job tasks, using methods such as principal components analysis, may provide additional insight.

Key success factors of health research centers: A mixed method study.

PubMed

Tofighi, Shahram; Teymourzadeh, Ehsan; Heydari, Majid

2017-08-01

In order to achieve success in future goals and activities, health research centers are required to identify their key success factors. This study aimed to extract and rank the factors affecting the success of research centers at one of the medical universities in Iran. This study is a mixed method (qualitative-quantitative) study, which was conducted between May to October in 2016. The study setting was 22 health research centers. In qualitative phase, we extracted the factors affecting the success in research centers through purposeful interviews with 10 experts of centers, and classified them into themes and sub-themes. In the quantitative phase, we prepared a questionnaire and scored and ranked the factors recognized by 54 of the study samples by Friedman test. Nine themes and 42 sub-themes were identified. Themes included: strategic orientation, management, human capital, support, projects, infrastructure, communications and collaboration, paradigm and innovation and they were rated respectively as components of success in research centers. Among the 42 identified factors, 10 factors were ranked respectively as the key factors of success, and included: science and technology road map, strategic plan, evaluation indexes, committed human resources, scientific evaluation of members and centers, innovation in research and implementation, financial support, capable researchers, equipment infrastructure and teamwork. According to the results, the strategic orientation was the most important component in the success of research centers. Therefore, managers and authorities of research centers should pay more attention to strategic areas in future planning, including the science and technology road map and strategic plan.

Key success factors of health research centers: A mixed method study

PubMed Central

Tofighi, Shahram; Teymourzadeh, Ehsan; Heydari, Majid

2017-01-01

Background In order to achieve success in future goals and activities, health research centers are required to identify their key success factors. Objective This study aimed to extract and rank the factors affecting the success of research centers at one of the medical universities in Iran. Methods This study is a mixed method (qualitative-quantitative) study, which was conducted between May to October in 2016. The study setting was 22 health research centers. In qualitative phase, we extracted the factors affecting the success in research centers through purposeful interviews with 10 experts of centers, and classified them into themes and sub-themes. In the quantitative phase, we prepared a questionnaire and scored and ranked the factors recognized by 54 of the study samples by Friedman test. Results Nine themes and 42 sub-themes were identified. Themes included: strategic orientation, management, human capital, support, projects, infrastructure, communications and collaboration, paradigm and innovation and they were rated respectively as components of success in research centers. Among the 42 identified factors, 10 factors were ranked respectively as the key factors of success, and included: science and technology road map, strategic plan, evaluation indexes, committed human resources, scientific evaluation of members and centers, innovation in research and implementation, financial support, capable researchers, equipment infrastructure and teamwork. Conclusion According to the results, the strategic orientation was the most important component in the success of research centers. Therefore, managers and authorities of research centers should pay more attention to strategic areas in future planning, including the science and technology road map and strategic plan. PMID:28979733

The sensitivity of relative toxicity rankings by the USF/NASA test method to some test variables

NASA Technical Reports Server (NTRS)

Hilado, C. J.; Labossiere, L. A.; Leon, H. A.; Kourtides, D. A.; Parker, J. A.; Hsu, M.-T. S.

1976-01-01

Pyrolysis temperature and the distance between the source and sensor of effluents are two important variables in tests for relative toxicity. Modifications of the USF/NASA toxicity screening test method to increase the upper temperature limit of pyrolysis, reduce the distance between the sample and the test animals, and increase the chamber volume available for animal occupancy, did not significantly alter rankings of relative toxicity of four representative materials. The changes rendered some differences no longer significant, but did not reverse any rankings. The materials studied were cotton, wool, aromatic polyamide, and polybenzimidazole.

Discriminative Multi-View Interactive Image Re-Ranking.

PubMed

Li, Jun; Xu, Chang; Yang, Wankou; Sun, Changyin; Tao, Dacheng

2017-07-01

Given an unreliable visual patterns and insufficient query information, content-based image retrieval is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose a discriminative multi-view interactive image re-ranking (DMINTIR), which integrates user relevance feedback capturing users' intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities. In addition, a discriminatively learned weight vector is obtained to reassign updated scores and target images for re-ranking. Compared with other multi-view learning techniques, our scheme not only generates a compact representation in the latent space from the redundant multi-view features but also maximally preserves the discriminative information in feature encoding by the large-margin principle. Furthermore, the generalization error bound of the proposed algorithm is theoretically analyzed and shown to be improved by the interactions between the latent space and discriminant function learning. Experimental results on two benchmark data sets demonstrate that our approach boosts baseline retrieval quality and is competitive with the other state-of-the-art re-ranking strategies.

Smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization.

PubMed

Lu, Canyi; Lin, Zhouchen; Yan, Shuicheng

2015-02-01

This paper presents a general framework for solving the low-rank and/or sparse matrix minimization problems, which may involve multiple nonsmooth terms. The iteratively reweighted least squares (IRLSs) method is a fast solver, which smooths the objective function and minimizes it by alternately updating the variables and their weights. However, the traditional IRLS can only solve a sparse only or low rank only minimization problem with squared loss or an affine constraint. This paper generalizes IRLS to solve joint/mixed low-rank and sparse minimization problems, which are essential formulations for many tasks. As a concrete example, we solve the Schatten-p norm and l2,q-norm regularized low-rank representation problem by IRLS, and theoretically prove that the derived solution is a stationary point (globally optimal if p,q ≥ 1). Our convergence proof of IRLS is more general than previous one that depends on the special properties of the Schatten-p norm and l2,q-norm. Extensive experiments on both synthetic and real data sets demonstrate that our IRLS is much more efficient.

International Ranking of Infant Mortality Rates: Taiwan Compared with European Countries.

PubMed

Liang, Fu-Wen; Lu, Tsung-Hsueh; Wu, Mei-Hwan; Lue, Hung-Chi; Chiang, Tung-Liang; Huang, Ya-Li; Chen, Lea-Hua

2016-08-01

Rankings of infant mortality rates are commonly cited international comparisons to assess the health status of individual countries. We compared the infant mortality rate of Taiwan with those of European countries for 2004 according to two definitions. First, the countries were ranked on the basis of crude infant, neonatal, and postneonatal mortality rates. The countries were then ranked according to the mortality rates calculated after exclusion of live births with a known birth weight of <1000 g, which is the definition set by the World Health Organization. Taiwan was ranked 11(th), 12(th), and 15(th) among 26 high-income countries for crude infant, neonatal, and postneonatal mortality rates, respectively. The ranks were 12(th), 16(th), and 15(th), respectively, for mortality rates, excluding live births with a birth weight of <1000 g. However, in only seven, four, and 10 countries were the mortality rate ratios statistically significantly lower than Taiwan in infant, neonatal, and postneonatal mortality, respectively, according to the second definition. The ranking of Taiwan was similar (11(th) vs. 12(th)) according the two definitions. However, after consideration of the confidence interval, only six countries (Sweden, Finland, Czech Republic, Belgium, Austria, and Germany) had infant mortality rates statistically significantly lower than those of Taiwan in 2004. Copyright © 2015. Published by Elsevier B.V.

Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error.

PubMed

Beheshti, Iman; Demirel, Hasan; Farokhian, Farnaz; Yang, Chunlan; Matsuda, Hiroshi

2016-12-01

This paper presents an automatic computer-aided diagnosis (CAD) system based on feature ranking for detection of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) data. The proposed CAD system is composed of four systematic stages. First, global and local differences in the gray matter (GM) of AD patients compared to the GM of healthy controls (HCs) are analyzed using a voxel-based morphometry technique. The aim is to identify significant local differences in the volume of GM as volumes of interests (VOIs). Second, the voxel intensity values of the VOIs are extracted as raw features. Third, the raw features are ranked using a seven-feature ranking method, namely, statistical dependency (SD), mutual information (MI), information gain (IG), Pearson's correlation coefficient (PCC), t-test score (TS), Fisher's criterion (FC), and the Gini index (GI). The features with higher scores are more discriminative. To determine the number of top features, the estimated classification error based on training set made up of the AD and HC groups is calculated, with the vector size that minimized this error selected as the top discriminative feature. Fourth, the classification is performed using a support vector machine (SVM). In addition, a data fusion approach among feature ranking methods is introduced to improve the classification performance. The proposed method is evaluated using a data-set from ADNI (130 AD and 130 HC) with 10-fold cross-validation. The classification accuracy of the proposed automatic system for the diagnosis of AD is up to 92.48% using the sMRI data. An automatic CAD system for the classification of AD based on feature-ranking method and classification errors is proposed. In this regard, seven-feature ranking methods (i.e., SD, MI, IG, PCC, TS, FC, and GI) are evaluated. The optimal size of top discriminative features is determined by the classification error estimation in the training phase. The experimental results indicate that the performance of the proposed system is comparative to that of state-of-the-art classification models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Development of archetypes for non-ranking classification and comparison of European National Health Technology Assessment systems.

PubMed

Allen, Nicola; Pichler, Franz; Wang, Tina; Patel, Sundip; Salek, Sam

2013-12-01

European countries are increasingly utilising health technology assessment (HTA) to inform reimbursement decision-making. However, the current European HTA environment is very diverse, and projects are already underway to initiate a more efficient and aligned HTA practice within Europe. This study aims to identify a non-ranking method for classifying the diversity of European HTA agencies process and the organisational architecture of the national regulatory review to reimbursement systems. Using a previously developed mapping methodology, this research created process maps to describe national processes for regulatory review to reimbursement for 33 European jurisdictions. These process maps enabled the creation of 2 HTA taxonomic sets. The confluence of the two taxonomic sets was subsequently cross-referenced to identify 10 HTA archetype groups. HTA is a young, rapidly evolving field and it can be argued that optimal practices for performing HTA are yet to emerge. Therefore, a non-ranking classification approach could objectively characterise and compare the diversity observed in the current European HTA environment. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Using binary classification to prioritize and curate articles for the Comparative Toxicogenomics Database.

PubMed

Vishnyakova, Dina; Pasche, Emilie; Ruch, Patrick

2012-01-01

We report on the original integration of an automatic text categorization pipeline, so-called ToxiCat (Toxicogenomic Categorizer), that we developed to perform biomedical documents classification and prioritization in order to speed up the curation of the Comparative Toxicogenomics Database (CTD). The task can be basically described as a binary classification task, where a scoring function is used to rank a selected set of articles. Then components of a question-answering system are used to extract CTD-specific annotations from the ranked list of articles. The ranking function is generated using a Support Vector Machine, which combines three main modules: an information retrieval engine for MEDLINE (EAGLi), a gene normalization service (NormaGene) developed for a previous BioCreative campaign and finally, a set of answering components and entity recognizer for diseases and chemicals. The main components of the pipeline are publicly available both as web application and web services. The specific integration performed for the BioCreative competition is available via a web user interface at http://pingu.unige.ch:8080/Toxicat.

Ranking Bias in Association Studies

PubMed Central

Jeffries, Neal O.

2009-01-01

Background It is widely appreciated that genomewide association studies often yield overestimates of the association of a marker with disease when attention focuses upon the marker showing the strongest relationship. For example, in a case-control setting the largest (in absolute value) estimated odds ratio has been found to typically overstate the association as measured in a second, independent set of data. The most common reason given for this observation is that the choice of the most extreme test statistic is often conditional upon first observing a significant p value associated with the marker. A second, less appreciated reason is described here. Under common circumstances it is the multiple testing of many markers and subsequent focus upon those with most extreme test statistics (i.e. highly ranked results) that leads to bias in the estimated effect sizes. Conclusions This bias, termed ranking bias, is separate from that arising from conditioning on a significant p value and may often be a more important factor in generating bias. An analytic description of this bias, simulations demonstrating its extent, and identification of some factors leading to its exacerbation are presented. PMID:19172085

AUPress: A Comparison of an Open Access University Press with Traditional Presses

ERIC Educational Resources Information Center

McGreal, Rory; Chen, Nian-Shing

2011-01-01

This study is a comparison of AUPress with three other traditional (non-open access) Canadian university presses. The analysis is based on the rankings that are correlated with book sales on Amazon.com and Amazon.ca. Statistical methods include the sampling of the sales ranking of randomly selected books from each press. The results of one-way…

Fuzzy bi-objective linear programming for portfolio selection problem with magnitude ranking function

NASA Astrophysics Data System (ADS)

Kusumawati, Rosita; Subekti, Retno

2017-04-01

Fuzzy bi-objective linear programming (FBOLP) model is bi-objective linear programming model in fuzzy number set where the coefficients of the equations are fuzzy number. This model is proposed to solve portfolio selection problem which generate an asset portfolio with the lowest risk and the highest expected return. FBOLP model with normal fuzzy numbers for risk and expected return of stocks is transformed into linear programming (LP) model using magnitude ranking function.

Ranking and combining multiple predictors without labeled data

PubMed Central

Parisi, Fabio; Strino, Francesco; Nadler, Boaz; Kluger, Yuval

2014-01-01

In a broad range of classification and decision-making problems, one is given the advice or predictions of several classifiers, of unknown reliability, over multiple questions or queries. This scenario is different from the standard supervised setting, where each classifier’s accuracy can be assessed using available labeled data, and raises two questions: Given only the predictions of several classifiers over a large set of unlabeled test data, is it possible to (i) reliably rank them and (ii) construct a metaclassifier more accurate than most classifiers in the ensemble? Here we present a spectral approach to address these questions. First, assuming conditional independence between classifiers, we show that the off-diagonal entries of their covariance matrix correspond to a rank-one matrix. Moreover, the classifiers can be ranked using the leading eigenvector of this covariance matrix, because its entries are proportional to their balanced accuracies. Second, via a linear approximation to the maximum likelihood estimator, we derive the Spectral Meta-Learner (SML), an unsupervised ensemble classifier whose weights are equal to these eigenvector entries. On both simulated and real data, SML typically achieves a higher accuracy than most classifiers in the ensemble and can provide a better starting point than majority voting for estimating the maximum likelihood solution. Furthermore, SML is robust to the presence of small malicious groups of classifiers designed to veer the ensemble prediction away from the (unknown) ground truth. PMID:24474744

Biomarker selection and classification of "-omics" data using a two-step bayes classification framework.

PubMed

Assawamakin, Anunchai; Prueksaaroon, Supakit; Kulawonganunchai, Supasak; Shaw, Philip James; Varavithya, Vara; Ruangrajitpakorn, Taneth; Tongsima, Sissades

2013-01-01

Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel two-step machine-learning framework is presented to address this need. First, a Naïve Bayes estimator is used to rank features from which the top-ranked will most likely contain the most informative features for prediction of the underlying biological classes. The top-ranked features are then used in a Hidden Naïve Bayes classifier to construct a classification prediction model from these filtered attributes. In order to obtain the minimum set of the most informative biomarkers, the bottom-ranked features are successively removed from the Naïve Bayes-filtered feature list one at a time, and the classification accuracy of the Hidden Naïve Bayes classifier is checked for each pruned feature set. The performance of the proposed two-step Bayes classification framework was tested on different types of -omics datasets including gene expression microarray, single nucleotide polymorphism microarray (SNParray), and surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) proteomic data. The proposed two-step Bayes classification framework was equal to and, in some cases, outperformed other classification methods in terms of prediction accuracy, minimum number of classification markers, and computational time.

Using textons to rank crystallization droplets by the likely presence of crystals

PubMed Central

Ng, Jia Tsing; Dekker, Carien; Kroemer, Markus; Osborne, Michael; von Delft, Frank

2014-01-01

The visual inspection of crystallization experiments is an important yet time-consuming and subjective step in X-ray crystallo­graphy. Previously published studies have focused on automatically classifying crystallization droplets into distinct but ultimately arbitrary experiment outcomes; here, a method is described that instead ranks droplets by their likelihood of containing crystals or microcrystals, thereby prioritizing for visual inspection those images that are most likely to contain useful information. The use of textons is introduced to describe crystallization droplets objectively, allowing them to be scored with the posterior probability of a random forest classifier trained against droplets manually annotated for the presence or absence of crystals or microcrystals. Unlike multi-class classification, this two-class system lends itself naturally to unidirectional ranking, which is most useful for assisting sequential viewing because images can be arranged simply by using these scores: this places droplets with probable crystalline behaviour early in the viewing order. Using this approach, the top ten wells included at least one human-annotated crystal or microcrystal for 94% of the plates in a data set of 196 plates imaged with a Minstrel HT system. The algorithm is robustly transferable to at least one other imaging system: when the parameters trained from Minstrel HT images are applied to a data set imaged by the Rock Imager system, human-annotated crystals ranked in the top ten wells for 90% of the plates. Because rearranging images is fundamental to the approach, a custom viewer was written to seamlessly support such ranked viewing, along with another important output of the algorithm, namely the shape of the curve of scores, which is itself a useful overview of the behaviour of the plate; additional features with known usefulness were adopted from existing viewers. Evidence is presented that such ranked viewing of images allows faster but more accurate evaluation of drops, in particular for the identification of microcrystals. PMID:25286854

Simultaneous grouping and ranking with combination of SOM and TOPSIS for selection of preferable analytical procedure for furan determination in food.

PubMed

Jędrkiewicz, Renata; Tsakovski, Stefan; Lavenu, Aurore; Namieśnik, Jacek; Tobiszewski, Marek

2018-02-01

Novel methodology for grouping and ranking with application of self-organizing maps and multicriteria decision analysis is presented. The dataset consists of 22 objects that are analytical procedures applied to furan determination in food samples. They are described by 10 variables, referred to their analytical performance, environmental and economic aspects. Multivariate statistics analysis allows to limit the amount of input data for ranking analysis. Assessment results show that the most beneficial procedures are based on microextraction techniques with GC-MS final determination. It is presented how the information obtained from both tools complement each other. The applicability of combination of grouping and ranking is also discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Is there a relationship between high-quality performance in major teaching hospitals and residents' knowledge of quality and patient safety?

PubMed

Pingleton, Susan K; Horak, Bernard J; Davis, David A; Goldmann, Donald A; Keroack, Mark A; Dickler, Robert M

2009-11-01

The relationship of the quality of teaching hospitals' clinical performance to resident education in quality and patient safety is unclear. The authors studied residents' knowledge of these areas in major teaching hospitals with higher- and lower-quality performance rankings. They assessed the presence of formal and informal quality curricula to determine whether programmatic differences exist. The authors used qualitative research methodology with purposeful sampling. They gathered data from individual structured interviews with residents and key educational and quality leaders in six medical schools and teaching hospitals, which represented a range of quality performance rankings, geographic regions, and public or private status. No relationship emerged between a hospital's quality status, residents' curriculum, and the residents' understanding of quality. Residents' definitions of quality and safety and their knowledge of the practice-based learning and systems-based practice competencies were indistinguishable between hospitals. Residents in all programs had extensive patient safety knowledge acquired through an informal curriculum in the hospital setting. A formal curriculum existed in only two programs, both of them ambulatory settings. Residents' learning about quality and patient safety is extensive, largely through a positive informal curriculum in the teaching hospital and, less frequently, via a formal curriculum. No relationship was found between the quality performance of the teaching hospital and the residents' curriculum or understanding of quality or safety. Residents seem to learn through an informal curriculum provided by hospital initiatives and resources, and thus these data suggest the importance of major teaching hospitals in quality education.

CO2 Adsorption in Low-Rank Coals: Progress Toward Assessing the National Capacity to Store CO2 in the Subsurface

NASA Astrophysics Data System (ADS)

Stanton, R. W.; Burruss, R. C.; Flores, R. M.; Warwick, P. D.

2001-05-01

Subsurface environments for geologic storage of CO2 from combustion of fossil fuel include saline formations, depleted oil and gas reservoirs, and unmineable coalbeds. Of these environments, storage in petroleum reservoirs and coal beds offers a potential economic benefit of enhanced recovery of energy resources. Meaningful assessment of the volume and geographic distribution of storage sites requires quantitative estimates of geologic factors that control storage capacity. The factors that control the storage capacity of unmineable coalbeds are poorly understood. In preparation for a USGS assessment of CO2 storage capacity we have begun new measurements of CO2 and CH4 adsorption isotherms of low-rank coal samples from 4 basins. Initial results for 13 samples of low-rank coal beds from the Powder River Basin (9 subbituminous coals), Greater Green River Basin (1 subbituminous coal), Williston Basin (2 lignites) and the Gulf Coast (1 lignite) indicate that their adsorption capacity is up to 10 times higher than it is for CH4. These values contrast with published measurements of the CO2 adsorption capacity of bituminous coals from the Fruitland Formation, San Juan basin, and Gates Formation, British Columbia, that indicate about twice as much carbon dioxide as methane can be adsorbed on coals. Because CH4 adsorption isotherms are commonly measured on coals, CO2 adsorption capacity can be estimated if thecorrect relationship between the gases is known. However, use a factor to predict CO2 adsorption that is twice that of CH4 adsorption, which is common in the published literature, grossly underestimates the storage capacity of widely distributed, thick low-rank coal beds. Complete petrographic and chemical characterization of these low-rank coal samples is in progress. Significant variations in adsorption measurements among samples are depicted depending on the reporting basis used. Properties were measured on an "as received" (moist) basis but can be converted to a dry basis, ash-free basis (moist), or dry ash-free basis to emphasize the property having the greatest effect on the adsorption isotherm. Initial results show that moisture content has a strong effect on CO2 adsorption. Our current sample base covers a limited range of coal rank and composition. Full characterization of the storage capacity of coalbeds in the US will require additional samples that cover a broader range of coal compositions, ranks, and depositional environments. Even at this preliminary stage, we can use results from the recent USGS assessment of the Powder River Basin (Wyoming and Montana) to examine the impact of these new measurements on estimates of storage capacity. At depths greater than 500 feet, the Wyodak-Anderson coal zone contains 360 billion metric tons of coal. Using the new measurements of CO2 storage capacity, this coal zone could, theoretically, sequester about 290 trillion cubic feet (TCF) of CO2. This estimate contrasts sharply with an estimated capacity of 70 TCF based on the published values for bituminous coals.

Confidence intervals for correlations when data are not normal.

PubMed

Bishara, Anthony J; Hittner, James B

2017-02-01

With nonnormal data, the typical confidence interval of the correlation (Fisher z') may be inaccurate. The literature has been unclear as to which of several alternative methods should be used instead, and how extreme a violation of normality is needed to justify an alternative. Through Monte Carlo simulation, 11 confidence interval methods were compared, including Fisher z', two Spearman rank-order methods, the Box-Cox transformation, rank-based inverse normal (RIN) transformation, and various bootstrap methods. Nonnormality often distorted the Fisher z' confidence interval-for example, leading to a 95 % confidence interval that had actual coverage as low as 68 %. Increasing the sample size sometimes worsened this problem. Inaccurate Fisher z' intervals could be predicted by a sample kurtosis of at least 2, an absolute sample skewness of at least 1, or significant violations of normality hypothesis tests. Only the Spearman rank-order and RIN transformation methods were universally robust to nonnormality. Among the bootstrap methods, an observed imposed bootstrap came closest to accurate coverage, though it often resulted in an overly long interval. The results suggest that sample nonnormality can justify avoidance of the Fisher z' interval in favor of a more robust alternative. R code for the relevant methods is provided in supplementary materials.

Organic petrology and coalbed gas content, Wilcox Group (Paleocene-Eocene), northern Louisiana

USGS Publications Warehouse

Hackley, Paul C.; Warwick, Peter D.; Breland, F. Clayton

2007-01-01

Wilcox Group (Paleocene–Eocene) coal and carbonaceous shale samples collected from four coalbed methane test wells in northern Louisiana were characterized through an integrated analytical program. Organic petrographic analyses, gas desorption and adsorption isotherm measurements, and proximate–ultimate analyses were conducted to provide insight into conditions of peat deposition and the relationships between coal composition, rank, and coalbed gas storage characteristics. The results of petrographic analyses indicate that woody precursor materials were more abundant in stratigraphically higher coal zones in one of the CBM wells, consistent with progradation of a deltaic depositional system (Holly Springs delta complex) into the Gulf of Mexico during the Paleocene–Eocene. Comparison of petrographic analyses with gas desorption measurements suggests that there is not a direct relationship between coal type (sensu maceral composition) and coalbed gas storage. Moisture, as a function of coal rank (lignite–subbituminous A), exhibits an inverse relationship with measured gas content. This result may be due to higher moisture content competing for adsorption space with coalbed gas in shallower, lower rank samples. Shallower (< 600 m) coal samples consistently are undersaturated with respect to CH4 adsorption isotherms; deeper (> 600 m) coal samples containing less moisture range from under- to oversaturated with respect to their CH4adsorption capacity.

Upper-Room Ultraviolet Light and Negative Air Ionization to Prevent Tuberculosis Transmission

PubMed Central

Escombe, A. Roderick; Moore, David A. J; Gilman, Robert H; Navincopa, Marcos; Ticona, Eduardo; Mitchell, Bailey; Noakes, Catherine; Martínez, Carlos; Sheen, Patricia; Ramirez, Rocio; Quino, Willi; Gonzalez, Armando; Friedland, Jon S; Evans, Carlton A

2009-01-01

Background Institutional tuberculosis (TB) transmission is an important public health problem highlighted by the HIV/AIDS pandemic and the emergence of multidrug- and extensively drug-resistant TB. Effective TB infection control measures are urgently needed. We evaluated the efficacy of upper-room ultraviolet (UV) lights and negative air ionization for preventing airborne TB transmission using a guinea pig air-sampling model to measure the TB infectiousness of ward air. Methods and Findings For 535 consecutive days, exhaust air from an HIV-TB ward in Lima, Perú, was passed through three guinea pig air-sampling enclosures each housing approximately 150 guinea pigs, using a 2-d cycle. On UV-off days, ward air passed in parallel through a control animal enclosure and a similar enclosure containing negative ionizers. On UV-on days, UV lights and mixing fans were turned on in the ward, and a third animal enclosure alone received ward air. TB infection in guinea pigs was defined by monthly tuberculin skin tests. All guinea pigs underwent autopsy to test for TB disease, defined by characteristic autopsy changes or by the culture of Mycobacterium tuberculosis from organs. 35% (106/304) of guinea pigs in the control group developed TB infection, and this was reduced to 14% (43/303) by ionizers, and to 9.5% (29/307) by UV lights (both p < 0.0001 compared with the control group). TB disease was confirmed in 8.6% (26/304) of control group animals, and this was reduced to 4.3% (13/303) by ionizers, and to 3.6% (11/307) by UV lights (both p < 0.03 compared with the control group). Time-to-event analysis demonstrated that TB infection was prevented by ionizers (log-rank 27; p < 0.0001) and by UV lights (log-rank 46; p < 0.0001). Time-to-event analysis also demonstrated that TB disease was prevented by ionizers (log-rank 3.7; p = 0.055) and by UV lights (log-rank 5.4; p = 0.02). An alternative analysis using an airborne infection model demonstrated that ionizers prevented 60% of TB infection and 51% of TB disease, and that UV lights prevented 70% of TB infection and 54% of TB disease. In all analysis strategies, UV lights tended to be more protective than ionizers. Conclusions Upper-room UV lights and negative air ionization each prevented most airborne TB transmission detectable by guinea pig air sampling. Provided there is adequate mixing of room air, upper-room UV light is an effective, low-cost intervention for use in TB infection control in high-risk clinical settings. PMID:19296717

Mutagenicity and in vivo toxicity of combined particulate and semivolatile organic fractions of gasoline and diesel engine emissions.

PubMed

Seagrave, JeanClare; McDonald, Jacob D; Gigliotti, Andrew P; Nikula, Kristen J; Seilkop, Steven K; Gurevich, Michael; Mauderly, Joe L

2002-12-01

Exposure to engine emissions is associated with adverse health effects. However, little is known about the relative effects of emissions produced by different operating conditions, fuels, or technologies. Rapid screening techniques are needed to compare the biological effects of emissions with different characteristics. Here, we examined a set of engine emission samples using conventional bioassays. The samples included combined particulate material and semivolatile organic compound fractions of emissions collected from normal- and high-emitter gasoline and diesel vehicles collected at 72 degrees F, and from normal-emitter groups collected at 30 degrees F. The relative potency of the samples was determined by statistical analysis of the dose-response curves. All samples induced bacterial mutagenicity, with a 10-fold range of potency among the samples. Responses to intratracheal instillation in rats indicated generally parallel rankings of the samples by multiple endpoints reflecting cytotoxic, inflammatory, and lung parenchymal changes, allowing selection of a more limited set of parameters for future studies. The parameters selected to assess oxidative stress and macrophage function yielded little useful information. Responses to instillation indicated little difference in potency per unit of combined particulate material and semivolatile organic compound mass between normal-emitter gasoline and diesel vehicles, or between emissions collected at different temperatures. However, equivalent masses of emissions from high-emitter vehicles of both types were more potent than those from normal-emitters. While preliminary in terms of assessing contributions of different emissions to health hazards, the results indicate that a subset of this panel of assays will be useful in providing rapid, cost-effective feedback on the biological impact of modified technology.

Effect of Er,Cr:YSGG laser on human dentin fluid flow.

PubMed

Al-Omari, Wael M; Palamara, Joseph E

2013-11-01

The aim of the current investigation was to assess the rate and magnitude of dentin fluid flow of dentinal surfaces irradiated with Er,Cr:YSGG laser. Twenty extracted third molars were sectioned, mounted, and irradiated with Er,Cr:YSGG laser at 3.5 and 4.5 W power settings. Specimens were connected to an automated fluid flow measurement apparatus (Flodec). The rate, magnitude, and direction of dentin fluid flow were recorded at baseline and after irradiation. Nonparametric Wilcoxon signed ranks repeated measure t test revealed a statistically significant reduction in fluid flow for all the power settings. The 4.5-W power output reduced the flow significantly more than the 3.5 W. The samples showed a baseline outward flow followed by inward flow due to irradiation then followed by decreased outward flow. It was concluded that Er,Cr:YSGG laser irradiation at 3.5 and 4.5 W significantly reduced dentinal fluid flow rate. The reduction was directly proportional to power output.

Neural Representations of Physics Concepts.

PubMed

Mason, Robert A; Just, Marcel Adam

2016-06-01

We used functional MRI (fMRI) to assess neural representations of physics concepts (momentum, energy, etc.) in juniors, seniors, and graduate students majoring in physics or engineering. Our goal was to identify the underlying neural dimensions of these representations. Using factor analysis to reduce the number of dimensions of activation, we obtained four physics-related factors that were mapped to sets of voxels. The four factors were interpretable as causal motion visualization, periodicity, algebraic form, and energy flow. The individual concepts were identifiable from their fMRI signatures with a mean rank accuracy of .75 using a machine-learning (multivoxel) classifier. Furthermore, there was commonality in participants' neural representation of physics; a classifier trained on data from all but one participant identified the concepts in the left-out participant (mean accuracy = .71 across all nine participant samples). The findings indicate that abstract scientific concepts acquired in an educational setting evoke activation patterns that are identifiable and common, indicating that science education builds abstract knowledge using inherent, repurposed brain systems. © The Author(s) 2016.

Nonpareil 3: Fast Estimation of Metagenomic Coverage and Sequence Diversity.

PubMed

Rodriguez-R, Luis M; Gunturu, Santosh; Tiedje, James M; Cole, James R; Konstantinidis, Konstantinos T

2018-01-01

Estimations of microbial community diversity based on metagenomic data sets are affected, often to an unknown degree, by biases derived from insufficient coverage and reference database-dependent estimations of diversity. For instance, the completeness of reference databases cannot be generally estimated since it depends on the extant diversity sampled to date, which, with the exception of a few habitats such as the human gut, remains severely undersampled. Further, estimation of the degree of coverage of a microbial community by a metagenomic data set is prohibitively time-consuming for large data sets, and coverage values may not be directly comparable between data sets obtained with different sequencing technologies. Here, we extend Nonpareil, a database-independent tool for the estimation of coverage in metagenomic data sets, to a high-performance computing implementation that scales up to hundreds of cores and includes, in addition, a k -mer-based estimation as sensitive as the original alignment-based version but about three hundred times as fast. Further, we propose a metric of sequence diversity ( N d ) derived directly from Nonpareil curves that correlates well with alpha diversity assessed by traditional metrics. We use this metric in different experiments demonstrating the correlation with the Shannon index estimated on 16S rRNA gene profiles and show that N d additionally reveals seasonal patterns in marine samples that are not captured by the Shannon index and more precise rankings of the magnitude of diversity of microbial communities in different habitats. Therefore, the new version of Nonpareil, called Nonpareil 3, advances the toolbox for metagenomic analyses of microbiomes. IMPORTANCE Estimation of the coverage provided by a metagenomic data set, i.e., what fraction of the microbial community was sampled by DNA sequencing, represents an essential first step of every culture-independent genomic study that aims to robustly assess the sequence diversity present in a sample. However, estimation of coverage remains elusive because of several technical limitations associated with high computational requirements and limiting statistical approaches to quantify diversity. Here we described Nonpareil 3, a new bioinformatics algorithm that circumvents several of these limitations and thus can facilitate culture-independent studies in clinical or environmental settings, independent of the sequencing platform employed. In addition, we present a new metric of sequence diversity based on rarefied coverage and demonstrate its use in communities from diverse ecosystems.

Social status drives social relationships in groups of unrelated female rhesus macaques

PubMed Central

Snyder-Mackler, Noah; Kohn, Jordan N.; Barreiro, Luis B.; Johnson, Zachary P.; Wilson, Mark E.; Tung, Jenny

2015-01-01

Strong social relationships confer health and fitness benefits in a number of species, motivating the need to understand the processes through which they arise. In female cercopithecine primates, both kinship and dominance rank are thought to influence rates of affiliative behaviour and social partner preference. Teasing apart the relative importance of these factors has been challenging, however, as female kin often occupy similar positions in the dominance hierarchy. Here, we isolated the specific effects of rank on social relationships in female rhesus macaques by analysing grooming patterns in 18 social groups that did not contain close relatives, and in which dominance ranks were experimentally randomized. We found that grooming was asymmetrically directed towards higher-ranking females and that grooming bouts temporarily decreased the likelihood of aggression between grooming partners, supporting the idea that grooming is associated with social tolerance. Even in the absence of kin, females formed the strongest grooming relationships with females adjacent to them in rank, a pattern that was strongest for the highest-ranking females. Using simulations, we show that three rules for allocating grooming based on dominance rank recapitulated most of the relationships we observed. Finally, we evaluated whether a female's tendency to engage in grooming behaviour was stable across time and social setting. We found that one measure, the rate of grooming females provided to others (but not the rate of grooming females received), exhibited modest stability after accounting for the primary effect of dominance rank. Together, our findings indicate that dominance rank has strong effects on social relationships in the absence of kin, suggesting the importance of considering social status and social connectedness jointly when investigating their health and fitness consequences. PMID:26769983

Desirability-based methods of multiobjective optimization and ranking for global QSAR studies. Filtering safe and potent drug candidates from combinatorial libraries.

PubMed

Cruz-Monteagudo, Maykel; Borges, Fernanda; Cordeiro, M Natália D S; Cagide Fajin, J Luis; Morell, Carlos; Ruiz, Reinaldo Molina; Cañizares-Carmenate, Yudith; Dominguez, Elena Rosa

2008-01-01

Up to now, very few applications of multiobjective optimization (MOOP) techniques to quantitative structure-activity relationship (QSAR) studies have been reported in the literature. However, none of them report the optimization of objectives related directly to the final pharmaceutical profile of a drug. In this paper, a MOOP method based on Derringer's desirability function that allows conducting global QSAR studies, simultaneously considering the potency, bioavailability, and safety of a set of drug candidates, is introduced. The results of the desirability-based MOOP (the levels of the predictor variables concurrently producing the best possible compromise between the properties determining an optimal drug candidate) are used for the implementation of a ranking method that is also based on the application of desirability functions. This method allows ranking drug candidates with unknown pharmaceutical properties from combinatorial libraries according to the degree of similarity with the previously determined optimal candidate. Application of this method will make it possible to filter the most promising drug candidates of a library (the best-ranked candidates), which should have the best pharmaceutical profile (the best compromise between potency, safety and bioavailability). In addition, a validation method of the ranking process, as well as a quantitative measure of the quality of a ranking, the ranking quality index (Psi), is proposed. The usefulness of the desirability-based methods of MOOP and ranking is demonstrated by its application to a library of 95 fluoroquinolones, reporting their gram-negative antibacterial activity and mammalian cell cytotoxicity. Finally, the combined use of the desirability-based methods of MOOP and ranking proposed here seems to be a valuable tool for rational drug discovery and development.

Social status drives social relationships in groups of unrelated female rhesus macaques.

PubMed

Snyder-Mackler, Noah; Kohn, Jordan N; Barreiro, Luis B; Johnson, Zachary P; Wilson, Mark E; Tung, Jenny

2016-01-01

Strong social relationships confer health and fitness benefits in a number of species, motivating the need to understand the processes through which they arise. In female cercopithecine primates, both kinship and dominance rank are thought to influence rates of affiliative behaviour and social partner preference. Teasing apart the relative importance of these factors has been challenging, however, as female kin often occupy similar positions in the dominance hierarchy. Here, we isolated the specific effects of rank on social relationships in female rhesus macaques by analysing grooming patterns in 18 social groups that did not contain close relatives, and in which dominance ranks were experimentally randomized. We found that grooming was asymmetrically directed towards higher-ranking females and that grooming bouts temporarily decreased the likelihood of aggression between grooming partners, supporting the idea that grooming is associated with social tolerance. Even in the absence of kin, females formed the strongest grooming relationships with females adjacent to them in rank, a pattern that was strongest for the highest-ranking females. Using simulations, we show that three rules for allocating grooming based on dominance rank recapitulated most of the relationships we observed. Finally, we evaluated whether a female's tendency to engage in grooming behaviour was stable across time and social setting. We found that one measure, the rate of grooming females provided to others (but not the rate of grooming females received), exhibited modest stability after accounting for the primary effect of dominance rank. Together, our findings indicate that dominance rank has strong effects on social relationships in the absence of kin, suggesting the importance of considering social status and social connectedness jointly when investigating their health and fitness consequences.

Residual association at C9orf72 suggests an alternative amyotrophic lateral sclerosis-causing hexanucleotide repeat

PubMed Central

Jones, Ashley R.; Woollacott, Ione; Shatunov, Aleksey; Cooper-Knock, Johnathan; Buchman, Vladimir; Sproviero, William; Smith, Bradley; Scott, Kirsten M.; Balendra, Rubika; Abel, Olubunmi; McGuffin, Peter; Ellis, Catherine M.; Shaw, Pamela J.; Morrison, Karen E.; Farmer, Anne; Lewis, Cathryn M.; Leigh, P. Nigel; Shaw, Christopher E.; Powell, John F.; Al-Chalabi, Ammar

2013-01-01

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease of motor neurons. Single-nucleotide polymorphism rs3849942 is associated with ALS, tagging a hexanucleotide repeat mutation in the C9orf72 gene. It is possible that there is more than 1 disease-causing genetic variation at this locus, in which case association might remain after removal of cases carrying the mutation. DNA from patients with ALS was therefore tested for the mutation. Genome-wide association testing was performed first using all samples, and then restricting the analysis to samples not carrying the mutation. rs3849942 and rs903603 were strongly associated with ALS when all samples were included (rs3849942, p = [3 × 2] × 10−6, rank 7/442,057; rs903603, p = [7 × 6] × 10−8, rank 2/442,057). Removal of the mutation-carrying cases resulted in loss of association for rs3849942 (p = [2 × 6] × 10−3, rank 1225/442,068), but had little effect on rs903603 (p = [1 × 9] × 10−5, rank 8/442,068). Those with a risk allele of rs903603 had an excess of apparent homozygosity for wild type repeat alleles, consistent with polymerase chain reaction failure of 1 allele because of massive repeat expansion. These results indicate residual association at the C9orf72 locus suggesting a second disease-causing repeat mutation. PMID:23587638

A curated compendium of monocyte transcriptome datasets of relevance to human monocyte immunobiology research

PubMed Central

Rinchai, Darawan; Boughorbel, Sabri; Presnell, Scott; Quinn, Charlie; Chaussabel, Damien

2016-01-01

Systems-scale profiling approaches have become widely used in translational research settings. The resulting accumulation of large-scale datasets in public repositories represents a critical opportunity to promote insight and foster knowledge discovery. However, resources that can serve as an interface between biomedical researchers and such vast and heterogeneous dataset collections are needed in order to fulfill this potential. Recently, we have developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB). This tool can be used to overlay deep molecular phenotyping data with rich contextual information about analytes, samples and studies along with ancillary clinical or immunological profiling data. In this note, we describe a curated compendium of 93 public datasets generated in the context of human monocyte immunological studies, representing a total of 4,516 transcriptome profiles. Datasets were uploaded to an instance of GXB along with study description and sample annotations. Study samples were arranged in different groups. Ranked gene lists were generated based on relevant group comparisons. This resource is publicly available online at http://monocyte.gxbsidra.org/dm3/landing.gsp. PMID:27158452

Model diagnostics in reduced-rank estimation

PubMed Central

Chen, Kun

2016-01-01

Reduced-rank methods are very popular in high-dimensional multivariate analysis for conducting simultaneous dimension reduction and model estimation. However, the commonly-used reduced-rank methods are not robust, as the underlying reduced-rank structure can be easily distorted by only a few data outliers. Anomalies are bound to exist in big data problems, and in some applications they themselves could be of the primary interest. While naive residual analysis is often inadequate for outlier detection due to potential masking and swamping, robust reduced-rank estimation approaches could be computationally demanding. Under Stein's unbiased risk estimation framework, we propose a set of tools, including leverage score and generalized information score, to perform model diagnostics and outlier detection in large-scale reduced-rank estimation. The leverage scores give an exact decomposition of the so-called model degrees of freedom to the observation level, which lead to exact decomposition of many commonly-used information criteria; the resulting quantities are thus named information scores of the observations. The proposed information score approach provides a principled way of combining the residuals and leverage scores for anomaly detection. Simulation studies confirm that the proposed diagnostic tools work well. A pattern recognition example with hand-writing digital images and a time series analysis example with monthly U.S. macroeconomic data further demonstrate the efficacy of the proposed approaches. PMID:28003860

Model diagnostics in reduced-rank estimation.

PubMed

Chen, Kun

2016-01-01

Reduced-rank methods are very popular in high-dimensional multivariate analysis for conducting simultaneous dimension reduction and model estimation. However, the commonly-used reduced-rank methods are not robust, as the underlying reduced-rank structure can be easily distorted by only a few data outliers. Anomalies are bound to exist in big data problems, and in some applications they themselves could be of the primary interest. While naive residual analysis is often inadequate for outlier detection due to potential masking and swamping, robust reduced-rank estimation approaches could be computationally demanding. Under Stein's unbiased risk estimation framework, we propose a set of tools, including leverage score and generalized information score, to perform model diagnostics and outlier detection in large-scale reduced-rank estimation. The leverage scores give an exact decomposition of the so-called model degrees of freedom to the observation level, which lead to exact decomposition of many commonly-used information criteria; the resulting quantities are thus named information scores of the observations. The proposed information score approach provides a principled way of combining the residuals and leverage scores for anomaly detection. Simulation studies confirm that the proposed diagnostic tools work well. A pattern recognition example with hand-writing digital images and a time series analysis example with monthly U.S. macroeconomic data further demonstrate the efficacy of the proposed approaches.

Setting priorities for a research agenda to combat drug-resistant tuberculosis in children.

PubMed

Velayutham, B; Nair, D; Ramalingam, S; Perez-Velez, C M; Becerra, M C; Swaminathan, S

2015-12-21

Numerous knowledge gaps hamper the prevention and treatment of childhood drug-resistant tuberculosis (TB). Identifying research priorities is vital to inform and develop strategies to address this neglected problem. To systematically identify and rank research priorities in childhood drug-resistant TB. Adapting the Child Health and Nutrition Research Initiative (CHNRI) methodology, we compiled 53 research questions in four research areas, then classified the questions into three research types. We invited experts in childhood drug-resistant TB to score these questions through an online survey. A total of 81 respondents participated in the survey. The top-ranked research question was to identify the best combination of existing diagnostic tools for early diagnosis. Highly ranked treatment-related questions centred on the reasons for and interventions to improve treatment outcomes, adverse effects of drugs and optimal treatment duration. The prevalence of drug-resistant TB was the highest-ranked question in the epidemiology area. The development type questions that ranked highest focused on interventions for optimal diagnosis, treatment and modalities for treatment delivery. This is the first effort to identify and rank research priorities for childhood drug-resistant TB. The result is a resource to guide research to improve prevention and treatment of drug-resistant TB in children.

Priming cases disturb visual search patterns in screening mammography

NASA Astrophysics Data System (ADS)

Lewis, Sarah J.; Reed, Warren M.; Tan, Alvin N. K.; Brennan, Patrick C.; Lee, Warwick; Mello-Thoms, Claudia

2015-03-01

Rationale and Objectives: To investigate the effect of inserting obvious cancers into a screening set of mammograms on the visual search of radiologists. Previous research presents conflicting evidence as to the impact of priming in scenarios where prevalence is naturally low, such as in screening mammography. Materials and Methods: An observer performance and eye position analysis study was performed. Four expert breast radiologists were asked to interpret two sets of 40 screening mammograms. The Control Set contained 36 normal and 4 malignant cases (located at case # 9, 14, 25 and 37). The Primed Set contained the same 34 normal and 4 malignant cases (in the same location) plus 2 "primer" malignant cases replacing 2 normal cases (located at positions #20 and 34). Primer cases were defined as lower difficulty cases containing salient malignant features inserted before cases of greater difficulty. Results: Wilcoxon Signed Rank Test indicated no significant differences in sensitivity or specificity between the two sets (P > 0.05). The fixation count in the malignant cases (#25, 37) in the Primed Set after viewing the primer cases (#20, 34) decreased significantly (Z = -2.330, P = 0.020). False-Negatives errors were mostly due to sampling in the Primed Set (75%) in contrast to in the Control Set (25%). Conclusion: The overall performance of radiologists is not affected by the inclusion of obvious cancer cases. However, changes in visual search behavior, as measured by eye-position recording, suggests visual disturbance by the inclusion of priming cases in screening mammography.

The Structural and Rank-Order Stability of Temperament in Young Children Based on a Laboratory-Observational Measure

PubMed Central

Dyson, Margaret W.; Olino, Thomas M.; Durbin, C. Emily; Goldsmith, H. Hill; Bufferd, Sara J.; Miller, Anna R.; Klein, Daniel N.

2015-01-01

It is generally assumed that temperament traits exhibit structural and rank-order stability over time. Most of the research on structural and rank-order stability has relied on parent-report measures. The present study used an alternative approach, a laboratory-observational measure (Laboratory Temperament Assessment Battery [Lab-TAB]), to examine the structural and rank-order stability of temperament traits in a community sample of young children (N = 447). Using structural equation modeling (SEM), we found that a similar five-factor structure consisting of the dimensions of Positive Affect/Interest, Sociability, Dysphoria, Fear/Inhibition, and Impulsivity vs. Constraint provided an adequate fit to the data at both age 3 and 6 years, suggesting good structural stability. Moreover, all five latent factors exhibited significant, albeit modest, rank-order stability from age 3 to 6. In addition, there were significant heterotypic associations of age 3 Sociability with age 6 PA/Interest, and age 3 Impulsivity vs. Constraint with age 6 Fear/Inhibition. PMID:25894709

Face the hierarchy: ERP and oscillatory brain responses in social rank processing.

PubMed

Breton, Audrey; Jerbi, Karim; Henaff, Marie-Anne; Cheylus, Anne; Baudouin, Jean-Yves; Schmitz, Christina; Krolak-Salmon, Pierre; Van der Henst, Jean-Baptiste

2014-01-01

Recognition of social hierarchy is a key feature that helps us navigate through our complex social environment. Neuroimaging studies have identified brain structures involved in the processing of hierarchical stimuli but the precise temporal dynamics of brain activity associated with such processing remains largely unknown. Here, we used electroencephalography to examine the effect of social hierarchy on neural responses elicited by faces. In contrast to previous studies, the key manipulation was that a hierarchical context was constructed, not by varying facial expressions, but by presenting neutral-expression faces in a game setting. Once the performance-based hierarchy was established, participants were presented with high-rank, middle-rank and low-rank player faces and had to evaluate the rank of each face with respect to their own position. Both event-related potentials and task-related oscillatory activity were investigated. Three main findings emerge from the study. First, the experimental manipulation had no effect on the early N170 component, which may suggest that hierarchy did not modulate the structural encoding of neutral-expression faces. Second, hierarchy significantly modulated the amplitude of the late positive potential (LPP) within a 400-700 ms time-window, with more a prominent LPP occurring when the participants processed the face of the highest-rank player. Third, high-rank faces were associated with the highest reduction of alpha power. Taken together these findings provide novel electrophysiological evidence for enhanced allocation of attentional resource in the presence of high-rank faces. At a broader level, this study brings new insights into the neural processing underlying social categorization.

Risk-based approach to developing a national residue sampling plan for testing under European Union regulation for veterinary medicinal products and coccidiostat feed additives in domestic animal production.

PubMed

Danaher, Martin; Shanahan, Conor; Butler, Francis; Evans, Rhodri; O'Sullivan, Dan; Glynn, Denise; Camon, Tim; Lawlor, Peadar; O'Keeffe, Michael

2016-07-01

A ranking system for veterinary medicinal products and coccidiostat feed additives has been developed as a tool to be applied in a risk-based approach to the residue testing programme for foods of animal origin in the Irish National Residue Control Plan (NRCP). Three characteristics of substances that may occur as residues in food are included in the developed risk ranking system: Potency, as measured by the acceptable daily intake assigned by the European Medicines Agency Committee for Medicinal Products for Veterinary Use, to each substance; Usage, as measured by the three factors of Number of Doses, use on Individual animals or for Group treatment, and Withdrawal Period; and Residue Occurrence, as measured by the number of Non-Compliant Samples in the NRCP. For both Number of Doses and Non-Compliant Samples, data for the 5-year period 2008-12 have been used. The risk ranking system for substances was developed for beef cattle, sheep and goats, pigs, chickens and dairy cattle using a scoring system applied to the various parameters described above to give an overall score based on the following equation: Potency × Usage (Number of Doses + Individual/Group Use + Withdrawal Period) × Residue Occurrence. Applying this risk ranking system, the following substances are ranked very highly: antimicrobials such as amoxicillin (for all species except pigs), marbofloxacillin (for beef cattle), oxytetracycline (for all species except chickens), sulfadiazine with trimethoprim (for pigs and chickens) and tilmicosin (for chickens); antiparasitic drugs, such as the benzimidazoles triclabendazole (for beef and dairy cattle), fenbendazole/oxfendazole (for sheep/goats and dairy cattle) and albendazole (for dairy cattle), the avermectin ivermectin (for beef cattle), and anti-fluke drugs closantel and rafoxanide (for sheep/goats); the anticoccidials monensin, narasin, nicarbazin and toltrazuril (for chickens). The risk ranking system described is a relatively simple system designed to provide a reliable basis for selecting the veterinary medicinal products and coccidiostat feed additives that might be prioritised for residue testing.

A ranking algorithm for spacelab crew and experiment scheduling

NASA Technical Reports Server (NTRS)

Grone, R. D.; Mathis, F. H.

1980-01-01

The problem of obtaining an optimal or near optimal schedule for scientific experiments to be performed on Spacelab missions is addressed. The current capabilities in this regard are examined and a method of ranking experiments in order of difficulty is developed to support the existing software. Experimental data is obtained from applying this method to the sets of experiments corresponding to Spacelab mission 1, 2, and 3. Finally, suggestions are made concerning desirable modifications and features of second generation software being developed for this problem.

The gas-phase metallicities of star-forming galaxies in aperture-matched SDSS samples follow potential rather than mass or average surface density

NASA Astrophysics Data System (ADS)

D'Eugenio, Francesco; Colless, Matthew; Groves, Brent; Bian, Fuyan; Barone, Tania M.

2018-05-01

We present a comparative study of the relation between the aperture-based gas-phase metallicity and three structural parameters of star-forming galaxies: mass (M ≡ M*), average potential (Φ ≡ M*/Re) and average surface mass density (Σ ≡ M_*/R_e^2; where Re is the effective radius). We use a volume-limited sample drawn from the publicly available SDSS DR7, and base our analysis on aperture-matched sampling by selecting sets of galaxies where the SDSS fibre probes a fixed fraction of Re. We find that between 0.5 and 1.5 Re, the gas-phase metallicity correlates more tightly with Φ than with either {M} or Σ, in that for all aperture-matched samples, the potential-metallicity relation has (i) less scatter, (ii) higher Spearman rank correlation coefficient and (iii) less residual trend with Re than either the mass-metallicity relation and the average surface density-metallicity relation. Our result is broadly consistent with the current models of gas enrichment and metal loss. However, a more natural explanation for our findings is a local relation between the gas-phase metallicity and escape velocity.

[A method for forecasting the seasonal dynamic of malaria in the municipalities of Colombia].

PubMed

Velásquez, Javier Oswaldo Rodríguez

2010-03-01

To develop a methodology for forecasting the seasonal dynamic of malaria outbreaks in the municipalities of Colombia. Epidemiologic ranges were defined by multiples of 50 cases for the six municipalities with the highest incidence, 25 cases for the municipalities that ranked 10th and 11th by incidence, 10 for the municipality that ranked 193rd, and 5 for the municipality that ranked 402nd. The specific probability values for each epidemiologic range appearing in each municipality, as well as the S/k value--the ratio between entropy (S) and the Boltzmann constant (k)--were calculated for each three-week set, along with the differences in this ratio divided by the consecutive sets of weeks. These mathematical ratios were used to determine the values for forecasting the case dynamic, which were compared with the actual epidemiologic data from the period 2003-2007. The probability of the epidemiologic ranges appearing ranged from 0.019 and 1.00, while the differences in the S/k ratio between the sets of consecutive weeks ranged from 0.23 to 0.29. Three ratios were established to determine whether the dynamic corresponded to an outbreak. These ratios were corroborated with real epidemiological data from 810 Colombian municipalities. This methodology allows us to forecast the malaria case dynamic and outbreaks in the municipalities of Colombia and can be used in planning interventions and public health policies.

Kernel-Based Measure of Variable Importance for Genetic Association Studies.

PubMed

Gallego, Vicente; Luz Calle, M; Oller, Ramon

2017-06-17

The identification of genetic variants that are associated with disease risk is an important goal of genetic association studies. Standard approaches perform univariate analysis where each genetic variant, usually Single Nucleotide Polymorphisms (SNPs), is tested for association with disease status. Though many genetic variants have been identified and validated so far using this univariate approach, for most complex diseases a large part of their genetic component is still unknown, the so called missing heritability. We propose a Kernel-based measure of variable importance (KVI) that provides the contribution of a SNP, or a group of SNPs, to the joint genetic effect of a set of genetic variants. KVI can be used for ranking genetic markers individually, sets of markers that form blocks of linkage disequilibrium or sets of genetic variants that lie in a gene or a genetic pathway. We prove that, unlike the univariate analysis, KVI captures the relationship with other genetic variants in the analysis, even when measured at the individual level for each genetic variable separately. This is specially relevant and powerful for detecting genetic interactions. We illustrate the results with data from an Alzheimer's disease study and show through simulations that the rankings based on KVI improve those rankings based on two measures of importance provided by the Random Forest. We also prove with a simulation study that KVI is very powerful for detecting genetic interactions.

Decision by Sampling

ERIC Educational Resources Information Center

Stewart, Neil; Chater, Nick; Brown, Gordon D. A.

2006-01-01

We present a theory of decision by sampling (DbS) in which, in contrast with traditional models, there are no underlying psychoeconomic scales. Instead, we assume that an attribute's subjective value is constructed from a series of binary, ordinal comparisons to a sample of attribute values drawn from memory and is its rank within the sample. We…

RANK Expression and Osteoclastogenesis in Human Monocytes in Peripheral Blood from Rheumatoid Arthritis Patients

PubMed Central

Kobashigawa, Tsuyoshi

2016-01-01

Rheumatoid arthritis (RA) appears as inflammation of synovial tissue and joint destruction. Receptor activator of NF-κB (RANK) is a member of the TNF receptor superfamily and a receptor for the RANK ligand (RANKL). In this study, we examined the expression of RANKhigh and CCR6 on CD14+ monocytes from patients with RA and healthy volunteers. Peripheral blood samples were obtained from both the RA patients and the healthy volunteers. Osteoclastogenesis from monocytes was induced by RANKL and M-CSF in vitro. To study the expression of RANKhigh and CCR6 on CD14+ monocytes, two-color flow cytometry was performed. Levels of expression of RANK on monocytes were significantly correlated with the level of osteoclastogenesis in the healthy volunteers. The expression of RANKhigh on CD14+ monocyte in RA patients without treatment was elevated and that in those receiving treatment was decreased. In addition, the high-level expression of RANK on CD14+ monocytes was correlated with the high-level expression of CCR6 in healthy volunteers. Monocytes expressing both RANK and CCR6 differentiate into osteoclasts. The expression of CD14+RANKhigh in untreated RA patients was elevated. RANK and CCR6 expressed on monocytes may be novel targets for the regulation of bone resorption in RA and osteoporosis. PMID:27822475

Use of National Burden to Define Operative Emergency General Surgery.

PubMed

Scott, John W; Olufajo, Olubode A; Brat, Gabriel A; Rose, John A; Zogg, Cheryl K; Haider, Adil H; Salim, Ali; Havens, Joaquim M

2016-06-15

Emergency general surgery (EGS) represents 11% of surgical admissions and 50% of surgical mortality in the United States. However, there is currently no established definition of the EGS procedures. To define a set of procedures accounting for at least 80% of the national burden of operative EGS. A retrospective review was conducted using data from the 2008-2011 National Inpatient Sample. Adults (age, ≥18 years) with primary EGS diagnoses consistent with the American Association for the Surgery of Trauma definition, admitted urgently or emergently, who underwent an operative procedure within 2 days of admission were included in the analyses. Procedures were ranked to account for national mortality and complication burden. Among ranked procedures, contributions to total EGS frequency, mortality, and hospital costs were assessed. The data query and analysis were performed between November 15, 2015, and February 16, 2016. Overall procedure frequency, in-hospital mortality, major complications, and inpatient costs calculated per 3-digit International Classification of Diseases, Ninth Revision, Clinical Modification procedure codes. The study identified 421 476 patient encounters associated with operative EGS, weighted to represent 2.1 million nationally over the 4-year study period. The overall mortality rate was 1.23% (95% CI, 1.18%-1.28%), the complication rate was 15.0% (95% CI, 14.6%-15.3%), and mean cost per admission was $13 241 (95% CI, $12 957-$13 525). After ranking the 35 procedure groups by contribution to EGS mortality and morbidity burden, a final set of 7 operative EGS procedures were identified, which collectively accounted for 80.0% of procedures, 80.3% of deaths, 78.9% of complications, and 80.2% of inpatient costs nationwide. These 7 procedures included partial colectomy, small-bowel resection, cholecystectomy, operative management of peptic ulcer disease, lysis of peritoneal adhesions, appendectomy, and laparotomy. Only 7 procedures account for most admissions, deaths, complications, and inpatient costs attributable to the 512 079 EGS procedures performed in the United States each year. National quality benchmarks and cost reduction efforts should focus on these common, complicated, and costly EGS procedures.

Biological water-quality assessment of selected streams in the Milwaukee Metropolitan Sewerage District Planning Area of Wisconsin, 2007

USGS Publications Warehouse

Scudder Eikenberry, Barbara C.; Bell, Amanda H.; Sullivan, Daniel J.; Lutz, Michelle A.; Alvarez, David A.

2010-01-01

Changes in the water quality of stream ecosystems in an urban area may manifest in conspicuous ways, such as in murky or smelly streamwater, or in less conspicuous ways, such as fewer native or pollution-sensitive organisms. In 2004, and again in 2007, the U.S. Geological Survey sampled stream organisms—algae, invertebrates, and fish—in 14 Milwaukee area streams to assess water quality as part of the ongoing Milwaukee Metropolitan Sewerage District (MMSD) Corridor Study. In addition, passive-sampling devices (SPMDs, “semipermeable membrane devices”) were deployed at a subset of sites in order to evaluate the potential exposure of stream organisms to certain toxic chemicals. Results of the 2007 sampling effort are the focus of this report. Results of sampling from 2007 are compared with results from 2004. The water quality of sampled streams was assessed by evaluating biological-assemblage data, metrics computed from assemblage data, and an aggregate bioassessment ranking method that combined data for algae, invertebrates, and fish. These data contain information about the abundance (number) of different species in each group of stream organisms and the balance between species that can or cannot tolerate polluted or disturbed conditions. In 2007, the highest numbers of algal, invertebrate, and fish species were found at the Milwaukee River at Milwaukee, the largest sampled site. Algal results indicated water quality concerns at 10 of the 14 sampled sites due to the occurrence of nuisance algae or low percentages of pollution-sensitive algae. When compared to 2004, total algal biovolume was higher in 2007 at 12 of 14 sites, due mostly to more nuisance green algae from unknown causes. Results of several metrics, including the Hilsenhoff Biotic Index (HBI-10), suggest that invertebrate assemblages in the Little Menomonee River, Underwood Creek, and Honey Creek were poorer quality in 2007 compared to 2004. Six sites received “very poor” quality ratings for fish in 2007, mostly because inadequate numbers of fish were collected at five sites to allow computation of an Index of Biotic Integrity (IBI); this resulted in three additional sites receiving “very poor” ratings compared to 2004. Some signs of potential improvement in the fish assemblage were evident at Lincoln Creek, possibly reflecting delayed effects of the restoration of stream habitat, completed in 2002; however, algae and invertebrates did not show signs of improvement. Aggregate bioassessment rankings across all groups of organisms for 2004 and 2007 indicated that water quality at the two Milwaukee River main stem sites (at Milwaukee and near Cedarburg), Jewel Creek, and the Menomonee River at Menomonee Falls was the least-degraded among all sampled sites. Rankings for Oak Creek and Little Menomonee suggested water quality was worse in 2007 compared to 2004 and placed these two sites together with Kinnickinnic River and Underwood Creek, two concrete-line sites, indicating the most-degraded water quality among all sampled sites. The aggregate ranking for Lincoln Creek in 2007 would have placed it in the most-degraded category but for the positive influence of the fish ranking when compared to poor algal and invertebrate rankings. Potential toxicity due to certain manmade chemicals, such as polycyclic aromatic hydrocarbons (PAHs), was found at all six sites where SPMDs were deployed. As was found in 2004, the highest potential toxicity in 2007 was observed at Lincoln Creek where chemical screening in 2007 also showed the highest total PAHs of all six sites; however, potential toxicity at Little Menomonee River, Honey Creek, and Kinnickinnic River was relatively high compared to Milwaukee River near Cedarburg. Although toxicity and chemical results in 2007 did not agree with aggregate rankings for Lincoln Creek because of fish, nor for Honey Creek, the results did agree with aggregate rankings at four of the six sites. In addition to toxicological and chemical influences, the more urbanized sites have high percentages of impervious surface area, resulting in frequent high stream flows that can adversely affect algal, invertebrate, and fish assemblages. Assessments of the ecological status of different groups of organisms and of potential chemical and physical stressors to organisms are important tools in evaluating streamwater quality.

Social Media Use in Academics: Undergraduate Perceptions and Practices

ERIC Educational Resources Information Center

Ciampa, Mark; Thrasher, Evelyn H.; Revels, Mark A.

2016-01-01

The aim of this research was to elicit student perceptions and practices regarding the use of social media in the academic setting. More specifically, the objectives of this study were to (1) assess student perceptions of technology use in an academic setting and to rank their preferences; (2) determine which resources and communication options…

Assessing Community Quality of Health Care.

PubMed

Herrin, Jeph; Kenward, Kevin; Joshi, Maulik S; Audet, Anne-Marie J; Hines, Stephen J

2016-02-01

To determine the agreement of measures of care in different settings-hospitals, nursing homes (NHs), and home health agencies (HHAs)-and identify communities with high-quality care in all settings. Publicly available quality measures for hospitals, NHs, and HHAs, linked to hospital service areas (HSAs). We constructed composite quality measures for hospitals, HHAs, and nursing homes. We used these measures to identify HSAs with exceptionally high- or low-quality of care across all settings, or only high hospital quality, and compared these with respect to sociodemographic and health system factors. We identified three dimensions of hospital quality, four HHA dimensions, and two NH dimensions; these were poorly correlated across the three care settings. HSAs that ranked high on all dimensions had more general practitioners per capita, and fewer specialists per capita, than HSAs that ranked highly on only the hospital measures. Higher quality hospital, HHA, and NH care are not correlated at the regional level; regions where all dimensions of care are high differ systematically from regions which score well on only hospital measures and from those which score well on none. © Health Research and Educational Trust.

How Many Environmental Impact Indicators Are Needed in the Evaluation of Product Life Cycles?

PubMed

Steinmann, Zoran J N; Schipper, Aafke M; Hauck, Mara; Huijbregts, Mark A J

2016-04-05

Numerous indicators are currently available for environmental impact assessments, especially in the field of Life Cycle Impact Assessment (LCIA). Because decision-making on the basis of hundreds of indicators simultaneously is unfeasible, a nonredundant key set of indicators representative of the overall environmental impact is needed. We aimed to find such a nonredundant set of indicators based on their mutual correlations. We have used Principal Component Analysis (PCA) in combination with an optimization algorithm to find an optimal set of indicators out of 135 impact indicators calculated for 976 products from the ecoinvent database. The first four principal components covered 92% of the variance in product rankings, showing the potential for indicator reduction. The same amount of variance (92%) could be covered by a minimal set of six indicators, related to climate change, ozone depletion, the combined effects of acidification and eutrophication, terrestrial ecotoxicity, marine ecotoxicity, and land use. In comparison, four commonly used resource footprints (energy, water, land, materials) together accounted for 84% of the variance in product rankings. We conclude that the plethora of environmental indicators can be reduced to a small key set, representing the major part of the variation in environmental impacts between product life cycles.

Tensor Rank Preserving Discriminant Analysis for Facial Recognition.

PubMed

Tao, Dapeng; Guo, Yanan; Li, Yaotang; Gao, Xinbo

2017-10-12

Facial recognition, one of the basic topics in computer vision and pattern recognition, has received substantial attention in recent years. However, for those traditional facial recognition algorithms, the facial images are reshaped to a long vector, thereby losing part of the original spatial constraints of each pixel. In this paper, a new tensor-based feature extraction algorithm termed tensor rank preserving discriminant analysis (TRPDA) for facial image recognition is proposed; the proposed method involves two stages: in the first stage, the low-dimensional tensor subspace of the original input tensor samples was obtained; in the second stage, discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition. On the one hand, the proposed TRPDA algorithm fully utilizes the natural structure of the input samples, and it applies an optimization criterion that can directly handle the tensor spectral analysis problem, thereby decreasing the computation cost compared those traditional tensor-based feature selection algorithms. On the other hand, the proposed TRPDA algorithm extracts feature by finding a tensor subspace that preserves most of the rank order information of the intra-class input samples. Experiments on the three facial databases are performed here to determine the effectiveness of the proposed TRPDA algorithm.

Rank preserving sparse learning for Kinect based scene classification.

PubMed

Tao, Dapeng; Jin, Lianwen; Yang, Zhao; Li, Xuelong

2013-10-01

With the rapid development of the RGB-D sensors and the promptly growing population of the low-cost Microsoft Kinect sensor, scene classification, which is a hard, yet important, problem in computer vision, has gained a resurgence of interest recently. That is because the depth of information provided by the Kinect sensor opens an effective and innovative way for scene classification. In this paper, we propose a new scheme for scene classification, which applies locality-constrained linear coding (LLC) to local SIFT features for representing the RGB-D samples and classifies scenes through the cooperation between a new rank preserving sparse learning (RPSL) based dimension reduction and a simple classification method. RPSL considers four aspects: 1) it preserves the rank order information of the within-class samples in a local patch; 2) it maximizes the margin between the between-class samples on the local patch; 3) the L1-norm penalty is introduced to obtain the parsimony property; and 4) it models the classification error minimization by utilizing the least-squares error minimization. Experiments are conducted on the NYU Depth V1 dataset and demonstrate the robustness and effectiveness of RPSL for scene classification.

BATS: a Bayesian user-friendly software for analyzing time series microarray experiments.

PubMed

Angelini, Claudia; Cutillo, Luisa; De Canditiis, Daniela; Mutarelli, Margherita; Pensky, Marianna

2008-10-06

Gene expression levels in a given cell can be influenced by different factors, namely pharmacological or medical treatments. The response to a given stimulus is usually different for different genes and may depend on time. One of the goals of modern molecular biology is the high-throughput identification of genes associated with a particular treatment or a biological process of interest. From methodological and computational point of view, analyzing high-dimensional time course microarray data requires very specific set of tools which are usually not included in standard software packages. Recently, the authors of this paper developed a fully Bayesian approach which allows one to identify differentially expressed genes in a 'one-sample' time-course microarray experiment, to rank them and to estimate their expression profiles. The method is based on explicit expressions for calculations and, hence, very computationally efficient. The software package BATS (Bayesian Analysis of Time Series) presented here implements the methodology described above. It allows an user to automatically identify and rank differentially expressed genes and to estimate their expression profiles when at least 5-6 time points are available. The package has a user-friendly interface. BATS successfully manages various technical difficulties which arise in time-course microarray experiments, such as a small number of observations, non-uniform sampling intervals and replicated or missing data. BATS is a free user-friendly software for the analysis of both simulated and real microarray time course experiments. The software, the user manual and a brief illustrative example are freely available online at the BATS website: http://www.na.iac.cnr.it/bats.

Authentication of vegetable oils on the basis of their physico-chemical properties with the aid of chemometrics.

PubMed

Zhang, Guowen; Ni, Yongnian; Churchill, Jane; Kokot, Serge

2006-09-15

In food production, reliable analytical methods for confirmation of purity or degree of spoilage are required by growers, food quality assessors, processors, and consumers. Seven parameters of physico-chemical properties, such as acid number, colority, density, refractive index, moisture and volatility, saponification value and peroxide value, were measured for quality and adulterated soybean, as well as quality and rancid rapeseed oils. Chemometrics methods were then applied for qualitative and quantitative discrimination and prediction of the oils by methods such exploratory principal component analysis (PCA), partial least squares (PLS), radial basis function-artificial neural networks (RBF-ANN), and multi-criteria decision making methods (MCDM), PROMETHEE and GAIA. In general, the soybean and rapeseed oils were discriminated by PCA, and the two spoilt oils behaved differently with the rancid rapeseed samples exhibiting more object scatter on the PC-scores plot, than the adulterated soybean oil. For the PLS and RBF-ANN prediction methods, suitable training models were devised, which were able to predict satisfactorily the category of the four different oil samples in the verification set. Rank ordering with the use of MCDM models indicated that the oil types can be discriminated on the PROMETHEE II scale. For the first time, it was demonstrated how ranking of oil objects with the use of PROMETHEE and GAIA could be utilized as a versatile indicator of quality performance of products on the basis of a standard selected by the stakeholder. In principle, this approach provides a very flexible method for assessment of product quality directly from the measured data.

Spring prey use by double-crested cormorants on the Penobscot River, Maine, USA

USGS Publications Warehouse

Blackwell, B.F.; Krohn, W.B.; Dube, N.R.; Godin, A.J.

1997-01-01

We analyzed 2 sets of data for Double-crested Cormorant (Phalacrocorax auritus) stomach contents (including esophageal contents) that were collected from April through June of 1986-1988 (N = 580) and 1992-1993 (N = 200) on the Penobscot River, Maine. Our objectives were to examine temporal and spatial variation in the spring diet and estimate the importance of Atlantic salmon (Salmo salar) smolts to the cormorant diet. We analyzed stomach contents relative to samples from 3 river sections: 5 mainstem dams collectively, above the head of tide, and free-flowing areas above and below the head of tide. Between years composition of taxa lists were compared (P = 0.05) relative to time and river section. We estimated taxon importance for data collected during 1992-1993 by ranking taxa according to 3 statistics: frequency of occurrence, mean percent volume, and numerical abundance. Data from 1986-88 were analyzed by frequency of occurrence only. Across the 3 river sections, the number of prey species recovered from cormorant stomachs increased from 15 in late April to at least 31 through May. Cormorants collected above the head of tide consumed 12 fish species (including freshwater, anadromous, and catadromous types), whereas birds collected below the head of tide consumed 28 freshwater and seasonally-available estuarine, marine benthic, and pelagic species. Salmon smolts were not recovered from stomachs collected in April, rare in stomach samples during the first week of June, and absent from the diet thereafter. In contrast, smolts were among the 5 most frequently occurring (1986-88) and highest ranking (1992-1993) prey taxa across the 3 river sections through May.

Estimation of the chemical rank for the three-way data: a principal norm vector orthogonal projection approach.

PubMed

Hong-Ping, Xie; Jian-Hui, Jiang; Guo-Li, Shen; Ru-Qin, Yu

2002-01-01

A new approach for estimating the chemical rank of the three-way array called the principal norm vector orthogonal projection method has been proposed. The method is based on the fact that the chemical rank of the three-way data array is equal to one of the column space of the unfolded matrix along the spectral or chromatographic mode. A vector with maximum Frobenius norm is selected among all the column vectors of the unfolded matrix as the principal norm vector (PNV). A transformation is conducted for the column vectors with an orthogonal projection matrix formulated by PNV. The mathematical rank of the column space of the residual matrix thus obtained should decrease by one. Such orthogonal projection is carried out repeatedly till the contribution of chemical species to the signal data is all deleted. At this time the decrease of the mathematical rank would equal that of the chemical rank, and the remaining residual subspace would entirely be due to the noise contribution. The chemical rank can be estimated easily by using an F-test. The method has been used successfully to the simulated HPLC-DAD type three-way data array and two real excitation-emission fluorescence data sets of amino acid mixtures and dye mixtures. The simulation with added relatively high level noise shows that the method is robust in resisting the heteroscedastic noise. The proposed algorithm is simple and easy to program with quite light computational burden.

Adaptive Set-Based Methods for Association Testing.

PubMed

Su, Yu-Chen; Gauderman, William James; Berhane, Kiros; Lewinger, Juan Pablo

2016-02-01

With a typical sample size of a few thousand subjects, a single genome-wide association study (GWAS) using traditional one single nucleotide polymorphism (SNP)-at-a-time methods can only detect genetic variants conferring a sizable effect on disease risk. Set-based methods, which analyze sets of SNPs jointly, can detect variants with smaller effects acting within a gene, a pathway, or other biologically relevant sets. Although self-contained set-based methods (those that test sets of variants without regard to variants not in the set) are generally more powerful than competitive set-based approaches (those that rely on comparison of variants in the set of interest with variants not in the set), there is no consensus as to which self-contained methods are best. In particular, several self-contained set tests have been proposed to directly or indirectly "adapt" to the a priori unknown proportion and distribution of effects of the truly associated SNPs in the set, which is a major determinant of their power. A popular adaptive set-based test is the adaptive rank truncated product (ARTP), which seeks the set of SNPs that yields the best-combined evidence of association. We compared the standard ARTP, several ARTP variations we introduced, and other adaptive methods in a comprehensive simulation study to evaluate their performance. We used permutations to assess significance for all the methods and thus provide a level playing field for comparison. We found the standard ARTP test to have the highest power across our simulations followed closely by the global model of random effects (GMRE) and a least absolute shrinkage and selection operator (LASSO)-based test. © 2015 WILEY PERIODICALS, INC.

A comprehensive performance evaluation on the prediction results of existing cooperative transcription factors identification algorithms.

PubMed

Lai, Fu-Jou; Chang, Hong-Tsun; Huang, Yueh-Min; Wu, Wei-Sheng

2014-01-01

Eukaryotic transcriptional regulation is known to be highly connected through the networks of cooperative transcription factors (TFs). Measuring the cooperativity of TFs is helpful for understanding the biological relevance of these TFs in regulating genes. The recent advances in computational techniques led to various predictions of cooperative TF pairs in yeast. As each algorithm integrated different data resources and was developed based on different rationales, it possessed its own merit and claimed outperforming others. However, the claim was prone to subjectivity because each algorithm compared with only a few other algorithms and only used a small set of performance indices for comparison. This motivated us to propose a series of indices to objectively evaluate the prediction performance of existing algorithms. And based on the proposed performance indices, we conducted a comprehensive performance evaluation. We collected 14 sets of predicted cooperative TF pairs (PCTFPs) in yeast from 14 existing algorithms in the literature. Using the eight performance indices we adopted/proposed, the cooperativity of each PCTFP was measured and a ranking score according to the mean cooperativity of the set was given to each set of PCTFPs under evaluation for each performance index. It was seen that the ranking scores of a set of PCTFPs vary with different performance indices, implying that an algorithm used in predicting cooperative TF pairs is of strength somewhere but may be of weakness elsewhere. We finally made a comprehensive ranking for these 14 sets. The results showed that Wang J's study obtained the best performance evaluation on the prediction of cooperative TF pairs in yeast. In this study, we adopted/proposed eight performance indices to make a comprehensive performance evaluation on the prediction results of 14 existing cooperative TFs identification algorithms. Most importantly, these proposed indices can be easily applied to measure the performance of new algorithms developed in the future, thus expedite progress in this research field.

What criteria do decision makers in Thailand use to set priorities for vaccine introduction?

PubMed

Pooripussarakul, Siriporn; Riewpaiboon, Arthorn; Bishai, David; Muangchana, Charung; Tantivess, Sripen

2016-08-02

There is a need to identify rational criteria and set priorities for vaccines. In Thailand, many licensed vaccines are being considering for introduction into the Expanded Program on Immunization; thus, the government has to make decisions about which vaccines should be adopted. This study aimed to set priorities for new vaccines and to facilitate decision analysis. We used a best-worst scaling study for rank-ordering of vaccines. The candidate vaccines were determined by a set of criteria, including burden of disease, target age group, budget impact, side effect, effectiveness, severity of disease, and cost of vaccine. The criteria were identified from a literature review and by in-depth, open-ended interviews with experts. The priority-setting model was conducted among three groups of stakeholders, including policy makers, healthcare professionals and healthcare administrators. The vaccine data were mapped and then calculated for the probability of selection. From the candidate vaccines, the probability of hepatitis B vaccine being selected by all respondents (96.67 %) was ranked first. This was followed, respectively, by pneumococcal conjugate vaccine-13 (95.09 %) and Haemophilus influenzae type b vaccine (90.87 %). The three groups of stakeholders (policy makers, healthcare professionals and healthcare administrators) showed the same ranking trends. Most severe disease, high fever rate and high disease burden showed the highest coefficients for criterion levels being selected by all respondents. This result can be implied that a vaccine which can prevent most severe disease with high disease burden and has low safety has a greater chance of being selected by respondents in this study. The priority setting of vaccines through a multiple-criteria approach could contribute to transparency and accountability in the decision-making process. This is a step forward in the development of an evidence-based approach that meets the need of developing country. The methodology is generalizable but its application to another country would require the criteria as relevant to that country.

Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval.

PubMed

Wang, Yang; Lin, Xuemin; Wu, Lin; Zhang, Wenjie

2017-03-01

Given a query photo issued by a user (q-user), the landmark retrieval is to return a set of photos with their landmarks similar to those of the query, while the existing studies on the landmark retrieval focus on exploiting geometries of landmarks for similarity matches between candidate photos and a query photo. We observe that the same landmarks provided by different users over social media community may convey different geometry information depending on the viewpoints and/or angles, and may, subsequently, yield very different results. In fact, dealing with the landmarks with low quality shapes caused by the photography of q-users is often nontrivial and has seldom been studied. In this paper, we propose a novel framework, namely, multi-query expansions, to retrieve semantically robust landmarks by two steps. First, we identify the top- k photos regarding the latent topics of a query landmark to construct multi-query set so as to remedy its possible low quality shape. For this purpose, we significantly extend the techniques of Latent Dirichlet Allocation. Then, motivated by the typical collaborative filtering methods, we propose to learn a collaborative deep networks-based semantically, nonlinear, and high-level features over the latent factor for landmark photo as the training set, which is formed by matrix factorization over collaborative user-photo matrix regarding the multi-query set. The learned deep network is further applied to generate the features for all the other photos, meanwhile resulting into a compact multi-query set within such space. Then, the final ranking scores are calculated over the high-level feature space between the multi-query set and all other photos, which are ranked to serve as the final ranking list of landmark retrieval. Extensive experiments are conducted on real-world social media data with both landmark photos together with their user information to show the superior performance over the existing methods, especially our recently proposed multi-query based mid-level pattern representation method [1].

Identifying acne treatment uncertainties via a James Lind Alliance Priority Setting Partnership

PubMed Central

Layton, Alison; Eady, E Anne; Peat, Maggie; Whitehouse, Heather; Levell, Nick; Ridd, Matthew; Cowdell, Fiona; Patel, Mahenda; Andrews, Stephen; Oxnard, Christine; Fenton, Mark; Firkins, Lester

2015-01-01

Objectives The Acne Priority Setting Partnership (PSP) was set up to identify and rank treatment uncertainties by bringing together people with acne, and professionals providing care within and beyond the National Health Service (NHS). Setting The UK with international participation. Participants Teenagers and adults with acne, parents, partners, nurses, clinicians, pharmacists, private practitioners. Methods Treatment uncertainties were collected via separate online harvesting surveys, embedded within the PSP website, for patients and professionals. A wide variety of approaches were used to promote the surveys to stakeholder groups with a particular emphasis on teenagers and young adults. Survey submissions were collated using keywords and verified as uncertainties by appraising existing evidence. The 30 most popular themes were ranked via weighted scores from an online vote. At a priority setting workshop, patients and professionals discussed the 18 highest-scoring questions from the vote, and reached consensus on the top 10. Results In the harvesting survey, 2310 people, including 652 professionals and 1456 patients (58% aged 24 y or younger), made submissions containing at least one research question. After checking for relevance and rephrasing, a total of 6255 questions were collated into themes. Valid votes ranking the 30 most common themes were obtained from 2807 participants. The top 10 uncertainties prioritised at the workshop were largely focused on management strategies, optimum use of common prescription medications and the role of non-drug based interventions. More female than male patients took part in the harvesting surveys and vote. A wider range of uncertainties were provided by patients compared to professionals. Conclusions Engaging teenagers and young adults in priority setting is achievable using a variety of promotional methods. The top 10 uncertainties reveal an extensive knowledge gap about widely used interventions and the relative merits of drug versus non-drug based treatments in acne management. PMID:26187120

Social norms and rank-based nudging: Changing willingness to pay for healthy food.

PubMed

Aldrovandi, Silvio; Brown, Gordon D A; Wood, Alex M

2015-09-01

People's evaluations in the domain of healthy eating are at least partly determined by the choice context. We systematically test reference level and rank-based models of relative comparisons against each other and explore their application to social norms nudging, an intervention that aims at influencing consumers' behavior by addressing their inaccurate beliefs about their consumption relative to the consumption of others. Study 1 finds that the rank of a product or behavior among others in the immediate comparison context, rather than its objective attributes, influences its evaluation. Study 2 finds that when a comparator is presented in isolation the same rank-based process occurs based on information retrieved from memory. Study 3 finds that telling people how their consumption ranks within a normative comparison sample increases willingness to pay for a healthy food by over 30% relative to the normal social norms intervention that tells them how they compare to the average. We conclude that social norms interventions should present rank information (e.g., "you are in the most unhealthy 10% of eaters") rather than information relative to the average (e.g., "you consume 500 calories more than the average person"). (c) 2015 APA, all rights reserved).

Abundances of polycyclic aromatic hydrocarbons (PAHs) in 14 chinese and american coals and their relation to coal rank and weathering

USGS Publications Warehouse

Wang, R.; Liu, Gaisheng; Zhang, Jiahua; Chou, C.-L.; Liu, J.

2010-01-01

The abundances of 16 polycyclic aromatic hydrocarbons (PAHs) on the priority list of the United States Environmental Protection Agency (U.S. EPA) have been determined in 14 Chinese and American coals. The ranks of the samples range from lignite, bituminous coal, anthracite, to natural coke. Soxhlet extraction was conducted on each coal for 48 h. The extract was analyzed on a gas chromatograph-mass spectrometer (GC-MS). The results show that the total PAH content ranged from 0.31 to 57.6 ??g/g of coal (on a dry basis). It varied with coal rank and is highest in the maturity range of bituminous coal rank. High-molecular-weight (HMW) PAHs are predominant in low-rank coals, but low-molecular-weight (LMW) PAHs are predominant in high-rank coals. The low-sulfur coals have a higher PAH content than high-sulfur coals. It may be explained by an increasing connection between disulfide bonds and PAHs in high-sulfur coal. In addition, it leads us to conclude that the PAH content of coals may be related to the depositional environment. ?? 2010 American Chemical Society.

Pure state `really' informationally complete with rank-1 POVM

NASA Astrophysics Data System (ADS)

Wang, Yu; Shang, Yun

2018-03-01

What is the minimal number of elements in a rank-1 positive operator-valued measure (POVM) which can uniquely determine any pure state in d-dimensional Hilbert space H_d? The known result is that the number is no less than 3d-2. We show that this lower bound is not tight except for d=2 or 4. Then we give an upper bound 4d-3. For d=2, many rank-1 POVMs with four elements can determine any pure states in H_2. For d=3, we show eight is the minimal number by construction. For d=4, the minimal number is in the set of {10,11,12,13}. We show that if this number is greater than 10, an unsettled open problem can be solved that three orthonormal bases cannot distinguish all pure states in H_4. For any dimension d, we construct d+2k-2 adaptive rank-1 positive operators for the reconstruction of any unknown pure state in H_d, where 1≤ k ≤ d.

What Are Essential Concepts in "Astronomy 101"? A New Approach to Find Consensus from Two Different Samples of Instructors

ERIC Educational Resources Information Center

Zeilik, Michael; Morris-Dueer, Vicki J.

2004-01-01

In the summers of 1997, 1998, and 1999, we gave attendees (N=44) at a workshop called Teaching Astronomy Conceptually a cognitive task: to rank 200 concepts often taught in "Astronomy 101." Prior to these workshops, we asked an expert panel (N=18) of Astronomy 101 teachers to also rank these concepts. Among the workshop participants, the…

Estimation of Rank Correlation for Clustered Data

PubMed Central

Rosner, Bernard; Glynn, Robert

2017-01-01

It is well known that the sample correlation coefficient (Rxy) is the maximum likelihood estimator (MLE) of the Pearson correlation (ρxy) for i.i.d. bivariate normal data. However, this is not true for ophthalmologic data where X (e.g., visual acuity) and Y (e.g., visual field) are available for each eye and there is positive intraclass correlation for both X and Y in fellow eyes. In this paper, we provide a regression-based approach for obtaining the MLE of ρxy for clustered data, which can be implemented using standard mixed effects model software. This method is also extended to allow for estimation of partial correlation by controlling both X and Y for a vector U of other covariates. In addition, these methods can be extended to allow for estimation of rank correlation for clustered data by (a) converting ranks of both X and Y to the probit scale, (b) estimating the Pearson correlation between probit scores for X and Y, and (c) using the relationship between Pearson and rank correlation for bivariate normally distributed data. The validity of the methods in finite-sized samples is supported by simulation studies. Finally, two examples from ophthalmology and analgesic abuse are used to illustrate the methods. PMID:28399615

Evaluating user reputation in online rating systems via an iterative group-based ranking method

NASA Astrophysics Data System (ADS)

Gao, Jian; Zhou, Tao

2017-05-01

Reputation is a valuable asset in online social lives and it has drawn increased attention. Due to the existence of noisy ratings and spamming attacks, how to evaluate user reputation in online rating systems is especially significant. However, most of the previous ranking-based methods either follow a debatable assumption or have unsatisfied robustness. In this paper, we propose an iterative group-based ranking method by introducing an iterative reputation-allocation process into the original group-based ranking method. More specifically, the reputation of users is calculated based on the weighted sizes of the user rating groups after grouping all users by their rating similarities, and the high reputation users' ratings have larger weights in dominating the corresponding user rating groups. The reputation of users and the user rating group sizes are iteratively updated until they become stable. Results on two real data sets with artificial spammers suggest that the proposed method has better performance than the state-of-the-art methods and its robustness is considerably improved comparing with the original group-based ranking method. Our work highlights the positive role of considering users' grouping behaviors towards a better online user reputation evaluation.

A multimedia retrieval framework based on semi-supervised ranking and relevance feedback.

PubMed

Yang, Yi; Nie, Feiping; Xu, Dong; Luo, Jiebo; Zhuang, Yueting; Pan, Yunhe

2012-04-01

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

The application of fuzzy Delphi and fuzzy inference system in supplier ranking and selection

NASA Astrophysics Data System (ADS)

Tahriri, Farzad; Mousavi, Maryam; Hozhabri Haghighi, Siamak; Zawiah Md Dawal, Siti

2014-06-01

In today's highly rival market, an effective supplier selection process is vital to the success of any manufacturing system. Selecting the appropriate supplier is always a difficult task because suppliers posses varied strengths and weaknesses that necessitate careful evaluations prior to suppliers' ranking. This is a complex process with many subjective and objective factors to consider before the benefits of supplier selection are achieved. This paper identifies six extremely critical criteria and thirteen sub-criteria based on the literature. A new methodology employing those criteria and sub-criteria is proposed for the assessment and ranking of a given set of suppliers. To handle the subjectivity of the decision maker's assessment, an integration of fuzzy Delphi with fuzzy inference system has been applied and a new ranking method is proposed for supplier selection problem. This supplier selection model enables decision makers to rank the suppliers based on three classifications including "extremely preferred", "moderately preferred", and "weakly preferred". In addition, in each classification, suppliers are put in order from highest final score to the lowest. Finally, the methodology is verified and validated through an example of a numerical test bed.

Exchange-Hole Dipole Dispersion Model for Accurate Energy Ranking in Molecular Crystal Structure Prediction.

PubMed

Whittleton, Sarah R; Otero-de-la-Roza, A; Johnson, Erin R

2017-02-14

Accurate energy ranking is a key facet to the problem of first-principles crystal-structure prediction (CSP) of molecular crystals. This work presents a systematic assessment of B86bPBE-XDM, a semilocal density functional combined with the exchange-hole dipole moment (XDM) dispersion model, for energy ranking using 14 compounds from the first five CSP blind tests. Specifically, the set of crystals studied comprises 11 rigid, planar compounds and 3 co-crystals. The experimental structure was correctly identified as the lowest in lattice energy for 12 of the 14 total crystals. One of the exceptions is 4-hydroxythiophene-2-carbonitrile, for which the experimental structure was correctly identified once a quasi-harmonic estimate of the vibrational free-energy contribution was included, evidencing the occasional importance of thermal corrections for accurate energy ranking. The other exception is an organic salt, where charge-transfer error (also called delocalization error) is expected to cause the base density functional to be unreliable. Provided the choice of base density functional is appropriate and an estimate of temperature effects is used, XDM-corrected density-functional theory is highly reliable for the energetic ranking of competing crystal structures.