ERIC Educational Resources Information Center
Fidalgo, Angel M.; Alavi, Seyed Mohammad; Amirian, Seyed Mohammad Reza
2014-01-01
This study examines three controversial aspects in differential item functioning (DIF) detection by logistic regression (LR) models: first, the relative effectiveness of different analytical strategies for detecting DIF; second, the suitability of the Wald statistic for determining the statistical significance of the parameters of interest; and…
Statistical Significance Testing.
ERIC Educational Resources Information Center
McLean, James E., Ed.; Kaufman, Alan S., Ed.
1998-01-01
The controversy about the use or misuse of statistical significance testing has become the major methodological issue in educational research. This special issue contains three articles that explore the controversy, three commentaries on these articles, an overall response, and three rejoinders by the first three authors. They are: (1)…
Statistically significant relational data mining :
Berry, Jonathan W.; Leung, Vitus Joseph; Phillips, Cynthia Ann; Pinar, Ali; Robinson, David Gerald; Berger-Wolf, Tanya; Bhowmick, Sanjukta; Casleton, Emily; Kaiser, Mark; Nordman, Daniel J.; Wilson, Alyson G.
2014-02-01
This report summarizes the work performed under the project (3z(BStatitically significant relational data mining.(3y (BThe goal of the project was to add more statistical rigor to the fairly ad hoc area of data mining on graphs. Our goal was to develop better algorithms and better ways to evaluate algorithm quality. We concetrated on algorithms for community detection, approximate pattern matching, and graph similarity measures. Approximate pattern matching involves finding an instance of a relatively small pattern, expressed with tolerance, in a large graph of data observed with uncertainty. This report gathers the abstracts and references for the eight refereed publications that have appeared as part of this work. We then archive three pieces of research that have not yet been published. The first is theoretical and experimental evidence that a popular statistical measure for comparison of community assignments favors over-resolved communities over approximations to a ground truth. The second are statistically motivated methods for measuring the quality of an approximate match of a small pattern in a large graph. The third is a new probabilistic random graph model. Statisticians favor these models for graph analysis. The new local structure graph model overcomes some of the issues with popular models such as exponential random graph models and latent variable models.
NASA Astrophysics Data System (ADS)
Baluev, Roman V.
2013-11-01
We consider the `multifrequency' periodogram, in which the putative signal is modelled as a sum of two or more sinusoidal harmonics with independent frequencies. It is useful in cases when the data may contain several periodic components, especially when their interaction with each other and with the data sampling patterns might produce misleading results. Although the multifrequency statistic itself was constructed earlier, for example by G. Foster in his CLEANest algorithm, its probabilistic properties (the detection significance levels) are still poorly known and much of what is deemed known is not rigorous. These detection levels are nonetheless important for data analysis. We argue that to prove the simultaneous existence of all n components revealed in a multiperiodic variation, it is mandatory to apply at least 2n - 1 significance tests, among which most involve various multifrequency statistics, and only n tests are single-frequency ones. The main result of this paper is an analytic estimation of the statistical significance of the frequency tuples that the multifrequency periodogram can reveal. Using the theory of extreme values of random fields (the generalized Rice method), we find a useful approximation to the relevant false alarm probability. For the double-frequency periodogram, this approximation is given by the elementary formula (π/16)W2e- zz2, where W denotes the normalized width of the settled frequency range, and z is the observed periodogram maximum. We carried out intensive Monte Carlo simulations to show that the practical quality of this approximation is satisfactory. A similar analytic expression for the general multifrequency periodogram is also given, although with less numerical verification.
Statistical significance of the gallium anomaly
Giunti, Carlo; Laveder, Marco
2011-06-15
We calculate the statistical significance of the anomalous deficit of electron neutrinos measured in the radioactive source experiments of the GALLEX and SAGE solar neutrino detectors, taking into account the uncertainty of the detection cross section. We found that the statistical significance of the anomaly is {approx}3.0{sigma}. A fit of the data in terms of neutrino oscillations favors at {approx}2.7{sigma} short-baseline electron neutrino disappearance with respect to the null hypothesis of no oscillations.
Significant results: statistical or clinical?
2016-01-01
The null hypothesis significance test method is popular in biological and medical research. Many researchers have used this method for their research without exact knowledge, though it has both merits and shortcomings. Readers will know its shortcomings, as well as several complementary or alternative methods, as such the estimated effect size and the confidence interval. PMID:27066201
Statistical Significance of Threading Scores
Fayyaz Movaghar, Afshin; Launay, Guillaume; Schbath, Sophie; Gibrat, Jean-François
2012-01-01
Abstract We present a general method for assessing threading score significance. The threading score of a protein sequence, thread onto a given structure, should be compared with the threading score distribution of a random amino-acid sequence, of the same length, thread on the same structure; small p-values point significantly high scores. We claim that, due to general protein contact map properties, this reference distribution is a Weibull extreme value distribution whose parameters depend on the threading method, the structure, the length of the query and the random sequence simulation model used. These parameters can be estimated off-line with simulated sequence samples, for different sequence lengths. They can further be interpolated at the exact length of a query, enabling the quick computation of the p-value. PMID:22149633
Finding Statistically Significant Communities in Networks
Lancichinetti, Andrea; Radicchi, Filippo; Ramasco, José J.; Fortunato, Santo
2011-01-01
Community structure is one of the main structural features of networks, revealing both their internal organization and the similarity of their elementary units. Despite the large variety of methods proposed to detect communities in graphs, there is a big need for multi-purpose techniques, able to handle different types of datasets and the subtleties of community structure. In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics. It is based on the local optimization of a fitness function expressing the statistical significance of clusters with respect to random fluctuations, which is estimated with tools of Extreme and Order Statistics. OSLOM can be used alone or as a refinement procedure of partitions/covers delivered by other techniques. We have also implemented sequential algorithms combining OSLOM with other fast techniques, so that the community structure of very large networks can be uncovered. Our method has a comparable performance as the best existing algorithms on artificial benchmark graphs. Several applications on real networks are shown as well. OSLOM is implemented in a freely available software (http://www.oslom.org), and we believe it will be a valuable tool in the analysis of networks. PMID:21559480
Social significance of community structure: Statistical view
NASA Astrophysics Data System (ADS)
Li, Hui-Jia; Daniels, Jasmine J.
2015-01-01
Community structure analysis is a powerful tool for social networks that can simplify their topological and functional analysis considerably. However, since community detection methods have random factors and real social networks obtained from complex systems always contain error edges, evaluating the significance of a partitioned community structure is an urgent and important question. In this paper, integrating the specific characteristics of real society, we present a framework to analyze the significance of a social community. The dynamics of social interactions are modeled by identifying social leaders and corresponding hierarchical structures. Instead of a direct comparison with the average outcome of a random model, we compute the similarity of a given node with the leader by the number of common neighbors. To determine the membership vector, an efficient community detection algorithm is proposed based on the position of the nodes and their corresponding leaders. Then, using a log-likelihood score, the tightness of the community can be derived. Based on the distribution of community tightness, we establish a connection between p -value theory and network analysis, and then we obtain a significance measure of statistical form . Finally, the framework is applied to both benchmark networks and real social networks. Experimental results show that our work can be used in many fields, such as determining the optimal number of communities, analyzing the social significance of a given community, comparing the performance among various algorithms, etc.
Social significance of community structure: statistical view.
Li, Hui-Jia; Daniels, Jasmine J
2015-01-01
Community structure analysis is a powerful tool for social networks that can simplify their topological and functional analysis considerably. However, since community detection methods have random factors and real social networks obtained from complex systems always contain error edges, evaluating the significance of a partitioned community structure is an urgent and important question. In this paper, integrating the specific characteristics of real society, we present a framework to analyze the significance of a social community. The dynamics of social interactions are modeled by identifying social leaders and corresponding hierarchical structures. Instead of a direct comparison with the average outcome of a random model, we compute the similarity of a given node with the leader by the number of common neighbors. To determine the membership vector, an efficient community detection algorithm is proposed based on the position of the nodes and their corresponding leaders. Then, using a log-likelihood score, the tightness of the community can be derived. Based on the distribution of community tightness, we establish a connection between p-value theory and network analysis, and then we obtain a significance measure of statistical form . Finally, the framework is applied to both benchmark networks and real social networks. Experimental results show that our work can be used in many fields, such as determining the optimal number of communities, analyzing the social significance of a given community, comparing the performance among various algorithms, etc.
The insignificance of statistical significance testing
Johnson, Douglas H.
1999-01-01
Despite their use in scientific journals such as The Journal of Wildlife Management, statistical hypothesis tests add very little value to the products of research. Indeed, they frequently confuse the interpretation of data. This paper describes how statistical hypothesis tests are often viewed, and then contrasts that interpretation with the correct one. I discuss the arbitrariness of P-values, conclusions that the null hypothesis is true, power analysis, and distinctions between statistical and biological significance. Statistical hypothesis testing, in which the null hypothesis about the properties of a population is almost always known a priori to be false, is contrasted with scientific hypothesis testing, which examines a credible null hypothesis about phenomena in nature. More meaningful alternatives are briefly outlined, including estimation and confidence intervals for determining the importance of factors, decision theory for guiding actions in the face of uncertainty, and Bayesian approaches to hypothesis testing and other statistical practices.
Statistical Significance vs. Practical Significance: An Exploration through Health Education
ERIC Educational Resources Information Center
Rosen, Brittany L.; DeMaria, Andrea L.
2012-01-01
The purpose of this paper is to examine the differences between statistical and practical significance, including strengths and criticisms of both methods, as well as provide information surrounding the application of various effect sizes and confidence intervals within health education research. Provided are recommendations, explanations and…
Determining the Statistical Significance of Relative Weights
ERIC Educational Resources Information Center
Tonidandel, Scott; LeBreton, James M.; Johnson, Jeff W.
2009-01-01
Relative weight analysis is a procedure for estimating the relative importance of correlated predictors in a regression equation. Because the sampling distribution of relative weights is unknown, researchers using relative weight analysis are unable to make judgments regarding the statistical significance of the relative weights. J. W. Johnson…
Statistical significance testing and clinical trials.
Krause, Merton S
2011-09-01
The efficacy of treatments is better expressed for clinical purposes in terms of these treatments' outcome distributions and their overlapping rather than in terms of the statistical significance of these distributions' mean differences, because clinical practice is primarily concerned with the outcome of each individual client rather than with the mean of the variety of outcomes in any group of clients. Reports of the obtained outcome distributions for the comparison groups of all competently designed and executed randomized clinical trials should be publicly available no matter what the statistical significance of the mean differences among these groups, because all of these studies' outcome distributions provide clinically useful information about the efficacy of the treatments compared.
Systematic identification of statistically significant network measures
NASA Astrophysics Data System (ADS)
Ziv, Etay; Koytcheff, Robin; Middendorf, Manuel; Wiggins, Chris
2005-01-01
We present a graph embedding space (i.e., a set of measures on graphs) for performing statistical analyses of networks. Key improvements over existing approaches include discovery of “motif hubs” (multiple overlapping significant subgraphs), computational efficiency relative to subgraph census, and flexibility (the method is easily generalizable to weighted and signed graphs). The embedding space is based on scalars, functionals of the adjacency matrix representing the network. Scalars are global, involving all nodes; although they can be related to subgraph enumeration, there is not a one-to-one mapping between scalars and subgraphs. Improvements in network randomization and significance testing—we learn the distribution rather than assuming Gaussianity—are also presented. The resulting algorithm establishes a systematic approach to the identification of the most significant scalars and suggests machine-learning techniques for network classification.
Statistical Significance of Clustering using Soft Thresholding
Huang, Hanwen; Liu, Yufeng; Yuan, Ming; Marron, J. S.
2015-01-01
Clustering methods have led to a number of important discoveries in bioinformatics and beyond. A major challenge in their use is determining which clusters represent important underlying structure, as opposed to spurious sampling artifacts. This challenge is especially serious, and very few methods are available, when the data are very high in dimension. Statistical Significance of Clustering (SigClust) is a recently developed cluster evaluation tool for high dimensional low sample size data. An important component of the SigClust approach is the very definition of a single cluster as a subset of data sampled from a multivariate Gaussian distribution. The implementation of SigClust requires the estimation of the eigenvalues of the covariance matrix for the null multivariate Gaussian distribution. We show that the original eigenvalue estimation can lead to a test that suffers from severe inflation of type-I error, in the important case where there are a few very large eigenvalues. This paper addresses this critical challenge using a novel likelihood based soft thresholding approach to estimate these eigenvalues, which leads to a much improved SigClust. Major improvements in SigClust performance are shown by both mathematical analysis, based on the new notion of Theoretical Cluster Index, and extensive simulation studies. Applications to some cancer genomic data further demonstrate the usefulness of these improvements. PMID:26755893
[Significance of medical statistics in insurance medicine].
Becher, J
2001-03-01
Knowledge of medical statistics is of great benefit to every insurance medical officer as they facilitate communication with actuaries, allow officers to make their own calculations and are the basis for correctly interpreting medical journals. Only about 20% of original work in medicine today is published without statistics or only with descriptive statistics--and this trend is falling. The reader of medical publications should be in a position to make a critical analysis of the methodology and content, since one cannot always rely on the conclusions drawn by the authors: statistical errors appear very frequently in medical publications. Due to the specific methodological features involved, the assessment of meta-analyses demands special attention. The number of published meta-analyses has risen 40-fold over the last ten years. Important examples for the practical use of statistical methods in insurance medicine include estimating extramortality from published survival analyses and evaluating diagnostic test results. The purpose of this article is to highlight statistical problems and issues of relevance to insurance medicine and to establish the bases for understanding them.
Statistics by Example, Detecting Patterns.
ERIC Educational Resources Information Center
Mosteller, Frederick; And Others
This booklet is part of a series of four pamphlets, each intended to stand alone, which provide problems in probability and statistics at the secondary school level. Twelve different real-life examples (written by professional statisticians and experienced teachers) have been collected in this booklet to illustrate the ideas of mean, variation,…
Testing the Difference of Correlated Agreement Coefficients for Statistical Significance
ERIC Educational Resources Information Center
Gwet, Kilem L.
2016-01-01
This article addresses the problem of testing the difference between two correlated agreement coefficients for statistical significance. A number of authors have proposed methods for testing the difference between two correlated kappa coefficients, which require either the use of resampling methods or the use of advanced statistical modeling…
The Use of Meta-Analytic Statistical Significance Testing
ERIC Educational Resources Information Center
Polanin, Joshua R.; Pigott, Terri D.
2015-01-01
Meta-analysis multiplicity, the concept of conducting multiple tests of statistical significance within one review, is an underdeveloped literature. We address this issue by considering how Type I errors can impact meta-analytic results, suggest how statistical power may be affected through the use of multiplicity corrections, and propose how…
Reviewer Bias for Statistically Significant Results: A Reexamination.
ERIC Educational Resources Information Center
Fagley, N. S.; McKinney, I. Jean
1983-01-01
Reexamines the article by Atkinson, Furlong, and Wampold (1982) and questions their conclusion that reviewers were biased toward statistically significant results. A statistical power analysis shows the power of their bogus study was low. Low power in a study reporting nonsignificant findings is a valid reason for recommending not to publish.…
A Tutorial on Hunting Statistical Significance by Chasing N.
Szucs, Denes
2016-01-01
There is increasing concern about the replicability of studies in psychology and cognitive neuroscience. Hidden data dredging (also called p-hacking) is a major contributor to this crisis because it substantially increases Type I error resulting in a much larger proportion of false positive findings than the usually expected 5%. In order to build better intuition to avoid, detect and criticize some typical problems, here I systematically illustrate the large impact of some easy to implement and so, perhaps frequent data dredging techniques on boosting false positive findings. I illustrate several forms of two special cases of data dredging. First, researchers may violate the data collection stopping rules of null hypothesis significance testing by repeatedly checking for statistical significance with various numbers of participants. Second, researchers may group participants post hoc along potential but unplanned independent grouping variables. The first approach 'hacks' the number of participants in studies, the second approach 'hacks' the number of variables in the analysis. I demonstrate the high amount of false positive findings generated by these techniques with data from true null distributions. I also illustrate that it is extremely easy to introduce strong bias into data by very mild selection and re-testing. Similar, usually undocumented data dredging steps can easily lead to having 20-50%, or more false positives.
A Tutorial on Hunting Statistical Significance by Chasing N
Szucs, Denes
2016-01-01
There is increasing concern about the replicability of studies in psychology and cognitive neuroscience. Hidden data dredging (also called p-hacking) is a major contributor to this crisis because it substantially increases Type I error resulting in a much larger proportion of false positive findings than the usually expected 5%. In order to build better intuition to avoid, detect and criticize some typical problems, here I systematically illustrate the large impact of some easy to implement and so, perhaps frequent data dredging techniques on boosting false positive findings. I illustrate several forms of two special cases of data dredging. First, researchers may violate the data collection stopping rules of null hypothesis significance testing by repeatedly checking for statistical significance with various numbers of participants. Second, researchers may group participants post hoc along potential but unplanned independent grouping variables. The first approach ‘hacks’ the number of participants in studies, the second approach ‘hacks’ the number of variables in the analysis. I demonstrate the high amount of false positive findings generated by these techniques with data from true null distributions. I also illustrate that it is extremely easy to introduce strong bias into data by very mild selection and re-testing. Similar, usually undocumented data dredging steps can easily lead to having 20–50%, or more false positives. PMID:27713723
Shukla, R.; Yu Daohai; Fulk, F.
1995-12-31
Short-term toxicity tests with aquatic organisms are a valuable measurement tool in the assessment of the toxicity of effluents, environmental samples and single chemicals. Currently toxicity tests are utilized in a wide range of US EPA regulatory activities including effluent discharge compliance. In the current approach for determining the No Observed Effect Concentration, an effluent concentration is presumed safe if there is no statistically significant difference in toxicant response versus control response. The conclusion of a safe concentration may be due to the fact that it truly is safe, or alternatively, that the ability of the statistical test to detect an effect, given its existence, is inadequate. Results of research of a new statistical approach, the basis of which is to move away from a demonstration of no difference to a demonstration of equivalence, will be discussed. The concept of observed confidence distributions, first suggested by Cox, is proposed as a measure of the strength of evidence for practically equivalent responses between a given effluent concentration and the control. The research included determination of intervals of practically equivalent responses as a function of the variability of control response. The approach is illustrated using reproductive data from tests with Ceriodaphnia dubia and survival and growth data from tests with fathead minnow. The data are from the US EPA`s National Reference Toxicant Database.
The questioned p value: clinical, practical and statistical significance.
Jiménez-Paneque, Rosa
2016-09-09
The use of p-value and statistical significance have been questioned since the early 80s in the last century until today. Much has been discussed about it in the field of statistics and its applications, especially in Epidemiology and Public Health. As a matter of fact, the p-value and its equivalent, statistical significance, are difficult concepts to grasp for the many health professionals some way involved in research applied to their work areas. However, its meaning should be clear in intuitive terms although it is based on theoretical concepts of the field of Statistics. This paper attempts to present the p-value as a concept that applies to everyday life and therefore intuitively simple but whose proper use cannot be separated from theoretical and methodological elements of inherent complexity. The reasons behind the criticism received by the p-value and its isolated use are intuitively explained, mainly the need to demarcate statistical significance from clinical significance and some of the recommended remedies for these problems are approached as well. It finally refers to the current trend to vindicate the p-value appealing to the convenience of its use in certain situations and the recent statement of the American Statistical Association in this regard.
Statistical significance test for transition matrices of atmospheric Markov chains
NASA Technical Reports Server (NTRS)
Vautard, Robert; Mo, Kingtse C.; Ghil, Michael
1990-01-01
Low-frequency variability of large-scale atmospheric dynamics can be represented schematically by a Markov chain of multiple flow regimes. This Markov chain contains useful information for the long-range forecaster, provided that the statistical significance of the associated transition matrix can be reliably tested. Monte Carlo simulation yields a very reliable significance test for the elements of this matrix. The results of this test agree with previously used empirical formulae when each cluster of maps identified as a distinct flow regime is sufficiently large and when they all contain a comparable number of maps. Monte Carlo simulation provides a more reliable way to test the statistical significance of transitions to and from small clusters. It can determine the most likely transitions, as well as the most unlikely ones, with a prescribed level of statistical significance.
Robust statistical methods for automated outlier detection
NASA Technical Reports Server (NTRS)
Jee, J. R.
1987-01-01
The computational challenge of automating outlier, or blunder point, detection in radio metric data requires the use of nonstandard statistical methods because the outliers have a deleterious effect on standard least squares methods. The particular nonstandard methods most applicable to the task are the robust statistical techniques that have undergone intense development since the 1960s. These new methods are by design more resistant to the effects of outliers than standard methods. Because the topic may be unfamiliar, a brief introduction to the philosophy and methods of robust statistics is presented. Then the application of these methods to the automated outlier detection problem is detailed for some specific examples encountered in practice.
Statistical Significance and Effect Size: Two Sides of a Coin.
ERIC Educational Resources Information Center
Fan, Xitao
This paper suggests that statistical significance testing and effect size are two sides of the same coin; they complement each other, but do not substitute for one another. Good research practice requires that both should be taken into consideration to make sound quantitative decisions. A Monte Carlo simulation experiment was conducted, and a…
Interpretation of Statistical Significance Testing: A Matter of Perspective.
ERIC Educational Resources Information Center
McClure, John; Suen, Hoi K.
1994-01-01
This article compares three models that have been the foundation for approaches to the analysis of statistical significance in early childhood research--the Fisherian and the Neyman-Pearson models (both considered "classical" approaches), and the Bayesian model. The article concludes that all three models have a place in the analysis of research…
Your Chi-Square Test Is Statistically Significant: Now What?
ERIC Educational Resources Information Center
Sharpe, Donald
2015-01-01
Applied researchers have employed chi-square tests for more than one hundred years. This paper addresses the question of how one should follow a statistically significant chi-square test result in order to determine the source of that result. Four approaches were evaluated: calculating residuals, comparing cells, ransacking, and partitioning. Data…
Statistical Fault Detection & Diagnosis Expert System
Wegerich, Stephan
1996-12-18
STATMON is an expert system that performs real-time fault detection and diagnosis of redundant sensors in any industrial process requiring high reliability. After a training period performed during normal operation, the expert system monitors the statistical properties of the incoming signals using a pattern recognition test. If the test determines that statistical properties of the signals have changed, the expert system performs a sequence of logical steps to determine which sensor or machine component has degraded.
Estimation of the geochemical threshold and its statistical significance
Miesch, A.T.
1981-01-01
A statistic is proposed for estimating the geochemical threshold and its statistical significance, or it may be used to identify a group of extreme values that can be tested for significance by other means. The statistic is the maximum gap between adjacent values in an ordered array after each gap has been adjusted for the expected frequency. The values in the ordered array are geochemical values transformed by either ln(?? - ??) or ln(?? - ??) and then standardized so that the mean is zero and the variance is unity. The expected frequency is taken from a fitted normal curve with unit area. The midpoint of an adjusted gap that exceeds the corresponding critical value may be taken as an estimate of the geochemical threshold, and the associated probability indicates the likelihood that the threshold separates two geochemical populations. The adjusted gap test may fail to identify threshold values if the variation tends to be continuous from background values to the higher values that reflect mineralized ground. However, the test will serve to identify other anomalies that may be too subtle to have been noted by other means. ?? 1981.
Advances in Significance Testing for Cluster Detection
NASA Astrophysics Data System (ADS)
Coleman, Deidra Andrea
Over the past two decades, much attention has been given to data driven project goals such as the Human Genome Project and the development of syndromic surveillance systems. A major component of these types of projects is analyzing the abundance of data. Detecting clusters within the data can be beneficial as it can lead to the identification of specified sequences of DNA nucleotides that are related to important biological functions or the locations of epidemics such as disease outbreaks or bioterrorism attacks. Cluster detection techniques require efficient and accurate hypothesis testing procedures. In this dissertation, we improve upon the hypothesis testing procedures for cluster detection by enhancing distributional theory and providing an alternative method for spatial cluster detection using syndromic surveillance data. In Chapter 2, we provide an efficient method to compute the exact distribution of the number and coverage of h-clumps of a collection of words. This method involves defining a Markov chain using a minimal deterministic automaton to reduce the number of states needed for computation. We allow words of the collection to contain other words of the collection making the method more general. We use our method to compute the distributions of the number and coverage of h-clumps in the Chi motif of H. influenza.. In Chapter 3, we provide an efficient algorithm to compute the exact distribution of multiple window discrete scan statistics for higher-order, multi-state Markovian sequences. This algorithm involves defining a Markov chain to efficiently keep track of probabilities needed to compute p-values of the statistic. We use our algorithm to identify cases where the available approximation does not perform well. We also use our algorithm to detect unusual clusters of made free throw shots by National Basketball Association players during the 2009-2010 regular season. In Chapter 4, we give a procedure to detect outbreaks using syndromic
Beyond Statistical Significance: Implications of Network Structure on Neuronal Activity
Vlachos, Ioannis; Aertsen, Ad; Kumar, Arvind
2012-01-01
It is a common and good practice in experimental sciences to assess the statistical significance of measured outcomes. For this, the probability of obtaining the actual results is estimated under the assumption of an appropriately chosen null-hypothesis. If this probability is smaller than some threshold, the results are deemed statistically significant and the researchers are content in having revealed, within their own experimental domain, a “surprising” anomaly, possibly indicative of a hitherto hidden fragment of the underlying “ground-truth”. What is often neglected, though, is the actual importance of these experimental outcomes for understanding the system under investigation. We illustrate this point by giving practical and intuitive examples from the field of systems neuroscience. Specifically, we use the notion of embeddedness to quantify the impact of a neuron's activity on its downstream neurons in the network. We show that the network response strongly depends on the embeddedness of stimulated neurons and that embeddedness is a key determinant of the importance of neuronal activity on local and downstream processing. We extrapolate these results to other fields in which networks are used as a theoretical framework. PMID:22291581
Statistical keyword detection in literary corpora
NASA Astrophysics Data System (ADS)
Herrera, J. P.; Pury, P. A.
2008-05-01
Understanding the complexity of human language requires an appropriate analysis of the statistical distribution of words in texts. We consider the information retrieval problem of detecting and ranking the relevant words of a text by means of statistical information referring to the spatial use of the words. Shannon's entropy of information is used as a tool for automatic keyword extraction. By using The Origin of Species by Charles Darwin as a representative text sample, we show the performance of our detector and compare it with another proposals in the literature. The random shuffled text receives special attention as a tool for calibrating the ranking indices.
Sibling Competition & Growth Tradeoffs. Biological vs. Statistical Significance
Kramer, Karen L.; Veile, Amanda; Otárola-Castillo, Erik
2016-01-01
Early childhood growth has many downstream effects on future health and reproduction and is an important measure of offspring quality. While a tradeoff between family size and child growth outcomes is theoretically predicted in high-fertility societies, empirical evidence is mixed. This is often attributed to phenotypic variation in parental condition. However, inconsistent study results may also arise because family size confounds the potentially differential effects that older and younger siblings can have on young children’s growth. Additionally, inconsistent results might reflect that the biological significance associated with different growth trajectories is poorly understood. This paper addresses these concerns by tracking children’s monthly gains in height and weight from weaning to age five in a high fertility Maya community. We predict that: 1) as an aggregate measure family size will not have a major impact on child growth during the post weaning period; 2) competition from young siblings will negatively impact child growth during the post weaning period; 3) however because of their economic value, older siblings will have a negligible effect on young children’s growth. Accounting for parental condition, we use linear mixed models to evaluate the effects that family size, younger and older siblings have on children’s growth. Congruent with our expectations, it is younger siblings who have the most detrimental effect on children’s growth. While we find statistical evidence of a quantity/quality tradeoff effect, the biological significance of these results is negligible in early childhood. Our findings help to resolve why quantity/quality studies have had inconsistent results by showing that sibling competition varies with sibling age composition, not just family size, and that biological significance is distinct from statistical significance. PMID:26938742
Fostering Students' Statistical Literacy through Significant Learning Experience
ERIC Educational Resources Information Center
Krishnan, Saras
2015-01-01
A major objective of statistics education is to develop students' statistical literacy that enables them to be educated users of data in context. Teaching statistics in today's educational settings is not an easy feat because teachers have a huge task in keeping up with the demands of the new generation of learners. The present day students have…
Chládek, J; Brázdil, M; Halámek, J; Plešinger, F; Jurák, P
2013-01-01
We present an off-line analysis procedure for exploring brain activity recorded from intra-cerebral electroencephalographic data (SEEG). The objective is to determine the statistical differences between different types of stimulations in the time-frequency domain. The procedure is based on computing relative signal power change and subsequent statistical analysis. An example of characteristic statistically significant event-related de/synchronization (ERD/ERS) detected across different frequency bands following different oddball stimuli is presented. The method is used for off-line functional classification of different brain areas.
Statistical fingerprinting for malware detection and classification
Prowell, Stacy J.; Rathgeb, Christopher T.
2015-09-15
A system detects malware in a computing architecture with an unknown pedigree. The system includes a first computing device having a known pedigree and operating free of malware. The first computing device executes a series of instrumented functions that, when executed, provide a statistical baseline that is representative of the time it takes the software application to run on a computing device having a known pedigree. A second computing device executes a second series of instrumented functions that, when executed, provides an actual time that is representative of the time the known software application runs on the second computing device. The system detects malware when there is a difference in execution times between the first and the second computing devices.
Statistical detection of systematic election irregularities
Klimek, Peter; Yegorov, Yuri; Hanel, Rudolf; Thurner, Stefan
2012-01-01
Democratic societies are built around the principle of free and fair elections, and that each citizen’s vote should count equally. National elections can be regarded as large-scale social experiments, where people are grouped into usually large numbers of electoral districts and vote according to their preferences. The large number of samples implies statistical consequences for the polling results, which can be used to identify election irregularities. Using a suitable data representation, we find that vote distributions of elections with alleged fraud show a kurtosis substantially exceeding the kurtosis of normal elections, depending on the level of data aggregation. As an example, we show that reported irregularities in recent Russian elections are, indeed, well-explained by systematic ballot stuffing. We develop a parametric model quantifying the extent to which fraudulent mechanisms are present. We formulate a parametric test detecting these statistical properties in election results. Remarkably, this technique produces robust outcomes with respect to the resolution of the data and therefore, allows for cross-country comparisons. PMID:23010929
Statistical detection of systematic election irregularities.
Klimek, Peter; Yegorov, Yuri; Hanel, Rudolf; Thurner, Stefan
2012-10-09
Democratic societies are built around the principle of free and fair elections, and that each citizen's vote should count equally. National elections can be regarded as large-scale social experiments, where people are grouped into usually large numbers of electoral districts and vote according to their preferences. The large number of samples implies statistical consequences for the polling results, which can be used to identify election irregularities. Using a suitable data representation, we find that vote distributions of elections with alleged fraud show a kurtosis substantially exceeding the kurtosis of normal elections, depending on the level of data aggregation. As an example, we show that reported irregularities in recent Russian elections are, indeed, well-explained by systematic ballot stuffing. We develop a parametric model quantifying the extent to which fraudulent mechanisms are present. We formulate a parametric test detecting these statistical properties in election results. Remarkably, this technique produces robust outcomes with respect to the resolution of the data and therefore, allows for cross-country comparisons.
Tipping points in the arctic: eyeballing or statistical significance?
Carstensen, Jacob; Weydmann, Agata
2012-02-01
Arctic ecosystems have experienced and are projected to experience continued large increases in temperature and declines in sea ice cover. It has been hypothesized that small changes in ecosystem drivers can fundamentally alter ecosystem functioning, and that this might be particularly pronounced for Arctic ecosystems. We present a suite of simple statistical analyses to identify changes in the statistical properties of data, emphasizing that changes in the standard error should be considered in addition to changes in mean properties. The methods are exemplified using sea ice extent, and suggest that the loss rate of sea ice accelerated by factor of ~5 in 1996, as reported in other studies, but increases in random fluctuations, as an early warning signal, were observed already in 1990. We recommend to employ the proposed methods more systematically for analyzing tipping points to document effects of climate change in the Arctic.
Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok?
NASA Astrophysics Data System (ADS)
Vu, Minh Tue; Aribarg, Thannob; Supratid, Siriporn; Raghavan, Srivatsan V.; Liong, Shie-Yui
2016-11-01
Artificial neural network (ANN) is an established technique with a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data. The present study utilizes ANN as a method of statistically downscaling global climate models (GCMs) during the rainy season at meteorological site locations in Bangkok, Thailand. The study illustrates the applications of the feed forward back propagation using large-scale predictor variables derived from both the ERA-Interim reanalyses data and present day/future GCM data. The predictors are first selected over different grid boxes surrounding Bangkok region and then screened by using principal component analysis (PCA) to filter the best correlated predictors for ANN training. The reanalyses downscaled results of the present day climate show good agreement against station precipitation with a correlation coefficient of 0.8 and a Nash-Sutcliffe efficiency of 0.65. The final downscaled results for four GCMs show an increasing trend of precipitation for rainy season over Bangkok by the end of the twenty-first century. The extreme values of precipitation determined using statistical indices show strong increases of wetness. These findings will be useful for policy makers in pondering adaptation measures due to flooding such as whether the current drainage network system is sufficient to meet the changing climate and to plan for a range of related adaptation/mitigation measures.
Wilkinson, Michael
2014-03-01
Decisions about support for predictions of theories in light of data are made using statistical inference. The dominant approach in sport and exercise science is the Neyman-Pearson (N-P) significance-testing approach. When applied correctly it provides a reliable procedure for making dichotomous decisions for accepting or rejecting zero-effect null hypotheses with known and controlled long-run error rates. Type I and type II error rates must be specified in advance and the latter controlled by conducting an a priori sample size calculation. The N-P approach does not provide the probability of hypotheses or indicate the strength of support for hypotheses in light of data, yet many scientists believe it does. Outcomes of analyses allow conclusions only about the existence of non-zero effects, and provide no information about the likely size of true effects or their practical/clinical value. Bayesian inference can show how much support data provide for different hypotheses, and how personal convictions should be altered in light of data, but the approach is complicated by formulating probability distributions about prior subjective estimates of population effects. A pragmatic solution is magnitude-based inference, which allows scientists to estimate the true magnitude of population effects and how likely they are to exceed an effect magnitude of practical/clinical importance, thereby integrating elements of subjective Bayesian-style thinking. While this approach is gaining acceptance, progress might be hastened if scientists appreciate the shortcomings of traditional N-P null hypothesis significance testing.
Assessing statistical significance in multivariable genome wide association analysis
Buzdugan, Laura; Kalisch, Markus; Navarro, Arcadi; Schunk, Daniel; Fehr, Ernst; Bühlmann, Peter
2016-01-01
Motivation: Although Genome Wide Association Studies (GWAS) genotype a very large number of single nucleotide polymorphisms (SNPs), the data are often analyzed one SNP at a time. The low predictive power of single SNPs, coupled with the high significance threshold needed to correct for multiple testing, greatly decreases the power of GWAS. Results: We propose a procedure in which all the SNPs are analyzed in a multiple generalized linear model, and we show its use for extremely high-dimensional datasets. Our method yields P-values for assessing significance of single SNPs or groups of SNPs while controlling for all other SNPs and the family wise error rate (FWER). Thus, our method tests whether or not a SNP carries any additional information about the phenotype beyond that available by all the other SNPs. This rules out spurious correlations between phenotypes and SNPs that can arise from marginal methods because the ‘spuriously correlated’ SNP merely happens to be correlated with the ‘truly causal’ SNP. In addition, the method offers a data driven approach to identifying and refining groups of SNPs that jointly contain informative signals about the phenotype. We demonstrate the value of our method by applying it to the seven diseases analyzed by the Wellcome Trust Case Control Consortium (WTCCC). We show, in particular, that our method is also capable of finding significant SNPs that were not identified in the original WTCCC study, but were replicated in other independent studies. Availability and implementation: Reproducibility of our research is supported by the open-source Bioconductor package hierGWAS. Contact: peter.buehlmann@stat.math.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153677
Evaluating clinical significance: incorporating robust statistics with normative comparison tests.
van Wieringen, Katrina; Cribbie, Robert A
2014-05-01
The purpose of this study was to evaluate a modified test of equivalence for conducting normative comparisons when distribution shapes are non-normal and variances are unequal. A Monte Carlo study was used to compare the empirical Type I error rates and power of the proposed Schuirmann-Yuen test of equivalence, which utilizes trimmed means, with that of the previously recommended Schuirmann and Schuirmann-Welch tests of equivalence when the assumptions of normality and variance homogeneity are satisfied, as well as when they are not satisfied. The empirical Type I error rates of the Schuirmann-Yuen were much closer to the nominal α level than those of the Schuirmann or Schuirmann-Welch tests, and the power of the Schuirmann-Yuen was substantially greater than that of the Schuirmann or Schuirmann-Welch tests when distributions were skewed or outliers were present. The Schuirmann-Yuen test is recommended for assessing clinical significance with normative comparisons.
Statistical significance of the rich-club phenomenon in complex networks
NASA Astrophysics Data System (ADS)
Jiang, Zhi-Qiang; Zhou, Wei-Xing
2008-04-01
We propose that the rich-club phenomenon in complex networks should be defined in the spirit of bootstrapping, in which a null model is adopted to assess the statistical significance of the rich-club detected. Our method can serve as a definition of the rich-club phenomenon and is applied to analyze three real networks and three model networks. The results show significant improvement compared with previously reported results. We report a dilemma with an exceptional example, showing that there does not exist an omnipotent definition for the rich-club phenomenon.
Lies, damned lies and statistics: Clinical importance versus statistical significance in research.
Mellis, Craig
2017-02-28
Correctly performed and interpreted statistics play a crucial role for both those who 'produce' clinical research, and for those who 'consume' this research. Unfortunately, however, there are many misunderstandings and misinterpretations of statistics by both groups. In particular, there is a widespread lack of appreciation for the severe limitations with p values. This is a particular problem with small sample sizes and low event rates - common features of many published clinical trials. These issues have resulted in increasing numbers of false positive clinical trials (false 'discoveries'), and the well-publicised inability to replicate many of the findings. While chance clearly plays a role in these errors, many more are due to either poorly performed or badly misinterpreted statistics. Consequently, it is essential that whenever p values appear, these need be accompanied by both 95% confidence limits and effect sizes. These will enable readers to immediately assess the plausible range of results, and whether or not the effect is clinically meaningful.
Timescales for detecting a significant acceleration in sea level rise
Haigh, Ivan D.; Wahl, Thomas; Rohling, Eelco J.; Price, René M.; Pattiaratchi, Charitha B.; Calafat, Francisco M.; Dangendorf, Sönke
2014-01-01
There is observational evidence that global sea level is rising and there is concern that the rate of rise will increase, significantly threatening coastal communities. However, considerable debate remains as to whether the rate of sea level rise is currently increasing and, if so, by how much. Here we provide new insights into sea level accelerations by applying the main methods that have been used previously to search for accelerations in historical data, to identify the timings (with uncertainties) at which accelerations might first be recognized in a statistically significant manner (if not apparent already) in sea level records that we have artificially extended to 2100. We find that the most important approach to earliest possible detection of a significant sea level acceleration lies in improved understanding (and subsequent removal) of interannual to multidecadal variability in sea level records. PMID:24728012
Timescales for detecting a significant acceleration in sea level rise.
Haigh, Ivan D; Wahl, Thomas; Rohling, Eelco J; Price, René M; Pattiaratchi, Charitha B; Calafat, Francisco M; Dangendorf, Sönke
2014-04-14
There is observational evidence that global sea level is rising and there is concern that the rate of rise will increase, significantly threatening coastal communities. However, considerable debate remains as to whether the rate of sea level rise is currently increasing and, if so, by how much. Here we provide new insights into sea level accelerations by applying the main methods that have been used previously to search for accelerations in historical data, to identify the timings (with uncertainties) at which accelerations might first be recognized in a statistically significant manner (if not apparent already) in sea level records that we have artificially extended to 2100. We find that the most important approach to earliest possible detection of a significant sea level acceleration lies in improved understanding (and subsequent removal) of interannual to multidecadal variability in sea level records.
Damage detection in mechanical structures using extreme value statistic.
Worden, K.; Allen, D. W.; Sohn, H.; Farrar, C. R.
2002-01-01
The first and most important objective of any damage identification algorithms is to ascertain with confidence if damage is present or not. Many methods have been proposed for damage detection based on ideas of novelty detection founded in pattern recognition and multivariate statistics. The philosophy of novelty detection is simple. Features are first extracted from a baseline system to be monitored, and subsequent data are then compared to see if the new features are outliers, which significantly depart from the rest of population. In damage diagnosis problems, the assumption is that outliers are generated from a damaged condition of the monitored system. This damage classification necessitates the establishment of a decision boundary. Choosing this threshold value is often based on the assumption that the parent distribution of data is Gaussian in nature. While the problem of novelty detection focuses attention on the outlier or extreme values of the data i.e. those points in the tails of the distribution, the threshold selection using the normality assumption weighs the central population of data. Therefore, this normality assumption might impose potentially misleading behavior on damage classification, and is likely to lead the damage diagnosis astray. In this paper, extreme value statistics is integrated with the novelty detection to specifically model the tails of the distribution of interest. Finally, the proposed technique is demonstrated on simulated numerical data and time series data measured from an eight degree-of-freedom spring-mass system.
Fault Diagnostics Using Statistical Change Detection in the Bispectral Domain
NASA Astrophysics Data System (ADS)
Eugene Parker, B.; Ware, H. A.; Wipf, D. P.; Tompkins, W. R.; Clark, B. R.; Larson, E. C.; Vincent Poor, H.
2000-07-01
It is widely accepted that structural defects in rotating machinery components (e.g. bearings and gears) can be detected through monitoring of vibration and/or sound emissions. Traditional diagnostic vibration analysis attempts to match spectral lines with a priori -known defect frequencies that are characteristic of the affected machinery components. Emphasis herein is on use of bispectral-based statistical change detection algorithms for machinery health monitoring. The bispectrum, a third-order statistic, helps identify pairs of phase-related spectral components, which is useful for fault detection and isolation. In particular, the bispectrum helps sort through the clutter of usual (second-order) vibration spectra to extract useful information associated with the health of particular components. Seeded and non-seeded helicopter gearbox fault results (CH-46E and CH-47D, respectively) show that bispectral algorithms can detect faults at the level of an individual component (i.e. bearings or gears). Fault isolation is implicit with detection based on characteristic a priori -known defect frequencies. Important attributes of the bispectral SCD approach include: (1) it does not require a priori training data as is needed for traditional pattern-classifier-based approaches (and thereby avoids the significant time and cost investments necessary to obtain such data); (2) being based on higher-order moment-based energy detection, it makes no assumptions about the statistical model of the bispectral sequences that are generated; (3) it is operating-regime independent (i.e. works across different operating conditions, flight regimes, torque levels, etc., without knowledge of same); (4) it can be used to isolate faults to the level of specific machinery components (e.g. bearings and gears); and (5) it can be implemented using relatively inexpensive computer hardware, since only low-frequency vibrations need to be processed. The bispectral SCD algorithm thus represents a
Understanding the Sampling Distribution and Its Use in Testing Statistical Significance.
ERIC Educational Resources Information Center
Breunig, Nancy A.
Despite the increasing criticism of statistical significance testing by researchers, particularly in the publication of the 1994 American Psychological Association's style manual, statistical significance test results are still popular in journal articles. For this reason, it remains important to understand the logic of inferential statistics. A…
A decision surface-based taxonomy of detection statistics
NASA Astrophysics Data System (ADS)
Bouffard, François
2012-09-01
Current and past literature on the topic of detection statistics - in particular those used in hyperspectral target detection - can be intimidating for newcomers, especially given the huge number of detection tests described in the literature. Detection tests for hyperspectral measurements, such as those generated by dispersive or Fourier transform spectrometers used in remote sensing of atmospheric contaminants, are of paramount importance if any level of analysis automation is to be achieved. The detection statistics used in hyperspectral target detection are generally borrowed and adapted from other fields such as radar signal processing or acoustics. Consequently, although remarkable efforts have been made to clarify and categorize the vast number of available detection tests, understanding their differences, similarities, limits and other intricacies is still an exacting journey. Reasons for this state of affairs include heterogeneous nomenclature and mathematical notation, probably due to the multiple origins of hyperspectral target detection formalisms. Attempts at sorting out detection statistics using ambiguously defined properties may also cause more harm than good. Ultimately, a detection statistic is entirely characterized by its decision boundary. Thus, we propose to catalogue detection statistics according to the shape of their decision surfaces, which greatly simplifies this taxonomy exercise. We make a distinction between the topology resulting from the mathematical formulation of the statistic and mere parameters that adjust the boundary's precise shape, position and orientation. Using this simple approach, similarities between various common detection statistics are found, limit cases are reduced to simpler statistics, and a general understanding of the available detection tests and their properties becomes much easier to achieve.
Detecting Statistically Significant Communities of Triangle Motifs in Undirected Networks
2015-03-16
to LFR benchmark graphs , relative to the method proposed by Perry et al. [6]. 6 Distribution A: Approved for public release; distribution is...trials. Specifically, let (X,Y ) denote any two observed triangles, then for a Bernoulli(p) graph : E(X) = E(Y ) = p3 (1) 7 Distribution A: Approved...the observed adjacency matrix and consider the null hypothesis H0: number of triangles in A is consistent with Bernoulli graph with probability p
ERIC Educational Resources Information Center
Monterde-i-Bort, Hector; Frias-Navarro, Dolores; Pascual-Llobell, Juan
2010-01-01
The empirical study we present here deals with a pedagogical issue that has not been thoroughly explored up until now in our field. Previous empirical studies in other sectors have identified the opinions of researchers about this topic, showing that completely unacceptable interpretations have been made of significance tests and other statistical…
Statistical Detection of Atypical Aircraft Flights
NASA Technical Reports Server (NTRS)
Statler, Irving; Chidester, Thomas; Shafto, Michael; Ferryman, Thomas; Amidan, Brett; Whitney, Paul; White, Amanda; Willse, Alan; Cooley, Scott; Jay, Joseph; Rosenthal, Loren; Swickard, Andrea; Bates, Derrick; Scherrer, Chad; Webb, Bobbie-Jo; Lawrence, Robert; Mosbrucker, Chris; Prothero, Gary; Andrei, Adi; Romanowski, Tim; Robin, Daniel; Prothero, Jason; Lynch, Robert; Lowe, Michael
2006-01-01
A computational method and software to implement the method have been developed to sift through vast quantities of digital flight data to alert human analysts to aircraft flights that are statistically atypical in ways that signify that safety may be adversely affected. On a typical day, there are tens of thousands of flights in the United States and several times that number throughout the world. Depending on the specific aircraft design, the volume of data collected by sensors and flight recorders can range from a few dozen to several thousand parameters per second during a flight. Whereas these data have long been utilized in investigating crashes, the present method is oriented toward helping to prevent crashes by enabling routine monitoring of flight operations to identify portions of flights that may be of interest with respect to safety issues.
Detecting cell death with optical coherence tomography and envelope statistics
NASA Astrophysics Data System (ADS)
Farhat, Golnaz; Yang, Victor X. D.; Czarnota, Gregory J.; Kolios, Michael C.
2011-02-01
Currently no standard clinical or preclinical noninvasive method exists to monitor cell death based on morphological changes at the cellular level. In our past work we have demonstrated that quantitative high frequency ultrasound imaging can detect cell death in vitro and in vivo. In this study we apply quantitative methods previously used with high frequency ultrasound to optical coherence tomography (OCT) to detect cell death. The ultimate goal of this work is to use these methods for optically-based clinical and preclinical cancer treatment monitoring. Optical coherence tomography data were acquired from acute myeloid leukemia cells undergoing three modes of cell death. Significant increases in integrated backscatter were observed for cells undergoing apoptosis and mitotic arrest, while necrotic cells induced a decrease. These changes appear to be linked to structural changes observed in histology obtained from the cell samples. Signal envelope statistics were analyzed from fittings of the generalized gamma distribution to histograms of envelope intensities. The parameters from this distribution demonstrated sensitivities to morphological changes in the cell samples. These results indicate that OCT integrated backscatter and first order envelope statistics can be used to detect and potentially differentiate between modes of cell death in vitro.
Configurational Statistics of Magnetic Bead Detection with Magnetoresistive Sensors
Henriksen, Anders Dahl; Ley, Mikkel Wennemoes Hvitfeld; Flyvbjerg, Henrik; Hansen, Mikkel Fougt
2015-01-01
Magnetic biosensors detect magnetic beads that, mediated by a target, have bound to a functionalized area. This area is often larger than the area of the sensor. Both the sign and magnitude of the average magnetic field experienced by the sensor from a magnetic bead depends on the location of the bead relative to the sensor. Consequently, the signal from multiple beads also depends on their locations. Thus, a given coverage of the functionalized area with magnetic beads does not result in a given detector response, except on the average, over many realizations of the same coverage. We present a systematic theoretical analysis of how this location-dependence affects the sensor response. The analysis is done for beads magnetized by a homogeneous in-plane magnetic field. We determine the expected value and standard deviation of the sensor response for a given coverage, as well as the accuracy and precision with which the coverage can be determined from a single sensor measurement. We show that statistical fluctuations between samples may reduce the sensitivity and dynamic range of a sensor significantly when the functionalized area is larger than the sensor area. Hence, the statistics of sampling is essential to sensor design. For illustration, we analyze three important published cases for which statistical fluctuations are dominant, significant, and insignificant, respectively. PMID:26496495
Configurational Statistics of Magnetic Bead Detection with Magnetoresistive Sensors.
Henriksen, Anders Dahl; Ley, Mikkel Wennemoes Hvitfeld; Flyvbjerg, Henrik; Hansen, Mikkel Fougt
2015-01-01
Magnetic biosensors detect magnetic beads that, mediated by a target, have bound to a functionalized area. This area is often larger than the area of the sensor. Both the sign and magnitude of the average magnetic field experienced by the sensor from a magnetic bead depends on the location of the bead relative to the sensor. Consequently, the signal from multiple beads also depends on their locations. Thus, a given coverage of the functionalized area with magnetic beads does not result in a given detector response, except on the average, over many realizations of the same coverage. We present a systematic theoretical analysis of how this location-dependence affects the sensor response. The analysis is done for beads magnetized by a homogeneous in-plane magnetic field. We determine the expected value and standard deviation of the sensor response for a given coverage, as well as the accuracy and precision with which the coverage can be determined from a single sensor measurement. We show that statistical fluctuations between samples may reduce the sensitivity and dynamic range of a sensor significantly when the functionalized area is larger than the sensor area. Hence, the statistics of sampling is essential to sensor design. For illustration, we analyze three important published cases for which statistical fluctuations are dominant, significant, and insignificant, respectively.
Steganography forensics method for detecting least significant bit replacement attack
NASA Astrophysics Data System (ADS)
Wang, Xiaofeng; Wei, Chengcheng; Han, Xiao
2015-01-01
We present an image forensics method to detect least significant bit replacement steganography attack. The proposed method provides fine-grained forensics features by using the hierarchical structure that combines pixels correlation and bit-planes correlation. This is achieved via bit-plane decomposition and difference matrices between the least significant bit-plane and each one of the others. Generated forensics features provide the susceptibility (changeability) that will be drastically altered when the cover image is embedded with data to form a stego image. We developed a statistical model based on the forensics features and used least square support vector machine as a classifier to distinguish stego images from cover images. Experimental results show that the proposed method provides the following advantages. (1) The detection rate is noticeably higher than that of some existing methods. (2) It has the expected stability. (3) It is robust for content-preserving manipulations, such as JPEG compression, adding noise, filtering, etc. (4) The proposed method provides satisfactory generalization capability.
Detection of bearing damage by statistic vibration analysis
NASA Astrophysics Data System (ADS)
Sikora, E. A.
2016-04-01
The condition of bearings, which are essential components in mechanisms, is crucial to safety. The analysis of the bearing vibration signal, which is always contaminated by certain types of noise, is a very important standard for mechanical condition diagnosis of the bearing and mechanical failure phenomenon. In this paper the method of rolling bearing fault detection by statistical analysis of vibration is proposed to filter out Gaussian noise contained in a raw vibration signal. The results of experiments show that the vibration signal can be significantly enhanced by application of the proposed method. Besides, the proposed method is used to analyse real acoustic signals of a bearing with inner race and outer race faults, respectively. The values of attributes are determined according to the degree of the fault. The results confirm that the periods between the transients, which represent bearing fault characteristics, can be successfully detected.
Why Are People Bad at Detecting Randomness? A Statistical Argument
ERIC Educational Resources Information Center
Williams, Joseph J.; Griffiths, Thomas L.
2013-01-01
Errors in detecting randomness are often explained in terms of biases and misconceptions. We propose and provide evidence for an account that characterizes the contribution of the inherent statistical difficulty of the task. Our account is based on a Bayesian statistical analysis, focusing on the fact that a random process is a special case of…
ERIC Educational Resources Information Center
Norris, John M.
2015-01-01
Traditions of statistical significance testing in second language (L2) quantitative research are strongly entrenched in how researchers design studies, select analyses, and interpret results. However, statistical significance tests using "p" values are commonly misinterpreted by researchers, reviewers, readers, and others, leading to…
"What If" Analyses: Ways to Interpret Statistical Significance Test Results Using EXCEL or "R"
ERIC Educational Resources Information Center
Ozturk, Elif
2012-01-01
The present paper aims to review two motivations to conduct "what if" analyses using Excel and "R" to understand the statistical significance tests through the sample size context. "What if" analyses can be used to teach students what statistical significance tests really do and in applied research either prospectively to estimate what sample size…
A Review of Post-1994 Literature on Whether Statistical Significance Tests Should Be Banned.
ERIC Educational Resources Information Center
Sullivan, Jeremy R.
This paper summarizes the literature regarding statistical significance testing with an emphasis on: (1) the post-1994 literature in various disciplines; (2) alternatives to statistical significance testing; and (3) literature exploring why researchers have demonstrably failed to be influenced by the 1994 American Psychological Association…
The Historical Growth of Statistical Significance Testing in Psychology--and Its Future Prospects.
ERIC Educational Resources Information Center
Hubbard, Raymond; Ryan, Patricia A.
2000-01-01
Examined the historical growth in the popularity of statistical significance testing using a random sample of data from 12 American Psychological Association journals. Results replicate and extend findings from a study that used only one such journal. Discusses the role of statistical significance testing and the use of replication and…
Crow, C.J.
1985-01-01
Middle Ordovician age Chickamauga Group carbonates crop out along the Birmingham and Murphrees Valley anticlines in central Alabama. The macrofossil contents on exposed surfaces of seven bioherms have been counted to determine their various paleontologic characteristics. Twelve groups of organisms are present in these bioherms. Dominant organisms include bryozoans, algae, brachiopods, sponges, pelmatozoans, stromatoporoids and corals. Minor accessory fauna include predators, scavengers and grazers such as gastropods, ostracods, trilobites, cephalopods and pelecypods. Vertical and horizontal niche zonation has been detected for some of the bioherm dwelling fauna. No one bioherm of those studied exhibits all 12 groups of organisms; rather, individual bioherms display various subsets of the total diversity. Statistical treatment (G-test) of the diversity data indicates a lack of statistical homogeneity of the bioherms, both within and between localities. Between-locality population heterogeneity can be ascribed to differences in biologic responses to such gross environmental factors as water depth and clarity, and energy levels. At any one locality, gross aspects of the paleoenvironments are assumed to have been more uniform. Significant differences among bioherms at any one locality may have resulted from patchy distribution of species populations, differential preservation and other factors.
Detection of small target using recursive higher order statistics
NASA Astrophysics Data System (ADS)
Hou, Wang; Sun, Hongyuan; Lei, Zhihui
2014-02-01
In this paper, a recursive higher order statistics algorithm is proposed for small target detection in temporal domain. Firstly, the background of image sequence is normalized. Then, the higher order statistics are recursively solved in image sequence to obtain the feature image. Finally, the feature image is segmented with threshold to detect the small target. To validate the algorithm proposed in this paper, five simulated and one semi-simulation image sequences are created. The ROC curves are employed for evaluation of experimental results. Experiment results show that our method is very effective for small target detection.
Gehrmann, Thies; Reinders, Marcel J.T.
2015-01-01
Background: With more and more genomes being sequenced, detecting synteny between genomes becomes more and more important. However, for microorganisms the genomic divergence quickly becomes large, resulting in different codon usage and shuffling of gene order and gene elements such as exons. Results: We present Proteny, a methodology to detect synteny between diverged genomes. It operates on the amino acid sequence level to be insensitive to codon usage adaptations and clusters groups of exons disregarding order to handle diversity in genomic ordering between genomes. Furthermore, Proteny assigns significance levels to the syntenic clusters such that they can be selected on statistical grounds. Finally, Proteny provides novel ways to visualize results at different scales, facilitating the exploration and interpretation of syntenic regions. We test the performance of Proteny on a standard ground truth dataset, and we illustrate the use of Proteny on two closely related genomes (two different strains of Aspergillus niger) and on two distant genomes (two species of Basidiomycota). In comparison to other tools, we find that Proteny finds clusters with more true homologies in fewer clusters that contain more genes, i.e. Proteny is able to identify a more consistent synteny. Further, we show how genome rearrangements, assembly errors, gene duplications and the conservation of specific genes can be easily studied with Proteny. Availability and implementation: Proteny is freely available at the Delft Bioinformatics Lab website http://bioinformatics.tudelft.nl/dbl/software. Contact: t.gehrmann@tudelft.nl Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26116928
NASA Technical Reports Server (NTRS)
Xu, Kuan-Man
2006-01-01
A new method is proposed to compare statistical differences between summary histograms, which are the histograms summed over a large ensemble of individual histograms. It consists of choosing a distance statistic for measuring the difference between summary histograms and using a bootstrap procedure to calculate the statistical significance level. Bootstrapping is an approach to statistical inference that makes few assumptions about the underlying probability distribution that describes the data. Three distance statistics are compared in this study. They are the Euclidean distance, the Jeffries-Matusita distance and the Kuiper distance. The data used in testing the bootstrap method are satellite measurements of cloud systems called cloud objects. Each cloud object is defined as a contiguous region/patch composed of individual footprints or fields of view. A histogram of measured values over footprints is generated for each parameter of each cloud object and then summary histograms are accumulated over all individual histograms in a given cloud-object size category. The results of statistical hypothesis tests using all three distances as test statistics are generally similar, indicating the validity of the proposed method. The Euclidean distance is determined to be most suitable after comparing the statistical tests of several parameters with distinct probability distributions among three cloud-object size categories. Impacts on the statistical significance levels resulting from differences in the total lengths of satellite footprint data between two size categories are also discussed.
A network-based method to assess the statistical significance of mild co-regulation effects.
Horvát, Emőke-Ágnes; Zhang, Jitao David; Uhlmann, Stefan; Sahin, Özgür; Zweig, Katharina Anna
2013-01-01
Recent development of high-throughput, multiplexing technology has initiated projects that systematically investigate interactions between two types of components in biological networks, for instance transcription factors and promoter sequences, or microRNAs (miRNAs) and mRNAs. In terms of network biology, such screening approaches primarily attempt to elucidate relations between biological components of two distinct types, which can be represented as edges between nodes in a bipartite graph. However, it is often desirable not only to determine regulatory relationships between nodes of different types, but also to understand the connection patterns of nodes of the same type. Especially interesting is the co-occurrence of two nodes of the same type, i.e., the number of their common neighbours, which current high-throughput screening analysis fails to address. The co-occurrence gives the number of circumstances under which both of the biological components are influenced in the same way. Here we present SICORE, a novel network-based method to detect pairs of nodes with a statistically significant co-occurrence. We first show the stability of the proposed method on artificial data sets: when randomly adding and deleting observations we obtain reliable results even with noise exceeding the expected level in large-scale experiments. Subsequently, we illustrate the viability of the method based on the analysis of a proteomic screening data set to reveal regulatory patterns of human microRNAs targeting proteins in the EGFR-driven cell cycle signalling system. Since statistically significant co-occurrence may indicate functional synergy and the mechanisms underlying canalization, and thus hold promise in drug target identification and therapeutic development, we provide a platform-independent implementation of SICORE with a graphical user interface as a novel tool in the arsenal of high-throughput screening analysis.
NASA Astrophysics Data System (ADS)
Vermeesch, Pieter
2011-02-01
In my Eos Forum of 24 November 2009 (90(47), 443), I used the chi-square test to reject the null hypothesis that earthquakes occur independent of the weekday to make the point that statistical significance should not be confused with geological significance. Of the five comments on my article, only the one by Sornette and Pisarenko [2011] disputes this conclusion, while the remaining comments take issue with certain aspects of the geophysical case study. In this reply I will address all of these points, after providing some necessary further background about statistical tests. Two types of error can result from a hypothesis test. A Type I error occurs when a true null hypothesis is erroneously rejected by chance. A Type II error occurs when a false null hypothesis is erroneously accepted by chance. By definition, the p value is the probability, under the null hypothesis, of obtaining a test statistic at least as extreme as the one observed. In other words, the smaller the p value, the lower the probability that a Type I error has been made. In light of the exceedingly small p value of the earthquake data set, Tseng and Chen's [2011] assertion that a Type I error has been committed is clearly wrong. How about Type II errors?
Cheng, Chia-Ying; Huang, Chung-Yuan; Sun, Chuen-Tsai
2008-02-01
A major task for postgenomic systems biology researchers is to systematically catalogue molecules and their interactions within living cells. Advancements in complex-network theory are being made toward uncovering organizing principles that govern cell formation and evolution, but we lack understanding of how molecules and their interactions determine how complex systems function. Molecular bridge motifs include isolated motifs that neither interact nor overlap with others, whereas brick motifs act as network foundations that play a central role in defining global topological organization. To emphasize their structural organizing and evolutionary characteristics, we define bridge motifs as consisting of weak links only and brick motifs as consisting of strong links only, then propose a method for performing two tasks simultaneously, which are as follows: 1) detecting global statistical features and local connection structures in biological networks and 2) locating functionally and statistically significant network motifs. To further understand the role of biological networks in system contexts, we examine functional and topological differences between bridge and brick motifs for predicting biological network behaviors and functions. After observing brick motif similarities between E. coli and S. cerevisiae, we note that bridge motifs differentiate C. elegans from Drosophila and sea urchin in three types of networks. Similarities (differences) in bridge and brick motifs imply similar (different) key circuit elements in the three organisms. We suggest that motif-content analyses can provide researchers with global and local data for real biological networks and assist in the search for either isolated or functionally and topologically overlapping motifs when investigating and comparing biological system functions and behaviors.
A computationally efficient order statistics based outlier detection technique for EEG signals.
Giri, Bapun K; Sarkar, Soumajyoti; Mazumder, Satyaki; Das, Koel
2015-01-01
Detecting artifacts in EEG data produced by muscle activity, eye blinks and electrical noise is a common and important problem in EEG applications. We present a novel outlier detection method based on order statistics. We propose a 2 step procedure comprising of detecting noisy EEG channels followed by detection of noisy epochs in the outlier channels. The performance of our method is tested systematically using simulated and real EEG data. Our technique produces significant improvement in detecting EEG artifacts over state-of-the-art outlier detection technique used in EEG applications. The proposed method can serve as a general outlier detection tool for different types of noisy signals.
Statistical detection of EEG synchrony using empirical bayesian inference.
Singh, Archana K; Asoh, Hideki; Takeda, Yuji; Phillips, Steven
2015-01-01
There is growing interest in understanding how the brain utilizes synchronized oscillatory activity to integrate information across functionally connected regions. Computing phase-locking values (PLV) between EEG signals is a popular method for quantifying such synchronizations and elucidating their role in cognitive tasks. However, high-dimensionality in PLV data incurs a serious multiple testing problem. Standard multiple testing methods in neuroimaging research (e.g., false discovery rate, FDR) suffer severe loss of power, because they fail to exploit complex dependence structure between hypotheses that vary in spectral, temporal and spatial dimension. Previously, we showed that a hierarchical FDR and optimal discovery procedures could be effectively applied for PLV analysis to provide better power than FDR. In this article, we revisit the multiple comparison problem from a new Empirical Bayes perspective and propose the application of the local FDR method (locFDR; Efron, 2001) for PLV synchrony analysis to compute FDR as a posterior probability that an observed statistic belongs to a null hypothesis. We demonstrate the application of Efron's Empirical Bayes approach for PLV synchrony analysis for the first time. We use simulations to validate the specificity and sensitivity of locFDR and a real EEG dataset from a visual search study for experimental validation. We also compare locFDR with hierarchical FDR and optimal discovery procedures in both simulation and experimental analyses. Our simulation results showed that the locFDR can effectively control false positives without compromising on the power of PLV synchrony inference. Our results from the application locFDR on experiment data detected more significant discoveries than our previously proposed methods whereas the standard FDR method failed to detect any significant discoveries.
A Computer Program for Detection of Statistical Outliers
ERIC Educational Resources Information Center
Pascale, Pietro J.; Lovas, Charles M.
1976-01-01
Presents a Fortran program which computes the rejection criteria of ten procedures for detecting outlying observations. These criteria are defined on comment cards. Journal sources for the statistical equations are listed. After applying rejection rules, the program calculates the mean and standard deviation of the censored sample. (Author/RC)
Statistical Studies on Sequential Probability Ratio Test for Radiation Detection
Warnick Kernan, Ding Yuan, et al.
2007-07-01
A Sequential Probability Ratio Test (SPRT) algorithm helps to increase the reliability and speed of radiation detection. This algorithm is further improved to reduce spatial gap and false alarm. SPRT, using Last-in-First-Elected-Last-Out (LIFELO) technique, reduces the error between the radiation measured and resultant alarm. Statistical analysis determines the reduction of spatial error and false alarm.
A higher-order-statistics-based approach to face detection
NASA Astrophysics Data System (ADS)
Li, Chunming; Li, Yushan; Wu, Ruihong; Li, Qiuming; Zhuang, Qingde; Zhang, Zhan
2005-02-01
A face detection method based on higher order statistics is proposed in this paper. Firstly, the object model and noise model are established to extract moving object from the background according to the fact that higher order statistics is nonsense to Gaussian noise. Secondly, the improved Sobel operator is used to extract the edge image of moving object. And a projection function is used to detect the face in the edge image. Lastly, PCA(Principle Component Analysis) method is used to do face recognition. The performance of the system is evaluated on the real video sequences. It is shown that the proposed method is simple and robust to the detection of human faces in the video sequences.
Coulson, Melissa; Healey, Michelle; Fidler, Fiona; Cumming, Geoff
2010-01-01
A statistically significant result, and a non-significant result may differ little, although significance status may tempt an interpretation of difference. Two studies are reported that compared interpretation of such results presented using null hypothesis significance testing (NHST), or confidence intervals (CIs). Authors of articles published in psychology, behavioral neuroscience, and medical journals were asked, via email, to interpret two fictitious studies that found similar results, one statistically significant, and the other non-significant. Responses from 330 authors varied greatly, but interpretation was generally poor, whether results were presented as CIs or using NHST. However, when interpreting CIs respondents who mentioned NHST were 60% likely to conclude, unjustifiably, the two results conflicted, whereas those who interpreted CIs without reference to NHST were 95% likely to conclude, justifiably, the two results were consistent. Findings were generally similar for all three disciplines. An email survey of academic psychologists confirmed that CIs elicit better interpretations if NHST is not invoked. Improved statistical inference can result from encouragement of meta-analytic thinking and use of CIs but, for full benefit, such highly desirable statistical reform requires also that researchers interpret CIs without recourse to NHST.
Coulson, Melissa; Healey, Michelle; Fidler, Fiona; Cumming, Geoff
2010-01-01
A statistically significant result, and a non-significant result may differ little, although significance status may tempt an interpretation of difference. Two studies are reported that compared interpretation of such results presented using null hypothesis significance testing (NHST), or confidence intervals (CIs). Authors of articles published in psychology, behavioral neuroscience, and medical journals were asked, via email, to interpret two fictitious studies that found similar results, one statistically significant, and the other non-significant. Responses from 330 authors varied greatly, but interpretation was generally poor, whether results were presented as CIs or using NHST. However, when interpreting CIs respondents who mentioned NHST were 60% likely to conclude, unjustifiably, the two results conflicted, whereas those who interpreted CIs without reference to NHST were 95% likely to conclude, justifiably, the two results were consistent. Findings were generally similar for all three disciplines. An email survey of academic psychologists confirmed that CIs elicit better interpretations if NHST is not invoked. Improved statistical inference can result from encouragement of meta-analytic thinking and use of CIs but, for full benefit, such highly desirable statistical reform requires also that researchers interpret CIs without recourse to NHST. PMID:21607077
Wang, Bo; Shi, Zhanquan; Weber, Georg F; Kennedy, Michael A
2013-10-01
Nuclear magnetic resonance (NMR) spectroscopy-based metabonomics is of growing importance for discovery of human disease biomarkers. Identification and validation of disease biomarkers using statistical significance analysis (SSA) is critical for translation to clinical practice. SSA is performed by assessing a null hypothesis test using a derivative of the Student's t test, e.g., a Welch's t test. Choosing how to correct the significance level for rejecting null hypotheses in the case of multiple testing to maintain a constant family-wise type I error rate is a common problem in such tests. The multiple testing problem arises because the likelihood of falsely rejecting the null hypothesis, i.e., a false positive, grows as the number of tests applied to the same data set increases. Several methods have been introduced to address this problem. Bonferroni correction (BC) assumes all variables are independent and therefore sacrifices sensitivity for detecting true positives in partially dependent data sets. False discovery rate (FDR) methods are more sensitive than BC but uniformly ascribe highest stringency to lowest p value variables. Here, we introduce standard deviation step down (SDSD), which is more sensitive and appropriate than BC for partially dependent data sets. Sensitivity and type I error rate of SDSD can be adjusted based on the degree of variable dependency. SDSD generates fundamentally different profiles of critical p values compared with FDR methods potentially leading to reduced type II error rates. SDSD is increasingly sensitive for more concentrated metabolites. SDSD is demonstrated using NMR-based metabonomics data collected on three different breast cancer cell line extracts.
Detection of Doppler Microembolic Signals Using High Order Statistics
Geryes, Maroun; Hassan, Walid; Mcheick, Ali
2016-01-01
Robust detection of the smallest circulating cerebral microemboli is an efficient way of preventing strokes, which is second cause of mortality worldwide. Transcranial Doppler ultrasound is widely considered the most convenient system for the detection of microemboli. The most common standard detection is achieved through the Doppler energy signal and depends on an empirically set constant threshold. On the other hand, in the past few years, higher order statistics have been an extensive field of research as they represent descriptive statistics that can be used to detect signal outliers. In this study, we propose new types of microembolic detectors based on the windowed calculation of the third moment skewness and fourth moment kurtosis of the energy signal. During energy embolus-free periods the distribution of the energy is not altered and the skewness and kurtosis signals do not exhibit any peak values. In the presence of emboli, the energy distribution is distorted and the skewness and kurtosis signals exhibit peaks, corresponding to the latter emboli. Applied on real signals, the detection of microemboli through the skewness and kurtosis signals outperformed the detection through standard methods. The sensitivities and specificities reached 78% and 91% and 80% and 90% for the skewness and kurtosis detectors, respectively. PMID:28096889
Statistical feature selection for enhanced detection of brain tumor
NASA Astrophysics Data System (ADS)
Chaddad, Ahmad; Colen, Rivka R.
2014-09-01
Feature-based methods are widely used in the brain tumor recognition system. Robust of early cancer detection is one of the most powerful image processing tools. Specifically, statistical features, such as geometric mean, harmonic mean, mean excluding outliers, median, percentiles, skewness and kurtosis, have been extracted from brain tumor glioma to aid in discriminating two levels namely, Level I and Level II using fluid attenuated inversion recovery (FLAIR) sequence in the diagnosis of brain tumor. Statistical feature describes the major characteristics of each level from glioma which is an important step to evaluate heterogeneity of cancer area pixels. In this paper, we address the task of feature selection to identify the relevant subset of features in the statistical domain, while discarding those that are either redundant or confusing, thereby improving the performance of feature-based scheme to distinguish between Level I and Level II. We apply a Decision Structure algorithm to find the optimal combination of nonhomogeneity based statistical features for the problem at hand. We employ a Naïve Bayes classifier to evaluate the performance of the optimal statistical feature based scheme in terms of its glioma Level I and Level II discrimination capability and use real-data collected from 17 patients have a glioblastoma multiforme (GBM). Dataset provided from 3 Tesla MR imaging system by MD Anderson Cancer Center. For the specific data analyzed, it is shown that the identified dominant features yield higher classification accuracy, with lower number of false alarms and missed detections, compared to the full statistical based feature set. This work has been proposed and analyzed specific GBM types which Level I and Level II and the dominant features were considered as feature aid to prognostic indicators. These features were selected automatically to be better able to determine prognosis from classical imaging studies.
Statistically normalized coherent change detection for synthetic aperture sonar imagery
NASA Astrophysics Data System (ADS)
G-Michael, Tesfaye; Tucker, J. D.; Roberts, Rodney G.
2016-05-01
Coherent Change Detection (CCD) is a process of highlighting an area of activity in scenes (seafloor) under survey and generated from pairs of synthetic aperture sonar (SAS) images of approximately the same location observed at two different time instances. The problem of CCD and subsequent anomaly feature extraction/detection is complicated due to several factors such as the presence of random speckle pattern in the images, changing environmental conditions, and platform instabilities. These complications make the detection of weak target activities even more difficult. Typically, the degree of similarity between two images measured at each pixel locations is the coherence between the complex pixel values in the two images. Higher coherence indicates little change in the scene represented by the pixel and lower coherence indicates change activity in the scene. Such coherence estimation scheme based on the pixel intensity correlation is an ad-hoc procedure where the effectiveness of the change detection is determined by the choice of threshold which can lead to high false alarm rates. In this paper, we propose a novel approach for anomalous change pattern detection using the statistical normalized coherence and multi-pass coherent processing. This method may be used to mitigate shadows by reducing the false alarms resulting in the coherent map due to speckles and shadows. Test results of the proposed methods on a data set of SAS images will be presented, illustrating the effectiveness of the normalized coherence in terms statistics from multi-pass survey of the same scene.
ERIC Educational Resources Information Center
Thompson, Bruce
This paper evaluates the logic underlying various criticisms of statistical significance testing and makes specific recommendations for scientific and editorial practice that might better increase the knowledge base. Reliance on the traditional hypothesis testing model has led to a major bias against nonsignificant results and to misinterpretation…
ERIC Educational Resources Information Center
Snyder, Patricia; Lawson, Stephen
Magnitude of effect measures (MEMs), when adequately understood and correctly used, are important aids for researchers who do not want to rely solely on tests of statistical significance in substantive result interpretation. The MEM tells how much of the dependent variable can be controlled, predicted, or explained by the independent variables.…
Alphas and Asterisks: The Development of Statistical Significance Testing Standards in Sociology
ERIC Educational Resources Information Center
Leahey, Erin
2005-01-01
In this paper, I trace the development of statistical significance testing standards in sociology by analyzing data from articles published in two prestigious sociology journals between 1935 and 2000. I focus on the role of two key elements in the diffusion literature, contagion and rationality, as well as the role of institutional factors. I…
Statistical Significance of the Trends in Monthly Heavy Precipitation Over the US
Mahajan, Salil; North, Dr. Gerald R.; Saravanan, Dr. R.; Genton, Dr. Marc G.
2012-01-01
Trends in monthly heavy precipitation, defined by a return period of one year, are assessed for statistical significance in observations and Global Climate Model (GCM) simulations over the contiguous United States using Monte Carlo non-parametric and parametric bootstrapping techniques. The results from the two Monte Carlo approaches are found to be similar to each other, and also to the traditional non-parametric Kendall's {tau} test, implying the robustness of the approach. Two different observational data-sets are employed to test for trends in monthly heavy precipitation and are found to exhibit consistent results. Both data-sets demonstrate upward trends, one of which is found to be statistically significant at the 95% confidence level. Upward trends similar to observations are observed in some climate model simulations of the twentieth century, but their statistical significance is marginal. For projections of the twenty-first century, a statistically significant upwards trend is observed in most of the climate models analyzed. The change in the simulated precipitation variance appears to be more important in the twenty-first century projections than changes in the mean precipitation. Stochastic fluctuations of the climate-system are found to be dominate monthly heavy precipitation as some GCM simulations show a downwards trend even in the twenty-first century projections when the greenhouse gas forcings are strong.
Weighing the costs of different errors when determining statistical significant during monitoring
Technology Transfer Automated Retrieval System (TEKTRAN)
Selecting appropriate significance levels when constructing confidence intervals and performing statistical analyses with rangeland monitoring data is not a straightforward process. This process is burdened by the conventional selection of “95% confidence” (i.e., Type I error rate, a =0.05) as the d...
Interpreting Statistical Significance Test Results: A Proposed New "What If" Method.
ERIC Educational Resources Information Center
Kieffer, Kevin M.; Thompson, Bruce
As the 1994 publication manual of the American Psychological Association emphasized, "p" values are affected by sample size. As a result, it can be helpful to interpret the results of statistical significant tests in a sample size context by conducting so-called "what if" analyses. However, these methods can be inaccurate…
Recent Literature on Whether Statistical Significance Tests Should or Should Not Be Banned.
ERIC Educational Resources Information Center
Deegear, James
This paper summarizes the literature regarding statistical significant testing with an emphasis on recent literature in various discipline and literature exploring why researchers have demonstrably failed to be influenced by the American Psychological Association publication manual's encouragement to report effect sizes. Also considered are…
ERIC Educational Resources Information Center
Linting, Marielle; van Os, Bart Jan; Meulman, Jacqueline J.
2011-01-01
In this paper, the statistical significance of the contribution of variables to the principal components in principal components analysis (PCA) is assessed nonparametrically by the use of permutation tests. We compare a new strategy to a strategy used in previous research consisting of permuting the columns (variables) of a data matrix…
ERIC Educational Resources Information Center
Spinella, Sarah
2011-01-01
As result replicability is essential to science and difficult to achieve through external replicability, the present paper notes the insufficiency of null hypothesis statistical significance testing (NHSST) and explains the bootstrap as a plausible alternative, with a heuristic example to illustrate the bootstrap method. The bootstrap relies on…
Simulated performance of an order statistic threshold strategy for detection of narrowband signals
NASA Technical Reports Server (NTRS)
Satorius, E.; Brady, R.; Deich, W.; Gulkis, S.; Olsen, E.
1988-01-01
The application of order statistics to signal detection is becoming an increasingly active area of research. This is due to the inherent robustness of rank estimators in the presence of large outliers that would significantly degrade more conventional mean-level-based detection systems. A detection strategy is presented in which the threshold estimate is obtained using order statistics. The performance of this algorithm in the presence of simulated interference and broadband noise is evaluated. In this way, the robustness of the proposed strategy in the presence of the interference can be fully assessed as a function of the interference, noise, and detector parameters.
2010-01-01
Background The null hypothesis significance test (NHST) is the most frequently used statistical method, although its inferential validity has been widely criticized since its introduction. In 1988, the International Committee of Medical Journal Editors (ICMJE) warned against sole reliance on NHST to substantiate study conclusions and suggested supplementary use of confidence intervals (CI). Our objective was to evaluate the extent and quality in the use of NHST and CI, both in English and Spanish language biomedical publications between 1995 and 2006, taking into account the International Committee of Medical Journal Editors recommendations, with particular focus on the accuracy of the interpretation of statistical significance and the validity of conclusions. Methods Original articles published in three English and three Spanish biomedical journals in three fields (General Medicine, Clinical Specialties and Epidemiology - Public Health) were considered for this study. Papers published in 1995-1996, 2000-2001, and 2005-2006 were selected through a systematic sampling method. After excluding the purely descriptive and theoretical articles, analytic studies were evaluated for their use of NHST with P-values and/or CI for interpretation of statistical "significance" and "relevance" in study conclusions. Results Among 1,043 original papers, 874 were selected for detailed review. The exclusive use of P-values was less frequent in English language publications as well as in Public Health journals; overall such use decreased from 41% in 1995-1996 to 21% in 2005-2006. While the use of CI increased over time, the "significance fallacy" (to equate statistical and substantive significance) appeared very often, mainly in journals devoted to clinical specialties (81%). In papers originally written in English and Spanish, 15% and 10%, respectively, mentioned statistical significance in their conclusions. Conclusions Overall, results of our review show some improvements in
Discrete Fourier Transform: statistical effect size and significance of Fourier components.
NASA Astrophysics Data System (ADS)
Crockett, Robin
2016-04-01
A key analytical technique in the context of investigating cyclic/periodic features in time-series (and other sequential data) is the Discrete (Fast) Fourier Transform (DFT/FFT). However, assessment of the statistical effect-size and significance of the Fourier components in the DFT/FFT spectrum can be subjective and variable. This presentation will outline an approach and method for the statistical evaluation of the effect-size and significance of individual Fourier components from their DFT/FFT coefficients. The effect size is determined in terms of the proportions of the variance in the time-series that individual components account for. The statistical significance is determined using an hypothesis-test / p-value approach with respect to a null hypothesis that the time-series has no linear dependence on a given frequency (of a Fourier component). This approach also allows spectrograms to be presented in terms of these statistical parameters. The presentation will use sunspot cycles as an illustrative example.
Robust Statistical Detection of Power-Law Cross-Correlation
NASA Astrophysics Data System (ADS)
Blythe, Duncan A. J.; Nikulin, Vadim V.; Müller, Klaus-Robert
2016-06-01
We show that widely used approaches in statistical physics incorrectly indicate the existence of power-law cross-correlations between financial stock market fluctuations measured over several years and the neuronal activity of the human brain lasting for only a few minutes. While such cross-correlations are nonsensical, no current methodology allows them to be reliably discarded, leaving researchers at greater risk when the spurious nature of cross-correlations is not clear from the unrelated origin of the time series and rather requires careful statistical estimation. Here we propose a theory and method (PLCC-test) which allows us to rigorously and robustly test for power-law cross-correlations, correctly detecting genuine and discarding spurious cross-correlations, thus establishing meaningful relationships between processes in complex physical systems. Our method reveals for the first time the presence of power-law cross-correlations between amplitudes of the alpha and beta frequency ranges of the human electroencephalogram.
Evidence for t{bar t} production at the Tevatron: Statistical significance and cross section
Koningsberg, J.; CDF Collaboration
1994-09-01
We summarize here the results of the ``counting experiments`` by the CDF Collaboration in the search of t{bar t} production in p{bar p} collisions at {radical}s = 1800 TeV at the Tevatron. We analyze their statistical significance by calculating the probability that the observed excess is a fluctuation of the expected backgrounds, and assuming the excess is from top events, extract a measurement of the t{bar t} production cross-section.
A new statistical approach to climate change detection and attribution
NASA Astrophysics Data System (ADS)
Ribes, Aurélien; Zwiers, Francis W.; Azaïs, Jean-Marc; Naveau, Philippe
2017-01-01
We propose here a new statistical approach to climate change detection and attribution that is based on additive decomposition and simple hypothesis testing. Most current statistical methods for detection and attribution rely on linear regression models where the observations are regressed onto expected response patterns to different external forcings. These methods do not use physical information provided by climate models regarding the expected response magnitudes to constrain the estimated responses to the forcings. Climate modelling uncertainty is difficult to take into account with regression based methods and is almost never treated explicitly. As an alternative to this approach, our statistical model is only based on the additivity assumption; the proposed method does not regress observations onto expected response patterns. We introduce estimation and testing procedures based on likelihood maximization, and show that climate modelling uncertainty can easily be accounted for. Some discussion is provided on how to practically estimate the climate modelling uncertainty based on an ensemble of opportunity. Our approach is based on the " models are statistically indistinguishable from the truth" paradigm, where the difference between any given model and the truth has the same distribution as the difference between any pair of models, but other choices might also be considered. The properties of this approach are illustrated and discussed based on synthetic data. Lastly, the method is applied to the linear trend in global mean temperature over the period 1951-2010. Consistent with the last IPCC assessment report, we find that most of the observed warming over this period (+0.65 K) is attributable to anthropogenic forcings (+0.67 ± 0.12 K, 90 % confidence range), with a very limited contribution from natural forcings (-0.01± 0.02 K).
NASA Astrophysics Data System (ADS)
Alves, Gelio; Wang, Guanghui; Ogurtsov, Aleksey Y.; Drake, Steven K.; Gucek, Marjan; Suffredini, Anthony F.; Sacks, David B.; Yu, Yi-Kuo
2016-02-01
Correct and rapid identification of microorganisms is the key to the success of many important applications in health and safety, including, but not limited to, infection treatment, food safety, and biodefense. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is challenging correct microbial identification because of the large number of choices present. To properly disentangle candidate microbes, one needs to go beyond apparent morphology or simple `fingerprinting'; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptidome profiles of microbes to better separate them and by designing an analysis method that yields accurate statistical significance. Here, we present an analysis pipeline that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using MS/MS data of 81 samples, each composed of a single known microorganism, that the proposed pipeline can correctly identify microorganisms at least at the genus and species levels. We have also shown that the proposed pipeline computes accurate statistical significances, i.e., E-values for identified peptides and unified E-values for identified microorganisms. The proposed analysis pipeline has been implemented in MiCId, a freely available software for Microorganism Classification and Identification. MiCId is available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.
Krumbholz, Aniko; Anielski, Patricia; Gfrerer, Lena; Graw, Matthias; Geyer, Hans; Schänzer, Wilhelm; Dvorak, Jiri; Thieme, Detlef
2014-01-01
Clenbuterol is a well-established β2-agonist, which is prohibited in sports and strictly regulated for use in the livestock industry. During the last few years clenbuterol-positive results in doping controls and in samples from residents or travellers from a high-risk country were suspected to be related the illegal use of clenbuterol for fattening. A sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed to detect low clenbuterol residues in hair with a detection limit of 0.02 pg/mg. A sub-therapeutic application study and a field study with volunteers, who have a high risk of contamination, were performed. For the application study, a total dosage of 30 µg clenbuterol was applied to 20 healthy volunteers on 5 subsequent days. One month after the beginning of the application, clenbuterol was detected in the proximal hair segment (0-1 cm) in concentrations between 0.43 and 4.76 pg/mg. For the second part, samples of 66 Mexican soccer players were analyzed. In 89% of these volunteers, clenbuterol was detectable in their hair at concentrations between 0.02 and 1.90 pg/mg. A comparison of both parts showed no statistical difference between sub-therapeutic application and contamination. In contrast, discrimination to a typical abuse of clenbuterol is apparently possible. Due to these findings results of real doping control samples can be evaluated.
Statistical method for detecting structural change in the growth process.
Ninomiya, Yoshiyuki; Yoshimoto, Atsushi
2008-03-01
Due to competition among individual trees and other exogenous factors that change the growth environment, each tree grows following its own growth trend with some structural changes in growth over time. In the present article, a new method is proposed to detect a structural change in the growth process. We formulate the method as a simple statistical test for signal detection without constructing any specific model for the structural change. To evaluate the p-value of the test, the tube method is developed because the regular distribution theory is insufficient. Using two sets of tree diameter growth data sampled from planted forest stands of Cryptomeria japonica in Japan, we conduct an analysis of identifying the effect of thinning on the growth process as a structural change. Our results demonstrate that the proposed method is useful to identify the structural change caused by thinning. We also provide the properties of the method in terms of the size and power of the test.
NASA Astrophysics Data System (ADS)
Eggert, Silke; Walter, Thomas R.
2009-06-01
The study of volcanic triggering and interaction with the tectonic surroundings has received special attention in recent years, using both direct field observations and historical descriptions of eruptions and earthquake activity. Repeated reports of clustered eruptions and earthquakes may imply that interaction is important in some subregions. However, the subregions likely to suffer such clusters have not been systematically identified, and the processes responsible for the observed interaction remain unclear. We first review previous works about the clustered occurrence of eruptions and earthquakes, and describe selected events. We further elaborate available databases and confirm a statistically significant relationship between volcanic eruptions and earthquakes on the global scale. Moreover, our study implies that closed volcanic systems in particular tend to be activated in association with a tectonic earthquake trigger. We then perform a statistical study at the subregional level, showing that certain subregions are especially predisposed to concurrent eruption-earthquake sequences, whereas such clustering is statistically less significant in other subregions. Based on this study, we argue that individual and selected observations may bias the perceptible weight of coupling. The activity at volcanoes located in the predisposed subregions (e.g., Japan, Indonesia, Melanesia), however, often unexpectedly changes in association with either an imminent or a past earthquake.
Zou, Fei; Fine, Jason P.; Hu, Jianhua; Lin, D. Y.
2004-01-01
Assessing genome-wide statistical significance is an important and difficult problem in multipoint linkage analysis. Due to multiple tests on the same genome, the usual pointwise significance level based on the chi-square approximation is inappropriate. Permutation is widely used to determine genome-wide significance. Theoretical approximations are available for simple experimental crosses. In this article, we propose a resampling procedure to assess the significance of genome-wide QTL mapping for experimental crosses. The proposed method is computationally much less intensive than the permutation procedure (in the order of 102 or higher) and is applicable to complex breeding designs and sophisticated genetic models that cannot be handled by the permutation and theoretical methods. The usefulness of the proposed method is demonstrated through simulation studies and an application to a Drosophila backcross. PMID:15611194
On the statistical significance of surface air temperature trends in the Eurasian Arctic region
NASA Astrophysics Data System (ADS)
Franzke, C.
2012-12-01
This study investigates the statistical significance of the trends of station temperature time series from the European Climate Assessment & Data archive poleward of 60°N. The trends are identified by different methods and their significance is assessed by three different null models of climate noise. All stations show a warming trend but only 17 out of the 109 considered stations have trends which cannot be explained as arising from intrinsic climate fluctuations when tested against any of the three null models. Out of those 17, only one station exhibits a warming trend which is significant against all three null models. The stations with significant warming trends are located mainly in Scandinavia and Iceland.
Wang, Yuedong; Guo, Sun-Wei
2004-01-01
Array-based comparative genomic hybridization (ABCGH) is an emerging high-resolution and high-throughput molecular genetic technique that allows genome-wide screening for chromosome alterations associated with tumorigenesis. Like the cDNA microarrays, ABCGH uses two differentially labeled test and reference DNAs which are cohybridized to cloned genomic fragments immobilized on glass slides. The hybridized DNAs are then detected in two different fluorochromes, and the significant deviation from unity in the ratios of the digitized intensity values is indicative of copy-number differences between the test and reference genomes. Proper statistical analyses need to account for many sources of variation besides genuine differences between the two genomes. In particular, spatial correlations, the variable nature of the ratio variance and non-Normal distribution call for careful statistical modeling. We propose two new statistics, the standard t-statistic and its modification with variances smoothed along the genome, and two tests for each statistic, the standard t-test and a test based on the hybrid adaptive spline (HAS). Simulations indicate that the smoothed t-statistic always improves the performance over the standard t-statistic. The t-tests are more powerful in detecting isolated alterations while those based on HAS are more powerful in detecting a cluster of alterations. We apply the proposed methods to the identification of genomic alterations in endometrium in women with endometriosis.
Significance probability mapping: the final touch in t-statistic mapping.
Hassainia, F; Petit, D; Montplaisir, J
1994-01-01
Significance Probability Mapping (SPM), based on Student's t-statistic, is widely used for comparing mean brain topography maps of two groups. The map resulting from this process represents the distribution of t-values over the entire scalp. However, t-values by themselves cannot reveal whether or not group differences are significant. Significance levels associated with a few t-values are therefore commonly indicated on map legends to give the reader an idea of the significance levels of t-values. Nevertheless, a precise significance level topography cannot be achieved with these few significance values. We introduce a new kind of map which directly displays significance level topography in order to relieve the reader from converting multiple t-values to their corresponding significance probabilities, and to obtain a good quantification and a better localization of regions with significant differences between groups. As an illustration of this type of map, we present a comparison of EEG activity in Alzheimer's patients and age-matched control subjects for both wakefulness and REM sleep.
A statistical method (cross-validation) for bone loss region detection after spaceflight
Zhao, Qian; Li, Wenjun; Li, Caixia; Chu, Philip W.; Kornak, John; Lang, Thomas F.
2010-01-01
Astronauts experience bone loss after the long spaceflight missions. Identifying specific regions that undergo the greatest losses (e.g. the proximal femur) could reveal information about the processes of bone loss in disuse and disease. Methods for detecting such regions, however, remains an open problem. This paper focuses on statistical methods to detect such regions. We perform statistical parametric mapping to get t-maps of changes in images, and propose a new cross-validation method to select an optimum suprathreshold for forming clusters of pixels. Once these candidate clusters are formed, we use permutation testing of longitudinal labels to derive significant changes. PMID:20632144
How to get statistically significant effects in any ERP experiment (and why you shouldn't).
Luck, Steven J; Gaspelin, Nicholas
2017-01-01
ERP experiments generate massive datasets, often containing thousands of values for each participant, even after averaging. The richness of these datasets can be very useful in testing sophisticated hypotheses, but this richness also creates many opportunities to obtain effects that are statistically significant but do not reflect true differences among groups or conditions (bogus effects). The purpose of this paper is to demonstrate how common and seemingly innocuous methods for quantifying and analyzing ERP effects can lead to very high rates of significant but bogus effects, with the likelihood of obtaining at least one such bogus effect exceeding 50% in many experiments. We focus on two specific problems: using the grand-averaged data to select the time windows and electrode sites for quantifying component amplitudes and latencies, and using one or more multifactor statistical analyses. Reanalyses of prior data and simulations of typical experimental designs are used to show how these problems can greatly increase the likelihood of significant but bogus results. Several strategies are described for avoiding these problems and for increasing the likelihood that significant effects actually reflect true differences among groups or conditions.
Iacucci, Ernesto; Zingg, Hans H; Perkins, Theodore J
2012-01-01
High-throughput molecular biology studies, such as microarray assays of gene expression, two-hybrid experiments for detecting protein interactions, or ChIP-Seq experiments for transcription factor binding, often result in an "interesting" set of genes - say, genes that are co-expressed or bound by the same factor. One way of understanding the biological meaning of such a set is to consider what processes or functions, as defined in an ontology, are over-represented (enriched) or under-represented (depleted) among genes in the set. Usually, the significance of enrichment or depletion scores is based on simple statistical models and on the membership of genes in different classifications. We consider the more general problem of computing p-values for arbitrary integer additive statistics, or weighted membership functions. Such membership functions can be used to represent, for example, prior knowledge on the role of certain genes or classifications, differential importance of different classifications or genes to the experimenter, hierarchical relationships between classifications, or different degrees of interestingness or evidence for specific genes. We describe a generic dynamic programming algorithm that can compute exact p-values for arbitrary integer additive statistics. We also describe several optimizations for important special cases, which can provide orders-of-magnitude speed up in the computations. We apply our methods to datasets describing oxidative phosphorylation and parturition and compare p-values based on computations of several different statistics for measuring enrichment. We find major differences between p-values resulting from these statistics, and that some statistics recover "gold standard" annotations of the data better than others. Our work establishes a theoretical and algorithmic basis for far richer notions of enrichment or depletion of gene sets with respect to gene ontologies than has previously been available.
Statistical significance estimation of a signal within the GooFit framework on GPUs
NASA Astrophysics Data System (ADS)
Cristella, Leonardo; Di Florio, Adriano; Pompili, Alexis
2017-03-01
In order to test the computing capabilities of GPUs with respect to traditional CPU cores a high-statistics toy Monte Carlo technique has been implemented both in ROOT/RooFit and GooFit frameworks with the purpose to estimate the statistical significance of the structure observed by CMS close to the kinematical boundary of the J/ψϕ invariant mass in the three-body decay B+ → J/ψϕK+. GooFit is a data analysis open tool under development that interfaces ROOT/RooFit to CUDA platform on nVidia GPU. The optimized GooFit application running on GPUs hosted by servers in the Bari Tier2 provides striking speed-up performances with respect to the RooFit application parallelised on multiple CPUs by means of PROOF-Lite tool. The considerable resulting speed-up, evident when comparing concurrent GooFit processes allowed by CUDA Multi Process Service and a RooFit/PROOF-Lite process with multiple CPU workers, is presented and discussed in detail. By means of GooFit it has also been possible to explore the behaviour of a likelihood ratio test statistic in different situations in which the Wilks Theorem may or may not apply because its regularity conditions are not satisfied.
Tables of Significance Points for the Variance-Weighted Kolmogorov-Smirnov Statistics.
1981-02-19
NIEDERHAUSEN NO001-76- C - 075 UNCLASSIFIED TR-298 NL Lmmi TABLES OF SIGNIFICANCE POINTS FOR THE VARIANCE-WEIGHTED KOLMOGOROV-SMIRNOV STATISTICS BY Heinrich...Niederhausen TECHNICAL REPORT NO. 298 FEBRUARY 19, 1981 Prepared Under Contract N00014-76- C -0475 (NR-042-267) For the Office of Naval Research Herbert...satisfying 0 < V0 < 10 and v, < Vi-i ¥i IN,* The following functions define a 4-Sheffer sequence (see (A.12)) for the derivative operator D: d i if x ɘ f(, c
NASA Astrophysics Data System (ADS)
Eggert, S.; Walter, T. R.
2009-04-01
The study of volcanic triggering and coupling to the tectonic surroundings has received special attention in recent years, using both direct field observations and historical descriptions of eruptions and earthquake activity. Repeated reports of volcano-earthquake interactions in, e.g., Europe and Japan, may imply that clustered occurrence is important in some regions. However, the regions likely to suffer clustered eruption-earthquake activity have not been systematically identified, and the processes responsible for the observed interaction are debated. We first review previous works about the correlation of volcanic eruptions and earthquakes, and describe selected local clustered events. Following an overview of previous statistical studies, we further elaborate the databases of correlated eruptions and earthquakes from a global perspective. Since we can confirm a relationship between volcanic eruptions and earthquakes on the global scale, we then perform a statistical study on the regional level, showing that time and distance between events follow a linear relationship. In the time before an earthquake, a period of volcanic silence often occurs, whereas in the time after, an increase in volcanic activity is evident. Our statistical tests imply that certain regions are especially predisposed to concurrent eruption-earthquake pairs, e.g., Japan, whereas such pairing is statistically less significant in other regions, such as Europe. Based on this study, we argue that individual and selected observations may bias the perceptible weight of coupling. Volcanoes located in the predisposed regions (e.g., Japan, Indonesia, Melanesia), however, indeed often have unexpectedly changed in association with either an imminent or a past earthquake.
StegoWall: blind statistical detection of hidden data
NASA Astrophysics Data System (ADS)
Voloshynovskiy, Sviatoslav V.; Herrigel, Alexander; Rytsar, Yuri B.; Pun, Thierry
2002-04-01
Novel functional possibilities, provided by recent data hiding technologies, carry out the danger of uncontrolled (unauthorized) and unlimited information exchange that might be used by people with unfriendly interests. The multimedia industry as well as the research community recognize the urgent necessity for network security and copyright protection, or rather the lack of adequate law for digital multimedia protection. This paper advocates the need for detecting hidden data in digital and analog media as well as in electronic transmissions, and for attempting to identify the underlying hidden data. Solving this problem calls for the development of an architecture for blind stochastic hidden data detection in order to prevent unauthorized data exchange. The proposed architecture is called StegoWall; its key aspects are the solid investigation, the deep understanding, and the prediction of possible tendencies in the development of advanced data hiding technologies. The basic idea of our complex approach is to exploit all information about hidden data statistics to perform its detection based on a stochastic framework. The StegoWall system will be used for four main applications: robust watermarking, secret communications, integrity control and tamper proofing, and internet/network security.
Fisher, Aaron; Anderson, G Brooke; Peng, Roger; Leek, Jeff
2014-01-01
Scatterplots are the most common way for statisticians, scientists, and the public to visually detect relationships between measured variables. At the same time, and despite widely publicized controversy, P-values remain the most commonly used measure to statistically justify relationships identified between variables. Here we measure the ability to detect statistically significant relationships from scatterplots in a randomized trial of 2,039 students in a statistics massive open online course (MOOC). Each subject was shown a random set of scatterplots and asked to visually determine if the underlying relationships were statistically significant at the P < 0.05 level. Subjects correctly classified only 47.4% (95% CI [45.1%-49.7%]) of statistically significant relationships, and 74.6% (95% CI [72.5%-76.6%]) of non-significant relationships. Adding visual aids such as a best fit line or scatterplot smooth increased the probability a relationship was called significant, regardless of whether the relationship was actually significant. Classification of statistically significant relationships improved on repeat attempts of the survey, although classification of non-significant relationships did not. Our results suggest: (1) that evidence-based data analysis can be used to identify weaknesses in theoretical procedures in the hands of average users, (2) data analysts can be trained to improve detection of statistically significant results with practice, but (3) data analysts have incorrect intuition about what statistically significant relationships look like, particularly for small effects. We have built a web tool for people to compare scatterplots with their corresponding p-values which is available here: http://glimmer.rstudio.com/afisher/EDA/.
Fisher, Aaron; Anderson, G. Brooke; Peng, Roger
2014-01-01
Scatterplots are the most common way for statisticians, scientists, and the public to visually detect relationships between measured variables. At the same time, and despite widely publicized controversy, P-values remain the most commonly used measure to statistically justify relationships identified between variables. Here we measure the ability to detect statistically significant relationships from scatterplots in a randomized trial of 2,039 students in a statistics massive open online course (MOOC). Each subject was shown a random set of scatterplots and asked to visually determine if the underlying relationships were statistically significant at the P < 0.05 level. Subjects correctly classified only 47.4% (95% CI [45.1%–49.7%]) of statistically significant relationships, and 74.6% (95% CI [72.5%–76.6%]) of non-significant relationships. Adding visual aids such as a best fit line or scatterplot smooth increased the probability a relationship was called significant, regardless of whether the relationship was actually significant. Classification of statistically significant relationships improved on repeat attempts of the survey, although classification of non-significant relationships did not. Our results suggest: (1) that evidence-based data analysis can be used to identify weaknesses in theoretical procedures in the hands of average users, (2) data analysts can be trained to improve detection of statistically significant results with practice, but (3) data analysts have incorrect intuition about what statistically significant relationships look like, particularly for small effects. We have built a web tool for people to compare scatterplots with their corresponding p-values which is available here: http://glimmer.rstudio.com/afisher/EDA/. PMID:25337457
RT-PSM, a real-time program for peptide-spectrum matching with statistical significance.
Wu, Fang-Xiang; Gagné, Pierre; Droit, Arnaud; Poirier, Guy G
2006-01-01
The analysis of complex biological peptide mixtures by tandem mass spectrometry (MS/MS) produces a huge body of collision-induced dissociation (CID) MS/MS spectra. Several methods have been developed for identifying peptide-spectrum matches (PSMs) by assigning MS/MS spectra to peptides in a database. However, most of these methods either do not give the statistical significance of PSMs (e.g., SEQUEST) or employ time-consuming computational methods to estimate the statistical significance (e.g., PeptideProphet). In this paper, we describe a new algorithm, RT-PSM, which can be used to identify PSMs and estimate their accuracy statistically in real time. RT-PSM first computes PSM scores between an MS/MS spectrum and a set of candidate peptides whose masses are within a preset tolerance of the MS/MS precursor ion mass. Then the computed PSM scores of all candidate peptides are employed to fit the expectation value distribution of the scores into a second-degree polynomial function in PSM score. The statistical significance of the best PSM is estimated by extrapolating the fitting polynomial function to the best PSM score. RT-PSM was tested on two pairs of MS/MS spectrum datasets and protein databases to investigate its performance. The MS/MS spectra were acquired using an ion trap mass spectrometer equipped with a nano-electrospray ionization source. The results show that RT-PSM has good sensitivity and specificity. Using a 55,577-entry protein database and running on a standard Pentium-4, 2.8-GHz CPU personal computer, RT-PSM can process peptide spectra on a sequential, one-by-one basis in 0.047 s on average, compared to more than 7 s per spectrum on average for Sequest and X!Tandem, in their current batch-mode processing implementations. RT-PSM is clearly shown to be fast enough for real-time PSM assignment of MS/MS spectra generated every 3 s or so by a 3D ion trap or by a QqTOF instrument.
Jefferson, L; Cooper, E; Hewitt, C; Torgerson, T; Cook, L; Tharmanathan, P; Cockayne, S; Torgerson, D
2016-01-01
Objective Time-lag from study completion to publication is a potential source of publication bias in randomised controlled trials. This study sought to update the evidence base by identifying the effect of the statistical significance of research findings on time to publication of trial results. Design Literature searches were carried out in four general medical journals from June 2013 to June 2014 inclusive (BMJ, JAMA, the Lancet and the New England Journal of Medicine). Setting Methodological review of four general medical journals. Participants Original research articles presenting the primary analyses from phase 2, 3 and 4 parallel-group randomised controlled trials were included. Main outcome measures Time from trial completion to publication. Results The median time from trial completion to publication was 431 days (n = 208, interquartile range 278–618). A multivariable adjusted Cox model found no statistically significant difference in time to publication for trials reporting positive or negative results (hazard ratio: 0.86, 95% CI 0.64 to 1.16, p = 0.32). Conclusion In contrast to previous studies, this review did not demonstrate the presence of time-lag bias in time to publication. This may be a result of these articles being published in four high-impact general medical journals that may be more inclined to publish rapidly, whatever the findings. Further research is needed to explore the presence of time-lag bias in lower quality studies and lower impact journals. PMID:27757242
NASA Astrophysics Data System (ADS)
Hu, Rui; Wang, Bin
2001-02-01
Finding out statistically significant words in DNA and protein sequences forms the basis for many genetic studies. By applying the maximal entropy principle, we give one systematic way to study the nonrandom occurrence of words in DNA or protein sequences. Through comparison with experimental results, it was shown that patterns of regulatory binding sites in Saccharomyces cerevisiae ( yeast) genomes tend to occur significantly in the promoter regions. We studied two correlated gene families of yeast. The method successfully extracts the binding sites verified by experiments in each family. Many putative regulatory sites in the upstream regions are proposed. The study also suggested that some regulatory sites are active in both directions, while others show directional preference.
Consequences of statistical sense determination for WIMP directional detection
NASA Astrophysics Data System (ADS)
Green, Anne M.; Morgan, Ben
2008-01-01
We study the consequences of limited recoil sense reconstruction on the number of events required to reject isotropy and detect a WIMP signal using a directional detector. For a constant probability of determining the sense correctly, 3-d readout and zero background, we find that as the probability is decreased from 1.0 to 0.75 the number of events required increases by a factor of a few. As the probability is decreased further the number of events increases sharply, and isotropy can be rejected more easily by discarding the sense information and using axial statistics. This however requires an order of magnitude more events than vectorial data with perfect sense determination. We also consider energy dependent probabilities of correctly measuring the sense. Our main finding is that correctly determining the sense of the abundant, but less anisotropic, low energy recoils is most important.
Detection and integration of genotyping errors in statistical genetics.
Sobel, Eric; Papp, Jeanette C; Lange, Kenneth
2002-02-01
Detection of genotyping errors and integration of such errors in statistical analysis are relatively neglected topics, given their importance in gene mapping. A few inopportunely placed errors, if ignored, can tremendously affect evidence for linkage. The present study takes a fresh look at the calculation of pedigree likelihoods in the presence of genotyping error. To accommodate genotyping error, we present extensions to the Lander-Green-Kruglyak deterministic algorithm for small pedigrees and to the Markov-chain Monte Carlo stochastic algorithm for large pedigrees. These extensions can accommodate a variety of error models and refrain from simplifying assumptions, such as allowing, at most, one error per pedigree. In principle, almost any statistical genetic analysis can be performed taking errors into account, without actually correcting or deleting suspect genotypes. Three examples illustrate the possibilities. These examples make use of the full pedigree data, multiple linked markers, and a prior error model. The first example is the estimation of genotyping error rates from pedigree data. The second-and currently most useful-example is the computation of posterior mistyping probabilities. These probabilities cover both Mendelian-consistent and Mendelian-inconsistent errors. The third example is the selection of the true pedigree structure connecting a group of people from among several competing pedigree structures. Paternity testing and twin zygosity testing are typical applications.
Robust Statistical Detection of Power-Law Cross-Correlation
Blythe, Duncan A. J.; Nikulin, Vadim V.; Müller, Klaus-Robert
2016-01-01
We show that widely used approaches in statistical physics incorrectly indicate the existence of power-law cross-correlations between financial stock market fluctuations measured over several years and the neuronal activity of the human brain lasting for only a few minutes. While such cross-correlations are nonsensical, no current methodology allows them to be reliably discarded, leaving researchers at greater risk when the spurious nature of cross-correlations is not clear from the unrelated origin of the time series and rather requires careful statistical estimation. Here we propose a theory and method (PLCC-test) which allows us to rigorously and robustly test for power-law cross-correlations, correctly detecting genuine and discarding spurious cross-correlations, thus establishing meaningful relationships between processes in complex physical systems. Our method reveals for the first time the presence of power-law cross-correlations between amplitudes of the alpha and beta frequency ranges of the human electroencephalogram. PMID:27250630
Cosmology with phase statistics: parameter forecasts and detectability of BAO
NASA Astrophysics Data System (ADS)
Eggemeier, Alexander; Smith, Robert E.
2017-04-01
We consider an alternative to conventional three-point statistics such as the bispectrum, which is purely based on the Fourier phases of the density field: the line correlation function. This statistic directly probes the non-linear clustering regime and contains information highly complementary to that contained in the power spectrum. In this work, we determine, for the first time, its potential to constrain cosmological parameters and detect baryon acoustic oscillations (hereafter BAOs). We show how to compute the line correlation function for a discrete sampled set of tracers that follow a local Lagrangian biasing scheme and demonstrate how it breaks the degeneracy between the amplitude of density fluctuations and the bias parameters of the model. We then derive analytic expressions for its covariance and show that it can be written as a sum of a Gaussian piece plus non-Gaussian corrections. We compare our predictions with a large ensemble of N-body simulations and confirm that BAOs do indeed modulate the signal of the line correlation function for scales 50-100 h-1Mpc and that the characteristic S-shape feature would be detectable in upcoming Stage IV surveys at the level of ∼4σ. We then focus on the cosmological information content and compute Fisher forecasts for an idealized Stage III galaxy redshift survey of volume V ∼ 10 h-3 Gpc3 and out to z = 1. We show that combining the line correlation function with the galaxy power spectrum and a Planck-like microwave background survey yields improvements up to a factor of 2 for parameters such as σ8, b1 and b2, compared with using only the two-point information alone.
Detecting the Significant Flux Backbone of Escherichia coli metabolism.
Güell, Oriol; Sagués, Francesc; Serrano, M Ángeles
2017-04-09
The heterogeneity of computationally predicted reaction fluxes in metabolic networks within a single flux state can be exploited to detect their significant flux backbone. Here, we disclose the backbone of Escherichia coli, and compare it with the backbones of other bacteria. We find that, in general, the core of the backbones is mainly composed of reactions in energy metabolism corresponding to ancient pathways. In E. coli, the synthesis of nucleotides and the metabolism of lipids form smaller cores which rely critically on energy metabolism. Moreover, the consideration of different media leads to the identification of pathways sensitive to environmental changes. The metabolic backbone of an organism is thus useful for tracing, simultaneously, both its evolution and adaptation fingerprints. This article is protected by copyright. All rights reserved.
Performance optimization for pedestrian detection on degraded video using natural scene statistics
NASA Astrophysics Data System (ADS)
Winterlich, Anthony; Denny, Patrick; Kilmartin, Liam; Glavin, Martin; Jones, Edward
2014-11-01
We evaluate the effects of transmission artifacts such as JPEG compression and additive white Gaussian noise on the performance of a state-of-the-art pedestrian detection algorithm, which is based on integral channel features. Integral channel features combine the diversity of information obtained from multiple image channels with the computational efficiency of the Viola and Jones detection framework. We utilize "quality aware" spatial image statistics to blindly categorize distorted video frames by distortion type and level without the use of an explicit reference. We combine quality statistics with a multiclassifier detection framework for optimal pedestrian detection performance across varying image quality. Our detection method provides statistically significant improvements over current approaches based on single classifiers, on two large pedestrian databases containing a wide variety of artificially added distortion. The improvement in detection performance is further demonstrated on real video data captured from multiple cameras containing varying levels of sensor noise and compression. The results of our research have the potential to be used in real-time in-vehicle networks to improve pedestrian detection performance across a wide range of image and video quality.
Algorithms for Detecting Significantly Mutated Pathways in Cancer
NASA Astrophysics Data System (ADS)
Vandin, Fabio; Upfal, Eli; Raphael, Benjamin J.
Recent genome sequencing studies have shown that the somatic mutations that drive cancer development are distributed across a large number of genes. This mutational heterogeneity complicates efforts to distinguish functional mutations from sporadic, passenger mutations. Since cancer mutations are hypothesized to target a relatively small number of cellular signaling and regulatory pathways, a common approach is to assess whether known pathways are enriched for mutated genes. However, restricting attention to known pathways will not reveal novel cancer genes or pathways. An alterative strategy is to examine mutated genes in the context of genome-scale interaction networks that include both well characterized pathways and additional gene interactions measured through various approaches. We introduce a computational framework for de novo identification of subnetworks in a large gene interaction network that are mutated in a significant number of patients. This framework includes two major features. First, we introduce a diffusion process on the interaction network to define a local neighborhood of "influence" for each mutated gene in the network. Second, we derive a two-stage multiple hypothesis test to bound the false discovery rate (FDR) associated with the identified subnetworks. We test these algorithms on a large human protein-protein interaction network using mutation data from two recent studies: glioblastoma samples from The Cancer Genome Atlas and lung adenocarcinoma samples from the Tumor Sequencing Project. We successfully recover pathways that are known to be important in these cancers, such as the p53 pathway. We also identify additional pathways, such as the Notch signaling pathway, that have been implicated in other cancers but not previously reported as mutated in these samples. Our approach is the first, to our knowledge, to demonstrate a computationally efficient strategy for de novo identification of statistically significant mutated subnetworks. We
2014-01-01
Background Most work on the topic of activity landscapes has focused on their quantitative description and visual representation, with the aim of aiding navigation of SAR. Recent developments have addressed applications such as quantifying the proportion of activity cliffs, investigating the predictive abilities of activity landscape methods and so on. However, all these publications have worked under the assumption that the activity landscape models are “real” (i.e., statistically significant). Results The current study addresses for the first time, in a quantitative manner, the significance of a landscape or individual cliffs in the landscape. In particular, we question whether the activity landscape derived from observed (experimental) activity data is different from a randomly generated landscape. To address this we used the SALI measure with six different data sets tested against one or more molecular targets. We also assessed the significance of the landscapes for single and multiple representations. Conclusions We find that non-random landscapes are data set and molecular representation dependent. For the data sets and representations used in this work, our results suggest that not all representations lead to non-random landscapes. This indicates that not all molecular representations should be used to a) interpret the SAR and b) combined to generate consensus models. Our results suggest that significance testing of activity landscape models and in particular, activity cliffs, is key, prior to the use of such models. PMID:24694189
Statistics, Probability, Significance, Likelihood: Words Mean What We Define Them to Mean
ERIC Educational Resources Information Center
Drummond, Gordon B.; Tom, Brian D. M.
2011-01-01
Statisticians use words deliberately and specifically, but not necessarily in the way they are used colloquially. For example, in general parlance "statistics" can mean numerical information, usually data. In contrast, one large statistics textbook defines the term "statistic" to denote "a characteristic of a…
NASA Astrophysics Data System (ADS)
Kellerer-Pirklbauer, Andreas
2016-04-01
Longer data series (e.g. >10 a) of ground temperatures in alpine regions are helpful to improve the understanding regarding the effects of present climate change on distribution and thermal characteristics of seasonal frost- and permafrost-affected areas. Beginning in 2004 - and more intensively since 2006 - a permafrost and seasonal frost monitoring network was established in Central and Eastern Austria by the University of Graz. This network consists of c.60 ground temperature (surface and near-surface) monitoring sites which are located at 1922-3002 m a.s.l., at latitude 46°55'-47°22'N and at longitude 12°44'-14°41'E. These data allow conclusions about general ground thermal conditions, potential permafrost occurrence, trend during the observation period, and regional pattern of changes. Calculations and analyses of several different temperature-related parameters were accomplished. At an annual scale a region-wide statistical significant warming during the observation period was revealed by e.g. an increase in mean annual temperature values (mean, maximum) or the significant lowering of the surface frost number (F+). At a seasonal scale no significant trend of any temperature-related parameter was in most cases revealed for spring (MAM) and autumn (SON). Winter (DJF) shows only a weak warming. In contrast, the summer (JJA) season reveals in general a significant warming as confirmed by several different temperature-related parameters such as e.g. mean seasonal temperature, number of thawing degree days, number of freezing degree days, or days without night frost. On a monthly basis August shows the statistically most robust and strongest warming of all months, although regional differences occur. Despite the fact that the general ground temperature warming during the last decade is confirmed by the field data in the study region, complications in trend analyses arise by temperature anomalies (e.g. warm winter 2006/07) or substantial variations in the winter
The Detection and Statistics of Giant Arcs behind CLASH Clusters
NASA Astrophysics Data System (ADS)
Xu, Bingxiao; Postman, Marc; Meneghetti, Massimo; Seitz, Stella; Zitrin, Adi; Merten, Julian; Maoz, Dani; Frye, Brenda; Umetsu, Keiichi; Zheng, Wei; Bradley, Larry; Vega, Jesus; Koekemoer, Anton
2016-02-01
We developed an algorithm to find and characterize gravitationally lensed galaxies (arcs) to perform a comparison of the observed and simulated arc abundance. Observations are from the Cluster Lensing And Supernova survey with Hubble (CLASH). Simulated CLASH images are created using the MOKA package and also clusters selected from the high-resolution, hydrodynamical simulations, MUSIC, over the same mass and redshift range as the CLASH sample. The algorithm's arc elongation accuracy, completeness, and false positive rate are determined and used to compute an estimate of the true arc abundance. We derive a lensing efficiency of 4 ± 1 arcs (with length ≥6″ and length-to-width ratio ≥7) per cluster for the X-ray-selected CLASH sample, 4 ± 1 arcs per cluster for the MOKA-simulated sample, and 3 ± 1 arcs per cluster for the MUSIC-simulated sample. The observed and simulated arc statistics are in full agreement. We measure the photometric redshifts of all detected arcs and find a median redshift zs = 1.9 with 33% of the detected arcs having zs > 3. We find that the arc abundance does not depend strongly on the source redshift distribution but is sensitive to the mass distribution of the dark matter halos (e.g., the c-M relation). Our results show that consistency between the observed and simulated distributions of lensed arc sizes and axial ratios can be achieved by using cluster-lensing simulations that are carefully matched to the selection criteria used in the observations.
THE DETECTION AND STATISTICS OF GIANT ARCS BEHIND CLASH CLUSTERS
Xu, Bingxiao; Zheng, Wei; Postman, Marc; Bradley, Larry; Meneghetti, Massimo; Koekemoer, Anton; Seitz, Stella; Zitrin, Adi; Merten, Julian; Maoz, Dani; Frye, Brenda; Vega, Jesus
2016-02-01
We developed an algorithm to find and characterize gravitationally lensed galaxies (arcs) to perform a comparison of the observed and simulated arc abundance. Observations are from the Cluster Lensing And Supernova survey with Hubble (CLASH). Simulated CLASH images are created using the MOKA package and also clusters selected from the high-resolution, hydrodynamical simulations, MUSIC, over the same mass and redshift range as the CLASH sample. The algorithm's arc elongation accuracy, completeness, and false positive rate are determined and used to compute an estimate of the true arc abundance. We derive a lensing efficiency of 4 ± 1 arcs (with length ≥6″ and length-to-width ratio ≥7) per cluster for the X-ray-selected CLASH sample, 4 ± 1 arcs per cluster for the MOKA-simulated sample, and 3 ± 1 arcs per cluster for the MUSIC-simulated sample. The observed and simulated arc statistics are in full agreement. We measure the photometric redshifts of all detected arcs and find a median redshift z{sub s} = 1.9 with 33% of the detected arcs having z{sub s} > 3. We find that the arc abundance does not depend strongly on the source redshift distribution but is sensitive to the mass distribution of the dark matter halos (e.g., the c–M relation). Our results show that consistency between the observed and simulated distributions of lensed arc sizes and axial ratios can be achieved by using cluster-lensing simulations that are carefully matched to the selection criteria used in the observations.
Sassenhagen, Jona; Alday, Phillip M
2016-11-01
Experimental research on behavior and cognition frequently rests on stimulus or subject selection where not all characteristics can be fully controlled, even when attempting strict matching. For example, when contrasting patients to controls, variables such as intelligence or socioeconomic status are often correlated with patient status. Similarly, when presenting word stimuli, variables such as word frequency are often correlated with primary variables of interest. One procedure very commonly employed to control for such nuisance effects is conducting inferential tests on confounding stimulus or subject characteristics. For example, if word length is not significantly different for two stimulus sets, they are considered as matched for word length. Such a test has high error rates and is conceptually misguided. It reflects a common misunderstanding of statistical tests: interpreting significance not to refer to inference about a particular population parameter, but about 1. the sample in question, 2. the practical relevance of a sample difference (so that a nonsignificant test is taken to indicate evidence for the absence of relevant differences). We show inferential testing for assessing nuisance effects to be inappropriate both pragmatically and philosophically, present a survey showing its high prevalence, and briefly discuss an alternative in the form of regression including nuisance variables.
Detecting Significant Change in Wavefront Error: How long does it take?
Koenig, Darren E.; Applegate, Raymond A.; Marsack, Jason D.; Sarver, Edwin J.; Nguyen, Lan Chi
2010-01-01
Purpose Measurement noise in ocular wavefront sensing limits detection of statistically significant change in high-order wavefront error (HO WFE). Consequently, measurement noise is problematic when trying to detect progressive change in HO WFE. Our aim is to 1) determine the necessary amount of time to detect age-related change in HO WFE given measurement variability and HO WFE composition and magnitude and 2) minimize the length of time necessary to detect change. Methods Five subjects with 0.26 to 1.57 micrometers root mean square HO WFE (HO RMS) over a 6 mm pupil were measured 12 times in 10–15 minutes using a custom Shack-Hartmann wavefront sensor. Each individual’s standard deviation of measures was used to calculate the 95% confidence interval around their mean HO RMS. Data previously reported on the rate of change in the HO RMS due to normal aging and pupil diameter was used to calculate time to detect change exceeding this interval given measurement variability. Results Single measurements limit statistical detection to a range of 8 to 30 years. Increasing the number of WFE measurements per visit decreases time to detection (e.g., 7 measurements reduce the range to 3 to 14 years). The number of years to detect a change requires consideration of the subject’s measurement variability, level and distribution of aberrations and age. Uncertainty in locating pupil centre accounts for 39 ± 8% of the total variability. Conclusions The ability to detect change in HO WFE over a short period of time due to normal aging is difficult but possible with current WFE measurement technology. Single measurements of HO WFE become less predictive of true HO WFE with increasing measurement variability. Multiple measurements reduce the variability. Even with proper fixation and instrument alignment, pupil centre location uncertainty in HO WFE measurements is a nontrivial contributor to measurement variability. PMID:19469015
Statistical language analysis for automatic exfiltration event detection.
Robinson, David Gerald
2010-04-01
This paper discusses the recent development a statistical approach for the automatic identification of anomalous network activity that is characteristic of exfiltration events. This approach is based on the language processing method eferred to as latent dirichlet allocation (LDA). Cyber security experts currently depend heavily on a rule-based framework for initial detection of suspect network events. The application of the rule set typically results in an extensive list of uspect network events that are then further explored manually for suspicious activity. The ability to identify anomalous network events is heavily dependent on the experience of the security personnel wading through the network log. Limitations f this approach are clear: rule-based systems only apply to exfiltration behavior that has previously been observed, and experienced cyber security personnel are rare commodities. Since the new methodology is not a discrete rule-based pproach, it is more difficult for an insider to disguise the exfiltration events. A further benefit is that the methodology provides a risk-based approach that can be implemented in a continuous, dynamic or evolutionary fashion. This permits uspect network activity to be identified early with a quantifiable risk associated with decision making when responding to suspicious activity.
Automatic brain tumor detection in MRI: methodology and statistical validation
NASA Astrophysics Data System (ADS)
Iftekharuddin, Khan M.; Islam, Mohammad A.; Shaik, Jahangheer; Parra, Carlos; Ogg, Robert
2005-04-01
Automated brain tumor segmentation and detection are immensely important in medical diagnostics because it provides information associated to anatomical structures as well as potential abnormal tissue necessary to delineate appropriate surgical planning. In this work, we propose a novel automated brain tumor segmentation technique based on multiresolution texture information that combines fractal Brownian motion (fBm) and wavelet multiresolution analysis. Our wavelet-fractal technique combines the excellent multiresolution localization property of wavelets to texture extraction of fractal. We prove the efficacy of our technique by successfully segmenting pediatric brain MR images (MRIs) from St. Jude Children"s Research Hospital. We use self-organizing map (SOM) as our clustering tool wherein we exploit both pixel intensity and multiresolution texture features to obtain segmented tumor. Our test results show that our technique successfully segments abnormal brain tissues in a set of T1 images. In the next step, we design a classifier using Feed-Forward (FF) neural network to statistically validate the presence of tumor in MRI using both the multiresolution texture and the pixel intensity features. We estimate the corresponding receiver operating curve (ROC) based on the findings of true positive fractions and false positive fractions estimated from our classifier at different threshold values. An ROC, which can be considered as a gold standard to prove the competence of a classifier, is obtained to ascertain the sensitivity and specificity of our classifier. We observe that at threshold 0.4 we achieve true positive value of 1.0 (100%) sacrificing only 0.16 (16%) false positive value for the set of 50 T1 MRI analyzed in this experiment.
Lee, L.; Helsel, D.
2005-01-01
Trace contaminants in water, including metals and organics, often are measured at sufficiently low concentrations to be reported only as values below the instrument detection limit. Interpretation of these "less thans" is complicated when multiple detection limits occur. Statistical methods for multiply censored, or multiple-detection limit, datasets have been developed for medical and industrial statistics, and can be employed to estimate summary statistics or model the distributions of trace-level environmental data. We describe S-language-based software tools that perform robust linear regression on order statistics (ROS). The ROS method has been evaluated as one of the most reliable procedures for developing summary statistics of multiply censored data. It is applicable to any dataset that has 0 to 80% of its values censored. These tools are a part of a software library, or add-on package, for the R environment for statistical computing. This library can be used to generate ROS models and associated summary statistics, plot modeled distributions, and predict exceedance probabilities of water-quality standards. ?? 2005 Elsevier Ltd. All rights reserved.
Petykhov, A B; Maev, I V; Deriabin, V E
2012-01-01
Anthropometry--a technique, allowing to obtain the necessary features for the characteristic of human body's changes in norm and at pathology. Statistical analysis of anthropometric parameters, such as--body mass, length, waist line, hip, shoulder and wrist circumferences, skin rolls of fat thickness: on triceps, under a bladebone, on a breast, on a venter and on a biceps, with calculation of indexes and an assessment of possible age influence was carried out for the first time in domestic medicine. Complexes of showing interrelations anthropometric characteristics were detected. Correlation coefficients (r) were counted and the factorial (on a method main a component with the subsequent rotation--a varimax method), covariance and discriminative analyses (with application of the Kaiser and Wilks criterions and F-test) is applied. Study of intergroup variability of body composition was carried out on separate characteristics in healthy individuals groups (135 surveyed aged 45,6 +/- 1,2 years, 56,3% men and 43,7% women) and at internal pathology: patients after a gastrectomy--121 (57,7 +/- 1,2 years, 52% men and 48% women); after Billroth operation--214 (56,1 +/- 1,0 years, 53% men and 47% women); after enterectomy--103 (44,5 +/- 1,8 years, 53% men and 47% women); after mixed genesis protein-energy wasting--206 (29,04 +/- 1,6 years, 79% men and 21% women). The group of interlocking characteristics which includes anthropometric parameters of hypodermic lipopexia (rolls of fat thickness on triceps, a biceps, under a bladebone, on a venter) and fatty body mass was defined by results of the analysis. These characteristics are interconnected with age and growth and have more expressed dependence at women, that reflects development of a fatty component of a body, at assessment of body mass index at women (unlike men). The waist-hip circumference index differs irrespective of body composition indicators that doesn't allow to characterize it with the terms of truncal or
Significance of antibody detection in the diagnosis of cryptococcal meningitis.
Patil, Shripad A; Katyayani, S; Arvind, N
2012-01-01
Cryptococcus neoformans is the causative agent of Cryptococcosis, a chronic and life-threatening infection common in AIDS patients. Sonicated proteins of cryptococci were reported to contain antigenic properties. In the present study antigens are prepared from cryptococcal culture filtrate and by sonication. Secretory antigens are prepared by precipitation of culture filtrate using saturated ammonium sulfate followed by dialysis. Prepared antigens are tested for the presence of antibodies in the CSF samples of cryptococcal meningitis cases by ELISA. Comparison is made between India ink staining, latex antigen test, and the antibodies to the sonicated and secretory antigens. The results indicate that although antigen could be detected in the majority of samples, antibody could also be detected to the extent of 80-85%. It is interesting to note that some samples that were negative for India ink staining also showed high antibody responses. Hence, antibody detection could be a valuable marker in association with India ink staining for the early diagnosis of the cryptococcal infection. This test may also counter false positivity encountered in latex antigen test. Antibody detection assay would be a viable alternative, which has 83% sensitivity and 100% specificity. Thus the presently described test aids in immunodiagnosis of cryptococcal infection.
NASA Astrophysics Data System (ADS)
Casati, Michele
2014-05-01
The assertion that solar activity may play a significant role in the trigger of large volcanic eruptions is, and has been discussed by many geophysicists. Numerous scientific papers have established a possible correlation between these events and the electromagnetic coupling between the Earth and the Sun, but none of them has been able to highlight a possible statistically significant relationship between large volcanic eruptions and any of the series, such as geomagnetic activity, solar wind, sunspots number. In our research, we compare the 148 volcanic eruptions with index VEI4, the major 37 historical volcanic eruptions equal to or greater than index VEI5, recorded from 1610 to 2012 , with its sunspots number. Staring, as the threshold value, a monthly sunspot number of 46 (recorded during the great eruption of Krakatoa VEI6 historical index, August 1883), we note some possible relationships and conduct a statistical test. • Of the historical 31 large volcanic eruptions with index VEI5+, recorded between 1610 and 1955, 29 of these were recorded when the SSN<46. The remaining 2 eruptions were not recorded when the SSN<46, but rather during solar maxima of the solar cycle of the year 1739 and in the solar cycle No. 14 (Shikotsu eruption of 1739 and Ksudach 1907). • Of the historical 8 large volcanic eruptions with index VEI6+, recorded from 1610 to the present, 7 of these were recorded with SSN<46 and more specifically, within the three large solar minima known : Maunder (1645-1710), Dalton (1790-1830) and during the solar minimums occurred between 1880 and 1920. As the only exception, we note the eruption of Pinatubo of June 1991, recorded in the solar maximum of cycle 22. • Of the historical 6 major volcanic eruptions with index VEI5+, recorded after 1955, 5 of these were not recorded during periods of low solar activity, but rather during solar maxima, of the cycles 19,21 and 22. The significant tests, conducted with the chi-square χ ² = 7,782, detect a
Advanced Statistical Signal Processing Techniques for Landmine Detection Using GPR
2014-07-12
based ground penetrating radars for the detection of subsurface objects that are low in metal content and hard to detect. The derived techniques...penetrating radars for the detection of subsurface objects that are low in metal content and hard to detect. The derived techniques include the exploitation...5.00 4.00 3.00 9.00 T. Glenn, J. Wilson, D. Ho. A MULTIMODAL MATCHING PURSUITS DISSIMILARITY MEASURE APPLIED TO LANDMINE/CLUTTER DISCRIMINATION
Avalanche Photodiode Statistics in Triggered-avalanche Detection Mode
NASA Technical Reports Server (NTRS)
Tan, H. H.
1984-01-01
The output of a triggered avalanche mode avalanche photodiode is modeled as Poisson distributed primary avalanche events plus conditionally Poisson distributed trapped carrier induced secondary events. The moment generating function as well as the mean and variance of the diode output statistics are derived. The dispersion of the output statistics is shown to always exceed that of the Poisson distribution. Several examples are considered in detail.
Statistical Anomaly Detection for Monitoring of Human Dynamics
NASA Astrophysics Data System (ADS)
Kamiya, K.; Fuse, T.
2015-05-01
Understanding of human dynamics has drawn attention to various areas. Due to the wide spread of positioning technologies that use GPS or public Wi-Fi, location information can be obtained with high spatial-temporal resolution as well as at low cost. By collecting set of individual location information in real time, monitoring of human dynamics is recently considered possible and is expected to lead to dynamic traffic control in the future. Although this monitoring focuses on detecting anomalous states of human dynamics, anomaly detection methods are developed ad hoc and not fully systematized. This research aims to define an anomaly detection problem of the human dynamics monitoring with gridded population data and develop an anomaly detection method based on the definition. According to the result of a review we have comprehensively conducted, we discussed the characteristics of the anomaly detection of human dynamics monitoring and categorized our problem to a semi-supervised anomaly detection problem that detects contextual anomalies behind time-series data. We developed an anomaly detection method based on a sticky HDP-HMM, which is able to estimate the number of hidden states according to input data. Results of the experiment with synthetic data showed that our proposed method has good fundamental performance with respect to the detection rate. Through the experiment with real gridded population data, an anomaly was detected when and where an actual social event had occurred.
Le Goualher, G; Argenti, A M; Duyme, M; Baaré, W F; Hulshoff Pol, H E; Boomsma, D I; Zouaoui, A; Barillot, C; Evans, A C
2000-05-01
Principal Component Analysis allows a quantitative description of shape variability with a restricted number of parameters (or modes) which can be used to quantify the difference between two shapes through the computation of a modal distance. A statistical test can then be applied to this set of measurements in order to detect a statistically significant difference between two groups. We have applied this methodology to highlight evidence of genetic encoding of the shape of neuroanatomical structures. To investigate genetic constraint, we studied if shapes were more similar within 10 pairs of monozygotic twins than within interpairs and compared the results with those obtained from 10 pairs of dizygotic twins. The statistical analysis was performed using a Mantel permutation test. We show, using simulations, that this statistical test applied on modal distances can detect a possible genetic encoding. When applied to real data, this study highlighted genetic constraints on the shape of the central sulcus. We found from 10 pairs of monozygotic twins that the intrapair modal distance of the central sulcus was significantly smaller than the interpair modal distance, for both the left central sulcus (Z = -2.66; P < 0.005) and the right central sulcus (Z = -2.26; P < 0.05). Genetic constraints on the definition of the central sulcus shape were confirmed by applying the same experiment to 10 pairs of normal young individuals (Z = -1.39; Z = -0.63, i.e., values not significant at the P < 0.05 level) and 10 pairs of dizygotic twins (Z = 0.47; Z = 0.03, i.e., values not significant at the P < 0.05 level).
Surface Electromyographic Onset Detection Based On Statistics and Information Content
NASA Astrophysics Data System (ADS)
López, Natalia M.; Orosco, Eugenio; di Sciascio, Fernando
2011-12-01
The correct detection of the onset of muscular contraction is a diagnostic tool to neuromuscular diseases and an action trigger to control myoelectric devices. In this work, entropy and information content concepts were applied in algorithmic methods to automatic detection in surface electromyographic signals.
Outliers in Statistical Analysis: Basic Methods of Detection and Accommodation.
ERIC Educational Resources Information Center
Jacobs, Robert
Researchers are often faced with the prospect of dealing with observations within a given data set that are unexpected in terms of their great distance from the concentration of observations. For their potential to influence the mean disproportionately, thus affecting many statistical analyses, outlying observations require special care on the…
Statistical Methods for Detecting Anomalous Voting Patterns: A Case Study
2011-09-23
voting data. As a case study, we apply methods developed by Beber and Scacco to analyze polling station counts in Helmand province for the four...1 2) STATISTICAL MODELS FOR ANOMALY ANALYSIS .............................................. 2 a) The Beber -Scacco Model...carry out the necessary analysis. Beber and Scacco [4] have developed one such model for analyzing voting tallies. Their methods exploit the apparent
The statistics of single molecule detection: An overview
Enderlein, J.; Robbins, D.L.; Ambrose, W.P.
1995-12-31
An overview of our recent results in modeling single molecule detection in fluid flow is presented. Our mathematical approach is based on a path integral representation. The model accounts for all experimental details, such as light collection, laser excitation, hydrodynamics and diffusion, and molecular photophysics. Special attention is paid to multiple molecule crossings through the detection volume. Numerical realization of the theory is discussed. Measurements of burst size distributions in single B-phycoerythrin molecule detection experiments are presented and compared with theoretical predictions.
Extrasolar planets detections and statistics through gravitational microlensing
NASA Astrophysics Data System (ADS)
Cassan, A.
2014-10-01
Gravitational microlensing was proposed thirty years ago as a promising method to probe the existence and properties of compact objects in the Galaxy and its surroundings. The particularity and strength of the technique is based on the fact that the detection does not rely on the detection of the photon emission of the object itself, but on the way its mass affects the path of light of a background, almost aligned source. Detections thus include not only bright, but also dark objects. Today, the many successes of gravitational microlensing have largely exceeded the original promises. Microlensing contributed important results and breakthroughs in several astrophysical fields as it was used as a powerful tool to probe the Galactic structure (proper motions, extinction maps), to search for dark and compact massive objects in the halo and disk of the Milky Way, to probe the atmospheres of bulge red giant stars, to search for low-mass stars and brown dwarfs and to hunt for extrasolar planets. As an extrasolar planet detection method, microlensing nowadays stands in the top five of the successful observational techniques. Compared to other (complementary) detection methods, microlensing provides unique information on the population of exoplanets, because it allows the detection of very low-mass planets (down to the mass of the Earth) at large orbital distances from their star (0.5 to 10 AU). It is also the only technique that allows the discovery of planets at distances from Earth greater than a few kiloparsecs, up to the bulge of the Galaxy. Microlensing discoveries include the first ever detection of a cool super-Earth around an M-dwarf star, the detection of several cool Neptunes, Jupiters and super-Jupiters, as well as multi-planetary systems and brown dwarfs. So far, the least massive planet detected by microlensing has only three times the mass of the Earth and orbits a very low mass star at the edge of the brown dwarf regime. Several free-floating planetary
Elçi, Alper; Polat, Rahime
2011-01-01
The main objective of this study was to statistically evaluate the significance of seasonal groundwater quality change and to provide an assessment on the spatial distribution of specific groundwater quality parameters. The studied area was the Mount Nif karstic aquifer system located in the southeast of the city of Izmir. Groundwater samples were collected at 57 sampling points in the rainy winter and dry summer seasons. Groundwater quality indicators of interest were electrical conductivity (EC), nitrate, chloride, sulfate, sodium, some heavy metals, and arsenic. Maps showing the spatial distributions and temporal changes of these parameters were created to further interpret spatial patterns and seasonal changes in groundwater quality. Furthermore, statistical tests were conducted to confirm whether the seasonal changes for each quality parameter were statistically significant. It was evident from the statistical tests that the seasonal changes in most groundwater quality parameters were statistically not significant. However, the increase in EC values and aluminum concentrations from winter to summer was found to be significant. Furthermore, a negative correlation between sampling elevation and groundwater quality was found. It was shown that with simple statistical testing, important conclusions can be drawn from limited monitoring data. It was concluded that less groundwater recharge in the dry period of the year does not always imply higher concentrations for all groundwater quality parameters because water circulation times, lithology, quality and extent of recharge, and land use patterns also play an important role on the alteration of groundwater quality.
Optimizing automated gas turbine fault detection using statistical pattern recognition
NASA Astrophysics Data System (ADS)
Loukis, E.; Mathioudakis, K.; Papailiou, K.
1992-06-01
A method enabling the automated diagnosis of Gas Turbine Compressor blade faults, based on the principles of statistical pattern recognition is initially presented. The decision making is based on the derivation of spectral patterns from dynamic measurements data and then the calculation of discriminants with respect to reference spectral patterns of the faults while it takes into account their statistical properties. A method of optimizing the selection of discriminants using dynamic measurements data is also presented. A few scalar discriminants are derived, in such a way that the maximum available discrimination potential is exploited. In this way the success rate of automated decision making is further improved, while the need for intuitive discriminant selection is eliminated. The effectiveness of the proposed methods is demonstrated by application to data coming from an Industrial Gas Turbine while extension to other aspects of Fault Diagnosis is discussed.
Links to sources of cancer-related statistics, including the Surveillance, Epidemiology and End Results (SEER) Program, SEER-Medicare datasets, cancer survivor prevalence data, and the Cancer Trends Progress Report.
ERIC Educational Resources Information Center
Rogosa, David
1981-01-01
The form of the Johnson-Neyman region of significance is shown to be determined by the statistic for testing the null hypothesis that the population within-group regressions are parallel. Results are obtained for both simultaneous and nonsimultaneous regions of significance. (Author)
Statistical algorithms for target detection in coherent active polarimetric images.
Goudail, F; Réfrégier, P
2001-12-01
We address the problem of small-target detection with a polarimetric imager that provides orthogonal state contrast images. Such active systems allow one to measure the degree of polarization of the light backscattered by purely depolarizing isotropic materials. To be independent of the spatial nonuniformities of the illumination beam, small-target detection on the orthogonal state contrast image must be performed without using the image of backscattered intensity. We thus propose and develop a simple and efficient target detection algorithm based on a nonlinear pointwise transformation of the orthogonal state contrast image followed by a maximum-likelihood algorithm optimal for additive Gaussian perturbations. We demonstrate the efficiency of this suboptimal technique in comparison with the optimal one, which, however, assumes a priori knowledge about the scene that is not available in practice. We illustrate the performance of this approach on both simulated and real polarimetric images.
Detecting modules in biological networks by edge weight clustering and entropy significance
Lecca, Paola; Re, Angela
2015-01-01
Detection of the modular structure of biological networks is of interest to researchers adopting a systems perspective for the analysis of omics data. Computational systems biology has provided a rich array of methods for network clustering. To date, the majority of approaches address this task through a network node classification based on topological or external quantifiable properties of network nodes. Conversely, numerical properties of network edges are underused, even though the information content which can be associated with network edges has augmented due to steady advances in molecular biology technology over the last decade. Properly accounting for network edges in the development of clustering approaches can become crucial to improve quantitative interpretation of omics data, finally resulting in more biologically plausible models. In this study, we present a novel technique for network module detection, named WG-Cluster (Weighted Graph CLUSTERing). WG-Cluster's notable features, compared to current approaches, lie in: (1) the simultaneous exploitation of network node and edge weights to improve the biological interpretability of the connected components detected, (2) the assessment of their statistical significance, and (3) the identification of emerging topological properties in the detected connected components. WG-Cluster utilizes three major steps: (i) an unsupervised version of k-means edge-based algorithm detects sub-graphs with similar edge weights, (ii) a fast-greedy algorithm detects connected components which are then scored and selected according to the statistical significance of their scores, and (iii) an analysis of the convolution between sub-graph mean edge weight and connected component score provides a summarizing view of the connected components. WG-Cluster can be applied to directed and undirected networks of different types of interacting entities and scales up to large omics data sets. Here, we show that WG-Cluster can be
The Development of Statistical Indices for Detecting Cheaters.
ERIC Educational Resources Information Center
Angoff, William H.
Comparison data on SAT verbal and mathematical were collected on pairs of examinees in three samples for later use in detecting instances of willful copying. Two of the samples were constructed with the knowledge that no examinee could possibly have copied from the answer sheet of any other examinee in the sample. The third sample was taken…
Statistical Inference for Detecting Structures and Anomalies in Networks
2015-08-27
community structure in dynamic networks, along with the discovery of a detectability phase transition as a function of the rate of change and the...local in- formation, about the known nodes and their neighbors. But when this fraction crosses a critical threshold, our knowledge becomes global
Incipient Fault Detection Using Higher-Order Statistics
1991-08-01
109 5.2 Simulated Wear Experiment..................................109 5.2. 1 Experimental Design ... Design .................................... 128 5.3.2 Collected Data.........................................130 5.3.3 Results...detecting crankshaft drill wear (Liu and Wiu, 1990) using thrust force and axial acceleration amplitude signals. Acoustic emission spectrum features and
Ultrabroadband direct detection of nonclassical photon statistics at telecom wavelength
Wakui, Kentaro; Eto, Yujiro; Benichi, Hugo; Izumi, Shuro; Yanagida, Tetsufumi; Ema, Kazuhiro; Numata, Takayuki; Fukuda, Daiji; Takeoka, Masahiro; Sasaki, Masahide
2014-01-01
Broadband light sources play essential roles in diverse fields, such as high-capacity optical communications, optical coherence tomography, optical spectroscopy, and spectrograph calibration. Although a nonclassical state from spontaneous parametric down-conversion may serve as a quantum counterpart, its detection and characterization have been a challenging task. Here we demonstrate the direct detection of photon numbers of an ultrabroadband (110 nm FWHM) squeezed state in the telecom band centred at 1535 nm wavelength, using a superconducting transition-edge sensor. The observed photon-number distributions violate Klyshko's criterion for the nonclassicality. From the observed photon-number distribution, we evaluate the second- and third-order correlation functions, and characterize a multimode structure, which implies that several tens of orthonormal modes of squeezing exist in the single optical pulse. Our results and techniques open up a new possibility to generate and characterize frequency-multiplexed nonclassical light sources for quantum info-communications technology. PMID:24694515
Ultrabroadband direct detection of nonclassical photon statistics at telecom wavelength.
Wakui, Kentaro; Eto, Yujiro; Benichi, Hugo; Izumi, Shuro; Yanagida, Tetsufumi; Ema, Kazuhiro; Numata, Takayuki; Fukuda, Daiji; Takeoka, Masahiro; Sasaki, Masahide
2014-04-03
Broadband light sources play essential roles in diverse fields, such as high-capacity optical communications, optical coherence tomography, optical spectroscopy, and spectrograph calibration. Although a nonclassical state from spontaneous parametric down-conversion may serve as a quantum counterpart, its detection and characterization have been a challenging task. Here we demonstrate the direct detection of photon numbers of an ultrabroadband (110 nm FWHM) squeezed state in the telecom band centred at 1535 nm wavelength, using a superconducting transition-edge sensor. The observed photon-number distributions violate Klyshko's criterion for the nonclassicality. From the observed photon-number distribution, we evaluate the second- and third-order correlation functions, and characterize a multimode structure, which implies that several tens of orthonormal modes of squeezing exist in the single optical pulse. Our results and techniques open up a new possibility to generate and characterize frequency-multiplexed nonclassical light sources for quantum info-communications technology.
Two New Statistics To Detect Answer Copying. Research Report.
ERIC Educational Resources Information Center
Sotaridona, Leonardo S.; Meijer, Rob R.
Two new indices to detect answer copying on a multiple-choice test, S(1) and S(2) (subscripts), are proposed. The S(1) index is similar to the K-index (P. Holland, 1996) and the K-overscore(2), (K2) index (L. Sotaridona and R. Meijer, in press), but the distribution of the number of matching incorrect answers of the source (examinee s) and the…
How Do Statistical Detection Methods Compare to Entropy Measures
2012-08-28
project ( GNU /GPL) that makes possible the detection of hidden information in different digital media. StegSecret is a java-based multiplatform...variety of UNIX platforms, Windows and MacOS. VSL studio: VSL is free image steganography and steganalysis software in form of graphical block ...verification: it’s the average value of the LSBs on the current block of 128 bytes. So, if there is a random message embedded, this green curve will
Zou, X H; Zhu, Y P; Ren, G Q; Li, G C; Zhang, J; Zou, L J; Feng, Z B; Li, B H
2017-02-20
Objective: To evaluate the significance of bacteria detection with filter paper method on diagnosis of diabetic foot wound infection. Methods: Eighteen patients with diabetic foot ulcer conforming to the study criteria were hospitalized in Liyuan Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology from July 2014 to July 2015. Diabetic foot ulcer wounds were classified according to the University of Texas diabetic foot classification (hereinafter referred to as Texas grade) system, and general condition of patients with wounds in different Texas grade was compared. Exudate and tissue of wounds were obtained, and filter paper method and biopsy method were adopted to detect the bacteria of wounds of patients respectively. Filter paper method was regarded as the evaluation method, and biopsy method was regarded as the control method. The relevance, difference, and consistency of the detection results of two methods were tested. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of filter paper method in bacteria detection were calculated. Receiver operating characteristic (ROC) curve was drawn based on the specificity and sensitivity of filter paper method in bacteria detection of 18 patients to predict the detection effect of the method. Data were processed with one-way analysis of variance and Fisher's exact test. In patients tested positive for bacteria by biopsy method, the correlation between bacteria number detected by biopsy method and that by filter paper method was analyzed with Pearson correlation analysis. Results: (1) There were no statistically significant differences among patients with wounds in Texas grade 1, 2, and 3 in age, duration of diabetes, duration of wound, wound area, ankle brachial index, glycosylated hemoglobin, fasting blood sugar, blood platelet count, erythrocyte sedimentation rate, C-reactive protein, aspartate aminotransferase, serum creatinine, and
A powerful weighted statistic for detecting group differences of directed biological networks
Yuan, Zhongshang; Ji, Jiadong; Zhang, Xiaoshuai; Xu, Jing; Ma, Daoxin; Xue, Fuzhong
2016-01-01
Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. Different physiological conditions such as cases and controls may manifest as different networks. Statistical comparison between biological networks can provide not only new insight into the disease mechanism but statistical guidance for drug development. However, the methods developed in previous studies are inadequate to capture the changes in both the nodes and edges, and often ignore the network structure. In this study, we present a powerful weighted statistical test for group differences of directed biological networks, which is independent of the network attributes and can capture the changes in both the nodes and edges, as well as simultaneously accounting for the network structure through putting more weights on the difference of nodes locating on relatively more important position. Simulation studies illustrate that this method had better performance than previous ones under various sample sizes and network structures. One application to GWAS of leprosy successfully identifies the specific gene interaction network contributing to leprosy. Another real data analysis significantly identifies a new biological network, which is related to acute myeloid leukemia. One potential network responsible for lung cancer has also been significantly detected. The source R code is available on our website. PMID:27686331
A powerful weighted statistic for detecting group differences of directed biological networks.
Yuan, Zhongshang; Ji, Jiadong; Zhang, Xiaoshuai; Xu, Jing; Ma, Daoxin; Xue, Fuzhong
2016-09-30
Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. Different physiological conditions such as cases and controls may manifest as different networks. Statistical comparison between biological networks can provide not only new insight into the disease mechanism but statistical guidance for drug development. However, the methods developed in previous studies are inadequate to capture the changes in both the nodes and edges, and often ignore the network structure. In this study, we present a powerful weighted statistical test for group differences of directed biological networks, which is independent of the network attributes and can capture the changes in both the nodes and edges, as well as simultaneously accounting for the network structure through putting more weights on the difference of nodes locating on relatively more important position. Simulation studies illustrate that this method had better performance than previous ones under various sample sizes and network structures. One application to GWAS of leprosy successfully identifies the specific gene interaction network contributing to leprosy. Another real data analysis significantly identifies a new biological network, which is related to acute myeloid leukemia. One potential network responsible for lung cancer has also been significantly detected. The source R code is available on our website.
ERIC Educational Resources Information Center
Thompson, Bruce; Snyder, Patricia A.
1998-01-01
Investigates two aspects of research analyses in quantitative research studies reported in the 1996 issues of "Journal of Counseling & Development" (JCD). Acceptable methodological practice regarding significance testing and evaluation of score reliability has evolved considerably. Contemporary thinking on these issues is described; practice as…
Statistical Fault Detection for Parallel Applications with AutomaDeD
Bronevetsky, G; Laguna, I; Bagchi, S; de Supinski, B R; Ahn, D; Schulz, M
2010-03-23
Today's largest systems have over 100,000 cores, with million-core systems expected over the next few years. The large component count means that these systems fail frequently and often in very complex ways, making them difficult to use and maintain. While prior work on fault detection and diagnosis has focused on faults that significantly reduce system functionality, the wide variety of failure modes in modern systems makes them likely to fail in complex ways that impair system performance but are difficult to detect and diagnose. This paper presents AutomaDeD, a statistical tool that models the timing behavior of each application task and tracks its behavior to identify any abnormalities. If any are observed, AutomaDeD can immediately detect them and report to the system administrator the task where the problem began. This identification of the fault's initial manifestation can provide administrators with valuable insight into the fault's root causes, making it significantly easier and cheaper for them to understand and repair it. Our experimental evaluation shows that AutomaDeD detects a wide range of faults immediately after they occur 80% of the time, with a low false-positive rate. Further, it identifies weaknesses of the current approach that motivate future research.
Key statistics related to CO/sub 2/ emissions: Significant contributing countries
Kellogg, M.A.; Edmonds, J.A.; Scott, M.J.; Pomykala, J.S.
1987-07-01
This country selection task report describes and applies a methodology for identifying a set of countries responsible for significant present and anticipated future emissions of CO/sub 2/ and other radiatively important gases (RIGs). The identification of countries responsible for CO/sub 2/ and other RIGs emissions will help determine to what extent a select number of countries might be capable of influencing future emissions. Once identified, those countries could potentially exercise cooperative collective control of global emissions and thus mitigate the associated adverse affects of those emissions. The methodology developed consists of two approaches: the resource approach and the emissions approach. While conceptually very different, both approaches yield the same fundamental conclusion. The core of any international initiative to control global emissions must include three key countries: the US, USSR, and the People's Republic of China. It was also determined that broader control can be achieved through the inclusion of sixteen additional countries with significant contributions to worldwide emissions.
A High-Order Statistical Tensor Based Algorithm for Anomaly Detection in Hyperspectral Imagery
Geng, Xiurui; Sun, Kang; Ji, Luyan; Zhao, Yongchao
2014-01-01
Recently, high-order statistics have received more and more interest in the field of hyperspectral anomaly detection. However, most of the existing high-order statistics based anomaly detection methods require stepwise iterations since they are the direct applications of blind source separation. Moreover, these methods usually produce multiple detection maps rather than a single anomaly distribution image. In this study, we exploit the concept of coskewness tensor and propose a new anomaly detection method, which is called COSD (coskewness detector). COSD does not need iteration and can produce single detection map. The experiments based on both simulated and real hyperspectral data sets verify the effectiveness of our algorithm. PMID:25366706
Statistically qualified neuro-analytic failure detection method and system
Vilim, Richard B.; Garcia, Humberto E.; Chen, Frederick W.
2002-03-02
An apparatus and method for monitoring a process involve development and application of a statistically qualified neuro-analytic (SQNA) model to accurately and reliably identify process change. The development of the SQNA model is accomplished in two stages: deterministic model adaption and stochastic model modification of the deterministic model adaptation. Deterministic model adaption involves formulating an analytic model of the process representing known process characteristics, augmenting the analytic model with a neural network that captures unknown process characteristics, and training the resulting neuro-analytic model by adjusting the neural network weights according to a unique scaled equation error minimization technique. Stochastic model modification involves qualifying any remaining uncertainty in the trained neuro-analytic model by formulating a likelihood function, given an error propagation equation, for computing the probability that the neuro-analytic model generates measured process output. Preferably, the developed SQNA model is validated using known sequential probability ratio tests and applied to the process as an on-line monitoring system. Illustrative of the method and apparatus, the method is applied to a peristaltic pump system.
ERIC Educational Resources Information Center
Oshima, T. C.; Raju, Nambury S.; Nanda, Alice O.
2006-01-01
A new item parameter replication method is proposed for assessing the statistical significance of the noncompensatory differential item functioning (NCDIF) index associated with the differential functioning of items and tests framework. In this new method, a cutoff score for each item is determined by obtaining a (1-alpha ) percentile rank score…
Quantitative linkage: a statistical procedure for its detection and estimation.
Hill, A P
1975-05-01
A new approach for detecting and estimating quantitative linkage which uses sibship data is presented. Using a nested analysis of variance design (with marker genotype nested within sibship), it is shown that under the null hypothesis of no linkage, the expected between marker genotype within sibship mean square (EMSbeta) is equal to the expected within marker genotype within sibship mean square (EMSe), while under the alternative hypothesis of linkage, the first is greater than the second. Thus the regular F-ratio, MSbeta/MSe, can be used to test for quantitative linkage. This is true for both backcross and intercross matings and whether or not there is dominance at the marker locus. A second test involving the comparison of the within marker genotype within sibship variances is available for intercross matings. A maximum likelihood procedure for the estimation for the recombination frequency is also presented.
Tables of square-law signal detection statistics for Hann spectra with 50 percent overlap
NASA Technical Reports Server (NTRS)
Deans, Stanley R.; Cullers, D. Kent
1991-01-01
The Search for Extraterrestrial Intelligence, currently being planned by NASA, will require that an enormous amount of data be analyzed in real time by special purpose hardware. It is expected that overlapped Hann data windows will play an important role in this analysis. In order to understand the statistical implication of this approach, it has been necessary to compute detection statistics for overlapped Hann spectra. Tables of signal detection statistics are given for false alarm rates from 10(exp -14) to 10(exp -1) and signal detection probabilities from 0.50 to 0.99; the number of computed spectra ranges from 4 to 2000.
Adams, James; Kruger, Uwe; Geis, Elizabeth; Gehn, Eva; Fimbres, Valeria; Pollard, Elena; Mitchell, Jessica; Ingram, Julie; Hellmers, Robert; Quig, David; Hahn, Juergen
2017-01-01
Introduction A number of previous studies examined a possible association of toxic metals and autism, and over half of those studies suggest that toxic metal levels are different in individuals with Autism Spectrum Disorders (ASD). Additionally, several studies found that those levels correlate with the severity of ASD. Methods In order to further investigate these points, this paper performs the most detailed statistical analysis to date of a data set in this field. First morning urine samples were collected from 67 children and adults with ASD and 50 neurotypical controls of similar age and gender. The samples were analyzed to determine the levels of 10 urinary toxic metals (UTM). Autism-related symptoms were assessed with eleven behavioral measures. Statistical analysis was used to distinguish participants on the ASD spectrum and neurotypical participants based upon the UTM data alone. The analysis also included examining the association of autism severity with toxic metal excretion data using linear and nonlinear analysis. “Leave-one-out” cross-validation was used to ensure statistical independence of results. Results and Discussion Average excretion levels of several toxic metals (lead, tin, thallium, antimony) were significantly higher in the ASD group. However, ASD classification using univariate statistics proved difficult due to large variability, but nonlinear multivariate statistical analysis significantly improved ASD classification with Type I/II errors of 15% and 18%, respectively. These results clearly indicate that the urinary toxic metal excretion profiles of participants in the ASD group were significantly different from those of the neurotypical participants. Similarly, nonlinear methods determined a significantly stronger association between the behavioral measures and toxic metal excretion. The association was strongest for the Aberrant Behavior Checklist (including subscales on Irritability, Stereotypy, Hyperactivity, and Inappropriate
Potts, T.T.; Hylko, J.M.; Almond, D.
2007-07-01
A company's overall safety program becomes an important consideration to continue performing work and for procuring future contract awards. When injuries or accidents occur, the employer ultimately loses on two counts - increased medical costs and employee absences. This paper summarizes the human and organizational components that contributed to successful safety programs implemented by WESKEM, LLC's Environmental, Safety, and Health Departments located in Paducah, Kentucky, and Oak Ridge, Tennessee. The philosophy of 'safety, compliance, and then production' and programmatic components implemented at the start of the contracts were qualitatively identified as contributing factors resulting in a significant accumulation of safe work hours and an Experience Modification Rate (EMR) of <1.0. Furthermore, a study by the Associated General Contractors of America quantitatively validated components, already found in the WESKEM, LLC programs, as contributing factors to prevent employee accidents and injuries. Therefore, an investment in the human and organizational components now can pay dividends later by reducing the EMR, which is the key to reducing Workers' Compensation premiums. Also, knowing your employees' demographics and taking an active approach to evaluate and prevent fatigue may help employees balance work and non-work responsibilities. In turn, this approach can assist employers in maintaining a healthy and productive workforce. For these reasons, it is essential that safety needs be considered as the starting point when performing work. (authors)
Meta-analysis using effect size distributions of only statistically significant studies.
van Assen, Marcel A L M; van Aert, Robbie C M; Wicherts, Jelte M
2015-09-01
Publication bias threatens the validity of meta-analytic results and leads to overestimation of the effect size in traditional meta-analysis. This particularly applies to meta-analyses that feature small studies, which are ubiquitous in psychology. Here we develop a new method for meta-analysis that deals with publication bias. This method, p-uniform, enables (a) testing of publication bias, (b) effect size estimation, and (c) testing of the null-hypothesis of no effect. No current method for meta-analysis possesses all 3 qualities. Application of p-uniform is straightforward because no additional data on missing studies are needed and no sophisticated assumptions or choices need to be made before applying it. Simulations show that p-uniform generally outperforms the trim-and-fill method and the test of excess significance (TES; Ioannidis & Trikalinos, 2007b) if publication bias exists and population effect size is homogenous or heterogeneity is slight. For illustration, p-uniform and other publication bias analyses are applied to the meta-analysis of McCall and Carriger (1993) examining the association between infants' habituation to a stimulus and their later cognitive ability (IQ). We conclude that p-uniform is a valuable technique for examining publication bias and estimating population effects in fixed-effect meta-analyses, and as sensitivity analysis to draw inferences about publication bias.
Saha, Ranajit; Pan, Sudip; Chattaraj, Pratim K
2016-11-05
The validity of the maximum hardness principle (MHP) is tested in the cases of 50 chemical reactions, most of which are organic in nature and exhibit anomeric effect. To explore the effect of the level of theory on the validity of MHP in an exothermic reaction, B3LYP/6-311++G(2df,3pd) and LC-BLYP/6-311++G(2df,3pd) (def2-QZVP for iodine and mercury) levels are employed. Different approximations like the geometric mean of hardness and combined hardness are considered in case there are multiple reactants and/or products. It is observed that, based on the geometric mean of hardness, while 82% of the studied reactions obey the MHP at the B3LYP level, 84% of the reactions follow this rule at the LC-BLYP level. Most of the reactions possess the hardest species on the product side. A 50% null hypothesis is rejected at a 1% level of significance.
Wang, Q.; Denton, D.L.; Shukla, R.
2000-01-01
As a follow up to the recommendations of the September 1995 SETAC Pellston Workshop on Whole Effluent Toxicity (WET) on test methods and appropriate endpoints, this paper will discuss the applications and statistical properties of using a statistical criterion of minimum significant difference (MSD). The authors examined the upper limits of acceptable MSDs as acceptance criterion in the case of normally distributed data. The implications of this approach are examined in terms of false negative rate as well as false positive rate. Results indicated that the proposed approach has reasonable statistical properties. Reproductive data from short-term chronic WET test with Ceriodaphnia dubia tests were used to demonstrate the applications of the proposed approach. The data were collected by the North Carolina Department of Environment, Health, and Natural Resources (Raleigh, NC, USA) as part of their National Pollutant Discharge Elimination System program.
NASA Astrophysics Data System (ADS)
Wang, Ping; Dai, Xin-Gang
2016-09-01
The term "APEC Blue" has been created to describe the clear sky days since the Asia-Pacific Economic Cooperation (APEC) summit held in Beijing during November 5-11, 2014. The duration of the APEC Blue is detected from November 1 to November 14 (hereafter Blue Window) by moving t test in statistics. Observations show that APEC Blue corresponds to low air pollution with respect to PM2.5, PM10, SO2, and NO2 under strict emission-control measures (ECMs) implemented in Beijing and surrounding areas. Quantitative assessment shows that ECM is more effective on reducing aerosols than the chemical constituents. Statistical investigation has revealed that the window also resulted from intensified wind variability, as well as weakened static stability of atmosphere (SSA). The wind and ECMs played key roles in reducing air pollution during November 1-7 and 11-13, and strict ECMs and weak SSA become dominant during November 7-10 under weak wind environment. Moving correlation manifests that the emission reduction for aerosols can increase the apparent wind cleanup effect, leading to significant negative correlations of them, and the period-wise changes in emission rate can be well identified by multi-scale correlations basing on wavelet decomposition. In short, this case study manifests statistically how human interference modified air quality in the mega city through controlling local and surrounding emissions in association with meteorological condition.
Spatial-Temporal Change Detection in NDVI Data Through Statistical Parametric Mapping
NASA Astrophysics Data System (ADS)
McKenna, S. A.; Yadav, V.; Gutierrez, K.
2011-12-01
Detection of significant changes in vegetation patterns provides a quantitative means of defining phenological response to changing climate. These changes may be indicative of long-term trends or shorter-duration conditions. In either case, quantifying the significance of the change patterns is critical in order to better understand the underlying processes. Spatial and temporal correlation within imaged data sets complicates change detection and must be taken into account. We apply a novel approach, Statistical Parametric Mapping (SPM), to change detection in Normalized Difference Vegetation Index (NDVI) data. SPM has been developed for identification of regions of anomalous activation in human brain imaging given functional magnetic resonance imaging (fMRI) data. Here, we adapt SPM to work on identifying anomalous regions of vegetation density within 30 years of weekly NDVI imagery. Significant change in any given image pixel is defined as a deviation from the expected value. Expected values are calculated using sinusoidal regression models fit to previous data at that location. The amount of deviation of an observation from the expected value is calculated using a modified t-test that accounts for temporal correlation in the regression data. The t-tests are applied independently to each pixel to create a t-statistic map for every time step. For a given time step, the probability that the maximum t-value exceeds a given threshold can be calculated to determine times with significant deviations, but standard techniques are not applicable due to the large number of pixels searched to find the maximum. SPM takes into account the spatial correlation of the t-statistic map to determine the significance of the maximum observed t-value. Theory developed for truncated Gaussian fields as part of SPM provides the expected number and size of regions within the t-statistic map that exceed a given threshold. The significance of the excursion regions can be assessed and then
NASA Technical Reports Server (NTRS)
Gofford, Jason; Reeves, James N.; Tombesi, Francesco; Braito, Valentina; Turner, T. Jane; Miller, Lance; Cappi, Massimo
2013-01-01
We present the results of a new spectroscopic study of Fe K-band absorption in active galactic nuclei (AGN). Using data obtained from the Suzaku public archive we have performed a statistically driven blind search for Fe XXV Healpha and/or Fe XXVI Lyalpha absorption lines in a large sample of 51 Type 1.0-1.9 AGN. Through extensive Monte Carlo simulations we find that statistically significant absorption is detected at E greater than or approximately equal to 6.7 keV in 20/51 sources at the P(sub MC) greater than or equal tov 95 per cent level, which corresponds to approximately 40 per cent of the total sample. In all cases, individual absorption lines are detected independently and simultaneously amongst the two (or three) available X-ray imaging spectrometer detectors, which confirms the robustness of the line detections. The most frequently observed outflow phenomenology consists of two discrete absorption troughs corresponding to Fe XXV Healpha and Fe XXVI Lyalpha at a common velocity shift. From xstar fitting the mean column density and ionization parameter for the Fe K absorption components are log (N(sub H) per square centimeter)) is approximately equal to 23 and log (Xi/erg centimeter per second) is approximately equal to 4.5, respectively. Measured outflow velocities span a continuous range from less than1500 kilometers per second up to approximately100 000 kilometers per second, with mean and median values of approximately 0.1 c and approximately 0.056 c, respectively. The results of this work are consistent with those recently obtained using XMM-Newton and independently provides strong evidence for the existence of very highly ionized circumnuclear material in a significant fraction of both radio-quiet and radio-loud AGN in the local universe.
A flexible spatial scan statistic with a restricted likelihood ratio for detecting disease clusters.
Tango, Toshiro; Takahashi, Kunihiko
2012-12-30
Spatial scan statistics are widely used tools for detection of disease clusters. Especially, the circular spatial scan statistic proposed by Kulldorff (1997) has been utilized in a wide variety of epidemiological studies and disease surveillance. However, as it cannot detect noncircular, irregularly shaped clusters, many authors have proposed different spatial scan statistics, including the elliptic version of Kulldorff's scan statistic. The flexible spatial scan statistic proposed by Tango and Takahashi (2005) has also been used for detecting irregularly shaped clusters. However, this method sets a feasible limitation of a maximum of 30 nearest neighbors for searching candidate clusters because of heavy computational load. In this paper, we show a flexible spatial scan statistic implemented with a restricted likelihood ratio proposed by Tango (2008) to (1) eliminate the limitation of 30 nearest neighbors and (2) to have surprisingly much less computational time than the original flexible spatial scan statistic. As a side effect, it is shown to be able to detect clusters with any shape reasonably well as the relative risk of the cluster becomes large via Monte Carlo simulation. We illustrate the proposed spatial scan statistic with data on mortality from cerebrovascular disease in the Tokyo Metropolitan area, Japan.
NASA Astrophysics Data System (ADS)
Morgan, Ben; Green, Anne M.
2005-12-01
The direction dependence of the WIMP direct detection rate provides a powerful tool for distinguishing a WIMP signal from possible backgrounds. We study the number of events required to discriminate a WIMP signal from an isotropic background for a detector with 2-d readout using nonparametric circular statistics. We also examine the number of events needed to (i) detect a deviation from rotational symmetry, due to flattening of the Milky Way halo and (ii) detect a deviation in the mean direction due to a tidal stream. If the senses of the recoils are measured then of order 20--70 events (depending on the plane of the 2-d readout and the detector location) will be sufficient to reject isotropy of the raw recoil angles at 90% confidence. If the senses can not be measured these number increase by roughly 2 orders of magnitude (compared with an increase of 1 order of magnitude for the case of full 3-d readout). The distributions of the reduced angles, with the (time-dependent) direction of solar motion subtracted, are far more anisotropic, however, and if the isotropy tests are applied to these angles then the numbers of events required are similar to the case of 3-d readout. A deviation from rotational symmetry will only be detectable if the Milky Way halo is significantly flattened. The deviation in the mean direction due to a tidal stream is potentially detectable, however, depending on the density and direction of the stream. The meridian plane (which contains the Earth’s spin axis) is, for all detector locations, the optimum readout plane for rejecting isotropy. However readout in this plane can not be used for detecting flattening of the Milky Way halo or a stream with direction perpendicular to the galactic plane. In these cases the optimum readout plane depends on the detector location.
Escoto Ponce de León, M C; Mancilla Díaz, J M; Camacho Ruiz, E J
2008-09-01
The current study used clinical and statistical significance tests to investigate the effects of two forms (didactic or interactive) of a universal prevention program on attitudes about shape and weight, eating behaviors, the influence of body aesthetic models, and self-esteem. Three schools were randomly assigned to one, interactive, didactic, or a control condition. Children (61 girls and 59 boys, age 9-11 years) were evaluated at pre-intervention, post-intervention, and at 6-month follow-up. Programs comprised eight, 90-min sessions. Statistical and clinical significance tests showed more changes in boys and girls with the interactive program versus the didactic intervention and control groups. The findings support the use of interactive programs that highlight identified risk factors and construction of identity based on positive traits distinct to physical appearance.
A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic.
Qi, Jin-Peng; Qi, Jie; Zhang, Qing
2016-01-01
Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals.
A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic
Qi, Jin-Peng; Qi, Jie; Zhang, Qing
2016-01-01
Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals. PMID:27413364
Willems, Sander; Fraiture, Marie-Alice; Deforce, Dieter; De Keersmaecker, Sigrid C J; De Loose, Marc; Ruttink, Tom; Herman, Philippe; Van Nieuwerburgh, Filip; Roosens, Nancy
2016-02-01
Because the number and diversity of genetically modified (GM) crops has significantly increased, their analysis based on real-time PCR (qPCR) methods is becoming increasingly complex and laborious. While several pioneers already investigated Next Generation Sequencing (NGS) as an alternative to qPCR, its practical use has not been assessed for routine analysis. In this study a statistical framework was developed to predict the number of NGS reads needed to detect transgene sequences, to prove their integration into the host genome and to identify the specific transgene event in a sample with known composition. This framework was validated by applying it to experimental data from food matrices composed of pure GM rice, processed GM rice (noodles) or a 10% GM/non-GM rice mixture, revealing some influential factors. Finally, feasibility of NGS for routine analysis of GM crops was investigated by applying the framework to samples commonly encountered in routine analysis of GM crops.
High significance detection of the tSZ effect relativistic corrections
NASA Astrophysics Data System (ADS)
Hurier, G.
2016-12-01
The thermal Sunyaev-Zel'dovich (tSZ) effect is produced by the interaction of cosmic microwave background (CMB) photons with the hot (a few keV) and diffuse gas of electrons inside galaxy clusters integrated along the line of sight. This effect produces a distortion of CMB blackbody emission law. This distortion law depends on the electronic temperature of the intra-cluster hot gas, Te, through the so-called tSZ relativistic corrections. In this work, we have performed a statistical analysis of the tSZ spectral distortion on large galaxy cluster samples. We performed a stacking analysis for several electronic temperature bins, using both spectroscopic measurements of X-ray temperatures and a scaling relation between X-ray luminosities and electronic temperatures. We report the first high significance detection of the relativistic tSZ at a significance of 5.3σ. We also demonstrate that the observed tSZ relativistic corrections are consistent with X-ray deduced temperatures. This measurement of the tSZ spectral law demonstrates that tSZ effect spectral distorsion can be used as a probe to measure galaxy cluster temperatures.
Damage detection of engine bladed-disks using multivariate statistical analysis
NASA Astrophysics Data System (ADS)
Fang, X.; Tang, J.
2006-03-01
The timely detection of damage in aero-engine bladed-disks is an extremely important and challenging research topic. Bladed-disks have high modal density and, particularly, their vibration responses are subject to significant uncertainties due to manufacturing tolerance (blade-to-blade difference or mistuning), operating condition change and sensor noise. In this study, we present a new methodology for the on-line damage detection of engine bladed-disks using their vibratory responses during spin-up or spin-down operations which can be measured by blade-tip-timing sensing technique. We apply a principle component analysis (PCA)-based approach for data compression, feature extraction, and denoising. The non-model based damage detection is achieved by analyzing the change between response features of the healthy structure and of the damaged one. We facilitate such comparison by incorporating the Hotelling's statistic T2 analysis, which yields damage declaration with a given confidence level. The effectiveness of the method is demonstrated by case studies.
Detection and implication of significant temporal b-value variation during earthquake sequences
NASA Astrophysics Data System (ADS)
Gulia, Laura; Tormann, Thessa; Schorlemmer, Danijel; Wiemer, Stefan
2016-04-01
Earthquakes tend to cluster in space and time and periods of increased seismic activity are also periods of increased seismic hazard. Forecasting models currently used in statistical seismology and in Operational Earthquake Forecasting (e.g. ETAS) consider the spatial and temporal changes in the activity rates whilst the spatio-temporal changes in the earthquake size distribution, the b-value, are not included. Laboratory experiments on rock samples show an increasing relative proportion of larger events as the system approaches failure, and a sudden reversal of this trend after the main event. The increasing fraction of larger events during the stress increase period can be mathematically represented by a systematic b-value decrease, while the b-value increases immediately following the stress release. We investigate whether these lab-scale observations also apply to natural earthquake sequences and can help to improve our understanding of the physical processes generating damaging earthquakes. A number of large events nucleated in low b-value regions and spatial b-value variations have been extensively documented in the past. Detecting temporal b-value evolution with confidence is more difficult, one reason being the very different scales that have been suggested for a precursory drop in b-value, from a few days to decadal scale gradients. We demonstrate with the results of detailed case studies of the 2009 M6.3 L'Aquila and 2011 M9 Tohoku earthquakes that significant and meaningful temporal b-value variability can be detected throughout the sequences, which e.g. suggests that foreshock probabilities are not generic but subject to significant spatio-temporal variability. Such potential conclusions require and motivate the systematic study of many sequences to investigate whether general patterns exist that might eventually be useful for time-dependent or even real-time seismic hazard assessment.
A New Approach to the Detection and Statistical Classification of Ca2+ Sparks
Bányász, Tamás; Chen-Izu, Ye; Balke, C. W.; Izu, Leighton T.
2007-01-01
The availability of high-speed, two-dimensional (2-D) confocal microscopes and the expanding armamentarium of fluorescent probes presents unprecedented opportunities and new challenges for studying the spatial and temporal dynamics of cellular processes. The need to remove subjectivity from the detection process, the difficulty of the human eye to detect subtle changes in fluorescence in these 2-D images, and the large volume of data produced by these confocal microscopes call for the need to develop algorithms to automatically mark the changes in fluorescence. These fluorescence signal changes are often subtle, so the statistical estimate of the likelihood that the detected signal is not noise is an integral part of the detection algorithm. This statistical estimation is fundamental to our new approach to detection; in earlier Ca2+ spark detectors, this statistical assessment was incidental to detection. Importantly, the use of the statistical properties of the signal local to the spark, instead of over the whole image, reduces the false positive and false negative rates. We developed an automatic spark detection algorithm based on these principles and used it to detect sparks on an inhomogeneous background of transverse tubule-labeled rat ventricular cells. Because of the large region of the cell surveyed by the confocal microscope, we can detect a large enough number of sparks to measure the dynamic changes in spark frequency in individual cells. We also found, in contrast to earlier results, that cardiac sparks are spatially symmetric. This new approach puts the detection of fluorescent signals on a firm statistical foundation. PMID:17400702
A new approach to the detection and statistical classification of Ca2+ sparks.
Bányász, Tamás; Chen-Izu, Ye; Balke, C W; Izu, Leighton T
2007-06-15
The availability of high-speed, two-dimensional (2-D) confocal microscopes and the expanding armamentarium of fluorescent probes presents unprecedented opportunities and new challenges for studying the spatial and temporal dynamics of cellular processes. The need to remove subjectivity from the detection process, the difficulty of the human eye to detect subtle changes in fluorescence in these 2-D images, and the large volume of data produced by these confocal microscopes call for the need to develop algorithms to automatically mark the changes in fluorescence. These fluorescence signal changes are often subtle, so the statistical estimate of the likelihood that the detected signal is not noise is an integral part of the detection algorithm. This statistical estimation is fundamental to our new approach to detection; in earlier Ca(2+) spark detectors, this statistical assessment was incidental to detection. Importantly, the use of the statistical properties of the signal local to the spark, instead of over the whole image, reduces the false positive and false negative rates. We developed an automatic spark detection algorithm based on these principles and used it to detect sparks on an inhomogeneous background of transverse tubule-labeled rat ventricular cells. Because of the large region of the cell surveyed by the confocal microscope, we can detect a large enough number of sparks to measure the dynamic changes in spark frequency in individual cells. We also found, in contrast to earlier results, that cardiac sparks are spatially symmetric. This new approach puts the detection of fluorescent signals on a firm statistical foundation.
Metoyer, Candace N.; Walsh, Stephen J.; Tardiff, Mark F.; Chilton, Lawrence
2008-10-30
The detection and identification of weak gaseous plumes using thermal imaging data is complicated by many factors. These include variability due to atmosphere, ground and plume temperature, and background clutter. This paper presents an analysis of one formulation of the physics-based model that describes the at-sensor observed radiance. The motivating question for the analyses performed in this paper is as follows. Given a set of backgrounds, is there a way to predict the background over which the probability of detecting a given chemical will be the highest? Two statistics were developed to address this question. These statistics incorporate data from the long-wave infrared band to predict the background over which chemical detectability will be the highest. These statistics can be computed prior to data collection. As a preliminary exploration into the predictive ability of these statistics, analyses were performed on synthetic hyperspectral images. Each image contained one chemical (either carbon tetrachloride or ammonia) spread across six distinct background types. The statistics were used to generate predictions for the background ranks. Then, the predicted ranks were compared to the empirical ranks obtained from the analyses of the synthetic images. For the simplified images under consideration, the predicted and empirical ranks showed a promising amount of agreement. One statistic accurately predicted the best and worst background for detection in all of the images. Future work may include explorations of more complicated plume ingredients, background types, and noise structures.
NASA Astrophysics Data System (ADS)
Fujimoto, K.; Yanagisawa, T.; Uetsuhara, M.
Automated detection and tracking of faint objects in optical, or bearing-only, sensor imagery is a topic of immense interest in space surveillance. Robust methods in this realm will lead to better space situational awareness (SSA) while reducing the cost of sensors and optics. They are especially relevant in the search for high area-to-mass ratio (HAMR) objects, as their apparent brightness can change significantly over time. A track-before-detect (TBD) approach has been shown to be suitable for faint, low signal-to-noise ratio (SNR) images of resident space objects (RSOs). TBD does not rely upon the extraction of feature points within the image based on some thresholding criteria, but rather directly takes as input the intensity information from the image file. Not only is all of the available information from the image used, TBD avoids the computational intractability of the conventional feature-based line detection (i.e., "string of pearls") approach to track detection for low SNR data. Implementation of TBD rooted in finite set statistics (FISST) theory has been proposed recently by Vo, et al. Compared to other TBD methods applied so far to SSA, such as the stacking method or multi-pass multi-period denoising, the FISST approach is statistically rigorous and has been shown to be more computationally efficient, thus paving the path toward on-line processing. In this paper, we intend to apply a multi-Bernoulli filter to actual CCD imagery of RSOs. The multi-Bernoulli filter can explicitly account for the birth and death of multiple targets in a measurement arc. TBD is achieved via a sequential Monte Carlo implementation. Preliminary results with simulated single-target data indicate that a Bernoulli filter can successfully track and detect objects with measurement SNR as low as 2.4. Although the advent of fast-cadence scientific CMOS sensors have made the automation of faint object detection a realistic goal, it is nonetheless a difficult goal, as measurements
Yuan, Zhongshang; Ji, Jiadong; Zhang, Tao; Liu, Yi; Zhang, Xiaoshuai; Chen, Wei; Xue, Fuzhong
2016-12-20
Traditional epidemiology often pays more attention to the identification of a single factor rather than to the pathway that is related to a disease, and therefore, it is difficult to explore the disease mechanism. Systems epidemiology aims to integrate putative lifestyle exposures and biomarkers extracted from multiple omics platforms to offer new insights into the pathway mechanisms that underlie disease at the human population level. One key but inadequately addressed question is how to develop powerful statistics to identify whether one candidate pathway is associated with a disease. Bearing in mind that a pathway difference can result from not only changes in the nodes but also changes in the edges, we propose a novel statistic for detecting group differences between pathways, which in principle, captures the nodes changes and edge changes, as well as simultaneously accounting for the pathway structure simultaneously. The proposed test has been proven to follow the chi-square distribution, and various simulations have shown it has better performance than other existing methods. Integrating genome-wide DNA methylation data, we analyzed one real data set from the Bogalusa cohort study and significantly identified a potential pathway, Smoking → SOCS3 → PIK3R1, which was strongly associated with abdominal obesity. The proposed test was powerful and efficient at identifying pathway differences between two groups, and it can be extended to other disciplines that involve statistical comparisons between pathways. The source code in R is available on our website. Copyright © 2016 John Wiley & Sons, Ltd.
Run-Length and Edge Statistics Based Approach for Image Splicing Detection
NASA Astrophysics Data System (ADS)
Dong, Jing; Wang, Wei; Tan, Tieniu; Shi, Yun Q.
In this paper, a simple but efficient approach for blind image splicing detection is proposed. Image splicing is a common and fundamental operation used for image forgery. The detection of image splicing is a preliminary but desirable study for image forensics. Passive detection approaches of image splicing are usually regarded as pattern recognition problems based on features which are sensitive to splicing. In the proposed approach, we analyze the discontinuity of image pixel correlation and coherency caused by splicing in terms of image run-length representation and sharp image characteristics. The statistical features extracted from image run-length representation and image edge statistics are used for splicing detection. The support vector machine (SVM) is used as the classifier. Our experimental results demonstrate that the two proposed features outperform existing ones both in detection accuracy and computational complexity.
Use of power analysis to develop detectable significance criteria for sea urchin toxicity tests
Carr, R.S.; Biedenbach, J.M.
1999-01-01
When sufficient data are available, the statistical power of a test can be determined using power analysis procedures. The term “detectable significance” has been coined to refer to this criterion based on power analysis and past performance of a test. This power analysis procedure has been performed with sea urchin (Arbacia punctulata) fertilization and embryological development data from sediment porewater toxicity tests. Data from 3100 and 2295 tests for the fertilization and embryological development tests, respectively, were used to calculate the criteria and regression equations describing the power curves. Using Dunnett's test, a minimum significant difference (MSD) (β = 0.05) of 15.5% and 19% for the fertilization test, and 16.4% and 20.6% for the embryological development test, for α ≤ 0.05 and α ≤ 0.01, respectively, were determined. The use of this second criterion reduces type I (false positive) errors and helps to establish a critical level of difference based on the past performance of the test.
A Statistical Detection of an Anomaly from a Few Noisy Tomographic Projections
NASA Astrophysics Data System (ADS)
Fillatre, Lionel; Nikiforov, Igor
2005-12-01
The problem of detecting an anomaly/target from a very limited number of noisy tomographic projections is addressed from the statistical point of view. The imaged object is composed of an environment, considered as a nuisance parameter, with a possibly hidden anomaly/target. The GLR test is used to solve the problem. When the projection linearly depends on the nuisance parameters, the GLR test coincides with an optimal statistical invariant test.
Huang, Ruili; Southall, Noel; Xia, Menghang; Cho, Ming-Hsuang; Jadhav, Ajit; Nguyen, Dac-Trung; Inglese, James; Tice, Raymond R.; Austin, Christopher P.
2009-01-01
In support of the U.S. Tox21 program, we have developed a simple and chemically intuitive model we call weighted feature significance (WFS) to predict the toxicological activity of compounds, based on the statistical enrichment of structural features in toxic compounds. We trained and tested the model on the following: (1) data from quantitative high–throughput screening cytotoxicity and caspase activation assays conducted at the National Institutes of Health Chemical Genomics Center, (2) data from Salmonella typhimurium reverse mutagenicity assays conducted by the U.S. National Toxicology Program, and (3) hepatotoxicity data published in the Registry of Toxic Effects of Chemical Substances. Enrichments of structural features in toxic compounds are evaluated for their statistical significance and compiled into a simple additive model of toxicity and then used to score new compounds for potential toxicity. The predictive power of the model for cytotoxicity was validated using an independent set of compounds from the U.S. Environmental Protection Agency tested also at the National Institutes of Health Chemical Genomics Center. We compared the performance of our WFS approach with classical classification methods such as Naive Bayesian clustering and support vector machines. In most test cases, WFS showed similar or slightly better predictive power, especially in the prediction of hepatotoxic compounds, where WFS appeared to have the best performance among the three methods. The new algorithm has the important advantages of simplicity, power, interpretability, and ease of implementation. PMID:19805409
NASA Astrophysics Data System (ADS)
Ahmed, Sheehan H.; Brooks, Alyson M.; Christensen, Charlotte R.
2017-04-01
We investigate whether the inclusion of baryonic physics influences the formation of thin, coherently rotating planes of satellites such as those seen around the Milky Way and Andromeda. For four Milky Way-mass simulations, each run both as dark matter-only and with baryons included, we are able to identify a planar configuration that significantly maximizes the number of plane satellite members. The maximum plane member satellites are consistently different between the dark matter-only and baryonic versions of the same run due to the fact that satellites are both more likely to be destroyed and to infall later in the baryonic runs. Hence, studying satellite planes in dark matter-only simulations is misleading, because they will be composed of different satellite members than those that would exist if baryons were included. Additionally, the destruction of satellites in the baryonic runs leads to less radially concentrated satellite distributions, a result that is critical to making planes that are statistically significant compared to a random distribution. Since all planes pass through the centre of the galaxy, it is much harder to create a plane of a given height from a random distribution if the satellites have a low radial concentration. We identify Andromeda's low radial satellite concentration as a key reason why the plane in Andromeda is highly significant. Despite this, when corotation is considered, none of the satellite planes identified for the simulated galaxies are as statistically significant as the observed planes around the Milky Way and Andromeda, even in the baryonic runs.
Turk-Browne, Nicholas B.; Scholl, Brian J.; Chun, Marvin M.; Johnson, Marcia K.
2009-01-01
Our environment contains regularities distributed in space and time that can be detected by way of statistical learning. This unsupervised learning occurs without intent or awareness, but little is known about how it relates to other types of learning, how it affects perceptual processing, and how quickly it can occur. Here we use fMRI during statistical learning to explore these questions. Participants viewed statistically structured versus unstructured sequences of shapes while performing a task unrelated to the structure. Robust neural responses to statistical structure were observed, and these responses were notable in four ways: First, responses to structure were observed in the striatum and medial temporal lobe, suggesting that statistical learning may be related to other forms of associative learning and relational memory. Second, statistical regularities yielded greater activation in category-specific visual regions (object-selective lateral occipital cortex and word-selective ventral occipito-temporal cortex), demonstrating that these regions are sensitive to information distributed in time. Third, evidence of learning emerged early during familiarization, showing that statistical learning can operate very quickly and with little exposure. Finally, neural signatures of learning were dissociable from subsequent explicit familiarity, suggesting that learning can occur in the absence of awareness. Overall, our findings help elucidate the underlying nature of statistical learning. PMID:18823241
A Statistical Test for Detecting Answer Copying on Multiple-Choice Tests
ERIC Educational Resources Information Center
van der Linden, Wim J.; Sotaridona, Leonardo
2004-01-01
A statistical test for the detection of answer copying on multiple-choice tests is presented. The test is based on the idea that the answers of examinees to test items may be the result of three possible processes: (1) knowing, (2) guessing, and (3) copying, but that examinees who do not have access to the answers of other examinees can arrive at…
ERIC Educational Resources Information Center
Turk-Browne, Nicholas B.; Scholl, Brian J.; Chun, Marvin M.; Johnson, Marcia K.
2009-01-01
Our environment contains regularities distributed in space and time that can be detected by way of statistical learning. This unsupervised learning occurs without intent or awareness, but little is known about how it relates to other types of learning, how it affects perceptual processing, and how quickly it can occur. Here we use fMRI during…
Dual-band, infrared buried mine detection using a statistical pattern recognition approach
Buhl, M.R.; Hernandez, J.E.; Clark, G.A.; Sengupta, S.K.
1993-08-01
The main objective of this work was to detect surrogate land mines, which were buried in clay and sand, using dual-band, infrared images. A statistical pattern recognition approach was used to achieve this objective. This approach is discussed and results of applying it to real images are given.
An Algorithm to Improve Test Answer Copying Detection Using the Omega Statistic
ERIC Educational Resources Information Center
Maeda, Hotaka; Zhang, Bo
2017-01-01
The omega (?) statistic is reputed to be one of the best indices for detecting answer copying on multiple choice tests, but its performance relies on the accurate estimation of copier ability, which is challenging because responses from the copiers may have been contaminated. We propose an algorithm that aims to identify and delete the suspected…
Linden, Ariel
2008-04-01
Prior to implementing a disease management (DM) strategy, a needs assessment should be conducted to determine whether sufficient opportunity exists for an intervention to be successful in the given population. A central component of this assessment is a sample size analysis to determine whether the population is of sufficient size to allow the expected program effect to achieve statistical significance. This paper discusses the parameters that comprise the generic sample size formula for independent samples and their interrelationships, followed by modifications for the DM setting. In addition, a table is provided with sample size estimates for various effect sizes. Examples are described in detail along with strategies for overcoming common barriers. Ultimately, conducting these calculations up front will help set appropriate expectations about the ability to demonstrate the success of the intervention.
Parkhomenko, Elena; Tritchler, David; Lemire, Mathieu; Hu, Pingzhao; Beyene, Joseph
2009-12-15
In high-dimensional studies such as genome-wide association studies, the correction for multiple testing in order to control total type I error results in decreased power to detect modest effects. We present a new analytical approach based on the higher criticism statistic that allows identification of the presence of modest effects. We apply our method to the genome-wide study of rheumatoid arthritis provided in the Genetic Analysis Workshop 16 Problem 1 data set. There is evidence for unknown bias in this study that could be explained by the presence of undetected modest effects. We compared the asymptotic and empirical thresholds for the higher criticism statistic. Using the asymptotic threshold we detected the presence of modest effects genome-wide. We also detected modest effects using 90th percentile of the empirical null distribution as a threshold; however, there is no such evidence when the 95th and 99th percentiles were used. While the higher criticism method suggests that there is some evidence for modest effects, interpreting individual single-nucleotide polymorphisms with significant higher criticism statistics is of undermined value. The goal of higher criticism is to alert the researcher that genetic effects remain to be discovered and to promote the use of more targeted and powerful studies to detect the remaining effects.
NASA Astrophysics Data System (ADS)
Wang, H. J.; Shi, W. L.; Chen, X. H.
2006-05-01
The West Development Policy being implemented in China is causing significant land use and land cover (LULC) changes in West China. With the up-to-date satellite database of the Global Land Cover Characteristics Database (GLCCD) that characterizes the lower boundary conditions, the regional climate model RIEMS-TEA is used to simulate possible impacts of the significant LULC variation. The model was run for five continuous three-month periods from 1 June to 1 September of 1993, 1994, 1995, 1996, and 1997, and the results of the five groups are examined by means of a student t-test to identify the statistical significance of regional climate variation. The main results are: (1) The regional climate is affected by the LULC variation because the equilibrium of water and heat transfer in the air-vegetation interface is changed. (2) The integrated impact of the LULC variation on regional climate is not only limited to West China where the LULC varies, but also to some areas in the model domain where the LULC does not vary at all. (3) The East Asian monsoon system and its vertical structure are adjusted by the large scale LULC variation in western China, where the consequences axe the enhancement of the westward water vapor transfer from the east east and the relevant increase of wet-hydrostatic energy in the middle-upper atmospheric layers. (4) The ecological engineering in West China affects significantly the regional climate in Northwest China, North China and the middle-lower reaches of the Yangtze River; there are obvious effects in South, Northeast, and Southwest China, but minor effects in Tibet.
Kossobokov, V.G.; Romashkova, L.L.; Keilis-Borok, V. I.; Healy, J.H.
1999-01-01
Algorithms M8 and MSc (i.e., the Mendocino Scenario) were used in a real-time intermediate-term research prediction of the strongest earthquakes in the Circum-Pacific seismic belt. Predictions are made by M8 first. Then, the areas of alarm are reduced by MSc at the cost that some earthquakes are missed in the second approximation of prediction. In 1992-1997, five earthquakes of magnitude 8 and above occurred in the test area: all of them were predicted by M8 and MSc identified correctly the locations of four of them. The space-time volume of the alarms is 36% and 18%, correspondingly, when estimated with a normalized product measure of empirical distribution of epicenters and uniform time. The statistical significance of the achieved results is beyond 99% both for M8 and MSc. For magnitude 7.5 + , 10 out of 19 earthquakes were predicted by M8 in 40% and five were predicted by M8-MSc in 13% of the total volume considered. This implies a significance level of 81% for M8 and 92% for M8-MSc. The lower significance levels might result from a global change in seismic regime in 1993-1996, when the rate of the largest events has doubled and all of them become exclusively normal or reversed faults. The predictions are fully reproducible; the algorithms M8 and MSc in complete formal definitions were published before we started our experiment [Keilis-Borok, V.I., Kossobokov, V.G., 1990. Premonitory activation of seismic flow: Algorithm M8, Phys. Earth and Planet. Inter. 61, 73-83; Kossobokov, V.G., Keilis-Borok, V.I., Smith, S.W., 1990. Localization of intermediate-term earthquake prediction, J. Geophys. Res., 95, 19763-19772; Healy, J.H., Kossobokov, V.G., Dewey, J.W., 1992. A test to evaluate the earthquake prediction algorithm, M8. U.S. Geol. Surv. OFR 92-401]. M8 is available from the IASPEI Software Library [Healy, J.H., Keilis-Borok, V.I., Lee, W.H.K. (Eds.), 1997. Algorithms for Earthquake Statistics and Prediction, Vol. 6. IASPEI Software Library]. ?? 1999 Elsevier
Molecular and statistical approaches to the detection and correction of errors in genotype databases
Brzustowicz, L.M.; Xie, X.; Merette, C.; Townsend, L.; Gilliam, T.C.; Ott, J. )
1993-11-01
Errors in genotyping data have been shown to have a significant effect on the estimation of recombination fractions in high-resolution genetic maps. Previous estimates of errors in existing databases have been limited to the analysis of relatively few markers and have suggested rates in the range 0.5%-1.5%. The present study capitalizes on the fact that within the Centre d'Etude du Polymorphisme Humain (CEPH) collection of reference families, 21 individuals are members of more than one family, with separate DNA samples provided by CEPH for each appearance of these individuals. By comparing the genotypes of these individuals in each of the families in which they occur, an estimated error rate of 1.4% was calculated for all loci in the version 4.0 CEPH database. Removing those individuals who were clearly identified by CEPH as appearing in more than one family resulted in a 3.0% error rate for the remaining samples, suggesting that some error checking of the identified repeated individuals may occur prior to data submission. An error rate of 3.0% for version 4.0 data was also obtained for four chromosome 5 markers that were retyped through the entire CEPH collection. The effects of these errors on a multipoint map were significant, with a total sex-averaged length of 36.09 cM with the errors, and 19.47 cM with the errors corrected. Several statistical approaches to detect and allow for errors during linkage analysis are presented. One method, which identified families containing possible errors on the basis of the impact on the maximum lod score, showed particular promise, especially when combined with the limited retyping of the identified families. The impact of the demonstrated error rate in an established genotype database on high-resolution mapping is significant, raising the question of the overall value of incorporating such existing data into new genetic maps. 15 refs., 8 tabs.
Anomaly detection in hyperspectral imagery: statistics vs. graph-based algorithms
NASA Astrophysics Data System (ADS)
Berkson, Emily E.; Messinger, David W.
2016-05-01
Anomaly detection (AD) algorithms are frequently applied to hyperspectral imagery, but different algorithms produce different outlier results depending on the image scene content and the assumed background model. This work provides the first comparison of anomaly score distributions between common statistics-based anomaly detection algorithms (RX and subspace-RX) and the graph-based Topological Anomaly Detector (TAD). Anomaly scores in statistical AD algorithms should theoretically approximate a chi-squared distribution; however, this is rarely the case with real hyperspectral imagery. The expected distribution of scores found with graph-based methods remains unclear. We also look for general trends in algorithm performance with varied scene content. Three separate scenes were extracted from the hyperspectral MegaScene image taken over downtown Rochester, NY with the VIS-NIR-SWIR ProSpecTIR instrument. In order of most to least cluttered, we study an urban, suburban, and rural scene. The three AD algorithms were applied to each scene, and the distributions of the most anomalous 5% of pixels were compared. We find that subspace-RX performs better than RX, because the data becomes more normal when the highest variance principal components are removed. We also see that compared to statistical detectors, anomalies detected by TAD are easier to separate from the background. Due to their different underlying assumptions, the statistical and graph-based algorithms highlighted different anomalies within the urban scene. These results will lead to a deeper understanding of these algorithms and their applicability across different types of imagery.
Kim, Jiyu; Jung, Inkyung
2017-01-01
Spatial scan statistics with circular or elliptic scanning windows are commonly used for cluster detection in various applications, such as the identification of geographical disease clusters from epidemiological data. It has been pointed out that the method may have difficulty in correctly identifying non-compact, arbitrarily shaped clusters. In this paper, we evaluated the Gini coefficient for detecting irregularly shaped clusters through a simulation study. The Gini coefficient, the use of which in spatial scan statistics was recently proposed, is a criterion measure for optimizing the maximum reported cluster size. Our simulation study results showed that using the Gini coefficient works better than the original spatial scan statistic for identifying irregularly shaped clusters, by reporting an optimized and refined collection of clusters rather than a single larger cluster. We have provided a real data example that seems to support the simulation results. We think that using the Gini coefficient in spatial scan statistics can be helpful for the detection of irregularly shaped clusters.
Kim, Jiyu; Jung, Inkyung
2017-01-01
Spatial scan statistics with circular or elliptic scanning windows are commonly used for cluster detection in various applications, such as the identification of geographical disease clusters from epidemiological data. It has been pointed out that the method may have difficulty in correctly identifying non-compact, arbitrarily shaped clusters. In this paper, we evaluated the Gini coefficient for detecting irregularly shaped clusters through a simulation study. The Gini coefficient, the use of which in spatial scan statistics was recently proposed, is a criterion measure for optimizing the maximum reported cluster size. Our simulation study results showed that using the Gini coefficient works better than the original spatial scan statistic for identifying irregularly shaped clusters, by reporting an optimized and refined collection of clusters rather than a single larger cluster. We have provided a real data example that seems to support the simulation results. We think that using the Gini coefficient in spatial scan statistics can be helpful for the detection of irregularly shaped clusters. PMID:28129368
NASA Astrophysics Data System (ADS)
Mamin, H. J.; Budakian, R.; Chui, B. W.; Rugar, D.
2005-07-01
We have detected and manipulated the naturally occurring N statistical polarization in nuclear spin ensembles using magnetic resonance force microscopy. Using protocols previously developed for detecting single electron spins, we have measured signals from ensembles of nuclear spins in a volume of roughly (150nm)3 with a sensitivity of roughly 2000 net spins in a 2.5h averaging window. Three systems have been studied, F19 nuclei in CaF2 , and H1 nuclei (protons) in both polymethylmethacrylate and collagen, a naturally occurring protein. By detecting the statistical polarization, we not only can work with relatively small ensembles, but we eliminate any need to wait a longitudinal relaxation time T1 to polarize the spins. We have also made use of the fact that the statistical polarization, which can be considered a form of spin noise, has a finite correlation time. A method similar to one previously proposed by Carlson [Bull. Am. Phys. Soc. 44, 541 (1999)] has been used to suppress the effect of the statistical uncertainty and extract meaningful information from time-averaged measurements. By implementing this method, we have successfully made nutation and transverse spin relaxation time measurements in CaF2 at low temperatures.
Efficient detection of wound-bed and peripheral skin with statistical colour models.
Veredas, Francisco J; Mesa, Héctor; Morente, Laura
2015-04-01
A pressure ulcer is a clinical pathology of localised damage to the skin and underlying tissue caused by pressure, shear or friction. Reliable diagnosis supported by precise wound evaluation is crucial in order to success on treatment decisions. This paper presents a computer-vision approach to wound-area detection based on statistical colour models. Starting with a training set consisting of 113 real wound images, colour histogram models are created for four different tissue types. Back-projections of colour pixels on those histogram models are used, from a Bayesian perspective, to get an estimate of the posterior probability of a pixel to belong to any of those tissue classes. Performance measures obtained from contingency tables based on a gold standard of segmented images supplied by experts have been used for model selection. The resulting fitted model has been validated on a training set consisting of 322 wound images manually segmented and labelled by expert clinicians. The final fitted segmentation model shows robustness and gives high mean performance rates [(AUC: .9426 (SD .0563); accuracy: .8777 (SD .0799); F-score: 0.7389 (SD .1550); Cohen's kappa: .6585 (SD .1787)] when segmenting significant wound areas that include healing tissues.
Statistical detection of slow-mode waves in solar polar regions with SDO/AIA
Su, J. T.
2014-10-01
Observations from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory are utilized to statistically investigate the propagating quasi-periodic oscillations in the solar polar plume and inter-plume regions. On average, the periods are found to be nearly equal in the three coronal channels of AIA 171 Å, 193 Å, and 211 Å, and the wavelengths increase with temperature from 171 Å, 193 Å, and 211 Å. The phase speeds may be inferred from the above parameters. Furthermore, the speed ratios of v {sub 193}/v {sub 171} and v {sub 211}/v {sub 171} are derived, e.g., 1.4 ± 0.8 and 2.0 ± 1.9 in the plume regions, respectively, which are equivalent to the theoretical ones for acoustic waves. We find that there are no significant differences for the detected parameters between the plume and inter-plume regions. To our knowledge, this is the first time that we have simultaneously obtained the phase speeds of slow-mode waves in the three channels in the open coronal magnetic structures due to the method adopted in the present work, which is able to minimize the influence of the jets or eruptions on wave signals.
NASA Astrophysics Data System (ADS)
Wilson, Mark; Mitra, Sunanda; Roberson, Glenn H.; Shieh, Yao-Yang
1997-10-01
Currently early detection of breast cancer is primarily accomplished by mammography and suspicious findings may lead to a decision for performing a biopsy. Digital enhancement and pattern recognition techniques may aid in early detection of some patterns such as microcalcification clusters indicating onset of DCIS (ductal carcinoma in situ) that accounts for 20% of all mammographically detected breast cancers and could be treated when detected early. These individual calcifications are hard to detect due to size and shape variability and inhomogeneous background texture. Our study addresses only early detection of microcalcifications that allows the radiologist to interpret the x-ray findings in computer-aided enhanced form easier than evaluating the x-ray film directly. We present an algorithm which locates microcalcifications based on local grayscale variability and of tissue structures and image statistics. Threshold filters with lower and upper bounds computed from the image statistics of the entire image and selected subimages were designed to enhance the entire image. This enhanced image was used as the initial image for identifying the micro-calcifications based on the variable box threshold filters at different resolutions. The test images came from the Texas Tech University Health Sciences Center and the MIAS mammographic database, which are classified into various categories including microcalcifications. Classification of other types of abnormalities in mammograms based on their characteristic features is addressed in later studies.
Nicol, Samuel; Roach, Jennifer K.; Griffith, Brad
2013-01-01
Over the past 50 years, the number and size of high-latitude lakes have decreased throughout many regions; however, individual lake trends have been variable in direction and magnitude. This spatial heterogeneity in lake change makes statistical detection of temporal trends challenging, particularly in small analysis areas where weak trends are difficult to separate from inter- and intra-annual variability. Factors affecting trend detection include inherent variability, trend magnitude, and sample size. In this paper, we investigated how the statistical power to detect average linear trends in lake size of 0.5, 1.0 and 2.0 %/year was affected by the size of the analysis area and the number of years of monitoring in National Wildlife Refuges in Alaska. We estimated power for large (930–4,560 sq km) study areas within refuges and for 2.6, 12.9, and 25.9 sq km cells nested within study areas over temporal extents of 4–50 years. We found that: (1) trends in study areas could be detected within 5–15 years, (2) trends smaller than 2.0 %/year would take >50 years to detect in cells within study areas, and (3) there was substantial spatial variation in the time required to detect change among cells. Power was particularly low in the smallest cells which typically had the fewest lakes. Because small but ecologically meaningful trends may take decades to detect, early establishment of long-term monitoring will enhance power to detect change. Our results have broad applicability and our method is useful for any study involving change detection among variable spatial and temporal extents.
NASA Astrophysics Data System (ADS)
Govindan, R. B.; Al-Shargabi, Tareq; Andescavage, Nickie N.; Metzler, Marina; Lenin, R. B.; Plessis, Adré du
2017-01-01
Phase differences of two signals in perfect synchrony exhibit a narrow band distribution, whereas the phase differences of two asynchronous signals exhibit uniform distribution. We assess the statistical significance of the phase synchronization between two signals by using a signed rank test to compare the distribution of their phase differences to the theoretically expected uniform distribution for two asynchronous signals. Using numerical simulation of a second order autoregressive (AR2) process, we show that the proposed approach correctly identifies the coupling between the AR2 process and the driving white noise. We also identify the optimal p-value that distinguishes coupled scenarios from uncoupled ones. To identify the limiting cases, we study the phase synchronization between two independent white noises as a function of bandwidth of the filter in a different second simulation. We identify the frequency bandwidth below which the proposed approach fails and suggest using a data-driven approach for those scenarios. Finally, we demonstrate the application of this approach to study the coupling between beat-to-beat cardiac intervals and continuous blood pressure obtained from critically-ill infants to characterize the baroreflex function.
Recommended methods for statistical analysis of data containing less-than-detectable measurements
Atwood, C.L.; Blackwood, L.G.; Harris, G.A.; Loehr, C.A.
1991-09-01
This report is a manual for statistical workers dealing with environmental measurements, when some of the measurements are not given exactly but are only reported as less than detectable. For some statistical settings with such data, many methods have been proposed in the literature, while for others few or none have been proposed. This report gives a recommended method in each of the settings considered. The body of the report gives a brief description of each recommended method. Appendix A gives example programs using the statistical package SAS, for those methods that involve nonstandard methods. Appendix B presents the methods that were compared and the reasons for selecting each recommended method, and explains any fine points that might be of interest. 7 refs., 4 figs.
Recommended methods for statistical analysis of data containing less-than-detectable measurements
Atwood, C.L.; Blackwood, L.G.; Harris, G.A.; Loehr, C.A.
1990-09-01
This report is a manual for statistical workers dealing with environmental measurements, when some of the measurements are not given exactly but are only reported as less than detectable. For some statistical settings with such data, many methods have been proposed in the literature, while for others few or none have been proposed. This report gives a recommended method in each of the settings considered. The body of the report gives a brief description of each recommended method. Appendix A gives example programs using the statistical package SAS, for those methods that involve nonstandard methods. Appendix B presents the methods that were compared and the reasons for selecting each recommended method, and explains any fine points that might be of interest. This is an interim version. Future revisions will complete the recommendations. 34 refs., 2 figs., 11 tabs.
Statistics provide guidance for indigenous organic carbon detection on Mars missions.
Sephton, Mark A; Carter, Jonathan N
2014-08-01
Data from the Viking and Mars Science Laboratory missions indicate the presence of organic compounds that are not definitively martian in origin. Both contamination and confounding mineralogies have been suggested as alternatives to indigenous organic carbon. Intuitive thought suggests that we are repeatedly obtaining data that confirms the same level of uncertainty. Bayesian statistics may suggest otherwise. If an organic detection method has a true positive to false positive ratio greater than one, then repeated organic matter detection progressively increases the probability of indigeneity. Bayesian statistics also reveal that methods with higher ratios of true positives to false positives give higher overall probabilities and that detection of organic matter in a sample with a higher prior probability of indigenous organic carbon produces greater confidence. Bayesian statistics, therefore, provide guidance for the planning and operation of organic carbon detection activities on Mars. Suggestions for future organic carbon detection missions and instruments are as follows: (i) On Earth, instruments should be tested with analog samples of known organic content to determine their true positive to false positive ratios. (ii) On the mission, for an instrument with a true positive to false positive ratio above one, it should be recognized that each positive detection of organic carbon will result in a progressive increase in the probability of indigenous organic carbon being present; repeated measurements, therefore, can overcome some of the deficiencies of a less-than-definitive test. (iii) For a fixed number of analyses, the highest true positive to false positive ratio method or instrument will provide the greatest probability that indigenous organic carbon is present. (iv) On Mars, analyses should concentrate on samples with highest prior probability of indigenous organic carbon; intuitive desires to contrast samples of high prior probability and low prior
NASA Astrophysics Data System (ADS)
He, Yu-Hao; Chao-Lin, Lü; Zhang, Wei-Jun; Zhang, Lu; Wu, Jun-Jie; Chen, Si-Jing; You, Li-Xing; Wang, Zhen
2015-06-01
A new method to study the transient detection efficiency (DE) and pulse amplitude of superconducting nanowire single photon detectors (SNSPD) during the current recovery process is proposed — statistically analyzing the single photon response under photon illumination with a high repetition rate. The transient DE results match well with the DEs deduced from the static current dependence of DE combined with the waveform of a single-photon detection event. This proves that static measurement results can be used to analyze the transient current recovery process after a detection event. The results are relevant for understanding the current recovery process of SNSPDs after a detection event and for determining the counting rate of SNSPDs. Project supported by the Strategic Priority Research Program (B) of the Chinese Academy of Sciences (Grant No. XDB04010200), the National Basic Research Program of China (Grant No. 2011CBA00202), and the National Natural Science Foundation of China (Grant No. 61401441).
A statistical approach of fatigue crack detection for a structural hotspot
NASA Astrophysics Data System (ADS)
Jin, Pei; Zhou, Li
2012-04-01
This work focuses on an unsupervised, data driven statistical approach to detect and monitor fatigue crack growth in lug joint samples using surface mounted piezoelectric sensors. Early and faithful detection of fatigue cracks in a lug joint can guide in taking preventive measures, thus avoiding any possible fatal structural failure. The on-line damage state at any given fatigue cycle is estimated using a damage index approach as the dynamical properties of a structure change with the initiation of a new crack or the growth of an existing crack. Using the measurements performed on an intact lug joint as baseline, damage indices are evaluated from the frequency response of the lug joint with an unknown damage state. As the damage indices are evaluated, a Bayesian analysis is committed and a statistical metric is evaluated to identify damage state(say crack length).
Signal waveform detection with statistical automaton for internet and web service streaming.
Tseng, Kuo-Kun; Ji, Yuzhu; Liu, Yiming; Huang, Nai-Lun; Zeng, Fufu; Lin, Fang-Ying
2014-01-01
In recent years, many approaches have been suggested for Internet and web streaming detection. In this paper, we propose an approach to signal waveform detection for Internet and web streaming, with novel statistical automatons. The system records network connections over a period of time to form a signal waveform and compute suspicious characteristics of the waveform. Network streaming according to these selected waveform features by our newly designed Aho-Corasick (AC) automatons can be classified. We developed two versions, that is, basic AC and advanced AC-histogram waveform automata, and conducted comprehensive experimentation. The results confirm that our approach is feasible and suitable for deployment.
Hu, Juju; Hu, Haijiang; Ji, Yinghua
2010-03-15
Periodic nonlinearity that ranges from tens of nanometers to a few nanometers in heterodyne interferometer limits its use in high accuracy measurement. A novel method is studied to detect the nonlinearity errors based on the electrical subdivision and the analysis method of statistical signal in heterodyne Michelson interferometer. Under the movement of micropositioning platform with the uniform velocity, the method can detect the nonlinearity errors by using the regression analysis and Jackknife estimation. Based on the analysis of the simulations, the method can estimate the influence of nonlinearity errors and other noises for the dimensions measurement in heterodyne Michelson interferometer.
Signal Waveform Detection with Statistical Automaton for Internet and Web Service Streaming
Liu, Yiming; Huang, Nai-Lun; Zeng, Fufu; Lin, Fang-Ying
2014-01-01
In recent years, many approaches have been suggested for Internet and web streaming detection. In this paper, we propose an approach to signal waveform detection for Internet and web streaming, with novel statistical automatons. The system records network connections over a period of time to form a signal waveform and compute suspicious characteristics of the waveform. Network streaming according to these selected waveform features by our newly designed Aho-Corasick (AC) automatons can be classified. We developed two versions, that is, basic AC and advanced AC-histogram waveform automata, and conducted comprehensive experimentation. The results confirm that our approach is feasible and suitable for deployment. PMID:25032231
Malm, Christer B; Khoo, Nelson S; Granlund, Irene; Lindstedt, Emilia; Hult, Andreas
2016-01-01
The discovery of erythropoietin (EPO) simplified blood doping in sports, but improved detection methods, for EPO has forced cheating athletes to return to blood transfusion. Autologous blood transfusion with cryopreserved red blood cells (RBCs) is the method of choice, because no valid method exists to accurately detect such event. In endurance sports, it can be estimated that elite athletes improve performance by up to 3% with blood doping, regardless of method. Valid detection methods for autologous blood doping is important to maintain credibility of athletic performances. Recreational male (N = 27) and female (N = 11) athletes served as Transfusion (N = 28) and Control (N = 10) subjects in two different transfusion settings. Hematological variables and physical performance were measured before donation of 450 or 900 mL whole blood, and until four weeks after re-infusion of the cryopreserved RBC fraction. Blood was analyzed for transferrin, iron, Hb, EVF, MCV, MCHC, reticulocytes, leucocytes and EPO. Repeated measures multivariate analysis of variance (MANOVA) and pattern recognition using Principal Component Analysis (PCA) and Orthogonal Projections of Latent Structures (OPLS) discriminant analysis (DA) investigated differences between Control and Transfusion groups over time. Significant increase in performance (15 ± 8%) and VO2max (17 ± 10%) (mean ± SD) could be measured 48 h after RBC re-infusion, and remained increased for up to four weeks in some subjects. In total, 533 blood samples were included in the study (Clean = 220, Transfused = 313). In response to blood transfusion, the largest change in hematological variables occurred 48 h after blood donation, when Control and Transfused groups could be separated with OPLS-DA (R2 = 0.76/Q2 = 0.59). RBC re-infusion resulted in the best model (R2 = 0.40/Q2 = 0.10) at the first sampling point (48 h), predicting one false positive and one false negative. Over all, a 25% and 86% false positives ratio was
2015-06-01
THEORETIC STATISTICAL METHODS FOR DETECTING AND LOCALIZING DISTRIBUTIONAL CHANGE IN MULTIVARIATE DATA by Matthew A. Hawks June 2015...existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this...DISTRIBUTIONAL CHANGE IN MULTIVARIATE DATA 5. FUNDING NUMBERS 6. AUTHOR(S) Hawks, Matthew A. 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES
A statistical model of the photomultiplier gain process with applications to optical pulse detection
NASA Technical Reports Server (NTRS)
Tan, H. H.
1982-01-01
A Markov diffusion model was used to determine an approximate probability density for the random gain. This approximate density preserves the correct second-order statistics and appears to be in reasonably good agreement with experimental data. The receiver operating curve for a pulse counter detector of PMT cathode emission events was analyzed using this density. The error performance of a simple binary direct detection optical communication system was also derived. Previously announced in STAR as N82-25100
A statistical model of the photomultiplier gain process with applications to optical pulse detection
NASA Technical Reports Server (NTRS)
Tan, H. H.
1982-01-01
A Markov diffusion model was used to determine an approximate probability density for the random gain. This approximate density preserves the correct second-order statistics and appears to be in reasonably good agreement with experimental data. The receiver operating curve for a pulse counter detector of PMT cathode emission events was analyzed using this density. The error performance of a simple binary direct detection optical communication system was also derived.
Banks-Leite, Cristina; Pardini, Renata; Boscolo, Danilo; Cassano, Camila Righetto; Püttker, Thomas; Barros, Camila Santos; Barlow, Jos
2014-01-01
1. In recent years, there has been a fast development of models that adjust for imperfect detection. These models have revolutionized the analysis of field data, and their use has repeatedly demonstrated the importance of sampling design and data quality. There are, however, several practical limitations associated with the use of detectability models which restrict their relevance to tropical conservation science. 2. We outline the main advantages of detectability models, before examining their limitations associated with their applicability to the analysis of tropical communities, rare species and large-scale data sets. Finally, we discuss whether detection probability needs to be controlled before and/or after data collection. 3. Models that adjust for imperfect detection allow ecologists to assess data quality by estimating uncertainty and to obtain adjusted ecological estimates of populations and communities. Importantly, these models have allowed informed decisions to be made about the conservation and management of target species. 4. Data requirements for obtaining unadjusted estimates are substantially lower than for detectability-adjusted estimates, which require relatively high detection/recapture probabilities and a number of repeated surveys at each location. These requirements can be difficult to meet in large-scale environmental studies where high levels of spatial replication are needed, or in the tropics where communities are composed of many naturally rare species. However, while imperfect detection can only be adjusted statistically, covariates of detection probability can also be controlled through study design. Using three study cases where we controlled for covariates of detection probability through sampling design, we show that the variation in unadjusted ecological estimates from nearly 100 species was qualitatively the same as that obtained from adjusted estimates. Finally, we discuss that the decision as to whether one should control for
Effects of measurement statistics on the detection of damage in the Alamosa Canyon Bridge
Doebling, S.W.; Farrar, C.R.; Goodman, R.S.
1996-12-31
This paper presents a comparison of the statistics on the measured model parameters of a bridge structure to the expected changes in those parameters caused by damage. It is then determined if the changes resulting from damage are statistically significant. This paper considers the most commonly used modal parameters for indication of damage: modal frequency, mode shape, and mode shape curvature. The approach is divided into two steps. First, the relative uncertainties (arising from random error sources) of the measured modal frequencies, mode shapes, and mode shape curvatures are determined by Monte Carlo analysis of the measured data. Based on these uncertainties, 95% statistical confidence bounds are computed for these parameters. The second step is the determination of the measured change in these parameters resulting from structural damage. Changes which are outside the 95% bounds are considered to be statistically significant. It is proposed that this statistical significance can be used to selectively filter which modes are used for damage identification. The primary conclusion of the paper is that the selection of the appropriate parameters to use in the damage identification algorithm must take into account not only the sensitivity of the damage indicator to the structural deterioration, but also the uncertainty inherent in the measurement of the parameters used to compute the indicator.
2012-03-01
previously undetectable asteroid or comet on a collision course with Earth early enough to establish an effective plan of action to save millions of lives...NEAR EARTH OBJECT DETECTION USING A POISSON STATISTICAL MODEL FOR DETECTION ON IMAGES MODELED FROM THE PANORAMIC SURVEY...the United States. AFIT/GE/ENG/12-33 NEAR EARTH OBJECT DETECTION USING POISSON STATISTICAL MODEL FOR DETECTION ON IMAGES MODELED FROM THE
NASA Technical Reports Server (NTRS)
Friedlander, Alan L.; Harry, David P., III
1960-01-01
An exploratory analysis of vehicle guidance during the approach to a target planet is presented. The objective of the guidance maneuver is to guide the vehicle to a specific perigee distance with a high degree of accuracy and minimum corrective velocity expenditure. The guidance maneuver is simulated by considering the random sampling of real measurements with significant error and reducing this information to prescribe appropriate corrective action. The instrumentation system assumed includes optical and/or infrared devices to indicate range and a reference angle in the trajectory plane. Statistical results are obtained by Monte-Carlo techniques and are shown as the expectation of guidance accuracy and velocity-increment requirements. Results are nondimensional and applicable to any planet within limits of two-body assumptions. The problem of determining how many corrections to make and when to make them is a consequence of the conflicting requirement of accurate trajectory determination and propulsion. Optimum values were found for a vehicle approaching a planet along a parabolic trajectory with an initial perigee distance of 5 radii and a target perigee of 1.02 radii. In this example measurement errors were less than i minute of arc. Results indicate that four corrections applied in the vicinity of 50, 16, 15, and 1.5 radii, respectively, yield minimum velocity-increment requirements. Thrust devices capable of producing a large variation of velocity-increment size are required. For a vehicle approaching the earth, miss distances within 32 miles are obtained with 90-percent probability. Total velocity increments used in guidance are less than 3300 feet per second with 90-percent probability. It is noted that the above representative results are valid only for the particular guidance scheme hypothesized in this analysis. A parametric study is presented which indicates the effects of measurement error size, initial perigee, and initial energy on the guidance
NASA Astrophysics Data System (ADS)
Zeng, Bobo; Wang, Guijin; Ruan, Zhiwei; Lin, Xinggang; Meng, Long
2012-07-01
High-performance pedestrian detection with good accuracy and fast speed is an important yet challenging task in computer vision. We design a novel feature named pair normalized channel feature (PNCF), which simultaneously combines and normalizes two channel features in image channels, achieving a highly discriminative power and computational efficiency. PNCF applies to both gradient channels and color channels so that shape and appearance information are described and integrated in the same feature. To efficiently explore the formidably large PNCF feature space, we propose a statistics-based feature learning method to select a small number of potentially discriminative candidate features, which are fed into the boosting algorithm. In addition, channel compression and a hybrid pyramid are employed to speed up the multiscale detection. Experiments illustrate the effectiveness of PNCF and its learning method. Our proposed detector outperforms the state-of-the-art on several benchmark datasets in both detection accuracy and efficiency.
Statistical method for detecting phase shifts in alpha rhythm from human electroencephalogram data
NASA Astrophysics Data System (ADS)
Naruse, Yasushi; Takiyama, Ken; Okada, Masato; Umehara, Hiroaki
2013-04-01
We developed a statistical method for detecting discontinuous phase changes (phase shifts) in fluctuating alpha rhythms in the human brain from electroencephalogram (EEG) data obtained in a single trial. This method uses the state space models and the line process technique, which is a Bayesian method for detecting discontinuity in an image. By applying this method to simulated data, we were able to detect the phase and amplitude shifts in a single simulated trial. Further, we demonstrated that this method can detect phase shifts caused by a visual stimulus in the alpha rhythm from experimental EEG data even in a single trial. The results for the experimental data showed that the timings of the phase shifts in the early latency period were similar between many of the trials, and that those in the late latency period were different between the trials. The conventional averaging method can only detect phase shifts that occur at similar timings between many of the trials, and therefore, the phase shifts that occur at differing timings cannot be detected using the conventional method. Consequently, our obtained results indicate the practicality of our method. Thus, we believe that our method will contribute to studies examining the phase dynamics of nonlinear alpha rhythm oscillators.
NASA Astrophysics Data System (ADS)
Ortega-Martinez, Antonio; Padilla-Martinez, Juan Pablo; Franco, Walfre
2016-04-01
The skin contains several fluorescent molecules or fluorophores that serve as markers of structure, function and composition. UV fluorescence excitation photography is a simple and effective way to image specific intrinsic fluorophores, such as the one ascribed to tryptophan which emits at a wavelength of 345 nm upon excitation at 295 nm, and is a marker of cellular proliferation. Earlier, we built a clinical UV photography system to image cellular proliferation. In some samples, the naturally low intensity of the fluorescence can make it difficult to separate the fluorescence of cells in higher proliferation states from background fluorescence and other imaging artifacts -- like electronic noise. In this work, we describe a statistical image segmentation method to separate the fluorescence of interest. Statistical image segmentation is based on image averaging, background subtraction and pixel statistics. This method allows to better quantify the intensity and surface distributions of fluorescence, which in turn simplify the detection of borders. Using this method we delineated the borders of highly-proliferative skin conditions and diseases, in particular, allergic contact dermatitis, psoriatic lesions and basal cell carcinoma. Segmented images clearly define lesion borders. UV fluorescence excitation photography along with statistical image segmentation may serve as a quick and simple diagnostic tool for clinicians.
Performance analysis of Wald-statistic based network detection methods for radiation sources
Sen, Satyabrata; Rao, Nageswara S; Wu, Qishi; Barry, M. L..; Grieme, M.; Brooks, Richard R; Cordone, G.
2016-01-01
There have been increasingly large deployments of radiation detection networks that require computationally fast algorithms to produce prompt results over ad-hoc sub-networks of mobile devices, such as smart-phones. These algorithms are in sharp contrast to complex network algorithms that necessitate all measurements to be sent to powerful central servers. In this work, at individual sensors, we employ Wald-statistic based detection algorithms which are computationally very fast, and are implemented as one of three Z-tests and four chi-square tests. At fusion center, we apply the K-out-of-N fusion to combine the sensors hard decisions. We characterize the performance of detection methods by deriving analytical expressions for the distributions of underlying test statistics, and by analyzing the fusion performances in terms of K, N, and the false-alarm rates of individual detectors. We experimentally validate our methods using measurements from indoor and outdoor characterization tests of the Intelligence Radiation Sensors Systems (IRSS) program. In particular, utilizing the outdoor measurements, we construct two important real-life scenarios, boundary surveillance and portal monitoring, and present the results of our algorithms.
Tavares, Gilberto; Zsigraiová, Zdena; Semiao, Viriato; Carvalho, Maria da Graca
2011-07-01
This work proposes the application of two multivariate statistical methods, principal component analysis (PCA) and partial least square (PLS), to a continuous process of a municipal solid waste (MSW) moving grate-type incinerator for process control--monitoring, fault detection and diagnosis--through the extraction of information from historical data. PCA model is built for process monitoring capable of detecting abnormal situations and the original 16-variable process dimension is reduced to eight, the first 4 being able to capture together 86% of the total process variation. PLS model is constructed to predict the generated superheated steam flow rate allowing for control of its set points. The model retained six of the original 13 variables, explaining together 90% of the input variation and almost 98% of the output variation. The proposed methodology is demonstrated by applying those multivariate statistical methods to process data continuously measured in an actual incinerator. Both models exhibited very good performance in fault detection and isolation. In predicting the generated superheated steam flow rate for its set point control the PLS model performed very well with low prediction errors (RMSE of 3.1 and 4.1).
Osche, G R
2000-08-20
Single- and multiple-pulse detection statistics are presented for aperture-averaged direct detection optical receivers operating against partially developed speckle fields. A partially developed speckle field arises when the probability density function of the received intensity does not follow negative exponential statistics. The case of interest here is the target surface that exhibits diffuse as well as specular components in the scattered radiation. An approximate expression is derived for the integrated intensity at the aperture, which leads to single- and multiple-pulse discrete probability density functions for the case of a Poisson signal in Poisson noise with an additive coherent component. In the absence of noise, the single-pulse discrete density function is shown to reduce to a generalized negative binomial distribution. The radar concept of integration loss is discussed in the context of direct detection optical systems where it is shown that, given an appropriate set of system parameters, multiple-pulse processing can be more efficient than single-pulse processing over a finite range of the integration parameter n.
Irshad, Humayun; Roux, Ludovic; Racoceanu, Daniel
2013-01-01
Accurate counting of mitosis in breast cancer histopathology plays a critical role in the grading process. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. This work aims at improving the accuracy of mitosis detection by selecting the color channels that better capture the statistical and morphological features having mitosis discrimination from other objects. The proposed framework includes comprehensive analysis of first and second order statistical features together with morphological features in selected color channels and a study on balancing the skewed dataset using SMOTE method for increasing the predictive accuracy of mitosis classification. The proposed framework has been evaluated on MITOS data set during an ICPR 2012 contest and ranked second from 17 finalists. The proposed framework achieved 74% detection rate, 70% precision and 72% F-Measure. In future work, we plan to apply our mitosis detection tool to images produced by different types of slide scanners, including multi-spectral and multi-focal microscopy.
A Space–Time Permutation Scan Statistic for Disease Outbreak Detection
2005-01-01
Background The ability to detect disease outbreaks early is important in order to minimize morbidity and mortality through timely implementation of disease prevention and control measures. Many national, state, and local health departments are launching disease surveillance systems with daily analyses of hospital emergency department visits, ambulance dispatch calls, or pharmacy sales for which population-at-risk information is unavailable or irrelevant. Methods and Findings We propose a prospective space–time permutation scan statistic for the early detection of disease outbreaks that uses only case numbers, with no need for population-at-risk data. It makes minimal assumptions about the time, geographical location, or size of the outbreak, and it adjusts for natural purely spatial and purely temporal variation. The new method was evaluated using daily analyses of hospital emergency department visits in New York City. Four of the five strongest signals were likely local precursors to citywide outbreaks due to rotavirus, norovirus, and influenza. The number of false signals was at most modest. Conclusion If such results hold up over longer study times and in other locations, the space–time permutation scan statistic will be an important tool for local and national health departments that are setting up early disease detection surveillance systems. PMID:15719066
Oberer, Richard B.
2002-10-01
The current practice of nondestructive assay (NDA) of fissile materials using neutrons is dominated by the ^{3}He detector. This has been the case since the mid 1980s when Fission Multiplicity Detection (FMD) was replaced with thermal well counters and neutron multiplicity counting (NMC). The thermal well counters detect neutrons by neutron capture in the ^{3}He detector subsequent to moderation. The process of detection requires from 30 to 60 μs. As will be explained in Section 3.3 the rate of detecting correlated neutrons (signal) from the same fission are independent of this time but the rate of accidental correlations (noise) are proportional to this time. The well counters are at a distinct disadvantage when there is a large source of uncorrelated neutrons present from (α, n) reactions for example. Plastic scintillating detectors, as were used in FMD, require only about 20 ns to detect neutrons from fission. One thousandth as many accidental coincidences are therefore accumulated. The major problem with the use of fast-plastic scintillation detectors, however, is that both neutrons and gamma rays are detected. The pulses from the two are indistinguishable in these detectors. For this thesis, a new technique was developed to use higher-order time correlation statistics to distinguish combinations of neutron and gamma ray detections in fast-plastic scintillation detectors. A system of analysis to describe these correlations was developed based on simple physical principles. Other sources of correlations from non-fission events are identified and integrated into the analysis developed for fission events. A number of ratios and metric are identified to determine physical properties of the source from the correlations. It is possible to determine both the quantity being measured and detection efficiency from these ratios from a single measurement without a separate calibration. To account for detector dead-time, an alternative analytical technique
NASA Technical Reports Server (NTRS)
Moore, G. K.
1976-01-01
An investigation was carried out to determine the feasibility of mapping lineaments on SKYLAB photographs of central Tennessee and to determine the hydrologic significance of these lineaments, particularly as concerns the occurrence and productivity of ground water. Sixty-nine percent more lineaments were found on SKYLAB photographs by stereo viewing than by projection viewing, but longer lineaments were detected by projection viewing. Most SKYLAB lineaments consisted of topographic depressions and they followed or paralleled the streams. The remainder were found by vegetation alinements and the straight sides of ridges. Test drilling showed that the median yield of wells located on SKYLAB lineaments were about six times the median yield of wells located by random drilling. The best single detection method, in terms of potential savings, was stereo viewing. Larger savings might be achieved by locating wells on lineaments detected by both stereo viewing and projection.
Greenhalgh, T.
1997-01-01
It is possible to be seriously misled by taking the statistical competence (and/or the intellectual honesty) of authors for granted. Some common errors committed (deliberately or inadvertently) by the authors of papers are given in the final box. PMID:9277611
NASA Astrophysics Data System (ADS)
Chung, Moo K.; Kim, Seung-Goo; Schaefer, Stacey M.; van Reekum, Carien M.; Peschke-Schmitz, Lara; Sutterer, Matthew J.; Davidson, Richard J.
2014-03-01
The sparse regression framework has been widely used in medical image processing and analysis. However, it has been rarely used in anatomical studies. We present a sparse shape modeling framework using the Laplace- Beltrami (LB) eigenfunctions of the underlying shape and show its improvement of statistical power. Tradition- ally, the LB-eigenfunctions are used as a basis for intrinsically representing surface shapes as a form of Fourier descriptors. To reduce high frequency noise, only the first few terms are used in the expansion and higher frequency terms are simply thrown away. However, some lower frequency terms may not necessarily contribute significantly in reconstructing the surfaces. Motivated by this idea, we present a LB-based method to filter out only the significant eigenfunctions by imposing a sparse penalty. For dense anatomical data such as deformation fields on a surface mesh, the sparse regression behaves like a smoothing process, which will reduce the error of incorrectly detecting false negatives. Hence the statistical power improves. The sparse shape model is then applied in investigating the influence of age on amygdala and hippocampus shapes in the normal population. The advantage of the LB sparse framework is demonstrated by showing the increased statistical power.
Detection of coronal mass ejections using AdaBoost on grayscale statistic features
NASA Astrophysics Data System (ADS)
Zhang, Ling; Yin, Jian-qin; Lin, Jia-ben; Wang, Xiao-fan; Guo, Juan
2016-10-01
We present an automatic algorithm to detect coronal mass ejections (CMEs) in Large Angle Spectrometric Coronagraph (LASCO) C2 running difference images. The algorithm includes 3 steps: (1) split the running difference images into blocks according to slice size and analyze the grayscale statistics of the blocks from a set of images with and without CMEs; (2) select the optimal parameters for slice size, gray threshold and fraction of the bright points and (3) use AdaBoost to combine the weak classifiers designed according to the optimal parameters. Experimental results show that our method is effective and has a high accuracy rate.
NASA Astrophysics Data System (ADS)
Taboada, Fernando L.
2002-09-01
Low probability of intercept (LPI) is that property of an emitter that because of its low power, wide bandwidth, frequency variability, or other design attributes, makes it difficult to be detected or identified by means of passive intercept devices such as radar warning, electronic support and electronic intelligence receivers. In order to detect LPI radar waveforms new signal processing techniques are required. This thesis first develops a MATLAB toolbox to generate important types of LPI waveforms based on frequency and phase modulation. The power spectral density and the periodic ambiguity function are examined for each waveforms. These signals are then used to test a novel signal processing technique that detects the waveforms parameters and classifies the intercepted signal in various degrees of noise. The technique is based on the use of parallel filter (sub-band) arrays and higher order statistics (third-order cumulant estimator). Each sub-band signal is treated individually and is followed by the third-order estimator in order to suppress any symmetrical noise that might be present. The significance of this technique is that it separates the LPI waveforms in small frequency bands, providing a detailed time-frequency description of the unknown signal. Finally, the resulting output matrix is processed by a feature extraction routine to detect the waveforms parameters. Identification of the signal is based on the modulation parameters detected.
Whittington, S L
1991-06-01
Heterogeneity and small sample size are problems that affect many paleodemographic studies. The former can cause the overall distribution of age at death to be an amalgam that does not accurately reflect the distributions of any of the groups composing the heterogeneous population. The latter can make it difficult to separate significant from nonsignificant demographic differences between groups. Survival analysis, a methodology that involves the survival distribution function and various regression models, can be applied to distributions of age at death in order to reveal statistically significant demographic differences and to control for heterogeneity. Survival analysis was used on demographic data from a heterogeneous sample of skeletons of low status Maya who lived in and around Copan, Honduras, between A.D. 400 and 1200. Results contribute to understanding the collapse of Classic Maya civilization.
Detection of object-based manipulation by the statistical features of object contour.
Richao, Chen; Gaobo, Yang; Ningbo, Zhu
2014-03-01
Object-based manipulations, such as adding or removing objects for digital video, are usually malicious forgery operations. Compared with the conventional double MPEG compression or frame-based tampering, it makes more sense to detect these object-based manipulations because they might directly affect our understanding towards the video content. In this paper, a passive video forensics scheme is proposed for object-based forgery operations. After extracting the adjustable width areas around object boundary, several statistical features such as the moment features of detailed wavelet coefficients and the average gradient of each colour channel are obtained and input into support vector machine (SVM) as feature vectors for the classification of natural objects and forged ones. Experimental results on several videos sequence with static background show that the proposed approach can achieve an accuracy of correct detection from 70% to 95%.
Du, Fei; Li, Yibo; Jin, Shijiu
2015-08-18
An accurate performance analysis on the MDL criterion for source enumeration in array processing is presented in this paper. The enumeration results of MDL can be predicted precisely by the proposed procedure via the statistical analysis of the sample eigenvalues, whose distributive properties are investigated with the consideration of their interactions. A novel approach is also developed for the performance evaluation when the source number is underestimated by a number greater than one, which is denoted as "multiple-missed detection", and the probability of a specific underestimated source number can be estimated by ratio distribution analysis. Simulation results are included to demonstrate the superiority of the presented method over available results and confirm the ability of the proposed approach to perform multiple-missed detection analysis.
NASA Astrophysics Data System (ADS)
Løvsletten, Ola; Rypdal, Martin; Rypdal, Kristoffer; Fredriksen, Hege-Beate
2015-04-01
We explore the statistics of instrumental surface temperature records on 5°× 5°, 2°× 2°, and equal-area grids. In particular, we compute the significance of determinstic trends against two parsimonious null models; auto-regressive processes of order 1, AR(1), and fractional Gaussian noises (fGn's). Both of these two null models contain a memory parameter which quantifies the temporal climate variability, with white noise nested in both classes of models. Estimates of the persistence parameters show significant positive serial correlation for most grid cells, with higher persistence over occeans compared to land areas. This shows that, in a trend detection framework, we need to take into account larger spurious trends than what follows from the frequently used white noise assumption. Tested against the fGn null hypothesis, we find that ~ 68% (~ 47%) of the time series have significant trends at the 5% (1%) significance level. If we assume an AR(1) null hypothesis instead, then the result is that ~ 94% (~ 88%) of the time series have significant trends at the 5% (1%) significance level. For both null models, the locations where we do not find significant trends are mostly the ENSO regions and the North-Atlantic. We try to discriminate between the two null models by means of likelihood-ratios. If we at each grid point choose the null model preferred by the model selection test, we find that ~ 82% (~ 73%) of the time series have significant trends at the 5% (1%). We conclude that there is emerging evidence of significant warming trends also at regional scales, although with a much lower signal-to-noise ratio compared to global mean temperatures. Another finding is that many temperature records are consistent with error models for internal variability that exhibit long-range dependence, whereas the temperature fluctuations of the tropical oceans are strongly influenced by the ENSO, and therefore seemingly more consistent with random processes with short
Shia, Jinru
2016-01-01
The last two decades have seen significant advancement in our understanding of colorectal tumors with DNA mismatch repair (MMR) deficiency. The ever-emerging revelations of new molecular and genetic alterations in various clinical conditions have necessitated constant refinement of disease terminology and classification. Thus, a case with the clinical condition of hereditary non-polyposis colorectal cancer as defined by the Amsterdam criteria may be one of Lynch syndrome characterized by a germline defect in one of the several MMR genes, one of the yet-to-be-defined “Lynch-like syndrome” if there is evidence of MMR deficiency in the tumor but no detectable germline MMR defect or tumor MLH1 promoter methylation, or “familial colorectal cancer type X” if there is no evidence of MMR deficiency. The detection of these conditions carries significant clinical implications. The detection tools and strategies are constantly evolving. The Bethesda guidelines symbolize a selective approach that uses clinical information and tumor histology as the basis to select high-risk individuals. Such a selective approach has subsequently been found to have limited sensitivity, and is thus gradually giving way to the alternative universal approach that tests all newly diagnosed colorectal cancers. Notably, the universal approach also has its own limitations; its cost-effectiveness in real practice, in particular, remains to be determined. Meanwhile, technological advances such as the next-generation sequencing are offering the promise of direct genetic testing for MMR deficiency at an affordable cost probably in the near future. This article reviews the up-to-date molecular definitions of the various conditions related to MMR deficiency, and discusses the tools and strategies that have been used in detecting these conditions. Special emphasis will be placed on the evolving nature and the clinical importance of the disease definitions and the detection strategies. PMID:25716099
The statistical power to detect cross-scale interactions at macroscales
Wagner, Tyler; Fergus, C. Emi; Stow, Craig A.; Cheruvelil, Kendra S.; Soranno, Patricia A.
2016-01-01
Macroscale studies of ecological phenomena are increasingly common because stressors such as climate and land-use change operate at large spatial and temporal scales. Cross-scale interactions (CSIs), where ecological processes operating at one spatial or temporal scale interact with processes operating at another scale, have been documented in a variety of ecosystems and contribute to complex system dynamics. However, studies investigating CSIs are often dependent on compiling multiple data sets from different sources to create multithematic, multiscaled data sets, which results in structurally complex, and sometimes incomplete data sets. The statistical power to detect CSIs needs to be evaluated because of their importance and the challenge of quantifying CSIs using data sets with complex structures and missing observations. We studied this problem using a spatially hierarchical model that measures CSIs between regional agriculture and its effects on the relationship between lake nutrients and lake productivity. We used an existing large multithematic, multiscaled database, LAke multiscaled GeOSpatial, and temporal database (LAGOS), to parameterize the power analysis simulations. We found that the power to detect CSIs was more strongly related to the number of regions in the study rather than the number of lakes nested within each region. CSI power analyses will not only help ecologists design large-scale studies aimed at detecting CSIs, but will also focus attention on CSI effect sizes and the degree to which they are ecologically relevant and detectable with large data sets.
Statistical foundations of audit trail analysis for the detection of computer misuse
Helman, P. . Computer Science Dept.); Liepins, G. Univ. of Tennessee, Knoxville, TN . Computer Science Dept.)
1993-09-01
The authors model computer transactions as generated by two stationary stochastic processes, the legitimate (normal) process N and the misuse process M. They define misuse (anomaly) detection to be the identification of transactions most likely to have been generated by M. They formally demonstrate that the accuracy of misuse detectors is bounded by a function of the difference of the densities of the processes N and M over the space of transactions. In practice, detection accuracy can be far below this bound, and generally improves with increasing sample size of historical (training) data. Careful selection of transaction attributes also can improve detection accuracy; they suggest several criteria for attribute selection, including adequate sampling rate and separation between models. They demonstrate that exactly optimizing even the simplest of these criteria is NP-hard, thus motivating a heuristic approach. They further differentiate between modeling (density estimation) and nonmodeling approaches. They introduce a frequentist method as a special case of the former, and Wisdom and Sense, developed at Los Alamos National Laboratory, as a special case of the latter. For nonmodeling approaches such as Wisdom and Sense that generate statistical rules, they show that the rules must be maximally specific to ensure consistency with Bayesian analysis. Finally, they provide suggestions for testing detection systems and present limited test results using Wisdom and Sense and the frequentist approach.
NASA Astrophysics Data System (ADS)
Huang, Yizhen
2005-07-01
Digital image forgery detection is becoming increasing important. In recently 2 years, a new upsurge has been started to study direct detection methods, which utilize the hardware features of digital cameras. Such features may be weakened or lost once tampered, or they may not be consistent if synthesizing several images into a single one. This manuscript first clarifies the concept of trueness of digital images and summarizes these methods with their crack by a general model. The recently proposed EM algorithm plus Fourier transform that checks the Color Filter Array (CFA) interpolation statistical feature (ISF) is taken as a case study. We propose 3 methods to recover the CFA-ISF of a fake image: (1) artificial CFA interpolation (2) a linear CFA-ISF recovery model with optimal uniform measure (3) a quadratic CFA-ISF recovery model with least square measure. A software prototype CFA-ISF Indicator & Adjustor integrating the detection and anti-detection algorithms is developed and shown. Experiments under our product validate the effectiveness of our methods.
Rönnegård, Lars; Valdar, William
2012-07-24
A number of recent works have introduced statistical methods for detecting genetic loci that affect phenotypic variability, which we refer to as variability-controlling quantitative trait loci (vQTL). These are genetic variants whose allelic state predicts how much phenotype values will vary about their expected means. Such loci are of great potential interest in both human and non-human genetic studies, one reason being that a detected vQTL could represent a previously undetected interaction with other genes or environmental factors. The simultaneous publication of these new methods in different journals has in many cases precluded opportunity for comparison. We survey some of these methods, the respective trade-offs they imply, and the connections between them. The methods fall into three main groups: classical non-parametric, fully parametric, and semi-parametric two-stage approximations. Choosing between alternatives involves balancing the need for robustness, flexibility, and speed. For each method, we identify important assumptions and limitations, including those of practical importance, such as their scope for including covariates and random effects. We show in simulations that both parametric methods and their semi-parametric approximations can give elevated false positive rates when they ignore mean-variance relationships intrinsic to the data generation process. We conclude that choice of method depends on the trait distribution, the need to include non-genetic covariates, and the population size and structure, coupled with a critical evaluation of how these fit with the assumptions of the statistical model.
Dipnall, Joanna F.
2016-01-01
Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009–2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. Results After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). Conclusion The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and
Perles, Stephanie J.; Wagner, Tyler; Irwin, Brian J.; Manning, Douglas R.; Callahan, Kristina K.; Marshall, Matthew R.
2014-01-01
Forests are socioeconomically and ecologically important ecosystems that are exposed to a variety of natural and anthropogenic stressors. As such, monitoring forest condition and detecting temporal changes therein remain critical to sound public and private forestland management. The National Parks Service’s Vital Signs monitoring program collects information on many forest health indicators, including species richness, cover by exotics, browse pressure, and forest regeneration. We applied a mixed-model approach to partition variability in data for 30 forest health indicators collected from several national parks in the eastern United States. We then used the estimated variance components in a simulation model to evaluate trend detection capabilities for each indicator. We investigated the extent to which the following factors affected ability to detect trends: (a) sample design: using simple panel versus connected panel design, (b) effect size: increasing trend magnitude, (c) sample size: varying the number of plots sampled each year, and (d) stratified sampling: post-stratifying plots into vegetation domains. Statistical power varied among indicators; however, indicators that measured the proportion of a total yielded higher power when compared to indicators that measured absolute or average values. In addition, the total variability for an indicator appeared to influence power to detect temporal trends more than how total variance was partitioned among spatial and temporal sources. Based on these analyses and the monitoring objectives of theVital Signs program, the current sampling design is likely overly intensive for detecting a 5 % trend·year−1 for all indicators and is appropriate for detecting a 1 % trend·year−1 in most indicators.
NASA Astrophysics Data System (ADS)
Perles, Stephanie J.; Wagner, Tyler; Irwin, Brian J.; Manning, Douglas R.; Callahan, Kristina K.; Marshall, Matthew R.
2014-09-01
Forests are socioeconomically and ecologically important ecosystems that are exposed to a variety of natural and anthropogenic stressors. As such, monitoring forest condition and detecting temporal changes therein remain critical to sound public and private forestland management. The National Parks Service's Vital Signs monitoring program collects information on many forest health indicators, including species richness, cover by exotics, browse pressure, and forest regeneration. We applied a mixed-model approach to partition variability in data for 30 forest health indicators collected from several national parks in the eastern United States. We then used the estimated variance components in a simulation model to evaluate trend detection capabilities for each indicator. We investigated the extent to which the following factors affected ability to detect trends: (a) sample design: using simple panel versus connected panel design, (b) effect size: increasing trend magnitude, (c) sample size: varying the number of plots sampled each year, and (d) stratified sampling: post-stratifying plots into vegetation domains. Statistical power varied among indicators; however, indicators that measured the proportion of a total yielded higher power when compared to indicators that measured absolute or average values. In addition, the total variability for an indicator appeared to influence power to detect temporal trends more than how total variance was partitioned among spatial and temporal sources. Based on these analyses and the monitoring objectives of the Vital Signs program, the current sampling design is likely overly intensive for detecting a 5 % trend·year-1 for all indicators and is appropriate for detecting a 1 % trend·year-1 in most indicators.
Kim, Jihoon; Grillo, Janice M; Ohno-Machado, Lucila
2011-01-01
Objective To determine whether statistical and machine-learning methods, when applied to electronic health record (EHR) access data, could help identify suspicious (ie, potentially inappropriate) access to EHRs. Methods From EHR access logs and other organizational data collected over a 2-month period, the authors extracted 26 features likely to be useful in detecting suspicious accesses. Selected events were marked as either suspicious or appropriate by privacy officers, and served as the gold standard set for model evaluation. The authors trained logistic regression (LR) and support vector machine (SVM) models on 10-fold cross-validation sets of 1291 labeled events. The authors evaluated the sensitivity of final models on an external set of 58 events that were identified as truly inappropriate and investigated independently from this study using standard operating procedures. Results The area under the receiver operating characteristic curve of the models on the whole data set of 1291 events was 0.91 for LR, and 0.95 for SVM. The sensitivity of the baseline model on this set was 0.8. When the final models were evaluated on the set of 58 investigated events, all of which were determined as truly inappropriate, the sensitivity was 0 for the baseline method, 0.76 for LR, and 0.79 for SVM. Limitations The LR and SVM models may not generalize because of interinstitutional differences in organizational structures, applications, and workflows. Nevertheless, our approach for constructing the models using statistical and machine-learning techniques can be generalized. An important limitation is the relatively small sample used for the training set due to the effort required for its construction. Conclusion The results suggest that statistical and machine-learning methods can play an important role in helping privacy officers detect suspicious accesses to EHRs. PMID:21672912
[Significance of high sensitive CRP assay for early detection of newborn babies infection diseases].
Otsuki, Takaaki; Okabe, Hidetoshi
2002-01-01
We have evaluated the accuracy of high sensitive CRP assay method using evanescent wave Immunoassy system(Evanet 20) and significance of this assay on the early detection of infectious diseases in newborn babies. In this assay system, prozone phenomenon was not detected up to 40 mg/dl. The reproducibility of this assay was quite good and the intra run CV value of the same sample was less than 5% for the assay of serum, plasma and whole blood. There was a high correlation between the CRP values in the serum and plasma(r = 0.98, regression formula y = 0.89x + 4.07). Similarly, the values in whole blood and serum samples were quite well correlated(r = 0.98, regression formula y = 0.91x - 6.75). Various humoral elements such as bilirubin, hemoglobin and Chyl did not significantly influence this assay method. A slight increase in blood CRP was clearly demonstrated in the early phase of infectious diseases of newborn babies and monitoring of CRP by this assay system seemed to be quite useful to detect the early phase of infectious diseases in newborn babies. This assay system requires only a small quantity of whole blood to perform quantitative analysis of very small amounts of other substances. Accordingly, this assay system seems to be quite effective for monitoring minute increases in various proteinaceous blood components in emergent laboratory examination or POCT.
NASA Astrophysics Data System (ADS)
Wang, Jiahui; Li, Feng; Doi, Kunio; Li, Qiang
2009-11-01
Accurate detection of diffuse lung disease is an important step for computerized diagnosis and quantification of this disease. It is also a difficult clinical task for radiologists. We developed a computerized scheme to assist radiologists in the detection of diffuse lung disease in multi-detector computed tomography (CT). Two radiologists selected 31 normal and 37 abnormal CT scans with ground glass opacity, reticular, honeycombing and nodular disease patterns based on clinical reports. The abnormal cases in our database must contain at least an abnormal area with a severity of moderate or severe level that was subjectively rated by the radiologists. Because statistical texture features may lack the power to distinguish a nodular pattern from a normal pattern, the abnormal cases that contain only a nodular pattern were excluded. The areas that included specific abnormal patterns in the selected CT images were then delineated as reference standards by an expert chest radiologist. The lungs were first segmented in each slice by use of a thresholding technique, and then divided into contiguous volumes of interest (VOIs) with a 64 × 64 × 64 matrix size. For each VOI, we determined and employed statistical texture features, such as run-length and co-occurrence matrix features, to distinguish abnormal from normal lung parenchyma. In particular, we developed new run-length texture features with clear physical meanings to considerably improve the accuracy of our detection scheme. A quadratic classifier was employed for distinguishing between normal and abnormal VOIs by the use of a leave-one-case-out validation scheme. A rule-based criterion was employed to further determine whether a case was normal or abnormal. We investigated the impact of new and conventional texture features, VOI size and the dimensionality for regions of interest on detecting diffuse lung disease. When we employed new texture features for 3D VOIs of 64 × 64 × 64 voxels, our system achieved the
NASA Astrophysics Data System (ADS)
Greenberg, Ariela Caren
Differential item functioning (DIF) and differential distractor functioning (DDF) are methods used to screen for item bias (Camilli & Shepard, 1994; Penfield, 2008). Using an applied empirical example, this mixed-methods study examined the congruency and relationship of DIF and DDF methods in screening multiple-choice items. Data for Study I were drawn from item responses of 271 female and 236 male low-income children on a preschool science assessment. Item analyses employed a common statistical approach of the Mantel-Haenszel log-odds ratio (MH-LOR) to detect DIF in dichotomously scored items (Holland & Thayer, 1988), and extended the approach to identify DDF (Penfield, 2008). Findings demonstrated that the using MH-LOR to detect DIF and DDF supported the theoretical relationship that the magnitude and form of DIF and are dependent on the DDF effects, and demonstrated the advantages of studying DIF and DDF in multiple-choice items. A total of 4 items with DIF and DDF and 5 items with only DDF were detected. Study II incorporated an item content review, an important but often overlooked and under-published step of DIF and DDF studies (Camilli & Shepard). Interviews with 25 female and 22 male low-income preschool children and an expert review helped to interpret the DIF and DDF results and their comparison, and determined that a content review process of studied items can reveal reasons for potential item bias that are often congruent with the statistical results. Patterns emerged and are discussed in detail. The quantitative and qualitative analyses were conducted in an applied framework of examining the validity of the preschool science assessment scores for evaluating science programs serving low-income children, however, the techniques can be generalized for use with measures across various disciplines of research.
Look what else we found - clinically significant abnormalities detected during routine ROP screening
Jayadev, Chaitra; Vinekar, Anand; Bauer, Noel; Mangalesh, Shwetha; Mahendradas, Padmamalini; Kemmanu, Vasudha; Mallipatna, Ashwin; Shetty, Bhujang
2015-01-01
Purpose: The purpose of this study was to report the spectrum of anterior and posterior segment diagnoses in Asian Indian premature infants detected serendipitously during routine retinopathy of prematurity (ROP) screening during a 1 year period. Methods: A retrospective review of all Retcam (Clarity MSI, USA) imaging sessions during the year 2011 performed on infants born either <2001 g at birth and/or <34.1 weeks of gestation recruited for ROP screening was performed. All infants had a minimum of seven images at each session, which included the dilated anterior segment, disc, and macula center and the four quadrants using the 130° lens. Results: Of the 8954 imaging sessions of 1450 new infants recruited in 2011, there were 111 (7.66%) with a diagnosis other than ROP. Anterior segment diagnoses seen in 31 (27.9%) cases included clinically significant cataract, lid abnormalities, anophthalmos, microphthalmos, and corneal diseases. Posterior segment diagnoses in 80 (72.1%) cases included retinal hemorrhages, cherry red spots, and neonatal uveitis of infective etiologies. Of the 111 cases, 15 (13.5%) underwent surgical procedures and 24 (21.6%) underwent medical procedures; importantly, two eyes with retinoblastoma were detected which were managed timely. Conclusions: This study emphasizes the importance of ocular digital imaging in premature infants. Visually significant, potentially life-threatening, and even treatable conditions were detected serendipitously during routine ROP screening that may be missed or detected late otherwise. This pilot data may be used to advocate for a possible universal infant eye screening program using digital imaging. PMID:26139795
Automatic detection of significant and subtle arterial lesions from coronary CT angiography
NASA Astrophysics Data System (ADS)
Kang, Dongwoo; Slomka, Piotr; Nakazato, Ryo; Cheng, Victor Y.; Min, James K.; Li, Debiao; Berman, Daniel S.; Kuo, C.-C. Jay; Dey, Damini
2012-02-01
Visual analysis of three-dimensional (3D) Coronary Computed Tomography Angiography (CCTA) remains challenging due to large number of image slices and tortuous character of the vessels. We aimed to develop an accurate, automated algorithm for detection of significant and subtle coronary artery lesions compared to expert interpretation. Our knowledge-based automated algorithm consists of centerline extraction which also classifies 3 main coronary arteries and small branches in each main coronary artery, vessel linearization, lumen segmentation with scan-specific lumen attenuation ranges, and lesion location detection. Presence and location of lesions are identified using a multi-pass algorithm which considers expected or "normal" vessel tapering and luminal stenosis from the segmented vessel. Expected luminal diameter is derived from the scan by automated piecewise least squares line fitting over proximal and mid segments (67%) of the coronary artery, considering small branch locations. We applied this algorithm to 21 CCTA patient datasets, acquired with dual-source CT, where 7 datasets had 17 lesions with stenosis greater than or equal to 25%. The reference standard was provided by visual and quantitative identification of lesions with any >=25% stenosis by an experienced expert reader. Our algorithm identified 16 out of the 17 lesions confirmed by the expert. There were 16 additional lesions detected (average 0.13/segment); 6 out of 16 of these were actual lesions with <25% stenosis. On persegment basis, sensitivity was 94%, specificity was 86% and accuracy was 87%. Our algorithm shows promising results in the high sensitivity detection and localization of significant and subtle CCTA arterial lesions.
Significance of parametric spectral ratio methods in detection and recognition of whispered speech
NASA Astrophysics Data System (ADS)
Mathur, Arpit; Reddy, Shankar M.; Hegde, Rajesh M.
2012-12-01
In this article the significance of a new parametric spectral ratio method that can be used to detect whispered speech segments within normally phonated speech is described. Adaptation methods based on the maximum likelihood linear regression (MLLR) are then used to realize a mismatched train-test style speech recognition system. This proposed parametric spectral ratio method computes a ratio spectrum of the linear prediction (LP) and the minimum variance distortion-less response (MVDR) methods. The smoothed ratio spectrum is then used to detect whispered segments of speech within neutral speech segments effectively. The proposed LP-MVDR ratio method exhibits robustness at different SNRs as indicated by the whisper diarization experiments conducted on the CHAINS and the cell phone whispered speech corpus. The proposed method also performs reasonably better than the conventional methods for whisper detection. In order to integrate the proposed whisper detection method into a conventional speech recognition engine with minimal changes, adaptation methods based on the MLLR are used herein. The hidden Markov models corresponding to neutral mode speech are adapted to the whispered mode speech data in the whispered regions as detected by the proposed ratio method. The performance of this method is first evaluated on whispered speech data from the CHAINS corpus. The second set of experiments are conducted on the cell phone corpus of whispered speech. This corpus is collected using a set up that is used commercially for handling public transactions. The proposed whisper speech recognition system exhibits reasonably better performance when compared to several conventional methods. The results shown indicate the possibility of a whispered speech recognition system for cell phone based transactions.
Wagner, Tyler; Irwin, Brian J.; James R. Bence,; Daniel B. Hayes,
2016-01-01
Monitoring to detect temporal trends in biological and habitat indices is a critical component of fisheries management. Thus, it is important that management objectives are linked to monitoring objectives. This linkage requires a definition of what constitutes a management-relevant “temporal trend.” It is also important to develop expectations for the amount of time required to detect a trend (i.e., statistical power) and for choosing an appropriate statistical model for analysis. We provide an overview of temporal trends commonly encountered in fisheries management, review published studies that evaluated statistical power of long-term trend detection, and illustrate dynamic linear models in a Bayesian context, as an additional analytical approach focused on shorter term change. We show that monitoring programs generally have low statistical power for detecting linear temporal trends and argue that often management should be focused on different definitions of trends, some of which can be better addressed by alternative analytical approaches.
Towards spatial localisation of harmful algal blooms; statistics-based spatial anomaly detection
NASA Astrophysics Data System (ADS)
Shutler, J. D.; Grant, M. G.; Miller, P. I.
2005-10-01
Harmful algal blooms are believed to be increasing in occurrence and their toxins can be concentrated by filter-feeding shellfish and cause amnesia or paralysis when ingested. As a result fisheries and beaches in the vicinity of blooms may need to be closed and the local population informed. For this avoidance planning timely information on the existence of a bloom, its species and an accurate map of its extent would be prudent. Current research to detect these blooms from space has mainly concentrated on spectral approaches towards determining species. We present a novel statistics-based background-subtraction technique that produces improved descriptions of an anomaly's extent from remotely-sensed ocean colour data. This is achieved by extracting bulk information from a background model; this is complemented by a computer vision ramp filtering technique to specifically detect the perimeter of the anomaly. The complete extraction technique uses temporal-variance estimates which control the subtraction of the scene of interest from the time-weighted background estimate, producing confidence maps of anomaly extent. Through the variance estimates the method learns the associated noise present in the data sequence, providing robustness, and allowing generic application. Further, the use of the median for the background model reduces the effects of anomalies that appear within the time sequence used to generate it, allowing seasonal variations in the background levels to be closely followed. To illustrate the detection algorithm's application, it has been applied to two spectrally different oceanic regions.
A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features
Amudha, P.; Karthik, S.; Sivakumari, S.
2015-01-01
Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different. PMID:26221625
Nielsen, Mette J.; Kazankov, Konstantin; Leeming, Diana J.; Karsdal, Morten A.; Krag, Aleksander; Barrera, Francisco; McLeod, Duncan; George, Jacob; Grønbæk, Henning
2015-01-01
Background and Aim Detection of advanced fibrosis (Metavir F≥3) is important to identify patients with a high urgency of antiviral treatments vs. those whose treatment could be deferred (F≤2). The aim was to assess the diagnostic value of novel serological extracellular matrix protein fragments as potential biomarkers for clinically significant and advanced fibrosis. Methods Specific protein fragments of matrix metalloprotease degraded type I, III, IV and VI collagen (C1M, C3M, C4M, C6M) and type III and IV collagen formation (Pro-C3 and P4NP7S) were assessed in plasma from 403 chronic hepatitis C patients by specific ELISAs. Patients were stratified according to Metavir Fibrosis stage; F0 (n = 46), F1 (n = 161), F2 (n = 95), F3 (n = 44) and F4 (n = 33) based on liver biopsy. Results Pro-C3 was significantly elevated in patients with significant fibrosis (≥F2) compared to F0-F1 (p<0.05), while the markers C3M, C4M, C6M and P4NP7S were significantly elevated in patients with advanced fibrosis (≥F3) compared to F0-F2 (p<0.05). C1M showed no difference between fibrosis stages. Using Receiver Operating Characteristics analysis, the best marker for detecting ≥F2 and ≥F3 was Pro-C3 with AUC = 0.75 and AUC = 0.86. Combination of Pro-C3 and C4M with age, BMI and gender in a multiple ordered logistic regression model improved the diagnostic value for detecting ≥F2 and ≥F3 with AUC = 0.80 and AUC = 0.88. Conclusion The Pro-C3 protein fragment provided clinically relevant diagnostic accuracy as a single marker of liver fibrosis. A model combining Pro-C3 and C4M along with patient’s age, body mass index and gender increased the diagnostic power for identifying clinically significant fibrosis. PMID:26406331
Early snowmelt events: detection, distribution, and significance in a major sub-arctic watershed
NASA Astrophysics Data System (ADS)
Alese Semmens, Kathryn; Ramage, Joan; Bartsch, Annett; Liston, Glen E.
2013-03-01
High latitude drainage basins are experiencing higher average temperatures, earlier snowmelt onset in spring, and an increase in rain on snow (ROS) events in winter, trends that climate models project into the future. Snowmelt-dominated basins are most sensitive to winter temperature increases that influence the frequency of ROS events and the timing and duration of snowmelt, resulting in changes to spring runoff. Of specific interest in this study are early melt events that occur in late winter preceding melt onset in the spring. The study focuses on satellite determination and characterization of these early melt events using the Yukon River Basin (Canada/USA) as a test domain. The timing of these events was estimated using data from passive (Advanced Microwave Scanning Radiometer—EOS (AMSR-E)) and active (SeaWinds on Quick Scatterometer (QuikSCAT)) microwave remote sensors, employing detection algorithms for brightness temperature (AMSR-E) and radar backscatter (QuikSCAT). The satellite detected events were validated with ground station meteorological and hydrological data, and the spatial and temporal variability of the events across the entire river basin was characterized. Possible causative factors for the detected events, including ROS, fog, and positive air temperatures, were determined by comparing the timing of the events to parameters from SnowModel and National Centers for Environmental Prediction North American Regional Reanalysis (NARR) outputs, and weather station data. All melt events coincided with above freezing temperatures, while a limited number corresponded to ROS (determined from SnowModel and ground data) and a majority to fog occurrence (determined from NARR). The results underscore the significant influence that warm air intrusions have on melt in some areas and demonstrate the large temporal and spatial variability over years and regions. The study provides a method for melt detection and a baseline from which to assess future change.
The functional significance of calcification of coronary arteries as detected on CT.
Timins, M E; Pinsk, R; Sider, L; Bear, G
1991-12-01
We evaluated the coronary arteries on computed tomography (CT) scans of the chest and on coronary angiograms of 27 patients who underwent both studies. We related the presence or absence of coronary artery calcification on CT to percentage stenosis on angiogram. For the left anterior descending artery (LAD), the likelihood of calcification rose proportionately with degree of stenosis; this was less true for the circumflex, and not true for the right coronary artery (RCA). The sensitivity of CT in detecting coronary artery calcification in patients with angiographic criteria of significant coronary artery disease (CAD) was 78% for the LAD, 63% for the circumflex, and 16% for the RCA. Specificities were 78%, 80%, and 100%, and positive predictive values were 88%, 83%, and 100%. The high positive predictive values suggest that coronary artery calcification diagnosed by chest CT has a high correlation with clinically significant CAD. Therefore, when we detect such calcification in a patient without documented heart disease, we suggest that a cardiac workup is indicated.
Du, Fei; Li, Yibo; Jin, Shijiu
2015-01-01
An accurate performance analysis on the MDL criterion for source enumeration in array processing is presented in this paper. The enumeration results of MDL can be predicted precisely by the proposed procedure via the statistical analysis of the sample eigenvalues, whose distributive properties are investigated with the consideration of their interactions. A novel approach is also developed for the performance evaluation when the source number is underestimated by a number greater than one, which is denoted as “multiple-missed detection”, and the probability of a specific underestimated source number can be estimated by ratio distribution analysis. Simulation results are included to demonstrate the superiority of the presented method over available results and confirm the ability of the proposed approach to perform multiple-missed detection analysis. PMID:26295232
Hébert-Dufresne, Laurent; Grochow, Joshua A.; Allard, Antoine
2016-01-01
We introduce a network statistic that measures structural properties at the micro-, meso-, and macroscopic scales, while still being easy to compute and interpretable at a glance. Our statistic, the onion spectrum, is based on the onion decomposition, which refines the k-core decomposition, a standard network fingerprinting method. The onion spectrum is exactly as easy to compute as the k-cores: It is based on the stages at which each vertex gets removed from a graph in the standard algorithm for computing the k-cores. Yet, the onion spectrum reveals much more information about a network, and at multiple scales; for example, it can be used to quantify node heterogeneity, degree correlations, centrality, and tree- or lattice-likeness. Furthermore, unlike the k-core decomposition, the combined degree-onion spectrum immediately gives a clear local picture of the network around each node which allows the detection of interesting subgraphs whose topological structure differs from the global network organization. This local description can also be leveraged to easily generate samples from the ensemble of networks with a given joint degree-onion distribution. We demonstrate the utility of the onion spectrum for understanding both static and dynamic properties on several standard graph models and on many real-world networks. PMID:27535466
NASA Astrophysics Data System (ADS)
Hébert-Dufresne, Laurent; Grochow, Joshua A.; Allard, Antoine
2016-08-01
We introduce a network statistic that measures structural properties at the micro-, meso-, and macroscopic scales, while still being easy to compute and interpretable at a glance. Our statistic, the onion spectrum, is based on the onion decomposition, which refines the k-core decomposition, a standard network fingerprinting method. The onion spectrum is exactly as easy to compute as the k-cores: It is based on the stages at which each vertex gets removed from a graph in the standard algorithm for computing the k-cores. Yet, the onion spectrum reveals much more information about a network, and at multiple scales; for example, it can be used to quantify node heterogeneity, degree correlations, centrality, and tree- or lattice-likeness. Furthermore, unlike the k-core decomposition, the combined degree-onion spectrum immediately gives a clear local picture of the network around each node which allows the detection of interesting subgraphs whose topological structure differs from the global network organization. This local description can also be leveraged to easily generate samples from the ensemble of networks with a given joint degree-onion distribution. We demonstrate the utility of the onion spectrum for understanding both static and dynamic properties on several standard graph models and on many real-world networks.
Structural damage detection using extended Kalman filter combined with statistical process control
NASA Astrophysics Data System (ADS)
Jin, Chenhao; Jang, Shinae; Sun, Xiaorong
2015-04-01
Traditional modal-based methods, which identify damage based upon changes in vibration characteristics of the structure on a global basis, have received considerable attention in the past decades. However, the effectiveness of the modalbased methods is dependent on the type of damage and the accuracy of the structural model, and these methods may also have difficulties when applied to complex structures. The extended Kalman filter (EKF) algorithm which has the capability to estimate parameters and catch abrupt changes, is currently used in continuous and automatic structural damage detection to overcome disadvantages of traditional methods. Structural parameters are typically slow-changing variables under effects of operational and environmental conditions, thus it would be difficult to observe the structural damage and quantify the damage in real-time with EKF only. In this paper, a Statistical Process Control (SPC) is combined with EFK method in order to overcome this difficulty. Based on historical measurements of damage-sensitive feathers involved in the state-space dynamic models, extended Kalman filter (EKF) algorithm is used to produce real-time estimations of these features as well as standard derivations, which can then be used to form control ranges for SPC to detect any abnormality of the selected features. Moreover, confidence levels of the detection can be adjusted by choosing different times of sigma and number of adjacent out-of-range points. The proposed method is tested using simulated data of a three floors linear building in different damage scenarios, and numerical results demonstrate high damage detection accuracy and light computation of this presented method.
NASA Astrophysics Data System (ADS)
Hurtado, Miguel A.
In this work, we consider the application of classical statistical inference to the fusion of data from different sensing technologies for object detection applications in order to increase the overall performance for a given active safety automotive system. Research evolved mainly around a centralized sensor fusion architecture assuming that three non-identical sensors, modeled by corresponding probability density functions (pdfs), provide discrete information of target being present or absent with associated probabilities of detection and false alarm for the sensor fusion engine. The underlying sensing technologies are the following standard automotive sensors: 24.5 GHz radar, high dynamic range infrared camera and a laser-radar. A complete mathematical framework was developed to select the optimal decision rule based on a generalized multinomial distribution resulting from a sum of weighted Bernoulli random variables from the Neyman-Pearson lemma and the likelihood ratio test. Moreover, to better understand the model and to obtain upper bounds on the performance of the fusion rules, we assumed exponential pdfs for each sensor and a parallel mathematical expression was obtained based on a generalized gamma distribution resulting from a sum of weighted exponential random variables for the situation when the continuous random vector of information is available. Mathematical expressions and results were obtained for modeling the following case scenarios: (i) non-identical sensors, (ii) identical sensors, (iii) combination of nonidentical and identical sensors, (iv) faulty sensor operation, (v) dominant sensor operation, (vi) negative sensor operation, and (vii) distributed sensor fusion. The second and final part of this research focused on: (a) simulation of statistical models for each sensing technology, (b) comparisons with distributed fusion, (c) overview of dynamic sensor fusion and adaptive decision rules.
Yokoyama, Shozo; Takenaka, Naomi
2005-04-01
Red-green color vision is strongly suspected to enhance the survival of its possessors. Despite being red-green color blind, however, many species have successfully competed in nature, which brings into question the evolutionary advantage of achieving red-green color vision. Here, we propose a new method of identifying positive selection at individual amino acid sites with the premise that if positive Darwinian selection has driven the evolution of the protein under consideration, then it should be found mostly at the branches in the phylogenetic tree where its function had changed. The statistical and molecular methods have been applied to 29 visual pigments with the wavelengths of maximal absorption at approximately 510-540 nm (green- or middle wavelength-sensitive [MWS] pigments) and at approximately 560 nm (red- or long wavelength-sensitive [LWS] pigments), which are sampled from a diverse range of vertebrate species. The results show that the MWS pigments are positively selected through amino acid replacements S180A, Y277F, and T285A and that the LWS pigments have been subjected to strong evolutionary conservation. The fact that these positively selected M/LWS pigments are found not only in animals with red-green color vision but also in those with red-green color blindness strongly suggests that both red-green color vision and color blindness have undergone adaptive evolution independently in different species.
Kurtz, S.E.; Fields, D.E.
1983-10-01
This report describes a version of the TERPED/P computer code that is very useful for small data sets. A new algorithm for determining the Kolmogorov-Smirnov (KS) statistics is used to extend program applicability. The TERPED/P code facilitates the analysis of experimental data and assists the user in determining its probability distribution function. Graphical and numerical tests are performed interactively in accordance with the user's assumption of normally or log-normally distributed data. Statistical analysis options include computation of the chi-square statistic and the KS one-sample test statistic and the corresponding significance levels. Cumulative probability plots of the user's data are generated either via a local graphics terminal, a local line printer or character-oriented terminal, or a remote high-resolution graphics device such as the FR80 film plotter or the Calcomp paper plotter. Several useful computer methodologies suffer from limitations of their implementations of the KS nonparametric test. This test is one of the more powerful analysis tools for examining the validity of an assumption about the probability distribution of a set of data. KS algorithms are found in other analysis codes, including the Statistical Analysis Subroutine (SAS) package and earlier versions of TERPED. The inability of these algorithms to generate significance levels for sample sizes less than 50 has limited their usefulness. The release of the TERPED code described herein contains algorithms to allow computation of the KS statistic and significance level for data sets of, if the user wishes, as few as three points. Values computed for the KS statistic are within 3% of the correct value for all data set sizes.
NASA Astrophysics Data System (ADS)
Flach, Milan; Mahecha, Miguel; Gans, Fabian; Rodner, Erik; Bodesheim, Paul; Guanche-Garcia, Yanira; Brenning, Alexander; Denzler, Joachim; Reichstein, Markus
2016-04-01
The number of available Earth observations (EOs) is currently substantially increasing. Detecting anomalous patterns in these multivariate time series is an important step in identifying changes in the underlying dynamical system. Likewise, data quality issues might result in anomalous multivariate data constellations and have to be identified before corrupting subsequent analyses. In industrial application a common strategy is to monitor production chains with several sensors coupled to some statistical process control (SPC) algorithm. The basic idea is to raise an alarm when these sensor data depict some anomalous pattern according to the SPC, i.e. the production chain is considered 'out of control'. In fact, the industrial applications are conceptually similar to the on-line monitoring of EOs. However, algorithms used in the context of SPC or process monitoring are rarely considered for supervising multivariate spatio-temporal Earth observations. The objective of this study is to exploit the potential and transferability of SPC concepts to Earth system applications. We compare a range of different algorithms typically applied by SPC systems and evaluate their capability to detect e.g. known extreme events in land surface processes. Specifically two main issues are addressed: (1) identifying the most suitable combination of data pre-processing and detection algorithm for a specific type of event and (2) analyzing the limits of the individual approaches with respect to the magnitude, spatio-temporal size of the event as well as the data's signal to noise ratio. Extensive artificial data sets that represent the typical properties of Earth observations are used in this study. Our results show that the majority of the algorithms used can be considered for the detection of multivariate spatiotemporal events and directly transferred to real Earth observation data as currently assembled in different projects at the European scale, e.g. http://baci-h2020.eu
Statistics, Handle with Care: Detecting Multiple Model Components with the Likelihood Ratio Test
NASA Astrophysics Data System (ADS)
Protassov, Rostislav; van Dyk, David A.; Connors, Alanna; Kashyap, Vinay L.; Siemiginowska, Aneta
2002-05-01
The likelihood ratio test (LRT) and the related F-test, popularized in astrophysics by Eadie and coworkers in 1971, Bevington in 1969, Lampton, Margon, & Bowyer, in 1976, Cash in 1979, and Avni in 1978, do not (even asymptotically) adhere to their nominal χ2 and F-distributions in many statistical tests common in astrophysics, thereby casting many marginal line or source detections and nondetections into doubt. Although the above authors illustrate the many legitimate uses of these statistics, in some important cases it can be impossible to compute the correct false positive rate. For example, it has become common practice to use the LRT or the F-test to detect a line in a spectral model or a source above background despite the lack of certain required regularity conditions. (These applications were not originally suggested by Cash or by Bevington.) In these and other settings that involve testing a hypothesis that is on the boundary of the parameter space, contrary to common practice, the nominal χ2 distribution for the LRT or the F-distribution for the F-test should not be used. In this paper, we characterize an important class of problems in which the LRT and the F-test fail and illustrate this nonstandard behavior. We briefly sketch several possible acceptable alternatives, focusing on Bayesian posterior predictive probability values. We present this method in some detail since it is a simple, robust, and intuitive approach. This alternative method is illustrated using the gamma-ray burst of 1997 May 8 (GRB 970508) to investigate the presence of an Fe K emission line during the initial phase of the observation. There are many legitimate uses of the LRT and the F-test in astrophysics, and even when these tests are inappropriate, there remain several statistical alternatives (e.g., judicious use of error bars and Bayes factors). Nevertheless, there are numerous cases of the inappropriate use of the LRT and similar tests in the literature, bringing substantive
2013-01-01
biomarkers, the incorporation of age and gender into statistical models significantly improved their predictive performance in the detection of HCC. PMID:24564861
Empirical Bayes scan statistics for detecting clusters of disease risk variants in genetic studies.
McCallum, Kenneth J; Ionita-Laza, Iuliana
2015-12-01
Recent developments of high-throughput genomic technologies offer an unprecedented detailed view of the genetic variation in various human populations, and promise to lead to significant progress in understanding the genetic basis of complex diseases. Despite this tremendous advance in data generation, it remains very challenging to analyze and interpret these data due to their sparse and high-dimensional nature. Here, we propose novel applications and new developments of empirical Bayes scan statistics to identify genomic regions significantly enriched with disease risk variants. We show that the proposed empirical Bayes methodology can be substantially more powerful than existing scan statistics methods especially so in the presence of many non-disease risk variants, and in situations when there is a mixture of risk and protective variants. Furthermore, the empirical Bayes approach has greater flexibility to accommodate covariates such as functional prediction scores and additional biomarkers. As proof-of-concept we apply the proposed methods to a whole-exome sequencing study for autism spectrum disorders and identify several promising candidate genes.
NASA Astrophysics Data System (ADS)
Hilliard, Antony
Energy Monitoring and Targeting is a well-established business process that develops information about utility energy consumption in a business or institution. While M&T has persisted as a worthwhile energy conservation support activity, it has not been widely adopted. This dissertation explains M&T challenges in terms of diagnosing and controlling energy consumption, informed by a naturalistic field study of M&T work. A Cognitive Work Analysis of M&T identifies structures that diagnosis can search, information flows un-supported in canonical support tools, and opportunities to extend the most popular tool for MM&T: Cumulative Sum of Residuals (CUSUM) charts. A design application outlines how CUSUM charts were augmented with a more contemporary statistical change detection strategy, Recursive Parameter Estimates, modified to better suit the M&T task using Representation Aiding principles. The design was experimentally evaluated in a controlled M&T synthetic task, and was shown to significantly improve diagnosis performance.
NASA Astrophysics Data System (ADS)
Zakaria, Chahnez; Curé, Olivier; Salzano, Gabriella; Smaïli, Kamel
In Computer Supported Cooperative Work (CSCW), it is crucial for project leaders to detect conflicting situations as early as possible. Generally, this task is performed manually by studying a set of documents exchanged between team members. In this paper, we propose a full-fledged automatic solution that identifies documents, subjects and actors involved in relational conflicts. Our approach detects conflicts in emails, probably the most popular type of documents in CSCW, but the methods used can handle other text-based documents. These methods rely on the combination of statistical and ontological operations. The proposed solution is decomposed in several steps: (i) we enrich a simple negative emotion ontology with terms occuring in the corpus of emails, (ii) we categorize each conflicting email according to the concepts of this ontology and (iii) we identify emails, subjects and team members involved in conflicting emails using possibilistic description logic and a set of proposed measures. Each of these steps are evaluated and validated on concrete examples. Moreover, this approach's framework is generic and can be easily adapted to domains other than conflicts, e.g. security issues, and extended with operations making use of our proposed set of measures.
Kyin May, Kyin; Htet Zaw, Min; Capistrano Canlas, Carolina; Hannah Seah, Mary; Menil Serrano, Catherine; Hartman, Mikael; Ho, Pei
2013-01-01
Objective: This study aims to evaluate the accuracy of AVF and AVG duplex ultrasound (US) compared to angiographic findings in patients with suspected failing dialysis access. Materials and Methods: From July 2008 to December 2010, US was performed on 35 hemodialysis patients with 51 vascular accesses having clinical feature or dialysis parameter suspicious of access problem. Peak systolic velocity ratio of ≥2 was the criteria for diagnosing stenosis ≥50%. Fistulogram was performed in all these patients. Results of US and fistulogram were compared using Kappa and Receiver Operator Characteristic (ROC) analyses. Results: In 51 accesses (35 AVF, 16 AVG), US diagnosed significant stenosis in 45 accesses according to the criteria and angiogram confirmed 44 significant stenoses. In AVF lesions, Kappa was 0.533 with 93.3% sensitivity and 60% specificity for US whereas in AVG lesions, Kappa was 0.636 with 100% sensitivity and 50% specificity. Overall Kappa value of 0.56 meant fair to good agreement. ROC demonstrated area under the curve being 0.79 for all cases and was significant (p = 0.016). Using the ≥50% criteria for stenosis diagnosed by US yielded the best sensitivity (95.5%) and specificity (57.1%). Conclusion: Duplex ultrasound study, using ≥50% criteria, is a sensitive tool for stenosis detection in patients with suspected failing AVF and AVG. PMID:23641285
Post, Gloria B; Louis, Judith B; Cooper, Keith R; Boros-Russo, Betty Jane; Lippincott, R Lee
2009-06-15
After detection of perfluorooctanoic acid (PFOA) in two New Jersey (NJ) public water systems (PWS) at concentrations up to 0.19 microg/L, a study of PFOA in 23 other NJ PWS was conducted in 2006. PFOA was detected in 15 (65%) of the systems at concentrations ranging from 0.005 to 0.039 microg/L. To assess the significance of these data, the contribution of drinking water to human exposure to PFOA was evaluated, and a health-based drinking water concentration protective for lifetime exposure of 0.04 microg/L was developed through a risk assessment approach. Both the exposure assessment and the health-based drinking water concentrations are based on the previously reported 100:1 ratio between the concentration of PFOA in serum and drinking water in a community with highly contaminated drinking water. The applicability of this ratio to lower drinking water concentrations was confirmed using data on serum levels and water concentrations from other communities. The health-based concentration is based on toxicological end points identified by U.S. Environmental Protection Agency (USEPA) in its 2005 draft risk assessment Recent information on PFOA's toxicity not considered in the USEPA risk assessment urther supports the health-based concentration of 0.04 microg/L. In additional sampling of 18 PWS in 2007-2008, PFOA in most systems was below the health-based concentration. However, PFOA was detected above the health-based concentration in five systems, including one not previously sampled.
NASA Astrophysics Data System (ADS)
Sheet, Debdoot; Karamalis, Athanasios; Kraft, Silvan; Noël, Peter B.; Vag, Tibor; Sadhu, Anup; Katouzian, Amin; Navab, Nassir; Chatterjee, Jyotirmoy; Ray, Ajoy K.
2013-03-01
Breast cancer is the most common form of cancer in women. Early diagnosis can significantly improve lifeexpectancy and allow different treatment options. Clinicians favor 2D ultrasonography for breast tissue abnormality screening due to high sensitivity and specificity compared to competing technologies. However, inter- and intra-observer variability in visual assessment and reporting of lesions often handicaps its performance. Existing Computer Assisted Diagnosis (CAD) systems though being able to detect solid lesions are often restricted in performance. These restrictions are inability to (1) detect lesion of multiple sizes and shapes, and (2) differentiate between hypo-echoic lesions from their posterior acoustic shadowing. In this work we present a completely automatic system for detection and segmentation of breast lesions in 2D ultrasound images. We employ random forests for learning of tissue specific primal to discriminate breast lesions from surrounding normal tissues. This enables it to detect lesions of multiple shapes and sizes, as well as discriminate between hypo-echoic lesion from associated posterior acoustic shadowing. The primal comprises of (i) multiscale estimated ultrasonic statistical physics and (ii) scale-space characteristics. The random forest learns lesion vs. background primal from a database of 2D ultrasound images with labeled lesions. For segmentation, the posterior probabilities of lesion pixels estimated by the learnt random forest are hard thresholded to provide a random walks segmentation stage with starting seeds. Our method achieves detection with 99.19% accuracy and segmentation with mean contour-to-contour error < 3 pixels on a set of 40 images with 49 lesions.
Kosaka, Priscila M; Tamayo, Javier; Ruz, José J; Puertas, Sara; Polo, Ester; Grazu, Valeria; de la Fuente, Jesús M; Calleja, Montserrat
2013-02-21
In the last decade, microcantilever biosensors have shown enormous potential for highly sensitive label-free detection of nucleic acid and proteins. Despite the enormous advances, the promise of applications of this technology in the biomedical field has been frustrated because of its low reproducibility. Here we tackle the reproducibility issue in microcantilever biosensors and provide the guidelines to minimize the deviations in the biosensor response between different assays. We use as a model system the label-free end-point detection of horseradish peroxidase. We choose the end-point detection mode because of its suitability for implementation in the clinical field that requires simplicity and point-of-care capability. Our study comprises the analysis of 1012 cantilevers with different antibody surface densities, two blocking strategies based on polyethylene-glycol (PEG) and bovine serum albumin (BSA) and stringent controls. The study reveals that the performance of the assay critically depends on both antibody surface density and blocking strategies. We find that the optimal conditions involve antibody surface densities near but below saturation and blocking with PEG. We find that the surface stress induced by the antibody-antigen binding is significantly correlated with the surface stress generated during the antibody attachment and blocking steps. The statistical correlation is harnessed to identify immobilization failure or success, and thus enhancing the specificity and sensitivity of the assay. This procedure enables achieving rates of true positives and true negatives of 90% and 91% respectively. The detection limit is of 10 ng mL(-1) (250 pM) that is similar to the detection limit obtained in our enzyme-linked immunosorbent assay (ELISA) and at least two orders of magnitude smaller than that achieved with well-established label-free biosensors such as a quartz crystal microbalance (QCM) and surface plasmon resonance (SPR) sensor.
Significance of Viable but Nonculturable Escherichia coli: Induction, Detection, and Control.
Ding, Tian; Suo, Yuanjie; Xiang, Qisen; Zhao, Xihong; Chen, Shiguo; Ye, Xingqian; Liu, Donghong
2017-03-28
Diseases caused by foodborne or waterborne pathogens are emerging. Many pathogens can enter into the viable but nonculturable (VBNC) state, which is a survival strategy when exposed to harsh environmental stresses. Pathogens in the VBNC state have the ability to evade conventional microbiological detection methods, posing a significant and potential health risk. Therefore, controlling VBNC bacteria in food processing and the environment is of great importance. As the typical one of the gram-negatives, Escherichia coli (E. coli) is a widespread foodborne and waterborne pathogenic bacterium and is able to enter into a VBNC state in extreme conditions (similar to the other gram-negative bacteria), including inducing factors and resuscitation stimulus. VBNC E. coli has the ability to recover both culturability and pathogenicity, which may bring potential health risk. This review describes the concrete factors (nonthermal treatment, chemical agents, and environmental factors) that induce E. coli into the VBNC state, the condition or stimulus required for resuscitation of VBNC E. coli, and the methods for detecting VBNC E. coli. Furthermore, the mechanism of genes and proteins involved in the VBNC E. coli is also discussed in this review.
Sibio, Simone; Fiorani, Cristina; Stolfi, Carmine; Divizia, Andrea; Pezzuto, Roberto; Montagnese, Fabrizio; Bagaglini, Giulia; Sammartino, Paolo; Sica, Giuseppe Sigismondo
2015-09-27
Peritoneal washing is now part of the standard clinical practice in several abdominal and pelvic neoplasias. However, in colorectal cancer surgery, intra-peritoneal free cancer cells (IFCC) presence is not routinely investigated and their prognostic meaning is still unclear. When peritoneal washing results are positive for the presence of IFCC a worse outcome is usually expected in these colorectal cancer operated patients, but it what is not clear is whether it is associated with an increased risk of local recurrence. It is authors' belief that one of the main reasons why IFCC are not researched as integral part of the routine staging system for colon cancer is that there still isn't a diagnostic or detection method with enough sensibility and specificity. However, the potential clinical implications of a routine research for the presence IFCC in colon neoplasias are enormous: not only to obtain a more accurate clinical staging but also to offer different therapy protocols, based on the presence of IFCC. Based on this, adjuvant chemotherapy could be offered to those patients found to be positive for IFCC; also, protocols of proactive intraperitoneal chemotherapy could be applied. Although presence of IFCC appears to have a valid prognostic significance, further studies are needed to standardize detection and examination procedures, to determine if there are and which are the stages more likely to benefit from routine search for IFCC.
Detecting trends in raptor counts: power and type I error rates of various statistical tests
Hatfield, J.S.; Gould, W.R.; Hoover, B.A.; Fuller, M.R.; Lindquist, E.L.
1996-01-01
We conducted simulations that estimated power and type I error rates of statistical tests for detecting trends in raptor population count data collected from a single monitoring site. Results of the simulations were used to help analyze count data of bald eagles (Haliaeetus leucocephalus) from 7 national forests in Michigan, Minnesota, and Wisconsin during 1980-1989. Seven statistical tests were evaluated, including simple linear regression on the log scale and linear regression with a permutation test. Using 1,000 replications each, we simulated n = 10 and n = 50 years of count data and trends ranging from -5 to 5% change/year. We evaluated the tests at 3 critical levels (alpha = 0.01, 0.05, and 0.10) for both upper- and lower-tailed tests. Exponential count data were simulated by adding sampling error with a coefficient of variation of 40% from either a log-normal or autocorrelated log-normal distribution. Not surprisingly, tests performed with 50 years of data were much more powerful than tests with 10 years of data. Positive autocorrelation inflated alpha-levels upward from their nominal levels, making the tests less conservative and more likely to reject the null hypothesis of no trend. Of the tests studied, Cox and Stuart's test and Pollard's test clearly had lower power than the others. Surprisingly, the linear regression t-test, Collins' linear regression permutation test, and the nonparametric Lehmann's and Mann's tests all had similar power in our simulations. Analyses of the count data suggested that bald eagles had increasing trends on at least 2 of the 7 national forests during 1980-1989.
Mengistu, M; Laryea, E; Miller, A; Wall, J R
1986-01-01
The clinical significance of a circulating autoantibody against a recently identified soluble human eye muscle-derived antigen was studied in patients with Graves' ophthalmopathy and autoimmune thyroid disorders. Tests were positive in 73% of patients with Graves' ophthalmopathy, including six of seven with no associated thyroid disease (euthyroid Graves' disease). Tests were also positive in 27% of patients with hyperthyroidism but no clinically apparent eye disease, in 13% of patients with Hashimoto's thyroiditis without eye disease, in two of 12 patients with subacute thyroiditis, in one of 20 patients with nonimmunological thyroid disorders but in none of 39 normal subjects. There were significant positive correlations between serum levels of the antibody (expressed as a titre) and the severity of the eye muscle component quantified as an index as well as the duration of the eye disease. Antibodies were detected in three of five patients with only lid lag and state who subsequently developed active ophthalmopathy, in six of nine patients who developed eye disease after treatment of their hyperthyroidism and in one of eight first degree relatives of patients with Graves' ophthalmopathy. In addition three of the 12 patients with autoimmune thyroid disease without apparent eye involvement, but positive antibody tests, have developed ophthalmopathy since the time of testing. These findings suggest that tests for antibodies against a soluble human eye muscle antigen may be useful clinically as a diagnostic test and to predict the onset of eye disease in predisposed patients and subjects. PMID:3539423
Clare, Elizabeth L
2014-01-01
The emerging field of ecological genomics contains several broad research areas. Comparative genomic and conservation genetic analyses are providing great insight into adaptive processes, species bottlenecks, population dynamics and areas of conservation priority. Now the same technological advances in high-throughput sequencing, coupled with taxonomically broad sequence repositories, are providing greater resolution and fundamentally new insights into functional ecology. In particular, we now have the capacity in some systems to rapidly identify thousands of species-level interactions using non-invasive methods based on the detection of trace DNA. This represents a powerful tool for conservation biology, for example allowing the identification of species with particularly inflexible niches and the investigation of food-webs or interaction networks with unusual or vulnerable dynamics. As they develop, these analyses will no doubt provide significant advances in the field of restoration ecology and the identification of appropriate locations for species reintroduction, as well as highlighting species at ecological risk. Here, I describe emerging patterns that have come from the various initial model systems, the advantages and limitations of the technique and key areas where these methods may significantly advance our empirical and applied conservation practices. PMID:25553074
Kumar, Rajesh; Srivastava, Subodh
2015-01-01
A framework for automated detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features is proposed and examined. The various stages involved in the proposed methodology include enhancement of microscopic images, segmentation of background cells, features extraction, and finally the classification. An appropriate and efficient method is employed in each of the design steps of the proposed framework after making a comparative analysis of commonly used method in each category. For highlighting the details of the tissue and structures, the contrast limited adaptive histogram equalization approach is used. For the segmentation of background cells, k-means segmentation algorithm is used because it performs better in comparison to other commonly used segmentation methods. In feature extraction phase, it is proposed to extract various biologically interpretable and clinically significant shapes as well as morphology based features from the segmented images. These include gray level texture features, color based features, color gray level texture features, Law's Texture Energy based features, Tamura's features, and wavelet features. Finally, the K-nearest neighborhood method is used for classification of images into normal and cancerous categories because it is performing better in comparison to other commonly used methods for this application. The performance of the proposed framework is evaluated using well-known parameters for four fundamental tissues (connective, epithelial, muscular, and nervous) of randomly selected 1000 microscopic biopsy images. PMID:27006938
The Approach of Statistically Validated Networks also Detects Under-Expressions
NASA Astrophysics Data System (ADS)
Mantegna, Rosario Nunzio
Society currently generates a gargantuan amount of new data each day and a significant amount of these data can be described and modeled in terms of networks and/or flows in them. One ubiquitous character of complex systems is the heterogeneity of their components, of their relationships, and of their pair similarities. To go beyond the detection and modeling of heterogeneity, it is highly informative to filter out features and relationships that cannot be explained by a random null hypothesis taking into account the heterogeneity of the system. Information filtering performed on networks and, more generally, on complex systems allows researchers to detect and characterize structures and phenomena that are present in the system of interest...
Prognostic significance of computed tomography-detected extramural vascular invasion in colon cancer
Yao, Xun; Yang, Su-Xing; Song, Xing-He; Cui, Yan-Cheng; Ye, Ying-Jiang; Wang, Yi
2016-01-01
AIM To compare disease-free survival (DFS) between extramural vascular invasion (EMVI)-positive and -negative colon cancer patients evaluated by computed tomography (CT). METHODS Colon cancer patients (n = 194) undergoing curative surgery between January 2009 and December 2013 were included. Each patient’s demographics, cancer characteristics, EMVI status, pathological status and survival outcomes were recorded. All included patients had been routinely monitored until December 2015. EMVI was defined as tumor tissue within adjacent vessels beyond the colon wall as seen on enhanced CT. Disease recurrence was defined as metachronous metastases, local recurrence, or death due to colon cancer. Kaplan-Meier analyses were used to compare DFS between the EMVI-positive and -negative groups. Cox’s proportional hazards models were used to measure the impact of confounding variables on survival rates. RESULTS EMVI was observed on CT (ctEMVI) in 60 patients (30.9%, 60/194). One year after surgery, there was no statistically significant difference regarding the rates of progressive events between EMVI-positive and -negative patients [11.7% (7/60) and 6.7% (9/134), respectively; P = 0.266]. At the study endpoint, the EMVI-positive patients had significantly more progressive events than the EMVI-negative patients [43.3% (26/60) and 14.9% (20/134), respectively; odds ratio = 4.4, P < 0.001]. Based on the Kaplan-Meier method, the cumulative 1-year DFS rates were 86.7% (95%CI: 82.3-91.1) and 92.4% (95%CI: 90.1-94.7) for EMVI-positive and EMVI-negative patients, respectively. The cumulative 3-year DFS rates were 49.5% (95%CI: 42.1-56.9) and 85.8% (95%CI: 82.6-89.0), respectively. Cox proportional hazards regression analysis revealed that ctEMVI was an independent predictor of DFS with a hazard ratio of 2.15 (95%CI: 1.12-4.14, P = 0.023). CONCLUSION ctEMVI may be helpful when evaluating disease progression in colon cancer patients. PMID:27610025
Nonparametric simulation-based statistics for detecting linkage in general pedigrees
Davis, S.; Schroeder, M.; Weeks, D.E.; Goldin, L.R.
1996-04-01
We present here four nonparametric statistics for linkage analysis that test whether pairs of affected relatives share marker alleles more often than expected. These statistics are based on simulating the null distribution of a given statistic conditional on the unaffecteds` marker genotypes. Each statistic uses a different measure of marker sharing: the SimAPM statistic uses the simulation-based affected-pedigree-member measure based on identity-by-state (IBS) sharing. The SimKIN (kinship) measure is 1.0 for identity-by-descent (IBD) sharing, 0.0 for no IBD sharing, and the kinship coefficient when the IBD status is ambiguous. The simulation-based IBD (SimIBD) statistic uses a recursive algorithm to determine the probability of two affecteds sharing a specific allele IBD. The SimISO statistic is identical to SimIBD, except that it also measures marker similarity between unaffected pairs. We evaluated our statistics on data simulated under different two-locus disease models, comparing our results to those obtained with several other nonparametric statistics. Use of IBD information produces dramatic increases in power over the SimAPM method, which uses only IBS information. The power of our best statistic in most cases meets or exceeds the power of the other nonparametric statistics. Furthermore, our statistics perform comparisons between all affected relative pairs within general pedigrees and are not restricted to sib pairs or nuclear families. 32 refs., 5 figs., 6 tabs.
Nonparametric simulation-based statistics for detecting linkage in general pedigrees.
Davis, S.; Schroeder, M.; Goldin, L. R.; Weeks, D. E.
1996-01-01
We present here four nonparametric statistics for linkage analysis that test whether pairs of affected relatives share marker alleles more often than expected. These statistics are based on simulating the null distribution of a given statistic conditional on the unaffecteds' marker genotypes. Each statistic uses a different measure of marker sharing: the SimAPM statistic uses the simulation-based affected-pedigree-member measure based on identity-by-state (IBS) sharing. The SimKIN (kinship) measure is 1.0 for identity-by-descent (IBD) sharing, 0.0 for no IBD status sharing, and the kinship coefficient when the IBD status is ambiguous. The simulation-based IBD (SimIBD) statistic uses a recursive algorithm to determine the probability of two affecteds sharing a specific allele IBD. The SimISO statistic is identical to SimIBD, except that it also measures marker similarity between unaffected pairs. We evaluated our statistics on data simulated under different two-locus disease models, comparing our results to those obtained with several other nonparametric statistics. Use of IBD information produces dramatic increases in power over the SimAPM method, which uses only IBS information. The power of our best statistic in most cases meets or exceeds the power of the other nonparametric statistics. Furthermore, our statistics perform comparisons between all affected relative pairs within general pedigrees and are not restricted to sib pairs or nuclear families. PMID:8644751
Application of frequency domain ARX models and extreme value statistics to damage detection
NASA Astrophysics Data System (ADS)
Fasel, Timothy R.; Sohn, Hoon; Farrar, Charles R.
2003-08-01
In this study, the applicability of an auto-regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is explored. Damage sensitive features that explicitly consider the nonlinear system input/output relationships produced by damage are extracted from the ARX model. Furthermore, because of the non-Gaussian nature of the extracted features, Extreme Value Statistics (EVS) is employed to develop a robust damage classifier. EVS is useful in this case because the data of interest are in the tails (extremes) of the damage sensitive feature distribution. The suitability of the ARX model, combined with EVS, to nonlinear damage detection is demonstrated using vibration data obtained from a laboratory experiment of a three-story building model. It is found that the current method, while able to discern when damage is present in the structure, is unable to localize the damage to a particular joint. An impedance-based method using piezoelectric (PZT) material as both an actuator and a sensor is then proposed as a possible solution to the problem of damage localization.
The Good, the Bad, and the Ugly: Statistical quality assessment of SZ detections
NASA Astrophysics Data System (ADS)
Aghanim, N.; Hurier, G.; Diego, J.-M.; Douspis, M.; Macias-Perez, J.; Pointecouteau, E.; Comis, B.; Arnaud, M.; Montier, L.
2015-08-01
We examine three approaches to the problem of source classification in catalogues. Our goal is to determine the confidence with which the elements in these catalogues can be distinguished in populations on the basis of their spectral energy distribution (SED). Our analysis is based on the projection of the measurements onto a comprehensive SED model of the main signals in the considered range of frequencies. We first consider likelihood analysis, which is halfway between supervised and unsupervised methods. Next, we investigate an unsupervised clustering technique. Finally, we consider a supervised classifier based on artificial neural networks. We illustrate the approach and results using catalogues from various surveys, such as X-rays (MCXC), optical (SDSS), and millimetric (Planck Sunyaev-Zeldovich (SZ)). We show that the results from the statistical classifications of the three methods are in very good agreement with each other, although the supervised neural network-based classification shows better performance allowing the best separation into populations of reliable and unreliable sources in catalogues. The latest method was applied to the SZ sources detected by the Planck satellite. It led to a classification assessing and thereby agreeing with the reliability assessment published in the Planck SZ catalogue. Our method could easily be applied to catalogues from future large surveys such as SRG/eROSITA and Euclid.
Detection of Cutting Tool Wear using Statistical Analysis and Regression Model
NASA Astrophysics Data System (ADS)
Ghani, Jaharah A.; Rizal, Muhammad; Nuawi, Mohd Zaki; Haron, Che Hassan Che; Ramli, Rizauddin
2010-10-01
This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals. A statistical-based method called Integrated Kurtosis-based Algorithm for Z-Filter technique, called I-kaz was used for developing a regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out using a CNC turning machine Colchester Master Tornado T4 in dry cutting condition. A Kistler 9255B dynamometer was used to measure the cutting force signals, which were transmitted, analyzed, and displayed in the DasyLab software. Various force signals from machining operation were analyzed, and each has its own I-kaz 3D coefficient. This coefficient was examined and its relationship with flank wear lands (VB) was determined. A regression model was developed due to this relationship, and results of the regression model shows that the I-kaz 3D coefficient value decreases as tool wear increases. The result then is used for real time tool wear monitoring.
Improving statistical keyword detection in short texts: Entropic and clustering approaches
NASA Astrophysics Data System (ADS)
Carretero-Campos, C.; Bernaola-Galván, P.; Coronado, A. V.; Carpena, P.
2013-03-01
In the last years, two successful approaches have been introduced to tackle the problem of statistical keyword detection in a text without the use of external information: (i) The entropic approach, where Shannon’s entropy of information is used to quantify the information content of the sequence of occurrences of each word in the text; and (ii) The clustering approach, which links the heterogeneity of the spatial distribution of a word in the text (clustering) with its relevance. In this paper, first we present some modifications to both techniques which improve their results. Then, we propose new metrics to evaluate the performance of keyword detectors based specifically on the needs of a typical user, and we employ them to find out which approach performs better. Although both approaches work well in long texts, we obtain in general that measures based on word-clustering perform at least as well as the entropic measure, which needs a convenient partition of the text to be applied, such as chapters of a book. In the latter approach we also show that the partition of the text chosen affects strongly its results. Finally, we focus on short texts, a case of high practical importance, such as short reports, web pages, scientific articles, etc. We show that the performance of word-clustering measures is also good in generic short texts since these measures are able to discriminate better the degree of relevance of low frequency words than the entropic approach.
Detecting mass substructure in galaxy clusters: an aperture mass statistic for gravitational flexion
NASA Astrophysics Data System (ADS)
Leonard, Adrienne; King, Lindsay J.; Wilkins, Stephen M.
2009-05-01
Gravitational flexion has been introduced as a technique by which one can map out and study substructure in clusters of galaxies. Previous analyses involving flexion have measured the individual galaxy-galaxy flexion signal, or used either parametric techniques or a Kaiser, Squires and Broadhurst (KSB)-type inversion to reconstruct the mass distribution in Abell 1689. In this paper, we present an aperture mass statistic for flexion, and apply it to the lensed images of background galaxies obtained by ray-tracing simulations through a simple analytic mass distribution and through a galaxy cluster from the Millennium Simulation. We show that this method is effective at detecting and accurately tracing structure within clusters of galaxies on subarcminute scales with high signal to noise even using a moderate background source number density and image resolution. In addition, the method provides much more information about both the overall shape and the small-scale structure of a cluster of galaxies than can be achieved through a weak lensing mass reconstruction using gravitational shear data. Lastly, we discuss how the zero-points of the aperture mass might be used to infer the masses of structures identified using this method.
Clinical significance of incidental FDG uptake in the prostate gland detected by PET/CT
Sahin, Ertan; Elboga, Umut; Kalender, Ebuzer; Basıbuyuk, Mustafa; Demir, Hasan Deniz; Celen, Yusuf Zeki
2015-01-01
The value of FDG-positron emission tomography/computed tomography (PET/CT) for detecting prostate cancer is unknown. We aimed to investigate the clinical value of incidental prostate FDG uptake on PET/CT scans. We reviewed 6128 male patients who underwent FDG-PET/CT scans and selected cases that reported hypermetabolic lesion in the prostate. The patients who have prior history of prostate carcinoma or prostate surgery were excluded from the study. We have analyzed the correlation between PET/CT findings and serum prostate-specific antigen (PSA) levels, imaging (USG), urological examinations and biopsy. Incidental 18F-FDG uptake of the prostate gland was observed in 79 patients (1.3%). While sixteen of them were excluded due to inadequate clinical data, the remaining 63 patients were included for further analysis. The patients were divided into two groups; 8 patients (12.7%) in the malignant group and 55 patients (87.3%) in the benign group. The SUVmax values were not significantly different between the two groups. In 6 (75%) patients with prostate cancer, FDG uptake was observed focally in the peripheral zone of the prostate glands. There was no significant correlation between the SUVmax and the PSA levels. Incidental 18F-FDG uptake in the prostate gland is a rare condition, but a substantial portion of it is associated with the cancer. Benign and malignant lesions of the prostate gland in FDG-PET/CT imaging could not be reliably distinguished. The peripheral focally FDG uptake of prostate glands should be further examined with the clinical and labaratory evaluations. PMID:26379847
Peltola, Emilia; Wester, Niklas; Holt, Katherine B; Johansson, Leena-Sisko; Koskinen, Jari; Myllymäki, Vesa; Laurila, Tomi
2017-02-15
We hypothesize that by using integrated carbon nanostructures on tetrahedral amorphous carbon (ta-C), it is possible to take the performance and characteristics of these bioelectrodes to a completely new level. The integrated carbon electrodes were realized by combining nanodiamonds (NDs) with ta-C thin films coated on Ti-coated Si-substrates. NDs were functionalized with mixture of carboxyl and amine groups NDandante or amine NDamine, carboxyl NDvox or hydroxyl groups NDH and drop-casted or spray-coated onto substrate. By utilizing these novel structures we show that (i) the detection limit for dopamine can be improved by two orders of magnitude [from 10µM to 50nM] in comparison to ta-C thin film electrodes and (ii) the coating method significantly affects electrochemical properties of NDs and (iii) the ND coatings selectively promote cell viability. NDandante and NDH showed most promising electrochemical properties. The viability of human mesenchymal stem cells and osteoblastic SaOS-2 cells was increased on all ND surfaces, whereas the viability of mouse neural stem cells and rat neuroblastic cells was improved on NDandante and NDH and reduced on NDamine and NDvox. The viability of C6 cells remained unchanged, indicating that these surfaces will not cause excess gliosis. In summary, we demonstrated here that by using functionalized NDs on ta-C thin films we can significantly improve sensitivity towards dopamine as well as selectively promote cell viability. Thus, these novel carbon nanostructures provide an interesting concept for development of various in vivo targeted sensor solutions.
Vanderperre, Benoît; Lucier, Jean-François; Bissonnette, Cyntia; Motard, Julie; Tremblay, Guillaume; Vanderperre, Solène; Wisztorski, Maxence; Salzet, Michel; Boisvert, François-Michel; Roucou, Xavier
2013-01-01
A fully mature mRNA is usually associated to a reference open reading frame encoding a single protein. Yet, mature mRNAs contain unconventional alternative open reading frames (AltORFs) located in untranslated regions (UTRs) or overlapping the reference ORFs (RefORFs) in non-canonical +2 and +3 reading frames. Although recent ribosome profiling and footprinting approaches have suggested the significant use of unconventional translation initiation sites in mammals, direct evidence of large-scale alternative protein expression at the proteome level is still lacking. To determine the contribution of alternative proteins to the human proteome, we generated a database of predicted human AltORFs revealing a new proteome mainly composed of small proteins with a median length of 57 amino acids, compared to 344 amino acids for the reference proteome. We experimentally detected a total of 1,259 alternative proteins by mass spectrometry analyses of human cell lines, tissues and fluids. In plasma and serum, alternative proteins represent up to 55% of the proteome and may be a potential unsuspected new source for biomarkers. We observed constitutive co-expression of RefORFs and AltORFs from endogenous genes and from transfected cDNAs, including tumor suppressor p53, and provide evidence that out-of-frame clones representing AltORFs are mistakenly rejected as false positive in cDNAs screening assays. Functional importance of alternative proteins is strongly supported by significant evolutionary conservation in vertebrates, invertebrates, and yeast. Our results imply that coding of multiple proteins in a single gene by the use of AltORFs may be a common feature in eukaryotes, and confirm that translation of unconventional ORFs generates an as yet unexplored proteome. PMID:23950983
Munjal, D D; Picken, J; Pritchard, J
1984-01-01
We evaluated the clinical usefulness of lipid-bound sialic acid (LSA) as a "tumor marker" and assessed individual and carcinoembryonic antigen (CEA) in cancer patients. Serum LSA and CEA concentrations were measured by the resorcinol method after total lipid extraction and isolation of the sialolipid fraction, and by Abbott enzyme immunoassay procedures, respectively. Results indicate that the frequency of elevation and mean LSA values were highest in patients with lung cancer (318 mg/liter), intermediate in miscellaneous (210 mg/liter) and colorectal cancers (200 mg/liter), and lowest in breast cancer (175 mg/liter); while mean CEA values were highest in colorectal cancer (162.5 micrograms/liter), followed by lung (33.8 micrograms/liter), miscellaneous (30.3 micrograms/liter), and breast cancers (11.6 micrograms/liter). Statistically, LSA and CEA values for cancer patients were significantly (P less than 0.001) higher than for normal subjects. The combined measurement of LSA and CEA in serum provides better detection potential for cancer patients than either of the two markers alone.
Significance of the detection of esters of p-hydroxybenzoic acid (parabens) in human breast tumours.
Harvey, Philip W; Everett, David J
2004-01-01
This issue of Journal of Applied Toxicology publishes the paper Concentrations of Parabens in Human Breast Tumours by Darbre et al. (2004), which reports that esters of p-hydroxybenzoic acid (parabens) can be detected in samples of tissue from human breast tumours. Breast tumour samples were supplied from 20 patients, in collaboration with the Edinburgh Breast Unit Research Group, and analysed by high-pressure liquid chromatography and tandem mass spectrometry. The parabens are used as antimicrobial preservatives in underarm deodorants and antiperspirants and in a wide range of other consumer products. The parabens also have inherent oestrogenic and other hormone related activity (increased progesterone receptor gene expression). As oestrogen is a major aetiological factor in the growth and development of the majority of human breast cancers, it has been previously suggested by Darbre that parabens and other chemicals in underarm cosmetics may contribute to the rising incidence of breast cancer. The significance of the finding of parabens in tumour samples is discussed here in terms of 1). Darbre et al's study design, 2). what can be inferred from this type of data (and what can not, such as the cause of these tumours), 3). the toxicology of these compounds and 4). the limitations of the existing toxicology database and the need to consider data that is appropriate to human exposures.
NASA Technical Reports Server (NTRS)
Moore, G. K. (Principal Investigator)
1976-01-01
The author has identified the following significant results. Lineaments were detected on Skylab photographs by stereo viewing, projection viewing, and composite viewing. Sixty-nine percent more lineaments were found by stereo viewing than by projection, but segments of projection lineaments are longer; total length of lineaments found by these two methods is nearly the same. Most Skylab lineaments consist of topographic depression: stream channel alinements, straight valley walls, elongated swales, and belts where sinkholes are abundant. Most of the remainder are vegetation alinements. Lineaments are most common in dissected areas having a thin soil cover. Results of test drilling show: (1) the median yield of test wells on Skylab lineaments is about six times the median yield of all existing wells; (2) three out of seven wells on Skylab lineaments yield more than 6.3 1/s (110 gal/min): (3) low yields are possible on lineaments as well as in other favorable locations; and (4) the largest well yields can be obtained at well locations of Skylab lineaments that also are favorably located with respect to topography and geologic structure, and are in the vicinity of wells with large yields.
Abbate, V; Kicman, A T; Evans-Brown, M; McVeigh, J; Cowan, D A; Wilson, C; Coles, S J; Walker, C J
2015-07-01
Twenty-four products suspected of containing anabolic steroids and sold in fitness equipment shops in the United Kingdom (UK) were analyzed for their qualitative and semi-quantitative content using full scan gas chromatography-mass spectrometry (GC-MS), accurate mass liquid chromatography-mass spectrometry (LC-MS), high pressure liquid chromatography with diode array detection (HPLC-DAD), UV-Vis, and nuclear magnetic resonance (NMR) spectroscopy. In addition, X-ray crystallography enabled the identification of one of the compounds, where reference standard was not available. Of the 24 products tested, 23 contained steroids including known anabolic agents; 16 of these contained steroids that were different to those indicated on the packaging and one product contained no steroid at all. Overall, 13 different steroids were identified; 12 of these are controlled in the UK under the Misuse of Drugs Act 1971. Several of the products contained steroids that may be considered to have considerable pharmacological activity, based on their chemical structures and the amounts present. This could unwittingly expose users to a significant risk to their health, which is of particular concern for naïve users.
Tousseyn, Simon; Dupont, Patrick; Goffin, Karolien; Sunaert, Stefan; Van Paesschen, Wim
2014-01-01
There is currently a lack of knowledge about electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) specificity. Our aim was to define sensitivity and specificity of blood oxygen level dependent (BOLD) responses to interictal epileptic spikes during EEG-fMRI for detecting the ictal onset zone (IOZ). We studied 21 refractory focal epilepsy patients who had a well-defined IOZ after a full presurgical evaluation and interictal spikes during EEG-fMRI. Areas of spike-related BOLD changes overlapping the IOZ in patients were considered as true positives; if no overlap was found, they were treated as false-negatives. Matched healthy case-controls had undergone similar EEG-fMRI in order to determine true-negative and false-positive fractions. The spike-related regressor of the patient was used in the design matrix of the healthy case-control. Suprathreshold BOLD changes in the brain of controls were considered as false positives, absence of these changes as true negatives. Sensitivity and specificity were calculated for different statistical thresholds at the voxel level combined with different cluster size thresholds and represented in receiver operating characteristic (ROC)-curves. Additionally, we calculated the ROC-curves based on the cluster containing the maximal significant activation. We achieved a combination of 100% specificity and 62% sensitivity, using a Z-threshold in the interval 3.4-3.5 and cluster size threshold of 350 voxels. We could obtain higher sensitivity at the expense of specificity. Similar performance was found when using the cluster containing the maximal significant activation. Our data provide a guideline for different EEG-fMRI settings with their respective sensitivity and specificity for detecting the IOZ. The unique cluster containing the maximal significant BOLD activation was a sensitive and specific marker of the IOZ.
Liu, Yang; Zhong, Wen-Wen; Kang, Hui-Yuan; Wang, Li-Li; Lu, Xue-Chun; Yu, Li; Zhu, Hong-Li
2014-06-01
The advances of treatment improved the prognosis of the patients with acute leukemia (AL) in the last decade, but the lack of general biomarker for predicting relapse in AL, which is one of the most important factors influencing the survival and prognosis. DNA methylation of ID4 gene promoter occurred frequently in patients with AL and was found to be highly related to the tumor progression. Based on the previous work of the setup of methylation-specific quantitative PCR system for ID4 gene, this study was designed to investigate the relation between the quantitative indicator of methylation density, percentage of methylation reference(PMR) value, and different disease status of AL. PMR of ID4 was detected by MS-PCR in bone marrow (BM) samples of 17 healthy persons and 54 AL patients in the status of newly diagnosis, complete remission and disease relapse. The results showed that at different disease status, PMR value in newly diagnosed group was significantly lower than that in complete remission group (P = 0.031). Among serial samples, PMR value remained very low at the status of patients with continuous complete remission (<1.5‰), and increased along with the accumulation of tumor cells at relapse. In 1 relapse case, the abnormal rise of PMR value occurred prior to morphological relapse. PMR value seemed to be related to body tumor cell load. It is concluded that the quantitative indicator of methylation density and PMR value may reflect the change of tumor cell load in acute leukemia patients. Dynamic monitoring of PMR maybe predict leukemia relapse.
A Multi-Scale Sampling Strategy for Detecting Physiologically Significant Signals in AVIRIS Imagery
NASA Technical Reports Server (NTRS)
Gamon, John A.; Lee, Lai-Fun; Qiu, Hong-Lie; Davis, Stephen; Roberts, Dar A.; Ustin, Susan L.
1998-01-01
Models of photosynthetic production at ecosystem and global scales require multiple input parameters specifying physical and physiological surface features. While certain physical parameters (e.g., absorbed photosynthetically active radiation) can be derived from current satellite sensors, other physiologically relevant measures (e.g., vegetation type, water status, carboxylation capacity, or photosynthetic light-use efficiency), are not generally directly available from current satellite sensors at the appropriate geographic scale. Consequently, many model parameters must be assumed or derived from independent sources, often at an inappropriate scale. An abundance of ecophysiological studies at the leaf and canopy scales suggests strong physiological control of vegetation-atmosphere CO2 and water vapor fluxes, particularly in evergreen vegetation subjected to diurnal or seasonal stresses. For example hot, dry conditions can lead to stomatal closure, and associated "downregulation" of photosynthetic biochemical processes, a phenomenon often manifested as a "midday photosynthetic depression". A recent study with the revised simple biosphere (SiB2) model demonstrated that photosynthetic downregulation can significantly impact global climate. However, at the global scale, the exact significance of downregulation remains unclear, largely because appropriate physiological measures are generally unavailable at this scale. Clearly, there is a need to develop reliable ways of extracting physiologically relevant information from remote sensing. Narrow-band spectrometers offer many opportunities for deriving physiological parameters needed for ecosystem and global scale photosynthetic models. Experimental studies on the ground at the leaf- to stand-scale have indicated that several narrow-band features can be used to detect plant physiological status. One physiological signal is caused by xanthophyll cycle pigment activity, and is often expressed as the Photochemical
Simonson, K.M.
1998-08-01
The rate at which a mine detection system falsely identifies man-made or natural clutter objects as mines is referred to as the system's false alarm rate (FAR). Generally expressed as a rate per unit area or time, the FAR is one of the primary metrics used to gauge system performance. In this report, an overview is given of statistical methods appropriate for the analysis of data relating to FAR. Techniques are presented for determining a suitable size for the clutter collection area, for summarizing the performance of a single sensor, and for comparing different sensors. For readers requiring more thorough coverage of the topics discussed, references to the statistical literature are provided. A companion report addresses statistical issues related to the estimation of mine detection probabilities.
NASA Astrophysics Data System (ADS)
Meng, X.; Daniels, C.; Smith, E.; Peng, Z.; Chen, X.; Wagner, L. S.; Fischer, K. M.; Hawman, R. B.
2015-12-01
Since 2001, the number of M>3 earthquakes increased significantly in Central and Eastern United States (CEUS), likely due to waste-water injection, also known as "induced earthquakes" [Ellsworth, 2013]. Because induced earthquakes are driven by short-term external forcing and hence may behave like earthquake swarms, which are not well characterized by branching point-process models, such as the Epidemic Type Aftershock Sequence (ETAS) model [Ogata, 1988]. In this study we focus on the 02/15/2014 M4.1 South Carolina and the 06/16/2014 M4.3 Oklahoma earthquakes, which likely represent intraplate tectonic and induced events, respectively. For the South Carolina event, only one M3.0 aftershock is identified by the ANSS catalog, which may be caused by a lack of low-magnitude events in this catalog. We apply a recently developed matched filter technique to detect earthquakes from 02/08/2014 to 02/22/2014 around the epicentral region. 15 seismic stations (both permanent and temporary USArray networks) within 100 km of the mainshock are used for detection. The mainshock and aftershock are used as templates for the initial detection. Newly detected events are employed as new templates, and the same detection procedure repeats until no new event can be added. Overall we have identified more than 10 events, including one foreshock occurred ~11 min before the M4.1 mainshock. However, the numbers of aftershocks are still much less than predicted with the modified Bath's law. For the Oklahoma event, we use 1270 events from the ANSS catalog and 182 events from a relocated catalog as templates to scan through continuous recordings 3 days before to 7 days after the mainshock. 12 seismic stations within the vicinity of the mainshock are included in the study. After obtaining more complete catalogs for both sequences, we plan to compare the statistical parameters (e.g., b, a, K, and p values) between the two sequences, as well as their spatial-temporal migration pattern, which may
2000-10-01
specific reverse-transcriptase polymerase chain reaction markers in the detection of metastases in the lymph nodes... chain reaction detection of cytokeratin-19 mRNA in bone marrow and blood of breast cancer patients. J Cancer Res Clin Oncol 1996; 122: 679-86. (43...directly drain a tumor and are most likely to harbor occult cells . Reverse transcriptase- polymerase chain reaction (RT-PCR) is a sensitive
NASA Astrophysics Data System (ADS)
Passaro, Marcello; Benveniste, Jérôme; Cipollini, Paolo; Fenoglio-Marc, Luciana
For more than two decades, it has been possible to map the Significant Wave Height (SWH) globally through Satellite Altimetry. SWH estimation is possible because the shape of an altimetric waveform, which usually presents a sharp leading edge and a slowly decaying trailing edge, depends on the sea state: in particular, the higher the sea state, the longer the rising time of the leading edge. The algorithm for SWH also depends on the width of the point target response (PTR) function, which is usually approximated by a constant value that contributes to the rising time. Particularly challenging for SWH detection are coastal data and low sea states. The first are usually flagged as unreliable due to land and calm water interference in the altimeter footprint; the second are characterized by an extremely sharp leading edge that is consequently poorly sampled in the digitalized waveform. ALES, a new algorithm for reprocessing altimetric waveforms, has recently been validated for sea surface height estimation (Passaro et al. 2014). The aim of this work is to check its validity also for SWH estimation in a particularly challenging area. The German Bight region presents both low sea state and coastal issues and is particularly suitable for validation, thanks to the extended network of buoys of the Bundesamt für Seeschifffahrt und Hydrographie (BSH). In-situ data include open sea, off-shore and coastal sea conditions, respectively at the Helgoland, lighthouse Alte Weser and Westerland locations. Reprocessed data from Envisat, Jason-1 and Jason-2 tracks are validated against those three buoys. The in-situ validation is applied both at the nearest point and at points along-track. The skill metrics is based on bias, standard deviation, slope of regression line, scatter index, number of cycles with correlation larger than 90%. The same metrics is applied to the altimeter data obtained by standard processing and the validation results are compared. Data are evaluated at high
Liu, Wei; Ding, Jinhui
2016-05-25
The application of the principle of the intention-to-treat (ITT) to the analysis of clinical trials is challenged in the presence of missing outcome data. The consequences of stopping an assigned treatment in a withdrawn subject are unknown. It is difficult to make a single assumption about missing mechanisms for all clinical trials because there are complicated reactions in the human body to drugs due to the presence of complex biological networks, leading to data missing randomly or non-randomly. Currently there is no statistical method that can tell whether a difference between two treatments in the ITT population of a randomized clinical trial with missing data is significant at a pre-specified level. Making no assumptions about the missing mechanisms, we propose a generalized complete-case (GCC) analysis based on the data of completers. An evaluation of the impact of missing data on the ITT analysis reveals that a statistically significant GCC result implies a significant treatment effect in the ITT population at a pre-specified significance level unless, relative to the comparator, the test drug is poisonous to the non-completers as documented in their medical records. Applications of the GCC analysis are illustrated using literature data, and its properties and limits are discussed.
NASA Astrophysics Data System (ADS)
Saleem, J. A.; Salvucci, G. D.
2002-05-01
Components of root zone outflow (evapotranspiration, drainage, and runoff processes) are dependent on soil moisture. In some cases, this dependence can be reasonably described at the point scale (e.g. the Darcy and Richards equations). However, as scale increases, these interrelationships become increasingly complex and uncertain. Small-scale processes are one of many factors that may influence large-scale behavior. Furthermore, these processes are often observed and measurable only at relatively large scales; such measurements are not suitable for simple point-scale analyses. One issue that can arise in large-scale investigations is spatial dependence among sites within a region. One can conceive of two possible models for soil moisture - outflow relationships at a given site. A simpler model is an "independent columns" approach; i.e. outflow at a site can be described as a function of the soil moisture at that site only. However, in many cases this is a not valid model; lateral interactions and flow among sites may exist and influence the components of outflow at measured point. In such cases, some sort of spatial aspect must be incorporated and addressed if moisture relations are to be successfully described, predicted or aggregated. Here we describe a test designed to detect such spatial dependence that uses precipitation as a surrogate for less available outflow data and applies methods used in multivariable regression. The long-time average dependence of root zone outflow on point soil moisture is measured using an estimation technique based on conditional averaging of precipitation according to soil moisture level. Using multivariable statistics, the residuals of this relationship are evaluated for dependence on variability remaining in spatially averaged moisture. A statistically significant relationship implies that lateral processes may be influencing the outflow processes and should be accounted for in attempts at describing or predicting such
Significance of "Not Detected but Amplified" Results by Real-Time PCR Method for HPV DNA Detection.
Kim, Taek Soo; Lim, Mi Suk; Hong, Yun Ji; Hwang, Sang Mee; Park, Kyoung Un; Song, Junghan; Kim, Eui-Chong
2016-01-01
Human papillomavirus (HPV) infection is an important etiologic factor in cervical carcinogenesis. Various HPV DNA detection methods have been evaluated for clinicopathological level. For the specimens with normal cytological finding, discrepancies among the detection methods were frequently found and adequate interpretation can be difficult. 6,322 clinical specimens were submitted and evaluated for real-time PCR and Hybrid Capture 2 (HC2). 573 positive or "Not Detected but Amplified" (NDBA) specimens by real-time PCR were additionally tested using genetic analyzer. For the reliability of real-time PCR, 325 retests were performed. Optimal cut-off cycle threshold (CT ) value was evaluated also. 78.7% of submitted specimens showed normal or nonspecific cytological finding. The distributions of HPV types by real-time PCR were not different between positive and NDBA cases. For positive cases by fragment analysis, concordance rates with real-time PCR and HC2 were 94.2% and 84.2%. In NDBA cases, fragment analysis and real-time PCR showed identical results in 77.0% and HC2 revealed 27.6% of concordance with fragment analysis. Optimal cut-off CT value was different for HPV types. NDBA results in real-time PCR should be regarded as equivocal, not negative. The adjustment of cut-off CT value for HPV types will be helpful for the appropriate result interpretation.
Significance of “Not Detected but Amplified” Results by Real-Time PCR Method for HPV DNA Detection
Kim, Taek Soo; Lim, Mi Suk; Hwang, Sang Mee; Song, Junghan; Kim, Eui-Chong
2016-01-01
Human papillomavirus (HPV) infection is an important etiologic factor in cervical carcinogenesis. Various HPV DNA detection methods have been evaluated for clinicopathological level. For the specimens with normal cytological finding, discrepancies among the detection methods were frequently found and adequate interpretation can be difficult. 6,322 clinical specimens were submitted and evaluated for real-time PCR and Hybrid Capture 2 (HC2). 573 positive or “Not Detected but Amplified” (NDBA) specimens by real-time PCR were additionally tested using genetic analyzer. For the reliability of real-time PCR, 325 retests were performed. Optimal cut-off cycle threshold (CT) value was evaluated also. 78.7% of submitted specimens showed normal or nonspecific cytological finding. The distributions of HPV types by real-time PCR were not different between positive and NDBA cases. For positive cases by fragment analysis, concordance rates with real-time PCR and HC2 were 94.2% and 84.2%. In NDBA cases, fragment analysis and real-time PCR showed identical results in 77.0% and HC2 revealed 27.6% of concordance with fragment analysis. Optimal cut-off CT value was different for HPV types. NDBA results in real-time PCR should be regarded as equivocal, not negative. The adjustment of cut-off CT value for HPV types will be helpful for the appropriate result interpretation. PMID:28097135
NASA Astrophysics Data System (ADS)
Monaco, E.; Memmolo, V.; Ricci, F.; Boffa, N. D.; Maio, L.
2015-03-01
Maintenance approaches based on sensorised structures and Structural Health Monitoring systems could represent one of the most promising innovations in the fields of aerostructures since many years, mostly when composites materials (fibers reinforced resins) are considered. Layered materials still suffer today of drastic reductions of maximum allowable stress values during the design phase as well as of costly and recurrent inspections during the life cycle phase that don't permit of completely exploit their structural and economic potentialities in today aircrafts. Those penalizing measures are necessary mainly to consider the presence of undetected hidden flaws within the layered sequence (delaminations) or in bonded areas (partial disbonding); in order to relax design and maintenance constraints a system based on sensors permanently installed on the structure to detect and locate eventual flaws can be considered (SHM system) once its effectiveness and reliability will be statistically demonstrated via a rigorous Probability Of Detection function definition and evaluation. This paper presents an experimental approach with a statistical procedure for the evaluation of detection threshold of a guided waves based SHM system oriented to delaminations detection on a typical wing composite layered panel. The experimental tests are mostly oriented to characterize the statistical distribution of measurements and damage metrics as well as to characterize the system detection capability using this approach. Numerically it is not possible to substitute part of the experimental tests aimed at POD where the noise in the system response is crucial. Results of experiments are presented in the paper and analyzed.
Moore, Gerald K.
1976-01-01
Lineaments were detected on SKYLAB photographs by stereo viewing , projection viewing, and composite viewing. Large well yields of 25 gal/min or more can be obtained in the study area by locating future wells on SKYLAB lineaments rather than on lineaments detected on either high-altitude aerial photographs, LANDSAT images, or by random drilling. Larger savings might be achieved by locating wells on lineaments detected by both stereo viewing and projection. The test site is underlain by dense, fractured, flat-lying limestones. Soil cover averages 4 ft thick in the Central Basin and about 40 ft thick on the Eastern Highland rim. Groundwater occurs mostly in horizontal, sheetlike solution cavities, and the trends of these cavities are controlled by joints. (Lardner-ISWS)
Significance of Follow-up in Detection of Pulmonary Metastasis of Colorectal Cancer
Shin, Jae Won; Lee, Sun Il
2010-01-01
Purpose This study was performed to evaluate the effectiveness of conventional chest radiography, carcinoembrionic antigen (CEA) level and abdominal computed tomography (CT) or chest CT for early detection of pulmonary metastasis after a curative resection of colorectal cancer. Methods We retrospectively reviewed 84 cases of pulmonary metastasis from a group of colorectal cancer patients who had a curative surgical resection from 2000 to 2006 at the Korea University Medical Center. Results Stage I tumors were detected in 4 patients, stage II tumors in 18, stage III tumors in 43 and stage IV tumors in 19. The detection rates for pulmonary metastasis were 28.5% by conventional chest radiography, 40.5% by increased CEA level and 28.5% by abdominal CT or chest CT. Among them, fourteen patients underwent a radical pneumonectomy. After detection of pulmonary metastasis, the survival outcome for the patients who underwent a resection of the lung was superior to the survival outcome of the patients who did not undergo a resection of the lung (43.7 months vs. 17.4 months, P = 0.001). For patients who underwent resections of the lung, pulmonary metastasis was detected by conventional chest radiography in 2 (14%) patients, by elevated CEA level in 6 (42%) patients, and by abdominal CT or chest CT in 6 (42%) patients. Conclusion Conventional chest radiography is no more useful in detecting early pulmonary metastasis after a curative colorectal surgery than a routine chest CT. Thus, we propose the use of routine chest CT for screening for lung metastasis. PMID:21152232
Significance of detection of extra metacentric microchromosome in amniotic cell culture.
Bernstein, R; Hakim, C; Hardwick, B; Nurse, G T
1978-01-01
A metacentric bisatellited microchromosome was detected in all metaphases from an amniotic culture performed because of maternal age. A wide-ranging survey of the literature failed to disclose any consistent anomaly associated with such a marker, but did reveal that the clinical picture of patients manifesting it could range from complete normality through mental retardation to a variety of deformities. The parents elected for termination, and the only deformity detected in the abortus was fixed talipes equinovarus. The implications of the finding of this marker chromosome on amniocentesis, believed to be reported for the first time here, are discussed particularly in the context of genetic counselling. Images PMID:641948
Ferrari, Ricardo J; Pinto, Carlos H Villa; da Silva, Bruno C Gregório; Bernardes, Danielle; Carvalho-Tavares, Juliana
2015-02-01
Intravital microscopy is an important experimental tool for the study of cellular and molecular mechanisms of the leukocyte-endothelial interactions in the microcirculation of various tissues and in different inflammatory conditions of in vivo specimens. However, due to the limited control over the conditions of the image acquisition, motion blur and artifacts, resulting mainly from the heartbeat and respiratory movements of the in vivo specimen, will very often be present. This problem can significantly undermine the results of either visual or computerized analysis of the acquired video images. Since only a fraction of the total number of images are usually corrupted by severe motion blur, it is necessary to have a procedure to automatically identify such images in the video for either further restoration or removal. This paper proposes a new technique for the detection of motion blur in intravital video microscopy based on directional statistics of local energy maps computed using a bank of 2D log-Gabor filters. Quantitative assessment using both artificially corrupted images and real microscopy data were conducted to test the effectiveness of the proposed method. Results showed an area under the receiver operating characteristic curve (AUC) of 0.95 (AUC = 0.95; 95 % CI 0.93-0.97) when tested on 329 video images visually ranked by four observers.
EEG-based Drowsiness Detection for Safe Driving Using Chaotic Features and Statistical Tests.
Mardi, Zahra; Ashtiani, Seyedeh Naghmeh Miri; Mikaili, Mohammad
2011-05-01
Electro encephalography (EEG) is one of the most reliable sources to detect sleep onset while driving. In this study, we have tried to demonstrate that sleepiness and alertness signals are separable with an appropriate margin by extracting suitable features. So, first of all, we have recorded EEG signals from 10 volunteers. They were obliged to avoid sleeping for about 20 hours before the test. We recorded the signals while subjects did a virtual driving game. They tried to pass some barriers that were shown on monitor. Process of recording was ended after 45 minutes. Then, after preprocessing of recorded signals, we labeled them by drowsiness and alertness by using times associated with pass times of the barriers or crash times to them. Then, we extracted some chaotic features (include Higuchi's fractal dimension and Petrosian's fractal dimension) and logarithm of energy of signal. By applying the two-tailed t-test, we have shown that these features can create 95% significance level of difference between drowsiness and alertness in each EEG channels. Ability of each feature has been evaluated by artificial neural network and accuracy of classification with all features was about 83.3% and this accuracy has been obtained without performing any optimization process on classifier.
Brennan, M E; Houssami, N
2006-10-01
In Australia, and many health care provider systems, primary care physicians are the first to see women with breast symptoms and are responsible for making decisions on whether to investigate and when to refer to specialist teams. We present an audit of new patient referrals from primary care triaged to a 'low-risk' (low likelihood of cancer) clinic on the basis of benign findings. The most common reason for referral was 'breast lump' (38%) followed by 'image-detected' abnormality (26%.) We have identified that (outside of population screening services) many women are being referred from primary care to specialist clinics for management of screen-detected lesions considered benign on imaging. Further research is needed to identify the reasons for such referrals and to develop appropriate educational strategies and clinical policy, both for the primary care and the specialist breast practitioner.
Detection of obstacles on runway using Ego-Motion compensation and tracking of significant features
NASA Technical Reports Server (NTRS)
Kasturi, Rangachar (Principal Investigator); Camps, Octavia (Principal Investigator); Gandhi, Tarak; Devadiga, Sadashiva
1996-01-01
This report describes a method for obstacle detection on a runway for autonomous navigation and landing of an aircraft. Detection is done in the presence of extraneous features such as tiremarks. Suitable features are extracted from the image and warping using approximately known camera and plane parameters is performed in order to compensate ego-motion as far as possible. Residual disparity after warping is estimated using an optical flow algorithm. Features are tracked from frame to frame so as to obtain more reliable estimates of their motion. Corrections are made to motion parameters with the residual disparities using a robust method, and features having large residual disparities are signaled as obstacles. Sensitivity analysis of the procedure is also studied. Nelson's optical flow constraint is proposed to separate moving obstacles from stationary ones. A Bayesian framework is used at every stage so that the confidence in the estimates can be determined.
The significance of clinical practice guidelines on adult varicocele detection and management
Shridharani, Anand; Owen, Ryan C; Elkelany, Osama O; Kim, Edward D
2016-01-01
Varicoceles are the most common correctable etiology of male factor infertility. However, the detection and management of varicoceles have not been standardized. This has led to decades of debate regarding the effect of varicocele on male infertility and subsequently whether repair leads to an improved fertility status. The current body of evidence investigating the role of varicocele and varicocelectomy is weak and conflicting. The stance taken by the AUA and ASRM suggests that there is insufficient outcomes data to support evidenced-based guidelines, citing evidence used to provide current recommendations are generally of a low quality level. On the other hand, the EAU Guidelines give a level 1a of evidence for management of varicoceles that are clinically palpable, associated with subnormal semen analyses and having otherwise unexplained fertility. Besides aiding with clinical varicocele detection and management, clinical practice opinion statements and guidelines aim to direct and strengthen the infrastructure of future studies. We review the current status of opinion statements and guidelines in varicocele and management detection with focus on their application in practice. PMID:26806081
Best, R; Harrell, A; Geesey, C; Libby, B; Wijesooriya, K
2014-06-15
Purpose: The purpose of this study is to inter-compare and find statistically significant differences between flattened field fixed-beam (FB) IMRT with flattening-filter free (FFF) volumetric modulated arc therapy (VMAT) for stereotactic body radiation therapy SBRT. Methods: SBRT plans using FB IMRT and FFF VMAT were generated for fifteen SBRT lung patients using 6 MV beams. For each patient, both IMRT and VMAT plans were created for comparison. Plans were generated utilizing RTOG 0915 (peripheral, 10 patients) and RTOG 0813 (medial, 5 patients) lung protocols. Target dose, critical structure dose, and treatment time were compared and tested for statistical significance. Parameters of interest included prescription isodose surface coverage, target dose heterogeneity, high dose spillage (location and volume), low dose spillage (location and volume), lung dose spillage, and critical structure maximum- and volumetric-dose limits. Results: For all criteria, we found equivalent or higher conformality with VMAT plans as well as reduced critical structure doses. Several differences passed a Student's t-test of significance: VMAT reduced the high dose spillage, evaluated with conformality index (CI), by an average of 9.4%±15.1% (p=0.030) compared to IMRT. VMAT plans reduced the lung volume receiving 20 Gy by 16.2%±15.0% (p=0.016) compared with IMRT. For the RTOG 0915 peripheral lesions, the volumes of lung receiving 12.4 Gy and 11.6 Gy were reduced by 27.0%±13.8% and 27.5%±12.6% (for both, p<0.001) in VMAT plans. Of the 26 protocol pass/fail criteria, VMAT plans were able to achieve an average of 0.2±0.7 (p=0.026) more constraints than the IMRT plans. Conclusions: FFF VMAT has dosimetric advantages over fixed beam IMRT for lung SBRT. Significant advantages included increased dose conformity, and reduced organs-at-risk doses. The overall improvements in terms of protocol pass/fail criteria were more modest and will require more patient data to establish difference
NASA Astrophysics Data System (ADS)
Zhao, J. Q.; Yang, J.; Li, P. X.; Liu, M. Y.; Shi, Y. M.
2016-06-01
Accurate and timely change detection of Earth's surface features is extremely important for understanding relationships and interactions between people and natural phenomena. Many traditional methods of change detection only use a part of polarization information and the supervised threshold selection. Those methods are insufficiency and time-costing. In this paper, we present a novel unsupervised change-detection method based on quad-polarimetric SAR data and automatic threshold selection to solve the problem of change detection. First, speckle noise is removed for the two registered SAR images. Second, the similarity measure is calculated by the test statistic, and automatic threshold selection of KI is introduced to obtain the change map. The efficiency of the proposed method is demonstrated by the quad-pol SAR images acquired by Radarsat-2 over Wuhan of China.
de Marcellus, Pierre; Bertrand, Marylène; Nuevo, Michel; Westall, Frances; Le Sergeant d'Hendecourt, Louis
2011-11-01
The delivery of extraterrestrial organic materials to primitive Earth from meteorites or micrometeorites has long been postulated to be one of the origins of the prebiotic molecules involved in the subsequent apparition of life. Here, we report on experiments in which vacuum UV photo-irradiation of interstellar/circumstellar ice analogues containing H(2)O, CH(3)OH, and NH(3) led to the production of several molecules of prebiotic interest. These were recovered at room temperature in the semi-refractory, water-soluble residues after evaporation of the ice. In particular, we detected small quantities of hydantoin (2,4-imidazolidinedione), a species suspected to play an important role in the formation of poly- and oligopeptides. In addition, hydantoin is known to form under extraterrestrial, abiotic conditions, since it has been detected, along with various other derivatives, in the soluble part of organic matter of primitive carbonaceous meteorites. This result, together with other related experiments reported recently, points to the potential importance of the photochemistry of interstellar "dirty" ices in the formation of organics in Solar System materials. Such molecules could then have been delivered to the surface of primitive Earth, as well as other telluric (exo-) planets, to help trigger first prebiotic reactions with the capacity to lead to some form of primitive biomolecular activity.
[Prognostic significance of sequencing-based MRD detection in multiple myeloma].
Takamatsu, Hiroyuki
2015-08-01
Stem cell transplantation in conjunction with therapeutic agents such as proteasome inhibitors and immunomodulatory drugs can dramatically improve response rates and the prognoses of patients with multiple myeloma (MM). However, most patients with MM are considered to be incurable, and relapse owing to minimal residual disease (MRD) is the main cause of death among these patients. We utilized a deep sequencing method, which employs consensus primers and next-generation sequencing (NGS), to amplify and sequence all rearranged immunoglobulin gene segments present in a myeloma clone. This technique has been shown to have 1-2 log greater sensitivity than both allele-specific oligonucleotide-polymerase chain reaction (ASO-PCR) (sensitivity 10(-5)) and multiparameter flow cytometry (MFC) (sensitivity at least 10(-4)). To investigate the value of sensitive detection of MRD in autograft by NGS, we compared progression-free survival (PFS) in 11 MRDNGS(-) cases (Group 1) with that in 12 MRDNGS(+) cases in which MRD was not detected by ASO-PCR (MRDASO(-)) (Group 2). Neither group received any post-ASCT therapy. Group 1 showed a better PFS than Group 2 (P=0.027). MRD-negativity in autografts, as revealed by NGS, is more closely associated with durable remission of MM than that revealed by ASO-PCR.
Fu, Rong; Wang, Pei; Ma, Weiping; Taguchi, Ayumu; Wong, Chee-Hong; Zhang, Qing; Gazdar, Adi; Hanash, Samir M; Zhou, Qinghua; Zhong, Hua; Feng, Ziding
2017-03-01
In this article, we propose a new statistical method-MutRSeq-for detecting differentially expressed single nucleotide variants (SNVs) based on RNA-seq data. Specifically, we focus on nonsynonymous mutations and employ a hierarchical likelihood approach to jointly model observed mutation events as well as read count measurements from RNA-seq experiments. We then introduce a likelihood ratio-based test statistic, which detects changes not only in overall expression levels, but also in allele-specific expression patterns. In addition, this method can jointly test multiple mutations in one gene/pathway. The simulation studies suggest that the proposed method achieves better power than a few competitors under a range of different settings. In the end, we apply this method to a breast cancer data set and identify genes with nonsynonymous mutations differentially expressed between the triple negative breast cancer tumors and other subtypes of breast cancer tumors.
2008-01-04
detection strategies in this paper a nearly perfect Gaussian representation of the noise is needed. In this Appendix, a method is shown to...Maryland Jonathan M. nichols Optical Techniques Branch Optical Sciences Division Frank Bucholtz Photonics Technology Branch Optical Sciences Division i...6 DETECTION STRATEGIES
Buhl, M.R.; Clark, G.A.; Candy, J.V.; Thomas, G.H.
1993-07-16
The goal of this work was to detect ``single-leg separated`` Bjoerk-Shiley Convexo-Concave heart valves which had been implanted in sheep. A ``single-leg separated`` heart valve contains a fracture in the outlet strut resulting in an increased risk of mechanical failure. The approach presented in this report detects such fractures by applying statistical pattern recognition with the nearest neighbor classifier to the acoustic signatures of the valve opening. This approach is discussed and results of applying it to real data are given.
Buhl, M.R.; Clark, G.A.; Candy, J.V.; Thomas, G.H.
1993-12-01
The goal of this work was to detect ``single-leg separated`` Bjoerk-Shiley Convexo-Concave heart valves which had been implanted in sheep. A ``single-leg separated`` heart valve contains a fracture in the outlet strut resulting in an increased risk of mechanical failure. The approach presented in this report detects such fractures by applying statistical pattern recognition with the nearest neighbor classifier to the acoustic signatures of the valve opening. This approach is discussed and results of applying it to real data are given.
Malm, Christer B.; Khoo, Nelson S.; Granlund, Irene; Lindstedt, Emilia; Hult, Andreas
2016-01-01
The discovery of erythropoietin (EPO) simplified blood doping in sports, but improved detection methods, for EPO has forced cheating athletes to return to blood transfusion. Autologous blood transfusion with cryopreserved red blood cells (RBCs) is the method of choice, because no valid method exists to accurately detect such event. In endurance sports, it can be estimated that elite athletes improve performance by up to 3% with blood doping, regardless of method. Valid detection methods for autologous blood doping is important to maintain credibility of athletic performances. Recreational male (N = 27) and female (N = 11) athletes served as Transfusion (N = 28) and Control (N = 10) subjects in two different transfusion settings. Hematological variables and physical performance were measured before donation of 450 or 900 mL whole blood, and until four weeks after re-infusion of the cryopreserved RBC fraction. Blood was analyzed for transferrin, iron, Hb, EVF, MCV, MCHC, reticulocytes, leucocytes and EPO. Repeated measures multivariate analysis of variance (MANOVA) and pattern recognition using Principal Component Analysis (PCA) and Orthogonal Projections of Latent Structures (OPLS) discriminant analysis (DA) investigated differences between Control and Transfusion groups over time. Significant increase in performance (15 ± 8%) and VO2max (17 ± 10%) (mean ± SD) could be measured 48 h after RBC re-infusion, and remained increased for up to four weeks in some subjects. In total, 533 blood samples were included in the study (Clean = 220, Transfused = 313). In response to blood transfusion, the largest change in hematological variables occurred 48 h after blood donation, when Control and Transfused groups could be separated with OPLS-DA (R2 = 0.76/Q2 = 0.59). RBC re-infusion resulted in the best model (R2 = 0.40/Q2 = 0.10) at the first sampling point (48 h), predicting one false positive and one false negative. Over all, a 25% and 86% false positives ratio was
An Automated Statistical Process Control Study of Inline Mixing Using Spectrophotometric Detection
ERIC Educational Resources Information Center
Dickey, Michael D.; Stewart, Michael D.; Willson, C. Grant
2006-01-01
An experiment is described, which is designed for a junior-level chemical engineering "fundamentals of measurements and data analysis" course, where students are introduced to the concept of statistical process control (SPC) through a simple inline mixing experiment. The students learn how to create and analyze control charts in an effort to…
Can Significant Trends in Surface Temperature and Precipitation be Detected over South America?
NASA Astrophysics Data System (ADS)
Lee, H.; Mechoso, C. R.; de Barros Soares, D.; Barkhordarian, A.; Loikith, P. C.
2015-12-01
This paper explores the existence of significant trends in near-surface temperature and precipitation over the South American continent by using observational data and estimates of natural variability based on simulations with numerical climate models. Trends are computed from three observational datasets in the period 1975-2004 for temperature and 1955-2004 for precipitation. Significance of the trends is tested against the null hypothesis that they arise from natural variability alone, which is estimated from the output of a suite of CMIP5 pre-industrial control experiments. Trends obtained from the observational datasets are compared with those simulated by CMIP5 historical runs, in which observed external transient forcing is imposed, and with those from simulations with natural-only forcing. In the case of temperature, an overall warming trend is found over the entire South American continent (0.23 C per decade). Significant trends (at the 95% level) are found in a region that corresponds roughly to Brazil with maximum warming over the north-central part. The average trends over South America in the observations broadly agree with those in the CMIP5 historical simulations for all seasons. This agreement is less close for the natural-only forcing simulations. The maximum warming over north-central Brazil is generally underestimated by the models. In the case of precipitation, trends vary both in sign and intensity according to region and season. The only significant trends in precipitation are obtained in La Plata Basin. Over the southern part of the basin (south of the Tropic of Capricorn), a significant decrease in precipitation is found during winter (-1.6 mm/month per decade) and an increase in all other seasons (4.2 mm/month per decade during summer). Over the northern part of La Plata Basin, the only significant trend in precipitation is a decrease during winter (-1.2 mm/month per decade).
Sheikh, Bahman
2017-03-01
The relevance and significance of the findings of chemicals of emerging concern at nanogram concentrations in recycled water is critically important for the consumers of these crops. The relevance and significance of these chemicals at these concentrations is placed in perspective in terms of the number of years of consumption necessary to accrue one acceptable daily intake every day, over a lifetime, specifically for carbamazepine. In this paper, the number of years is calculated and found to far exceed the maximum human life expectancy, even assuming that the individual consumes a mix of fruits and vegetables irrigated with recycled water throughout an 80-year life span, excluding other food crops free from carbamazepine.
Bamidis, P D; Lithari, C; Konstantinidis, S T
2010-12-01
With the number of scientific papers published in journals, conference proceedings, and international literature ever increasing, authors and reviewers are not only facilitated with an abundance of information, but unfortunately continuously confronted with risks associated with the erroneous copy of another's material. In parallel, Information Communication Technology (ICT) tools provide to researchers novel and continuously more effective ways to analyze and present their work. Software tools regarding statistical analysis offer scientists the chance to validate their work and enhance the quality of published papers. Moreover, from the reviewers and the editor's perspective, it is now possible to ensure the (text-content) originality of a scientific article with automated software tools for plagiarism detection. In this paper, we provide a step-bystep demonstration of two categories of tools, namely, statistical analysis and plagiarism detection. The aim is not to come up with a specific tool recommendation, but rather to provide useful guidelines on the proper use and efficiency of either category of tools. In the context of this special issue, this paper offers a useful tutorial to specific problems concerned with scientific writing and review discourse. A specific neuroscience experimental case example is utilized to illustrate the young researcher's statistical analysis burden, while a test scenario is purpose-built using open access journal articles to exemplify the use and comparative outputs of seven plagiarism detection software pieces.
Bamidis, P D; Lithari, C; Konstantinidis, S T
2010-01-01
With the number of scientific papers published in journals, conference proceedings, and international literature ever increasing, authors and reviewers are not only facilitated with an abundance of information, but unfortunately continuously confronted with risks associated with the erroneous copy of another's material. In parallel, Information Communication Technology (ICT) tools provide to researchers novel and continuously more effective ways to analyze and present their work. Software tools regarding statistical analysis offer scientists the chance to validate their work and enhance the quality of published papers. Moreover, from the reviewers and the editor's perspective, it is now possible to ensure the (text-content) originality of a scientific article with automated software tools for plagiarism detection. In this paper, we provide a step-bystep demonstration of two categories of tools, namely, statistical analysis and plagiarism detection. The aim is not to come up with a specific tool recommendation, but rather to provide useful guidelines on the proper use and efficiency of either category of tools. In the context of this special issue, this paper offers a useful tutorial to specific problems concerned with scientific writing and review discourse. A specific neuroscience experimental case example is utilized to illustrate the young researcher's statistical analysis burden, while a test scenario is purpose-built using open access journal articles to exemplify the use and comparative outputs of seven plagiarism detection software pieces. PMID:21487489
Nhu, Nguyen Van; Singh, Mahendra; Leonhard, Kai
2008-05-08
We have computed molecular descriptors for sizes, shapes, charge distributions, and dispersion interactions for 67 compounds using quantum chemical ab initio and density functional theory methods. For the same compounds, we have fitted the three perturbed-chain polar statistical associating fluid theory (PCP-SAFT) equation of state (EOS) parameters to experimental data and have performed a statistical analysis for relations between the descriptors and the EOS parameters. On this basis, an analysis of the physical significance of the parameters, the limits of the present descriptors, and the PCP-SAFT EOS has been performed. The result is a method that can be used to estimate the vapor pressure curve including the normal boiling point, the liquid volume, the enthalpy of vaporization, the critical data, mixture properties, and so on. When only two of the three parameters are predicted and one is adjusted to experimental normal boiling point data, excellent predictions of all investigated pure compound and mixture properties are obtained. We are convinced that the methodology presented in this work will lead to new EOS applications as well as improved EOS models whose predictive performance is likely to surpass that of most present quantum chemically based, quantitative structure-property relationship, and group contribution methods for a broad range of chemical substances.
Maric, Marija; de Haan, Else; Hogendoorn, Sanne M; Wolters, Lidewij H; Huizenga, Hilde M
2015-03-01
Single-case experimental designs are useful methods in clinical research practice to investigate individual client progress. Their proliferation might have been hampered by methodological challenges such as the difficulty applying existing statistical procedures. In this article, we describe a data-analytic method to analyze univariate (i.e., one symptom) single-case data using the common package SPSS. This method can help the clinical researcher to investigate whether an intervention works as compared with a baseline period or another intervention type, and to determine whether symptom improvement is clinically significant. First, we describe the statistical method in a conceptual way and show how it can be implemented in SPSS. Simulation studies were performed to determine the number of observation points required per intervention phase. Second, to illustrate this method and its implications, we present a case study of an adolescent with anxiety disorders treated with cognitive-behavioral therapy techniques in an outpatient psychotherapy clinic, whose symptoms were regularly assessed before each session. We provide a description of the data analyses and results of this case study. Finally, we discuss the advantages and shortcomings of the proposed method.
Detecting significant change in stream benthic macroinvertebrate communities in wilderness areas
Milner, Alexander M.; Woodward, Andrea; Freilich, Jerome E.; Black, Robert W.; Resh, Vincent H.
2015-01-01
Within a region, both MDS analyses typically identified similar years as exceeding reference condition variation, illustrating the utility of the approach for identifying wider spatial scale effects that influence more than one stream. MDS responded to both simulated water temperature stress and a pollutant event, and generally outlying years on MDS plots could be explained by environmental variables, particularly higher precipitation. Multivariate control charts successfully identified whether shifts in community structure identified by MDS were significant and whether the shift represented a press disturbance (long-term change) or a pulse disturbance. We consider a combination of TD and MDS with control charts to be a potentially powerful tool for determining years significantly outside of a reference condition variation.
Statistical analysis and ground-based testing of the on-orbit Space Shuttle damage detection sensors
NASA Astrophysics Data System (ADS)
Miles, Brian H.; Tanner, Elizabeth A.; Carter, John P.; Kamerman, Gary W.; Schwartz, Robert
2005-05-01
The loss of Space Shuttle Columbia and her crew led to the creation of the Columbia Accident Investigation Board (CAIB), which concluded that a piece of external fuel tank insulating foam impacted the Shuttle"s wing leading edge. The foam created a hole in the reinforced carbon/carbon (RCC) insulating material which gravely compromised the Shuttle"s thermal protection system (TPS). In response to the CAIB recommendation, the upcoming Return to Flight Shuttle Mission (STS-114) NASA will include a Shuttle deployed sensor suite which, among other sensors, will include two laser sensing systems, Sandia National Lab"s Laser Dynamic Range Imager (LDRI) and Neptec"s Laser Camera System (LCS) to collect 3-D imagery of the Shuttle"s exterior. Herein is described a ground-based statistical testing procedure that will be used by NASA as part of a damage detection performance assessment studying the performance of each of the two laser radar systems in detecting and identifying impact damage to the Shuttle. A statistical framework based on binomial and Bayesian statistics is used to describe the probability of detection and associated statistical confidence. A mock-up of a section of Shuttle wing RCC with interchangeable panels includes a random pattern of 1/4" and 1" diameter holes on the simulated RCC panels and is cataloged prior to double-blind testing. A team of ladar sensor operators will acquire laser radar imagery of the wing mock-up using a robotic platform in a laboratory at Johnson Space Center to execute linear image scans of the wing mock-up. The test matrix will vary robotic platform motion to simulate boom wobble and alter lighting and background conditions at the 6.5 foot and 10 foot sensor-wing stand-off distances to be used on orbit. A separate team of image analysts will process and review the data and characterize and record the damage that is found. A suite of software programs has been developed to support hole location definition, damage disposition
Pietropaolo, Massimo; Towns, Roberto; Eisenbarth, George S.
2012-01-01
Type 1 diabetes mellitus (T1D) is an autoimmune disease encompassing the T-cell-mediated destruction of pancreatic β cells and the production of autoantibodies against islet proteins. In humoral autoimmunity in T1D, the detection of islet autoantibodies and the examination of their associations with genetic factors and cellular autoimmunity constitute major areas in both basic research and clinical practice. Although insulin is a key autoantigen and may be primus inter pares in importance among T1D autoantigens, an abundant body of research has also revealed other autoantigens associated with the disease process. Solid evidence indicates that autoantibodies against islet targets serve as key markers to enroll newly diagnosed T1D patients and their family members in intervention trials aimed at preventing or halting the disease process. The next challenge is perfecting mechanistic bioassays to be used as end points for disease amelioration following immunomodulatory therapies aimed at blocking immune-mediated β-cell injury and, in turn, preserving β-cell function in type 1 diabetes mellitus. PMID:23028135
2008-03-31
Superresolution ”, Invited paper, The Computer Journal, April 2007; doi: 10.1093/comjnl/bxm007 4 8. M. Elad, P. Milanfar, R. Rubinstein, “Analysis versus...Elad, and P. Milanfar, “Video-to-Video Dynamic Superresolution for Grayscale and Color Sequences ”, EURASIP Journal of Applied Signal Processing...Special Issue on Superresolution Imaging, Volume 2006, Article ID 61859, Pages 1-15. 12. M. Shahram, and P. Milanfar, “Statistical and Information
Fold change rank ordering statistics: a new method for detecting differentially expressed genes
2014-01-01
Background Different methods have been proposed for analyzing differentially expressed (DE) genes in microarray data. Methods based on statistical tests that incorporate expression level variability are used more commonly than those based on fold change (FC). However, FC based results are more reproducible and biologically relevant. Results We propose a new method based on fold change rank ordering statistics (FCROS). We exploit the variation in calculated FC levels using combinatorial pairs of biological conditions in the datasets. A statistic is associated with the ranks of the FC values for each gene, and the resulting probability is used to identify the DE genes within an error level. The FCROS method is deterministic, requires a low computational runtime and also solves the problem of multiple tests which usually arises with microarray datasets. Conclusion We compared the performance of FCROS with those of other methods using synthetic and real microarray datasets. We found that FCROS is well suited for DE gene identification from noisy datasets when compared with existing FC based methods. PMID:24423217
Aouinti, Safa; Malouche, Dhafer; Giudicelli, Véronique; Kossida, Sofia; Lefranc, Marie-Paule
2015-01-01
The adaptive immune responses of humans and of other jawed vertebrate species (gnasthostomata) are characterized by the B and T cells and their specific antigen receptors, the immunoglobulins (IG) or antibodies and the T cell receptors (TR) (up to 2.1012 different IG and TR per individual). IMGT, the international ImMunoGeneTics information system (http://www.imgt.org), was created in 1989 by Marie-Paule Lefranc (Montpellier University and CNRS) to manage the huge and complex diversity of these antigen receptors. IMGT built on IMGT-ONTOLOGY concepts of identification (keywords), description (labels), classification (gene and allele nomenclature) and numerotation (IMGT unique numbering), is at the origin of immunoinformatics, a science at the interface between immunogenetics and bioinformatics. IMGT/HighV-QUEST, the first web portal, and so far the only one, for the next generation sequencing (NGS) analysis of IG and TR, is the paradigm for immune repertoire standardized outputs and immunoprofiles of the adaptive immune responses. It provides the identification of the variable (V), diversity (D) and joining (J) genes and alleles, analysis of the V-(D)-J junction and complementarity determining region 3 (CDR3) and the characterization of the ‘IMGT clonotype (AA)’ (AA for amino acid) diversity and expression. IMGT/HighV-QUEST compares outputs of different batches, up to one million nucleotide sequencesfor the statistical module. These high throughput IG and TR repertoire immunoprofiles are of prime importance in vaccination, cancer, infectious diseases, autoimmunity and lymphoproliferative disorders, however their comparative statistical analysis still remains a challenge. We present a standardized statistical procedure to analyze IMGT/HighV-QUEST outputs for the evaluation of the significance of the IMGT clonotype (AA) diversity differences in proportions, per gene of a given group, between NGS IG and TR repertoire immunoprofiles. The procedure is generic and
Pathway Cross-Talk Analysis in Detecting Significant Pathways in Barrett’s Esophagus Patients
Xu, Zhengyuan; Yan, Yan; He, Jian; Shan, Xinfang; Wu, Weiguo
2017-01-01
Background The pathological mechanism of Barrett’s esophagus (BE) is still unclear. In the present study, pathway cross-talks were analyzed to identify hub pathways for BE, with the purpose of finding an efficient and cost-effective detection method to discover BE at its early stage and take steps to prevent its progression. Material/Methods We collected and preprocessed gene expression profile data, original pathway data, and protein-protein interaction (PPI) data. Then, we constructed a background pathway cross-talk network (BPCN) based on the original pathway data and PPI data, and a disease pathway cross-talk network (DPCN) based on the differential pathways between the PPI data and the BE and normal control. Finally, a comprehensive analysis was conducted on these 2 networks to identify hub pathway cross-talks for BE, so as to better understand the pathological mechanism of BE from the pathway level. Results A total of 12 411 genes, 300 pathways (6919 genes), and 787 896 PPI interactions (16 730 genes) were separately obtained from their own databases. Then, we constructed a BPCN with 300 nodes (42 293 interactions) and a DPCN with 296 nodes (15 073 interactions). We identified 4 hub pathways: AMP signaling pathway, cGMP-PKG signaling pathway, natural killer cell-mediated cytotoxicity, and osteoclast differentiation. We found that these pathways might play important roles during the occurrence and development of BE. Conclusions We predicted that these pathways (such as AMP signaling pathway and cAMP signaling pathway) could be used as potential biomarkers for early diagnosis and therapy of BE. PMID:28263955
Clin, Bénédicte; Luc, Amandine; Morlais, Fabrice; Paris, Chrisophe; Ameille, Jacques; Brochard, Patrick; De Girolamo, Julien; Gislard, Antoine; Laurent, François; Letourneux, Marc; Schorle, Evelyne; Launoy, Guy; Pairon, Jean-Claude
2011-01-01
SUMMARY Objective The aim of the study was to analyse the relationships between CT pulmonary nodules mentioned by radiologists and cumulative exposure to asbestos or asbestos-related pleuro-pulmonary diseases, among 5,662 asbestos-exposed subjects, and the relationships between pulmonary nodules and thoracic cancer, in order to determine whether a specific surveillance strategy according to cumulative asbestos exposure, should be adopted. Design Standardised Incidence and Mortality Ratios for lung cancer and pleural mesothelioma were calculated among patients with and without mention of pulmonary nodules, and compared via the Comparative Morbidity Figure. Results A significant over-incidence of primary lung cancer and pleural mesothelioma was observed among subjects presenting with pulmonary nodule(s) (SIR respectively 1.95 [1.22; 2.95] and 11.88 [3.20; 30.41]). However, there was no significant relationship between pulmonary nodules mentioned by radiologists and cumulative asbestos exposure or between pulmonary nodules and the presence of asbestos-related benign diseases. Conclusions This study confirms the expected excess of lung cancer in subjects presenting with pulmonary nodules in the radiologist’s diagnostic report, and shows the absence of relationship between these nodules and the level of cumulative asbestos exposure. Consequently, our study offers no argument in favour of specific surveillance modalities with regard to these nodules based on estimated cumulative asbestos exposure. PMID:22118184
Bartnik, A.; Wachulak, P.; Fiedorowicz, H.; Fok, T.; Jarocki, R.; Szczurek, M.
2013-11-15
In this work, spectral investigations of photoionized He plasmas were performed. The photoionized plasmas were created by irradiation of helium stream, with intense pulses from laser-plasma extreme ultraviolet (EUV) source. The EUV source was based on a double-stream Xe/Ne gas-puff target irradiated with 10 ns/10 J Nd:YAG laser pulses. The most intense emission from the source spanned a relatively narrow spectral region below 20 nm, however, spectrally integrated intensity at longer wavelengths was also significant. The EUV radiation was focused onto a gas stream, injected into a vacuum chamber synchronously with the EUV pulse. The long-wavelength part of the EUV radiation was used for backlighting of the photoionized plasmas to obtain absorption spectra. Both emission and absorption spectra in the EUV range were investigated. Significant differences between absorption spectra acquired for neutral helium and low temperature photoionized plasmas were demonstrated for the first time. Strong increase of intensities and spectral widths of absorption lines, together with a red shift of the K-edge, was shown.
NASA Astrophysics Data System (ADS)
Nielsen, Allan A.; Conradsen, Knut; Skriver, Henning
2016-10-01
Test statistics for comparison of real (as opposed to complex) variance-covariance matrices exist in the statistics literature [1]. In earlier publications we have described a test statistic for the equality of two variance-covariance matrices following the complex Wishart distribution with an associated p-value [2]. We showed their application to bitemporal change detection and to edge detection [3] in multilook, polarimetric synthetic aperture radar (SAR) data in the covariance matrix representation [4]. The test statistic and the associated p-value is described in [5] also. In [6] we focussed on the block-diagonal case, we elaborated on some computer implementation issues, and we gave examples on the application to change detection in both full and dual polarization bitemporal, bifrequency, multilook SAR data. In [7] we described an omnibus test statistic Q for the equality of k variance-covariance matrices following the complex Wishart distribution. We also described a factorization of Q = R2 R3 … Rk where Q and Rj determine if and when a difference occurs. Additionally, we gave p-values for Q and Rj. Finally, we demonstrated the use of Q and Rj and the p-values to change detection in truly multitemporal, full polarization SAR data. Here we illustrate the methods by means of airborne L-band SAR data (EMISAR) [8,9]. The methods may be applied to other polarimetric SAR data also such as data from Sentinel-1, COSMO-SkyMed, TerraSAR-X, ALOS, and RadarSat-2 and also to single-pol data. The account given here closely follows that given our recent IEEE TGRS paper [7]. Selected References [1] Anderson, T. W., An Introduction to Multivariate Statistical Analysis, John Wiley, New York, third ed. (2003). [2] Conradsen, K., Nielsen, A. A., Schou, J., and Skriver, H., "A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data," IEEE Transactions on Geoscience and Remote Sensing 41(1): 4-19, 2003. [3] Schou, J
Prendes, Jorge; Chabert, Marie; Pascal, Frederic; Giros, Alain; Tourneret, Jean-Yves
2015-03-01
Remote sensing images are commonly used to monitor the earth surface evolution. This surveillance can be conducted by detecting changes between images acquired at different times and possibly by different kinds of sensors. A representative case is when an optical image of a given area is available and a new image is acquired in an emergency situation (resulting from a natural disaster for instance) by a radar satellite. In such a case, images with heterogeneous properties have to be compared for change detection. This paper proposes a new approach for similarity measurement between images acquired by heterogeneous sensors. The approach exploits the considered sensor physical properties and specially the associated measurement noise models and local joint distributions. These properties are inferred through manifold learning. The resulting similarity measure has been successfully applied to detect changes between many kinds of images, including pairs of optical images and pairs of optical-radar images.
Murphy, Emma L; Comiskey, Catherine M
2013-10-01
In times of scarce resources it is important for services to make evidence based decisions when identifying clients with poor outcomes. chi-Squared Automatic Interaction Detection (CHAID) modelling was used to identify characteristics of clients experiencing statistically significant poor outcomes. A national, longitudinal study recruited and interviewed, using the Maudsley Addiction Profile (MAP), 215 clients starting methadone treatment and 78% were interviewed one year later. Four CHAID analyses were conducted to model the interactions between the primary outcome variable, used heroin in the last 90 days prior to one year interview and variables on drug use, treatment history, social functioning and demographics. Results revealed that regardless of these other variables, males over 22 years of age consistently demonstrated significantly poorer outcomes than all other clients. CHAID models can be easily applied by service providers to provide ongoing evidence on clients exhibiting poor outcomes and requiring priority within services.
NASA Astrophysics Data System (ADS)
Shojiguchi, A.; Tanaka, T.; Okada, M.
Recently a modified algorithm of code-division multiple-access (CDMA) parallel interference canceler (PIC) has been proposed by Tanaka based on statistical neurodynamics. In this paper we apply the modified algorithm to the linear PIC (LPIC) and investigate its stability. We show that the stable (unstable) fixed points of the modified algorithm correspond to the stable (unstable) replica symmetry solutions with the Gaussian prior. We also show the modified algorithm is a special case of Kabashima's belief-propagation algorithm with Gaussian prior.
NASA Astrophysics Data System (ADS)
Nir, Yaacov; Eldar, Iris
1987-01-01
Eight ancient water wells, representing the late Bronze Age to the Crusades period (ca. 3100 700 B.P.), have recently been excavated (six by the authors) and reopened at archaeological sites (tels) along the southern and central Mediterranean coast of Israel. Evidence of ancient freshwater levels directly reflects on possible neotectonics of the region and on eustatic changes of sea level. There is substantial disagreement about the tectonic stability of the Israel Mediterranean coastal region during the past 3500 yr, whether there was a large-magnitude tectonic event (one of the largest known for recent times) during the period in discussion or whether the region was tectonically quiet. We tested the instability hypothesis by using geoarchaeological data from the wells and found no evidence for significant tectonic deformation of the central and southern Israel coast in the past 3100 yr. The “ancient water-well” method can, with appropriate modifications, be used all around the Mediterranean and other coasts elsewhere in the world where ground-water-sea-level relations are alike. Now in the digging of wells we must not disdain reflection, but must devote much acuteness and skill to the consideration of the natural principles of things. Vitruvius Pollio, Architectura, Book VIII, Chapter VI (25 B.C.)
Detection of signal transients based on wavelet and statistics for machine fault diagnosis
NASA Astrophysics Data System (ADS)
Zhu, Z. K.; Yan, Ruqiang; Luo, Liheng; Feng, Z. H.; Kong, F. R.
2009-05-01
This paper presents a transient detection method that combines continuous wavelet transform (CWT) and Kolmogorov-Smirnov (K-S) test for machine fault diagnosis. According to this method, the CWT represents the signal in the time-scale plane, and the proposed "step-by-step detection" based on K-S test identifies the transient coefficients. Simulation study shows that the transient feature can be effectively identified in the time-scale plane with the K-S test. Moreover, the transients can be further transformed back into the time domain through the inverse CWT. The proposed method is then utilized in the gearbox vibration transient detection for fault diagnosis, and the results show that the transient features both expressed in the time-scale plane and re-constructed in the time domain characterize the gearbox condition and fault severity development more clearly than the original time domain signal. The proposed method is also applied to the vibration signals of cone bearings with the localized fault in the inner race, outer race and the rolling elements, respectively. The detected transients indicate not only the existence of the bearing faults, but also the information about the fault severity to a certain degree.
Ioannidis, John P. A.
2017-01-01
A typical rule that has been used for the endorsement of new medications by the Food and Drug Administration is to have two trials, each convincing on its own, demonstrating effectiveness. “Convincing” may be subjectively interpreted, but the use of p-values and the focus on statistical significance (in particular with p < .05 being coined significant) is pervasive in clinical research. Therefore, in this paper, we calculate with simulations what it means to have exactly two trials, each with p < .05, in terms of the actual strength of evidence quantified by Bayes factors. Our results show that different cases where two trials have a p-value below .05 have wildly differing Bayes factors. Bayes factors of at least 20 in favor of the alternative hypothesis are not necessarily achieved and they fail to be reached in a large proportion of cases, in particular when the true effect size is small (0.2 standard deviations) or zero. In a non-trivial number of cases, evidence actually points to the null hypothesis, in particular when the true effect size is zero, when the number of trials is large, and when the number of participants in both groups is low. We recommend use of Bayes factors as a routine tool to assess endorsement of new medications, because Bayes factors consistently quantify strength of evidence. Use of p-values may lead to paradoxical and spurious decision-making regarding the use of new medications. PMID:28273140
NASA Astrophysics Data System (ADS)
Salih, A. L.; Mühlbauer, M.; Grumpe, A.; Pasckert, J. H.; Wöhler, C.; Hiesinger, H.
2016-06-01
The analysis of the impact crater size-frequency distribution (CSFD) is a well-established approach to the determination of the age of planetary surfaces. Classically, estimation of the CSFD is achieved by manual crater counting and size determination in spacecraft images, which, however, becomes very time-consuming for large surface areas and/or high image resolution. With increasing availability of high-resolution (nearly) global image mosaics of planetary surfaces, a variety of automated methods for the detection of craters based on image data and/or topographic data have been developed. In this contribution a template-based crater detection algorithm is used which analyses image data acquired under known illumination conditions. Its results are used to establish the CSFD for the examined area, which is then used to estimate the absolute model age of the surface. The detection threshold of the automatic crater detection algorithm is calibrated based on a region with available manually determined CSFD such that the age inferred from the manual crater counts corresponds to the age inferred from the automatic crater detection results. With this detection threshold, the automatic crater detection algorithm can be applied to a much larger surface region around the calibration area. The proposed age estimation method is demonstrated for a Kaguya Terrain Camera image mosaic of 7.4 m per pixel resolution of the floor region of the lunar crater Tsiolkovsky, which consists of dark and flat mare basalt and has an area of nearly 10,000 km2. The region used for calibration, for which manual crater counts are available, has an area of 100 km2. In order to obtain a spatially resolved age map, CSFDs and surface ages are computed for overlapping quadratic regions of about 4.4 x 4.4 km2 size offset by a step width of 74 m. Our constructed surface age map of the floor of Tsiolkovsky shows age values of typically 3.2-3.3 Ga, while for small regions lower (down to 2.9 Ga) and higher
Detecting hippocampal shape changes in Alzheimer's disease using statistical shape models
NASA Astrophysics Data System (ADS)
Shen, Kaikai; Bourgeat, Pierrick; Fripp, Jurgen; Meriaudeau, Fabrice; Salvado, Olivier
2011-03-01
The hippocampus is affected at an early stage in the development of Alzheimer's disease (AD). Using brain Magnetic Resonance (MR) images, we can investigate the effect of AD on the morphology of the hippocampus. Statistical shape models (SSM) are usually used to describe and model the hippocampal shape variations among the population. We use the shape variation from SSM as features to classify AD from normal control cases (NC). Conventional SSM uses principal component analysis (PCA) to compute the modes of variations among the population. Although these modes are representative of variations within the training data, they are not necessarily discriminant on labelled data. In this study, a Hotelling's T 2 test is used to qualify the landmarks which can be used for PCA. The resulting variation modes are used as predictors of AD from NC. The discrimination ability of these predictors is evaluated in terms of their classification performances using support vector machines (SVM). Using only landmarks statistically discriminant between AD and NC in SSM showed a better separation between AD and NC. These predictors also showed better correlation to the cognitive scores such as mini-mental state examination (MMSE) and Alzheimer's disease assessment scale (ADAS).
Xu, Kai; Yoshida, Ruriko
2010-01-01
Although exchange of genetic information by recombination plays an important role in the evolution of viruses, it is not clear how it generates diversity. Understanding recombination events helps with the study of the evolution of new virus strains or new viruses. Geminiviruses are plant viruses which have ambisense single-stranded circular DNA genomes and are one of the most economically important plant viruses in agricultural production. Small circular single-stranded DNA satellites, termed DNA-β, have recently been found to be associated with some geminivirus infections. In this paper we analyze several DNA-β sequences of geminiviruses for recombination events using phylogenetic and statistical analysis and we find that one strain from ToLCMaB has a recombination pattern and is a recombinant molecule between two strains from two species, PaLCuB-[IN:Chi:05] (major parent) and ToLCB-[IN:CP:04] (minor parent). We propose that this recombination event contributed to the evolution of the strain of ToLCMaB in South India. The Hidden Markov Chain (HMM) method developed by Webb et al. (2009) estimating phylogenetic tree through out the whole alignment provide us a recombination history of these DNA-β strains. It is the first time that this statistic method has been used on DNA-β recombination study and give a clear recombination history of DNA-β recombination. PMID:21423447
Zhang, Yan-Song; Xu, Jun; Luo, Guang-Hua; Wang, Rong-Chao; Zhu, Jiang; Zhang, Xiao-Ying; Nilsson-Ehle, Peter; Xu, Ning
2006-01-01
AIM: To establish a more sensitive method for detection of free cancer cells in peritoneal washes from gastric cancer patients during surgery and to evaluate its clinical significance. METHODS: The carcinoembryonic antigen (CEA) mRNA levels in peritoneal washes from 65 cases of gastric cancer were detected by real-time RT-PCR. Peritoneal lavage cytology (PLC) was applied simultaneously to detection of free cancer cells. Negative controls included peritoneal washes from 5 cases of benign gastric disease and blood samples from 5 adult healthy volunteers. RESULTS: There was no CEA mRNA in peritoneal washes from benign gastric disease patients and in blood of adult healthy volunteers. The positive percentage of free cancer cells detected by real-time RT-PCR was 47.7% and only 12.3% by PLC. The positive rate of CEA mRNA was significantly related with serosa invasion between peritoneal metastasis and stage of gastric cancer. CONCLUSION: Real-time RT-PCR is a sensitive and rapid method for the detection of free cancer cells in peritoneal washes. The presence of free cancer cells in peritoneal washes is related to the pathologic stage of gastric cancer. PMID:16552810
NASA Astrophysics Data System (ADS)
Brunet, M.; Asín, J.; Sigró, J.; Bañon, M.; García, F.; Aguilar, E.; Palenzuela, J. E.; Jones, P. D.
2009-04-01
The substitution of ancient shelters by modern Stevenson screens is thought to be the main cause of breaking homogeneity of long temperature records. It is also known that the so-called screen bias has induced a warm (cold) bias in long maximum (minimum) temperature records, whose magnitude is dependent on the latitude, on the moment of the year / day or on the meteorological conditions of the measurement (Parker, 1994). In the Mediterranean region, screen change is responsible for a strong positive bias in daily maximum temperature records (of about 1 °C at the annual scale, which shows a clear annual cycle with higher values in summer and lower in winter), meanwhile daily minimum temperatures show a small cold bias (about 0.2 °C, without seasonal differential behaviour) compared to the modern observations (e. g. Nichols et al., 1996; Brunet et al., 2008). Therefore, the use of the uncorrected data in assessments of long-term temperature variability and change will give negatively-biased results in term of trends, largely underestimating (slightly overestimating) maximum (minimum) long-term rate of temperature change, respectively; this bias would affect so to trend's estimation of the derived daily mean and diurnal temperature range series. Here we present an exploratory statistical analysis aimed at the minimisation of the "screen bias" from the affected Western Mediterranean air temperature time series. Our approach lies in the statistical analysis of about 6 years (5 years as calibration and 1 year as validation periods) of daily paired maximum and minimum temperature observations taken under a replicated ancient MONTSOURI shelter (one of the open stands used in the past to protect thermometers from direct or indirect radiation and wetting) and the modern STEVENSON screen installed in two experimental sites, the meteorological gardens of La Coruña and Murcia, Spain. These sites are representing the 2 most contrasted Mediterranean climate types: Oceanic
Guilera, Georgina; Gómez-Benito, Juana; Hidalgo, Maria Dolores; Sánchez-Meca, Julio
2013-12-01
This article presents a meta-analysis of studies investigating the effectiveness of the Mantel-Haenszel (MH) procedure when used to detect differential item functioning (DIF). Studies were located electronically in the main databases, representing the codification of 3,774 different simulation conditions, 1,865 related to Type I error and 1,909 to statistical power. The homogeneity of effect-size distributions was assessed by the Q statistic. The extremely high heterogeneity in both error rates (I² = 94.70) and power (I² = 99.29), due to the fact that numerous studies test the procedure in extreme conditions, means that the main interest of the results lies in explaining the variability in detection rates. One-way analysis of variance was used to determine the effects of each variable on detection rates, showing that the MH test was more effective when purification procedures were used, when the data fitted the Rasch model, when test contamination was below 20%, and with sample sizes above 500. The results imply a series of recommendations for practitioners who wish to study DIF with the MH test. A limitation, one inherent to all meta-analyses, is that not all the possible moderator variables, or the levels of variables, have been explored. This serves to remind us of certain gaps in the scientific literature (i.e., regarding the direction of DIF or variances in ability distribution) and is an aspect that methodologists should consider in future simulation studies.
Röling, Wilfred F M; Aerts, Joost W; Patty, C H Lucas; ten Kate, Inge Loes; Ehrenfreund, Pascale; Direito, Susana O L
2015-06-01
The detection of biomarkers plays a central role in our effort to establish whether there is, or was, life beyond Earth. In this review, we address the importance of considering mineralogy in relation to the selection of locations and biomarker detection methodologies with characteristics most promising for exploration. We review relevant mineral-biomarker and mineral-microbe interactions. The local mineralogy on a particular planet reflects its past and current environmental conditions and allows a habitability assessment by comparison with life under extreme conditions on Earth. The type of mineral significantly influences the potential abundances and types of biomarkers and microorganisms containing these biomarkers. The strong adsorptive power of some minerals aids in the preservation of biomarkers and may have been important in the origin of life. On the other hand, this strong adsorption as well as oxidizing properties of minerals can interfere with efficient extraction and detection of biomarkers. Differences in mechanisms of adsorption and in properties of minerals and biomarkers suggest that it will be difficult to design a single extraction procedure for a wide range of biomarkers. While on Mars samples can be used for direct detection of biomarkers such as nucleic acids, amino acids, and lipids, on other planetary bodies remote spectrometric detection of biosignatures has to be relied upon. The interpretation of spectral signatures of photosynthesis can also be affected by local mineralogy. We identify current gaps in our knowledge and indicate how they may be filled to improve the chances of detecting biomarkers on Mars and beyond.
Accounting for imperfect detection and survey bias in statistical analysis of presence-only data
Dorazio, Robert M.
2014-01-01
Using mathematical proof and simulation-based comparisons, I demonstrate that biases induced by errors in detection or biased selection of survey locations can be reduced or eliminated by using the hierarchical model to analyse presence-only data in conjunction with counts observed in planned surveys. I show that a relatively small number of high-quality data (from planned surveys) can be used to leverage the information in presence-only observations, which usually have broad spatial coverage but may not be informative of both occurrence and detectability of individuals. Because a variety of sampling protocols can be used in planned surveys, this approach to the analysis of presence-only data is widely applicable. In addition, since the point-process model is formulated at the level of an individual, it can be extended to account for biological interactions between individuals and temporal changes in their spatial distributions.
A Streaming Statistical Algorithm for Detection of SSH Keystroke Packets in TCP Connections
2011-01-01
Purdue University, West Lafayette IN 47904, USA, sguha@purdue.edu Paul Kidwell Lawrence Livermore Laboratories, Livermore CA 94551, USA, kidwellpaul...NAME OF RESPONSIBLE PERSON a. REPORT unclassified b. ABSTRACT unclassified c. THIS PAGE unclassified Guha, Kidwell , Barthur, Cleveland, Gerth...Guha, Kidwell , Barthur, Cleveland, Gerth, and Bullard: SSH Keystroke Packet Detection ICS-2011—Monterey, pp. 000–000, c© 2011 INFORMS 3 the widely-used
Smith, D.L.; Sagalovsky, L.; Micklich, B.J.; Harper, M.K.; Novick, A.H.
1994-06-01
A least-squares algorithm developed for analysis of fast-neutron transmission data resulting from non-destructive interrogation of sealed luggage and containers is subjected to a probabilistic interpretation. The approach is to convert knowledge of uncertainties in the derived areal elemental densities, as provided by this algorithm, into probability information that can be used to judge whether an interrogated object is either benign or potentially contains an illicit substance that should be investigated further. Two approaches are considered in this paper. One involves integration of a normalized probability density function associated with the least-squares solution. The other tests this solution against a hypothesis that the interrogated object indeed contains illicit material. This is accomplished by an application of the F-distribution from statistics. These two methods of data interpretation are applied to specific sets of neutron transmission results produced by Monte Carlo simulation.
NASA Technical Reports Server (NTRS)
Natarajan, Suresh; Gardner, C. S.
1987-01-01
Receiver timing synchronization of an optical Pulse-Position Modulation (PPM) communication system can be achieved using a phased-locked loop (PLL), provided the photodetector output is suitably processed. The magnitude of the PLL phase error is a good indicator of the timing error at the receiver decoder. The statistics of the phase error are investigated while varying several key system parameters such as PPM order, signal and background strengths, and PPL bandwidth. A practical optical communication system utilizing a laser diode transmitter and an avalanche photodiode in the receiver is described, and the sampled phase error data are presented. A linear regression analysis is applied to the data to obtain estimates of the relational constants involving the phase error variance and incident signal power.
Just post it: the lesson from two cases of fabricated data detected by statistics alone.
Simonsohn, Uri
2013-10-01
I argue that requiring authors to post the raw data supporting their published results has the benefit, among many others, of making fraud much less likely to go undetected. I illustrate this point by describing two cases of suspected fraud I identified exclusively through statistical analysis of reported means and standard deviations. Analyses of the raw data behind these published results provided invaluable confirmation of the initial suspicions, ruling out benign explanations (e.g., reporting errors, unusual distributions), identifying additional signs of fabrication, and also ruling out one of the suspected fraud's explanations for his anomalous results. If journals, granting agencies, universities, or other entities overseeing research promoted or required data posting, it seems inevitable that fraud would be reduced.
Statistical power of latent growth curve models to detect quadratic growth.
Diallo, Thierno M O; Morin, Alexandre J S; Parker, Philip D
2014-06-01
Latent curve models (LCMs) have been used extensively to analyze longitudinal data. However, little is known about the power of LCMs to detect nonlinear trends when they are present in the data. For this study, we utilized simulated data to investigate the power of LCMs to detect the mean of the quadratic slope, Type I error rates, and rates of nonconvergence during the estimation of quadratic LCMs. Five factors were examined: the number of time points, growth magnitude, interindividual variability, sample size, and the R (2)s of the measured variables. The results showed that the empirical Type I error rates were close to the nominal value of 5 %. The empirical power to detect the mean of the quadratic slope was affected by the simulation factors. Finally, a substantial proportion of samples failed to converge under conditions of no to small variation in the quadratic factor, small sample sizes, and small R (2) of the repeated measures. In general, we recommended that quadratic LCMs be based on samples of (a) at least 250 but ideally 400, when four measurement points are available; (b) at least 100 but ideally 150, when six measurement points are available; (c) at least 50 but ideally 100, when ten measurement points are available.
Robust Statistical Approaches for RSS-Based Floor Detection in Indoor Localization
Razavi, Alireza; Valkama, Mikko; Lohan, Elena Simona
2016-01-01
Floor detection for indoor 3D localization of mobile devices is currently an important challenge in the wireless world. Many approaches currently exist, but usually the robustness of such approaches is not addressed or investigated. The goal of this paper is to show how to robustify the floor estimation when probabilistic approaches with a low number of parameters are employed. Indeed, such an approach would allow a building-independent estimation and a lower computing power at the mobile side. Four robustified algorithms are to be presented: a robust weighted centroid localization method, a robust linear trilateration method, a robust nonlinear trilateration method, and a robust deconvolution method. The proposed approaches use the received signal strengths (RSS) measured by the Mobile Station (MS) from various heard WiFi access points (APs) and provide an estimate of the vertical position of the MS, which can be used for floor detection. We will show that robustification can indeed increase the performance of the RSS-based floor detection algorithms. PMID:27258279
Robust Statistical Approaches for RSS-Based Floor Detection in Indoor Localization.
Razavi, Alireza; Valkama, Mikko; Lohan, Elena Simona
2016-05-31
Floor detection for indoor 3D localization of mobile devices is currently an important challenge in the wireless world. Many approaches currently exist, but usually the robustness of such approaches is not addressed or investigated. The goal of this paper is to show how to robustify the floor estimation when probabilistic approaches with a low number of parameters are employed. Indeed, such an approach would allow a building-independent estimation and a lower computing power at the mobile side. Four robustified algorithms are to be presented: a robust weighted centroid localization method, a robust linear trilateration method, a robust nonlinear trilateration method, and a robust deconvolution method. The proposed approaches use the received signal strengths (RSS) measured by the Mobile Station (MS) from various heard WiFi access points (APs) and provide an estimate of the vertical position of the MS, which can be used for floor detection. We will show that robustification can indeed increase the performance of the RSS-based floor detection algorithms.
Kulp, Marjean Taylor; Ying, Gui-shuang; Huang, Jiayan; Maguire, Maureen; Quinn, Graham; Ciner, Elise B.; Cyert, Lynn A.; Orel-Bixler, Deborah A.; Moore, Bruce D.
2014-01-01
Purpose. To evaluate, by receiver operating characteristic (ROC) analysis, the ability of noncycloplegic retinoscopy (NCR), Retinomax Autorefractor (Retinomax), and SureSight Vision Screener (SureSight) to detect significant refractive errors (RE) among preschoolers. Methods. Refraction results of eye care professionals using NCR, Retinomax, and SureSight (n = 2588) and of nurse and lay screeners using Retinomax and SureSight (n = 1452) were compared with masked cycloplegic retinoscopy results. Significant RE was defined as hyperopia greater than +3.25 diopters (D), myopia greater than 2.00 D, astigmatism greater than 1.50 D, and anisometropia greater than 1.00 D interocular difference in hyperopia, greater than 3.00 D interocular difference in myopia, or greater than 1.50 D interocular difference in astigmatism. The ability of each screening test to identify presence, type, and/or severity of significant RE was summarized by the area under the ROC curve (AUC) and calculated from weighted logistic regression models. Results. For detection of each type of significant RE, AUC of each test was high; AUC was better for detecting the most severe levels of RE than for all REs considered important to detect (AUC 0.97–1.00 vs. 0.92–0.93). The area under the curve of each screening test was high for myopia (AUC 0.97–0.99). Noncycloplegic retinoscopy and Retinomax performed better than SureSight for hyperopia (AUC 0.92–0.99 and 0.90–0.98 vs. 0.85–0.94, P ≤ 0.02), Retinomax performed better than NCR for astigmatism greater than 1.50 D (AUC 0.95 vs. 0.90, P = 0.01), and SureSight performed better than Retinomax for anisometropia (AUC 0.85–1.00 vs. 0.76–0.96, P ≤ 0.07). Performance was similar for nurse and lay screeners in detecting any significant RE (AUC 0.92–1.00 vs. 0.92–0.99). Conclusions. Each test had a very high discriminatory power for detecting children with any significant RE. PMID:24481262
Outlier detection using some methods of mathematical statistic in meteorological time-series
NASA Astrophysics Data System (ADS)
Elias, Michal; Dousa, Jan
2016-06-01
In many applications of Global Navigate Satellite Systems the meteorological time-series play a very important role, especially when representing source of input data for other calculations such as corrections for very precise positioning. We are interested in those corrections which are related to the troposphere delay modelling. Time-series might contain some non-homogeneities, depending on the type of the data source. In this paper the outlier detection is discussed. For investigation we used method based on the autoregressive model and the results of its application were compared with the regression model.
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
Khuu, Sieu K.; Cham, Joey; Hayes, Anthony
2017-01-01
In the present study, we investigated the detection of contours defined by constant curvature and the statistics of curved contours in natural scenes. In Experiment 1, we examined the degree to which human sensitivity to contours is affected by changing the curvature angle and disrupting contour curvature continuity by varying the orientation of end elements. We find that (1) changing the angle of contour curvature decreased detection performance, while (2) end elements oriented in the direction (i.e., clockwise) of curvature facilitated contour detection regardless of the curvature angle of the contour. In Experiment 2 we further established that the relative effect of end—element orientation on contour detection was not only dependent on their orientation (collinear or cocircular), but also their spatial separation from the contour, and whether the contour shape was curved or not (i.e., C-shaped or S-shaped). Increasing the spatial separation of end-elements reduced contour detection performance regardless of their orientation or the contour shape. However, at small separations, cocircular end-elements facilitated the detection of C-shaped contours, but not S-shaped contours. The opposite result was observed for collinear end-elements, which improved the detection of S- shaped, but not C-shaped contours. These dissociative results confirmed that the visual system specifically codes contour curvature, but the association of contour elements occurs locally. Finally, we undertook an analysis of natural images that mapped contours with a constant angular change and determined the frequency of occurrence of end elements with different orientations. Analogous to our behavioral data, this image analysis revealed that the mapped end elements of constantly curved contours are likely to be oriented clockwise to the angle of curvature. Our findings indicate that the visual system is selectively sensitive to contours defined by constant curvature and that this might
Khuu, Sieu K; Cham, Joey; Hayes, Anthony
2016-01-01
In the present study, we investigated the detection of contours defined by constant curvature and the statistics of curved contours in natural scenes. In Experiment 1, we examined the degree to which human sensitivity to contours is affected by changing the curvature angle and disrupting contour curvature continuity by varying the orientation of end elements. We find that (1) changing the angle of contour curvature decreased detection performance, while (2) end elements oriented in the direction (i.e., clockwise) of curvature facilitated contour detection regardless of the curvature angle of the contour. In Experiment 2 we further established that the relative effect of end-element orientation on contour detection was not only dependent on their orientation (collinear or cocircular), but also their spatial separation from the contour, and whether the contour shape was curved or not (i.e., C-shaped or S-shaped). Increasing the spatial separation of end-elements reduced contour detection performance regardless of their orientation or the contour shape. However, at small separations, cocircular end-elements facilitated the detection of C-shaped contours, but not S-shaped contours. The opposite result was observed for collinear end-elements, which improved the detection of S- shaped, but not C-shaped contours. These dissociative results confirmed that the visual system specifically codes contour curvature, but the association of contour elements occurs locally. Finally, we undertook an analysis of natural images that mapped contours with a constant angular change and determined the frequency of occurrence of end elements with different orientations. Analogous to our behavioral data, this image analysis revealed that the mapped end elements of constantly curved contours are likely to be oriented clockwise to the angle of curvature. Our findings indicate that the visual system is selectively sensitive to contours defined by constant curvature and that this might reflect
Inoue, Nagamu; Yoshida, Toshifumi; Bessyo, Rieko; Yoneno, Kazuaki; Imaeda, Hiroyuki; Ogata, Haruhiko; Kanai, Takanori; Sugino, Yoshinori; Iwao, Yasushi
2017-01-01
Background The guidelines for colonoscopy present withdrawal time (WT) and adenoma detection rate (ADR) as the quality indicator. The purpose of this retrospective study is to analyze the predicting factors with polyp detection rate (PDR) as a surrogate for ADR by using comprehensive health checkup data, and assess the correlation between PDR per each colonic segment and WT, and factors influencing WT. Methods One thousand and thirty six consecutive health checkup cases from April 2015 to March 2016 were enrolled in this study, and 880 subjects who undertook colonoscopy without polyp removal or biopsy were divided into the two groups (polyp not detected group vs polyp detected group). The two groups were compared by subjects and clinical characteristics with univariate analysis followed by multivariate analysis. Colonoscopies with longer WT (≥ 6 min) and those with shorter WT (< 6 min) were compared by PDR per each colonic segment, and also by subjects and clinical characteristics. Results A total of 1009 subjects included two incomplete colonoscopies (CIR, 99.9%) and overall PDR was 35.8%. A multiple logistic regression model demonstrated that age, gender, and WT were significantly related factors for polyp detection (odds ratio, 1.036; 1.771; 1.217). PDR showed a linear increase as WT increased from 3 min to 9 min (r = 0.989, p = 0.000) and PDR with long WT group was higher than that with short WT group per each colonic segment, significantly in transverse (2.3 times, p = 0.004) and sigmoid colon (2.1 times, p = 0.001). Not only bowel preparation quality but also insertion difficulty evaluated by endoscopist were significant factors relating with WT (odds ratio, 3.811; 1.679). Conclusion This study suggests that endoscopists should be recommended to take more time up to 9 min of WT to observe transverse and sigmoid colon, especially when they feel no difficulty during scope insertion. PMID:28328936
Kim, Ki-Yeol; Kim, Jin; Kim, Hyung Jun; Nam, Woong; Cha, In-Ho
2010-05-01
Array comparative genomic hybridization (aCGH) provides a genome-wide technique for identifying chromosomal aberrations in human diseases, including cancer. Chromosomal aberrations in cancers are defined as regions that contain an increased or decreased DNA copy number, relative to normal samples. The identification of genomic regions associated with systematic aberrations provides insights into initiation and progression of cancer, and improves diagnosis, prognosis, and therapy strategies. The McNemar test can be used to detect differentially expressed genes after discretization of gene expressions in a microarray experiment for the matched dataset. In this study, we propose a method to detect significantly altered DNA regions, shifted McNemar test, which is based on the standard McNemar test and takes into account changes in copy number variations and the region size throughout the whole genome. In addition, this novel method can be used to detect genomic regions associated with the progress of oral squamous cell carcinoma (OSCC). The performance of the proposed method was evaluated based on the homogeneity within the selected regions and the classification accuracies of the selected regions. This method might be useful for identifying new candidate genes that neighbor known genes based on the whole-genomic variation because it detects significant chromosomal regions, not independent probes.
Farrington, C. Paddy; Noufaily, Angela; Andrews, Nick J.; Charlett, Andre
2016-01-01
A large-scale multiple surveillance system for infectious disease outbreaks has been in operation in England and Wales since the early 1990s. Changes to the statistical algorithm at the heart of the system were proposed and the purpose of this paper is to compare two new algorithms with the original algorithm. Test data to evaluate performance are created from weekly counts of the number of cases of each of more than 2000 diseases over a twenty-year period. The time series of each disease is separated into one series giving the baseline (background) disease incidence and a second series giving disease outbreaks. One series is shifted forward by twelve months and the two are then recombined, giving a realistic series in which it is known where outbreaks have been added. The metrics used to evaluate performance include a scoring rule that appropriately balances sensitivity against specificity and is sensitive to variation in probabilities near 1. In the context of disease surveillance, a scoring rule can be adapted to reflect the size of outbreaks and this was done. Results indicate that the two new algorithms are comparable to each other and better than the algorithm they were designed to replace. PMID:27513749
Heggeseth, Brianna; Harley, Kim; Warner, Marcella; Jewell, Nicholas; Eskenazi, Brenda
2015-01-01
It has been hypothesized that environmental exposures at key development periods such as in utero play a role in childhood growth and obesity. To investigate whether in utero exposure to endocrine-disrupting chemicals, dichlorodiphenyltrichloroethane (DDT) and its metabolite, dichlorodiphenyldichloroethane (DDE), is associated with childhood physical growth, we took a novel statistical approach to analyze data from the CHAMACOS cohort study. To model heterogeneity in the growth patterns, we used a finite mixture model in combination with a data transformation to characterize body mass index (BMI) with four groups and estimated the association between exposure and group membership. In boys, higher maternal concentrations of DDT and DDE during pregnancy are associated with a BMI growth pattern that is stable until about age five followed by increased growth through age nine. In contrast, higher maternal DDT exposure during pregnancy is associated with a flat, relatively stable growth pattern in girls. This study suggests that in utero exposure to DDT and DDE may be associated with childhood BMI growth patterns, not just BMI level, and both the magnitude of exposure and sex may impact the relationship.
NASA Astrophysics Data System (ADS)
Leviandier, Thierry; Alber, A.; Le Ber, F.; Piégay, H.
2012-02-01
Seven methods designed to delineate homogeneous river segments, belonging to four families, namely — tests of homogeneity, contrast enhancing, spatially constrained classification, and hidden Markov models — are compared, firstly on their principles, then on a case study, and on theoretical templates. These templates contain patterns found in the case study but not considered in the standard assumptions of statistical methods, such as gradients and curvilinear structures. The influence of data resolution, noise and weak satisfaction of the assumptions underlying the methods is investigated. The control of the number of reaches obtained in order to achieve meaningful comparisons is discussed. No method is found that outperforms all the others on all trials. However, the methods with sequential algorithms (keeping at order n + 1 all breakpoints found at order n) fail more often than those running complete optimisation at any order. The Hubert-Kehagias method and Hidden Markov Models are the most successful at identifying subpatterns encapsulated within the templates. Ergodic Hidden Markov Models are, moreover, liable to exhibit transition areas.
Statistical Detection of Anthropogenic Temporal Changes in the Distribution of Tropical Cyclones
NASA Astrophysics Data System (ADS)
Joannes-boyau, R.; Bodin, T.; Scheffers, A.; Sambridge, M.
2012-12-01
Recent studies highlighting the potential impact of climate change on tropical cyclones have added fuel to the already controversial debates. The link between climate change and tropical cyclone intensity and frequency has been disputed, as both appear to remain in the natural variability. The difficulty lies in our ability to distinguish natural changes from anthropogenic-induced anomalies. The increased anthropogenic atmospheric carbon dioxide leads to environmental changes such as warmer Sea Surface Temperatures (SST) and thus could impact tropical cyclones intensities and frequencies. However, recent studies show that, against an increasing SST, no global trend in respect to cyclone frequency has yet emerged. Scientists have warned to consider the heterogeneity of the existing dataset; especially since the historical tropical cyclone record is frequently accused to be incomplete. Given the abundance of cyclone record data and its likely sensitivity to a number of environmental factors, the real limitation comes from our ability to understand the record as a whole. Thus, strong arguments against the impartiality of proposed models are often debated. We will present an impartial and independent statistical tool applicable to a wide variety of physical and biological phenomena such as processes described by power laws, to observe temporal variations in the tropical cyclone track record from 1842 to 2010. This methodology allows us to observe the impact of anthropogenic-induced modifications on climatic events, without being clustered in subjective parameterised models.
Shen, Kai-kai; Fripp, Jurgen; Mériaudeau, Fabrice; Chételat, Gaël; Salvado, Olivier; Bourgeat, Pierrick
2012-02-01
The hippocampus is affected at an early stage in the development of Alzheimer's disease (AD). With the use of structural magnetic resonance (MR) imaging, we can investigate the effect of AD on the morphology of the hippocampus. The hippocampal shape variations among a population can be usually described using statistical shape models (SSMs). Conventional SSMs model the modes of variations among the population via principal component analysis (PCA). Although these modes are representative of variations within the training data, they are not necessarily discriminative on labeled data or relevant to the differences between the subpopulations. We use the shape descriptors from SSM as features to classify AD from normal control (NC) cases. In this study, a Hotelling's T2 test is performed to select a subset of landmarks which are used in PCA. The resulting variation modes are used as predictors of AD from NC. The discrimination ability of these predictors is evaluated in terms of their classification performances with bagged support vector machines (SVMs). Restricting the model to landmarks with better separation between AD and NC increases the discrimination power of SSM. The predictors extracted on the subregions also showed stronger correlation with the memory-related measurements such as Logical Memory, Auditory Verbal Learning Test (AVLT) and the memory subscores of Alzheimer Disease Assessment Scale (ADAS).
Wille, Anja; Gruissem, Wilhelm; Bühlmann, Peter; Hennig, Lars
2007-11-01
Accurately identifying differentially expressed genes from microarray data is not a trivial task, partly because of poor variance estimates of gene expression signals. Here, after analyzing 380 replicated microarray experiments, we found that probesets have typical, distinct variances that can be estimated based on a large number of microarray experiments. These probeset-specific variances depend at least in part on the function of the probed gene: genes for ribosomal or structural proteins often have a small variance, while genes implicated in stress responses often have large variances. We used these variance estimates to develop a statistical test for differentially expressed genes called EVE (external variance estimation). The EVE algorithm performs better than the t-test and LIMMA on some real-world data, where external information from appropriate databases is available. Thus, EVE helps to maximize the information gained from a typical microarray experiment. Nonetheless, only a large number of replicates will guarantee to identify nearly all truly differentially expressed genes. However, our simulation studies suggest that even limited numbers of replicates will usually result in good coverage of strongly differentially expressed genes.
POPITA, CRISTIAN; POPITA, ANCA RALUCA; SITAR-TAUT, ADELA; PETRUT, BOGDAN; FETICA, BOGDAN; COMAN, IOAN
2017-01-01
Background and aim Multiparametric-magnetic resonance imaging (mp-MRI) is the main imaging modality used for prostate cancer detection. The aim of this study is to evaluate the diagnostic performance of mp-MRI at 1.5-Tesla (1.5-T) for the detection of clinically significant prostate cancer. Methods In this ethical board approved prospective study, 39 patients with suspected prostate cancer were included. Patients with a history of positive prostate biopsy and patients treated for prostate cancer were excluded. All patients were examined at 1.5-T MRI, before standard transrectal ultrasonography–guided biopsy. Results The overall sensitivity, specificity, positive predictive value and negative predictive value for mp-MRI were 100%, 73.68%, 80% and 100%, respectively. Conclusion Our results showed that 1.5 T mp-MRI has a high sensitivity for detection of clinically significant prostate cancer and high negative predictive value in order to rule out significant disease. PMID:28246496
Graph-based and statistical approaches for detecting spectrally variable target materials
NASA Astrophysics Data System (ADS)
Ziemann, Amanda K.; Theiler, James
2016-05-01
In discriminating target materials from background clutter in hyperspectral imagery, one must contend with variability in both. Most algorithms focus on the clutter variability, but for some materials there is considerable variability in the spectral signatures of the target. This is especially the case for solid target materials, whose signatures depend on morphological properties (particle size, packing density, etc.) that are rarely known a priori. In this paper, we investigate detection algorithms that explicitly take into account the diversity of signatures for a given target. In particular, we investigate variable target detectors when applied to new representations of the hyperspectral data: a manifold learning based approach, and a residual based approach. The graph theory and manifold learning based approach incorporates multiple spectral signatures of the target material of interest; this is built upon previous work that used a single target spectrum. In this approach, we first build an adaptive nearest neighbors (ANN) graph on the data and target spectra, and use a biased locally linear embedding (LLE) transformation to perform nonlinear dimensionality reduction. This biased transformation results in a lower-dimensional representation of the data that better separates the targets from the background. The residual approach uses an annulus based computation to represent each pixel after an estimate of the local background is removed, which suppresses local backgrounds and emphasizes the target-containing pixels. We will show detection results in the original spectral space, the dimensionality-reduced space, and the residual space, all using subspace detectors: ranked spectral angle mapper (rSAM), subspace adaptive matched filter (ssAMF), and subspace adaptive cosine/coherence estimator (ssACE). Results of this exploratory study will be shown on a ground-truthed hyperspectral image with variable target spectra and both full and mixed pixel targets.
Lin, Jen-Jen; Cheng, Jung-Yu; Huang, Li-Fei; Lin, Ying-Hsiu; Wan, Yung-Liang; Tsui, Po-Hsiang
2017-05-01
The Nakagami distribution is an approximation useful to the statistics of ultrasound backscattered signals for tissue characterization. Various estimators may affect the Nakagami parameter in the detection of changes in backscattered statistics. In particular, the moment-based estimator (MBE) and maximum likelihood estimator (MLE) are two primary methods used to estimate the Nakagami parameters of ultrasound signals. This study explored the effects of the MBE and different MLE approximations on Nakagami parameter estimations. Ultrasound backscattered signals of different scatterer number densities were generated using a simulation model, and phantom experiments and measurements of human liver tissues were also conducted to acquire real backscattered echoes. Envelope signals were employed to estimate the Nakagami parameters by using the MBE, first- and second-order approximations of MLE (MLE1 and MLE2, respectively), and Greenwood approximation (MLEgw) for comparisons. The simulation results demonstrated that, compared with the MBE and MLE1, the MLE2 and MLEgw enabled more stable parameter estimations with small sample sizes. Notably, the required data length of the envelope signal was 3.6 times the pulse length. The phantom and tissue measurement results also showed that the Nakagami parameters estimated using the MLE2 and MLEgw could simultaneously differentiate various scatterer concentrations with lower standard deviations and reliably reflect physical meanings associated with the backscattered statistics. Therefore, the MLE2 and MLEgw are suggested as estimators for the development of Nakagami-based methodologies for ultrasound tissue characterization.
Thyagarajan, Nithyanandan; Udaya Shankar, N.; Subrahmanyan, Ravi; Arcus, Wayne; Emrich, David; Herne, David; Bernardi, Gianni; Greenhill, Lincoln J.; Bowman, Judd D.; Briggs, Frank; Gaensler, Bryan M.; Bunton, John D.; DeSouza, Ludi; Cappallo, Roger J.; Corey, Brian E.; Goeke, Robert F.; Hewitt, Jacqueline N.; Hazelton, Bryna J.; Johnston-Hollitt, Melanie; Kaplan, David L.; and others
2013-10-10
In this paper, we explore for the first time the relative magnitudes of three fundamental sources of uncertainty, namely, foreground contamination, thermal noise, and sample variance, in detecting the H I power spectrum from the epoch of reionization (EoR). We derive limits on the sensitivity of a Fourier synthesis telescope to detect EoR based on its array configuration and a statistical representation of images made by the instrument. We use the Murchison Widefield Array (MWA) configuration for our studies. Using a unified framework for estimating signal and noise components in the H I power spectrum, we derive an expression for and estimate the contamination from extragalactic point-like sources in three-dimensional k-space. Sensitivity for EoR H I power spectrum detection is estimated for different observing modes with MWA. With 1000 hr of observing on a single field using the 128 tile MWA, EoR detection is feasible (S/N >1 for k ∼< 0.8 Mpc{sup –1}). Bandpass shaping and refinements to the EoR window are found to be effective in containing foreground contamination, which makes the instrument tolerant to imaging errors. We find that for a given observing time, observing many independent fields of view does not offer an advantage over a single field observation when thermal noise dominates over other uncertainties in the derived power spectrum.
A novel scheme for detection of diffuse lung disease in MDCT by use of statistical texture features
NASA Astrophysics Data System (ADS)
Wang, Jiahui; Li, Feng; Doi, Kunio; Li, Qiang
2009-02-01
The successful development of high performance computer-aided-diagnostic systems has potential to assist radiologists in the detection and diagnosis of diffuse lung disease. We developed in this study an automated scheme for the detection of diffuse lung disease on multi-detector computed tomography (MDCT). Our database consisted of 68 CT scans, which included 31 normal and 37 abnormal cases with three kinds of abnormal patterns, i.e., ground glass opacity, reticular, and honeycombing. Two radiologists first selected the CT scans with abnormal patterns based on clinical reports. The areas that included specific abnormal patterns in the selected CT images were then delineated as reference standards by an expert chest radiologist. To detect abnormal cases with diffuse lung disease, the lungs were first segmented from the background in each slice by use of a texture analysis technique, and then divided into contiguous volumes of interest (VOIs) with a 64×64×64 matrix size. For each VOI, we calculated many statistical texture features, including the mean and standard deviation of CT values, features determined from the run length matrix, and features from the co-occurrence matrix. A quadratic classifier was employed for distinguishing between normal and abnormal VOIs by use of a leave-one-case-out validation scheme. A rule-based criterion was employed to further determine whether a case was normal or abnormal. For the detection of abnormal VOIs, our CAD system achieved a sensitivity of 86% and a specificity of 90%. For the detection of abnormal cases, it achieved a sensitivity of 89% and a specificity of 90%. This preliminary study indicates that our CAD system would be useful for the detection of diffuse lung disease.
NASA Astrophysics Data System (ADS)
von Larcher, Thomas; Harlander, Uwe; Alexandrov, Kiril; Wang, Yongtai
2010-05-01
Experiments on baroclinic wave instabilities in a rotating cylindrical gap have been long performed, e.g., to unhide regular waves of different zonal wave number, to better understand the transition to the quasi-chaotic regime, and to reveal the underlying dynamical processes of complex wave flows. We present the application of appropriate multivariate data analysis methods on time series data sets acquired by the use of non-intrusive measurement techniques of a quite different nature. While the high accurate Laser-Doppler-Velocimetry (LDV ) is used for measurements of the radial velocity component at equidistant azimuthal positions, a high sensitive thermographic camera measures the surface temperature field. The measurements are performed at particular parameter points, where our former studies show that kinds of complex wave patterns occur [1, 2]. Obviously, the temperature data set has much more information content as the velocity data set due to the particular measurement techniques. Both sets of time series data are analyzed by using multivariate statistical techniques. While the LDV data sets are studied by applying the Multi-Channel Singular Spectrum Analysis (M - SSA), the temperature data sets are analyzed by applying the Empirical Orthogonal Functions (EOF ). Our goal is (a) to verify the results yielded with the analysis of the velocity data and (b) to compare the data analysis methods. Therefor, the temperature data are processed in a way to become comparable to the LDV data, i.e. reducing the size of the data set in such a manner that the temperature measurements would imaginary be performed at equidistant azimuthal positions only. This approach initially results in a great loss of information. But applying the M - SSA to the reduced temperature data sets enable us to compare the methods. [1] Th. von Larcher and C. Egbers, Experiments on transitions of baroclinic waves in a differentially heated rotating annulus, Nonlinear Processes in Geophysics
Aerts, Joost W.; Patty, C.H. Lucas; ten Kate, Inge Loes; Ehrenfreund, Pascale; Direito, Susana O.L.
2015-01-01
Abstract The detection of biomarkers plays a central role in our effort to establish whether there is, or was, life beyond Earth. In this review, we address the importance of considering mineralogy in relation to the selection of locations and biomarker detection methodologies with characteristics most promising for exploration. We review relevant mineral-biomarker and mineral-microbe interactions. The local mineralogy on a particular planet reflects its past and current environmental conditions and allows a habitability assessment by comparison with life under extreme conditions on Earth. The type of mineral significantly influences the potential abundances and types of biomarkers and microorganisms containing these biomarkers. The strong adsorptive power of some minerals aids in the preservation of biomarkers and may have been important in the origin of life. On the other hand, this strong adsorption as well as oxidizing properties of minerals can interfere with efficient extraction and detection of biomarkers. Differences in mechanisms of adsorption and in properties of minerals and biomarkers suggest that it will be difficult to design a single extraction procedure for a wide range of biomarkers. While on Mars samples can be used for direct detection of biomarkers such as nucleic acids, amino acids, and lipids, on other planetary bodies remote spectrometric detection of biosignatures has to be relied upon. The interpretation of spectral signatures of photosynthesis can also be affected by local mineralogy. We identify current gaps in our knowledge and indicate how they may be filled to improve the chances of detecting biomarkers on Mars and beyond. Key Words: DNA—Lipids—Photosynthesis—Extremophiles—Mineralogy—Subsurface. Astrobiology 15, 492–507. PMID:26060985
Non-detection of a statistically anisotropic power spectrum in large-scale structure
Pullen, Anthony R.; Hirata, Christopher M. E-mail: chirata@tapir.caltech.edu
2010-05-01
We search a sample of photometric luminous red galaxies (LRGs) measured by the Sloan Digital Sky Survey (SDSS) for a quadrupolar anisotropy in the primordial power spectrum, in which P( k-vector ) is an isotropic power spectrum P-bar (k) multiplied by a quadrupolar modulation pattern. We first place limits on the 5 coefficients of a general quadrupole anisotropy. We also consider axisymmetric quadrupoles of the form P( k-vector ) = P-bar (k)(1+g{sub *}[( k-circumflex ⋅ n-circumflex ){sup 2}−(1/3)]) where n-circumflex is the axis of the anisotropy. When we force the symmetry axis n-circumflex to be in the direction (l,b) = (94°,26°) identified in the recent Groeneboom et al. analysis of the cosmic microwave background, we find g{sub *} = 0.006±0.036 (1σ). With uniform priors on n-circumflex and g{sub *} we find that −0.41 < g{sub *} < +0.38 with 95% probability, with the wide range due mainly to the large uncertainty of asymmetries aligned with the Galactic Plane. In none of these three analyses do we detect evidence for quadrupolar power anisotropy in large scale structure.
NASA Astrophysics Data System (ADS)
Darrouzet, Fabien; Ranvier, Sylvain; De Keyser, Johan; Lamy, Hervé; Lichtenberger, Janos; Décréau, Pierrette
2013-04-01
Whistlers are VLF (3-30 kHz) emissions initiated by lightning, propagating along magnetic field lines, observed on ground and in space. Whistler wave analysis is an effective tool for studying the plasmasphere. Whistlers acquire particular frequency-time characteristics while they propagate through the magnetospheric plasma, and in particular through the plasmasphere. Their propagation time depends on the plasma density along their propagation paths. It is possible to derive the plasmaspheric electron density distribution from these propagation times. We therefore have started a project to detect whistlers with VLF measurements. A VLF antenna has been installed in early 2011 in Humain, Belgium (50.11^°N, 5.15^°E). The VLF antenna is made of two perpendicular magnetic loops, oriented North-South and East-West, and with an area of approximately 50 m2 each. This antenna is part of AWDAnet, the Automatic Whistler Detector and Analyzer system's network. This network covers low, mid and high magnetic latitudes, including conjugate locations. We use the AWDA system to retrieve automatically electron density profiles from whistler measurements made in Belgium. In this poster, the first results of whistler occurrence are shown, as well as the first comparison with density measurements made with the WHISPER instrument onboard Cluster.
2012-01-01
Background Coronary artery calcifications (CAC) are markers of coronary atherosclerosis, but do not correlate well with stenosis severity. This study intended to evaluate clinical situations where a combined approach of coronary calcium scoring (CS) and nuclear stress test (SPECT-MPI) is useful for the detection of relevant CAD. Methods Patients with clinical indication for invasive coronary angiography (ICA) were included into our study during 08/2005-09/2008. At first all patients underwent CS procedure as part of the study protocol performed by either using a multidetector computed tomography (CT) scanner or a dual-source CT imager. CAC were automatically defined by dedicated software and the Agatston score was semi-automatically calculated. A stress-rest SPECT-MPI study was performed afterwards and scintigraphic images were evaluated quantitatively. Then all patients underwent ICA. Thereby significant CAD was defined as luminal stenosis ≥75% in quantitative coronary analysis (QCA) in ≥1 epicardial vessel. To compare data lacking Gaussian distribution an unpaired Wilcoxon-Test (Mann–Whitney) was used. Otherwise a Students t-test for unpaired samples was applied. Calculations were considered to be significant at a p-value of <0.05. Results We consecutively included 351 symptomatic patients (mean age: 61.2±12.3 years; range: 18–94 years; male: n=240) with a mean Agatston score of 258.5±512.2 (range: 0–4214). ICA verified exclusion of significant CAD in 66/67 (98.5%) patients without CAC. CAC was detected in remaining 284 patients. In 132/284 patients (46.5%) with CS>0 significant CAD was confirmed by ICA, and excluded in 152/284 (53.5%) patients. Sensitivity for CAD detection by CS alone was calculated as 99.2%, specificity was 30.3%, and negative predictive value was 98.5%. An additional SPECT in patients with CS>0 increased specificity to 80.9% while reducing sensitivity to 87.9%. Diagnostic accuracy was 84.2%. Conclusions In patients without CS=0
NASA Astrophysics Data System (ADS)
Liu, Guosheng; Seo, Eun-Kyoung
2013-02-01
has been long believed that the dominant microwave signature of snowfall over land is the brightness temperature decrease caused by ice scattering. However, our analysis of multiyear satellite data revealed that on most of occasions, brightness temperatures are rather higher under snowfall than nonsnowfall conditions, likely due to the emission by cloud liquid water. This brightness temperature increase masks the scattering signature and complicates the snowfall detection problem. In this study, we propose a statistical method for snowfall detection, which is developed by using CloudSat radar to train high-frequency passive microwave observations. To capture the major variations of the brightness temperatures and reduce the dimensionality of independent variables, the detection algorithm is designed to use the information contained in the first three principal components resulted from Empirical Orthogonal Function (EOF) analysis, which capture ~99% of the total variances of brightness temperatures. Given a multichannel microwave observation, the algorithm first transforms the brightness temperature vector into EOF space and then retrieves a probability of snowfall by using the CloudSat radar-trained look-up table. Validation has been carried out by case studies and averaged horizontal snowfall fraction maps. The result indicated that the algorithm has clear skills in identifying snowfall areas even over mountainous regions.
Statistical modeling and trend detection of extreme sea level records in the Pearl River Estuary
NASA Astrophysics Data System (ADS)
Wang, Weiwen; Zhou, Wen
2017-03-01
Sea level rise has become an important issue in global climate change studies. This study investigates trends in sea level records, particularly extreme records, in the Pearl River Estuary, using measurements from two tide gauge stations in Macau and Hong Kong. Extremes in the original sea level records (daily higher high water heights) and in tidal residuals with and without the 18.6-year nodal modulation are investigated separately. Thresholds for defining extreme sea levels are calibrated based on extreme value theory. Extreme events are then modeled by peaks-over-threshold models. The model applied to extremes in original sea level records does not include modeling of their durations, while a geometric distribution is added to model the duration of extremes in tidal residuals. Realistic modeling results are recommended in all stationary models. Parametric trends of extreme sea level records are then introduced to nonstationary models through a generalized linear model framework. The result shows that, in recent decades, since the 1960s, no significant trends can be found in any type of extreme at any station, which may be related to a reduction in the influence of tropical cyclones in the region. For the longer-term record since the 1920s at Macau, a regime shift of tidal amplitudes around the 1970s may partially explain the diverse trend of extremes in original sea level records and tidal residuals.
Kato, Hiroki; Shimosegawa, Eku; Fujino, Koichi; Hatazawa, Jun
2016-01-01
Background Integrated SPECT/CT enables non-uniform attenuation correction (AC) using built-in CT instead of the conventional uniform AC. The effect of CT-based AC on voxel-based statistical analyses of brain SPECT findings has not yet been clarified. Here, we assessed differences in the detectability of regional cerebral blood flow (CBF) reduction using SPECT voxel-based statistical analyses based on the two types of AC methods. Subjects and Methods N-isopropyl-p-[123I]iodoamphetamine (IMP) CBF SPECT images were acquired for all the subjects and were reconstructed using 3D-OSEM with two different AC methods: Chang’s method (Chang’s AC) and the CT-based AC method. A normal database was constructed for the analysis using SPECT findings obtained for 25 healthy normal volunteers. Voxel-based Z-statistics were also calculated for SPECT findings obtained for 15 patients with chronic cerebral infarctions and 10 normal subjects. We assumed that an analysis with a higher specificity would likely produce a lower mean absolute Z-score for normal brain tissue, and a more sensitive voxel-based statistical analysis would likely produce a higher absolute Z-score for in old infarct lesions, where the CBF was severely decreased. Results The inter-subject variation in the voxel values in the normal database was lower using CT-based AC, compared with Chang’s AC, for most of the brain regions. The absolute Z-score indicating a SPECT count reduction in infarct lesions was also significantly higher in the images reconstructed using CT-based AC, compared with Chang’s AC (P = 0.003). The mean absolute value of the Z-score in the 10 intact brains was significantly lower in the images reconstructed using CT-based AC than in those reconstructed using Chang’s AC (P = 0.005). Conclusions Non-uniform CT-based AC by integrated SPECT/CT significantly improved sensitivity and the specificity of the voxel-based statistical analyses for regional SPECT count reductions, compared with
NASA Astrophysics Data System (ADS)
Shen, Wen; Li, Suiqiong; Horikawa, Shin; Petrenko, Valery A.; Barbaree, James; Chin, Bryan A.
2011-06-01
This work demonstrated a direct detection of Salmonella on fresh food produce using groups of magnetoelastic biosensors. The magnetoelastic biosensors were coated with E2 phage, which specifically binds with S. typhimurium. The resonance frequency of the biosensor is measured using a pulse excitation system, which allows simultaneous detection of multiple sensors. Multiple measurement and control biosensors were placed on fresh food surfaces that had been spiked with a known amount of Salmonella. Binding with bacteria was allowed to occur for 30 minutes in a humid air environment. The resonance frequencies of the groups of biosensors were then measured to determine the amount of bound bacteria. By using a statistical experimental design and by taking the average of repeated measurements, possible detection errors are decreased. By using multiple sensors at each site of interest, a higher portion of the contaminated surface has contact with biosensors, allowing for more complete information on the food produce surface. Results from SEM pictures of the sensor surface agree with the sensor frequency response results.
Cohn, T.A.; England, J.F.; Berenbrock, C.E.; Mason, R.R.; Stedinger, J.R.; Lamontagne, J.R.
2013-01-01
he Grubbs-Beck test is recommended by the federal guidelines for detection of low outliers in flood flow frequency computation in the United States. This paper presents a generalization of the Grubbs-Beck test for normal data (similar to the Rosner (1983) test; see also Spencer and McCuen (1996)) that can provide a consistent standard for identifying multiple potentially influential low flows. In cases where low outliers have been identified, they can be represented as “less-than” values, and a frequency distribution can be developed using censored-data statistical techniques, such as the Expected Moments Algorithm. This approach can improve the fit of the right-hand tail of a frequency distribution and provide protection from lack-of-fit due to unimportant but potentially influential low flows (PILFs) in a flood series, thus making the flood frequency analysis procedure more robust.
Xiao, Ruobing; Cerny, Jan; Devitt, Katherine; Dresser, Karen; Nath, Rajneesh; Ramanathan, Muthalagu; Rodig, Scott J; Chen, Benjamin J; Woda, Bruce A; Yu, Hongbo
2014-06-01
It has been recognized that monoclonal gammopathy of undetermined significance (MGUS) precedes a diagnosis of plasma cell myeloma in most patients. Recent gene expression array analysis has revealed that an MYC activation signature is detected in plasma cell myeloma but not in MGUS. In this study, we performed immunohistochemical studies using membrane CD138 and nuclear MYC double staining on bone marrow biopsies from patients who met the diagnostic criteria of plasma cell myeloma or MGUS. Our study demonstrated nuclear MYC expression in CD138-positive plasma cells in 22 of 26 (84%) plasma cell myeloma samples and in none of the 29 bone marrow samples from patients with MGUS. In addition, our data on the follow-up biopsies from plasma cell myeloma patients with high MYC expression demonstrated that evaluation of MYC expression in plasma cells can be useful in detecting residual disease. We also demonstrated that plasma cells gained MYC expression in 5 of 8 patients (62.5%) when progressing from MGUS to plasma cell myeloma. Analysis of additional lymphomas with plasmacytic differentiation, including lymphoplasmacytic lymphoma, marginal zone lymphoma, and plasmablastic lymphoma, reveals that MYC detection can be a useful tool in the diagnosis of plasma cell myeloma.
Vasas, Vera; Hanley, Daniel; Kevan, Peter G; Chittka, Lars
2017-03-17
Many pollinating insects acquire their entire nutrition from visiting flowers, and they must therefore be efficient both at detecting flowers and at recognizing familiar rewarding flower types. A crucial first step in recognition is the identification of edges and the segmentation of the visual field into areas that belong together. Honeybees and bumblebees acquire visual information through three types of photoreceptors; however, they only use a single receptor type-the one sensitive to longer wavelengths-for edge detection and movement detection. Here, we show that these long-wavelength receptors (peak sensitivity at ~544 nm, i.e., green) provide the most consistent signals in response to natural objects. Using our multispectral image database of flowering plants, we found that long-wavelength receptor responses had, depending on the specific scenario, up to four times higher signal-to-noise ratios than the short- and medium-wavelength receptors. The reliability of the long-wavelength receptors emerges from an intricate interaction between flower coloration and the bee's visual system. This finding highlights the adaptive significance of bees using only long-wavelength receptors to locate flowers among leaves, before using information provided by all three receptors to distinguish the rewarding flower species through trichromatic color vision.
Minor changes in the indicator used to measure fine PM, which cause only modest changes in Mass concentrations, can lead to dramatic changes in the statistical relationship of fine PM mass with cardiovascular mortality. An epidemiologic study in Phoenix (Mar et al., 2000), augme...
Coklin, Tatjana; Farber, Jeffrey M; Parrington, Lorna J; Bin Kingombe, Cesar I; Ross, William H; Dixon, Brent R
2011-03-01
The effectiveness of molecular methods for the detection of species of Giardia and Cryptosporidium in fecal samples is often reduced by low or intermittent cyst and oocyst shedding, and/or the presence of polymerase chain reaction (PCR) inhibitors. The present study investigates the use of immunomagnetic separation (IMS) as an additional concentration step before PCR in the detection of these common protozoan parasites in dairy cattle. The IMS-PCR assays were optimized for amplifying fragments of the 16S ribosomal RNA (rRNA), β-giardin, and glutamate dehydrogenase (GDH) genes of Giardia duodenalis, as well as fragments of the 18S rRNA, heat shock protein (HSP)-70, and Cryptosporidium oocyst wall protein (COWP) genes of Cryptosporidium spp. In all cases, IMS-PCR was more sensitive than PCR alone. A significantly greater number of Giardia-positive samples were identified using IMS-PCR of the 16S rRNA gene (P < 0.01) and of the GDH gene (P < 0.01), as compared with PCR without any additional concentration step. In the case of Cryptosporidium, IMS-PCR of the COWP gene (P = 0.02) resulted in a significantly greater number of positives than did PCR without the IMS concentration step. The greatest number of positives, however, was obtained using IMS-PCR to amplify a portion of the 16S rRNA gene of Giardia and a portion of the HSP-70 gene of Cryptosporidium. A further comparison of the optimized IMS-PCR assays to immunofluorescence microscopy suggested that the IMS-PCR assays were considerably more sensitive than microscopy was in the detection of Giardia cysts and Cryptosporidium oocysts in fecal samples.
NASA Astrophysics Data System (ADS)
Meng, X.; Peng, Z.
2014-12-01
It is now well established that extraction of fossil fuels and/or waste water disposal do cause earthquakes in Central and Eastern United States (CEUS). However, the physics underneath of the nucleation of induced earthquakes still remain elusive. In particular, do induced and tectonic earthquake sequences in CEUS share the same statistics, for example the Omori's law [Utsu et al., 1995] and the Gutenberg-Richter's law? Some studies have show that most naturally occurring earthquake sequences are driven by cascading-type triggering. Hence, they would follow the typical Gutenberg-Richter relation and Omori's aftershock decay and could be well described by multi-dimensional point-process models such as Epidemic Type Aftershock Sequence (ETAS) [Ogata, 1988; Zhuang et al., 2012]. However, induced earthquakes are likely driven by external forcing such as injected fluid pressure, and hence would not be well described by the ETAS model [Llenos and Michael, 2013]. Existing catalogs in CEUS (e.g. the ANSS catalog) have relatively high magnitude of completeness [e.g., Van Der Elst et al., 2013] and hence may not be ideal for a detailed ETAS modeling analysis. A waveform matched filter technique has been successfully applied to detect many missing earthquakes in CEUS with a sparse network in Illinois [Yang et al., 2009] and on single station in Texas, Oklahoma and Colorado [e.g., Van Der Elst et al., 2013]. In addition, the deployment of the USArray station in CEUS also helped to expand the station coverage. In this study, we systematically detect missing events during 14 moderate-size (M>=4) earthquake sequences since 2000 in CEUS and quantify their statistical parameters (e.g. b, a, K, and p values) and spatio-temporal evolutions. Then we compare the statistical parameters and the spatio-temporal evolution pattern between induced and naturally occurring earthquake sequences to see if one or more diagnostic parameters exist. Our comprehensive analysis of earthquake sequences
NASA Astrophysics Data System (ADS)
Iwasaki, Atsushi; Todoroki, Akira; Shimamura, Yoshinobu; Kobayashi, Hideo
2004-10-01
The present paper proposes a new damage diagnosis method for structural health monitoring that does not require data on damaged-state structures. Structural health monitoring is an essential technology for aged civil structures and advanced composite structures. For damage diagnostic methods, most current structural health monitoring systems adopt parametric methods based on modeling, or non-parametric methods such as artificial neural networks. The conventional methods require FEM modeling of structure or data for training the damaged-state structure. These processes require judgment by a human, resulting in high cost. The present paper proposes a new automatic damage diagnostic method for structural health monitoring that does not require these processes by using a system identification and statistical similarity test of the identified systems using an F-test. As an example of damage diagnosis using the new method, the present study describes delamination detection of a CFRP beam. System identification among the strain data measured on the surface of a composite beam is used for damage diagnosis. The results show that the new statistical damage diagnostic method successfully diagnoses damage without the use of modeling and without learning data for damaged structures.
Gusto, Gaelle; Schbath, Sophie
2005-01-01
We propose an original statistical method to estimate how the occurrences of a given process along a genome, genes or motifs for instance, may be influenced by the occurrences of a second process. More precisely, the aim is to detect avoided and/or favored distances between two motifs, for instance, suggesting possible interactions at a molecular level. For this, we consider occurrences along the genome as point processes and we use the so-called Hawkes' model. In such model, the intensity at position t depends linearly on the distances to past occurrences of both processes via two unknown profile functions to estimate. We perform a non parametric estimation of both profiles by using B-spline decompositions and a constrained maximum likelihood method. Finally, we use the AIC criterion for the model selection. Simulations show the excellent behavior of our estimation procedure. We then apply it to study (i) the dependence between gene occurrences along the E. coli genome and the occurrences of a motif known to be part of the major promoter for this bacterium, and (ii) the dependence between the yeast S. cerevisiae genes and the occurrences of putative polyadenylation signals. The results are coherent with known biological properties or previous predictions, meaning this method can be of great interest for functional motif detection, or to improve knowledge of some biological mechanisms.
Salman, A; Shufan, E; Zeiri, L; Huleihel, M
2014-07-01
Herpes viruses are involved in a variety of human disorders. Herpes Simplex Virus type 1 (HSV-1) is the most common among the herpes viruses and is primarily involved in human cutaneous disorders. Although the symptoms of infection by this virus are usually minimal, in some cases HSV-1 might cause serious infections in the eyes and the brain leading to blindness and even death. A drug, acyclovir, is available to counter this virus. The drug is most effective when used during the early stages of the infection, which makes early detection and identification of these viral infections highly important for successful treatment. In the present study we evaluated the potential of Raman spectroscopy as a sensitive, rapid, and reliable method for the detection and identification of HSV-1 viral infections in cell cultures. Using Raman spectroscopy followed by advanced statistical methods enabled us, with sensitivity approaching 100%, to differentiate between a control group of Vero cells and another group of Vero cells that had been infected with HSV-1. Cell sites that were "rich in membrane" gave the best results in the differentiation between the two categories. The major changes were observed in the 1195-1726 cm(-1) range of the Raman spectrum. The features in this range are attributed mainly to proteins, lipids, and nucleic acids.
NASA Astrophysics Data System (ADS)
Larsen, L.; Watts, D.; Khurana, A.; Anderson, J. L.; Xu, C.; Merritts, D. J.
2015-12-01
The classic signal of self-organization in nature is pattern formation. However, the interactions and feedbacks that organize depositional landscapes do not always result in regular or fractal patterns. How might we detect their existence and effects in these "irregular" landscapes? Emergent landscapes such as newly forming deltaic marshes or some restoration sites provide opportunities to study the autogenic processes that organize landscapes and their physical signatures. Here we describe a quest to understand autogenic vs. allogenic controls on landscape evolution in Big Spring Run, PA, a landscape undergoing restoration from bare-soil conditions to a target wet meadow landscape. The contemporary motivation for asking questions about autogenic vs. allogenic controls is to evaluate how important initial conditions or environmental controls may be for the attainment of management objectives. However, these questions can also inform interpretation of the sedimentary record by enabling researchers to separate signals that may have arisen through self-organization processes from those resulting from environmental perturbations. Over three years at Big Spring Run, we mapped the dynamic evolution of floodplain vegetation communities and distributions of abiotic variables and topography. We used principal component analysis and transition probability analysis to detect associative interactions between vegetation and geomorphic variables and convergent cross-mapping on lidar data to detect causal interactions between biomass and topography. Exploratory statistics revealed that plant communities with distinct morphologies exerted control on landscape evolution through stress divergence (i.e., channel initiation) and promoting the accumulation of fine sediment in channels. Together, these communities participated in a negative feedback that maintains low energy and multiple channels. Because of the spatially explicit nature of this feedback, causal interactions could not
NASA Astrophysics Data System (ADS)
Zhan, Yimin; Mechefske, Chris K.
2007-07-01
Optimal maintenance decision analysis is heavily dependent on the accuracy of condition indicators. A condition indicator that is subject to such varying operating conditions as load is unable to provide precise condition information of the monitored object for making optimal operational maintenance decisions even if the maintenance program is established within a rigorous theoretical framework. For this reason, the performance of condition monitoring techniques applied to rotating machinery under varying load conditions has been a long-term concern and has attracted intensive research interest. Part I of this study proposed a novel technique based on adaptive autoregressive modeling and hypothesis tests. The method is able to automatically search for the optimal time-series model order and establish a compromised autoregressive model fitting based on the healthy gear motion residual signals under varying load conditions. The condition of the monitored gearbox is numerically represented by a modified Kolmogorov-Smirnov test statistic. Part II of this study is devoted to applications of the proposed technique to entire lifetime condition detection of three gearboxes with distinct physical specifications, distinct load conditions, and distinct failure modes. A comprehensive and thorough comparative study is conducted between the proposed technique and several counterparts. The detection technique is further enhanced by a proposed method to automatically identify and generate fault alerts with the aid of the Wilcoxon rank-sum test and thus requires no supervision from maintenance personnel. Experimental analysis demonstrated that the proposed technique applied to automatic identification and generation of fault alerts also features two highly desirable properties, i.e. few false alerts and early alert for incipient faults. Furthermore, it is found that the proposed technique is able to identify two types of abnormalities, i.e. strong ghost components abruptly
Dilsizian, V.; Perrone-Filardi, P.; Cannon, R.O. 3d.; Freedman, N.M.; Bacharach, S.L.; Bonow, R.O. )
1991-08-01
Although quantitation of exercise thallium tomograms has enhanced the noninvasive diagnosis and localization of coronary artery disease, the detection of stenosis of the left circumflex coronary artery remains suboptimal. Because posterolateral regional wall motion during exercise is well assessed by radionuclide angiography, this study determined whether regional dysfunction of the posterolateral wall during exercise radionuclide angiography is more sensitive in identifying left circumflex disease than thallium perfusion abnormalities assessed by single-photon emission computed tomography (SPECT). One hundred ten consecutive patients with CAD were studied, of whom 70 had a significant stenosis of the left circumflex coronary artery or a major obtuse marginal branch. Both regional function and segmental thallium activity of the posterolateral wall were assessed using visual and quantitative analysis. Left ventricular regional function was assessed objectively by dividing the left ventricular region of interest into 20 sectors; the 8 sectors corresponding to the posterolateral free wall were used to assess function in the left circumflex artery distribution. Similarly, using circumferential profile analysis of short-axis thallium tomograms, left ventricular myocardial activity was subdivided into 64 sectors; the 16 sectors corresponding to the posterolateral region were used to assess thallium perfusion abnormalities in the left circumflex artery territory. Qualitative posterolateral wall motion analysis detected 76% of patients with left circumflex coronary artery stenosis, with a specificity of 83%, compared with only 44% by qualitative thallium tomography (p less than 0.001) and a specificity of 92%.
Adams, Michael C; Barbano, David M
2015-06-01
Our objective was to develop a statistical approach that could be used to determine whether a handler's fat, protein, or other solids mid-infrared (MIR) spectrophotometer test values were different, on average, from a milk regulatory laboratory's MIR test values when split-sampling test values are not available. To accomplish this objective, the Proc GLM procedure of SAS (SAS Institute Inc., Cary, NC) was used to develop a multiple linear regression model to evaluate 4 mo of MIR producer payment testing data (112 to 167 producers per month) from 2 different MIR instruments. For each of the 4 mo and each of the 2 components (fat or protein), the GLM model was Response=Instrument+Producer+Date+2-Way Interactions+3-Way Interaction. Instrument was significant in determining fat and protein tests for 3 of the 4 mo, and Producer was significant in determining fat and protein tests for all 4 mo. This model was also used to establish fat and protein least significant differences (LSD) between instruments. Fat LSD between instruments ranged from 0.0108 to 0.0144% (α=0.05) for the 4 mo studied, whereas protein LSD between instruments ranged from 0.0046 to 0.0085% (α=0.05). In addition, regression analysis was used to determine the effects of component concentration and date of sampling on fat and protein differences between 2 MIR instruments. This statistical approach could be performed monthly to document a regulatory laboratory's verification that a given handler's instrument has obtained a different test result, on average, from that of the regulatory laboratory's and that an adjustment to producer payment may be required.
NASA Astrophysics Data System (ADS)
de Laat, Jos; van Weele, Michiel; van der A, Ronald
2015-04-01
An important new landmark in present day ozone research is presented through MLS satellite observations of significant ozone increases during the ozone hole season that are attributed unequivocally to declining ozone depleting substances. For many decades the Antarctic ozone hole has been the prime example of both the detrimental effects of human activities on our environment as well as how to construct effective and successful environmental policies. Nowadays atmospheric concentrations of ozone depleting substances are on the decline and first signs of recovery of stratospheric ozone and ozone in the Antarctic ozone hole have been observed. The claimed detection of significant recovery, however, is still subject of debate. In this talk we will discuss first current uncertainties in the assessment of ozone recovery in the Antarctic ozone hole by using multi-variate regression methods, and, secondly present an alternative approach to identify ozone hole recovery unequivocally. Even though multi-variate regression methods help to reduce uncertainties in estimates of ozone recovery, great care has to be taken in their application due to the existence of uncertainties and degrees of freedom in the choice of independent variables. We show that taking all uncertainties into account in the regressions the formal recovery of ozone in the Antarctic ozone hole cannot be established yet, though is likely before the end of the decade (before 2020). Rather than focusing on time and area averages of total ozone columns or ozone profiles, we argue that the time evolution of the probability distribution of vertically resolved ozone in the Antarctic ozone hole contains a better fingerprint for the detection of ozone recovery in the Antarctic ozone hole. The advantages of this method over more tradition methods of trend analyses based on spatio-temporal average ozone are discussed. The 10-year record of MLS satellite measurements of ozone in the Antarctic ozone hole shows a
Ramanathan, Arvind; Savol, Andrej J; Agarwal, Pratul K; Chennubhotla, Chakra S
2012-11-01
Biomolecular simulations at millisecond and longer time-scales can provide vital insights into functional mechanisms. Because post-simulation analyses of such large trajectory datasets can be a limiting factor in obtaining biological insights, there is an emerging need to identify key dynamical events and relating these events to the biological function online, that is, as simulations are progressing. Recently, we have introduced a novel computational technique, quasi-anharmonic analysis (QAA) (Ramanathan et al., PLoS One 2011;6:e15827), for partitioning the conformational landscape into a hierarchy of functionally relevant sub-states. The unique capabilities of QAA are enabled by exploiting anharmonicity in the form of fourth-order statistics for characterizing atomic fluctuations. In this article, we extend QAA for analyzing long time-scale simulations online. In particular, we present HOST4MD--a higher-order statistical toolbox for molecular dynamics simulations, which (1) identifies key dynamical events as simulations are in progress, (2) explores potential sub-states, and (3) identifies conformational transitions that enable the protein to access those sub-states. We demonstrate HOST4MD on microsecond timescale simulations of the enzyme adenylate kinase in its apo state. HOST4MD identifies several conformational events in these simulations, revealing how the intrinsic coupling between the three subdomains (LID, CORE, and NMP) changes during the simulations. Further, it also identifies an inherent asymmetry in the opening/closing of the two binding sites. We anticipate that HOST4MD will provide a powerful and extensible framework for detecting biophysically relevant conformational coordinates from long time-scale simulations.
NASA Astrophysics Data System (ADS)
Liu, Fangfang
The thesis is composed of three independent projects: (i) analyzing transposon-sequencing data to infer functions of genes on bacteria growth (chapter 2), (ii) developing semi-parametric Bayesian method for differential gene expression analysis with RNA-sequencing data (chapter 3), (iii) solving group selection problem for survival data (chapter 4). All projects are motivated by statistical challenges raised in biological research. The first project is motivated by the need to develop statistical models to accommodate the transposon insertion sequencing (Tn-Seq) data, Tn-Seq data consist of sequence reads around each transposon insertion site. The detection of transposon insertion at a given site indicates that the disruption of genomic sequence at this site does not cause essential function loss and the bacteria can still grow. Hence, such measurements have been used to infer the functions of each gene on bacteria growth. We propose a zero-inflated Poisson regression method for analyzing the Tn-Seq count data, and derive an Expectation-Maximization (EM) algorithm to obtain parameter estimates. We also propose a multiple testing procedure that categorizes genes into each of the three states, hypo-tolerant, tolerant, and hyper-tolerant, while controlling false discovery rate. Simulation studies show our method provides good estimation of model parameters and inference on gene functions. In the second project, we model the count data from RNA-sequencing experiment for each gene using a Poisson-Gamma hierarchical model, or equivalently, a negative binomial (NB) model. We derive a full semi-parametric Bayesian approach with Dirichlet process as the prior for the fold changes between two treatment means. An inference strategy using Gibbs algorithm is developed for differential expression analysis. We evaluate our method with several simulation studies, and the results demonstrate that our method outperforms other methods including the popularly applied ones such as edge
ERIC Educational Resources Information Center
Tabor, Josh
2010-01-01
On the 2009 AP[c] Statistics Exam, students were asked to create a statistic to measure skewness in a distribution. This paper explores several of the most popular student responses and evaluates which statistic performs best when sampling from various skewed populations. (Contains 8 figures, 3 tables, and 4 footnotes.)
Díaz, Ester; Ayala, Guillermo; Díaz, María Elena; Gong, Liang-Wei; Toomre, Derek
2010-01-01
Analyzing the morphological appearance and the spatial distribution of large dense-core vesicles (granules) in the cell cytoplasm is central to the understanding of regulated exocytosis. This paper is concerned with the automatic detection of granules and the statistical analysis of their spatial locations in different cell groups. We model the locations of granules of a given cell as a realization of a finite spatial point process and the point patterns associated with the cell groups as replicated point patterns of different spatial point processes. First, an algorithm to segment the granules using electron microscopy images is proposed. Second, the relative locations of the granules with respect to the plasma membrane are characterized by two functional descriptors: the empirical cumulative distribution function of the distances from the granules to the plasma membrane and the density of granules within a given distance to the plasma membrane. The descriptors of the different cells for each group are compared using bootstrap procedures. Our results show that these descriptors and the testing procedure allow discriminating between control and treated cells. The application of these novel tools to studies of secretion should help in the analysis of diseases associated with dysfunctional secretion, such as diabetes.
NASA Astrophysics Data System (ADS)
Neubert, A.; Fripp, J.; Engstrom, C.; Schwarz, R.; Lauer, L.; Salvado, O.; Crozier, S.
2012-12-01
Recent advances in high resolution magnetic resonance (MR) imaging of the spine provide a basis for the automated assessment of intervertebral disc (IVD) and vertebral body (VB) anatomy. High resolution three-dimensional (3D) morphological information contained in these images may be useful for early detection and monitoring of common spine disorders, such as disc degeneration. This work proposes an automated approach to extract the 3D segmentations of lumbar and thoracic IVDs and VBs from MR images using statistical shape analysis and registration of grey level intensity profiles. The algorithm was validated on a dataset of volumetric scans of the thoracolumbar spine of asymptomatic volunteers obtained on a 3T scanner using the relatively new 3D T2-weighted SPACE pulse sequence. Manual segmentations and expert radiological findings of early signs of disc degeneration were used in the validation. There was good agreement between manual and automated segmentation of the IVD and VB volumes with the mean Dice scores of 0.89 ± 0.04 and 0.91 ± 0.02 and mean absolute surface distances of 0.55 ± 0.18 mm and 0.67 ± 0.17 mm respectively. The method compares favourably to existing 3D MR segmentation techniques for VBs. This is the first time IVDs have been automatically segmented from 3D volumetric scans and shape parameters obtained were used in preliminary analyses to accurately classify (100% sensitivity, 98.3% specificity) disc abnormalities associated with early degenerative changes.
NASA Astrophysics Data System (ADS)
Leich, Marcus; Kiltz, Stefan; Krätzer, Christian; Dittmann, Jana; Vielhauer, Claus
2011-03-01
According to the European Commission around 200,000 counterfeit Euro coins are removed from circulation every year. While approaches exist to automatically detect these coins, satisfying error rates are usually only reached for low quality forgeries, so-called "local classes". High-quality minted forgeries ("common classes") pose a problem for these methods as well as for trained humans. This paper presents a first approach for statistical analysis of coins based on high resolution 3D data acquired with a chromatic white light sensor. The goal of this analysis is to determine whether two coins are of common origin. The test set for these first and new investigations consists of 62 coins from not more than five different sources. The analysis is based on the assumption that, apart from markings caused by wear such as scratches and residue consisting of grease and dust, coins from equal origin have a more similar height field than coins from different mints. First results suggest that the selected approach is heavily affected by influences of wear like dents and scratches and the further research is required the eliminate this influence. A course for future work is outlined.
2015-12-01
frequency combs. Ultrasensitive detection of methane, isotopic carbon dioxide, carbon monoxide, formaldehyde, acetylene, and ethylene was performed in...performance spectroscopic sensor based on mid-IR frequency combs. Ultrasensitive detection of methane, isotopic carbon diox ide, carbon monoxide, fo...trace point detection of methane, carbon dioxide, isotopic (13C02) carbon dioxide, carbon monoxide, ethylene, acetylene, and formaldehyde and
Crump, Kenny S; Duport, Philippe; Jiang, Huixia; Shilnikova, Natalia S; Krewski, Daniel; Zielinski, Jan M
2012-01-01
A database containing 800 datasets on the incidence of specific tumor types from 262 radiation carcinogenicity experiments identified in a comprehensive literature search through September 2000 was analyzed for evidence of hormesis. This database includes lifetime studies of tumorigenic responses in mice, rats, and dogs to exposures to alpha, beta, gamma, neutron, or x-ray radiation. A J-shaped dose response, in the form of a significant decreased response at some low dose followed by a significant increased response at a higher dose, was found in only four datasets from three experiments. Three of these datasets involved the same control animals and two also shared dosed animals; the J shape in the fourth dataset appeared to be the result of an outlier within an otherwise monotonic dose response. A meta-analysis was conducted to determine whether there was an excess of dose groups with decreases in tumor response below that in controls at doses below no-observed-effect levels (NOELs) in individual datasets. Because the probability of a decreased response is generally not equal to the probability of an increased response even in the null case, the meta-analysis focused on comparing the number of statistically significant diminished responses to the number expected, assuming no dose effect below the NOEL. Only 54 dose groups out of the total of 2579 in the database had doses below the dataset-specific NOEL and that satisfied an a priori criterion for sufficient power to detect a reduced response. Among these 54, a liberal criterion for defining a significant decreases identified 15 such decreases, versus 54 × 0.2 = 10.8 expected. The excess in significant reductions was accounted for almost entirely by the excess from neutron experiments (10 observed, 6.2 expected). Nine of these 10 dose groups involved only 2 distinct control groups, and 2 pairs from the 10 even shared dosed animals. Given this high degree of overlap, this small excess did not appear remarkable
Abe, T; Tsuiki, T; Murai, K; Sasamori, S
1990-12-01
A statistical study of 41 cases with denture foreign bodies in the air and upper food passages which were treated in our department during the past 21 years was done. (1) Males were more frequently affected. The ratio of male to female was about 2 to 1. (2) Of 41 dentures, 2, 2 and 37 were lodged in the air passages, hypopharynx and esophagus respectively. (3) There were 5 complete mandibular dentures in 41 cases. (4) The causes of the denture foreign bodies were originated to the problem of denture itself in 29 cases, that of the patient himself in 2 cases and both in 10 cases. (5) Of 39 problematic dentures, 16 showed the breakage such as plate fracture and clasp deformity, but the other 23 showed no breakage. In this latter group, poor holding of the denture was ascribed to miss-making or miss-planning. (6) Of 12 patients with problems in their physical function, 5 had suffered from cerebrovascular disease and 3 from geriatric dementia. (7) The denture foreign body in aged patients with physical hypofunction tends to increase in recent years. (8) Of 39 dentures tried to remove by esophagoscopy, 18 were done with difficulty and they were detachable partial dentures with one artificial tooth and 2-arm-clasps lodged at the first and/or second isthmus of the esophagus. Though we have a denture removed successfully at the third trial, we have no case needed external esophagotomy. (9) Duplicated denture models were made in 20 cases prior to the procedure, and we certify that these models play an important role for the safer removal of denture foreign bodies.
Asano, Yoshitaka; Shinoda, Jun; Okumura, Ayumi; Aki, Tatsuki; Takenaka, Shunsuke; Miwa, Kazuhiro; Yamada, Mikito; Ito, Takeshi; Yokoyama, Kazutoshi
2012-01-01
Diffusion tensor imaging (DTI) has recently evolved as valuable technique to investigate diffuse axonal injury (DAI). This study examined whether fractional anisotropy (FA) images analyzed by statistical parametric mapping (FA-SPM images) are superior to T(2)*-weighted gradient recalled echo (T2*GRE) images or fluid-attenuated inversion recovery (FLAIR) images for detecting minute lesions in traumatic brain injury (TBI) patients. DTI was performed in 25 patients with cognitive impairments in the chronic stage after mild or moderate TBI. The FA maps obtained from the DTI were individually compared with those from age-matched healthy control subjects using voxel-based analysis and FA-SPM images (p < 0.001). Abnormal low-intensity areas on T2*GRE images (T2* lesions) were found in 10 patients (40.0%), abnormal high-intensity areas on FLAIR images in 4 patients (16.0%), and areas with significantly decreased FA on FA-SPM image in 16 patients (64.0%). Nine of 10 patients with T2* lesions had FA-SPM lesions. FA-SPM lesions topographically included most T2* lesions in the white matter and the deep brain structures, but did not include T2* lesions in the cortex/near-cortex or lesions containing substantial hemosiderin regardless of location. All 4 patients with abnormal areas on FLAIR images had FA-SPM lesions. FA-SPM imaging is useful for detecting minute lesions because of DAI in the white matter and the deep brain structures, which may not be visualized on T2*GRE or FLAIR images, and may allow the detection of minute brain lesions in patients with post-traumatic cognitive impairment.
NASA Astrophysics Data System (ADS)
Tejos, Nicolas; Prochaska, J. Xavier; Crighton, Neil H. M.; Morris, Simon L.; Werk, Jessica K.; Theuns, Tom; Padilla, Nelson; Bielby, Rich M.; Finn, Charles W.
2016-01-01
Modern analyses of structure formation predict a universe tangled in a `cosmic web' of dark matter and diffuse baryons. These theories further predict that at low z, a significant fraction of the baryons will be shock-heated to T ˜ 105-107 K yielding a warm-hot intergalactic medium (WHIM), but whose actual existence has eluded a firm observational confirmation. We present a novel experiment to detect the WHIM, by targeting the putative filaments connecting galaxy clusters. We use HST/COS to observe a remarkable quasi-stellar object (QSO) sightline that passes within Δd = 3 Mpc from the seven intercluster axes connecting seven independent cluster pairs at redshifts 0.1 ≤ z ≤ 0.5. We find tentative excesses of total H I, narrow H I (NLA; Doppler parameters b < 50 km s-1), broad H I (BLA; b ≥ 50 km s-1) and O VI absorption lines within rest-frame velocities of Δv ≲ 1000 km s-1 from the cluster-pairs redshifts, corresponding to ˜2, ˜1.7, ˜6 and ˜4 times their field expectations, respectively. Although the excess of O VI likely comes from gas close to individual galaxies, we conclude that most of the excesses of NLAs and BLAs are truly intergalactic. We find the covering fractions, fc, of BLAs close to cluster pairs are ˜4-7 times higher than the random expectation (at the ˜2σ c.l.), whereas the fc of NLAs and O VI are not significantly enhanced. We argue that a larger relative excess of BLAs compared to those of NLAs close to cluster pairs may be a signature of the WHIM in intercluster filaments. By extending this analysis to tens of sightlines, our experiment offers a promising route to detect the WHIM.
ERIC Educational Resources Information Center
Hidalgo-Montesinos, Maria Dolores; Lopez-Pina, Jose Antonio
2002-01-01
Examined the effect of test purification in detecting differential item functioning (DIF) by means of polytomous extensions of the Raju area measures (N. Raju, 1990) and the Lord statistic (F. Lord, 1980). Simulation results suggest the necessity of using a two-stage equating purification process with the Raju exact measures and the Lord statistic…
NASA Astrophysics Data System (ADS)
Tombesi, F.; Cappi, M.; Reeves, J. N.; Palumbo, G. G. C.; Yaqoob, T.; Braito, V.; Dadina, M.
2010-10-01
Context. Blue-shifted Fe K absorption lines have been detected in recent years between 7 and 10 keV in the X-ray spectra of several radio-quiet AGNs. The derived blue-shifted velocities of the lines can often reach mildly relativistic values, up to 0.2-0.4c. These findings are important because they suggest the presence of a previously unknown massive and highly ionized absorbing material outflowing from their nuclei, possibly connected with accretion disk winds/outflows. Aims: The scope of the present work is to statistically quantify the parameters and incidence of the blue-shifted Fe K absorption lines through a uniform analysis on a large sample of radio-quiet AGNs. This allows us to assess their global detection significance and to overcome any possible publication bias. Methods: We performed a blind search for narrow absorption features at energies greater than 6.4 keV in a sample of 42 radio-quiet AGNs observed with XMM-Newton. A simple uniform model composed by an absorbed power-law plus Gaussian emission and absorption lines provided a good fit for all the data sets. We derived the absorption lines parameters and calculated their detailed detection significance making use of the classical F-test and extensive Monte Carlo simulations. Results: We detect 36 narrow absorption lines on a total of 101 XMM-Newton EPIC pn observations. The number of absorption lines at rest-frame energies higher than 7 keV is 22. Their global probability to be generated by random fluctuations is very low, less than 3 × 10-8, and their detection have been independently confirmed by a spectral analysis of the MOS data, with associated random probability <10-7. We identify the lines as Fe XXV and Fe XXVI K-shell resonant absorption. They are systematically blue-shifted, with a velocity distribution ranging from zero up to ~0.3c, with a peak and mean value at ~0.1c. We detect variability of the lines on both EWs and blue-shifted velocities among different XMM-Newton observations
NASA Technical Reports Server (NTRS)
Wilson, Robert M.
2001-01-01
Since 1750, the number of cataclysmic volcanic eruptions (volcanic explosivity index (VEI)>=4) per decade spans 2-11, with 96 percent located in the tropics and extra-tropical Northern Hemisphere. A two-point moving average of the volcanic time series has higher values since the 1860's than before, being 8.00 in the 1910's (the highest value) and 6.50 in the 1980's, the highest since the 1910's peak. Because of the usual behavior of the first difference of the two-point moving averages, one infers that its value for the 1990's will measure approximately 6.50 +/- 1, implying that approximately 7 +/- 4 cataclysmic volcanic eruptions should be expected during the present decade (2000-2009). Because cataclysmic volcanic eruptions (especially those having VEI>=5) nearly always have been associated with short-term episodes of global cooling, the occurrence of even one might confuse our ability to assess the effects of global warming. Poisson probability distributions reveal that the probability of one or more events with a VEI>=4 within the next ten years is >99 percent. It is approximately 49 percent for an event with a VEI>=5, and 18 percent for an event with a VEI>=6. Hence, the likelihood that a climatically significant volcanic eruption will occur within the next ten years appears reasonably high.
Williams, Scott G. Buyyounouski, Mark K.; Pickles, Tom; Kestin, Larry; Martinez, Alvaro; Hanlon, Alexandra L.; Duchesne, Gillian M.
2008-03-15
Purpose: To define and incorporate the impact of the percentage of positive biopsy cores (PPC) into a predictive model of prostate cancer radiotherapy biochemical outcome. Methods and Materials: The data of 3264 men with clinically localized prostate cancer treated with external beam radiotherapy at four institutions were retrospectively analyzed. Standard prognostic and treatment factors plus the number of biopsy cores collected and the number positive for malignancy by transrectal ultrasound-guided biopsy were available. The primary endpoint was biochemical failure (bF, Phoenix definition). Multivariate proportional hazards analyses were performed and expressed as a nomogram and the model's predictive ability assessed using the concordance index (c-index). Results: The cohort consisted of 21% low-, 51% intermediate-, and 28% high-risk cancer patients, and 30% had androgen deprivation with radiotherapy. The median PPC was 50% (interquartile range [IQR] 29-67%), and median follow-up was 51 months (IQR 29-71 months). Percentage of positive biopsy cores displayed an independent association with the risk of bF (p = 0.01), as did age, prostate-specific antigen value, Gleason score, clinical stage, androgen deprivation duration, and radiotherapy dose (p < 0.001 for all). Including PPC increased the c-index from 0.72 to 0.73 in the overall model. The influence of PPC varied significantly with radiotherapy dose and clinical stage (p = 0.02 for both interactions), with doses <66 Gy and palpable tumors showing the strongest relationship between PPC and bF. Intermediate-risk patients were poorly discriminated regardless of PPC inclusion (c-index 0.65 for both models). Conclusions: Outcome models incorporating PPC show only minor additional ability to predict biochemical failure beyond those containing standard prognostic factors.
Kalinin, A V; Krasheninnikov, V N; Sviridov, A P; Titov, V N
2015-11-01
The content of clinically important fatty acids and individual triglycerides in food and biological mediums are traditionally detected by gas and fluid chromatography in various methodical modifications. The techniques are hard-to-get in laboratories of clinical biochemistry. The study was carried out to develop procedures and equipment for operative quantitative detection of concentration of fatty acids, primarily palmitic saturated fatty acid and oleic mono unsaturated fatty acid. Also detection was applied to sums ofpolyenoic (eicosapentaenoic and docosahexaenoic acid) fatty acids in biological mediums (cod-liver oil, tissues, blood plasma) using spectrometers of short-range infrared band of different types: with Fourier transform, diffraction and combined scattering. The evidences of reliable and reproducible quantitative detection offatty acids were received on the basis of technique of calibration (regression) by projection on latent structures using standard samples of mixtures of oils and fats. The evaluation is implemented concerning possibility of separate detection of content of palmitic and oleic triglycerides in mediums with presence of water The choice of technical conditions and mode of application of certain types of infrared spectrometers and techniques of their calibration is substantiated
Fukuoka, Kohei; Yanagisawa, Takaaki; Suzuki, Tomonari; Shirahata, Mitsuaki; Adachi, Jun-Ichi; Mishima, Kazuhiko; Fujimaki, Takamitsu; Katakami, Hideki; Matsutani, Masao; Nishikawa, Ryo
2016-11-01
OBJECTIVE Human chorionic gonadotropin (HCG) can be detected in a certain population of patients with a germinoma, but the frequency of germinoma HCG secretion and the prognostic value of HCG in the CSF are unknown. METHODS The authors measured HCG levels in sera and CSF in patients with a histologically confirmed germinoma by using a highly sensitive assay known as an immune complex transfer enzyme immunoassay (EIA), which is more than 100 times as sensitive as the conventional method, and they analyzed the correlation between HCG levels and the prognoses of patients with a germinoma. RESULTS HCG levels in sera and CSF of 35 patients with a germinoma were examined with the immune complex transfer EIA. The median CSF HCG levels in patients with a germinoma during the pretreatment and posttreatment evaluations were 192.5 pg/ml (range 1.2-13,116.5 pg/ml) and 18.7 pg/ml (1.2-283.9 pg/ml), respectively. Before treatment, the CSF HCG level was greater than the cutoff value in 85.7% of the patients with a germinoma. The authors compared survival rates among the patients by using a CSF HCG cutoff level of 1000 pg/ml, and the difference was statistically significant between the groups (p = 0.029, log-rank test). CONCLUSIONS Results of this study demonstrate that most germinomas secrete HCG. Patients with a germinoma that secretes higher amounts of HCG in their CSF experienced recurrence more frequently than those with lower CSF HCG levels.
Hua, Ang Kean
2017-01-01
Malacca River water quality is affected due to rapid urbanization development. The present study applied LULC changes towards water quality detection in Malacca River. The method uses LULC, PCA, CCA, HCA, NHCA, and ANOVA. PCA confirmed DS, EC, salinity, turbidity, TSS, DO, BOD, COD, As, Hg, Zn, Fe, E. coli, and total coliform. CCA confirmed 14 variables into two variates; first variate involves residential and industrial activities; and second variate involves agriculture, sewage treatment plant, and animal husbandry. HCA and NHCA emphasize that cluster 1 occurs in urban area with Hg, Fe, total coliform, and DO pollution; cluster 3 occurs in suburban area with salinity, EC, and DS; and cluster 2 occurs in rural area with salinity and EC. ANOVA between LULC and water quality data indicates that built-up area significantly polluted the water quality through E. coli, total coliform, EC, BOD, COD, TSS, Hg, Zn, and Fe, while agriculture activities cause EC, TSS, salinity, E. coli, total coliform, arsenic, and iron pollution; and open space causes contamination of turbidity, salinity, EC, and TSS. Research finding provided useful information in identifying pollution sources and understanding LULC with river water quality as references to policy maker for proper management of Land Use area.
2017-01-01
Malacca River water quality is affected due to rapid urbanization development. The present study applied LULC changes towards water quality detection in Malacca River. The method uses LULC, PCA, CCA, HCA, NHCA, and ANOVA. PCA confirmed DS, EC, salinity, turbidity, TSS, DO, BOD, COD, As, Hg, Zn, Fe, E. coli, and total coliform. CCA confirmed 14 variables into two variates; first variate involves residential and industrial activities; and second variate involves agriculture, sewage treatment plant, and animal husbandry. HCA and NHCA emphasize that cluster 1 occurs in urban area with Hg, Fe, total coliform, and DO pollution; cluster 3 occurs in suburban area with salinity, EC, and DS; and cluster 2 occurs in rural area with salinity and EC. ANOVA between LULC and water quality data indicates that built-up area significantly polluted the water quality through E. coli, total coliform, EC, BOD, COD, TSS, Hg, Zn, and Fe, while agriculture activities cause EC, TSS, salinity, E. coli, total coliform, arsenic, and iron pollution; and open space causes contamination of turbidity, salinity, EC, and TSS. Research finding provided useful information in identifying pollution sources and understanding LULC with river water quality as references to policy maker for proper management of Land Use area. PMID:28377790
Maity, Debabrata; Jiang, Juanjuan; Ehlers, Martin; Wu, Junchen; Schmuck, Carsten
2016-05-04
A cationic molecular peptide beacon NAP1 functionalized with a fluorescence resonance energy transfer-pair at its ends allows the ratiometric detection of ds-DNA with a preference for AT rich sequences. NAP1 most likely binds in a folded form into the minor groove of ds-DNA, which results in a remarkable change in its fluorescence properties. As NAP1 exhibits quite low cytotoxicity, it can also be used for imaging of nuclear DNA in cells.
Nondetect (ND) or below detection limit (BDL) results cannot be measured accurately, and, therefore, are reported as less than certain detection limit (DL) values. However, since the presence of some contaminants (e.g., dioxin) in environmental media may pose a threat to human he...
Imanishi, M; Newton, A E; Vieira, A R; Gonzalez-Aviles, G; Kendall Scott, M E; Manikonda, K; Maxwell, T N; Halpin, J L; Freeman, M M; Medalla, F; Ayers, T L; Derado, G; Mahon, B E; Mintz, E D
2015-08-01
Although rare, typhoid fever cases acquired in the United States continue to be reported. Detection and investigation of outbreaks in these domestically acquired cases offer opportunities to identify chronic carriers. We searched surveillance and laboratory databases for domestically acquired typhoid fever cases, used a space-time scan statistic to identify clusters, and classified clusters as outbreaks or non-outbreaks. From 1999 to 2010, domestically acquired cases accounted for 18% of 3373 reported typhoid fever cases; their isolates were less often multidrug-resistant (2% vs. 15%) compared to isolates from travel-associated cases. We identified 28 outbreaks and two possible outbreaks within 45 space-time clusters of ⩾2 domestically acquired cases, including three outbreaks involving ⩾2 molecular subtypes. The approach detected seven of the ten outbreaks published in the literature or reported to CDC. Although this approach did not definitively identify any previously unrecognized outbreaks, it showed the potential to detect outbreaks of typhoid fever that may escape detection by routine analysis of surveillance data. Sixteen outbreaks had been linked to a carrier. Every case of typhoid fever acquired in a non-endemic country warrants thorough investigation. Space-time scan statistics, together with shoe-leather epidemiology and molecular subtyping, may improve outbreak detection.
Vuento, M H; Stenman, U H; Pirhonen, J P; Mäkinen, J I; Laippala, P J; Salmi, T A
1997-01-01
We evaluated the utility of a single CA 125 measurement in combination with transvaginal sonography for early detection of ovarian and endometrial cancer in asymptomatic postmenopausal women. A sample of peripheral blood was taken from 1291 apparently healthy postmenopausal women, who were examined by conventional and color Doppler ultrasound for early detection of ovarian and endometrial cancer. Serum CA 125 was determined in all samples 3 years later by the IMx CA 125 assay (Abbott Laboratories, Abbott Park, IL). The cutoff level based on the 99th percentile was 30 U/ml. Elevated values were controlled by repeat sonography and an additional determination of CA 125. Record linkage with the files of the Finnish Cancer Registry was performed 3 1/2 years after the primary sonographic screening. The mean CA 125 concentration was 8.1 U/ml (range 0-1410 U/ml). Fourteen of the 1291 women had a CA 125 level greater than 30 U/ml. None of these had signs of either endometrial or ovarian malignancy in the primary sonography screening. Among the other women three cases of endometrial carcinoma (all stage Ib) and one ovarian carcinoma (stage Ia with borderline malignancy) were detected by sonography. All these patients had a CA 125 value <30 U/ml, the mean value being 11.4 U/ml (range 7.5-16.7 U/ml). During follow-up of 3.5 years, one stage Ia ovarian carcinoma, one abdominal carcinomatosis, and two endometrial carcinomas (both stage Ib) were diagnosed. In these patients the mean value for CA 125 was 12.7 U/ml (range 2.5-30.9 U/ml) at the primary sonography screening. A single CA 125 measurement provides no advantage in the early detection of ovarian and endometrial cancer in asymptomatic postmenopausal women compared with transvaginal sonography. The vast majority of women with an elevated CA 125 value have some reason other than an ovarian or endometrial malignancy for this finding.
Kitagishi, K; Sato, Y
2001-10-01
In capillary electrophoresis (CE), light flux passes through a capillary cell and is in most cases detected photometrically. Due to the thinness of the cell, a part of the light passes through the wall and misses hitting the sample. In most CE apparatuses, incident light is focused by converging lenses in order to condense light beams passing through the capillary. Considering the aberration of lenses and lens effects of capillary, we assumed that light beams inside were approximately parallel. Although the path lengths of light beams vary depending on their tracks, we could estimate the virtual light path length, L, by measuring absorbance when concentration and molar absorptivity of the sample solution were known. A light-restricting device consisting of narrow slits makes effectively L longer and signal intensity higher. On the other hand, noise increases as light width narrows. The signal-to-noise ratio showed a maximum at 68 microm of light width for a capillary with diameter of 75 microm. The optimized L was evaluated by the simulation. The experimental data verified it even in indirect UV detection. Our approach could help to design the optics of CE apparatuses.
Skou, Nikolaj; Egund, Niels
2017-03-01
Background Diagnosis and treatment of patellofemoral disorders including osteoarthritis are currently often based on imaging and clinical assessment with patients in the supine position. Purpose To evaluate differences in patellar position in the trochlear groove and to assess the detection of medial and lateral patellofemoral (PF) osteoarthritis (OA) on axial radiographs in supine and standing positions, respectively. Material and Methods Thirty-five women and 23 men (mean age, 56 years; age range, 18-87 years) referred for routine radiographic examinations of the knees were included. Axial radiographs of the PF joint in both supine non-weight-bearing and standing weight-bearing position in 30° knee flexion were obtained of 111 knees. Measurements performed on the radiographs: patellar tilt, patellar displacement, joint space width, and grade of OA according to Ahlbäck. Results From supine to standing position the patella moved medially and medial joint space width and lateral patellar tilt angle decreased ( P < 0.0001 for the three measured parameters). In the standing position, medial PF OA was observed in 19 knees compared to three knees in the supine position. Fourteen knees had lateral PF OA with almost unchanged grade of OA irrespective of position. Conclusion In weight-bearing positions, the patella is positioned medially in the trochlear groove compared to supine non-weight-bearing positions. Therefore, this study suggests that the common occurrence of medial PF OA can generally not be detected on axial radiographs in supine non-weight-bearing positions and confirms the importance of imaging the PF joint in standing weight-bearing positions.
Hillengass, Jens; Ritsch, Judith; Merz, Maximilian; Wagner, Barbara; Kunz, Christina; Hielscher, Thomas; Laue, Hendrik; Bäuerle, Tobias; Zechmann, Christian M; Ho, Anthony D; Schlemmer, Heinz-Peter; Goldschmidt, Hartmut; Moehler, Thomas M; Delorme, Stefan
2016-07-01
This prospective study aimed to investigate the prognostic significance of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) as a non-invasive imaging technique delivering the quantitative parameters amplitude A (reflecting blood volume) and exchange rate constant kep (reflecting vascular permeability) in patients with asymptomatic monoclonal plasma cell diseases. We analysed DCE-MRI parameters in 33 healthy controls and 148 patients with monoclonal gammopathy of undetermined significance (MGUS) or smouldering multiple myeloma (SMM) according to the 2003 IMWG guidelines. All individuals underwent standardized DCE-MRI of the lumbar spine. Regions of interest were drawn manually on T1-weighted images encompassing the bone marrow of each of the 5 lumbar vertebrae sparing the vertebral vessel. Prognostic significance for median of amplitude A (univariate: P < 0·001, hazard ratio (HR) 2·42, multivariate P = 0·02, HR 2·7) and exchange rate constant kep (univariate P = 0·03, HR 1·92, multivariate P = 0·46, HR 1·5) for time to progression of 79 patients with SMM was found. Patients with amplitude A above the optimal cut-off point of 0·89 arbitrary units had a 2-year progression rate into symptomatic disease of 80%. In conclusion, DCE-MRI parameters are of prognostic significance for time to progression in patients with SMM but not in individuals with MGUS.
2002-09-01
Resulting Plots for Different LPI Radar Signals (1) FMCW Table 9 shows a FMCW signal with carrier frequency equal to 1 KHz, sampling frequency equal to...REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE: Detection and Classification of LPI Radar Signals using Parallel Filter...In order to detect LPI radar waveforms new signal processing techniques are required. This thesis first develops a MATLAB® toolbox to generate
Gladysz, Szymon; Yaitskova, Natalia; Christou, Julian C
2010-11-01
This paper is an introduction to the problem of modeling the probability density function of adaptive-optics speckle. We show that with the modified Rician distribution one cannot describe the statistics of light on axis. A dual solution is proposed: the modified Rician distribution for off-axis speckle and gamma-based distribution for the core of the point spread function. From these two distributions we derive optimal statistical discriminators between real sources and quasi-static speckles. In the second part of the paper the morphological difference between the two probability density functions is used to constrain a one-dimensional, "blind," iterative deconvolution at the position of an exoplanet. Separation of the probability density functions of signal and speckle yields accurate differential photometry in our simulations of the SPHERE planet finder instrument.
DAVIS, S.J.
2000-12-28
This document identifies critical characteristics of components to be dedicated for use in Safety Significant (SS) Systems, Structures, or Components (SSCs). This document identifies the requirements for the components of the common, radiation area, monitor alarm in the WESF pool cell. These are procured as Commercial Grade Items (CGI), with the qualification testing and formal dedication to be performed at the Waste Encapsulation Storage Facility (WESF) for use in safety significant systems. System modifications are to be performed in accordance with the approved design. Components for this change are commercially available and interchangeable with the existing alarm configuration This document focuses on the operational requirements for alarm, declaration of the safety classification, identification of critical characteristics, and interpretation of requirements for procurement. Critical characteristics are identified herein and must be verified, followed by formal dedication, prior to the components being used in safety related applications.
Ahn, Hyo-Sae; Son, Whee Sung; Shin, Ji-Hoon; Ahn, Myun-Whan
2016-01-01
Study Design Retrospective exploratory imaging study. Purpose To investigate the significance of the coronal magnetic resonance imaging (MRI) using Proset technique to detect the hidden zone in patients with mid-zone stenosis by comparing with conventional axial and sagittal MRI and to explore the morphologic characteristic patterns of the mid-zone stenosis. Overview of Literature Despite advancements in diagnostic modalities such as computed tomography and MRI, stenotic lesions under the pedicle and pars interarticularis, also called the mid-zone, are still difficult to definitely detect with the conventional axial and sagittal MRI due to its inherited anatomical peculiarity. Methods Of 180 patients scheduled to undergo selective nerve root block, 20 patients with mid-zone stenosis were analyzed using MRI. Characteristic group patterns were also explored morphologically by comparing MRI views of each group after verifying statistical differences between them. Hierarchical cluster analysis was performed to classify morphological characteristic groups based on three-dimensional radiologic grade for stenosis at all three zones. Results At the mid-zone, the stenosis of grade 2 or more was found in 14 cases in the coronal image,13 cases in the sagittal image, and 9 cases in the axial image (p<0.05). Especially, mid-zone stenosis was not detected in six of 20 cases at the axial images. At the entrance and exit-zone, coronal image was also associated with more accurate detection of hidden zone compared to other views such as axial and sagittal images. After repeated statistical verification, the morphological patterns of hidden zone were classified into 5 groups: 6 cases in group I; 1 case in group II; 4 cases in group III; 7 cases in group IV; and 2 cases in group V. Conclusions Coronal MRI using the Proset technique more accurately detected hidden zone of the mid-zone stenosis compared to conventional axial and sagittal images. PMID:27559443
NASA Astrophysics Data System (ADS)
Xiao, Yongshuang; Ma, Daoyuan; Xu, Shihong; Liu, Qinghua; Wang, Yanfeng; Xiao, Zhizhong; Li, Jun
2016-05-01
Oplegnathus fasciatus (rock bream) is a commercial rocky reef fish species in East Asia that has been considered for aquaculture. We estimated the population genetic diversity and population structure of the species along the coastal waters of China using fluorescent-amplified fragment length polymorphisms technology. Using 53 individuals from three populations and four pairs of selective primers, we amplified 1 264 bands, 98.73% of which were polymorphic. The Zhoushan population showed the highest Nei's genetic diversity and Shannon genetic diversity. The results of analysis of molecular variance (AMOVA) showed that 59.55% of genetic variation existed among populations and 40.45% occurred within populations, which indicated that a significant population genetic structure existed in the species. The pairwise fixation index F st ranged from 0.20 to 0.63 and were significant after sequential Bonferroni correction. The topology of an unweighted pair group method with arithmetic mean tree showed two significant genealogical branches corresponding to the sampling locations of North and South China. The AMOVA and STRUCTURE analyses suggested that the O. fasciatus populations examined should comprise two stocks.
Grabmer, C; Nachbauer, W; Schanda, K; Feurle, P; Loacker, K; Scholz, E; Schennach, H; Berger, T; Reindl, M; Gassner, C
2010-03-01
The T-cell immunoglobulin mucin (TIM) gene family encodes receptors on T-cells that regulate Th1- and Th2-cell-mediated immunity. Recently published data implied differential expression of human TIM molecules by mononuclear cells in cerebrospinal fluid of patients with multiple sclerosis (MS) and might therefore be involved in different phases of the pathogenesis of MS. The purpose of this study was to investigate the association of TIM1 gene polymorphism with susceptibility to and clinical progression in MS. In total, 272 patients with MS and 272 sex- and age-matched healthy blood donors from Western Austria were genotyped for 10 single nucleotide polymorphisms (SNPs). Five SNPs were located in the promoter region of TIM1 (rs7702920, rs41297577, rs41297579, rs9313422 and rs34333511). Another five SNPs were selected in exon 4 (rs1553316 and rs12522248) and in the intronic regions 4 and 7 of TIM1 (rs1553318, rs2279804 and rs2277025), respectively. None of these SNPs showed a significant association with MS after correction for multiple comparisons. Haplotype analysis of our data resulted in 11 haplotypes and showed no significant differences between MS patients and controls. Our findings suggest that even fine mapping of TIM1 shows no significant association of this gene with multiple sclerosis.
Benson, Robert; Conerly, Octavia D; Sander, William; Batt, Angela L; Boone, J Scott; Furlong, Edward T; Glassmeyer, Susan T; Kolpin, Dana W; Mash, Heath E; Schenck, Kathleen M; Simmons, Jane Ellen
2017-02-01
The source water and treated drinking water from twenty five drinking water treatment plants (DWTPs) across the United States were sampled in 2010-2012. Samples were analyzed for 247 contaminants using 15 chemical and microbiological methods. Most of these contaminants are not regulated currently either in drinking water or in discharges to ambient water by the U. S. Environmental Protection Agency (USEPA) or other U.S. regulatory agencies. This analysis shows that there is little public health concern for most of the contaminants detected in treated water from the 25 DWTPs participating in this study. For vanadium, the calculated Margin of Exposure (MOE) was less than the screening MOE in two DWTPs. For silicon, the calculated MOE was less than the screening MOE in one DWTP. Additional study, for example a national survey may be needed to determine the number of people ingesting vanadium and silicon above a level of concern. In addition, the concentrations of lithium found in treated water from several DWTPs are within the range previous research has suggested to have a human health effect. Additional investigation of this issue is necessary. Finally, new toxicological data suggest that exposure to manganese at levels in public water supplies may present a public health concern which will require a robust assessment of this information.
Fleg, J.L.; Gerstenblith, G.; Zonderman, A.B.; Becker, L.C.; Weisfeldt, M.L.; Costa, P.T. Jr.; Lakatta, E.G. )
1990-02-01
Although a silent ischemic electrocardiographic response to treadmill exercise in clinically healthy populations is associated with an increased likelihood of future coronary events (i.e., angina pectoris, myocardial infarction, or cardiac death), such a response has a low predictive value for future events because of the low prevalence of disease in asymptomatic populations. To examine whether detection of reduced regional perfusion by thallium scintigraphy improved the predictive value of exercise-induced ST segment depression, we performed maximal treadmill exercise electrocardiography (ECG) and thallium scintigraphy (201Tl) in 407 asymptomatic volunteers 40-96 years of age (mean = 60) from the Baltimore Longitudinal Study on Aging. The prevalence of exercise-induced silent ischemia, defined by concordant ST segment depression and a thallium perfusion defect, increased more than sevenfold from 2% in the fifth and sixth decades to 15% in the ninth decade. Over a mean follow-up period of 4.6 years, cardiac events developed in 9.8% of subjects and consisted of 20 cases of new angina pectoris, 13 myocardial infarctions, and seven deaths. Events occurred in 7% of individuals with both negative 201Tl and ECG, 8% of those with either test positive, and 48% of those in whom both tests were positive (p less than 0.001). By proportional hazards analysis, age, hypertension, exercise duration, and a concordant positive ECG and 201Tl result were independent predictors of coronary events. Furthermore, those with positive ECG and 201Tl had a 3.6-fold relative risk for subsequent coronary events, independent of conventional risk factors.
NASA Astrophysics Data System (ADS)
Fuhrmann, L.; Larsson, S.; Chiang, J.; Angelakis, E.; Zensus, J. A.; Nestoras, I.; Krichbaum, T. P.; Ungerechts, H.; Sievers, A.; Pavlidou, V.; Readhead, A. C. S.; Max-Moerbeck, W.; Pearson, T. J.
2014-07-01
The exact location of the γ-ray emitting region in blazars is still controversial. In order to attack this problem we present first results of a cross-correlation analysis between radio (11 cm to 0.8 mm wavelength, F-GAMMA programme) and γ-ray (0.1-300 GeV) ˜3.5 yr light curves of 54 Fermi-bright blazars. We perform a source stacking analysis and estimate significances and chance correlations using mixed source correlations. Our results reveal: (i) the first highly significant multiband radio and γ-ray correlations (radio lagging γ rays) when averaging over the whole sample, (ii) average time delays (source frame: 76 ± 23 to 7 ± 9 d), systematically decreasing from cm to mm/sub-mm bands with a frequency dependence τr, γ(ν) ∝ ν-1, in good agreement with jet opacity dominated by synchrotron self-absorption, (iii) a bulk γ-ray production region typically located within/upstream of the 3 mm core region (τ3mm, γ = 12 ± 8 d), (iv) mean distances between the region of γ-ray peak emission and the radio `τ = 1 photosphere' decreasing from 9.8 ± 3.0 pc (11 cm) to 0.9 ± 1.1 pc (2 mm) and 1.4 ± 0.8 pc (0.8 mm), (v) 3 mm/γ-ray correlations in nine individual sources at a significance level where one is expected by chance (probability: 4 × 10-6), (vi) opacity and `time lag core shift' estimates for quasar 3C 454.3 providing a lower limit for the distance of the bulk γ-ray production region from the supermassive black hole (SMBH) of ˜0.8-1.6 pc, i.e. at the outer edge of the broad-line region (BLR) or beyond. A 3 mm τ = 1 surface at ˜2-3 pc from the jet base (i.e. well outside the `canonical BLR') finally suggests that BLR material extends to several parsec distances from the SMBH.
2015-10-01
Award Number: W81XWH-11-1-0744 TITLE: Development of Assays for Detecting Significant Prostate Cancer Based on Molecular Alterations Associated...Significant Prostate Cancer Based on Molecular Alterations Associated with Cancer in Non-Neoplastic Prostate Tissue 5b. GRANT NUMBER W81XWH-11-1-0744 5c...for Public Release; Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT The goal of this project is to develop biopsy based assays to
Dolan, T E; Lynch, P D; Karazsia, J L; Serafy, J E
2016-03-01
An expansion is underway of a nuclear power plant on the shoreline of Biscayne Bay, Florida, USA. While the precise effects of its construction and operation are unknown, impacts on surrounding marine habitats and biota are considered by experts to be likely. The objective of the present study was to determine the adequacy of an ongoing monitoring survey of fish communities associated with mangrove habitats directly adjacent to the power plant to detect fish community changes, should they occur, at three spatial scales. Using seasonally resolved data recorded during 532 fish surveys over an 8-year period, power analyses were performed for four mangrove fish metrics (fish diversity, fish density, and the occurrence of two ecologically important fish species: gray snapper (Lutjanus griseus) and goldspotted killifish (Floridichthys carpio). Results indicated that the monitoring program at current sampling intensity allows for detection of <33% changes in fish density and diversity metrics in both the wet and the dry season in the two larger study areas. Sampling effort was found to be insufficient in either season to detect changes at this level (<33%) in species-specific occurrence metrics for the two fish species examined. The option of supplementing ongoing, biological monitoring programs for improved, focused change detection deserves consideration from both ecological and cost-benefit perspectives.
ERIC Educational Resources Information Center
Fidalgo, Angel M.
2011-01-01
Mantel-Haenszel (MH) methods constitute one of the most popular nonparametric differential item functioning (DIF) detection procedures. GMHDIF has been developed to provide an easy-to-use program for conducting DIF analyses. Some of the advantages of this program are that (a) it performs two-stage DIF analyses in multiple groups simultaneously;…
NASA Astrophysics Data System (ADS)
Jing, Yu; Wang, Yaxuan; Liu, Jianxin; Liu, Zhaoxia
2015-08-01
Edge detection is a crucial method for the location and quantity estimation of oil slick when oil spills on the sea. In this paper, we present a robust active contour edge detection algorithm for oil spill remote sensing images. In the proposed algorithm, we define a local Gaussian data fitting energy term with spatially varying means and variances, and this data fitting energy term is introduced into a global minimization active contour (GMAC) framework. The energy function minimization is achieved fast by a dual formulation of the weighted total variation norm. The proposed algorithm avoids the existence of local minima, does not require the definition of initial contour, and is robust to weak boundaries, high noise and severe intensity inhomogeneity exiting in oil slick remote sensing images. Furthermore, the edge detection of oil slick and the correction of intensity inhomogeneity are simultaneously achieved via the proposed algorithm. The experiment results have shown that a superior performance of proposed algorithm over state-of-the-art edge detection algorithms. In addition, the proposed algorithm can also deal with the special images with the object and background of the same intensity means but different variances.
Milward, Elizabeth A; Moscato, Pablo; Riveros, Carlos; Johnstone, Daniel M
2014-01-01
Interventions to delay or slow Alzheimer's disease (AD) progression are most effective when implemented at pre-clinical disease stages, making early diagnosis essential. For this reason, there is an increasing focus on discovery of predictive biomarkers for AD. Currently, the most reliable predictive biomarkers require either expensive (brain imaging) or invasive (cerebrospinal fluid collection) procedures, leading researchers to strive toward identifying robust biomarkers in blood. Yet promising early results from candidate blood biomarker studies are being refuted by subsequent findings in other cohorts or using different assay technologies. Recent evidence suggests that univariate blood biomarkers are not sufficiently sensitive or specific for the diagnosis of disorders as complex, multifactorial, and heterogeneous as AD. To overcome these present limitations, more consideration must be given to the development of 'biomarker panels' assessing multiple molecular entities. The selection of such panels should draw not only on traditional statistical approaches, whether parametric or non-parametric, but also on newer non-statistical approaches that have the capacity to retain and utilize information about all individual study participants rather than collapsing individual data into group summary values (e.g., mean, variance). These new approaches, facilitated by advances in computing, have the potential to preserve the context of interrelationships between different molecular entities, making them amenable to the development of panels that, as a multivariate collective, can overcome the challenge of individual variability and disease heterogeneity to accurately predict and classify AD. We argue that the AD research community should take fuller advantage of these approaches to accelerate discovery.
Parmar, Rudrangi; Ghanta, Ajay; Haware, Rahul V; Johnson, Paul R; Stagner, William C
2016-12-01
A sucrose octaacetate (SOA) gradient HPLC evaporative light scattering detection (ELSD) and low-wavelength UV-diode array detection (UV-DAD)-specific stability-indicating method development and validation comparison is reported. A central composite response surface design and multicriteria optimization was used to maximize molten SOA area-under-the-curve response and signal-to-noise ratio. The ELSD data were also analyzed using multivariate principal component analysis, analysis of variance, and standard least squares effects modeling. The method suitability and validation parameters of both methods were compared. To the authors' knowledge, this is the first report that validates an ELSD method using a molten analyte. SOA exhibited a low molar absorptivity of 439 absorption units/cm/M in water at 210 nm requiring low-wavelength UV-DAD detection. The low-wavelength UV-DAD method provided substantially better intraday and interday precision, intraday and interday goodness-of-fit, detection limit, and quantitation limit than ELSD. ELSD exhibited a 60-fold greater area-under-the-curve response, better resolution, and 58% more theoretical plates. On balance, the UV-DAD method was chosen for SOA chemical kinetic studies. This study illustrates that ELSD may not always be the best alternative to gradient HPLC low-wavelength UV detection.
Zhang, Xiaoshuai; Yang, Xiaowei; Yuan, Zhongshang; Liu, Yanxun; Li, Fangyu; Peng, Bin; Zhu, Dianwen; Zhao, Jinghua; Xue, Fuzhong
2013-01-01
For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods.
NASA Astrophysics Data System (ADS)
Li, Jianyong; Meng, Guojie; Wang, Min; Liao, Hua; Shen, Xuhui
2009-10-01
Ionospheric TEC (total electron content) time series are derived from GPS measurements at 13 stations around the epicenter of the 2008 Wenchuan earthquake. Defining anomaly bounds for a sliding window by quartile and 2-standard deviation of TEC values, this paper analyzed the characteristics of ionospheric changes before and after the destructive event. The Neyman-Pearson signal detection method is employed to compute the probabilities of TEC abnormalities. Result shows that one week before the Wenchuan earthquake, ionospheric TEC over the epicenter and its vicinities displays obvious abnormal disturbances, most of which are positive anomalies. The largest TEC abnormal changes appeared on May 9, three days prior to the seismic event. Signal detection shows that the largest possibility of TEC abnormity on May 9 is 50.74%, indicating that ionospheric abnormities three days before the main shock are likely related to the preparation process of the M S8.0 Wenchuan earthquake.
NASA Astrophysics Data System (ADS)
Gurcan, Metin N.; Petrick, Nicholas; Sahiner, Berkman; Chan, Heang-Ping; Cascade, Philip N.; Kazerooni, Ella A.; Hadjiiski, Lubomir M.
2001-07-01
We are developing a computer-aided diagnosis (CAD) system for lung nodule detection on thoracic helical computed tomography (CT) images. In the first stage of this CAD system, lung regions are identified and suspicious structures are segmented. These structures may include true lung nodules or normal structures that consist mainly of vascular structures. We have designed rule-based classifiers to distinguish nodules and normal structures using 2D and 3D features. After rule-based classification, linear discriminant analysis (LDA) is used to further reduce the number of false positive (FP) objects. We have performed a preliminary study using CT images from 17 patients with 31 lung nodules. When only LDA classification was applied to the segmented objects, the sensitivity was 84% (26/31) with 2.53 (1549/612) FP objects per slice. When the LDA followed the rule-based classifier, the number of FP objects per slice decreased to 1.75 (1072/612) at the same sensitivity. These preliminary results demonstrate the feasibility of our approach for nodule detection and FP reduction on CT images. The inclusion of rule-based classification leads to an improvement in detection accuracy for the CAD system.
NASA Astrophysics Data System (ADS)
Trendafilova, Irina
2010-08-01
This paper suggests an automated approach for fault detection and classification in roller bearings, which is based on pattern recognition and principal components analysis of the measured vibration signals. The signals recorded are pre-processed applying a wavelet transform in order to extract the appropriate high frequency (detailed) area needed for ball bearing fault detection. This is followed by a pattern recognition (PR) procedure used to recognise between signals coming from healthy bearings and those generated from different bearing faults. Four categories of signals are considered, namely no fault signals (from a healthy bearing), inner race fault, outer race fault and rolling element fault signals. The PR procedure uses the first six principal components extracted from the signals after a proper principal component analysis (PCA). In this work a modified PCA is suggested, which is much more appropriate for categorical data. The combination of the modified PCA and the PR method ensures that the fault is automatically detected and classified to one of the considered fault categories. The method suggested does not require the knowledge/determination of the specific fault frequencies and/or any expert analysis: once the signal filtering is done and the PC's are found the PR method automatically gives the answer if there is a fault present and its type.
Bint, Susan; Irving, Melita D.; Kyle, Phillipa M.; Akolekar, Ranjit; Mohammed, Shehla N.; Mackie Ogilvie, Caroline
2014-01-01
Purpose. To design and validate a prenatal chromosomal microarray testing strategy that moves away from size-based detection thresholds, towards a more clinically relevant analysis, providing higher resolution than G-banded chromosomes but avoiding the detection of copy number variants (CNVs) of unclear prognosis that cause parental anxiety. Methods. All prenatal samples fulfilling our criteria for karyotype analysis (n = 342) were tested by chromosomal microarray and only CNVs of established deletion/duplication syndrome regions and any other CNV >3 Mb were detected and reported. A retrospective full-resolution analysis of 249 of these samples was carried out to ascertain the performance of this testing strategy. Results. Using our prenatal analysis, 23/342 (6.7%) samples were found to be abnormal. Of the remaining samples, 249 were anonymized and reanalyzed at full-resolution; a further 46 CNVs were detected in 44 of these cases (17.7%). None of these additional CNVs were of clear clinical significance. Conclusion. This prenatal chromosomal microarray strategy detected all CNVs of clear prognostic value and did not miss any CNVs of clear clinical significance. This strategy avoided both the problems associated with interpreting CNVs of uncertain prognosis and the parental anxiety that are a result of such findings. PMID:24795849
Jor, Evert; Myrmel, Mette; Jonassen, Christine M
2010-10-01
A novel SYBR Green based real-time RT-PCR assay for detection of genogroup III bovine noroviruses (BoNoV) was developed and the assay applied to 419 faecal samples from calves with and without diarrhoea. The samples were obtained from 190 Norwegian dairy and beef herds. BoNoV was detected in 49.6% of the samples from 61.1% of the herds indicating that BoNoV is ubiquitous in Norway. The overall prevalence was not significantly different in diarrhoea and non-diarrhoea samples. Analyses of polymerase gene sequences revealed both genotype III/1 and III/2 with genotype III/2 (Newbury2-like) being the most prevalent. Detected capsid sequences were restricted to Newbury2-like and the chimeric Bo/Thirsk10/00/UK strain. The RNA polymerase genotypes of the circulating BoNoVs in Norway were predicted by melting temperature analysis. Additional data from a challenge experiment suggest that a high proportion of young calves are shedding low levels of BoNoV for a prolonged time after recovering from the associated diarrhoea. The findings may explain some of the discrepancies in detection rates from previous studies and explain why some studies have failed to detect significant prevalence differences between calves with and without diarrhoea. It may also shed new light on some epidemiological aspects of norovirus infections.
Shigematsu, Hideki; Okuda, Akinori; Morimoto, Yasuhiko; Masuda, Keisuke; Nakajima, Hiroshi; Koizumi, Munehisa; Tanaka, Yasuhito
2016-01-01
Study Design Case control study. Purpose To identify the most significant laboratory marker for early detection of surgical site infection (SSI) using multiple logistic regression analysis. Overview of Literature SSI is a serious complication of spinal instrumentation surgery. Early diagnosis and treatment are crucial. Methods We retrospectively reviewed the laboratory data of patients who underwent posterior lumbar instrumentation surgery for degenerative spinal disease from January 2003 to December 2014. Six laboratory markers for early SSI detection were considered: renewed elevation of the white blood cell count, higher at 7 than 4 days postoperatively; renewed elevation of the C-reactive protein (CRP) level, higher at 7 than 4 days postoperatively; CRP level of >10 mg/dL at 4 days postoperatively; neutrophil percentage of >75% at 4 days postoperatively; lymphocyte percentage of <10% at 4 days postoperatively; and lymphocyte count of <1,000/µL at 4 days postoperatively. Results Ninety patients were enrolled; five developed deep SSI. Multivariate regression analysis showed that a lymphocyte count of <1,000/µL at 4 days postoperatively was the sole significant independent laboratory marker for early detection of SSI (p=0.037; odds ratio, 11.9; 95% confidence interval, 1.2–122.7). Conclusions A lymphocyte count of <1,000/µL at 4 days postoperatively is the most significant laboratory marker for early detection of SSI. PMID:27994779
NASA Astrophysics Data System (ADS)
Khan, Shahjahan
Often scientific information on various data generating processes are presented in the from of numerical and categorical data. Except for some very rare occasions, generally such data represent a small part of the population, or selected outcomes of any data generating process. Although, valuable and useful information is lurking in the array of scientific data, generally, they are unavailable to the users. Appropriate statistical methods are essential to reveal the hidden "jewels" in the mess of the row data. Exploratory data analysis methods are used to uncover such valuable characteristics of the observed data. Statistical inference provides techniques to make valid conclusions about the unknown characteristics or parameters of the population from which scientifically drawn sample data are selected. Usually, statistical inference includes estimation of population parameters as well as performing test of hypotheses on the parameters. However, prediction of future responses and determining the prediction distributions are also part of statistical inference. Both Classical or Frequentists and Bayesian approaches are used in statistical inference. The commonly used Classical approach is based on the sample data alone. In contrast, increasingly popular Beyesian approach uses prior distribution on the parameters along with the sample data to make inferences. The non-parametric and robust methods are also being used in situations where commonly used model assumptions are unsupported. In this chapter,we cover the philosophical andmethodological aspects of both the Classical and Bayesian approaches.Moreover, some aspects of predictive inference are also included. In the absence of any evidence to support assumptions regarding the distribution of the underlying population, or if the variable is measured only in ordinal scale, non-parametric methods are used. Robust methods are employed to avoid any significant changes in the results due to deviations from the model
NASA Astrophysics Data System (ADS)
Khan, Shahjahan
Often scientific information on various data generating processes are presented in the from of numerical and categorical data. Except for some very rare occasions, generally such data represent a small part of the population, or selected outcomes of any data generating process. Although, valuable and useful information is lurking in the array of scientific data, generally, they are unavailable to the users. Appropriate statistical methods are essential to reveal the hidden “jewels” in the mess of the row data. Exploratory data analysis methods are used to uncover such valuable characteristics of the observed data. Statistical inference provides techniques to make valid conclusions about the unknown characteristics or parameters of the population from which scientifically drawn sample data are selected. Usually, statistical inference includes estimation of population parameters as well as performing test of hypotheses on the parameters. However, prediction of future responses and determining the prediction distributions are also part of statistical inference. Both Classical or Frequentists and Bayesian approaches are used in statistical inference. The commonly used Classical approach is based on the sample data alone. In contrast, increasingly popular Beyesian approach uses prior distribution on the parameters along with the sample data to make inferences. The non-parametric and robust methods are also being used in situations where commonly used model assumptions are unsupported. In this chapter,we cover the philosophical andmethodological aspects of both the Classical and Bayesian approaches.Moreover, some aspects of predictive inference are also included. In the absence of any evidence to support assumptions regarding the distribution of the underlying population, or if the variable is measured only in ordinal scale, non-parametric methods are used. Robust methods are employed to avoid any significant changes in the results due to deviations from the model
Grimm, Lars J. Ghate, Sujata V.; Yoon, Sora C.; Kim, Connie; Kuzmiak, Cherie M.; Mazurowski, Maciej A.
2014-03-15
Purpose: The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. Methods: Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). Results: Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502–0.739, 95% Confidence Interval: 0.543–0.680,p < 0.002). Conclusions: Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees.
Parker, S
2015-06-15
Purpose: To evaluate the ability of statistical process control methods to detect systematic errors when using a two dimensional (2D) detector array for routine electron beam energy verification. Methods: Electron beam energy constancy was measured using an aluminum wedge and a 2D diode array on four linear accelerators. Process control limits were established. Measurements were recorded in control charts and compared with both calculated process control limits and TG-142 recommended specification limits. The data was tested for normality, process capability and process acceptability. Additional measurements were recorded while systematic errors were intentionally introduced. Systematic errors included shifts in the alignment of the wedge, incorrect orientation of the wedge, and incorrect array calibration. Results: Control limits calculated for each beam were smaller than the recommended specification limits. Process capability and process acceptability ratios were greater than one in all cases. All data was normally distributed. Shifts in the alignment of the wedge were most apparent for low energies. The smallest shift (0.5 mm) was detectable using process control limits in some cases, while the largest shift (2 mm) was detectable using specification limits in only one case. The wedge orientation tested did not affect the measurements as this did not affect the thickness of aluminum over the detectors of interest. Array calibration dependence varied with energy and selected array calibration. 6 MeV was the least sensitive to array calibration selection while 16 MeV was the most sensitive. Conclusion: Statistical process control methods demonstrated that the data distribution was normally distributed, the process was capable of meeting specifications, and that the process was centered within the specification limits. Though not all systematic errors were distinguishable from random errors, process control limits increased the ability to detect systematic errors
Grossling, Bernardo F.
1975-01-01
Exploratory drilling is still in incipient or youthful stages in those areas of the world where the bulk of the potential petroleum resources is yet to be discovered. Methods of assessing resources from projections based on historical production and reserve data are limited to mature areas. For most of the world's petroleum-prospective areas, a more speculative situation calls for a critical review of resource-assessment methodology. The language of mathematical statistics is required to define more rigorously the appraisal of petroleum resources. Basically, two approaches have been used to appraise the amounts of undiscovered mineral resources in a geologic province: (1) projection models, which use statistical data on the past outcome of exploration and development in the province; and (2) estimation models of the overall resources of the province, which use certain known parameters of the province together with the outcome of exploration and development in analogous provinces. These two approaches often lead to widely different estimates. Some of the controversy that arises results from a confusion of the probabilistic significance of the quantities yielded by each of the two approaches. Also, inherent limitations of analytic projection models-such as those using the logistic and Gomperts functions --have often been ignored. The resource-assessment problem should be recast in terms that provide for consideration of the probability of existence of the resource and of the probability of discovery of a deposit. Then the two above-mentioned models occupy the two ends of the probability range. The new approach accounts for (1) what can be expected with reasonably high certainty by mere projections of what has been accomplished in the past; (2) the inherent biases of decision-makers and resource estimators; (3) upper bounds that can be set up as goals for exploration; and (4) the uncertainties in geologic conditions in a search for minerals. Actual outcomes can then
Georgouli, Konstantia; Martinez Del Rincon, Jesus; Koidis, Anastasios
2017-02-15
The main objective of this work was to develop a novel dimensionality reduction technique as a part of an integrated pattern recognition solution capable of identifying adulterants such as hazelnut oil in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. A novel Continuous Locality Preserving Projections (CLPP) technique is proposed which allows the modelling of the continuous nature of the produced in-house admixtures as data series instead of discrete points. The maintenance of the continuous structure of the data manifold enables the better visualisation of this examined classification problem and facilitates the more accurate utilisation of the manifold for detecting the adulterants. The performance of the proposed technique is validated with two different spectroscopic techniques (Raman and Fourier transform infrared, FT-IR). In all cases studied, CLPP accompanied by k-Nearest Neighbors (kNN) algorithm was found to outperform any other state-of-the-art pattern recognition techniques.
NASA Astrophysics Data System (ADS)
Calderon, Christopher P.; Weiss, Lucien E.; Moerner, W. E.
2014-05-01
Experimental advances have improved the two- (2D) and three-dimensional (3D) spatial resolution that can be extracted from in vivo single-molecule measurements. This enables researchers to quantitatively infer the magnitude and directionality of forces experienced by biomolecules in their native environment. Situations where such force information is relevant range from mitosis to directed transport of protein cargo along cytoskeletal structures. Models commonly applied to quantify single-molecule dynamics assume that effective forces and velocity in the x ,y (or x ,y,z) directions are statistically independent, but this assumption is physically unrealistic in many situations. We present a hypothesis testing approach capable of determining if there is evidence of statistical dependence between positional coordinates in experimentally measured trajectories; if the hypothesis of independence between spatial coordinates is rejected, then a new model accounting for 2D (3D) interactions can and should be considered. Our hypothesis testing technique is robust, meaning it can detect interactions, even if the noise statistics are not well captured by the model. The approach is demonstrated on control simulations and on experimental data (directed transport of intraflagellar transport protein 88 homolog in the primary cilium).
Arismendi, Ivan; Johnson, Sherri L.; Dunham, Jason
2015-01-01
Statistics of central tendency and dispersion may not capture relevant or desired characteristics of the distribution of continuous phenomena and, thus, they may not adequately describe temporal patterns of change. Here, we present two methodological approaches that can help to identify temporal changes in environmental regimes. First, we use higher-order statistical moments (skewness and kurtosis) to examine potential changes of empirical distributions at decadal extents. Second, we adapt a statistical procedure combining a non-metric multidimensional scaling technique and higher density region plots to detect potentially anomalous years. We illustrate the use of these approaches by examining long-term stream temperature data from minimally and highly human-influenced streams. In particular, we contrast predictions about thermal regime responses to changing climates and human-related water uses. Using these methods, we effectively diagnose years with unusual thermal variability and patterns in variability through time, as well as spatial variability linked to regional and local factors that influence stream temperature. Our findings highlight the complexity of responses of thermal regimes of streams and reveal their differential vulnerability to climate warming and human-related water uses. The two approaches presented here can be applied with a variety of other continuous phenomena to address historical changes, extreme events, and their associated ecological responses.
Wang, Lin; Wu, Chuanyong; Qiao, Lihua; Yu, Wenjun; Guo, Qiaomei; Zhao, Mingna; Yang, Guohua; Zhao, Hang; Lou, Jiatao
2017-01-01
Background: As the heterogeneity of CTCs is becoming increasingly better understood, it is clear that identifying particular subtypes of CTCs would be more relevant. Methods: We detected folate receptor (FR)-positive circulating tumor cells (FR+-CTCs) by a novel ligand-targeted polymerase chain reaction (LT-PCR) detection technique. Results: In the none-dynamic study, FR+-CTC levels of patients with lung cancer were significantly higher than controls (patients with benign lung diseases and healthy controls). With a threshold of 8.7 CTC units, FR+-CTC showed a sensitivity of 77.7% and specificity of 89.5% in the diagnosis of lung cancer. When compared with established clinical biomarkers including carcinoembryonic antigen (CEA), cytokeratin 19 fragment (CYFRA21-1), and neuron-specific enolase (NSE), FR+-CTC showed the highest diagnostic efficiency. Notably, the combination of FR+-CTC, CEA, NSE, and CYFRA21-1 could significantly improve the diagnostic efficacy in differentiating patients with lung cancer from benign lung disease. In our dynamic surveillance study, the CTC levels of 62 non-small cell lung cancer (NSCLC) patients decreased significantly after tumor resection. Conclusion: We established a LT-PCR-based FR+-CTC detection platform for patients with lung cancer that exhibits high sensitivity and specificity. This platform would be clinical useful in lung cancer diagnosis and treatment response assessment. PMID:28123603
Lee, L.; Helsel, D.
2007-01-01
Analysis of low concentrations of trace contaminants in environmental media often results in left-censored data that are below some limit of analytical precision. Interpretation of values becomes complicated when there are multiple detection limits in the data-perhaps as a result of changing analytical precision over time. Parametric and semi-parametric methods, such as maximum likelihood estimation and robust regression on order statistics, can be employed to model distributions of multiply censored data and provide estimates of summary statistics. However, these methods are based on assumptions about the underlying distribution of data. Nonparametric methods provide an alternative that does not require such assumptions. A standard nonparametric method for estimating summary statistics of multiply-censored data is the Kaplan-Meier (K-M) method. This method has seen widespread usage in the medical sciences within a general framework termed "survival analysis" where it is employed with right-censored time-to-failure data. However, K-M methods are equally valid for the left-censored data common in the geosciences. Our S-language software provides an analytical framework based on K-M methods that is tailored to the needs of the earth and environmental sciences community. This includes routines