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)…
Lack of Statistical Significance
ERIC Educational Resources Information Center
Kehle, Thomas J.; Bray, Melissa A.; Chafouleas, Sandra M.; Kawano, Takuji
2007-01-01
Criticism has been leveled against the use of statistical significance testing (SST) in many disciplines. However, the field of school psychology has been largely devoid of critiques of SST. Inspection of the primary journals in school psychology indicated numerous examples of SST with nonrandom samples and/or samples of convenience. In this…
Statistical or biological significance?
Saxon, Emma
2015-01-01
Oat plants grown at an agricultural research facility produce higher yields in Field 1 than in Field 2, under well fertilised conditions and with similar weather exposure; all oat plants in both fields are healthy and show no sign of disease. In this study, the authors hypothesised that the soil microbial community might be different in each field, and these differences might explain the difference in oat plant growth. They carried out a metagenomic analysis of the 16 s ribosomal 'signature' sequences from bacteria in 50 randomly located soil samples in each field to determine the composition of the bacterial community. The study identified >1000 species, most of which were present in both fields. The authors identified two plant growth-promoting species that were significantly reduced in soil from Field 2 (Student's t-test P < 0.05), and concluded that these species might have contributed to reduced yield. PMID:26541972
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
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. PMID:25679651
NASA Astrophysics Data System (ADS)
Kalimeris, A.; Potirakis, S. M.; Eftaxias, K.; Antonopoulos, G.; Kopanas, J.; Nomikos, C.
2016-05-01
A multi-spectral analysis of the kHz electromagnetic time series associated with Athens' earthquake (M = 5.9, 7 September 1999) is presented here, that results to the reliable discrimination of the fracto-electromagnetic emissions from the natural geo-electromagnetic field background. Five spectral analysis methods are utilized in order to resolve the statistically significant variability modes of the studied dynamical system out of a red noise background (the revised Multi-Taper Method, the Singular Spectrum Analysis, and the Wavelet Analysis among them). The performed analysis reveals the existence of three distinct epochs in the time series for the period before the earthquake, a "quiet", a "transitional" and an "active" epoch. Towards the end of the active epoch, during a sub-period which is approximately starting two days before the earthquake, the dynamical system passes into a high activity state, where electromagnetic signal emissions become powerful and statistically significant almost in all time-scales. The temporal behavior of the studied system in each one of these epochs is further searched through mathematical reconstruction in the time domain of those spectral features that were found to be statistically significant. The transition of the system from the quiet to the active state proved to be detectable first in the long time-scales and afterwards in the short scales. Finally, a Hurst exponent analysis revealed persistent characteristics embedded in the two strong EM bursts observed during the "active" epoch.
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…
Comments on the Statistical Significance Testing Articles.
ERIC Educational Resources Information Center
Knapp, Thomas R.
1998-01-01
Expresses a "middle-of-the-road" position on statistical significance testing, suggesting that it has its place but that confidence intervals are generally more useful. Identifies 10 errors of omission or commission in the papers reviewed that weaken the positions taken in their discussions. (SLD)
Statistical significance of normalized global alignment.
Peris, Guillermo; Marzal, Andrés
2014-03-01
The comparison of homologous proteins from different species is a first step toward a function assignment and a reconstruction of the species evolution. Though local alignment is mostly used for this purpose, global alignment is important for constructing multiple alignments or phylogenetic trees. However, statistical significance of global alignments is not completely clear, lacking a specific statistical model to describe alignments or depending on computationally expensive methods like Z-score. Recently we presented a normalized global alignment, defined as the best compromise between global alignment cost and length, and showed that this new technique led to better classification results than Z-score at a much lower computational cost. However, it is necessary to analyze the statistical significance of the normalized global alignment in order to be considered a completely functional algorithm for protein alignment. Experiments with unrelated proteins extracted from the SCOP ASTRAL database showed that normalized global alignment scores can be fitted to a log-normal distribution. This fact, obtained without any theoretical support, can be used to derive statistical significance of normalized global alignments. Results are summarized in a table with fitted parameters for different scoring schemes. PMID:24400820
Assessing the statistical significance of periodogram peaks
NASA Astrophysics Data System (ADS)
Baluev, R. V.
2008-04-01
The least-squares (or Lomb-Scargle) periodogram is a powerful tool that is routinely used in many branches of astronomy to search for periodicities in observational data. The problem of assessing the statistical significance of candidate periodicities for a number of periodograms is considered. Based on results in extreme value theory, improved analytic estimations of false alarm probabilities are given. These include an upper limit to the false alarm probability (or a lower limit to the significance). The estimations are tested numerically in order to establish regions of their practical applicability.
Statistical Significance of Trends in Exoplanetary Atmospheres
NASA Astrophysics Data System (ADS)
Harrington, Joseph; Bowman, M.; Blumenthal, S. D.; Loredo, T. J.; UCF Exoplanets Group
2013-10-01
Cowan and Agol (2011) and we (Harrington et al. 2007, 2010, 2011, 2012, 2013) have noted that at higher equilibrium temperatures, observed exoplanet fluxes are substantially higher than even the elevated equilibrium temperature predicts. With a substantial increase in the number of atmospheric flux measurements, we can now test the statistical significance of this trend. We can also cast the data on a variety of axes to search further for the physics behind both the jump in flux above about 2000 K and the wide scatter in fluxes at all temperatures. This work was supported by NASA Planetary Atmospheres grant NNX12AI69G and NASA Astrophysics Data Analysis Program grant NNX13AF38G.
On the statistical significance of climate trends
NASA Astrophysics Data System (ADS)
Franzke, Christian
2010-05-01
One of the major problems in climate science is the prediction of future climate change due to anthropogenic green-house gas emissions. The earth's climate is not changing in a uniform way because it is a complex nonlinear system of many interacting components. The overall warming trend can be interrupted by cooling periods due to natural variability. Thus, in order to statistically distinguish between internal climate variability and genuine trends one has to assume a certain null model of the climate variability. Traditionally a short-range, and not a long-range, dependent null model is chosen. Here I show evidence for the first time that temperature data at 8 stations across Antarctica are long-range dependent and that the choice of a long-range, rather than a short-range, dependent null model negates the statistical significance of temperature trends at 2 out of 3 stations. These results show the short comings of traditional trend analysis and imply that more attention should be given to the correlation structure of climate data, in particular if they are long-range dependent. In this study I use the Empirical Mode Decomposition (EMD) to decompose the univariate temperature time series into a finite number of Intrinsic Mode Functions (IMF) and an instantaneous mean. While there is no unambiguous definition of a trend, in this study we interpret the instantaneous mean as a trend which is possibly nonlinear. The EMD method has been shown to be a powerful method for extracting trends from noisy and nonlinear time series. I will show that this way of identifying trends is superior to the traditional linear least-square fits.
Statistical mechanics of community detection
NASA Astrophysics Data System (ADS)
Reichardt, Jörg; Bornholdt, Stefan
2006-07-01
Starting from a general ansatz, we show how community detection can be interpreted as finding the ground state of an infinite range spin glass. Our approach applies to weighted and directed networks alike. It contains the ad hoc introduced quality function from [J. Reichardt and S. Bornholdt, Phys. Rev. Lett. 93, 218701 (2004)] and the modularity Q as defined by Newman and Girvan [Phys. Rev. E 69, 026113 (2004)] as special cases. The community structure of the network is interpreted as the spin configuration that minimizes the energy of the spin glass with the spin states being the community indices. We elucidate the properties of the ground state configuration to give a concise definition of communities as cohesive subgroups in networks that is adaptive to the specific class of network under study. Further, we show how hierarchies and overlap in the community structure can be detected. Computationally efficient local update rules for optimization procedures to find the ground state are given. We show how the ansatz may be used to discover the community around a given node without detecting all communities in the full network and we give benchmarks for the performance of this extension. Finally, we give expectation values for the modularity of random graphs, which can be used in the assessment of statistical significance of community structure.
Statistical mechanics of community detection.
Reichardt, Jörg; Bornholdt, Stefan
2006-07-01
Starting from a general ansatz, we show how community detection can be interpreted as finding the ground state of an infinite range spin glass. Our approach applies to weighted and directed networks alike. It contains the ad hoc introduced quality function from [J. Reichardt and S. Bornholdt, Phys. Rev. Lett. 93, 218701 (2004)] and the modularity Q as defined by Newman and Girvan [Phys. Rev. E 69, 026113 (2004)] as special cases. The community structure of the network is interpreted as the spin configuration that minimizes the energy of the spin glass with the spin states being the community indices. We elucidate the properties of the ground state configuration to give a concise definition of communities as cohesive subgroups in networks that is adaptive to the specific class of network under study. Further, we show how hierarchies and overlap in the community structure can be detected. Computationally efficient local update rules for optimization procedures to find the ground state are given. We show how the ansatz may be used to discover the community around a given node without detecting all communities in the full network and we give benchmarks for the performance of this extension. Finally, we give expectation values for the modularity of random graphs, which can be used in the assessment of statistical significance of community structure. PMID:16907154
Statistical methodology for pathogen detection.
Ogliari, Paulo José; de Andrade, Dalton Francisco; Pacheco, Juliano Anderson; Franchin, Paulo Rogério; Batista, Cleide Rosana Vieira
2007-08-01
The main goal of the present study was to discuss the application of the McNemar test to the comparison of proportions in dependent samples. Data were analyzed from studies conducted to verify the suitability of replacing a conventional method with a new one for identifying the presence of Salmonella. It is shown that, in most situations, the McNemar test does not provide all the elements required by the microbiologist to make a final decision and that appropriate functions of the proportions need to be considered. Sample sizes suitable to guarantee a test with a high power in the detection of significant differences regarding the problem studied are obtained by simulation. Examples of functions that are of great value to the microbiologist are presented. PMID:17803152
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…
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.…
Advances in Testing the Statistical Significance of Mediation Effects
ERIC Educational Resources Information Center
Mallinckrodt, Brent; Abraham, W. Todd; Wei, Meifen; Russell, Daniel W.
2006-01-01
P. A. Frazier, A. P. Tix, and K. E. Barron (2004) highlighted a normal theory method popularized by R. M. Baron and D. A. Kenny (1986) for testing the statistical significance of indirect effects (i.e., mediator variables) in multiple regression contexts. However, simulation studies suggest that this method lacks statistical power relative to some…
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.
Decadal power in land air temperatures: Is it statistically significant?
NASA Astrophysics Data System (ADS)
Thejll, Peter A.
2001-12-01
The geographical distribution and properties of the well-known 10-11 year signal in terrestrial temperature records is investigated. By analyzing the Global Historical Climate Network data for surface air temperatures we verify that the signal is strongest in North America and is similar in nature to that reported earlier by R. G. Currie. The decadal signal is statistically significant for individual stations, but it is not possible to show that the signal is statistically significant globally, using strict tests. In North America, during the twentieth century, the decadal variability in the solar activity cycle is associated with the decadal part of the North Atlantic Oscillation index series in such a way that both of these signals correspond to the same spatial pattern of cooling and warming. A method for testing statistical results with Monte Carlo trials on data fields with specified temporal structure and specific spatial correlation retained is presented.
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…
A Comparison of Statistical Significance Tests for Selecting Equating Functions
ERIC Educational Resources Information Center
Moses, Tim
2009-01-01
This study compared the accuracies of nine previously proposed statistical significance tests for selecting identity, linear, and equipercentile equating functions in an equivalent groups equating design. The strategies included likelihood ratio tests for the loglinear models of tests' frequency distributions, regression tests, Kolmogorov-Smirnov…
Assigning statistical significance to proteotypic peptides via database searches
Alves, Gelio; Ogurtsov, Aleksey Y.; Yu, Yi-Kuo
2011-01-01
Querying MS/MS spectra against a database containing only proteotypic peptides reduces data analysis time due to reduction of database size. Despite the speed advantage, this search strategy is challenged by issues of statistical significance and coverage. The former requires separating systematically significant identifications from less confident identifications, while the latter arises when the underlying peptide is not present, due to single amino acid polymorphisms (SAPs) or post-translational modifications (PTMs), in the proteotypic peptide libraries searched. To address both issues simultaneously, we have extended RAId’s knowledge database to include proteotypic information, utilized RAId’s statistical strategy to assign statistical significance to proteotypic peptides, and modified RAId’s programs to allow for consideration of proteotypic information during database searches. The extended database alleviates the coverage problem since all annotated modifications, even those occurred within proteotypic peptides, may be considered. Taking into account the likelihoods of observation, the statistical strategy of RAId provides accurate E-value assignments regardless whether a candidate peptide is proteotypic or not. The advantage of including proteotypic information is evidenced by its superior retrieval performance when compared to regular database searches. PMID:21055489
Assigning statistical significance to proteotypic peptides via database searches.
Alves, Gelio; Ogurtsov, Aleksey Y; Yu, Yi-Kuo
2011-02-01
Querying MS/MS spectra against a database containing only proteotypic peptides reduces data analysis time due to reduction of database size. Despite the speed advantage, this search strategy is challenged by issues of statistical significance and coverage. The former requires separating systematically significant identifications from less confident identifications, while the latter arises when the underlying peptide is not present, due to single amino acid polymorphisms (SAPs) or post-translational modifications (PTMs), in the proteotypic peptide libraries searched. To address both issues simultaneously, we have extended RAId's knowledge database to include proteotypic information, utilized RAId's statistical strategy to assign statistical significance to proteotypic peptides, and modified RAId's programs to allow for consideration of proteotypic information during database searches. The extended database alleviates the coverage problem since all annotated modifications, even those that occurred within proteotypic peptides, may be considered. Taking into account the likelihoods of observation, the statistical strategy of RAId provides accurate E-value assignments regardless whether a candidate peptide is proteotypic or not. The advantage of including proteotypic information is evidenced by its superior retrieval performance when compared to regular database searches. PMID:21055489
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
A Statistical Approach to Autocorrelation Detection of Low Frequency Earthquakes
NASA Astrophysics Data System (ADS)
Aguiar, A. C.; Beroza, G. C.
2012-12-01
We have analyzed tremor data during the April, 2006 tremor episode in the Nankai Trough in SW Japan using the auto-correlation approach of Brown et al. (2008), which detects low frequency earthquakes (LFEs) based on pair-wise matching. We have found that the statistical behavior of the autocorrelations of each station is different and for this reason we have based our LFE detection method on the autocorrelation of each station individually. Analyzing one station at a time assures that the detection threshold will only depend on the station being analyzed. Once detections are found on each station individually, using a low detection threshold based on a Gaussian distribution of the correlation coefficients, the results are compared within stations and declared a detection if they are found in a statistically significant number of the stations, following multinomial statistics. We have compared our detections using the single station method to the detections found by Shelly et al. (2007) for the 2006 April 16 events and find a significant number of similar detections as well as many new detections that were not found using templates from known LFEs. We are working towards developing a sound statistical basis for event detection. This approach should improve our ability to detect LFEs within weak tremor signals where they are not already identified, and should be applicable to earthquake swarms and sequences in general.
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.
Statistical significance of climate sensitivity predictors obtained by data mining
NASA Astrophysics Data System (ADS)
Caldwell, Peter M.; Bretherton, Christopher S.; Zelinka, Mark D.; Klein, Stephen A.; Santer, Benjamin D.; Sanderson, Benjamin M.
2014-03-01
Several recent efforts to estimate Earth's equilibrium climate sensitivity (ECS) focus on identifying quantities in the current climate which are skillful predictors of ECS yet can be constrained by observations. This study automates the search for observable predictors using data from phase 5 of the Coupled Model Intercomparison Project. The primary focus of this paper is assessing statistical significance of the resulting predictive relationships. Failure to account for dependence between models, variables, locations, and seasons is shown to yield misleading results. A new technique for testing the field significance of data-mined correlations which avoids these problems is presented. Using this new approach, all 41,741 relationships we tested were found to be explainable by chance. This leads us to conclude that data mining is best used to identify potential relationships which are then validated or discarded using physically based hypothesis testing.
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
Statistical significance across multiple optimization models for community partition
NASA Astrophysics Data System (ADS)
Li, Ju; Li, Hui-Jia; Mao, He-Jin; Chen, Junhua
2016-05-01
The study of community structure is an important problem in a wide range of applications, which can help us understand the real network system deeply. However, due to the existence of random factors and error edges in real networks, how to measure the significance of community structure efficiently is a crucial question. In this paper, we present a novel statistical framework computing the significance of community structure across multiple optimization methods. Different from the universal approaches, we calculate the similarity between a given node and its leader and employ the distribution of link tightness to derive the significance score, instead of a direct comparison to a randomized model. Based on the distribution of community tightness, a new “p-value” form significance measure is proposed for community structure analysis. Specially, the well-known approaches and their corresponding quality functions are unified to a novel general formulation, which facilitates in providing a detailed comparison across them. To determine the position of leaders and their corresponding followers, an efficient algorithm is proposed based on the spectral theory. Finally, we apply the significance analysis to some famous benchmark networks and the good performance verified the effectiveness and efficiency of our framework.
ERIC Educational Resources Information Center
Gordon, Howard R. D.
A random sample of 113 members of the American Vocational Education Research Association (AVERA) was surveyed to obtain baseline information regarding AVERA members' perceptions of statistical significance tests. The Psychometrics Group Instrument was used to collect data from participants. Of those surveyed, 67% were male, 93% had earned a…
Statistical Fault Detection & Diagnosis Expert System
Energy Science and Technology Software Center (ESTSC)
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 hasmore » degraded.« less
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.
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. PMID:24109865
Statistical controversies in clinical research: statistical significance-too much of a good thing ….
Buyse, M; Hurvitz, S A; Andre, F; Jiang, Z; Burris, H A; Toi, M; Eiermann, W; Lindsay, M-A; Slamon, D
2016-05-01
The use and interpretation of P values is a matter of debate in applied research. We argue that P values are useful as a pragmatic guide to interpret the results of a clinical trial, not as a strict binary boundary that separates real treatment effects from lack thereof. We illustrate our point using the result of BOLERO-1, a randomized, double-blind trial evaluating the efficacy and safety of adding everolimus to trastuzumab and paclitaxel as first-line therapy for HER2+ advanced breast cancer. In this trial, the benefit of everolimus was seen only in the predefined subset of patients with hormone receptor-negative breast cancer at baseline (progression-free survival hazard ratio = 0.66, P = 0.0049). A strict interpretation of this finding, based on complex 'alpha splitting' rules to assess statistical significance, led to the conclusion that the benefit of everolimus was not statistically significant either overall or in the subset. We contend that this interpretation does not do justice to the data, and we argue that the benefit of everolimus in hormone receptor-negative breast cancer is both statistically compelling and clinically relevant. PMID:26861602
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.
Infants with Williams syndrome detect statistical regularities in continuous speech.
Cashon, Cara H; Ha, Oh-Ryeong; Graf Estes, Katharine; Saffran, Jenny R; Mervis, Carolyn B
2016-09-01
Williams syndrome (WS) is a rare genetic disorder associated with delays in language and cognitive development. The reasons for the language delay are unknown. Statistical learning is a domain-general mechanism recruited for early language acquisition. In the present study, we investigated whether infants with WS were able to detect the statistical structure in continuous speech. Eighteen 8- to 20-month-olds with WS were familiarized with 2min of a continuous stream of synthesized nonsense words; the statistical structure of the speech was the only cue to word boundaries. They were tested on their ability to discriminate statistically-defined "words" and "part-words" (which crossed word boundaries) in the artificial language. Despite significant cognitive and language delays, infants with WS were able to detect the statistical regularities in the speech stream. These findings suggest that an inability to track the statistical properties of speech is unlikely to be the primary basis for the delays in the onset of language observed in infants with WS. These results provide the first evidence of statistical learning by infants with developmental delays. PMID:27299804
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
2015-08-01
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.
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
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
2014-01-01
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
NASA Astrophysics Data System (ADS)
Haigh, Ivan D.; Wahl, Thomas; Rohling, Eelco J.; Price, René M.; Pattiaratchi, Charitha B.; Calafat, Francisco M.; Dangendorf, Sönke
2014-04-01
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.
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
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.
Statistical Analysis of Examination to Detect Cheating.
ERIC Educational Resources Information Center
Code, Ronald P.
1985-01-01
A number of statistical procedures that were developed in 1983 at the University of Medicine and Dentistry of New Jersey-Rutgers Medical School to verify the suspicion that a student cheated during an examination are described. (MLW)
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.
NASA Astrophysics Data System (ADS)
Williams, Arnold C.; Pachowicz, Peter W.
2004-09-01
Current mine detection research indicates that no single sensor or single look from a sensor will detect mines/minefields in a real-time manner at a performance level suitable for a forward maneuver unit. Hence, the integrated development of detectors and fusion algorithms are of primary importance. A problem in this development process has been the evaluation of these algorithms with relatively small data sets, leading to anecdotal and frequently over trained results. These anecdotal results are often unreliable and conflicting among various sensors and algorithms. Consequently, the physical phenomena that ought to be exploited and the performance benefits of this exploitation are often ambiguous. The Army RDECOM CERDEC Night Vision Laboratory and Electron Sensors Directorate has collected large amounts of multisensor data such that statistically significant evaluations of detection and fusion algorithms can be obtained. Even with these large data sets care must be taken in algorithm design and data processing to achieve statistically significant performance results for combined detectors and fusion algorithms. This paper discusses statistically significant detection and combined multilook fusion results for the Ellipse Detector (ED) and the Piecewise Level Fusion Algorithm (PLFA). These statistically significant performance results are characterized by ROC curves that have been obtained through processing this multilook data for the high resolution SAR data of the Veridian X-Band radar. We discuss the implications of these results on mine detection and the importance of statistical significance, sample size, ground truth, and algorithm design in performance evaluation.
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…
Modeling single-molecule detection statistics
NASA Astrophysics Data System (ADS)
Enderlein, Joerg; Robbins, David L.; Ambrose, W. P.; Goodwin, Peter M.; Keller, Richard A.
1997-05-01
We present experimental results of single B-phycoerythrin molecule detection in a fluid flow at different sample introduction rates. A new mathematical approach is used for calculating the resulting burst size distributions. The calculations are based upon a complete physical model including absorption, fluorescence and photobleaching characteristics of the fluorophore; its diffusion; the sample stream hydrodynamics; the spatially dependent optical detection efficiency; and the excitation laser beam characteristics. Special attention is paid to the phenomenon of `molecular noise'--fluctuations in the number of overlapping crossings of molecules through the detection volume. The importance of this study and its connections to experimental applications are discussed.
Smith, Ariana L; Wein, Alan J
2011-05-01
To evaluate the statistical and clinical efficacy of the pharmacological treatments of nocturia using non-antidiuretic agents. A literature review of treatments of nocturia specifically addressing the impact of alpha blockers, 5-alpha reductase inhibitors (5ARI) and antimuscarinics on reduction in nocturnal voids. Despite commonly reported statistically significant results, nocturia has shown a poor clinical response to traditional therapies for benign prostatic hyperplasia including alpha blockers and 5ARI. Similarly, nocturia has shown a poor clinical response to traditional therapies for overactive bladder including antimuscarinics. Statistical success has been achieved in some groups with a variety of alpha blockers and antimuscarinic agents, but the clinical significance of these changes is doubtful. It is likely that other types of therapy will need to be employed in order to achieve a clinically significant reduction in nocturia. PMID:21518417
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…
Dark census: Statistically detecting the satellite populations of distant galaxies
NASA Astrophysics Data System (ADS)
Cyr-Racine, Francis-Yan; Moustakas, Leonidas A.; Keeton, Charles R.; Sigurdson, Kris; Gilman, Daniel A.
2016-08-01
In the standard structure formation scenario based on the cold dark matter paradigm, galactic halos are predicted to contain a large population of dark matter subhalos. While the most massive members of the subhalo population can appear as luminous satellites and be detected in optical surveys, establishing the existence of the low mass and mostly dark subhalos has proven to be a daunting task. Galaxy-scale strong gravitational lenses have been successfully used to study mass substructures lying close to lensed images of bright background sources. However, in typical galaxy-scale lenses, the strong lensing region only covers a small projected area of the lens's dark matter halo, implying that the vast majority of subhalos cannot be directly detected in lensing observations. In this paper, we point out that this large population of dark satellites can collectively affect gravitational lensing observables, hence possibly allowing their statistical detection. Focusing on the region of the galactic halo outside the strong lensing area, we compute from first principles the statistical properties of perturbations to the gravitational time delay and position of lensed images in the presence of a mass substructure population. We find that in the standard cosmological scenario, the statistics of these lensing observables are well approximated by Gaussian distributions. The formalism developed as part of this calculation is very general and can be applied to any halo geometry and choice of subhalo mass function. Our results significantly reduce the computational cost of including a large substructure population in lens models and enable the use of Bayesian inference techniques to detect and characterize the distributed satellite population of distant lens galaxies.
Statistics and Machine Learning based Outlier Detection Techniques for Exoplanets
NASA Astrophysics Data System (ADS)
Goel, Amit; Montgomery, Michele
2015-08-01
Architectures of planetary systems are observable snapshots in time that can indicate formation and dynamic evolution of planets. The observable key parameters that we consider are planetary mass and orbital period. If planet masses are significantly less than their host star masses, then Keplerian Motion is defined as P^2 = a^3 where P is the orbital period in units of years and a is the orbital period in units of Astronomical Units (AU). Keplerian motion works on small scales such as the size of the Solar System but not on large scales such as the size of the Milky Way Galaxy. In this work, for confirmed exoplanets of known stellar mass, planetary mass, orbital period, and stellar age, we analyze Keplerian motion of systems based on stellar age to seek if Keplerian motion has an age dependency and to identify outliers. For detecting outliers, we apply several techniques based on statistical and machine learning methods such as probabilistic, linear, and proximity based models. In probabilistic and statistical models of outliers, the parameters of a closed form probability distributions are learned in order to detect the outliers. Linear models use regression analysis based techniques for detecting outliers. Proximity based models use distance based algorithms such as k-nearest neighbour, clustering algorithms such as k-means, or density based algorithms such as kernel density estimation. In this work, we will use unsupervised learning algorithms with only the proximity based models. In addition, we explore the relative strengths and weaknesses of the various techniques by validating the outliers. The validation criteria for the outliers is if the ratio of planetary mass to stellar mass is less than 0.001. In this work, we present our statistical analysis of the outliers thus detected.
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.
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.
On the statistical significance of the bulk flow measured by the Planck satellite
NASA Astrophysics Data System (ADS)
Atrio-Barandela, F.
2013-09-01
A recent analysis of data collected by the Planck satellite detected a net dipole at the location of X-ray selected galaxy clusters, corresponding to a large-scale bulk flow extending at least to z ~ 0.18, the median redshift of the cluster sample. The amplitude of this flow, as measured with Planck, is consistent with earlier findings based on data from the Wilkinson Microwave Anisotropy Probe (WMAP). However, the uncertainty assigned to the dipole by the Planck team is much larger than that found in the WMAP studies, leading the authors of the Planck study to conclude that the observed bulk flow is not statistically significant. Here, we show that two of the three implementations of random sampling used in the error analysis of the Planck study lead to systematic overestimates in the uncertainty of the measured dipole. Random simulations of the sky do not take into account that the actual realization of the sky leads to filtered data that have a 12% lower root-mean-square dispersion than the average simulation. Using rotations around the Galactic pole (the Z axis), increases the uncertainty of the X and Y components of the dipole and artificially reduces the significance of the dipole detection from 98-99% to less than 90% confidence. When either effect is taken into account, the corrected errors agree with those obtained using random distributions of clusters on Planck data, and the resulting statistical significance of the dipole measured by Planck is consistent with that of the WMAP results.
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.
2013-01-01
Background Relative validity (RV), a ratio of ANOVA F-statistics, is often used to compare the validity of patient-reported outcome (PRO) measures. We used the bootstrap to establish the statistical significance of the RV and to identify key factors affecting its significance. Methods Based on responses from 453 chronic kidney disease (CKD) patients to 16 CKD-specific and generic PRO measures, RVs were computed to determine how well each measure discriminated across clinically-defined groups of patients compared to the most discriminating (reference) measure. Statistical significance of RV was quantified by the 95% bootstrap confidence interval. Simulations examined the effects of sample size, denominator F-statistic, correlation between comparator and reference measures, and number of bootstrap replicates. Results The statistical significance of the RV increased as the magnitude of denominator F-statistic increased or as the correlation between comparator and reference measures increased. A denominator F-statistic of 57 conveyed sufficient power (80%) to detect an RV of 0.6 for two measures correlated at r = 0.7. Larger denominator F-statistics or higher correlations provided greater power. Larger sample size with a fixed denominator F-statistic or more bootstrap replicates (beyond 500) had minimal impact. Conclusions The bootstrap is valuable for establishing the statistical significance of RV estimates. A reasonably large denominator F-statistic (F > 57) is required for adequate power when using the RV to compare the validity of measures with small or moderate correlations (r < 0.7). Substantially greater power can be achieved when comparing measures of a very high correlation (r > 0.9). PMID:23721463
ERIC Educational Resources Information Center
Simpson, Robert G.
1981-01-01
Occasionally, differences in test scores seem to indicate that a student performs much better in one reading area than in another when, in reality, the differences may not be statistically significant. The author presents a table in which statistically significant differences between Woodcock test standard scores are identified. (Author)
"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…
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…
The Importance of Invariance Procedures as against Tests of Statistical Significance.
ERIC Educational Resources Information Center
Fish, Larry
A growing controversy surrounds the strict interpretation of statistical significance tests in social research. Statistical significance tests fail in particular to provide estimates for the stability of research results. Methods that do provide such estimates are known as invariance or cross-validation procedures. Invariance analysis is largely…
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…
NASA Astrophysics Data System (ADS)
Ritter, Axel; Muñoz-Carpena, Rafael
2013-02-01
SummarySuccess in the use of computer models for simulating environmental variables and processes requires objective model calibration and verification procedures. Several methods for quantifying the goodness-of-fit of observations against model-calculated values have been proposed but none of them is free of limitations and are often ambiguous. When a single indicator is used it may lead to incorrect verification of the model. Instead, a combination of graphical results, absolute value error statistics (i.e. root mean square error), and normalized goodness-of-fit statistics (i.e. Nash-Sutcliffe Efficiency coefficient, NSE) is currently recommended. Interpretation of NSE values is often subjective, and may be biased by the magnitude and number of data points, data outliers and repeated data. The statistical significance of the performance statistics is an aspect generally ignored that helps in reducing subjectivity in the proper interpretation of the model performance. In this work, approximated probability distributions for two common indicators (NSE and root mean square error) are derived with bootstrapping (block bootstrapping when dealing with time series), followed by bias corrected and accelerated calculation of confidence intervals. Hypothesis testing of the indicators exceeding threshold values is proposed in a unified framework for statistically accepting or rejecting the model performance. It is illustrated how model performance is not linearly related with NSE, which is critical for its proper interpretation. Additionally, the sensitivity of the indicators to model bias, outliers and repeated data is evaluated. The potential of the difference between root mean square error and mean absolute error for detecting outliers is explored, showing that this may be considered a necessary but not a sufficient condition of outlier presence. The usefulness of the approach for the evaluation of model performance is illustrated with case studies including those with
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…
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.
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. PMID:24039936
Brazilian Amazonia Deforestation Detection Using Spatio-Temporal Scan Statistics
NASA Astrophysics Data System (ADS)
Vieira, C. A. O.; Santos, N. T.; Carneiro, A. P. S.; Balieiro, A. A. S.
2012-07-01
The spatio-temporal models, developed for analyses of diseases, can also be used for others fields of study, including concerns about forest and deforestation. The aim of this paper is to quantitatively check priority areas in order to combat deforestation on the Amazon forest, using the space-time scan statistic. The study area location is at the south of the Amazonas State and cover around 297.183 kilometre squares, including the municipality of Boca do Acre, Labrea, Canutama, Humaita, Manicore, Novo Aripuana e Apui County on the north region of Brazil. This area has showed a significant change for land cover, which has increased the number of deforestation's alerts. Therefore this situation becomes a concern and gets more investigation, trying to stop factors that increase the number of cases in the area. The methodology includes the location and year that deforestation's alert occurred. These deforestation's alerts are mapped by the DETER (Detection System of Deforestation in Real Time in Amazonia), which is carry out by the Brazilian Space Agency (INPE). The software SatScanTM v7.0 was used in order to define space-time permutation scan statistic for detection of deforestation cases. The outcome of this experiment shows an efficient model to detect space-time clusters of deforestation's alerts. The model was efficient to detect the location, the size, the order and characteristics about activities at the end of the experiments. Two clusters were considered actives and kept actives up to the end of the study. These clusters are located in Canutama and Lábrea County. This quantitative spatial modelling of deforestation warnings allowed: firstly, identifying actives clustering of deforestation, in which the environment government official are able to concentrate their actions; secondly, identifying historic clustering of deforestation, in which the environment government official are able to monitoring in order to avoid them to became actives again; and finally
ERIC Educational Resources Information Center
Sullivan, Jeremy R.
2001-01-01
Summarizes the post-1994 literature in psychology and education regarding statistical significance testing, emphasizing limitations and defenses of statistical testing and alternatives or supplements to statistical significance testing. (SLD)
Armijo-Olivo, Susan; Warren, Sharon; Fuentes, Jorge; Magee, David J
2011-12-01
Statistical significance has been used extensively to evaluate the results of research studies. Nevertheless, it offers only limited information to clinicians. The assessment of clinical relevance can facilitate the interpretation of the research results into clinical practice. The objective of this study was to explore different methods to evaluate the clinical relevance of the results using a cross-sectional study as an example comparing different neck outcomes between subjects with temporomandibular disorders and healthy controls. Subjects were compared for head and cervical posture, maximal cervical muscle strength, endurance of the cervical flexor and extensor muscles, and electromyographic activity of the cervical flexor muscles during the CranioCervical Flexion Test (CCFT). The evaluation of clinical relevance of the results was performed based on the effect size (ES), minimal important difference (MID), and clinical judgement. The results of this study show that it is possible to have statistical significance without having clinical relevance, to have both statistical significance and clinical relevance, to have clinical relevance without having statistical significance, or to have neither statistical significance nor clinical relevance. The evaluation of clinical relevance in clinical research is crucial to simplify the transfer of knowledge from research into practice. Clinical researchers should present the clinical relevance of their results. PMID:21658987
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. PMID:25822617
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. PMID:25822617
Algorithm for Detecting Significant Locations from Raw GPS Data
NASA Astrophysics Data System (ADS)
Kami, Nobuharu; Enomoto, Nobuyuki; Baba, Teruyuki; Yoshikawa, Takashi
We present a fast algorithm for probabilistically extracting significant locations from raw GPS data based on data point density. Extracting significant locations from raw GPS data is the first essential step of algorithms designed for location-aware applications. Assuming that a location is significant if users spend a certain time around that area, most current algorithms compare spatial/temporal variables, such as stay duration and a roaming diameter, with given fixed thresholds to extract significant locations. However, the appropriate threshold values are not clearly known in priori and algorithms with fixed thresholds are inherently error-prone, especially under high noise levels. Moreover, for N data points, they are generally O(N 2) algorithms since distance computation is required. We developed a fast algorithm for selective data point sampling around significant locations based on density information by constructing random histograms using locality sensitive hashing. Evaluations show competitive performance in detecting significant locations even under high noise levels.
Fidler, Fiona; Burgman, Mark A; Cumming, Geoff; Buttrose, Robert; Thomason, Neil
2006-10-01
Over the last decade, criticisms of null-hypothesis significance testing have grown dramatically, and several alternative practices, such as confidence intervals, information theoretic, and Bayesian methods, have been advocated. Have these calls for change had an impact on the statistical reporting practices in conservation biology? In 2000 and 2001, 92% of sampled articles in Conservation Biology and Biological Conservation reported results of null-hypothesis tests. In 2005 this figure dropped to 78%. There were corresponding increases in the use of confidence intervals, information theoretic, and Bayesian techniques. Of those articles reporting null-hypothesis testing--which still easily constitute the majority--very few report statistical power (8%) and many misinterpret statistical nonsignificance as evidence for no effect (63%). Overall, results of our survey show some improvements in statistical practice, but further efforts are clearly required to move the discipline toward improved practices. PMID:17002771
Inferential Conditions in the Statistical Detection of Measurement Bias.
ERIC Educational Resources Information Center
Millsap, Roger E.; Meredith, William
1992-01-01
Inferential conditions in the statistical detection of measurement bias are discussed in the contexts of differential item functioning and predictive bias in educational and employment settings. It is concluded that bias measures that rely strictly on observed measures are not generally diagnostic of measurement bias or lack of bias. (SLD)
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 methods for detecting periodic fragments in DNA sequence data
2011-01-01
Background Period 10 dinucleotides are structurally and functionally validated factors that influence the ability of DNA to form nucleosomes, histone core octamers. Robust identification of periodic signals in DNA sequences is therefore required to understand nucleosome organisation in genomes. While various techniques for identifying periodic components in genomic sequences have been proposed or adopted, the requirements for such techniques have not been considered in detail and confirmatory testing for a priori specified periods has not been developed. Results We compared the estimation accuracy and suitability for confirmatory testing of autocorrelation, discrete Fourier transform (DFT), integer period discrete Fourier transform (IPDFT) and a previously proposed Hybrid measure. A number of different statistical significance procedures were evaluated but a blockwise bootstrap proved superior. When applied to synthetic data whose period-10 signal had been eroded, or for which the signal was approximately period-10, the Hybrid technique exhibited superior properties during exploratory period estimation. In contrast, confirmatory testing using the blockwise bootstrap procedure identified IPDFT as having the greatest statistical power. These properties were validated on yeast sequences defined from a ChIP-chip study where the Hybrid metric confirmed the expected dominance of period-10 in nucleosome associated DNA but IPDFT identified more significant occurrences of period-10. Application to the whole genomes of yeast and mouse identified ~ 21% and ~ 19% respectively of these genomes as spanned by period-10 nucleosome positioning sequences (NPS). Conclusions For estimating the dominant period, we find the Hybrid period estimation method empirically to be the most effective for both eroded and approximate periodicity. The blockwise bootstrap was found to be effective as a significance measure, performing particularly well in the problem of period detection in the
Effect size, confidence interval and statistical significance: a practical guide for biologists.
Nakagawa, Shinichi; Cuthill, Innes C
2007-11-01
Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non
Statistical analysis of spectral data for vegetation detection
NASA Astrophysics Data System (ADS)
Love, Rafael; Cathcart, J. Michael
2006-05-01
Identification and reduction of false alarms provide a critical component in the detection of landmines. Research at Georgia Tech over the past several years has focused on this problem through an examination of the signature characteristics of various background materials. These efforts seek to understand the physical basis and features of these signatures as an aid to the development of false target identification techniques. The investigation presented in this paper deal concentrated on the detection of foliage in long wave infrared imagery. Data collected by a hyperspectral long-wave infrared sensor provided the background signatures used in this study. These studies focused on an analysis of the statistical characteristics of both the intensity signature and derived emissivity data. Results from these studies indicate foliage signatures possess unique characteristics that can be exploited to enable detection of vegetation in LWIR images. This paper will present review of the approach and results of the statistical analysis.
NASA Astrophysics Data System (ADS)
Wilks, Daniel S.
1996-04-01
A simple approach to long-range forecasting of monthly or seasonal quantities is as the average of observations over some number of the most recent years. Finding this `optimal climate normal' (OCN) involves examining the relationships between the observed variable and averages of its values over the previous one to 30 years and selecting the averaging period yielding the best results. This procedure involves a multiplicity of comparisons, which will lead to misleadingly positive results for developments data. The statistical significance of these OCNs are assessed here using a resampling procedure, in which time series of U.S. Climate Division data are repeatedly shuffled to produce statistical distributions of forecast performance measures, under the null hypothesis that the OCNs exhibit no predictive skill. Substantial areas in the United States are found for which forecast performance appears to be significantly better than would occur by chance.Another complication in the assessment of the statistical significance of the OCNs derives from the spatial correlation exhibited by the data. Because of this correlation, instances of Type I errors (false rejections of local null hypotheses) will tend to occur with spatial coherency and accordingly have the potential to be confused with regions for which there may be real predictability. The `field significance' of the collections of local tests is also assessed here by simultaneously and coherently shuffling the time series for the Climate Divisions. Areas exhibiting significant local tests are large enough to conclude that seasonal OCN temperature forecasts exhibit significant skill over parts of the United States for all seasons except SON, OND, and NDJ, and that seasonal OCN precipitation forecasts are significantly skillful only in the fall. Statistical significance is weaker for monthly than for seasonal OCN temperature forecasts, and the monthly OCN precipitation forecasts do not exhibit significant predictive
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…
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.…
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…
Statistical Significance, Effect Size, and Replication: What Do the Journals Say?
ERIC Educational Resources Information Center
DeVaney, Thomas A.
2001-01-01
Studied the attitudes of representatives of journals in education, sociology, and psychology through an electronic survey completed by 194 journal representatives. Results suggest that the majority of journals do not have written policies concerning the reporting of results from statistical significance testing, and most indicated that statistical…
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.
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…
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...
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…
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 unless "corrected" effect…
Statistical tests for detecting movements in repeatedly measured geodetic networks
NASA Astrophysics Data System (ADS)
Niemeier, W.
1981-01-01
Geodetic networks with two or more measuring epochs can be found rather frequently, for example in connection with the investigation of recent crustal movements, in the field of monitoring problems in engineering surveying or in ordinary control networks. For these repeatedly measured networks the so-called congruency problem has to be solved, i.e. possible changes in the geometry of the net have to be found. In practice distortions of bench marks and an extension or densification of the net (differences in the 1st-order design) and/or changes in the measuring elements or techniques (differences in the 2nd-order design) can frequently be found between different epochs. In this paper a rigorous mathematical procedure is presented for this congruency analysis of multiple measured networks, taking into account these above-mentioned differences in the network design. As a first step, statistical tests are carried out to detect the epochs with departures from congruency. As a second step the individual points with significant movements within these critical epochs can be identified. A numerical example for the analysis of a monitoring network with 9 epochs is given.
Liu, Yang; Vijver, Martina G; Qiu, Hao; Baas, Jan; Peijnenburg, Willie J G M
2015-12-01
There is increasing attention from scientists and policy makers to the joint effects of multiple metals on organisms when present in a mixture. Using root elongation of lettuce (Lactuca sativa L.) as a toxicity endpoint, the combined effects of binary mixtures of Cu, Cd, and Ni were studied. The statistical MixTox model was used to search deviations from the reference models i.e. concentration addition (CA) and independent action (IA). The deviations were subsequently interpreted as 'interactions'. A comprehensive experiment was designed to test the reproducibility of the 'interactions'. The results showed that the toxicity of binary metal mixtures was equally well predicted by both reference models. We found statistically significant 'interactions' in four of the five total datasets. However, the patterns of 'interactions' were found to be inconsistent or even contradictory across the different independent experiments. It is recommended that a statistically significant 'interaction', must be treated with care and is not necessarily biologically relevant. Searching a statistically significant interaction can be the starting point for further measurements and modeling to advance the understanding of underlying mechanisms and non-additive interactions occurring inside the organisms. PMID:26188643
The Effects of Electrode Impedance on Data Quality and Statistical Significance in ERP Recordings
Kappenman, Emily S.; Luck, Steven J.
2010-01-01
To determine whether data quality is meaningfully reduced by high electrode impedance, EEG was recorded simultaneously from low- and high-impedance electrode sites during an oddball task. Low-frequency noise was found to be increased at high-impedance sites relative to low-impedance sites, especially when the recording environment was warm and humid. The increased noise at the high-impedance sites caused an increase in the number of trials needed to obtain statistical significance in analyses of P3 amplitude, but this could be partially mitigated by high-pass filtering and artifact rejection. High electrode impedance did not reduce statistical power for the N1 wave unless the recording environment was warm and humid. Thus, high electrode impedance may increase noise and decrease statistical power under some conditions, but these effects can be reduced by using a cool and dry recording environment and appropriate signal processing methods. PMID:20374541
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.
A statistical modeling approach for detecting generalized synchronization
Schumacher, Johannes; Haslinger, Robert; Pipa, Gordon
2012-01-01
Detecting nonlinear correlations between time series presents a hard problem for data analysis. We present a generative statistical modeling method for detecting nonlinear generalized synchronization. Truncated Volterra series are used to approximate functional interactions. The Volterra kernels are modeled as linear combinations of basis splines, whose coefficients are estimated via l1 and l2 regularized maximum likelihood regression. The regularization manages the high number of kernel coefficients and allows feature selection strategies yielding sparse models. The method's performance is evaluated on different coupled chaotic systems in various synchronization regimes and analytical results for detecting m:n phase synchrony are presented. Experimental applicability is demonstrated by detecting nonlinear interactions between neuronal local field potentials recorded in different parts of macaque visual cortex. PMID:23004851
A Generative Statistical Algorithm for Automatic Detection of Complex Postures
Amit, Yali; Biron, David
2015-01-01
This paper presents a method for automated detection of complex (non-self-avoiding) postures of the nematode Caenorhabditis elegans and its application to analyses of locomotion defects. Our approach is based on progressively detailed statistical models that enable detection of the head and the body even in cases of severe coilers, where data from traditional trackers is limited. We restrict the input available to the algorithm to a single digitized frame, such that manual initialization is not required and the detection problem becomes embarrassingly parallel. Consequently, the proposed algorithm does not propagate detection errors and naturally integrates in a “big data” workflow used for large-scale analyses. Using this framework, we analyzed the dynamics of postures and locomotion of wild-type animals and mutants that exhibit severe coiling phenotypes. Our approach can readily be extended to additional automated tracking tasks such as tracking pairs of animals (e.g., for mating assays) or different species. PMID:26439258
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.
Detection of a diffusive cloak via second-order statistics
NASA Astrophysics Data System (ADS)
Koirala, Milan; Yamilov, Alexey
2016-08-01
We propose a scheme to detect the diffusive cloak proposed by Schittny et al [Science 345, 427 (2014)]. We exploit the fact that diffusion of light is an approximation that disregards wave interference. The long-range contribution to intensity correlation is sensitive to locations of paths crossings and the interference inside the medium, allowing one to detect the size and position, including the depth, of the diffusive cloak. Our results also suggest that it is possible to separately manipulate the first- and the second-order statistics of wave propagation in turbid media.
Detection of a diffusive cloak via second-order statistics.
Koirala, Milan; Yamilov, Alexey
2016-08-15
We propose a scheme to detect the diffusive cloak proposed by Schittny et al. [Science345, 427 (2014).SCIEAS0036-807510.1126/science.1254524]. We exploit the fact that diffusion of light is an approximation that disregards wave interference. The long-range contribution to intensity correlation is sensitive to the locations of path crossings and the interference inside the medium, allowing one to detect the size and position, including the depth, of the diffusive cloak. Our results also suggest that it is possible to separately manipulate the first- and the second-order statistics of wave propagation in turbid media. PMID:27519108
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.
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 . Graphical Abstract ᅟ. PMID:26510657
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.
Pregnancy-associated breast cancer: significance of early detection.
Ulery, Maryann; Carter, Linnette; McFarlin, Barbara L; Giurgescu, Carmen
2009-01-01
Pregnancy-associated breast cancer (PABC) is defined as cancer of the breast diagnosed during pregnancy and up to 1 year postpartum. Delays in diagnosis are frequently associated with increased morbidity and mortality. The aim of this article is to determine the significance of early detection of PABC and to alert health care providers to include PABC in the differential diagnosis when evaluating a breast mass in the perinatal period. This integrative literature review evaluated 15 research studies by using the hypothetical deductive model of clinical reasoning to determine factors related to diagnosis of PABC. As women delay childbearing, the incidence of PABC increases with age. In the reviewed studies, breast cancer was diagnosed with greater frequency in the postpartum period than during any trimester in pregnancy. Delay in diagnosis is complicated by axillary lymph node metastasis, high-grade tumors at diagnosis, and poor outcomes. Early detection is a significant predictor of improved outcomes. Diagnostic modalities such as ultrasound, mammography, and biopsy can be safely used for diagnostic purposes in the evaluation of potential cases of PABC during pregnancy. PMID:19720336
Mass spectrometry-based protein identification with accurate statistical significance assignment
Alves, Gelio; Yu, Yi-Kuo
2015-01-01
Motivation: Assigning statistical significance accurately has become increasingly important as metadata of many types, often assembled in hierarchies, are constructed and combined for further biological analyses. Statistical inaccuracy of metadata at any level may propagate to downstream analyses, undermining the validity of scientific conclusions thus drawn. From the perspective of mass spectrometry-based proteomics, even though accurate statistics for peptide identification can now be achieved, accurate protein level statistics remain challenging. Results: We have constructed a protein ID method that combines peptide evidences of a candidate protein based on a rigorous formula derived earlier; in this formula the database P-value of every peptide is weighted, prior to the final combination, according to the number of proteins it maps to. We have also shown that this protein ID method provides accurate protein level E-value, eliminating the need of using empirical post-processing methods for type-I error control. Using a known protein mixture, we find that this protein ID method, when combined with the Sorić formula, yields accurate values for the proportion of false discoveries. In terms of retrieval efficacy, the results from our method are comparable with other methods tested. Availability and implementation: The source code, implemented in C++ on a linux system, is available for download at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbp/qmbp_ms/RAId/RAId_Linux_64Bit. Contact: yyu@ncbi.nlm.nih.gov Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25362092
Rudd, James; Moore, Jason H.; Urbanowicz, Ryan J.
2013-01-01
Permutation-based statistics for evaluating the significance of class prediction, predictive attributes, and patterns of association have only appeared within the learning classifier system (LCS) literature since 2012. While still not widely utilized by the LCS research community, formal evaluations of test statistic confidence are imperative to large and complex real world applications such as genetic epidemiology where it is standard practice to quantify the likelihood that a seemingly meaningful statistic could have been obtained purely by chance. LCS algorithms are relatively computationally expensive on their own. The compounding requirements for generating permutation-based statistics may be a limiting factor for some researchers interested in applying LCS algorithms to real world problems. Technology has made LCS parallelization strategies more accessible and thus more popular in recent years. In the present study we examine the benefits of externally parallelizing a series of independent LCS runs such that permutation testing with cross validation becomes more feasible to complete on a single multi-core workstation. We test our python implementation of this strategy in the context of a simulated complex genetic epidemiological data mining problem. Our evaluations indicate that as long as the number of concurrent processes does not exceed the number of CPU cores, the speedup achieved is approximately linear. PMID:24358057
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. PMID:25388545
Statistical method for determining and comparing limits of detection of bioassays.
Holstein, Carly A; Griffin, Maryclare; Hong, Jing; Sampson, Paul D
2015-10-01
The current bioassay development literature lacks the use of statistically robust methods for calculating the limit of detection of a given assay. Instead, researchers often employ simple methods that provide a rough estimate of the limit of detection, often without a measure of the confidence in the estimate. This scarcity of robust methods is likely due to a realistic preference for simple and accessible methods and to a lack of such methods that have reduced the concepts of limit of detection theory to practice for the specific application of bioassays. Here, we have developed a method for determining limits of detection for bioassays that is statistically robust and reduced to practice in a clear and accessible manner geared at researchers, not statisticians. This method utilizes a four-parameter logistic curve fit to translate signal intensity to analyte concentration, which is a curve that is commonly employed in quantitative bioassays. This method generates a 95% confidence interval of the limit of detection estimate to provide a measure of uncertainty and a means by which to compare the analytical sensitivities of different assays statistically. We have demonstrated this method using real data from the development of a paper-based influenza assay in our laboratory to illustrate the steps and features of the method. Using this method, assay developers can calculate statistically valid limits of detection and compare these values for different assays to determine when a change to the assay design results in a statistically significant improvement in analytical sensitivity. PMID:26376354
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.
Statistical detection of nanoparticles in cells by darkfield microscopy.
Gnerucci, Alessio; Romano, Giovanni; Ratto, Fulvio; Centi, Sonia; Baccini, Michela; Santosuosso, Ugo; Pini, Roberto; Fusi, Franco
2016-07-01
In the fields of nanomedicine, biophotonics and radiation therapy, nanoparticle (NP) detection in cell models often represents a fundamental step for many in vivo studies. One common question is whether NPs have or have not interacted with cells. In this context, we propose an imaging based technique to detect the presence of NPs in eukaryotic cells. Darkfield images of cell cultures at low magnification (10×) are acquired in different spectral ranges and recombined so as to enhance the contrast due to the presence of NPs. Image analysis is applied to extract cell-based parameters (i.e. mean intensity), which are further analyzed by statistical tests (Student's t-test, permutation test) in order to obtain a robust detection method. By means of a statistical sample size analysis, the sensitivity of the whole methodology is quantified in terms of the minimum cell number that is needed to identify the presence of NPs. The method is presented in the case of HeLa cells incubated with gold nanorods labeled with anti-CA125 antibodies, which exploits the overexpression of CA125 in ovarian cancers. Control cases are considered as well, including PEG-coated NPs and HeLa cells without NPs. PMID:27381231
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
2016-04-01
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).
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
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/. PMID:25337457
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
Statistical damage detection method for frame structures using a confidence interval
NASA Astrophysics Data System (ADS)
Li, Weiming; Zhu, Hongping; Luo, Hanbin; Xia, Yong
2010-03-01
A novel damage detection method is applied to a 3-story frame structure, to obtain statistical quantification control criterion of the existence, location and identification of damage. The mean, standard deviation, and exponentially weighted moving average (EWMA) are applied to detect damage information according to statistical process control (SPC) theory. It is concluded that the detection is insignificant with the mean and EWMA because the structural response is not independent and is not a normal distribution. On the other hand, the damage information is detected well with the standard deviation because the influence of the data distribution is not pronounced with this parameter. A suitable moderate confidence level is explored for more significant damage location and quantification detection, and the impact of noise is investigated to illustrate the robustness of the method.
FloatBoost learning and statistical face detection.
Li, Stan Z; Zhang, ZhenQiu
2004-09-01
A novel learning procedure, called FloatBoost, is proposed for learning a boosted classifier for achieving the minimum error rate. FloatBoost learning uses a backtrack mechanism after each iteration of AdaBoost learning to minimize the error rate directly, rather than minimizing an exponential function of the margin as in the traditional AdaBoost algorithms. A second contribution of the paper is a novel statistical model for learning best weak classifiers using a stagewise approximation of the posterior probability. These novel techniques lead to a classifier which requires fewer weak classifiers than AdaBoost yet achieves lower error rates in both training and testing, as demonstrated by extensive experiments. Applied to face detection, the FloatBoost learning method, together with a proposed detector pyramid architecture, leads to the first real-time multiview face detection system reported. PMID:15742888
Statistics over features for internal carotid arterial disorders detection.
Ubeyli, Elif Derya
2008-03-01
The objective of the present study is to extract the representative features of the internal carotid arterial (ICA) Doppler ultrasound signals and to present the accurate classification model. This paper presented the usage of statistics over the set of the extracted features (Lyapunov exponents and the power levels of the power spectral density estimates obtained by the eigenvector methods) in order to reduce the dimensionality of the extracted feature vectors. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Mixture of experts (ME) and modified mixture of experts (MME) architectures were formulated and used as basis for detection of arterial disorders. Three types of ICA Doppler signals (Doppler signals recorded from healthy subjects, subjects having stenosis, and subjects having occlusion) were classified. The classification results confirmed that the proposed ME and MME has potential in detecting the arterial disorders. PMID:18179791
Statistically robust detection of spontaneous, non-stereotypical neural signals.
Liu, Fan; Merwine, David K; Grzywacz, Norberto M
2006-06-15
Neural signals of interest are often temporally spontaneous and non-stereotypical in waveform. Detecting such signals is difficult, since one cannot use time-locking or simple template-matching techniques. We have sought a statistical method for automatically estimating the baseline in these conditions, and subsequently detecting the occurrence of neural signals. One could consider the signals as outliers in the distribution of neural activity and thus separate them from the baseline with median-based techniques. However, we found that baseline estimators that rely on the median are problematic. They introduce progressively greater estimation errors as the neural signal's duration, amplitude or frequency increases. Therefore, we tested several mode-based algorithms, taking advantage of the most probable state of the neural activity being the baseline. We found that certain mode-based algorithms perform baseline estimation well, with low susceptibility to changes in event duration, amplitude or frequency. Once the baseline is properly established, its median absolute deviation (MAD) can be determined. One can then use it to detect spontaneous signals robustly as outliers from the noise distribution. We also demonstrate how the choice of detection threshold in terms of MADs can be used to bias against false positives, without creating too many false negatives or vice versa. PMID:16430965
STATISTICAL METHOD FOR DETECTION OF A TREND IN ATMOSPHERIC SULFATE
Daily atmospheric concentrations of sulfate collected in northeastern Pennsylvania are regressed against meteorological factors, ozone, and time in order to determine if a significant trend in sulfate can be detected. he data used in this analysis were collected during the Sulfat...
NASA Astrophysics Data System (ADS)
Santer, B. D.; Wigley, T. M. L.; Boyle, J. S.; Gaffen, D. J.; Hnilo, J. J.; Nychka, D.; Parker, D. E.; Taylor, K. E.
2000-03-01
This paper examines trend uncertainties in layer-average free atmosphere temperatures arising from the use of different trend estimation methods. It also considers statistical issues that arise in assessing the significance of individual trends and of trend differences between data sets. Possible causes of these trends are not addressed. We use data from satellite and radiosonde measurements and from two reanalysis projects. To facilitate intercomparison, we compute from reanalyses and radiosonde data temperatures equivalent to those from the satellite-based Microwave Sounding Unit (MSU). We compare linear trends based on minimization of absolute deviations (LA) and minimization of squared deviations (LS). Differences are generally less than 0.05°C/decade over 1959-1996. Over 1979-1993, they exceed 0.10°C/decade for lower tropospheric time series and 0.15°C/decade for the lower stratosphere. Trend fitting by the LA method can degrade the lower-tropospheric trend agreement of 0.03°C/decade (over 1979-1996) previously reported for the MSU and radiosonde data. In assessing trend significance we employ two methods to account for temporal autocorrelation effects. With our preferred method, virtually none of the individual 1979-1993 trends in deep-layer temperatures are significantly different from zero. To examine trend differences between data sets we compute 95% confidence intervals for individual trends and show that these overlap for almost all data sets considered. Confidence intervals for lower-tropospheric trends encompass both zero and the model-projected trends due to anthropogenic effects. We also test the significance of a trend in d(t), the time series of differences between a pair of data sets. Use of d(t) removes variability common to both time series and facilitates identification of small trend differences. This more discerning test reveals that roughly 30% of the data set comparisons have significant differences in lower-tropospheric trends
Pectoral muscle detection in mammograms using local statistical features.
Liu, Li; Liu, Qian; Lu, Wei
2014-10-01
Mammography is a primary imaging method for breast cancer diagnosis. It is an important issue to accurately identify and separate pectoral muscles (PM) from breast tissues. Hough-transform-based methods are commonly adopted for PM detection. But their performances are susceptible when PM edges cannot be depicted by straight lines. In this study, we present a new pectoral muscle identification algorithm which utilizes statistical features of pixel responses. First, the Anderson-Darling goodness-of-fit test is used to extract a feature image by assuming non-Gaussianity for PM boundaries. Second, a global weighting scheme based on the location of PM was applied onto the feature image to suppress non-PM regions. From the weighted image, a preliminary set of pectoral muscles boundary components is detected via row-wise peak detection. An iterative procedure based on the edge continuity and orientation is used to determine the final PM boundary. Our results on a public mammogram database were assessed using four performance metrics: the false positive rate, the false negative rate, the Hausdorff distance, and the average distance. Compared to previous studies, our method demonstrates the state-of-art performance in terms of four measures. PMID:24482043
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.
Statistical modeling for particle impact noise detection testing
Prairie, R.R. ); Zimmer, W.J. )
1990-01-01
Particle Impact Noise Detection (PIND) testing is widely used to test electronic devices for the presence of conductive particles which can cause catastrophic failure. This paper develops a statistical model based on the rate of particles contaminating the part, the rate of particles induced by the test vibration, the escape rate, and the false alarm rate. Based on data from a large number of PIND tests for a canned transistor, the model is shown to fit the observed results closely. Knowledge of the parameters for which this fit is made is important in evaluating the effectiveness of the PIND test procedure and for developing background judgment about the performance of the PIND test. Furthermore, by varying the input parameters to the model, the resulting yield, failure rate and percent fallout can be examined and used to plan and implement PIND test programs.
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
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
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
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 "sample", such as the average score",…
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. PMID:17608782
Carr, J.R.; Roberts, K.P.
1989-02-01
Universal kriging is compared with ordinary kriging for estimation of earthquake ground motion. Ordinary kriging is based on a stationary random function model; universal kriging is based on a nonstationary random function model representing first-order drift. Accuracy of universal kriging is compared with that for ordinary kriging; cross-validation is used as the basis for comparison. Hypothesis testing on these results shows that accuracy obtained using universal kriging is not significantly different from accuracy obtained using ordinary kriging. Test based on normal distribution assumptions are applied to errors measured in the cross-validation procedure; t and F tests reveal no evidence to suggest universal and ordinary kriging are different for estimation of earthquake ground motion. Nonparametric hypothesis tests applied to these errors and jackknife statistics yield the same conclusion: universal and ordinary kriging are not significantly different for this application as determined by a cross-validation procedure. These results are based on application to four independent data sets (four different seismic events).
There's more than one way to conduct a replication study: Beyond statistical significance.
Anderson, Samantha F; Maxwell, Scott E
2016-03-01
As the field of psychology struggles to trust published findings, replication research has begun to become more of a priority to both scientists and journals. With this increasing emphasis placed on reproducibility, it is essential that replication studies be capable of advancing the field. However, we argue that many researchers have been only narrowly interpreting the meaning of replication, with studies being designed with a simple statistically significant or nonsignificant results framework in mind. Although this interpretation may be desirable in some cases, we develop a variety of additional "replication goals" that researchers could consider when planning studies. Even if researchers are aware of these goals, we show that they are rarely used in practice-as results are typically analyzed in a manner only appropriate to a simple significance test. We discuss each goal conceptually, explain appropriate analysis procedures, and provide 1 or more examples to illustrate these analyses in practice. We hope that these various goals will allow researchers to develop a more nuanced understanding of replication that can be flexible enough to answer the various questions that researchers might seek to understand. PMID:26214497
Mass detection on real and synthetic mammograms: human observer templates and local statistics
NASA Astrophysics Data System (ADS)
Castella, Cyril; Kinkel, Karen; Verdun, Francis R.; Eckstein, Miguel P.; Abbey, Craig K.; Bochud, François O.
2007-03-01
In this study we estimated human observer templates associated with the detection of a realistic mass signal superimposed on real and simulated but realistic synthetic mammographic backgrounds. Five trained naÃve observers participated in two-alternative forced-choice (2-AFC) experiments in which they were asked to detect a spherical mass signal extracted from a mammographic phantom. This signal was superimposed on statistically stationary clustered lumpy backgrounds (CLB) in one instance, and on nonstationary real mammographic backgrounds in another. Human observer linear templates were estimated using a genetic algorithm. An additional 2-AFC experiment was conducted with twin noise in order to determine which local statistical properties of the real backgrounds influenced the ability of the human observers to detect the signal. Results show that the estimated linear templates are not significantly different for stationary and nonstationary backgrounds. The estimated performance of the linear template compared with the human observer is within 5% in terms of percent correct (Pc) for the 2-AFC task. Detection efficiency is significantly higher on nonstationary real backgrounds than on globally stationary synthetic CLB. Using the twin-noise experiment and a new method to relate image features to observers trial to trial decisions, we found that the local statistical properties preventing or making the detection task easier were the standard deviation and three features derived from the neighborhood gray-tone difference matrix: coarseness, contrast and strength. These statistical features showed a dependency with the human performance only when they are estimated within an area sufficiently small around the searched location. These findings emphasize that nonstationary backgrounds need to be described by their local statistics and not by global ones like the noise Wiener spectrum.
Alexandrov, N. N.; Go, N.
1994-01-01
We have completed an exhaustive search for the common spatial arrangements of backbone fragments (SARFs) in nonhomologous proteins. This type of local structural similarity, incorporating short fragments of backbone atoms, arranged not necessarily in the same order along the polypeptide chain, appears to be important for protein function and stability. To estimate the statistical significance of the similarities, we have introduced a similarity score. We present several locally similar structures, with a large similarity score, which have not yet been reported. On the basis of the results of pairwise comparison, we have performed hierarchical cluster analysis of protein structures. Our analysis is not limited by comparison of single chains but also includes complex molecules consisting of several subunits. The SARFs with backbone fragments from different polypeptide chains provide a stable interaction between subunits in protein molecules. In many cases the active site of enzyme is located at the same position relative to the common SARFs, implying a function of the certain SARFs as a universal interface of the protein-substrate interaction. PMID:8069217
Statistical Analysis of Data with Non-Detectable Values
Frome, E.L.
2004-08-26
Environmental exposure measurements are, in general, positive and may be subject to left censoring, i.e. the measured value is less than a ''limit of detection''. In occupational monitoring, strategies for assessing workplace exposures typically focus on the mean exposure level or the probability that any measurement exceeds a limit. A basic problem of interest in environmental risk assessment is to determine if the mean concentration of an analyte is less than a prescribed action level. Parametric methods, used to determine acceptable levels of exposure, are often based on a two parameter lognormal distribution. The mean exposure level and/or an upper percentile (e.g. the 95th percentile) are used to characterize exposure levels, and upper confidence limits are needed to describe the uncertainty in these estimates. In certain situations it is of interest to estimate the probability of observing a future (or ''missed'') value of a lognormal variable. Statistical methods for random samples (without non-detects) from the lognormal distribution are well known for each of these situations. In this report, methods for estimating these quantities based on the maximum likelihood method for randomly left censored lognormal data are described and graphical methods are used to evaluate the lognormal assumption. If the lognormal model is in doubt and an alternative distribution for the exposure profile of a similar exposure group is not available, then nonparametric methods for left censored data are used. The mean exposure level, along with the upper confidence limit, is obtained using the product limit estimate, and the upper confidence limit on the 95th percentile (i.e. the upper tolerance limit) is obtained using a nonparametric approach. All of these methods are well known but computational complexity has limited their use in routine data analysis with left censored data. The recent development of the R environment for statistical data analysis and graphics has greatly
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.
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.
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
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.
Detection of Significant Groups in Hierarchical Clustering by Resampling
Sebastiani, Paola; Perls, Thomas T.
2016-01-01
Hierarchical clustering is a simple and reproducible technique to rearrange data of multiple variables and sample units and visualize possible groups in the data. Despite the name, hierarchical clustering does not provide clusters automatically, and “tree-cutting” procedures are often used to identify subgroups in the data by cutting the dendrogram that represents the similarities among groups used in the agglomerative procedure. We introduce a resampling-based technique that can be used to identify cut-points of a dendrogram with a significance level based on a reference distribution for the heights of the branch points. The evaluation on synthetic data shows that the technique is robust in a variety of situations. An example with real biomarker data from the Long Life Family Study shows the usefulness of the method. PMID:27551289
Detection of Significant Groups in Hierarchical Clustering by Resampling.
Sebastiani, Paola; Perls, Thomas T
2016-01-01
Hierarchical clustering is a simple and reproducible technique to rearrange data of multiple variables and sample units and visualize possible groups in the data. Despite the name, hierarchical clustering does not provide clusters automatically, and "tree-cutting" procedures are often used to identify subgroups in the data by cutting the dendrogram that represents the similarities among groups used in the agglomerative procedure. We introduce a resampling-based technique that can be used to identify cut-points of a dendrogram with a significance level based on a reference distribution for the heights of the branch points. The evaluation on synthetic data shows that the technique is robust in a variety of situations. An example with real biomarker data from the Long Life Family Study shows the usefulness of the method. PMID:27551289
Detection of significant pathways in osteoporosis based on graph clustering.
Xiao, Haijun; Shan, Liancheng; Zhu, Haiming; Xue, Feng
2012-12-01
Osteoporosis is the most common and serious skeletal disorder among the elderly, characterized by a low bone mineral density (BMD). Low bone mass in the elderly is highly dependent on their peak bone mass (PBM) as young adults. Circulating monocytes serve as early progenitors of osteoclasts and produce significant molecules for bone metabolism. An improved understanding of the biology and genetics of osteoclast differentiation at the pathway level is likely to be beneficial for the development of novel targeted approaches for osteoporosis. The objective of this study was to explore gene expression profiles comprehensively by grouping individual differentially expressed genes (DEGs) into gene sets and pathways using the graph clustering approach and Gene Ontology (GO) term enrichment analysis. The results indicated that the DEGs between high and low PBM samples were grouped into nine gene sets. The genes in clusters 1 and 8 (including GBP1, STAT1, CXCL10 and EIF2AK2) may be associated with osteoclast differentiation by the immune system response. The genes in clusters 2, 7 and 9 (including SOCS3, SOD2, ATF3, ADM EGR2 and BCL2A1) may be associated with osteoclast differentiation by responses to various stimuli. This study provides a number of candidate genes that warrant further investigation, including DDX60, HERC5, RSAD2, SIGLEC1, CMPK2, MX1, SEPING1, EPSTI1, C9orf72, PHLDA2, PFKFB3, PLEKHG2, ANKRD28, IL1RN and RNF19B. PMID:22992777
A Non-Parametric Surrogate-based Test of Significance for T-Wave Alternans Detection
Nemati, Shamim; Abdala, Omar; Bazán, Violeta; Yim-Yeh, Susie; Malhotra, Atul; Clifford, Gari
2010-01-01
We present a non-parametric adaptive surrogate test that allows for the differentiation of statistically significant T-Wave Alternans (TWA) from alternating patterns that can be solely explained by the statistics of noise. The proposed test is based on estimating the distribution of noise induced alternating patterns in a beat sequence from a set of surrogate data derived from repeated reshuffling of the original beat sequence. Thus, in assessing the significance of the observed alternating patterns in the data no assumptions are made about the underlying noise distribution. In addition, since the distribution of noise-induced alternans magnitudes is calculated separately for each sequence of beats within the analysis window, the method is robust to data non-stationarities in both noise and TWA. The proposed surrogate method for rejecting noise was compared to the standard noise rejection methods used with the Spectral Method (SM) and the Modified Moving Average (MMA) techniques. Using a previously described realistic multi-lead model of TWA, and real physiological noise, we demonstrate the proposed approach reduces false TWA detections, while maintaining a lower missed TWA detection compared with all the other methods tested. A simple averaging-based TWA estimation algorithm was coupled with the surrogate significance testing and was evaluated on three public databases; the Normal Sinus Rhythm Database (NRSDB), the Chronic Heart Failure Database (CHFDB) and the Sudden Cardiac Death Database (SCDDB). Differences in TWA amplitudes between each database were evaluated at matched heart rate (HR) intervals from 40 to 120 beats per minute (BPM). Using the two-sample Kolmogorov-Smirnov test, we found that significant differences in TWA levels exist between each patient group at all decades of heart rates. The most marked difference was generally found at higher heart rates, and the new technique resulted in a larger margin of separability between patient populations than
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
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
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.
Badr, Lina Kurdahi
2009-01-01
By adopting more appropriate statistical methods to appraise data from a previously published randomized controlled trial (RCT), we evaluated the statistical and clinical significance of an intervention on the 18 month neurodevelopmental outcome of infants with suspected brain injury. The intervention group (n =32) received extensive, individualized cognitive/sensorimotor stimulation by public health nurses (PHNs) while the control group (n = 30) received standard follow-up care. At 18 months 43 infants remained in the study (22 = intervention, 21 = control). The results indicate that there was a significant statistical change within groups and a clinical significance whereby more infants in the intervention group improved in mental, motor and neurological functioning at 18 months compared to the control group. The benefits of looking at clinical significance from a meaningful aspect for practitioners are emphasized. PMID:19276403
NASA Astrophysics Data System (ADS)
Alves, Gelio
After the sequencing of many complete genomes, we are in a post-genomic era in which the most important task has changed from gathering genetic information to organizing the mass of data as well as under standing how components interact with each other. The former is usually undertaking using bioinformatics methods, while the latter task is generally termed proteomics. Success in both parts demands correct statistical significance assignments for results found. In my dissertation. I study two concrete examples: global sequence alignment statistics and peptide sequencing/identification using mass spectrometry. High-performance liquid chromatography coupled to a mass spectrometer (HPLC/MS/MS), enabling peptide identifications and thus protein identifications, has become the tool of choice in large-scale proteomics experiments. Peptide identification is usually done by database searches methods. The lack of robust statistical significance assignment among current methods motivated the development of a novel de novo algorithm, RAId, whose score statistics then provide statistical significance for high scoring peptides found in our custom, enzyme-digested peptide library. The ease of incorporating post-translation modifications is another important feature of RAId. To organize the massive protein/DNA data accumulated, biologists often cluster proteins according to their similarity via tools such as sequence alignment. Homologous proteins share similar domains. To assess the similarity of two domains usually requires alignment from head to toe, ie. a global alignment. A good alignment score statistics with an appropriate null model enable us to distinguish the biologically meaningful similarity from chance similarity. There has been much progress in local alignment statistics, which characterize score statistics when alignments tend to appear as a short segment of the whole sequence. For global alignment, which is useful in domain alignment, there is still much room for
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.
Determination of significant variables in compound wear using a statistical model
Pumwa, J.; Griffin, R.B.; Smith, C.M.
1997-07-01
This paper will report on a study of dry compound wear of normalized 1018 steel on A2 tool steel. Compound wear is a combination of sliding and impact wear. The compound wear machine consisted of an A2 tool steel wear plate that could be rotated, and an indentor head that held the 1018 carbon steel wear pins. The variables in the system were the rpm of the wear plate, the force with which the indentor strikes the wear plate, and the frequency with which the indentor strikes the wear plate. A statistically designed experiment was used to analyze the effects of the different variables on the compound wear process. The model developed showed that wear could be reasonably well predicted using a defined variable that was called the workrate. The paper will discuss the results of the modeling and the metallurgical changes that occurred at the indentor interface, with the wear plate, during the wear process.
Detection of low contrasted membranes in electron microscope images: statistical contour validation
NASA Astrophysics Data System (ADS)
Karathanou, A.; Buessler, J.-L.; Kihl, H.; Urban, J.-P.
2009-02-01
Images of biological objects in transmission electron microscopy (TEM) are particularly noisy and low contrasted, making their processing a challenging task to accomplish. During these last years, several software tools were conceived for the automatic or semi-automatic acquisition of TEM images. However, tools for the automatic analysis of these images are still rare. Our study concerns in particular the automatic identification of artificial membranes at medium magnification for the control of an electron microscope. We recently proposed a segmentation strategy in order to detect the regions of interest. In this paper, we introduce a complementary technique to improve contour recognition by a statistical validation algorithm. Our technique explores the profile transition between two objects. A transition is validated if there exists a gradient orthogonal to the contour that is statistically significant.
Statistical behavior of ten million experimental detection limits
NASA Astrophysics Data System (ADS)
Voigtman, Edward; Abraham, Kevin T.
2011-02-01
Using a lab-constructed laser-excited fluorimeter, together with bootstrapping methodology, the authors have generated many millions of experimental linear calibration curves for the detection of rhodamine 6G tetrafluoroborate in ethanol solutions. The detection limits computed from them are in excellent agreement with both previously published theory and with comprehensive Monte Carlo computer simulations. Currie decision levels and Currie detection limits, each in the theoretical, chemical content domain, were found to be simply scaled reciprocals of the non-centrality parameter of the non-central t distribution that characterizes univariate linear calibration curves that have homoscedastic, additive Gaussian white noise. Accurate and precise estimates of the theoretical, content domain Currie detection limit for the experimental system, with 5% (each) probabilities of false positives and false negatives, are presented.
Chen, Shuo; Kang, Jian; Xing, Yishi; Wang, Guoqing
2015-12-01
Group-level functional connectivity analyses often aim to detect the altered connectivity patterns between subgroups with different clinical or psychological experimental conditions, for example, comparing cases and healthy controls. We present a new statistical method to detect differentially expressed connectivity networks with significantly improved power and lower false-positive rates. The goal of our method was to capture most differentially expressed connections within networks of constrained numbers of brain regions (by the rule of parsimony). By virtue of parsimony, the false-positive individual connectivity edges within a network are effectively reduced, whereas the informative (differentially expressed) edges are allowed to borrow strength from each other to increase the overall power of the network. We develop a test statistic for each network in light of combinatorics graph theory, and provide p-values for the networks (in the weak sense) by using permutation test with multiple-testing adjustment. We validate and compare this new approach with existing methods, including false discovery rate and network-based statistic, via simulation studies and a resting-state functional magnetic resonance imaging case-control study. The results indicate that our method can identify differentially expressed connectivity networks, whereas existing methods are limited. PMID:26416398
Chen, Shuo; Kang, Jian; Xing, Yishi; Wang, Guoqing
2016-01-01
Group-level functional connectivity analyses often aim to detect the altered connectivity patterns between subgroups with different clinical or psychological experimental conditions, for example, comparing cases and healthy controls. We present a new statistical method to detect differentially expressed connectivity networks with significantly improved power and lower false-positive rates. The goal of our method was to capture most differentially expressed connections within networks of constrained numbers of brain regions (by the rule of parsimony). By virtue of parsimony, the false-positive individual connectivity edges within a network are effectively reduced, whereas the informative (differentially expressed) edges are allowed to borrow strength from each other to increase the overall power of the network. We develop a test statistic for each network in light of combinatorics graph theory, and provide p-values for the networks (in the weak sense) by using permutation test with multiple-testing adjustment. We validate and compare this new approach with existing methods, including false discovery rate and network-based statistic, via simulation studies and a resting-state functional magnetic resonance imaging case–control study. The results indicate that our method can identify differentially expressed connectivity networks, whereas existing methods are limited. PMID:26416398
ERIC Educational Resources Information Center
Jacobson, Neil S.; Truax, Paula
1991-01-01
Describes ways of operationalizing clinically significant change, defined as extent to which therapy moves someone outside range of dysfunctional population or within range of functional population. Uses examples to show how clients can be categorized on basis of this definition. Proposes reliable change index (RC) to determine whether magnitude…
ERIC Educational Resources Information Center
Hojat, Mohammadreza; Xu, Gang
2004-01-01
Effect Sizes (ES) are an increasingly important index used to quantify the degree of practical significance of study results. This paper gives an introduction to the computation and interpretation of effect sizes from the perspective of the consumer of the research literature. The key points made are: (1) "ES" is a useful indicator of the…
ERIC Educational Resources Information Center
Onwuegbuzie, Anthony J.; Roberts, J. Kyle; Daniel, Larry G.
2005-01-01
In this article, the authors (a) illustrate how displaying disattenuated correlation coefficients alongside their unadjusted counterparts will allow researchers to assess the impact of unreliability on bivariate relationships and (b) demonstrate how a proposed new "what if reliability" analysis can complement null hypothesis significance tests of…
NASA Technical Reports Server (NTRS)
Staubert, R.
1985-01-01
Methods for calculating the statistical significance of excess events and the interpretation of the formally derived values are discussed. It is argued that a simple formula for a conservative estimate should generally be used in order to provide a common understanding of quoted values.
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.
Detection and analysis of statistical differences in anatomical shape.
Golland, Polina; Grimson, W Eric L; Shenton, Martha E; Kikinis, Ron
2005-02-01
We present a computational framework for image-based analysis and interpretation of statistical differences in anatomical shape between populations. Applications of such analysis include understanding developmental and anatomical aspects of disorders when comparing patients versus normal controls, studying morphological changes caused by aging, or even differences in normal anatomy, for example, differences between genders. Once a quantitative description of organ shape is extracted from input images, the problem of identifying differences between the two groups can be reduced to one of the classical questions in machine learning of constructing a classifier function for assigning new examples to one of the two groups while making as few misclassifications as possible. The resulting classifier must be interpreted in terms of shape differences between the two groups back in the image domain. We demonstrate a novel approach to such interpretation that allows us to argue about the identified shape differences in anatomically meaningful terms of organ deformation. Given a classifier function in the feature space, we derive a deformation that corresponds to the differences between the two classes while ignoring shape variability within each class. Based on this approach, we present a system for statistical shape analysis using distance transforms for shape representation and the support vector machines learning algorithm for the optimal classifier estimation and demonstrate it on artificially generated data sets, as well as real medical studies. PMID:15581813
NASA Technical Reports Server (NTRS)
Massey, J. L.
1976-01-01
The very low error probability obtained with long error-correcting codes results in a very small number of observed errors in simulation studies of practical size and renders the usual confidence interval techniques inapplicable to the observed error probability. A natural extension of the notion of a 'confidence interval' is made and applied to such determinations of error probability by simulation. An example is included to show the surprisingly great significance of as few as two decoding errors in a very large number of decoding trials.
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.
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.
Longitudinal change detection in diffusion MRI using multivariate statistical testing on tensors.
Grigis, Antoine; Noblet, Vincent; Heitz, Fabrice; Blanc, Frédéric; de Sèze, Jérome; Kremer, Stéphane; Rumbach, Lucien; Armspach, Jean-Paul
2012-05-01
This paper presents a longitudinal change detection framework for detecting relevant modifications in diffusion MRI, with application to neuromyelitis optica (NMO) and multiple sclerosis (MS). The core problem is to identify image regions that are significantly different between two scans. The proposed method is based on multivariate statistical testing which was initially introduced for tensor population comparison. We use this method in the context of longitudinal change detection by considering several strategies to build sets of tensors characterizing the variability of each voxel. These strategies make use of the variability existing in the diffusion weighted images (thanks to a bootstrap procedure), or in the spatial neighborhood of the considered voxel, or a combination of both. Results on synthetic evolutions and on real data are presented. Interestingly, experiments on NMO patients highlight the ability of the proposed approach to detect changes in the normal-appearing white matter (according to conventional MRI) that are related with physical status outcome. Experiments on MS patients highlight the ability of the proposed approach to detect changes in evolving and non-evolving lesions (according to conventional MRI). These findings might open promising prospects for the follow-up of NMO and MS pathologies. PMID:22387171
Lindmark, Anita; van Rompaye, Bart; Goetghebeur, Els; Glader, Eva-Lotta; Eriksson, Marie
2016-01-01
Background When profiling hospital performance, quality inicators are commonly evaluated through hospital-specific adjusted means with confidence intervals. When identifying deviations from a norm, large hospitals can have statistically significant results even for clinically irrelevant deviations while important deviations in small hospitals can remain undiscovered. We have used data from the Swedish Stroke Register (Riksstroke) to illustrate the properties of a benchmarking method that integrates considerations of both clinical relevance and level of statistical significance. Methods The performance measure used was case-mix adjusted risk of death or dependency in activities of daily living within 3 months after stroke. A hospital was labeled as having outlying performance if its case-mix adjusted risk exceeded a benchmark value with a specified statistical confidence level. The benchmark was expressed relative to the population risk and should reflect the clinically relevant deviation that is to be detected. A simulation study based on Riksstroke patient data from 2008–2009 was performed to investigate the effect of the choice of the statistical confidence level and benchmark value on the diagnostic properties of the method. Results Simulations were based on 18,309 patients in 76 hospitals. The widely used setting, comparing 95% confidence intervals to the national average, resulted in low sensitivity (0.252) and high specificity (0.991). There were large variations in sensitivity and specificity for different requirements of statistical confidence. Lowering statistical confidence improved sensitivity with a relatively smaller loss of specificity. Variations due to different benchmark values were smaller, especially for sensitivity. This allows the choice of a clinically relevant benchmark to be driven by clinical factors without major concerns about sufficiently reliable evidence. Conclusions The study emphasizes the importance of combining clinical relevance
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
2014-01-01
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
Tri-mean-based statistical differential gene expression detection.
Ji, Zhaohua; Wu, Chunguo; Wang, Yao; Guan, Renchu; Tu, Huawei; Wu, Xiaozhou; Liang, Yanchun
2012-01-01
Based on the assumption that only a subset of disease group has differential gene expression, traditional detection of differentially expressed genes is under the constraint that cancer genes are up- or down-regulated in all disease samples compared with normal samples. However, in 2005, Tomlins assumed and discussed the situation that only a subset of disease samples would be activated, which are often referred to as outliers. PMID:23155761
Statistical detection of the mid-Pleistocene transition
Maasch, K.A. )
1988-01-01
Statistical methods have been used to show quantitatively that the transition in mean and variance observed in delta O-18 records during the middle of the Pleistocene was abrupt. By applying these methods to all of the available records spanning the entire Pleistocene, it appears that this jump was global and primarily represents an increase in ice mass. At roughly the same time an abrupt decrease in sea surface temperature also occurred, indicative of sudden global cooling. This kind of evidence suggests a possible bifurcation of the climate system that must be accounted for in a complete explanation of the ice ages. Theoretical models including internal dynamics are capable of exhibiting this kind of rapid transition. 50 refs.
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
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)
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.
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.
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.
Detecting Maternal-Effect Loci by Statistical Cross-Fostering
Wolf, Jason; Cheverud, James M.
2012-01-01
Great progress has been made in understanding the genetic architecture of phenotypic variation, but it is almost entirely focused on how the genotype of an individual affects the phenotype of that same individual. However, in many species the genotype of the mother is a major determinant of the phenotype of her offspring. Therefore, a complete picture of genetic architecture must include these maternal genetic effects, but they can be difficult to identify because maternal and offspring genotypes are correlated and therefore, partially confounded. We present a conceptual framework that overcomes this challenge to separate direct and maternal effects in intact families through an analysis that we call “statistical cross-fostering.” Our approach combines genotype data from mothers and their offspring to remove the confounding effects of the offspring’s own genotype on measures of maternal genetic effects. We formalize our approach in an orthogonal model and apply this model to an experimental population of mice. We identify a set of six maternal genetic effect loci that explain a substantial portion of variation in body size at all ages. This variation would be missed in an approach focused solely on direct genetic effects, but is clearly a major component of genetic architecture. Our approach can easily be adapted to examine maternal effects in different systems, and because it does not require experimental manipulation, it provides a framework that can be used to understand the contribution of maternal genetic effects in both natural and experimental populations. PMID:22377636
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
Fast and accurate border detection in dermoscopy images using statistical region merging
NASA Astrophysics Data System (ADS)
Celebi, M. Emre; Kingravi, Hassan A.; Iyatomi, Hitoshi; Lee, JeongKyu; Aslandogan, Y. Alp; Van Stoecker, William; Moss, Randy; Malters, Joseph M.; Marghoob, Ashfaq A.
2007-03-01
As a result of advances in skin imaging technology and the development of suitable image processing techniques during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, since the accuracy of the subsequent steps crucially depends on it. In this paper, a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the Statistical Region Merging algorithm is presented. The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which a set of dermatologist-determined borders is used as the ground-truth. The proposed method is compared to six state-of-the-art automated methods (optimized histogram thresholding, orientation-sensitive fuzzy c-means, gradient vector flow snakes, dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method) and borders determined by a second dermatologist. The results demonstrate that the presented method achieves both fast and accurate border detection in dermoscopy images.
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.
Parameter-space correlations of the optimal statistic for continuous gravitational-wave detection
Pletsch, Holger J.
2008-11-15
The phase parameters of matched-filtering searches for continuous gravitational-wave signals are sky position, frequency, and frequency time-derivatives. The space of these parameters features strong global correlations in the optimal detection statistic. For observation times smaller than 1 yr, the orbital motion of the Earth leads to a family of global-correlation equations which describes the 'global maximum structure' of the detection statistic. The solution to each of these equations is a different hypersurface in parameter space. The expected detection statistic is maximal at the intersection of these hypersurfaces. The global maximum structure of the detection statistic from stationary instrumental-noise artifacts is also described by the global-correlation equations. This permits the construction of a veto method which excludes false candidate events.
Anomaly detection based on the statistics of hyperspectral imagery
NASA Astrophysics Data System (ADS)
Catterall, Stephen P.
2004-10-01
The purpose of this paper is to introduce a new anomaly detection algorithm for application to hyperspectral imaging (HSI) data. The algorithm uses characterisations of the joint (among wavebands) probability density function (pdf) of HSI data. Traditionally, the pdf has been assumed to be multivariate Gaussian or a mixture of multivariate Gaussians. Other distributions have been considered by previous authors, in particular Elliptically Contoured Distributions (ECDs). In this paper we focus on another distribution, which has only recently been defined and studied. This distribution has a more flexible and extensive set of parameters than the multivariate Gaussian does, yet the pdf takes on a relatively simple mathematical form. The result of all this is a model for the pdf of a hyperspectral image, consisting of a mixture of these distributions. Once a model for the pdf of a hyperspectral image has been obtained, it can be incorporated into an anomaly detector. The new anomaly detector is implemented and applied to some medium wave infra-red (MWIR) hyperspectral imagery. Comparison is made with a well-known anomaly detector, and it will be seen that the results are promising.
An Unsigned Mantel-Haenszel Statistic for Detecting Uniform and Nonuniform DIF.
ERIC Educational Resources Information Center
Kwak, Nohoon; And Others
This paper introduces a new method for detecting differential item functioning (DIF), the unsigned Mantel-Haenszel (UMH) statistic, and compares this method with two other chi-square methods, the Mantel-Haenszel (MH) and the absolute mean deviation (AMD) statistics, in terms of power and agreement between expected and actual false positive rates.…
A Statistical Analysis of Automated and Manually Detected Fires Using Environmental Satellites
NASA Astrophysics Data System (ADS)
Ruminski, M. G.; McNamara, D.
2003-12-01
The National Environmental Satellite and Data Information Service (NESDIS) of the National Oceanic and Atmospheric Administration (NOAA) has been producing an analysis of fires and smoke over the US since 1998. This product underwent significant enhancement in June 2002 with the introduction of the Hazard Mapping System (HMS), an interactive workstation based system that displays environmental satellite imagery (NOAA Geostationary Operational Environmental Satellite (GOES), NOAA Polar Operational Environmental Satellite (POES) and National Aeronautics and Space Administration (NASA) MODIS data) and fire detects from the automated algorithms for each of the satellite sensors. The focus of this presentation is to present statistics compiled on the fire detects since November 2002. The Automated Biomass Burning Algorithm (ABBA) detects fires using GOES East and GOES West imagery. The Fire Identification, Mapping and Monitoring Algorithm (FIMMA) utilizes NOAA POES 15/16/17 imagery and the MODIS algorithm uses imagery from the MODIS instrument on the Terra and Aqua spacecraft. The HMS allows satellite analysts to inspect and interrogate the automated fire detects and the input satellite imagery. The analyst can then delete those detects that are felt to be false alarms and/or add fire points that the automated algorithms have not selected. Statistics are compiled for the number of automated detects from each of the algorithms, the number of automated detects that are deleted and the number of fire points added by the analyst for the contiguous US and immediately adjacent areas of Mexico and Canada. There is no attempt to distinguish between wildfires and control or agricultural fires. A detailed explanation of the automated algorithms is beyond the scope of this presentation. However, interested readers can find a more thorough description by going to www.ssd.noaa.gov/PS/FIRE/hms.html and scrolling down to Individual Fire Layers. For the period November 2002 thru August
Fusion of Local Statistical Parameters for Buried Underwater Mine Detection in Sonar Imaging
NASA Astrophysics Data System (ADS)
Maussang, F.; Rombaut, M.; Chanussot, J.; Hétet, A.; Amate, M.
2008-12-01
Detection of buried underwater objects, and especially mines, is a current crucial strategic task. Images provided by sonar systems allowing to penetrate in the sea floor, such as the synthetic aperture sonars (SASs), are of great interest for the detection and classification of such objects. However, the signal-to-noise ratio is fairly low and advanced information processing is required for a correct and reliable detection of the echoes generated by the objects. The detection method proposed in this paper is based on a data-fusion architecture using the belief theory. The input data of this architecture are local statistical characteristics extracted from SAS data corresponding to the first-, second-, third-, and fourth-order statistical properties of the sonar images, respectively. The interest of these parameters is derived from a statistical model of the sonar data. Numerical criteria are also proposed to estimate the detection performances and to validate the method.
Ryan, Paul; Furlong, Heidi; Murphy, Conleth G; O'Sullivan, Finbarr; Walsh, Thomas N; Shanahan, Fergus; O'Sullivan, Gerald C
2015-08-01
We have previously reported that most patients with esophagogastric cancer (EGC) undergoing potentially curative resections have bone marrow micrometastases (BMM). We present 10-year outcome data of patients with EGC whose rib marrow was examined for micrometastases and correlate the findings with treatment and conventional pathologic tumor staging. A total of 88 patients with localized esophagogastric tumors had radical en-bloc esophagectomy, with 47 patients receiving neoadjuvant (5-fluorouracil/cisplatin based) chemoradiotherapy (CRT) and the remainder being treated with surgery alone. Rib marrow was examined for cytokeratin-18-positive cells. Standard demographic and pathologic features were recorded and patients were followed for a mean 10.04 years. Disease recurrences and all deaths in the follow-up period were recorded. No patients were lost to follow-up. 46 EGC-related and 10 non-EGC-related deaths occurred. Multivariate Cox analysis of interaction of neoadjuvant chemotherapy, nodal status, and BMM positivity showed that the contribution of BMM to disease-specific and overall survival is significant (P = 0.014). There is significant interaction with neoadjvant CRT (P < 0.005), and lymph node positivity (P < 0.001) but BMM positivity contributes to increase in risk of cancer-related death in patients treated with either CRT or surgery alone. Bone marrow micrometastases detected at the time of surgery for EGC is a long-term prognostic marker. Detection is a readily available, technically noncomplex test which offers a window on the metastatic process and a refinement of pathologic staging and is worthy of routine consideration. PMID:25914238
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. PMID:26304412
Recovery of gastrointestinal tract motility detection using Naive Bayesian and minimum statistics.
Ulusar, Umit D
2014-08-01
Loss of gastrointestinal motility is a significant medical setback for patients who experience abdominal surgery and contributes to the most common reason for prolonged hospital stays. Recent clinical studies suggest that initiating feeding early after abdominal surgery is beneficial. Early feeding is possible when the patients demonstrate bowel motility in the form of bowel sounds (BS). This work provides a data collection, processing and analysis methodology for detection of recovery of gastrointestinal track motility by observing BSs in auscultation recordings. The approach is suitable for real-time long-term continuous monitoring in clinical environments. The system was developed using a Naive Bayesian algorithm for pattern classification, and Minimum Statistics and spectral subtraction for noise attenuation. The solution was tested on 59h of recordings and 94.15% recognition accuracy was observed. PMID:24971526
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
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
NASA Astrophysics Data System (ADS)
Manolakis, D.; Jairam, L. G.; Zhang, D.; Rossacci, M.
2007-04-01
Remote detection of chemical vapors in the atmosphere has a wide range of civilian and military applications. In the past few years there has been significant interest in the detection of effluent plumes using hyperspectral imaging spectroscopy in the 8-13μm atmospheric window. A major obstacle in the full exploitation of this technology is the fact that everything in the infrared is a source of radiation. As a result, the emission from the gases of interest is always mixed with emission by the more abundant atmospheric constituents and by other objects in the sensor field of view. The radiance fluctuations in this background emission constitute an additional source of interference which is much stronger than the detector noise. In this paper we develop and evaluate parametric models for the statistical characterization of LWIR hyperspectral backgrounds. We consider models based on the theory of elliptically contoured distributions. Both models can handle heavy tails, which is a key stastical feature of hyperspectral imaging backgrounds. The paper provides a concise description of the underlying models, the algorithms used to estimate their parameters from the background spectral measurements, and the use of the developed models in the design and evaluation of chemical warfare agent detection algorithms.
Inferential statistics for transient signal detection in radio astronomy phased arrays
NASA Astrophysics Data System (ADS)
Schmid, Natalia A.; Prestage, Richard M.; Alkhweldi, Marwan
2015-05-01
In this paper we develop two statistical rules for the purpose of detecting pulsars and transients using signals from phased array feeds installed on a radio telescope in place of a traditional horn receiver. We assume a known response of the antenna arrays and known coupling among array elements. We briefly summarize a set of pre-processing steps applied to raw array data prior to signal detection and then derive two detection statistics assuming two models for the unknown radio source astronomical signal: (1) the signal is deterministic and (2) the signal is a random process. The performance of both detectors is analyzed using both real and simulated data.
Confounding factors in HGT detection: statistical error, coalescent effects, and multiple solutions.
Than, Cuong; Ruths, Derek; Innan, Hideki; Nakhleh, Luay
2007-05-01
Prokaryotic organisms share genetic material across species boundaries by means of a process known as horizontal gene transfer (HGT). This process has great significance for understanding prokaryotic genome diversification and unraveling their complexities. Phylogeny-based detection of HGT is one of the most commonly used methods for this task, and is based on the fundamental fact that HGT may cause gene trees to disagree with one another, as well as with the species phylogeny. Using these methods, we can compare gene and species trees, and infer a set of HGT events to reconcile the differences among these trees. In this paper, we address three factors that confound the detection of the true HGT events, including the donors and recipients of horizontally transferred genes. First, we study experimentally the effects of error in the estimated gene trees (statistical error) on the accuracy of inferred HGT events. Our results indicate that statistical error leads to overestimation of the number of HGT events, and that HGT detection methods should be designed with unresolved gene trees in mind. Second, we demonstrate, both theoretically and empirically, that based on topological comparison alone, the number of HGT scenarios that reconcile a pair of species/gene trees may be exponential. This number may be reduced when branch lengths in both trees are estimated correctly. This set of results implies that in the absence of additional biological information, and/or a biological model of how HGT occurs, multiple HGT scenarios must be sought, and efficient strategies for how to enumerate such solutions must be developed. Third, we address the issue of lineage sorting, how it confounds HGT detection, and how to incorporate it with HGT into a single stochastic framework that distinguishes between the two events by extending population genetics theories. This result is very important, particularly when analyzing closely related organisms, where coalescent effects may not be
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.
Garud, Nandita R; Rosenberg, Noah A
2015-06-01
Soft selective sweeps represent an important form of adaptation in which multiple haplotypes bearing adaptive alleles rise to high frequency. Most statistical methods for detecting selective sweeps from genetic polymorphism data, however, have focused on identifying hard selective sweeps in which a favored allele appears on a single haplotypic background; these methods might be underpowered to detect soft sweeps. Among exceptions is the set of haplotype homozygosity statistics introduced for the detection of soft sweeps by Garud et al. (2015). These statistics, examining frequencies of multiple haplotypes in relation to each other, include H12, a statistic designed to identify both hard and soft selective sweeps, and H2/H1, a statistic that conditional on high H12 values seeks to distinguish between hard and soft sweeps. A challenge in the use of H2/H1 is that its range depends on the associated value of H12, so that equal H2/H1 values might provide different levels of support for a soft sweep model at different values of H12. Here, we enhance the H12 and H2/H1 haplotype homozygosity statistics for selective sweep detection by deriving the upper bound on H2/H1 as a function of H12, thereby generating a statistic that normalizes H2/H1 to lie between 0 and 1. Through a reanalysis of resequencing data from inbred lines of Drosophila, we show that the enhanced statistic both strengthens interpretations obtained with the unnormalized statistic and leads to empirical insights that are less readily apparent without the normalization. PMID:25891325
Species identification of airborne molds and its significance for the detection of indoor pollution
Fradkin, A.; Tobin, R.S.; Tario, S.M.; Tucic-Porretta, M.; Malloch, D.
1987-01-01
The present study was undertaken to investigate species composition and prevalence of culturable particles of airborne fungi in 27 homes in Toronto, Canada. Its major objective is to examine the significance of species identification for the detection of indoor pollution.
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)
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
On the power for linkage detection using a test based on scan statistics.
Hernández, Sonia; Siegmund, David O; de Gunst, Mathisca
2005-04-01
We analyze some aspects of scan statistics, which have been proposed to help for the detection of weak signals in genetic linkage analysis. We derive approximate expressions for the power of a test based on moving averages of the identity by descent allele sharing proportions for pairs of relatives at several contiguous markers. We confirm these approximate formulae by simulation. The results show that when there is a single trait-locus on a chromosome, the test based on the scan statistic is slightly less powerful than that based on the customary allele sharing statistic. On the other hand, if two genes having a moderate effect on a trait lie close to each other on the same chromosome, scan statistics improve power to detect linkage. PMID:15772104
Quantile regression for the statistical analysis of immunological data with many non-detects
2012-01-01
Background Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical techniques currently available for the analysis of datasets with non-detects can only be used if a small percentage of the data are non-detects. Methods and results Quantile regression, a generalization of percentiles to regression models, models the median or higher percentiles and tolerates very high numbers of non-detects. We present a non-technical introduction and illustrate it with an implementation to real data from a clinical trial. We show that by using quantile regression, groups can be compared and that meaningful linear trends can be computed, even if more than half of the data consists of non-detects. Conclusion Quantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects. PMID:22769433
NASA Astrophysics Data System (ADS)
Lo, C. F.; Wu, K.; Whitehead, B. A.
1993-06-01
The statistical and neural networks methods have been applied to investigate the feasibility in detecting anomalies in turbopump vibration of SSME. The anomalies are detected based on the amplitude of peaks of fundamental and harmonic frequencies in the power spectral density. These data are reduced to the proper format from sensor data measured by strain gauges and accelerometers. Both methods are feasible to detect the vibration anomalies. The statistical method requires sufficient data points to establish a reasonable statistical distribution data bank. This method is applicable for on-line operation. The neural networks method also needs to have enough data basis to train the neural networks. The testing procedure can be utilized at any time so long as the characteristics of components remain unchanged.
NASA Technical Reports Server (NTRS)
Lo, C. F.; Wu, K.; Whitehead, B. A.
1993-01-01
The statistical and neural networks methods have been applied to investigate the feasibility in detecting anomalies in turbopump vibration of SSME. The anomalies are detected based on the amplitude of peaks of fundamental and harmonic frequencies in the power spectral density. These data are reduced to the proper format from sensor data measured by strain gauges and accelerometers. Both methods are feasible to detect the vibration anomalies. The statistical method requires sufficient data points to establish a reasonable statistical distribution data bank. This method is applicable for on-line operation. The neural networks method also needs to have enough data basis to train the neural networks. The testing procedure can be utilized at any time so long as the characteristics of components remain unchanged.
Wagner, R.F.; Insana, M.F.; Brown, D.G.
1987-05-01
Both radio-frequency (rf) and envelope-detected signal anlayses have lead to successful tissue discrimination in medical ultrasound. The extrapolation from tissue discrimination to a description of the tissue structure requires an analysis of the statistics of complex signals. To that end, first- and second-order statistics of complex random signals are reviewed, and an example is taken from rf signal analysis of the backscattered echoes from diffuse scatterers. In this case the scattering form factor of small scatterers can be easily separated from long-range structure and corrected for the transducer characteristics, thereby yielding an instrument-independent tissue signature. The statistics of the more economical envelope- and square-law-detected signals are derived next and found to be almost identical when normalized autocorrelation functions are used. Of the two nonlinear methods of detection, the square-law or intensity scheme gives rise to statistics that are more transparent to physical insight. Moreover, an analysis of the intensity-correlation structure indicates that the contributions to the total echo signal from the diffuse scatter and from the steady and variable components of coherent scatter can still be separated and used for tissue characterization. However, this anlaysis is not system independent. Finally, the statistical methods of this paper may be applied directly to envelope signals in nuclear-magnetic-resonance imaging because of the approximate equivalence of second-order statistics for magnitude and intensity.
Webb-Robertson, Bobbie-Jo M.; McCue, Lee Ann; Waters, Katrina M.; Matzke, Melissa M.; Jacobs, Jon M.; Metz, Thomas O.; Varnum, Susan M.; Pounds, Joel G.
2010-11-01
Liquid chromatography-mass spectrometry-based (LC-MS) proteomics uses peak intensities of proteolytic peptides to infer the differential abundance of peptides/proteins. However, substantial run-to-run variability in peptide intensities and observations (presence/absence) of peptides makes data analysis quite challenging. The missing abundance values in LC-MS proteomics data are difficult to address with traditional imputation-based approaches because the mechanisms by which data are missing are unknown a priori. Data can be missing due to random mechanisms such as experimental error, or non-random mechanisms such as a true biological effect. We present a statistical approach that uses a test of independence known as a G-test to test the null hypothesis of independence between the number of missing values and the experimental groups. We pair the G-test results evaluating independence of missing data (IMD) with a standard analysis of variance (ANOVA) that uses only means and variances computed from the observed data. Each peptide is therefore represented by two statistical confidence metrics, one for qualitative differential observation and one for quantitative differential intensity. We use two simulated and two real LC-MS datasets to demonstrate the robustness and sensitivity of the ANOVA-IMD approach for assigning confidence to peptides with significant differential abundance among experimental groups.
Statistical detection and modeling of the over-dispersion of winter storm occurrence
NASA Astrophysics Data System (ADS)
Raschke, M.
2015-08-01
In this communication, I improve the detection and modeling of the over-dispersion of winter storm occurrence. For this purpose, the generalized Poisson distribution and the Bayesian information criterion are introduced; the latter is used for statistical model selection. Moreover, I replace the frequently used dispersion statistics by an over-dispersion parameter which does not depend on the considered return period of storm events. These models and methods are applied in order to properly detect the over-dispersion in winter storm data for Germany, carrying out a joint estimation of the distribution models for different samples.
Ni, Weiping; Yan, Weidong; Bian, Hui; Wu, Junzheng
2014-01-01
A novel fast SAR image change detection method is presented in this paper. Based on a Bayesian approach, the prior information that speckles follow the Nakagami distribution is incorporated into the difference image (DI) generation process. The new DI performs much better than the familiar log ratio (LR) DI as well as the cumulant based Kullback-Leibler divergence (CKLD) DI. The statistical region merging (SRM) approach is first introduced to change detection context. A new clustering procedure with the region variance as the statistical inference variable is exhibited to tailor SAR image change detection purposes, with only two classes in the final map, the unchanged and changed classes. The most prominent advantages of the proposed modified SRM (MSRM) method are the ability to cope with noise corruption and the quick implementation. Experimental results show that the proposed method is superior in both the change detection accuracy and the operation efficiency. PMID:25258740
Gaonkar, Bilwaj; Davatzikos, Christos
2013-01-01
Multivariate pattern analysis (MVPA) methods such as support vector machines (SVMs) have been increasingly applied to fMRI and sMRI analyses, enabling the detection of distinctive imaging patterns. However, identifying brain regions that significantly contribute to the classification/group separation requires computationally expensive permutation testing. In this paper we show that the results of SVM-permutation testing can be analytically approximated. This approximation leads to more than a thousand fold speed up of the permutation testing procedure, thereby rendering it feasible to perform such tests on standard computers. The speed up achieved makes SVM based group difference analysis competitive with standard univariate group difference analysis methods. PMID:23583748
Ye, Sujuan; Wu, Yanying; Zhai, Xiaomo; Tang, Bo
2015-08-18
Simultaneous detection of cancer biomarkers holds great promise for the early diagnosis of different cancers. However, in the presence of high-concentration biomarkers, the signals of lower-expression biomarkers are overlapped. Existing techniques are not suitable for simultaneously detecting multiple biomarkers at concentrations with significantly different orders of magnitude. Here, we propose an asymmetric signal amplification method for simultaneously detecting multiple biomarkers with significantly different levels. Using the bifunctional probe, a linear amplification mode responds to high-concentration markers, and quadratic amplification mode responds to low-concentration markers. With the combined biobarcode probe and hybridization chain reaction (HCR) amplification method, the detection limits of microRNA (miRNA) and ATP via surface-enhanced Raman scattering (SERS) detection are 0.15 fM and 20 nM, respectively, with a breakthrough of detection concentration difference over 11 orders of magnitude. Furthermore, successful determination of miRNA and ATP in cancer cells supports the practicability of the assay. This methodology promises to open an exciting new avenue for the detection of various types of biomolecules. PMID:26218034
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.
Liu, Wei; Feng, Huanqing; Li, Chuanfu; Huang, Yufeng; Wu, Dehuang; Tong, Tong
2009-01-01
In this paper, we present a method that detects intracranial space-occupying lesions in two-dimensional (2D) brain high-resolution CT images. Use of statistical texture atlas technique localizes anatomy variation in the gray level distribution of brain images, and in turn, identifies the regions with lesions. The statistical texture atlas involves 147 HRCT slices of normal individuals and its construction is extremely time-consuming. To improve the performance of atlas construction, we have implemented the pixel-wise texture extraction procedure on Nvidia 8800GTX GPU with Compute Unified Device Architecture (CUDA) platform. Experimental results indicate that the extracted texture feature is distinctive and robust enough, and is suitable for detecting uniform and mixed density space-occupying lesions. In addition, a significant speedup against straight forward CPU version was achieved with CUDA. PMID:19963990
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
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.
NASA Astrophysics Data System (ADS)
Goovaerts, P.; Jacquez, G. M.; Marcus, A. W.
2004-12-01
Spatial data are periodically collected and processed to monitor, analyze and interpret developments in our changing environment. Remote sensing is a modern way of data collecting and has seen an enormous growth since launching of modern satellites and development of airborne sensors. In particular, the recent availability of high spatial resolution hyperspectral imagery (spatial resolution of less than 5 meters and including data collected over 64 or more bands of electromagnetic radiation for each pixel offers a great potential to significantly enhance environmental mapping and our ability to model spatial systems. High spatial resolution imagery contains a remarkable quantity of information that could be used to analyze spatial breaks (boundaries), areas of similarity (clusters), and spatial autocorrelation (associations) across the landscape. This paper addresses the specific issue of soil disturbance detection, which could indicate the presence of land mines or recent movements of troop and heavy equipment. A challenge presented by soil detection is to retain the measurement of fine-scale features (i.e. mineral soil changes, organic content changes, vegetation disturbance related changes, aspect changes) while still covering proportionally large spatial areas. An additional difficulty is that no ground data might be available for the calibration of spectral signatures, and little might be known about the size of patches of disturbed soils to be detected. This paper describes a new technique for automatic target detection which capitalizes on both spatial and across spectral bands correlation, does not require any a priori information on the target spectral signature but does not allow discrimination between targets. This approach involves successively a multivariate statistical analysis (principal component analysis) of all spectral bands, a geostatistical filtering of noise and regional background in the first principal components using factorial kriging, and
Statistical methods for detecting differentially abundant features in clinical metagenomic samples.
White, James Robert; Nagarajan, Niranjan; Pop, Mihai
2009-04-01
Numerous studies are currently underway to characterize the microbial communities inhabiting our world. These studies aim to dramatically expand our understanding of the microbial biosphere and, more importantly, hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora. An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them.We present a statistical method for comparing clinical metagenomic samples from two treatment populations on the basis of count data (e.g. as obtained through sequencing) to detect differentially abundant features. Our method, Metastats, employs the false discovery rate to improve specificity in high-complexity environments, and separately handles sparsely-sampled features using Fisher's exact test. Under a variety of simulations, we show that Metastats performs well compared to previously used methods, and significantly outperforms other methods for features with sparse counts. We demonstrate the utility of our method on several datasets including a 16S rRNA survey of obese and lean human gut microbiomes, COG functional profiles of infant and mature gut microbiomes, and bacterial and viral metabolic subsystem data inferred from random sequencing of 85 metagenomes. The application of our method to the obesity dataset reveals differences between obese and lean subjects not reported in the original study. For the COG and subsystem datasets, we provide the first statistically rigorous assessment of the differences between these populations. The methods described in this paper are the first to address clinical metagenomic datasets comprising samples from multiple subjects. Our methods are robust across datasets of varied complexity and sampling level. While designed for metagenomic applications, our software can also be applied
NASA Astrophysics Data System (ADS)
Choquet, Élodie; Pueyo, Laurent; Soummer, Rémi; Perrin, Marshall D.; Hagan, J. Brendan; Gofas-Salas, Elena; Rajan, Abhijith; Aguilar, Jonathan
2015-09-01
The ALICE program, for Archival Legacy Investigation of Circumstellar Environment, is currently conducting a virtual survey of about 400 stars, by re-analyzing the HST-NICMOS coronagraphic archive with advanced post-processing techniques. We present here the strategy that we adopted to identify detections and potential candidates for follow-up observations, and we give a preliminary overview of our detections. We present a statistical analysis conducted to evaluate the confidence level on these detection and the completeness of our candidate search.
Nakamura, Naoki; Tsunoda, Hiroko; Takahashi, Osamu; Kikuchi, Mari; Honda, Satoshi; Shikama, Naoto; Akahane, Keiko; Sekiguchi, Kenji
2012-11-01
Purpose: To determine the frequency and clinical significance of previously undetected incidental findings found on computed tomography (CT) simulation images for breast cancer patients. Methods and Materials: All CT simulation images were first interpreted prospectively by radiation oncologists and then double-checked by diagnostic radiologists. The official reports of CT simulation images for 881 consecutive postoperative breast cancer patients from 2009 to 2010 were retrospectively reviewed. Potentially important incidental findings (PIIFs) were defined as any previously undetected benign or malignancy-related findings requiring further medical follow-up or investigation. For all patients in whom a PIIF was detected, we reviewed the clinical records to determine the clinical significance of the PIIF. If the findings from the additional studies prompted by a PIIF required a change in management, the PIIF was also recorded as a clinically important incidental finding (CIIF). Results: There were a total of 57 (6%) PIIFs. The 57 patients in whom a PIIF was detected were followed for a median of 17 months (range, 3-26). Six cases of CIIFs (0.7% of total) were detected. Of the six CIIFs, three (50%) cases had not been noted by the radiation oncologist until the diagnostic radiologist detected the finding. On multivariate analysis, previous CT examination was an independent predictor for PIIF (p = 0.04). Patients who had not previously received chest CT examinations within 1 year had a statistically significantly higher risk of PIIF than those who had received CT examinations within 6 months (odds ratio, 3.54; 95% confidence interval, 1.32-9.50; p = 0.01). Conclusions: The rate of incidental findings prompting a change in management was low. However, radiation oncologists appear to have some difficulty in detecting incidental findings that require a change in management. Considering cost, it may be reasonable that routine interpretations are given to those who have not
Shamloo-Dashtpagerdi, Roohollah; Razi, Hooman; Aliakbari, Massumeh; Lindlöf, Angelica; Ebrahimi, Mahdi; Ebrahimie, Esmaeil
2015-01-01
Cis regulatory elements (CREs), located within promoter regions, play a significant role in the blueprint for transcriptional regulation of genes. There is a growing interest to study the combinatorial nature of CREs including presence or absence of CREs, the number of occurrences of each CRE, as well as of their order and location relative to their target genes. Comparative promoter analysis has been shown to be a reliable strategy to test the significance of each component of promoter architecture. However, it remains unclear what level of difference in the number of occurrences of each CRE is of statistical significance in order to explain different expression patterns of two genes. In this study, we present a novel statistical approach for pairwise comparison of promoters of Arabidopsis genes in the context of number of occurrences of each CRE within the promoters. First, using the sample of 1000 Arabidopsis promoters, the results of the goodness of fit test and non-parametric analysis revealed that the number of occurrences of CREs in a promoter sequence is Poisson distributed. As a promoter sequence contained functional and non-functional CREs, we addressed the issue of the statistical distribution of functional CREs by analyzing the ChIP-seq datasets. The results showed that the number of occurrences of functional CREs over the genomic regions was determined as being Poisson distributed. In accordance with the obtained distribution of CREs occurrences, we suggested the Audic and Claverie (AC) test to compare two promoters based on the number of occurrences for the CREs. Superiority of the AC test over Chi-square (2×2) and Fisher's exact tests was also shown, as the AC test was able to detect a higher number of significant CREs. The two case studies on the Arabidopsis genes were performed in order to biologically verify the pairwise test for promoter comparison. Consequently, a number of CREs with significantly different occurrences was identified between
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.
Sivasamy, Aneetha Avalappampatty; Sundan, Bose
2015-01-01
The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T(2) method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T(2) statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668
Avalappampatty Sivasamy, Aneetha; Sundan, Bose
2015-01-01
The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668
Chiang, Michael F.; Melia, Michele; Buffenn, Angela N.; Lambert, Scott R.; Recchia, Franco M.; Simpson, Jennifer L.; Yang, Michael B.
2013-01-01
Objective To evaluate the accuracy of detecting clinically significant retinopathy of prematurity (ROP) using wide-angle digital retinal photography. Methods Literature searches of PubMed and the Cochrane Library databases were conducted last on December 7, 2010, and yielded 414 unique citations. The authors assessed these 414 citations and marked 82 that potentially met the inclusion criteria. These 82 studies were reviewed in full text; 28 studies met inclusion criteria. The authors extracted from these studies information about study design, interventions, outcomes, and study quality. After data abstraction, 18 were excluded for study deficiencies or because they were superseded by a more recent publication. The methodologist reviewed the remaining 10 studies and assigned ratings of evidence quality; 7 studies were rated level I evidence and 3 studies were rated level III evidence. Results There is level I evidence from ≥5 studies demonstrating that digital retinal photography has high accuracy for detection of clinically significant ROP. Level III studies have reported high accuracy, without any detectable complications, from real-world operational programs intended to detect clinically significant ROP through remote site interpretation of wide-angle retinal photographs. Conclusions Wide-angle digital retinal photography has the potential to complement standard ROP care. It may provide advantages through objective documentation of clinical examination findings, improved recognition of disease progression by comparing previous photographs, and the creation of image libraries for education and research. Financial Disclosure(s) Proprietary or commercial disclosure may be found after the references. PMID:22541632
Akahori, Takuya; Gaensler, B. M.; Ryu, Dongsu E-mail: bryan.gaensler@sydney.edu.au
2014-08-01
Rotation measure (RM) grids of extragalactic radio sources have been widely used for studying cosmic magnetism. However, their potential for exploring the intergalactic magnetic field (IGMF) in filaments of galaxies is unclear, since other Faraday-rotation media such as the radio source itself, intervening galaxies, and the interstellar medium of our Galaxy are all significant contributors. We study statistical techniques for discriminating the Faraday rotation of filaments from other sources of Faraday rotation in future large-scale surveys of radio polarization. We consider a 30° × 30° field of view toward the south Galactic pole, while varying the number of sources detected in both present and future observations. We select sources located at high redshifts and toward which depolarization and optical absorption systems are not observed so as to reduce the RM contributions from the sources and intervening galaxies. It is found that a high-pass filter can satisfactorily reduce the RM contribution from the Galaxy since the angular scale of this component toward high Galactic latitudes would be much larger than that expected for the IGMF. Present observations do not yet provide a sufficient source density to be able to estimate the RM of filaments. However, from the proposed approach with forthcoming surveys, we predict significant residuals of RM that should be ascribable to filaments. The predicted structure of the IGMF down to scales of 0.°1 should be observable with data from the Square Kilometre Array, if we achieve selections of sources toward which sightlines do not contain intervening galaxies and RM errors are less than a few rad m{sup –2}.
Denton, Debra L; Diamond, Jerry; Zheng, Lei
2011-05-01
The U.S. Environmental Protection Agency (U.S. EPA) and state agencies implement the Clean Water Act, in part, by evaluating the toxicity of effluent and surface water samples. A common goal for both regulatory authorities and permittees is confidence in an individual test result (e.g., no-observed-effect concentration [NOEC], pass/fail, 25% effective concentration [EC25]), which is used to make regulatory decisions, such as reasonable potential determinations, permit compliance, and watershed assessments. This paper discusses an additional statistical approach (test of significant toxicity [TST]), based on bioequivalence hypothesis testing, or, more appropriately, test of noninferiority, which examines whether there is a nontoxic effect at a single concentration of concern compared with a control. Unlike the traditional hypothesis testing approach in whole effluent toxicity (WET) testing, TST is designed to incorporate explicitly both α and β error rates at levels of toxicity that are unacceptable and acceptable, given routine laboratory test performance for a given test method. Regulatory management decisions are used to identify unacceptable toxicity levels for acute and chronic tests, and the null hypothesis is constructed such that test power is associated with the ability to declare correctly a truly nontoxic sample as acceptable. This approach provides a positive incentive to generate high-quality WET data to make informed decisions regarding regulatory decisions. This paper illustrates how α and β error rates were established for specific test method designs and tests the TST approach using both simulation analyses and actual WET data. In general, those WET test endpoints having higher routine (e.g., 50th percentile) within-test control variation, on average, have higher method-specific α values (type I error rate), to maintain a desired type II error rate. This paper delineates the technical underpinnings of this approach and demonstrates the benefits
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.
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.
Hudorović, Narcis
2006-01-01
Transcranial Doppler can detect microembolic signals, which are characterized by unidirectional high intensity increase, short duration, and random occurrence, producing a "whistling" sound. Microembolic signals have been proven to represent solid or gaseous particles within the blood flow. Microemboli have been detected in a number of clinical cardiovascular settings: carotid artery stenosis, aortic arch plaques, atrial fibrillation, myocardial infarction, prosthetic heart valves, patent foramen ovale, valvular stenosis, during invasive procedures (angiography, percutaneous transluminal angioplasty) and surgery (carotid, cardiopulmonary bypass). Despite numerous studies performed so far, clinical significance of microembolic signals is still unclear. This article provides an overview of the development and current state of technical and clinical aspects of microembolus detection. PMID:17462357
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.
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
Baldewijns, Greet; Luca, Stijn; Nagels, William; Vanrumste, Bart; Croonenborghs, Tom
2015-01-01
It has been shown that gait speed and transfer times are good measures of functional ability in elderly. However, data currently acquired by systems that measure either gait speed or transfer times in the homes of elderly people require manual reviewing by healthcare workers. This reviewing process is time-consuming. To alleviate this burden, this paper proposes the use of statistical process control methods to automatically detect both positive and negative changes in transfer times. Three SPC techniques: tabular CUSUM, standardized CUSUM and EWMA, known for their ability to detect small shifts in the data, are evaluated on simulated transfer times. This analysis shows that EWMA is the best-suited method with a detection accuracy of 82% and an average detection time of 9.64 days. PMID:26737425
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
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
Early statistical detection of anthrax outbreaks by tracking over-the-counter medication sales
Goldenberg, Anna; Shmueli, Galit; Caruana, Richard A.; Fienberg, Stephen E.
2002-01-01
The recent series of anthrax attacks has reinforced the importance of biosurveillance systems for the timely detection of epidemics. This paper describes a statistical framework for monitoring grocery data to detect a large-scale but localized bioterrorism attack. Our system illustrates the potential of data sources that may be more timely than traditional medical and public health data. The system includes several layers, each customized to grocery data and tuned to finding footprints of an epidemic. We also propose an evaluation methodology that is suitable in the absence of data on large-scale bioterrorist attacks and disease outbreaks. PMID:11959973
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
NASA Astrophysics Data System (ADS)
Gilbert, Richard O.; O'Brien, Robert F.; Wilson, John E.; Pulsipher, Brent A.; McKinstry, Craig A.
2003-09-01
It may not be feasible to completely survey large tracts of land suspected of containing minefields. It is desirable to develop a characterization protocol that will confidently identify minefields within these large land tracts if they exist. Naturally, surveying areas of greatest concern and most likely locations would be necessary but will not provide the needed confidence that an unknown minefield had not eluded detection. Once minefields are detected, methods are needed to bound the area that will require detailed mine detection surveys. The US Department of Defense Strategic Environmental Research and Development Program (SERDP) is sponsoring the development of statistical survey methods and tools for detecting potential UXO targets. These methods may be directly applicable to demining efforts. Statistical methods are employed to determine the optimal geophysical survey transect spacing to have confidence of detecting target areas of a critical size, shape, and anomaly density. Other methods under development determine the proportion of a land area that must be surveyed to confidently conclude that there are no UXO present. Adaptive sampling schemes are also being developed as an approach for bounding the target areas. These methods and tools will be presented and the status of relevant research in this area will be discussed.
Vardhanabhuti, Saran; Blakemore, Steven J; Clark, Steven M; Ghosh, Sujoy; Stephens, Richard J; Rajagopalan, Dilip
2006-01-01
Signal quantification and detection of differential expression are critical steps in the analysis of Affymetrix microarray data. Many methods have been proposed in the literature for each of these steps. The goal of this paper is to evaluate several signal quantification methods (GCRMA, RSVD, VSN, MAS5, and Resolver) and statistical methods for differential expression (t test, Cyber-T, SAM, LPE, RankProducts, Resolver RatioBuild). Our particular focus is on the ability to detect differential expression via statistical tests. We have used two different datasets for our evaluation. First, we have used the HG-U133 Latin Square spike in dataset developed by Affymetrix. Second, we have used data from an in-house rat liver transcriptomics study following 30 different drug treatments generated using the Affymetrix RAE230A chip. Our overall recommendation based on this study is to use GCRMA for signal quantification. For detection of differential expression, GCRMA coupled with Cyber-T or SAM is the best approach, as measured by area under the receiver operating characteristic (ROC) curve. The integrated pipeline in Resolver RatioBuild combining signal quantification and detection of differential expression is an equally good alternative for detecting differentially expressed genes. For most of the differential expression algorithms we considered, the performance using MAS5 signal quantification was inferior to that of the other methods we evaluated. PMID:17233564
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
Peace, R A; Staff, R T; Gemmell, H G; McKiddie, F I; Metcalfe, M J
2002-08-01
The purpose of this study was to compare the performance of automatic detection of coronary artery disease (CAD) with that of expert observers. A male and female normal image template was constructed from normal stress technetium-99m single photon emission computed tomography (SPECT) studies. Mean and standard deviation images for each sex were created by registering normal studies to a standard shape and position. The test group consisted of 104 patients who had been routinely referred for SPECT and angiography. The gold standard for CAD was defined by angiography. The test group studies were registered to the respective templates and the Z-score was calculated for each voxel. Voxels with a Z-score greater than 5 indicated the presence of CAD. The performance of this method and that of three observers were compared by continuous receiver operating characteristic (CROC) analysis. The overall sensitivity and specificity for automatic detection were 73% and 92%, respectively. The area (Az) under the CROC curve (+/-1 SE) for automatic detection of CAD was 0.88+/-0.06. There was no statistically significant difference between the performances of the three observers in terms of Az and that of automatic detection (P> or =0.25, univariate Z-score test). The use of this automated statistical mapping approach shows a performance comparable with experienced observers, but avoids inter-observer and intra-observer variability. PMID:12124485
Hüneburg, Robert; Kukuk, Guido; Nattermann, Jacob; Endler, Christoph; Penner, Arndt-Hendrik; Wolter, Karsten; Schild, Hans; Strassburg, Christian; Sauerbruch, Tilman; Schmitz, Volker; Willinek, Winfried
2016-01-01
Background and study aims: Colorectal cancer (CRC) is one of the most common cancers worldwide, and several efforts have been made to reduce its occurrence or severity. Although colonoscopy is considered the gold standard in CRC prevention, it has its disadvantages: missed lesions, bleeding, and perforation. Furthermore, a high number of patients undergo this procedure even though no polyps are detected. Therefore, an initial screening examination may be warranted. Our aim was to compare the adenoma detection rate of magnetic resonance colonography (MRC) with that of optical colonoscopy. Patients and methods: A total of 25 patients with an intermediate risk for CRC (17 men, 8 women; mean age 57.6, standard deviation 11) underwent MRC with a 3.0-tesla magnet, followed by colonoscopy. The endoscopist was initially blinded to the results of MRC and unblinded immediately after examining the distal rectum. Following endoscopic excision, the size, anatomical localization, and appearance of all polyps were described according to the Paris classification. Results: A total of 93 lesions were detected during colonoscopy. These included a malignant infiltration of the transverse colon due to gastric cancer in 1 patient, 28 adenomas in 10 patients, 19 hyperplastic polyps in 9 patients, and 45 non-neoplastic lesions. In 5 patients, no lesion was detected. MRC detected significantly fewer lesions: 1 adenoma (P = 0.001) and 1 hyperplastic polyp (P = 0.004). The malignant infiltration was seen with both modalities. Of the 28 adenomas, 23 (82 %) were 5 mm or smaller; only 4 adenomas 10 mm or larger (14 %) were detected. Conclusion: MRC does not detect adenomas sufficiently independently of the location of the lesion. Even advanced lesions were missed. Therefore, colonoscopy should still be considered the current gold standard, even for diagnostic purposes. PMID:26878043
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
Improved bowel preparation increases polyp detection and unmasks significant polyp miss rate
Papanikolaou, Ioannis S; Sioulas, Athanasios D; Magdalinos, Nektarios; Beintaris, Iosif; Lazaridis, Lazaros-Dimitrios; Polymeros, Dimitrios; Malli, Chrysoula; Dimitriadis, George D; Triantafyllou, Konstantinos
2015-01-01
AIM: To retrospectively compare previous-day vs split-dose preparation in terms of bowel cleanliness and polyp detection in patients referred for polypectomy. METHODS: Fifty patients underwent two colonoscopies: one diagnostic in a private clinic and a second for polypectomy in a University Hospital. The latter procedures were performed within 12 wk of the index ones. Examinations were accomplished by two experienced endoscopists, different in each facility. Twenty-seven patients underwent screening/surveillance colonoscopy, while the rest were symptomatic. Previous day bowel preparation was utilized initially and split-dose for polypectomy. Colon cleansing was evaluated using the Aronchick scale. We measured the number of detected polyps, and the polyp miss rates per-polyp. RESULTS: Excellent/good preparation was reported in 38 cases with previous-day preparation (76%) vs 46 with split-dose (92%), respectively (P = 0.03). One hundred and twenty-six polyps were detected initially and 169 subsequently (P < 0.0001); 88 vs 126 polyps were diminutive (P < 0.0001), 25 vs 29 small (P = 0.048) and 13 vs 14 equal or larger than 10 mm. The miss rates for total, diminutive, small and large polyps were 25.4%, 30.1%, 13.7% and 6.6%, respectively. Multivariate analysis revealed that split-dose preparation was significantly associated (OR, P) with increased number of polyps detected overall (0.869, P < 0.001), in the right (0.418, P = 0.008) and in the left colon (0.452, P = 0.02). CONCLUSION: Split-dose preparation improved colon cleansing, enhanced polyp detection and unmasked significant polyp miss rates. PMID:26488024
NASA Astrophysics Data System (ADS)
Cho, Baek Hwan; Chang, Chuho; Lee, Jong-Ha; Ko, Eun Young; Seong, Yeong Kyeong; Woo, Kyoung-Gu
2013-02-01
The existence of microcalcifications (MCs) is an important marker of malignancy in breast cancer. In spite of the benefits in mass detection for dense breasts, ultrasonography is believed that it might not reliably detect MCs. For computer aided diagnosis systems, however, accurate detection of MCs has the possibility of improving the performance in both Breast Imaging-Reporting and Data System (BI-RADS) lexicon description for calcifications and malignancy classification. We propose a new efficient and effective method for MC detection using image enhancement and threshold adjacency statistics (TAS). The main idea of TAS is to threshold an image and to count the number of white pixels with a given number of adjacent white pixels. Our contribution is to adopt TAS features and apply image enhancement to facilitate MC detection in ultrasound images. We employed fuzzy logic, tophat filter, and texture filter to enhance images for MCs. Using a total of 591 images, the classification accuracy of the proposed method in MC detection showed 82.75%, which is comparable to that of Haralick texture features (81.38%). When combined, the performance was as high as 85.11%. In addition, our method also showed the ability in mass classification when combined with existing features. In conclusion, the proposed method exploiting image enhancement and TAS features has the potential to deal with MC detection in ultrasound images efficiently and extend to the real-time localization and visualization of MCs.
Lakhani, Paras; Langlotz, Curtis P
2010-12-01
The purpose of this investigation is to develop an automated method to accurately detect radiology reports that indicate non-routine communication of critical or significant results. Such a classification system would be valuable for performance monitoring and accreditation. Using a database of 2.3 million free-text radiology reports, a rule-based query algorithm was developed after analyzing hundreds of radiology reports that indicated communication of critical or significant results to a healthcare provider. This algorithm consisted of words and phrases used by radiologists to indicate such communications combined with specific handcrafted rules. This algorithm was iteratively refined and retested on hundreds of reports until the precision and recall did not significantly change between iterations. The algorithm was then validated on the entire database of 2.3 million reports, excluding those reports used during the testing and refinement process. Human review was used as the reference standard. The accuracy of this algorithm was determined using precision, recall, and F measure. Confidence intervals were calculated using the adjusted Wald method. The developed algorithm for detecting critical result communication has a precision of 97.0% (95% CI, 93.5-98.8%), recall 98.2% (95% CI, 93.4-100%), and F measure of 97.6% (ß=1). Our query algorithm is accurate for identifying radiology reports that contain non-routine communication of critical or significant results. This algorithm can be applied to a radiology reports database for quality control purposes and help satisfy accreditation requirements. PMID:19826871
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 Requirements For Pass-Fail Testing Of Contraband Detection Systems
NASA Astrophysics Data System (ADS)
Gilliam, David M.
2011-06-01
Contraband detection systems for homeland security applications are typically tested for probability of detection (PD) and probability of false alarm (PFA) using pass-fail testing protocols. Test protocols usually require specified values for PD and PFA to be demonstrated at a specified level of statistical confidence CL. Based on a recent more theoretical treatment of this subject [1], this summary reviews the definition of CL and provides formulas and spreadsheet functions for constructing tables of general test requirements and for determining the minimum number of tests required. The formulas and tables in this article may be generally applied to many other applications of pass-fail testing, in addition to testing of contraband detection systems.
Statistical stage transition detection method for small sample gene expression time series data.
Tominaga, Daisuke
2014-08-01
In terms of their internal (genetic) and external (phenotypic) states, living cells are always changing at varying rates. Periods of stable or low rate of change are often called States, Stages, or Phases, whereas high-rate periods are called Transitions or Transients. While states and transitions are observed phenotypically, such as cell differentiation, cancer progression, for example, are related with gene expression levels. On the other hand, stages of gene expression are definable based on changes of expression levels. Analyzing relations between state changes of phenotypes and stage transitions of gene expression levels is a general approach to elucidate mechanisms of life phenomena. Herein, we propose an algorithm to detect stage transitions in a time series of expression levels of a gene by defining statistically optimal division points. The algorithm shows detecting ability for simulated datasets. An annotation based analysis on detecting results for a dataset of initial development of Caenorhabditis elegans agrees with that are presented in the literature. PMID:24960588
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
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
Comparison of probability statistics for automated ship detection in SAR imagery
NASA Astrophysics Data System (ADS)
Henschel, Michael D.; Rey, Maria T.; Campbell, J. W. M.; Petrovic, D.
1998-12-01
This paper discuses the initial results of a recent operational trial of the Ocean Monitoring Workstation's (OMW) ship detection algorithm which is essentially a Constant False Alarm Rate filter applied to Synthetic Aperture Radar data. The choice of probability distribution and methodologies for calculating scene specific statistics are discussed in some detail. An empirical basis for the choice of probability distribution used is discussed. We compare the results using a l-look, k-distribution function with various parameter choices and methods of estimation. As a special case of sea clutter statistics the application of a (chi) 2-distribution is also discussed. Comparisons are made with reference to RADARSAT data collected during the Maritime Command Operation Training exercise conducted in Atlantic Canadian Waters in June 1998. Reference is also made to previously collected statistics. The OMW is a commercial software suite that provides modules for automated vessel detection, oil spill monitoring, and environmental monitoring. This work has been undertaken to fine tune the OMW algorithm's, with special emphasis on the false alarm rate of each algorithm.
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.
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.
Chung, Moo K; Kim, Seung-Goo; Schaefer, Stacey M; van Reekum, Carien M; Peschke-Schmitz, Lara; Sutterer, Matthew J; Davidson, Richard J
2014-03-21
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. Traditionally, 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. PMID:25302007
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. PMID:18350006
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
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
Detection of ground fog in mountainous areas from MODIS day-time data using a statistical approach
NASA Astrophysics Data System (ADS)
Schulz, H. M.; Thies, B.; Chang, S.-C.; Bendix, J.
2015-11-01
The mountain cloud forest of Taiwan can be delimited from other forest types using a map of the ground fog frequency. In order to create such a frequency map from remotely sensed data, an algorithm able to detect ground fog is necessary. Common techniques for ground fog detection based on weather satellite data can not be applied to fog occurrences in Taiwan as they rely on several assumptions regarding cloud properties. Therefore a new statistical method for the detection of ground fog from MODIS data in mountainous terrain is presented. Due to the sharpening of input data using MODIS bands 1 and 2 the method provides fog masks in a resolution of 250 m per pixel. The new technique is based on negative correlations between optical thickness and terrain height that can be observed if a cloud that is relatively plane-parallel is truncated by the terrain. A validation of the new technique using camera data has shown that the quality of fog detection is comparable to that of another modern fog detection scheme developed and validated for the temperate zones. The method is particularly applicable to optically thinner water clouds. Beyond a cloud optical thickness of ≈ 40, classification errors significantly increase.
NASA Astrophysics Data System (ADS)
Schulz, Hans Martin; Thies, Boris; Chang, Shih-Chieh; Bendix, Jörg
2016-03-01
The mountain cloud forest of Taiwan can be delimited from other forest types using a map of the ground fog frequency. In order to create such a frequency map from remotely sensed data, an algorithm able to detect ground fog is necessary. Common techniques for ground fog detection based on weather satellite data cannot be applied to fog occurrences in Taiwan as they rely on several assumptions regarding cloud properties. Therefore a new statistical method for the detection of ground fog in mountainous terrain from MODIS Collection 051 data is presented. Due to the sharpening of input data using MODIS bands 1 and 2, the method provides fog masks in a resolution of 250 m per pixel. The new technique is based on negative correlations between optical thickness and terrain height that can be observed if a cloud that is relatively plane-parallel is truncated by the terrain. A validation of the new technique using camera data has shown that the quality of fog detection is comparable to that of another modern fog detection scheme developed and validated for the temperate zones. The method is particularly applicable to optically thinner water clouds. Beyond a cloud optical thickness of ≈ 40, classification errors significantly increase.
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.
2014-01-01
Background In healthcare facilities, conventional surveillance techniques using rule-based guidelines may result in under- or over-reporting of methicillin-resistant Staphylococcus aureus (MRSA) outbreaks, as these guidelines are generally unvalidated. The objectives of this study were to investigate the utility of the temporal scan statistic for detecting MRSA clusters, validate clusters using molecular techniques and hospital records, and determine significant differences in the rate of MRSA cases using regression models. Methods Patients admitted to a community hospital between August 2006 and February 2011, and identified with MRSA > 48 hours following hospital admission, were included in this study. Between March 2010 and February 2011, MRSA specimens were obtained for spa typing. MRSA clusters were investigated using a retrospective temporal scan statistic. Tests were conducted on a monthly scale and significant clusters were compared to MRSA outbreaks identified by hospital personnel. Associations between the rate of MRSA cases and the variables year, month, and season were investigated using a negative binomial regression model. Results During the study period, 735 MRSA cases were identified and 167 MRSA isolates were spa typed. Nine different spa types were identified with spa type 2/t002 (88.6%) the most prevalent. The temporal scan statistic identified significant MRSA clusters at the hospital (n = 2), service (n = 16), and ward (n = 10) levels (P ≤ 0.05). Seven clusters were concordant with nine MRSA outbreaks identified by hospital staff. For the remaining clusters, seven events may have been equivalent to true outbreaks and six clusters demonstrated possible transmission events. The regression analysis indicated years 2009–2011, compared to 2006, and months March and April, compared to January, were associated with an increase in the rate of MRSA cases (P ≤ 0.05). Conclusions The application of the temporal scan
NASA Astrophysics Data System (ADS)
Gaidash, A. A.; Egorov, V. I.; Gleim, A. V.
2014-10-01
Quantum cryptography in theory allows distributing secure keys between two users so that any performed eavesdropping attempt would be immediately discovered. However, in practice an eavesdropper can obtain key information from multi-photon states when attenuated laser radiation is used as a source. In order to overcome this possibility, it is generally suggested to implement special cryptographic protocols, like decoy states or SARG04. We present an alternative method based on monitoring photon number statistics after detection. This method can therefore be used with any existing protocol.
Seo, Hyo Jung; Pagsisihan, Jefferson R.; Choi, Seung Hong; Cheon, Gi Jeong; Chung, June-Key; Lee, Dong Soo; Kang, Keon Wook
2015-01-01
Purpose We evaluated hemodynamic significance of stenosis on magnetic resonance angiography (MRA) using acetazolamide perfusion single photon emission computed tomography (SPECT). Materials and Methods Of 171 patients, stenosis in internal carotid artery (ICA) and middle cerebral artery (MCA) (ICA-MCA) on MRA and cerebrovascular reserve (CVR) of MCA territory on SPECT was measured using quantification and a 3-grade system. Stenosis and CVR grades were compared with each other, and their prognostic value for subsequent stroke was evaluated. Results Of 342 ICA-MCA, 151 (44%) presented stenosis on MRA; grade 1 in 69 (20%) and grade 2 in 82 (24%) cases. Decreased CVR was observed in 9% of grade 0 stenosis, 25% of grade 1, and 35% of grade 2. The average CVR of grade 0 was significantly different from grade 1 (p<0.001) and grade 2 stenosis (p=0.007). In quantitative analysis, average CVR index was -0.56±7.91 in grade 0, -1.81±6.66 in grade 1 and -1.18±5.88 in grade 2 stenosis. Agreement between stenosis and CVR grades was fair in patients with lateralizing and non-lateralizing symptoms (κ=0.230 and 0.346). Of the factors tested, both MRA and CVR were not significant prognostic factors (p=0.104 and 0.988, respectively), whereas hypertension and renal disease were significant factors (p<0.05, respectively). Conclusion A considerable proportion of ICA-MCA stenosis detected on MRA does not cause CVR impairment despite a fair correlation between them. Thus, hemodynamic state needs to be assessed for evaluating significance of stenosis, particularly in asymptomatic patients. PMID:26446655
Robust two-parameter invariant CFAR detection utilizing order statistics applied to Weibull clutter
NASA Astrophysics Data System (ADS)
Nagle, Daniel T.; Saniie, Jafar
1992-08-01
Constant False Alarm Rate (CFAR) detectors are designed to perform when the clutter information is partially unknown and/or varying. This is accomplished using local threshold estimates from background observations in which the CFAR level is maintained. However, when local observations contain target or irrelevant information, censoring is warranted to improve detection performance. Order Statistics (OS) processors have been shown to perform robustly (referring to type II errors or CFAR loss) for heterogeneous background clutter observations, and their performance has been analyzed for exponential clutter with unknown power. In this paper, several order statistics are used to create an invariant test statistic for Weibull clutter with two varying parameters (i.e., power and skewness). The robustness of a two-parameter invariant CFAR detector is analyzed and compared with an uncensored Weibull-Two Parameter (WTP) CFAR detector and conventional Cell Averaging (CA)-CFAR detector (i.e., designed invariant to exponential clutter). The performance trade-offs of these detectors are gaged for different scenarios of volatile clutter environments.
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
NASA Astrophysics Data System (ADS)
Svärd, Carl; Nyberg, Mattias; Frisk, Erik; Krysander, Mattias
2014-03-01
An important step in model-based fault detection is residual evaluation, where residuals are evaluated with the aim to detect changes in their behavior caused by faults. To handle residuals subject to time-varying uncertainties and disturbances, which indeed are present in practice, a novel statistical residual evaluation approach is presented. The main contribution is to base the residual evaluation on an explicit comparison of the probability distribution of the residual, estimated online using current data, with a no-fault residual distribution. The no-fault distribution is based on a set of a priori known no-fault residual distributions, and is continuously adapted to the current situation. As a second contribution, a method is proposed for estimating the required set of no-fault residual distributions off-line from no-fault training data. The proposed residual evaluation approach is evaluated with measurement data on a residual for fault detection in the gas-flow system of a Scania truck diesel engine. Results show that small faults can be reliably detected with the proposed approach in cases where regular methods fail.
The role of envelope statistics in detecting changes in interaural correlation
Goupell, Matthew J.
2012-01-01
The role of envelope statistics in binaural masking-level differences (BMLDs) and correlation change detection was investigated in normal-hearing listeners. Thresholds and just-noticeable differences (JNDs) were measured for different bandwidths and center frequencies (500, 2000, 4000, and 8000 Hz) using Gaussian noises (GNs) and low-fluctuation noises (LFNs). At a 500-Hz center frequency, GN NoSo thresholds were higher than, NoSπ thresholds were lower than, and correlation change detection JNDs were the same as LFN thresholds and JNDs. At higher center frequencies, GN NoSπ thresholds were the same or higher than LFN thresholds and GN correlation change detection JNDs were much smaller than LFN JNDs. Using a pulsed sine vocoder, a second experiment was performed to investigate if binaural adaptation might contribute to the difference in GN and LFN detection. There was no effect of pulse rate, thus providing no clear evidence that binaural adaptation plays a role in these tasks. Both a cross-correlation model and a model that utilized the fluctuations in the interaural differences could explain a majority of the variance in the LFN correlation change JNDs. PMID:22978885
NASA Astrophysics Data System (ADS)
Chakraborty, Jayasree; Rangayyan, Rangaraj M.; Banik, Shantanu; Mukhopadhyay, Sudipta; Desautels, J. E. L.
2012-03-01
We present a method using statistical measures of the orientation of texture to characterize and detect architectural distortion in prior mammograms of interval-cancer cases. Based on the orientation field, obtained by the application of a bank of Gabor filters to mammographic images, two types of co-occurrence matrices were derived to estimate the joint occurrence of the angles of oriented structures. For each of the matrices, Haralick's 14 texture features were computed. From a total of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases, 4,224 regions of interest (ROIs) were automatically obtained by applying Gabor filters and phase portrait analysis. For each ROI, statistical features were computed using the angle co-occurrence matrices. The performance of the features in the detection of architectural distortion was analyzed and compared with that of Haralick's features computed using the gray-level co-occurrence matrices of the ROIs. Using logistic regression for feature selection, an artificial neural network for classification, and the leave-one-image-out approach for cross-validation, the best result achieved was 0.77 in terms of the area under the receiver operating characteristic (ROC) curve. Analysis of the free-response ROC curve yielded a sensitivity of 80% at 5.4 false positives per image.
Using genomic annotations increases statistical power to detect eGenes
Duong, Dat; Zou, Jennifer; Hormozdiari, Farhad; Sul, Jae Hoon; Ernst, Jason; Han, Buhm; Eskin, Eleazar
2016-01-01
Motivation: Expression quantitative trait loci (eQTLs) are genetic variants that affect gene expression. In eQTL studies, one important task is to find eGenes or genes whose expressions are associated with at least one eQTL. The standard statistical method to determine whether a gene is an eGene requires association testing at all nearby variants and the permutation test to correct for multiple testing. The standard method however does not consider genomic annotation of the variants. In practice, variants near gene transcription start sites (TSSs) or certain histone modifications are likely to regulate gene expression. In this article, we introduce a novel eGene detection method that considers this empirical evidence and thereby increases the statistical power. Results: We applied our method to the liver Genotype-Tissue Expression (GTEx) data using distance from TSSs, DNase hypersensitivity sites, and six histone modifications as the genomic annotations for the variants. Each of these annotations helped us detected more candidate eGenes. Distance from TSS appears to be the most important annotation; specifically, using this annotation, our method discovered 50% more candidate eGenes than the standard permutation method. Contact: buhm.han@amc.seoul.kr or eeskin@cs.ucla.edu PMID:27307612
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.
[Hygienic significance of patulin in foods. 1. Analytical detection of patulin].
Koch, C E; Thurm, V; Paul, P
1979-01-01
The authors describe a thin-layer chromatographic method for determining patulin in fruit and vegetable products which is suited for routine work in hygiene practice. The samples are extracted with ethyl acetate, and the extracts are purified on a Florisil column. Separation is performed by means of a one-dimensional technique, using toluene/ehtyl acetate/formic acid (5 + 4 + 1), or, in the presence of interfering contaminants, by means of a two-dimensional technique, using benzene/methanol/glacial acetic acid (90 + 5 + 5) for the first run, and toluene/ethyl acetate/formic acid (5 + 4 + 1) for the second run. Patulin is detected by spraying with a benzidine solution, after chlorination. The limits of detection are 5 microgram/l of juice and 5 microgram/kg of fruit or vegetable. Derivatization with acetic anhydride/pyridine is used for corroborating the results obtained. The significance of 5-hydroxymethylfurfural as an interfering substance in apple juices is discussed. PMID:471032
Sibio, Simone; Fiorani, Cristina; Stolfi, Carmine; Divizia, Andrea; Pezzuto, Roberto; Montagnese, Fabrizio; Bagaglini, Giulia; Sammartino, Paolo; Sica, Giuseppe Sigismondo
2015-01-01
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. PMID:26425265
Early detection of illness associated with poisonings of public health significance.
Wolkin, Amy F; Patel, Manish; Watson, William; Belson, Martin; Rubin, Carol; Schier, Joshua; Kilbourne, Edwin M; Crawford, Carol Gotway; Wattigney, Wendy; Litovitz, Toby
2006-02-01
Since September 11, 2001, concern about potential terrorist attacks has increased in the United States. To reduce morbidity and mortality from outbreaks of illness from the intentional release of chemical agents, we examine data from the Toxic Exposure Surveillance System (TESS). TESS, a national system for timely collection of reports from US poison control centers, can facilitate early recognition of outbreaks of illness from chemical exposures. TESS data can serve as proxy markers for a diagnosis and may provide early alerts to potential outbreaks of covert events. We use 3 categories of information from TESS to detect potential outbreaks, including call volume, clinical effect, and substance-specific data. Analysis of the data identifies aberrations by comparing the observed number of events with a threshold based on historical data. Using TESS, we have identified several events of potential public health significance, including an arsenic poisoning at a local church gathering in Maine, the TOPOFF 2 national preparedness exercise, and contaminated food and water during the northeastern US blackout. Integration of poison control centers into the public health network will enhance the detection and response to emerging chemical threats. Traditionally, emergency physicians and other health care providers have used poison control centers for management information; their reporting to these centers is crucial in poisoning surveillance efforts. PMID:16431230
Liu, Ran; Jin, Cuiyun; Song, Fengjuan; Liu, Jing
2013-01-01
The conductivity and permittivity of tumors are known to differ significantly from those of normal tissues. Electrical impedance tomography (EIT) is a relatively new imaging method for exploiting these differences. However, the accuracy of data capture is one of the difficult problems urgently to be solved in the clinical application of EIT technology. A new concept of EIT sensitizers is put forward in this paper with the goal of expanding the contrast ratio of tumor and healthy tissue to enhance EIT imaging quality. The use of nanoparticles for changing tumor characteristics and determining the infiltration vector for easier detection has been widely accepted in the biomedical field. Ultra-pure water, normal saline, and gold nanoparticles, three kinds of material with large differences in electrical characteristics, are considered as sensitizers and undergo mathematical model analysis and animal experimentation. Our preliminary results suggest that nanoparticles are promising for sensitization work. Furthermore, in experimental and simulation results, we found that we should select different sensitizers for the detection of different types and stages of tumor. PMID:23319858
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
An adaptive resampling test for detecting the presence of significant predictors
McKeague, Ian W.; Qian, Min
2015-01-01
This paper investigates marginal screening for detecting the presence of significant predictors in high-dimensional regression. Screening large numbers of predictors is a challenging problem due to the non-standard limiting behavior of post-model-selected estimators. There is a common misconception that the oracle property for such estimators is a panacea, but the oracle property only holds away from the null hypothesis of interest in marginal screening. To address this difficulty, we propose an adaptive resampling test (ART). Our approach provides an alternative to the popular (yet conservative) Bonferroni method of controlling familywise error rates. ART is adaptive in the sense that thresholding is used to decide whether the centered percentile bootstrap applies, and otherwise adapts to the non-standard asymptotics in the tightest way possible. The performance of the approach is evaluated using a simulation study and applied to gene expression data and HIV drug resistance data. PMID:27073292
Liu, Bin; Zhou, Lin; Wang, Qian
2015-01-01
Epidermal growth factor receptor (EGFR) mutations can affect the therapeutic efficacy of drugs used to treat nonsmall-cell lung cancer (NSCLC). We aimed to develop methods to detect five common EGFR somatic mutations in tumor tissues from NSCLC patients by using a nanoscale mutation-sensitive switch consisting of a high-fidelity polymerase and phosphorothioate-modified allele-specific primers. The five clinically significant EGFR mutations examined here are S768I, T790M, L858R, and 15- and 18-bp deletion mutations in exon 19. Our assays showed sensitivities of 100 copies and specificities of more than three log scales for matched templates relative to mismatched templates by routine polymerase chain reaction (PCR), real-time PCR, and multiplex PCR. This assay would be superior to DNA sequencing in situations where mutant DNA is not abundant. PMID:25918867
Pepe, Pietro; Pennisi, Michele; Fraggetta, Filippo
2015-01-01
ABSTRACT Purpose: Detection rate for anterior prostate cancer (PCa) in men who underwent initial and repeat biopsy has been prospectively evaluated. Materials and Methods: From January 2013 to March 2014, 400 patients all of Caucasian origin (median age 63.5 years) underwent initial (285 cases) and repeat (115 cases) prostate biopsy; all the men had negative digital rectal examination and the indications to biopsy were: PSA values > 10 ng/mL, PSA between 4.1-10 or 2.6-4 ng/mL with free/total PSA≤25% and ≤20%, respectively. A median of 22 (initial biopsy) and 31 cores (repeat biopsy) were transperineally performed including 4 cores of the anterior zone (AZ) and 4 cores of the AZ plus 2 cores of the transition zone (TZ), respectively. Results: Median PSA was 7.9 ng/mL; overall, a PCa was found in 180 (45%) patients: in 135 (47.4%) and 45 (36%) of the men who underwent initial and repeat biopsy, respectively. An exclusive PCa of the anterior zone was found in the 8.9 (initial biopsy) vs 13.3% (repeat biopsy) of the men: a single microfocus of cancer was found in the 61.2% of the cases; moreover, in 7 out 18 AZ PCa the biopsy histology was predictive of significant cancer in 2 (28.5%) and 5 (71.5%) men who underwent initial and repeat biopsy, respectively. Conclusions: However AZ biopsies increased detection rate for PCa (10% of the cases), the majority of AZ PCa with histological findings predictive of clinically significant cancer were found at repeat biopsy (about 70% of the cases). PMID:26689509
AB029. The clinical significance of RigiScan plus detection
Gao, Bing; Mu, Hongtao; Zhang, Zhichao; Yuan, Yiming; Peng, Jing; Xin, Zhongcheng; Guo, Yinglu
2016-01-01
Erectile dysfunction (ED) is a common disease in male outpatient service, the penis hardness testing of ED in the clinical diagnosis has important significance, past some detection methods, such as nocturnal penile tumescence monitoring (NPT) due to time-consuming is not easy in outpatient service and a vasodilator agent intervention tests such as color Doppler detection due to the injection of drugs in the penis is difficult for patients to accept. In 1965 night rapid eye movement sleep phase 3–5 times of penile erection phenomenon was first reported by Fisher, in 1970 the night monitoring instrument of penis erectile hardness is used for evaluation of male erectile function. In 1985 researchers reported the RigiScan plus software was used to record of nocturnal erection, 1994 this method was gradually improved and used in clinical application by Levine. Sol reported that after taking sildenafil the patient was given audio-visual stimulation to induce the penis erects, then the RigiScan plus was used to record erection hardness in 2006. Due to the complexity of the etiology of ED, for the evaluation of penile erectile function should also be in many ways any single check has its limitations, the drug combined with audio-visual sense stimulation induced penile erection hardness monitoring (AVSS + RigiScan plus) in newly diagnosed patients with erectile function has a certain significance. Compared with AVSS + RigiScan examination, NPT is more expensive, time-consuming, and cumbersome, and patients feel unwell. And AVSS + RigiScan is simple, effective, easy, cheap, and the diagnostic accuracy rate matching to NPT, it is suitable for routine examination of patients with newly diagnosed ED.
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.
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.
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.
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
2010-01-01
Background Animals, including humans, exhibit a variety of biological rhythms. This article describes a method for the detection and simultaneous comparison of multiple nycthemeral rhythms. Methods A statistical method for detecting periodic patterns in time-related data via harmonic regression is described. The method is particularly capable of detecting nycthemeral rhythms in medical data. Additionally a method for simultaneously comparing two or more periodic patterns is described, which derives from the analysis of variance (ANOVA). This method statistically confirms or rejects equality of periodic patterns. Mathematical descriptions of the detecting method and the comparing method are displayed. Results Nycthemeral rhythms of incidents of bodily harm in Middle Franconia are analyzed in order to demonstrate both methods. Every day of the week showed a significant nycthemeral rhythm of bodily harm. These seven patterns of the week were compared to each other revealing only two different nycthemeral rhythms, one for Friday and Saturday and one for the other weekdays. PMID:21059197
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.
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.
COMPASS server for homology detection: improved statistical accuracy, speed and functionality
Sadreyev, Ruslan I.; Tang, Ming; Kim, Bong-Hyun; Grishin, Nick V.
2009-01-01
COMPASS is a profile-based method for the detection of remote sequence similarity and the prediction of protein structure. Here we describe a recently improved public web server of COMPASS, http://prodata.swmed.edu/compass. The server features three major developments: (i) improved statistical accuracy; (ii) increased speed from parallel implementation; and (iii) new functional features facilitating structure prediction. These features include visualization tools that allow the user to quickly and effectively analyze specific local structural region predictions suggested by COMPASS alignments. As an application example, we describe the structural, evolutionary and functional analysis of a protein with unknown function that served as a target in the recent CASP8 (Critical Assessment of Techniques for Protein Structure Prediction round 8). URL: http://prodata.swmed.edu/compass PMID:19435884
COMPASS server for homology detection: improved statistical accuracy, speed and functionality.
Sadreyev, Ruslan I; Tang, Ming; Kim, Bong-Hyun; Grishin, Nick V
2009-07-01
COMPASS is a profile-based method for the detection of remote sequence similarity and the prediction of protein structure. Here we describe a recently improved public web server of COMPASS, http://prodata.swmed.edu/compass. The server features three major developments: (i) improved statistical accuracy; (ii) increased speed from parallel implementation; and (iii) new functional features facilitating structure prediction. These features include visualization tools that allow the user to quickly and effectively analyze specific local structural region predictions suggested by COMPASS alignments. As an application example, we describe the structural, evolutionary and functional analysis of a protein with unknown function that served as a target in the recent CASP8 (Critical Assessment of Techniques for Protein Structure Prediction round 8). URL: http://prodata.swmed.edu/compass. PMID:19435884
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
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
Gu, Xun
2016-03-01
RNA-seq has been an increasingly popular high-throughput platform to identify differentially expressed (DE) genes, which is much more reproducible and accurate than the previous microarray technology. Yet, a number of statistical issues remain to be resolved in data analysis, largely due to the high-throughput data volume and over-dispersion of read counts. These problems become more challenging for those biologists who use RNA-seq to measure genome-wide expression profiles in different combinations of sampling resources (species or genotypes) or treatments. In this paper, the author first reviews the statistical methods available for detecting DE genes, which have implemented negative binomial (NB) models and/or quasi-likelihood (QL) approaches to account for the over-dispersion problem in RNA-seq samples. The author then studies how to carry out the DE test in the context of phylogeny, i.e., RNA-seq samples are from a range of species as phylogenetic replicates. The author proposes a computational framework to solve this phylo-DE problem: While an NB model is used to account for data over-dispersion within biological replicates, over-dispersion among phylogenetic replicates is taken into account by QL, plus some special treatments for phylogenetic bias. This work helps to design cost-effective RNA-seq experiments in the field of biodiversity or phenotype plasticity that may involve hundreds of species under a phylogenetic framework. PMID:26108230
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.
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
Tollenaar, R. A.; van Krieken, J. H.; van Slooten, H. J.; Bruinvels, D. J.; Nelemans, K. M.; van den Broek, L. J.; Hermans, J.; van Dierendonck, J. H.
1998-01-01
To evaluate the prognostic significance of immunohistochemically detected p53 and Bcl-2 proteins in colorectal cancer, tissue sections from 238 paraffin-embedded colorectal carcinomas were immunostained for p53 (MAb DO-7 and CM-1 antiserum) and Bcl-2 (MAb Bcl-2:124). Staining patterns were assessed semiquantitatively and correlated with each other and with sex, age, tumour site, Dukes' classification, tumour differentiation, mucinous characteristics, lymphocyte and eosinophilic granulocyte infiltration, and patient survival. In our series, 35% of carcinomas showed no nuclear staining and 34% (DO-7) to 40% (CM-1) showed staining in over 30% of tumour cell nuclei. A majority of carcinomas that had been immunostained with CM-1 showed cytoplasmic staining, but this was not observed with DO-7. With respect to Bcl-2, 51% of tumours were completely negative, 32% displayed weak and 15% moderate staining; only 3% showed strong positive staining. No evidence was found for reciprocity between Bcl-2 expression and nuclear p53 accumulation. From 13 cases containing tumour-associated adenoma, four were Bcl-2 negative in premalignant and malignant cells, in another four cases these cells showed similar staining intensities and in the remaining cases only the malignant colorectal cells were Bcl-2 negative. Therefore, our data indicate that Bcl-2 is dispensable in the progression towards carcinoma. Except for an association between nuclear p53 accumulation and mucinous tumours (P = 0.01), no significant correlation was found between the clinicopathological parameters mentioned above and immunostaining pattern of (nuclear or cytoplasmic) p53 or Bcl-2. PMID:9667656
Frome, EL
2005-09-20
Environmental exposure measurements are, in general, positive and may be subject to left censoring; i.e,. the measured value is less than a ''detection limit''. In occupational monitoring, strategies for assessing workplace exposures typically focus on the mean exposure level or the probability that any measurement exceeds a limit. Parametric methods used to determine acceptable levels of exposure, are often based on a two parameter lognormal distribution. The mean exposure level, an upper percentile, and the exceedance fraction are used to characterize exposure levels, and confidence limits are used to describe the uncertainty in these estimates. Statistical methods for random samples (without non-detects) from the lognormal distribution are well known for each of these situations. In this report, methods for estimating these quantities based on the maximum likelihood method for randomly left censored lognormal data are described and graphical methods are used to evaluate the lognormal assumption. If the lognormal model is in doubt and an alternative distribution for the exposure profile of a similar exposure group is not available, then nonparametric methods for left censored data are used. The mean exposure level, along with the upper confidence limit, is obtained using the product limit estimate, and the upper confidence limit on an upper percentile (i.e., the upper tolerance limit) is obtained using a nonparametric approach. All of these methods are well known but computational complexity has limited their use in routine data analysis with left censored data. The recent development of the R environment for statistical data analysis and graphics has greatly enhanced the availability of high-quality nonproprietary (open source) software that serves as the basis for implementing the methods in this paper.
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.
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.
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. PMID:25284752
Charidimou, Andreas; Linn, Jennifer; Vernooij, Meike W; Opherk, Christian; Akoudad, Saloua; Baron, Jean-Claude; Greenberg, Steven M; Jäger, Hans Rolf; Werring, David J
2015-08-01
Cortical superficial siderosis describes a distinct pattern of blood-breakdown product deposition limited to cortical sulci over the convexities of the cerebral hemispheres, sparing the brainstem, cerebellum and spinal cord. Although cortical superficial siderosis has many possible causes, it is emerging as a key feature of cerebral amyloid angiopathy, a common and important age-related cerebral small vessel disorder leading to intracerebral haemorrhage and dementia. In cerebral amyloid angiopathy cohorts, cortical superficial siderosis is associated with characteristic clinical symptoms, including transient focal neurological episodes; preliminary data also suggest an association with a high risk of future intracerebral haemorrhage, with potential implications for antithrombotic treatment decisions. Thus, cortical superficial siderosis is of relevance to neurologists working in neurovascular, memory and epilepsy clinics, and neurovascular emergency services, emphasizing the need for appropriate blood-sensitive magnetic resonance sequences to be routinely acquired in these clinical settings. In this review we focus on recent developments in neuroimaging and detection, aetiology, prevalence, pathophysiology and clinical significance of cortical superficial siderosis, with a particular emphasis on cerebral amyloid angiopathy. We also highlight important areas for future investigation and propose standards for evaluating cortical superficial siderosis in research studies. PMID:26115675
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.
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
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. PMID:23223515
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.
Boareto, Marcelo; Caticha, Nestor
2014-01-01
Microarray data analysis typically consists in identifying a list of differentially expressed genes (DEG), i.e., the genes that are differentially expressed between two experimental conditions. Variance shrinkage methods have been considered a better choice than the standard t-test for selecting the DEG because they correct the dependence of the error with the expression level. This dependence is mainly caused by errors in background correction, which more severely affects genes with low expression values. Here, we propose a new method for identifying the DEG that overcomes this issue and does not require background correction or variance shrinkage. Unlike current methods, our methodology is easy to understand and implement. It consists of applying the standard t-test directly on the normalized intensity data, which is possible because the probe intensity is proportional to the gene expression level and because the t-test is scale- and location-invariant. This methodology considerably improves the sensitivity and robustness of the list of DEG when compared with the t-test applied to preprocessed data and to the most widely used shrinkage methods, Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA). Our approach is useful especially when the genes of interest have small differences in expression and therefore get ignored by standard variance shrinkage methods.
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.
Statistical modeling, detection, and segmentation of stains in digitized fabric images
NASA Astrophysics Data System (ADS)
Gururajan, Arunkumar; Sari-Sarraf, Hamed; Hequet, Eric F.
2007-02-01
This paper will describe a novel and automated system based on a computer vision approach, for objective evaluation of stain release on cotton fabrics. Digitized color images of the stained fabrics are obtained, and the pixel values in the color and intensity planes of these images are probabilistically modeled as a Gaussian Mixture Model (GMM). Stain detection is posed as a decision theoretic problem, where the null hypothesis corresponds to absence of a stain. The null hypothesis and the alternate hypothesis mathematically translate into a first order GMM and a second order GMM respectively. The parameters of the GMM are estimated using a modified Expectation-Maximization (EM) algorithm. Minimum Description Length (MDL) is then used as the test statistic to decide the verity of the null hypothesis. The stain is then segmented by a decision rule based on the probability map generated by the EM algorithm. The proposed approach was tested on a dataset of 48 fabric images soiled with stains of ketchup, corn oil, mustard, ragu sauce, revlon makeup and grape juice. The decision theoretic part of the algorithm produced a correct detection rate (true positive) of 93% and a false alarm rate of 5% on these set of images.
Akrami, Yashar; Savage, Christopher; Scott, Pat; Conrad, Jan; Edsjö, Joakim E-mail: savage@fysik.su.se E-mail: conrad@fysik.su.se
2011-07-01
Models of weak-scale supersymmetry offer viable dark matter (DM) candidates. Their parameter spaces are however rather large and complex, such that pinning down the actual parameter values from experimental data can depend strongly on the employed statistical framework and scanning algorithm. In frequentist parameter estimation, a central requirement for properly constructed confidence intervals is that they cover true parameter values, preferably at exactly the stated confidence level when experiments are repeated infinitely many times. Since most widely-used scanning techniques are optimised for Bayesian statistics, one needs to assess their abilities in providing correct confidence intervals in terms of the statistical coverage. Here we investigate this for the Constrained Minimal Supersymmetric Standard Model (CMSSM) when only constrained by data from direct searches for dark matter. We construct confidence intervals from one-dimensional profile likelihoods and study the coverage by generating several pseudo-experiments for a few benchmark sets of pseudo-true parameters. We use nested sampling to scan the parameter space and evaluate the coverage for the benchmarks when either flat or logarithmic priors are imposed on gaugino and scalar mass parameters. The sampling algorithm has been used in the configuration usually adopted for exploration of the Bayesian posterior. We observe both under- and over-coverage, which in some cases vary quite dramatically when benchmarks or priors are modified. We show how most of the variation can be explained as the impact of explicit priors as well as sampling effects, where the latter are indirectly imposed by physicality conditions. For comparison, we also evaluate the coverage for Bayesian credible intervals, and observe significant under-coverage in those cases.
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
Using channel-specific statistical models to detect reverberation in cochlear implant stimuli.
Desmond, Jill M; Collins, Leslie M; Throckmorton, Chandra S
2013-08-01
Reverberation is especially detrimental for cochlear implant listeners; thus, mitigating its effects has the potential to provide significant improvements to cochlear implant communication. Efforts to model and correct for reverberation in acoustic listening scenarios can be quite complex, requiring estimation of the room transfer function and localization of the source and receiver. However, due to the limited resolution associated with cochlear implant stimulation, simpler processing for reverberation detection and mitigation may be possible for cochlear implants. This study models speech stimuli in a cochlear implant on a per-channel basis both in quiet and in reverberation, and assesses the efficacy of these models for detecting the presence of reverberation. This study was able to successfully detect reverberation in cochlear implant pulse trains, and the results appear to be robust to varying room conditions and cochlear implant stimulation parameters. Reverberant signals were detected 100% of the time for a long reverberation time of 1.2 s and 86% of the time for a shorter reverberation time of 0.5 s. PMID:23927111
Auto-Detection of Impact Crater Statistics and Crater Morphologies in Mars THEMIS Data
NASA Astrophysics Data System (ADS)
Plesko, C. S.; Brumby, S. P.; Asphaug, E.
2003-12-01
One of the challenges of planetary science is the development of tools adequate to provide automated crater statistics, for use in chronology, geomorphology and a variety of other investigations. We will present the current results of an ongoing effort to develop new tools for culling THEMIS imagery for crater statistics. Our eventual goal is to generate crater density and age maps of Mars. We are also developing tools to probe the morphologies and near-surface compositions of type-class craters. One crater type of particular significance is the rampart crater, which is unique to Mars. These are widely believed to be the result of impacts into volatile-rich surface materials. We will present the results of our examination of the spectral and morphological properties of several rampart craters in THEMIS IR images as a demonstration of image processing and automated feature extraction techniques. Using techniques developed at Los Alamos National Laboratory, we are able to obtain an automated count of craters in an image, their centroids and radii, extract spectra and compare them to spectral libraries of known reference minerals.
NASA Astrophysics Data System (ADS)
Marcos, R.; Turco, M.; Llasat, M. C.; Quintana-Seguí, P.
2012-04-01
In the context of climate studies, the analysis of long homogeneous time series is of the utmost importance. A homogeneous climate series is defined as a series whose variations are caused only by changes in weather and climate (Conrad and Pollak, 1950). Unfortunately, a time series is often affected by one or more artificial inhomogeneities. Regardless of the type and the effect of inhomogeneities, the analysis of a non-homogeneous series can be misleading. Consequently, it is crucial to determine, assign and adjust any discontinuities in the data, especially in those reference series used in climate change studies. The Twentieth Century Reanalysis (20CR) data can provide an independent estimate of, among other variables, surface temperature. However, the difference in scale affects its potential use as a tool to detect non-climatic inhomogeneities in a local temperature time series. To avoid this limitation, we propose a new approach based on a parsimonious statistical downscaling method to bridge the gap between reanalysis data and the local temperature time series. This method was applied to two high-quality international reference stations in the North-East of Spain (present in the ECA database, http://eca.knmi.nl/) whose temperature series are used, for example, in the report of climatic change in Catalonia, Cunillera et al., 2009: Ebre (Tortosa) and Fabra (Barcelona), for the periods 1940-2008 and 1914-2008, respectively. Both series show an anomalous period which is clearly identifiable by visual inspection. The statistical downscaling model was calibrated for these stations and independently tested over the reliable periods with good results. The model was then applied to reproduce the doubtful years. The results of the study are in agreement with the metadata: for the Fabra series, the method proposed clearly identifies the artificial inhomogeneity; whilst for the Ebre Observatory, there is no documented change in the station and the suspicious period
Detecting trends in raptor counts: power and type I error rates of various statistical tests
Hatfield, J.S.; Gould, W.R., IV; 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.
NASA Astrophysics Data System (ADS)
Michael, A. J.
2012-12-01
Detecting trends in the rate of sporadic events is a problem for earthquakes and other natural hazards such as storms, floods, or landslides. I use synthetic events to judge the tests used to address this problem in seismology and consider their application to other hazards. Recent papers have analyzed the record of magnitude ≥7 earthquakes since 1900 and concluded that the events are consistent with a constant rate Poisson process plus localized aftershocks (Michael, GRL, 2011; Shearer and Stark, PNAS, 2012; Daub et al., GRL, 2012; Parsons and Geist, BSSA, 2012). Each paper removed localized aftershocks and then used a different suite of statistical tests to test the null hypothesis that the remaining data could be drawn from a constant rate Poisson process. The methods include KS tests between event times or inter-event times and predictions from a Poisson process, the autocorrelation function on inter-event times, and two tests on the number of events in time bins: the Poisson dispersion test and the multinomial chi-square test. The range of statistical tests gives us confidence in the conclusions; which are robust with respect to the choice of tests and parameters. But which tests are optimal and how sensitive are they to deviations from the null hypothesis? The latter point was raised by Dimer (arXiv, 2012), who suggested that the lack of consideration of Type 2 errors prevents these papers from being able to place limits on the degree of clustering and rate changes that could be present in the global seismogenic process. I produce synthetic sets of events that deviate from a constant rate Poisson process using a variety of statistical simulation methods including Gamma distributed inter-event times and random walks. The sets of synthetic events are examined with the statistical tests described above. Preliminary results suggest that with 100 to 1000 events, a data set that does not reject the Poisson null hypothesis could have a variability that is 30% to
NASA Technical Reports Server (NTRS)
Druzhinin, I. P.; Khamyanova, N. V.; Yagodinskiy, V. N.
1974-01-01
Statistical evaluations of the significance of the relationship of abrupt changes in solar activity and discontinuities in the multi-year pattern of an epidemic process are reported. They reliably (with probability of more than 99.9%) show the real nature of this relationship and its great specific weight (about half) in the formation of discontinuities in the multi-year pattern of the processes in question.
Schanfield, M.S.; Stevens, J.O.; Bauman, D.
1981-01-01
Red blood cell (RBC) allo- or autoantibodies, which markedly reduce the survival of transfused or autologous RBC, are considered to be clinically significant antibodies. The occurrence of antibodies against high-incidence antigens, which are occasionally associated with clinically significant RBC destruction or are of unknown clinical significance, often creates delays in providing blood to patients. In the majority of cases these antibodies are benign; however, clinically significant examples of these antibodies have been reported. An in vitro homologous human mononuclear phagocyte assay (MPA) was used to study antibodies directed against specificities associated with variable clinical significance. Two antibodies reported to be clinically significant and 25 antibodies known to be clinically insignificant were tested by MPA. The results indicate that clinically significant antibodies have a significantly higher score than do clinically insignificant antibodies, with no overlap observed between the two groups. An additional eight antibodies with unknown clinical significance were tested. None of these antibodies had scores in the clinically significant range.
DeltAMT: A Statistical Algorithm for Fast Detection of Protein Modifications From LC-MS/MS Data*
Fu, Yan; Xiu, Li-Yun; Jia, Wei; Ye, Ding; Sun, Rui-Xiang; Qian, Xiao-Hong; He, Si-Min
2011-01-01
Identification of proteins and their modifications via liquid chromatography-tandem mass spectrometry is an important task for the field of proteomics. However, because of the complexity of tandem mass spectra, the majority of the spectra cannot be identified. The presence of unanticipated protein modifications is among the major reasons for the low spectral identification rate. The conventional database search approach to protein identification has inherent difficulties in comprehensive detection of protein modifications. In recent years, increasing efforts have been devoted to developing unrestrictive approaches to modification identification, but they often suffer from their lack of speed. This paper presents a statistical algorithm named DeltAMT (Delta Accurate Mass and Time) for fast detection of abundant protein modifications from tandem mass spectra with high-accuracy precursor masses. The algorithm is based on the fact that the modified and unmodified versions of a peptide are usually present simultaneously in a sample and their spectra are correlated with each other in precursor masses and retention times. By representing each pair of spectra as a delta mass and time vector, bivariate Gaussian mixture models are used to detect modification-related spectral pairs. Unlike previous approaches to unrestrictive modification identification that mainly rely upon the fragment information and the mass dimension in liquid chromatography-tandem mass spectrometry, the proposed algorithm makes the most of precursor information. Thus, it is highly efficient while being accurate and sensitive. On two published data sets, the algorithm effectively detected various modifications and other interesting events, yielding deep insights into the data. Based on these discoveries, the spectral identification rates were significantly increased and many modified peptides were identified. PMID:21321130
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
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.
NASA Astrophysics Data System (ADS)
Zechlin, Hannes-S.; Cuoco, Alessandro; Donato, Fiorenza; Fornengo, Nicolao; Vittino, Andrea
2016-08-01
The source-count distribution as a function of their flux, {dN}/{dS}, is one of the main quantities characterizing gamma-ray source populations. We employ statistical properties of the Fermi Large Area Telescope (LAT) photon counts map to measure the composition of the extragalactic gamma-ray sky at high latitudes (| b| ≥slant 30°) between 1 and 10 GeV. We present a new method, generalizing the use of standard pixel-count statistics, to decompose the total observed gamma-ray emission into (a) point-source contributions, (b) the Galactic foreground contribution, and (c) a truly diffuse isotropic background contribution. Using the 6 yr Fermi-LAT data set (P7REP), we show that the {dN}/{dS} distribution in the regime of so far undetected point sources can be consistently described with a power law with an index between 1.9 and 2.0. We measure {dN}/{dS} down to an integral flux of ∼ 2× {10}-11 {{cm}}-2 {{{s}}}-1, improving beyond the 3FGL catalog detection limit by about one order of magnitude. The overall {dN}/{dS} distribution is consistent with a broken power law, with a break at {2.1}-1.3+1.0× {10}-8 {{cm}}-2 {{{s}}}-1. The power-law index {n}1={3.1}-0.5+0.7 for bright sources above the break hardens to {n}2=1.97+/- 0.03 for fainter sources below the break. A possible second break of the {dN}/{dS} distribution is constrained to be at fluxes below 6.4× {10}-11 {{cm}}-2 {{{s}}}-1 at 95% confidence level. The high-latitude gamma-ray sky between 1 and 10 GeV is shown to be composed of ∼25% point sources, ∼69.3% diffuse Galactic foreground emission, and ∼6% isotropic diffuse background.
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
Huang, Chih-Chung
2011-05-01
The echogenicity of whole blood is known to vary during the flow cycle under pulsatile flow both in vitro and in vivo. However, the fundamental underlying mechanisms remain uncertain. The combined effects of flow acceleration and the shear rate were recently used to explain the cyclic variations of signals backscattered from flowing blood. However, testing this hypothesis requires determination of the spatial distributions of red blood cells (RBCs) in flowing blood. Recently, the Nakagami (m) and scaling (Ω) parameters have been used, respectively, to detect the spatial distributions of RBCs and the intensity of backscattering signal from blood under steady flow. For a better understanding of the relationship between the spatial distributions of RBCs and erythrocyte aggregation under pulsatile flow condition, these ultrasound backscattering statistical parameters were used, in this study, to characterize signals backscattered from both whole blood and RBC suspensions at different peak flow velocities (from 10 to 30 cm/s) and hematocrits (20% and 40%). The experiments were carried out by a 35-MHz ultrasound transducer. The m and Ω parameters were calculated for different blood properties and conditions, and the flow velocity in the center of blood flowing through a tube was measured synchronously. In whole blood, the results demonstrated that most RBCs were aggregated progressively toward the center of tube as the flow velocity started to accelerate, and that the increase in the intensity of the backscattered signal envelope to a maximum was attributable to larger rouleaux being formed in the center of tube. This phenomenon became apparent at a lower peak flow velocity with 40% hematocrit. However, there were no cyclic and spatial variations of the backscattering signal over a pulsatile cycle in RBC suspensions. PMID:21134805
Katsura, Masaki; Akahane, Masaaki; Sato, Jiro; Matsuda, Izuru; Ohtomo, Kuni
2016-01-01
Background Iterative reconstruction methods have attracted attention for reducing radiation doses in computed tomography (CT). Purpose To investigate the detectability of pancreatic calcification using dose-reduced CT reconstructed with model-based iterative construction (MBIR) and adaptive statistical iterative reconstruction (ASIR). Material and Methods This prospective study approved by Institutional Review Board included 85 patients (57 men, 28 women; mean age, 69.9 years; mean body weight, 61.2 kg). Unenhanced CT was performed three times with different radiation doses (reference-dose CT [RDCT], low-dose CT [LDCT], ultralow-dose CT [ULDCT]). From RDCT, LDCT, and ULDCT, images were reconstructed with filtered-back projection (R-FBP, used for establishing reference standard), ASIR (L-ASIR), and MBIR and ASIR (UL-MBIR and UL-ASIR), respectively. A lesion (pancreatic calcification) detection test was performed by two blinded radiologists with a five-point certainty level scale. Results Dose-length products of RDCT, LDCT, and ULDCT were 410, 97, and 36 mGy-cm, respectively. Nine patients had pancreatic calcification. The sensitivity for detecting pancreatic calcification with UL-MBIR was high (0.67–0.89) compared to L-ASIR or UL-ASIR (0.11–0.44), and a significant difference was seen between UL-MBIR and UL-ASIR for one reader (P = 0.014). The area under the receiver-operating characteristic curve for UL-MBIR (0.818–0.860) was comparable to that for L-ASIR (0.696–0.844). The specificity was lower with UL-MBIR (0.79–0.92) than with L-ASIR or UL-ASIR (0.96–0.99), and a significant difference was seen for one reader (P < 0.01). Conclusion In UL-MBIR, pancreatic calcification can be detected with high sensitivity, however, we should pay attention to the slightly lower specificity. PMID:27110389
A comparative study of four significance measures for periodicity detection in astronomical surveys
NASA Astrophysics Data System (ADS)
Süveges, Maria; Guy, Leanne P.; Eyer, Laurent; Cuypers, Jan; Holl, Berry; Lecoeur-Taïbi, Isabelle; Mowlavi, Nami; Nienartowicz, Krzysztof; Blanco, Diego Ordóñez; Rimoldini, Lorenzo; Ruiz, Idoia
2015-06-01
We study the problem of periodicity detection in massive data sets of photometric or radial velocity time series, as presented by ESA's Gaia mission. Periodicity detection hinges on the estimation of the false alarm probability of the extremum of the periodogram of the time series. We consider the problem of its estimation with two main issues in mind. First, for a given number of observations and signal-to-noise ratio, the rate of correct periodicity detections should be constant for all realized cadences of observations regardless of the observational time patterns, in order to avoid sky biases that are difficult to assess. Secondly, the computational loads should be kept feasible even for millions of time series. Using the Gaia case, we compare the FM method of Paltani and Schwarzenberg-Czerny, the Baluev method and the GEV method of Süveges, as well as a method for the direct estimation of a threshold. Three methods involve some unknown parameters, which are obtained by fitting a regression-type predictive model using easily obtainable covariates derived from observational time series. We conclude that the GEV and the Baluev methods both provide good solutions to the issues posed by a large-scale processing. The first of these yields the best scientific quality at the price of some moderately costly pre-processing. When this pre-processing is impossible for some reason (e.g. the computational costs are prohibitive or good regression models cannot be constructed), the Baluev method provides a computationally inexpensive alternative with slight biases in regions where time samplings exhibit strong aliases.
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
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.
Gavaldón, D G; Cisneros, M A; Rojas, N; Moles-Cervantes, L P
1995-01-01
The presence of specific serum antibodies has been used as a diagnostic test for human leptospirosis. The presence of these antibodies in humans is indicative of an active natural infection. Its detection after exposure denotes the presence of immunity. Serum samples from 206 adult blood donors were analyzed with a microscopic agglutination assay against 7 serovars of Leptospira interrogans. A total of 7% were positive with the following serovar distribution; shermani 53%, canicola 33%, pyrogens 20%, pomona 13% and icterohaemorrhagiae 6%. The highest frequency of seropositivity was found in the 20 year to 39 age group. These results in asymptomatic individuals show that leptospirosis is a frequent zoonosis in Mexico. PMID:8582567
Statistical approaches to detecting and analyzing tandem repeats in genomic sequences.
Anisimova, Maria; Pečerska, Julija; Schaper, Elke
2015-01-01
Tandem repeats (TRs) are frequently observed in genomes across all domains of life. Evidence suggests that some TRs are crucial for proteins with fundamental biological functions and can be associated with virulence, resistance, and infectious/neurodegenerative diseases. Genome-scale systematic studies of TRs have the potential to unveil core mechanisms governing TR evolution and TR roles in shaping genomes. However, TR-related studies are often non-trivial due to heterogeneous and sometimes fast evolving TR regions. In this review, we discuss these intricacies and their consequences. We present our recent contributions to computational and statistical approaches for TR significance testing, sequence profile-based TR annotation, TR-aware sequence alignment, phylogenetic analyses of TR unit number and order, and TR benchmarks. Importantly, all these methods explicitly rely on the evolutionary definition of a tandem repeat as a sequence of adjacent repeat units stemming from a common ancestor. The discussed work has a focus on protein TRs, yet is generally applicable to nucleic acid TRs, sharing similar features. PMID:25853125
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. PMID:22059641
Detection of Invalid Test Scores: The Usefulness of Simple Nonparametric Statistics
ERIC Educational Resources Information Center
Tendeiro, Jorge N.; Meijer, Rob R.
2014-01-01
In recent guidelines for fair educational testing it is advised to check the validity of individual test scores through the use of person-fit statistics. For practitioners it is unclear on the basis of the existing literature which statistic to use. An overview of relatively simple existing nonparametric approaches to identify atypical response…
Drozd, V; Polyanskaya, O; Ostapenko, V; Demidchik, Y; Biko, I; Reiners, C
2002-01-01
We report the results of ultrasound screening of the thyroid gland in 3,051 Belarus children 4-14 years of age exposed to radioactive fallout due to the Chernobyl accident. Screening was performed in 1990, 1993 and 1998. The study demonstrated that with time the prevalence of thyroid nodules in this contaminated region increased from 1.2% to 3.5%, mostly due to pathologically verified nodular goiter and non-verified small solid nodules and cysts. In contrast, the prevalence of thyroid carcinoma decreased from 0.6% in 1990 to 0.3% in 1993. We found 15 patients with carcinoma. On analysis of the ultrasound pattern of all carcinomas, we observed nodular and diffuse variants. Thus, we can conclude that systematic ultrasound screening is useful for the early detection of thyroid carcinoma in the population of Belarus exposed to radiation due to the Chernobyl accident. PMID:12199342
Nakashima, R; Hosono, Y; Mimori, T
2016-07-01
Anti-aminoacyl-tRNA synthetase (ARS) and anti-melanoma differentiation-associated gene 5 (MDA5) antibodies are closely associated with interstitial lung disease in polymyositis and dermatomyositis. Anti-ARS-positive patients develop common clinical characteristics termed anti-synthetase syndrome and share a common clinical course, in which they respond well to initial treatment with glucocorticoids but in which disease tends to recur when glucocorticoids are tapered. Anti-MDA5 antibody is associated with rapidly progressive interstitial lung disease and poor prognosis, particularly in Asia. Therefore, intensive immunosuppressive therapy is required for anti-MDA5-positive patients from the early phase of the disease. New enzyme-linked immunosorbent assays to detect anti-ARS and anti-MDA5 antibodies have recently been established and are suggested to be efficient and useful. These assays are expected to be widely applied in daily practice. PMID:27252271
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
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.
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
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.
Beyond Gaussian statistical analysis for man-made object detection in hyperspectral images
NASA Astrophysics Data System (ADS)
Bernhardt, Mark; Roberts, Joanne M.
1999-12-01
Emerging Hyper-Spectral imaging technology allows the acquisition of data 'cubes' which simultaneously have high- resolution spatial and spectral components. There is a wealth of information in this data and effective techniques for extracting and processing this information are vital. Previous work by ERIM on man-made object detection has demonstrated that there is a huge amount of discriminatory information in hyperspectral images. This work used the hypothesis that the spectral characteristics of natural backgrounds can be described by a multivariate Gaussian model. The Mahalanobis distance (derived from the covariance matrix) between the background and other objects in the spectral data is the key discriminant. Other work (by DERA and Pilkington Optronics Ltd) has confirmed these findings, but indicates that in order to obtain the lowest possible false alarm probability, a way of including higher order statistics is necessary. There are many ways in which this could be done ranging from neural networks to classical density estimation approaches. In this paper we report on a new method for extending the Gaussian approach to more complex spectral signatures. By using ideas from the theory of Support Vector Machines we are able to map the spectral data into a higher dimensional space. The co- ordinates of this space are derived from all possible multiplicative combinations of the original spectral line intensities, up to a given order d -- which is the main parameter of the method. The data in this higher dimensional space are then analyzed using a multivariate Gaussian approach. Thus when d equals 1 we recover the ERIM model -- in this case the mapping is the identity. In order for such an approach to be at all tractable we must solve the 'combinatorial explosion' problem implicit in this mapping for large numbers of spectral lines in the signature data. In order to do this we note that in the final analysis of this approach it is only the inner (dot) products
NASA Astrophysics Data System (ADS)
Baluev, Roman V.
2015-01-01
We consider the so-called Keplerian periodogram, in which the putative detectable signal is modelled by a highly non-linear Keplerian radial velocity function, appearing in Doppler exoplanetary surveys. We demonstrate that for planets on high-eccentricity orbits the Keplerian periodogram is far more efficient than the classic Lomb-Scargle periodogram and even the multiharmonic periodograms, in which the periodic signal is approximated by a truncated Fourier series. We provide a new numerical algorithm for computation of the Keplerian periodogram. This algorithm adaptively increases the parametric resolution where necessary, in order to uniformly cover all local optima of the Keplerian fit. Thanks to this improvement, the algorithm provides more smooth and reliable results with minimized computing demands. We also derive a fast analytic approximation to the false alarm probability levels of the Keplerian periodogram. This approximation has the form (Pz3/2 + Qz)Wexp ( - z), where z is the observed periodogram maximum, W is proportional to the settled frequency range, and the coefficients P and Q depend on the maximum eccentricity to scan.
Clinical significance of circulating miRNA detection in lung cancer.
Zhao, Chen; Lu, Funian; Chen, Hongxia; Zhao, Fuqiang; Zhu, Ziwen; Zhao, Xianda; Chen, Honglei
2016-05-01
Lung cancer is the most common cancer in the world and the leading cause of tumor death among males. MicroRNAs (miRNAs) are single-stranded RNAs of approximately 22 nucleotides and constituted a new class of gene regulators in humans. As a novel class of emerging biomarkers, the aberrant expression of miRNA has been detected in various tumors. miRNAs are secreted into circulation by microvesicles from the broken tumor cells and act as either oncogenes or tumor suppressors in tumor tissues. In this review, we summarized different circulating miRNAs and their expression level as well as predictable values in lung cancer patients which were investigated in recent 5 years. Circulating miRNAs are found to be dysregulated and have association with clinicopathological parameters and overall survival in lung cancer patients. In conclusion, circulating miRNAs have the potential for distinguishing lung cancer patients from healthy individuals, with the advantages of stabilities, noninvasiveness and cost-effectiveness. PMID:27034265
Carroll, Adam J.; Zhang, Peng; Whitehead, Lynne; Kaines, Sarah; Tcherkez, Guillaume; Badger, Murray R.
2015-01-01
This article describes PhenoMeter (PM), a new type of metabolomics database search that accepts metabolite response patterns as queries and searches the MetaPhen database of reference patterns for responses that are statistically significantly similar or inverse for the purposes of detecting functional links. To identify a similarity measure that would detect functional links as reliably as possible, we compared the performance of four statistics in correctly top-matching metabolic phenotypes of Arabidopsis thaliana metabolism mutants affected in different steps of the photorespiration metabolic pathway to reference phenotypes of mutants affected in the same enzymes by independent mutations. The best performing statistic, the PM score, was a function of both Pearson correlation and Fisher’s Exact Test of directional overlap. This statistic outperformed Pearson correlation, biweight midcorrelation and Fisher’s Exact Test used alone. To demonstrate general applicability, we show that the PM reliably retrieved the most closely functionally linked response in the database when queried with responses to a wide variety of environmental and genetic perturbations. Attempts to match metabolic phenotypes between independent studies were met with varying success and possible reasons for this are discussed. Overall, our results suggest that integration of pattern-based search tools into metabolomics databases will aid functional annotation of newly recorded metabolic phenotypes analogously to the way sequence similarity search algorithms have aided the functional annotation of genes and proteins. PM is freely available at MetabolomeExpress (https://www.metabolome-express.org/phenometer.php). PMID:26284240
Balasubramanian, Madhusudhanan; Arias-Castro, Ery; Medeiros, Felipe A.; Kriegman, David J.; Bowd, Christopher; Weinreb, Robert N.; Holst, Michael; Sample, Pamela A.; Zangwill, Linda M.
2014-01-01
Purpose. We evaluated three new pixelwise rates of retinal height changes (PixR) strategies to reduce false-positive errors while detecting glaucomatous progression. Methods. Diagnostic accuracy of nonparametric PixR-NP cluster test (CT), PixR-NP single threshold test (STT), and parametric PixR-P STT were compared to statistic image mapping (SIM) using the Heidelberg Retina Tomograph. We included 36 progressing eyes, 210 nonprogressing patient eyes, and 21 longitudinal normal eyes from the University of California, San Diego (UCSD) Diagnostic Innovations in Glaucoma Study. Multiple comparison problem due to simultaneous testing of retinal locations was addressed in PixR-NP CT by controlling family-wise error rate (FWER) and in STT methods by Lehmann-Romano's k-FWER. For STT methods, progression was defined as an observed progression rate (ratio of number of pixels with significant rate of decrease; i.e., red-pixels, to disk size) > 2.5%. Progression criterion for CT and SIM methods was presence of one or more significant (P < 1%) red-pixel clusters within disk. Results. Specificity in normals: CT = 81% (90%), PixR-NP STT = 90%, PixR-P STT = 90%, SIM = 90%. Sensitivity in progressing eyes: CT = 86% (86%), PixR-NP STT = 75%, PixR-P STT = 81%, SIM = 39%. Specificity in nonprogressing patient eyes: CT = 49% (55%), PixR-NP STT = 56%, PixR-P STT = 50%, SIM = 79%. Progression detected by PixR in nonprogressing patient eyes was associated with early signs of visual field change that did not yet meet our definition of glaucomatous progression. Conclusions. The PixR provided higher sensitivity in progressing eyes and similar specificity in normals than SIM, suggesting that PixR strategies can improve our ability to detect glaucomatous progression. Longer follow-up is necessary to determine whether nonprogressing eyes identified as progressing by these methods will develop glaucomatous progression. (ClinicalTrials.gov number, NCT00221897.) PMID:24519427
Wang, Hong-Qiang; Tsai, Chung-Jui
2013-01-01
With the rapid increase of omics data, correlation analysis has become an indispensable tool for inferring meaningful associations from a large number of observations. Pearson correlation coefficient (PCC) and its variants are widely used for such purposes. However, it remains challenging to test whether an observed association is reliable both statistically and biologically. We present here a new method, CorSig, for statistical inference of correlation significance. CorSig is based on a biology-informed null hypothesis, i.e., testing whether the true PCC (ρ) between two variables is statistically larger than a user-specified PCC cutoff (τ), as opposed to the simple null hypothesis of ρ = 0 in existing methods, i.e., testing whether an association can be declared without a threshold. CorSig incorporates Fisher's Z transformation of the observed PCC (r), which facilitates use of standard techniques for p-value computation and multiple testing corrections. We compared CorSig against two methods: one uses a minimum PCC cutoff while the other (Zhu's procedure) controls correlation strength and statistical significance in two discrete steps. CorSig consistently outperformed these methods in various simulation data scenarios by balancing between false positives and false negatives. When tested on real-world Populus microarray data, CorSig effectively identified co-expressed genes in the flavonoid pathway, and discriminated between closely related gene family members for their differential association with flavonoid and lignin pathways. The p-values obtained by CorSig can be used as a stand-alone parameter for stratification of co-expressed genes according to their correlation strength in lieu of an arbitrary cutoff. CorSig requires one single tunable parameter, and can be readily extended to other correlation measures. Thus, CorSig should be useful for a wide range of applications, particularly for network analysis of high-dimensional genomic data. Software
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.
Čunderlíková, B
2016-09-01
Our understanding of cancer has evolved mainly from results of studies utilizing experimental models. Simplification inherent to in vitro cell culture models enabled potential ways of cell behaviour in response to various external stimuli to be described, but it has led also to disappointments in clinical trials, presumably due to the lack of crucial tissue components, including extracellular matrix (ECM). ECM and its role in healthy and diseased tissues are being explored extensively and significance of ECM for cell behaviour has been evidenced experimentally. Part of the information gathered in such research that is relevant for natural conditions of a human body can be identified by carefully designed analyses of human tissue samples. This review summarizes published information on clinical significance of ECM in cancer and examines whether effects of ECM on cell behaviour evidenced in vitro, could be supported by clinically based data acquired from analysis of tissue samples. Based on current approaches of clinical immunohistochemical analyses, impact of ECM components on tumour cell behaviour is vague. Except of traditionally considered limitations, other reasons may include lack of stratification of analyzed cases based on clinicopathologic parameters, inclusion of patients treated postoperatively by different treatments or neglecting complexity of interactions among tumour constituents. Nevertheless, reliable immunohistochemical studies represent a source of crucial information for design of tumour models comprising ECM corresponding to real clinical situation. Knowledge gathered from such immunohistochemical studies combined with achievements in tissue engineering hold promise for reversal of the unfavourable trends in the current translational oncologic research. PMID:27443915
NASA Astrophysics Data System (ADS)
Korman, Murray S.; Alberts, W. C. K., II; Sabatier, James M.
2004-09-01
In nonlinear acoustic detection experiments involving a buried inert VS 2.2 anti-tank landmine, airborne sound at two closely spaced primary frequencies f1 and f2 couple into the ground and interact nonlinearly with the soil-top pressure plate interface. Scattering generates soil vibration at the surface at the combination frequencies | m f1 +- n f2 | , where m and n are integers. The normal component of the particle velocity at the soil surface has been measured with a laser Doppler velocimeter (LDV) and with a geophone by Sabatier et. al. [SPIE Proceedings Vol. 4742, (695-700), 2002; Vol. 5089, (476-486), 2003] at the gravel lane test site. Spatial profiles of the particle velocity measured for both primary components and for various combination frequencies indicate that the modal structure of the mine is playing an important role. Here, an experimental modal analysis is performed on a VS 1.6 inert anti-tank mine that is resting on sand but is not buried. Five top-plate mode shapes are described. The mine is then buried in dry finely sifted natural loess soil and excited at f1 = 120 Hz and f2 = 130 Hz. Spatial profiles at the primary components and the nonlinearly generated f1 - (f2 - f1) component are characterized by a single peak. For the 2f1+f2 and 2f2 + f1 components, the doubly peaked profiles can be attributed to the familiar mode shape of a timpani drum (that is shifted lower in frequency due to soil mass loading). Other nonlinear profiles appear to be due to a mixture of modes. This material is based upon work supported by the U. S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate under Contract DAAB15-02-C-0024.
NASA Astrophysics Data System (ADS)
Draper, David S.; van Westrenen, Wim
2007-12-01
As a complement to our efforts to update and revise the thermodynamic basis for predicting garnet-melt trace element partitioning using lattice-strain theory (van Westrenen and Draper in Contrib Mineral Petrol, this issue), we have performed detailed statistical evaluations of possible correlations between intensive and extensive variables and experimentally determined garnet-melt partitioning values for trivalent cations (rare earth elements, Y, and Sc) entering the dodecahedral garnet X-site. We applied these evaluations to a database containing over 300 partition coefficient determinations, compiled both from literature values and from our own work designed in part to expand that database. Available data include partitioning measurements in ultramafic to basaltic to intermediate bulk compositions, and recent studies in Fe-rich systems relevant to extraterrestrial petrogenesis, at pressures sufficiently high such that a significant component of majorite, the high-pressure form of garnet, is present. Through the application of lattice-strain theory, we obtained best-fit values for the ideal ionic radius of the dodecahedral garnet X-site, r 0(3+), its apparent Young’s modulus E(3+), and the strain-free partition coefficient D 0(3+) for a fictive REE element J of ionic radius r 0(3+). Resulting values of E, D 0, and r 0 were used in multiple linear regressions involving sixteen variables that reflect the possible influence of garnet composition and stoichiometry, melt composition and structure, major-element partitioning, pressure, and temperature. We find no statistically significant correlations between fitted r 0 and E values and any combination of variables. However, a highly robust correlation between fitted D 0 and garnet-melt Fe Mg exchange and D Mg is identified. The identification of more explicit melt-compositional influence is a first for this type of predictive modeling. We combine this statistically-derived expression for predicting D 0 with the new
Detecting reduced bone mineral density from dental radiographs using statistical shape models.
Allen, P Danny; Graham, Jim; Farnell, Damian J J; Harrison, Elizabeth J; Jacobs, Reinhilde; Nicopolou-Karayianni, Kety; Lindh, Christina; van der Stelt, Paul F; Horner, Keith; Devlin, Hugh
2007-11-01
We describe a novel method of estimating reduced bone mineral density (BMD) from dental panoramic tomograms (DPTs), which show the entire mandible. Careful expert width measurement of the inferior mandibular cortex has been shown to be predictive of BMD in hip and spine osteopenia and osteoporosis. We have implemented a method of automatic measurement of the width by active shape model search, using as training data 132 DPTs of female subjects whose BMD has been established by dual-energy X-ray absorptiometry. We demonstrate that widths measured after fully automatic search are significantly correlated with BMD, and exhibit less variability than manual measurements made by different experts. The correlation is highest towards the lateral region of the mandible, in a position different from that previously employed for manual width measurement. An receiver-operator characterstic (ROC) analysis for identifying osteopenia (T < -1: BMD more than one standard deviation below that of young healthy females) gives an area under curve (AUC) value of 0.64. Using a minimal interaction to initiate active shape model (ASM) search, the measurement can be made at the optimum region of the mandible, resulting in an AUC value of 0.71. Using an independent test set, AUC for detection of osteoporosis (T < -2.5) is 0.81. PMID:18046935
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.)
Yamauchi, M; Nakajima, H; Ohata, M; Hirakawa, J; Mizuhara, Y; Nakahara, M; Kimura, K; Fujisawa, K; Kameda, H
1991-08-01
Pooled sera collected from cirrhotic patients was fractionated by affinity chromatography with a fibronectin receptor monoclonal antibody against the beta-subunit of fibronectin receptor. Eluates were assayed using Western immunoblotting. The relative mobility of the protein reactive with fibronectin receptor antibody was nearly identical to that of the beta-subunit of fibronectin receptor, confirming that fibronectin receptor is present in human serum. Serum levels of the beta-subunit of fibronectin receptor were analyzed by sandwich enzyme-linked immunosorbent assay in patients with various liver diseases. The serum level of fibronectin receptor (micrograms/ml) was significantly higher in patients with chronic hepatitis (inactive, 2.59 +/- 0.04; active, 3.45 +/- 0.13), cirrhosis (4.77 +/- 0.30), alcoholic liver disease (2.96 +/- 0.16) and hepatocellular carcinoma (4.71 +/- 0.49) than in normal subjects (2.11 +/- 0.08). Strong positive correlation was observed between serum levels of fibronectin receptor and histological findings, particularly in the degree of hepatic fibrosis. Immunohistochemical studies with fibronectin receptor antibody revealed that the beta-subunit of fibronectin receptor was present on the plasma membrane of hepatocytes and sinusoidal lining cells in the normal liver and was increased in fibrotic areas and on the plasma membrane of hepatocytes and sinusoidal lining cells of fibrotic liver. The serum level of fibronectin receptor in patients with chronic liver diseases may therefore be a useful marker of hepatic fibrosis. PMID:1830562
Wawrzyniak, Ewa; Kotkowska, Aleksandra; Blonski, Jerzy Z; Siemieniuk-Rys, Monika; Ziolkowska, Ewelina; Giannopoulos, Krzysztof; Robak, Tadeusz; Korycka-Wolowiec, Anna
2014-02-01
The acquisition of new aberrations during the course of chronic lymphocytic leukemia (CLL) named clonal evolution (CE) is usually detected by one of the two methods: chromosome banding analysis (CBA) and interphase fluorescence in situ hybridization (I-FISH). The purpose of this study was to compare the usefulness of FISH and CBA for detecting CE and to evaluate its influence on clinical outcome. FISH and CBA were performed at two time points: baseline and follow-up. Thirty-eight previously untreated patients with CLL were included in this study. CBA and I-FISH revealed CE in 15 (39.5%) and 10 (26.3%) patients, respectively. High-risk CE was detected in six cases by CBA and in five cases by I-FISH. In four cases with CE-dependent 17p abnormalities detected by CBA, metaphase FISH was needed for the confirmation of 17p13.1 deletion. Time from first-line to second-line treatment (TTST) and overall survival (OS) did not differ between patients with and without CE, irrespective of the CE-detecting method used. However, shorter OS (P = 0.043) and TTST (P = 0.006) were observed for the patients with potentially relevant CE (rCE) detected by CBA, in which acquired aberrations were present in at least 20% of undivided cells and/or changed baseline karyotype to abnormal or complex and were not resulting from 13q deletion. Our results suggest that some, but not all, CE-dependent aberrations detected by CBA influence clinical outcome. Moreover, I-FISH, which was aimed at detecting aberrations of prognostic significance, was found to be more precise than CBA in their detection, especially TP53 deletion. PMID:24138550
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.
Zhao, Wei; Yu, Haixiang; Han, Zhifeng; Gao, Nan; Xue, Jinru; Wang, Yan
2015-01-01
Lung cancer is a type of malignant tumor with highest morbidity and mortality. This study tested three tumor marker levels including CEA, SCCA, and bFGF to explore their value in lung cancer diagnosis and pathological type judgment. Venous blood was extracted from lung cancer patients, lung benign lesion patients and healthy control. Electrochemiluminescence immunoassay was applied to detect serum CEA and SCCA content. ELISA was used to test serum bFGF level. Serum CEA, SCCA, and bFGF levels and positive rates were significantly higher in lung cancer group than that of lung benign disease group and health control (P < 0.05). bFGF showed higher detection sensitivity than CEA in lung cancer (P < 0.05). Three joint detection sensitivity was higher than single test (P < 0.05), while its specificity was lower (P < 0.05), and the accuracy presented no significant difference. Serum CEA and SCCA levels and positive rates were obviously higher in non-small cell lung cancer patients when compared with small cell lung cancer patients (P < 0.05), while bFGF level was similar between small cell lung cancer and non-small cell lung cancer. bFGF showed higher detection rate than SCCA in small cell lung cancer (P < 0.05). Three joint detection exhibited higher positive rate in small cell lung cancer and non-small lung cancer than single test. Serum CEA, SCCA and bFGF joint detection improved detection sensitivity in lung cancer and had important reference value for pathological type deduction. PMID:26464712
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
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. PMID:26060985
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.
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.
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. PMID:21487489
NASA Astrophysics Data System (ADS)
Maussang, F.; Chanussot, J.; Hétet, A.; Amate, M.
2007-12-01
An original algorithm for the detection of small objects in a noisy background is proposed. Its application to underwater objects detection by sonar imaging is addressed. This new method is based on the use of higher-order statistics (HOS) that are locally estimated on the images. The proposed algorithm is divided into two steps. In a first step, HOS (skewness and kurtosis) are estimated locally using a square sliding computation window. Small deterministic objects have different statistical properties from the background they are thus highlighted. The influence of the signal-to-noise ratio (SNR) on the results is studied in the case of Gaussian noise. Mathematical expressions of the estimators and of the expected performances are derived and are experimentally confirmed. In a second step, the results are focused by a matched filter using a theoretical model. This enables the precise localization of the regions of interest. The proposed method generalizes to other statistical distributions and we derive the theoretical expressions of the HOS estimators in the case of a Weibull distribution (both when only noise is present or when a small deterministic object is present within the filtering window). This enables the application of the proposed technique to the processing of synthetic aperture sonar data containing underwater mines whose echoes have to be detected and located. Results on real data sets are presented and quantitatively evaluated using receiver operating characteristic (ROC) curves.
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
Research of adaptive threshold edge detection algorithm based on statistics canny operator
NASA Astrophysics Data System (ADS)
Xu, Jian; Wang, Huaisuo; Huang, Hua
2015-12-01
The traditional Canny operator cannot get the optimal threshold in different scene, on this foundation, an improved Canny edge detection algorithm based on adaptive threshold is proposed. The result of the experiment pictures indicate that the improved algorithm can get responsible threshold, and has the better accuracy and precision in the edge detection.
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
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…
NASA Astrophysics Data System (ADS)
Panthou, G.; Vischel, T.; Lebel, T.; Quantin, G.; Favre, A.; Blanchet, J.; Ali, A.
2012-12-01
Studying trends in rainfall extremes at regional scale is required to provide reference climatology to evaluate General Circulation Model global predictions as well as to help managing and designing hydraulic works. The present study compares three methods to detect trends (linear and change-point) in series of daily rainfall annual maxima: (i) The first approach is widely used and consist in applying statistical stationarity tests (linear trend and change-point) on the point-wise maxima series; (ii) The second approach compares the performances of a constant and a time dependent Generalized Extreme Value (GEV) distribution fitted to the point-wise maxima series. (iii) The last method uses an original regional statistical model based on space-time GEV distribution which is used to detect changes in rainfall extremes directly at regional scale. The three methods are applied to detect trends in extreme daily rainfall over the Sahel during the period 1950-1990 for which a network of 128 daily rain gages is available. This region has experienced an intense drought since the end of the 1960s; it is thus an interesting case-study to illustrate how a regional climate change can affect the extreme rainfall distributions. One major result is that the statistical stationarity tests rarely detect non-stationarities in the series while the two GEV-based models converge to show that the extreme rainfall series have a negative break point around 1970. The study points out the limit of the widely used classical stationarity tests to detect trends in noisy series affected by sampling errors. The use of parametric time-dependent GEV seems to reduce this effect especially when a regional approach is used. From a climatological point of view, the results show that the great Sahelian drought has been accompanied by a decrease of extreme rainfall events, both in magnitude and occurence.
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
Statistical power of detecting trends in total suspended sediment loads to the Great Barrier Reef.
Darnell, Ross; Henderson, Brent; Kroon, Frederieke J; Kuhnert, Petra
2012-01-01
The export of pollutant loads from coastal catchments is of primary interest to natural resource management. For example, Reef Plan, a joint initiative by the Australian Government and the Queensland Government, has indicated that a 20% reduction in sediment is required by 2020. There is an obvious need to consider our ability to detect any trend if we are to set realistic targets or to reliably identify changes to catchment loads. We investigate the number of years of monitoring aquatic pollutant loads necessary to detect trends. Instead of modelling the trend in the annual loads directly, given their strong relationship to flow, we consider trends through the reduction in concentration for a given flow. Our simulations show very low power (<40%) of detecting changes of 20% over time periods of several decades, indicating that the chances of detecting trends of reasonable magnitudes over these time frames are very small. PMID:22551850
Zeng, W; Liu, B
1999-01-01
Digital watermarking has been proposed as the means for copyright protection of multimedia data. Many of existing watermarking schemes focused on the robust means to mark an image invisibly without really addressing the ends of these schemes. This paper first discusses some scenarios in which many current watermarking schemes fail to resolve the rightful ownership of an image. The key problems are then identified, and some crucial requirements for a valid invisible watermark detection are discussed. In particular, we show that, for the particular application of resolving rightful ownership using invisible watermarks, it might be crucial to require that the original image not be directly involved in the watermark detection process. A general framework for validly detecting the invisible watermarks is then proposed. Some requirements on the claimed signature/watermarks to be used for detection are discussed to prevent the existence of any counterfeit scheme. The optimal detection strategy within the framework is derived. We show the effectiveness of this technique based on some visual-model-based watermark encoding schemes. PMID:18267429
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
Nichols, W W; Curtis, G D; Johnston, H H
1982-01-01
A fully automated method for detecting significant bacteriuria is described which uses firefly luciferin and luciferase to detect bacterial ATP in urine. The automated method was calibrated and evaluated, using 308 urine specimens, against two reference culture methods. We obtained a specificity of 0.79 and sensitivity of 0.75 using a quantitative pour plate reference test and a specificity of 0.79 and a sensitivity of 0.90 using a semiquantitative standard loop reference test. The majority of specimens negative by the automated test but positive by the pour plate reference test were specimens which grew several bacterial species. We suggest that such disagreement was most likely for urine containing around 10(5) colony-forming units per ml (the culture threshold of positivity) and that these specimens were ones contaminated by urethral or vaginal flora. We propose standard procedures for calibrating and evaluating rapid or automated methods for the detection of significant bacteriuria and have analyzed our results using these procedures. We recommend that identical analyses should be reported for other evaluations of bacteriuria detection methods. PMID:6808012
Yoon, Hyun Jung; Hwang, Hye Sun; Moon, Jung Won; Lee, Kyung Soo
2015-01-01
Objective To assess the performance of adaptive statistical iterative reconstruction (ASIR)-applied ultra-low-dose CT (ULDCT) in detecting small lung nodules. Materials and Methods Thirty patients underwent both ULDCT and standard dose CT (SCT). After determining the reference standard nodules, five observers, blinded to the reference standard reading results, independently evaluated SCT and both subsets of ASIR- and filtered back projection (FBP)-driven ULDCT images. Data assessed by observers were compared statistically. Results Converted effective doses in SCT and ULDCT were 2.81 ± 0.92 and 0.17 ± 0.02 mSv, respectively. A total of 114 lung nodules were detected on SCT as a standard reference. There was no statistically significant difference in sensitivity between ASIR-driven ULDCT and SCT for three out of the five observers (p = 0.678, 0.735, < 0.01, 0.038, and < 0.868 for observers 1, 2, 3, 4, and 5, respectively). The sensitivity of FBP-driven ULDCT was significantly lower than that of ASIR-driven ULDCT in three out of the five observers (p < 0.01 for three observers, and p = 0.064 and 0.146 for two observers). In jackknife alternative free-response receiver operating characteristic analysis, the mean values of figure-of-merit (FOM) for FBP, ASIR-driven ULDCT, and SCT were 0.682, 0.772, and 0.821, respectively, and there were no significant differences in FOM values between ASIR-driven ULDCT and SCT (p = 0.11), but the FOM value of FBP-driven ULDCT was significantly lower than that of ASIR-driven ULDCT and SCT (p = 0.01 and 0.00). Conclusion Adaptive statistical iterative reconstruction-driven ULDCT delivering a radiation dose of only 0.17 mSv offers acceptable sensitivity in nodule detection compared with SCT and has better performance than FBP-driven ULDCT. PMID:26357505
ERIC Educational Resources Information Center
Liu, Ming-Tsung; Yu, Pao-Ta
2011-01-01
A personalized e-learning service provides learning content to fit learners' individual differences. Learning achievements are influenced by cognitive as well as non-cognitive factors such as mood, motivation, interest, and personal styles. This paper proposes the Learning Caution Indexes (LCI) to detect aberrant learning patterns. The philosophy…
Efficient snoring and breathing detection based on sub-band spectral statistics.
Sun, Xiang; Kim, Jin Young; Won, Yonggwan; Kim, Jung-Ja; Kim, Kyung-Ah
2015-01-01
Snoring, a common symptom in the general population may indicate the presence of obstructive sleep apnea (OSA). In order to detect snoring events in sleep sound recordings, a novel method was proposed in this paper. The proposed method operates by analyzing the acoustic characteristics of the snoring sounds. Based on these acoustic properties, the feature vectors are obtained using the mean and standard deviation of the sub-band spectral energy. A support vector machine is then applied to perform the frame-based classification procedure. This method was demonstrated experimentally to be effective for snoring detection. The database for detection included full-night audio recordings from four individuals who acknowledged having snoring habits. The performance of the proposed method was evaluated by classifying different events (snoring, breathing and silence) from the sleep sound recordings and comparing the classification against ground truth. The proposed algorithm was able to achieve an accuracy of 99.61% for detecting snoring events, 99.16% for breathing, and 99.55% for silence. PMID:26406075
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).
NASA Astrophysics Data System (ADS)
Vanli, O. Arda; Jung, Sungmoon
2014-01-01
Health monitoring of large structures with embedded, distributed sensor systems is gaining importance. This study proposes a new probabilistic model updating method in order to improve the damage prediction capability of a finite element analysis (FEA) model with experimental observations from a Lamb-wave sensing system. The approach statistically calibrates unknown parameters of the FEA model and estimates a bias-correcting function to achieve a good match between the model predictions and sensor observations. An experimental validation study is presented in which a set of controlled damages are generated on a composite panel. Time-series signals are collected with the damage condition using a Lamb-wave sensing system and a one dimensional FEA model of the panel is constructed to quantify the damages. The damage indices from both the experiments and the computational model are used to calibrate assumed parameters of the FEA model and to estimate a bias-correction function. The updated model is used to predict the size (extent) and location of damage. It is shown that the proposed model updating approach achieves a prediction accuracy that is superior to a purely statistical approach or a deterministic model calibration approach.
Statistical approaches to nonstationary EEGs for the detection of slow vertex responses.
Fujikake, M; Ninomija, S P; Fujita, H
1989-06-01
A slow vertex response (SVR) is an electric auditory evoked response used for an objective hearing power test. One of the aims of an objective hearing power test is to find infants whose hearing is less than that of normal infants. Early medical treatment is important for infants with a loss of hearing so that they do not have retarded growth. To measure SVRs, we generally use the averaged summation method of an electroencephalogram (EEG), because the signal-to-noise ratio (SVR to EEG and etc.) is very poor. To increase the reliability and stability of measured SVRs, and at the same time, to make the burden of testing light, it is necessary to device an effective measurement method of SVR. Two factors must be considered: (1) SVR waveforms change following the changes of EEGs caused by sleeping and (2) EEGs are considered as nonstationary data in prolonged measurement. In this paper, five statistical methods are used on two different models; a stationary model and a nonstationary model. Through the comparison of waves obtained by each method, we will clarify the statistical characteristics of the original data (EEGs including SVRs), and consider the conditions that effect the measurement method of an SVR. PMID:2794816
NASA Astrophysics Data System (ADS)
Lin, Y. Q.; Ren, W. X.; Fang, S. E.
2011-11-01
Although most vibration-based damage detection methods can acquire satisfactory verification on analytical or numerical structures, most of them may encounter problems when applied to real-world structures under varying environments. The damage detection methods that directly extract damage features from the periodically sampled dynamic time history response measurements are desirable but relevant research and field application verification are still lacking. In this second part of a two-part paper, the robustness and performance of the statistics-based damage index using the forward innovation model by stochastic subspace identification of a vibrating structure proposed in the first part have been investigated against two prestressed reinforced concrete (RC) beams tested in the laboratory and a full-scale RC arch bridge tested in the field under varying environments. Experimental verification is focused on temperature effects. It is demonstrated that the proposed statistics-based damage index is insensitive to temperature variations but sensitive to the structural deterioration or state alteration. This makes it possible to detect the structural damage for the real-scale structures experiencing ambient excitations and varying environmental conditions.
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.
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.
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
2016-01-01
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
NASA Astrophysics Data System (ADS)
Ciuonzo, D.; De Maio, A.; Orlando, D.
2016-06-01
This paper deals with the problem of adaptive multidimensional/multichannel signal detection in homogeneous Gaussian disturbance with unknown covariance matrix and structured deterministic interference. The aforementioned problem corresponds to a generalization of the well-known Generalized Multivariate Analysis of Variance (GMANOVA). In this first part of the work, we formulate the considered problem in canonical form and, after identifying a desirable group of transformations for the considered hypothesis testing, we derive a Maximal Invariant Statistic (MIS) for the problem at hand. Furthermore, we provide the MIS distribution in the form of a stochastic representation. Finally, strong connections to the MIS obtained in the open literature in simpler scenarios are underlined.
ERIC Educational Resources Information Center
Meyer, Donald L.
Bayesian statistical methodology and its possible uses in the behavioral sciences are discussed in relation to the solution of problems in both the use and teaching of fundamental statistical methods, including confidence intervals, significance tests, and sampling. The Bayesian model explains these statistical methods and offers a consistent…
Gentile, Mauro; De Vito, Alessandro; Azzini, Cristiano; Tamborino, Carmine; Casetta, Ilaria
2014-11-01
Contrast-transcranial Doppler and contrast-transcranial color-coded duplex sonography (c-TCCD) have been reported to have high sensitivity in detecting patent foramen ovale as compared with transesophageal echocardiography. An international consensus meeting (Jauss and Zanette 2000) recommended that the contrast agent for right-to left-shunt (RLS) detection using contrast-transcranial Doppler be prepared by mixing 9 mL of isotonic saline solution and 1 mL of air. The aim of our study was to determine whether adding blood to the contrast agent results in improved detection of RLS. We enrolled all consecutive patients admitted to our neurosonology laboratory for RLS diagnosis. For each patient, we performed c-TCCD both at rest and during the Valsalva maneuver using two different contrast agents: ANSs (1 mL of air mixed with 9 mL of normal saline) and ANSHBs (1 mL of air mixed with 8 mL of normal saline and 1 mL of the patient's blood). To classify RLS, we used a four-level visual categorization: (i) no occurrence of micro-embolic signals; (ii) grade I, 1-10 signals; (iii) grade II, >10 signals but no curtain; grade III, curtain pattern. We included 80 patients, 33 men and 47 women. RLS was detected in 18.8% at rest and in 35% during the Valsalva maneuver using ANSs, and in 31.3% and in 46.3% using ANSHBs, respectively (p < 0.0001). There was a statistically significant increase in the number of micro-embolic signals with the use of ANSHBs. The use of blood mixed with saline solution and air as a c-TCCD contrast agent produced an increase in positive tests and a higher grade of RLS compared with normal saline and air alone, either with or without the Valsalva maneuver. PMID:25220269
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.
Chwirot, Barbara W; Kowalska, Małgorzata; Płóciennik, Natalia; Piwiński, Mariusz; Michniewicz, Zbigniew; Chwirot, Stanisław
2003-05-01
To determine the extent of a natural variability of the spectra of the autofluorescence and its significance for a reproducibility of different approaches typically used in studies on fluorescence detection of colonic lesions. Two independent series of experiments have been conducted during three years in the same laboratory. Macroscopic tissue specimens obtained during operations of patients with colonic cancers were studied in vitro. The tissues were excited using UV lines of c.w. He-Cd laser and pulsed nitrogen laser and the autofluorescence spectra were recorded for areas visually diagnosed as normal or pathologically changed mucosa. Natural variability of the autofluorescence spectra of colonic tissues seems to be most important factor limiting sensitivity and specificity of the diagnostic algorithms. The mean fluorescence spectra obtained for normal mucosa and its neoplastic lesions differ significantly but the differences are difficult to observe because of the high natural variability among the individual spectra. Further studies of biological basis of the colonic autofluorescence are necessary for a progress in the field of fluorescence detection of colonic neoplastic lesions. PMID:15244272
Statistical modelling and power analysis for detecting trends in total suspended sediment loads
NASA Astrophysics Data System (ADS)
Wang, You-Gan; Wang, Shen S. J.; Dunlop, Jason
2015-01-01
The export of sediments from coastal catchments can have detrimental impacts on estuaries and near shore reef ecosystems such as the Great Barrier Reef. Catchment management approaches aimed at reducing sediment loads require monitoring to evaluate their effectiveness in reducing loads over time. However, load estimation is not a trivial task due to the complex behaviour of constituents in natural streams, the variability of water flows and often a limited amount of data. Regression is commonly used for load estimation and provides a fundamental tool for trend estimation by standardising the other time specific covariates such as flow. This study investigates whether load estimates and resultant power to detect trends can be enhanced by (i) modelling the error structure so that temporal correlation can be better quantified, (ii) making use of predictive variables, and (iii) by identifying an efficient and feasible sampling strategy that may be used to reduce sampling error. To achieve this, we propose a new regression model that includes an innovative compounding errors model structure and uses two additional predictive variables (average discounted flow and turbidity). By combining this modelling approach with a new, regularly optimised, sampling strategy, which adds uniformity to the event sampling strategy, the predictive power was increased to 90%. Using the enhanced regression model proposed here, it was possible to detect a trend of 20% over 20 years. This result is in stark contrast to previous conclusions presented in the literature.
A proposal for statistical evaluation of the detection of gunshot residues on a suspect.
Cardinetti, Bruno; Ciampini, Claudio; Abate, Sergio; Marchetrti, Christian; Ferrari, Francesco; Di Tullio, Donatello; D'Onofrio, Carlo; Orlando, Giovanni; Gravina, Luciano; Torresi, Luca; Saporita, Giuseppe
2006-01-01
The possibility of accidental contamination of a suspect by gunshot residues (GSRs) is considered. If two hypotheses are taken into account ("the suspect has shot a firearm" and "the suspect has not shot a firearm"), the likelihood ratio of the conditional probabilities of finding a number n of GSRs is defined. Choosing two Poisson distributions, the parameter lambda of the first one coincides with the mean number of GSRs that can be found on a firearm shooter, while the parameter mu of the second one is the mean number of GSRs that can be found on a nonshooter. In this scenario, the likelihood ratio of the conditional probabilities of finding a number n of GSRs in the two hypotheses can be easily calculated. The evaluation of the two parameters lambda and mu and of the goodness of the two probability distributions is performed by using different sets of data: "exclusive" lead-antimony-barium GSRs have been detected in two populations of 31 and 28 police officers at diverse fixed times since firearm practice, and in a population of 81 police officers who stated that they had not handled firearms for almost 1 month. The results show that the Poisson distributions well fit the data for both shooters and nonshooters, and that the probability of detection of two or more GSRs is normally greater if the suspect has shot firearms. PMID:16878785
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.
Enki, Doyo G; Garthwaite, Paul H; 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
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
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). PMID:22037419
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.
NASA Astrophysics Data System (ADS)
Chakraborty, Jayasree; Rangayyan, Rangaraj M.; Banik, Shantanu; Mukhopadhyay, Sudipta; Leo Desautels, J. E.
2012-07-01
Architectural distortion is an important sign of early breast cancer. Due to its subtlety, it is often missed during screening. We propose a method to detect architectural distortion in prior mammograms of interval-cancer cases based on statistical measures of oriented patterns. Oriented patterns were analyzed in the present work because regions with architectural distortion contain a large number of tissue structures spread over a wide angular range. Two new types of cooccurrence matrices were derived to estimate the joint occurrence of the angles of oriented structures. Statistical features were computed from each of the angle cooccurrence matrices to discriminate sites of architectural distortion from falsely detected regions in normal parts of mammograms. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases with the application of Gabor filters and phase portrait analysis. For each ROI, Haralick's 14 features were computed using the angle cooccurrence matrices. The best result obtained in terms of the area under the receiver operating characteristic (ROC) curve with the leave-one-patient-out method was 0.76; the free-response ROC curve indicated a sensitivity of 80% at 4.2 false positives per patient.
Nieto, A; Peña, L; Pérez-Alenza, M D; Sánchez, M A; Flores, J M; Castaño, M
2000-05-01
Eighty-nine canine mammary tumors and dysplasias of 66 bitches were investigated to determine the immunohistochemical expression of classical estrogen receptor (ER-alpha) and its clinical and pathologic associations and prognostic value. A complete clinical examination was performed and reproductive history was evaluated. After surgery, all animals were followed-up for 18 months, with clinical examinations every 3-4 months. ER-alpha expression was higher in tumors of genitally intact and young bitches (P < 0.01, P < 0.01) and in animals with regular estrous periods (P = 0.03). Malignant tumors of the bitches with a previous clinical history of pseudopregnancy expressed significantly more ER-alpha (P = 0.04). Immunoexpression of ER-alpha decreased significantly with tumor size (P = 0.05) and skin ulceration (P = 0.01). Low levels of ER-alpha were significantly associated with lymph node involvement (P < 0.01). Malignant tumors had lower ER-alpha expression than did benign tumors (P < 0.01). Proliferation index measured by proliferating cell nuclear antigen immunostaining was inversely correlated with ER-alpha scores (P = 0.05) in all tumors. Low ER-alpha levels in primary malignant tumors were significantly associated with the occurrence of metastases in the follow-up (P = 0.03). Multivariate analyses were performed to determine the prognostic significance of some follow-up variables. ER-alpha value, Ki-67 index, and age were independent factors that could predict disease-free survival. Lymph node status, age, and ER-alpha index were independent prognostic factors for the overall survival. The immunohistochemical detection of ER-alpha in canine mammary tumors is a simple technique with prognostic value that could be useful in selecting appropriate hormonal therapy. PMID:10810988
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
Himemoto, Yoshiaki; Hiramatsu, Takashi; Taruya, Atsushi; Kudoh, Hideaki
2007-01-15
We discuss a robust data analysis method to detect a stochastic background of gravitational waves in the presence of non-Gaussian noise. In contrast to the standard cross-correlation (SCC) statistic frequently used in the stochastic background searches, we consider a generalized cross-correlation (GCC) statistic, which is nearly optimal even in the presence of non-Gaussian noise. The detection efficiency of the GCC statistic is investigated analytically, particularly focusing on the statistical relation between the false-alarm and the false-dismissal probabilities, and the minimum detectable amplitude of gravitational-wave signals. We derive simple analytic formulas for these statistical quantities. The robustness of the GCC statistic is clarified based on these formulas, and one finds that the detection efficiency of the GCC statistic roughly corresponds to the one of the SCC statistic neglecting the contribution of non-Gaussian tails. This remarkable property is checked by performing the Monte Carlo simulations and successful agreement between analytic and simulation results was found.
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
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...
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
A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions
Paulsen, Jonas; Rødland, Einar A.; Holden, Lars; Holden, Marit; Hovig, Eivind
2014-01-01
Identification of three-dimensional (3D) interactions between regulatory elements across the genome is crucial to unravel the complex regulatory machinery that orchestrates proliferation and differentiation of cells. ChIA-PET is a novel method to identify such interactions, where physical contacts between regions bound by a specific protein are quantified using next-generation sequencing. However, determining the significance of the observed interaction frequencies in such datasets is challenging, and few methods have been proposed. Despite the fact that regions that are close in linear genomic distance have a much higher tendency to interact by chance, no methods to date are capable of taking such dependency into account. Here, we propose a statistical model taking into account the genomic distance relationship, as well as the general propensity of anchors to be involved in contacts overall. Using both real and simulated data, we show that the previously proposed statistical test, based on Fisher's exact test, leads to invalid results when data are dependent on genomic distance. We also evaluate our method on previously validated cell-line specific and constitutive 3D interactions, and show that relevant interactions are significant, while avoiding over-estimating the significance of short nearby interactions. PMID:25114054
NASA Astrophysics Data System (ADS)
Dhakal, N.; Jain, S.
2014-12-01
Changes in seasonality of extreme precipitation have important implications for public safety, stormwater infrastructure and, in general, adaptation strategies in a changing climate. In this context, an understanding of shifts in the extreme event seasons—emerging, weakening or intensification within seasonal windows is an important step. In this study, we applied a nonparametric circular method to assess the temporal changes in the seasonality of the extreme precipitation for 10 USHCN stations across the state of Maine. Two 30-year blocks (1951-1980 and 1981-2010) of daily annual maximum precipitation were used for the analysis. Extreme precipitation dates were used to compute the circular probability distribution. Important information regarding the multimodality in the seasonal distribution of extreme precipitation dates were obtained from the probabilistic assessment of seasonality using the kernel circular density estimates. Nonstationarity in seasonality was observed for most of the stations; some stations exhibit shifting of significant mode towards Spring season for the recent time period while some stations exhibit multimodality for both the time periods. Despite the limitation of being sensitive to the smoothing parameter, the kernel circular density estimates method is clearly superior and robust when dealing with diverse seasonal pattern of extreme rainfall comprising of multiple seasonal modes.
Abe, Noriko; Ohtake, Tohru; Saito, Katsuharu; Kumamoto, Kensuke; Sugino, Takashi; Takenoshita, Seiichi
2016-06-01
To elucidate the association between the lymphangiogenesis and clinicopathological factors including the survival in breast cancer, 91 Japanese patients with breast cancer were investigated. The lymphangiogenesis was evaluated by the count of lymph vessel density (LVD) with immunohistochemical method using D2-40 monoclonal antibody, a specific marker for lymphatic endothelial cells.D2-40-positive lymph vessels were detected in 87 of 91 cases, and were mainly distributed in the peritumoral lesions or around the tumor edge. There was a significant difference in disease-free survival (DFS) and overall survival (OS) between patients with high LVD and with low LVD (p=0.02, 0.01, respectively, log-rank test). In addition, LVD significantly correlated with the following clinicopathological factors: menopausal status (p<0.01), tumor size (p<0.01), lymph-node status (p=0.01) lymphatic vessel invasion (LVI) (p<0.01), blood vessel invasion (BVI) (p=0.03) and estrogen receptor status (ER) (p=0.02).Those data suggest that D2-40 monoclonal antibody is a useful marker for evaluating the LVD and its evaluation is helpful to predict the survival in breast cancer. PMID:27210308
Liang, Jin-Hua; Sun, Jin; Wang, Li; Fan, Lei; Chen, Yao-Yu; Qu, Xiao-Yan; Li, Tian-Nv; Li, Jian-Yong; Xu, Wei
2016-01-01
The aim of this study was to examine the prognostic value of bone marrow involvement (BMI) assessed by baseline PET-CT (PET(0)-BMI) in treatment-naïve patients with diffuse large B-cell lymphoma (DLBCL). All patients from a single centre diagnosed as DLBCL between 2005 and 2014 had data extracted from staging PET-CT (PET(0)-CT), bone marrow biopsy (BMB), and treatment records. The PET(3)-CT (PET-CT scan after cycle 3 of immunochemotherapy) was performed on all the patients with PET(0)-BMI positivity (PET(0)-BMI(+)). Of 169 patients, 20 (11.8%) had BMI on BMB, whereas 35 (20.7%) were PET(0)-BMI positive. Among PET(0)-BMI(+) patients, patients with maximum of standard uptake value (SUVmax) of bone marrow (SUVmax(BM)) more than 8.6 were significantly associated with high IPI score (3–5) (P=0.002), worse progression-free survival (PFS) and overall survival (OS) (P=0.025 and P=0.002, respectively). In the 68 stage IV cases, 3-year OS was higher in the patients with negative PET(0)-BMI (PET(0)-BMI(−)) than that with PET(0)-BMI(+) (84.2%±6.5% vs. 44.1%±8.6%; P=0.003), while 3-year PFS only shown a trend of statistic significance (P=0.077) between the two groups. Among the 69 patients of inter-risk of IPI (2–3), patients with PET(0)-BMI(+) had significantly inferior PFS and OS than that with PET(0)-BMI(−) (P=0.009 and P<0.001, respectively). The cut-off value of the decreased percentage of SUVmax(BM) between PET(0)-CT and PET(3)-CT (ΔSUVmax(BM)) was 70.0%, which can predict PFS (P=0.003) and OS (P=0.023). These data confirmed that along with the increased sensitivity and accuracy of identifying bone marrow by PET-CT, novel prognostic values of marrow involvement were found in patients with DLBCL. PMID:26919239
NASA Astrophysics Data System (ADS)
Cohn, T. A.; England, J. F.; Berenbrock, C. E.; Mason, R. R.; Stedinger, J. R.; Lamontagne, J. R.
2013-08-01
The 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.
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.
Myers, Jamie L.; Sekar, Raju; Richardson, Laurie L.
2007-01-01
Black band disease (BBD) is a pathogenic, sulfide-rich microbial mat dominated by filamentous cyanobacteria that infect corals worldwide. We isolated cyanobacteria from BBD into culture, confirmed their presence in the BBD community by using denaturing gradient gel electrophoresis (DGGE), and demonstrated their ecological significance in terms of physiological sulfide tolerance and photosynthesis-versus-irradiance values. Twenty-nine BBD samples were collected from nine host coral species, four of which have not previously been investigated, from reefs of the Florida Keys, the Bahamas, St. Croix, and the Philippines. From these samples, seven cyanobacteria were isolated into culture. Cloning and sequencing of the 16S rRNA gene using universal primers indicated that four isolates were related to the genus Geitlerinema and three to the genus Leptolyngbya. DGGE results, obtained using Cyanobacteria-specific 16S rRNA primers, revealed that the most common BBD cyanobacterial sequence, detected in 26 BBD field samples, was related to that of an Oscillatoria sp. The next most common sequence, 99% similar to that of the Geitlerinema BBD isolate, was present in three samples. One Leptolyngbya- and one Phormidium-related sequence were also found. Laboratory experiments using isolates of BBD Geitlerinema and Leptolyngbya revealed that they could carry out sulfide-resistant oxygenic photosynthesis, a relatively rare characteristic among cyanobacteria, and that they are adapted to the sulfide-rich, low-light BBD environment. The presence of the cyanotoxin microcystin in these cultures and in BBD suggests a role in BBD pathogenicity. Our results confirm the presence of Geitlerinema in the BBD microbial community and its ecological significance, which have been challenged, and provide evidence of a second ecologically significant BBD cyanobacterium, Leptolyngbya. PMID:17601818
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.
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
Curis, Emmanuel; Dubar, Faustine; Nicolis, Ioannis; Bénazeth, Simone; Biot, Christophe
2012-06-14
Antimalarial compounds ruthenoquine and methylruthenoquine were studied by X-ray absorption spectroscopy both in solid state and in solution, in normal (aqueous or CH(2)Cl(2) solutions) and oxidative (aqueous solution with H(2)O(2), either equimolar or in large excess) conditions, to detect small changes in the coordination sphere of the ruthenium atom. Since changes in the EXAFS spectra of these compounds are quite subtle, a complete procedure was developed to assess the different sources of uncertainties in fitted structural parameters, including the use of multivariate statistic methods for simultaneous comparison of edge energy correction ΔE(0) and distances, which can take into account the very strong correlation between these two parameters. Factors limiting the precision of distance determination depend on the recording mode. In transmission mode, the main source of uncertainty is the data reduction process, whereas in fluorescence mode, experimental noise is the main source of variability in the fitted parameters. However, it was shown that the effects of data reduction are systematic and almost identical for all compounds; hence, they can be ignored when comparing distances. Consequently, for both fluorescence and transmission recorded spectra, experimental noise is the limiting factor for distance comparisons, which leads to the use of statistical methods for comparing distances. Univariate methods, focusing on the distance only, are shown to be less powerful in detecting changes in distances than bivariate methods making a simultaneous comparison of ΔE(0) and distances. This bivariate comparison can be done either by using the Hotelling's T(2) test or by using a graphical comparison of Monte Carlo simulation results. We have shown that using these methods allows for the detection of very subtle changes in distances. When applied to ruthenoquine compounds, it suggests that the implication of the nonbinding doublet of the aminoquine nitrogen in either
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
NASA Astrophysics Data System (ADS)
Zheng, Hao; Holzworth, Robert H.; Brundell, James B.; Jacobson, Abram R.; Wygant, John R.; Hospodarsky, George B.; Mozer, Forrest S.; Bonnell, John
2016-03-01
Lightning-generated whistler waves are electromagnetic plasma waves in the very low frequency (VLF) band, which play an important role in the dynamics of radiation belt particles. In this paper, we statistically analyze simultaneous waveform data from the Van Allen Probes (Radiation Belt Storm Probes, RBSP) and global lightning data from the World Wide Lightning Location Network (WWLLN). Data were obtained between July to September 2013 and between March and April 2014. For each day during these periods, we predicted the most probable 10 min for which each of the two RBSP satellites would be magnetically conjugate to lightning producing regions. The prediction method uses integrated WWLLN stroke data for that day obtained during the three previous years. Using these predicted times for magnetic conjugacy to lightning activity regions, we recorded high time resolution, burst mode waveform data. Here we show that whistlers are observed by the satellites in more than 80% of downloaded waveform data. About 22.9% of the whistlers observed by RBSP are one-to-one coincident with source lightning strokes detected by WWLLN. About 40.1% more of whistlers are found to be one-to-one coincident with lightning if source regions are extended out 2000 km from the satellites footpoints. Lightning strokes with far-field radiated VLF energy larger than about 100 J are able to generate a detectable whistler wave in the inner magnetosphere. One-to-one coincidences between whistlers observed by RBSP and lightning strokes detected by WWLLN are clearly shown in the L shell range of L = 1-3. Nose whistlers observed in July 2014 show that it may be possible to extend this coincidence to the region of L≥4.
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
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. PMID:25828652
Bonfanti, Laura; Lippi, Giuseppe; Ciullo, Irene; Meschi, Tiziana; Ticinesi, Andrea; Aloe, Rosalia; Di Spigno, Francesco
2016-01-01
Background Cardiac troponin (cTn) testing has reduced the likelihood of erroneous discharge of patients with acute coronary syndrome (ACS) from the emergency department (ED), but doubts remain about optimal clinical use. This study was planned for evaluating the predictive significance of cTn values between the limit of detection of the method and the 99th percentile in ED patients evaluated for suspected ACS. Methods In this retrospective study all hospital records of patients admitted over a 6-month period to the ED and with at least one cTnI value comprised between the limit of detection (0.01 ng/mL) and the 99th percentile of the assay (0.05 ng/mL) were analyzed. Results A total of 4,749 patients with cTnI value between 0.01–0.05 ng/mL were identified among 57,879 ED visits throughout the study period. Overall, 2,189 patients (46.1%) were discharged from the ED, 2,529 (53.25%) were admitted to the hospital and 31 (0.65%) died during ED stay. A total number of 289 patients out of 2,189 who were discharged (i.e., 13.2%) had additional ED visits within 30 days. Among these, 6 were diagnosed with ACS, representing 0.27% of patients discharged [negative predictive value (NPV) 0.997; 95% CI, 0.994–0.999] and 2.1% of those with second admission (NPV 0.979; 95% CI, 0.955–0.992). Only one of the 2,529 patients admitted to the hospital (i.e., 0.04%) developed an ACS during hospital stay. Conclusions The results of our retrospective study suggest that the suitability of using a contemporary-sensitive cTnI immunoassay assay in the context of an appropriate protocol represents a safe and effective strategy for ruling in and ruling out ACS in patients presenting to the ED. PMID:27500153
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.)
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.
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.
ERIC Educational Resources Information Center
Cicchetti, Domenic V.; Koenig, Kathy; Klin, Ami; Volkmar, Fred R.; Paul, Rhea; Sparrow, Sara
2011-01-01
The objectives of this report are: (a) to trace the theoretical roots of the concept clinical significance that derives from Bayesian thinking, Marginal Utility/Diminishing Returns in Economics, and the "just noticeable difference", in Psychophysics. These concepts then translated into: Effect Size (ES), strength of agreement, clinical…
LaFleur, Bonnie; Lee, Wooin; Billhiemer, Dean; Lockhart, Craig; Liu, Junmei; Merchant, Nipun
2011-01-01
In analytic chemistry a detection limit (DL) is the lowest measurable amount of an analyte that can be distinguished from a blank; many biomedical measurement technologies exhibit this property. From a statistical perspective, these data present inferential challenges because instead of precise measures, one only has information that the value is somewhere between 0 and the DL (below detection limit, BDL). Substitution of BDL values, with 0 or the DL can lead to biased parameter estimates and a loss of statistical power. Statistical methods that make adjustments when dealing with these types of data, often called left-censored data, are available in many commercial statistical packages. Despite this availability, the use of these methods is still not widespread in biomedical literature. We have reviewed the statistical approaches of dealing with BDL values, and used simulations to examine the performance of the commonly used substitution methods and the most widely available statistical methods. We have illustrated these methods using a study undertaken at the Vanderbilt-Ingram Cancer Center, to examine the serum bile acid levels in patients with colorectal cancer and adenoma. We have found that the modern methods for BDL values identify disease-related differences that are often missed, with statistically naive approaches. PMID:21712958
Biot, Eric; Adenot, Pierre-Gaël; Hue-Beauvais, Cathy; Houba-Hérin, Nicole; Duranthon, Véronique; Devinoy, Eve; Beaujean, Nathalie; Gaudin, Valérie; Maurin, Yves; Debey, Pascale
2010-01-01
In eukaryotes, the interphase nucleus is organized in morphologically and/or functionally distinct nuclear “compartments”. Numerous studies highlight functional relationships between the spatial organization of the nucleus and gene regulation. This raises the question of whether nuclear organization principles exist and, if so, whether they are identical in the animal and plant kingdoms. We addressed this issue through the investigation of the three-dimensional distribution of the centromeres and chromocenters. We investigated five very diverse populations of interphase nuclei at different differentiation stages in their physiological environment, belonging to rabbit embryos at the 8-cell and blastocyst stages, differentiated rabbit mammary epithelial cells during lactation, and differentiated cells of Arabidopsis thaliana plantlets. We developed new tools based on the processing of confocal images and a new statistical approach based on G- and F- distance functions used in spatial statistics. Our original computational scheme takes into account both size and shape variability by comparing, for each nucleus, the observed distribution against a reference distribution estimated by Monte-Carlo sampling over the same nucleus. This implicit normalization allowed similar data processing and extraction of rules in the five differentiated nuclei populations of the three studied biological systems, despite differences in chromosome number, genome organization and heterochromatin content. We showed that centromeres/chromocenters form significantly more regularly spaced patterns than expected under a completely random situation, suggesting that repulsive constraints or spatial inhomogeneities underlay the spatial organization of heterochromatic compartments. The proposed technique should be useful for identifying further spatial features in a wide range of cell types. PMID:20628576
Qiao, Liangwei; Qu, Qingshan; Jiang, Xin
2016-01-01
Objective: To evaluate value of quantitative and qualitative detection of BK virus (BKV) and JC virus (JCV) in timely diagnosing polyomavirus-associated nephropathy (PVAN) occurring inrenal transplantation recipients. Methods: We collected 306 cases of urine specimen and 310 cases of blood specimen from 306 patients who underwent renal transplant. Levels of BKV and JCV in blood and urine were detected using real-time quantitative polymerase chain reaction (PCR). Results: Detection rate of BKV DNA was 33.3% (102/306) in urine and 34.8% (108/310); while that of JCV DNA was 30.7% (94/306) and 33.5% (104/310) respectively. The lowest detectable limit of BCK and JCV detection for patients who underwent renal transplant was 2×103 copies/ml, suggesting high specificity and sensitivity. Conclusion: Real-time quantitative PCR is able to monitor BCV and JCV in renal transplant recipients in a convenient and rapid way, thus it is beneficial for early discovery, diagnosis and treatment of PVAN. PMID:27182256
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 PMID:26999859
NASA Astrophysics Data System (ADS)
Goodman, J. W.
This book is based on the thesis that some training in the area of statistical optics should be included as a standard part of any advanced optics curriculum. Random variables are discussed, taking into account definitions of probability and random variables, distribution functions and density functions, an extension to two or more random variables, statistical averages, transformations of random variables, sums of real random variables, Gaussian random variables, complex-valued random variables, and random phasor sums. Other subjects examined are related to random processes, some first-order properties of light waves, the coherence of optical waves, some problems involving high-order coherence, effects of partial coherence on imaging systems, imaging in the presence of randomly inhomogeneous media, and fundamental limits in photoelectric detection of light. Attention is given to deterministic versus statistical phenomena and models, the Fourier transform, and the fourth-order moment of the spectrum of a detected speckle image.
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.
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…
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.
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.
Cottrell, R.Les; Logg, Connie; Chhaparia, Mahesh; Grigoriev, Maxim; Haro, Felipe; Nazir, Fawad; Sandford, Mark
2006-01-25
End-to-End fault and performance problems detection in wide area production networks is becoming increasingly hard as the complexity of the paths, the diversity of the performance, and dependency on the network increase. Several monitoring infrastructures are built to monitor different network metrics and collect monitoring information from thousands of hosts around the globe. Typically there are hundreds to thousands of time-series plots of network metrics which need to be looked at to identify network performance problems or anomalous variations in the traffic. Furthermore, most commercial products rely on a comparison with user configured static thresholds and often require access to SNMP-MIB information, to which a typical end-user does not usually have access. In our paper we propose new techniques to detect network performance problems proactively in close to realtime and we do not rely on static thresholds and SNMP-MIB information. We describe and compare the use of several different algorithms that we have implemented to detect persistent network problems using anomalous variations analysis in real end-to-end Internet performance measurements. We also provide methods and/or guidance for how to set the user settable parameters. The measurements are based on active probes running on 40 production network paths with bottlenecks varying from 0.5Mbits/s to 1000Mbit/s. For well behaved data (no missed measurements and no very large outliers) with small seasonal changes most algorithms identify similar events. We compare the algorithms' robustness with respect to false positives and missed events especially when there are large seasonal effects in the data. Our proposed techniques cover a wide variety of network paths and traffic patterns. We also discuss the applicability of the algorithms in terms of their intuitiveness, their speed of execution as implemented, and areas of applicability. Our encouraging results compare and evaluate the accuracy of our detection
Maity, Debabrata; Jiang, Juanjuan; Ehlers, Martin; Wu, Junchen; Schmuck, Carsten
2016-05-01
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. PMID:27071707
NASA Astrophysics Data System (ADS)
Narayanan, Gopal; Shi, Yun Qing
We first develop a probability mass function (PMF) for quantized block discrete cosine transform (DCT) coefficients in JPEG compression using statistical analysis of quantization, with a Generalized Gaussian model being considered as the PDF for non-quantized block DCT coefficients. We subsequently propose a novel method to detect potential JPEG compression history in bitmap images using the PMF that has been developed. We show that this method outperforms a classical approach to compression history detection in terms of effectiveness. We also show that it detects history with both independent JPEG group (IJG) and custom quantization tables.
Hendrickson, Jeanne E; Tormey, Christopher A
2016-06-01
Red blood cell (RBC) transfusion is a cornerstone of the management of patients with hematology/oncology disorders. However, a potentially deleterious consequence of transfusion is the development of alloantibodies against blood group antigens present on RBCs. Such alloantibodies can be an obstacle in providing compatible units for transfusion. Providers in this arena must fully understand the testing performed by blood banks, as well as the consequences of detected antibodies. This article reviews immunohematologic tests, describes how autoimmune hemolytic anemia is classified by autoantibodies; outlines RBC alloimmunization rates, and presents strategies to prevent/mitigate the impact of RBC alloimmunization. PMID:27113001
NASA Astrophysics Data System (ADS)
Skatter, Sondre; Fritsch, Sebastian; Schlomka, Jens-Peter
2016-05-01
The performance limits were explored for an X-ray Diffraction based explosives detection system for baggage scanning. This XDi system offers 4D imaging that comprises three spatial dimensions with voxel sizes in the order of ~(0.5cm)3, and one spectral dimension for material discrimination. Because only a very small number of photons are observed for an individual voxel, material discrimination cannot work reliably at the voxel level. Therefore, an initial 3D reconstruction is performed, which allows the identification of objects of interest. Combining all the measured photons that scattered within an object, more reliable spectra are determined on the object-level. As a case study we looked at two liquid materials, one threat and one innocuous, with very similar spectral characteristics, but with 15% difference in electron density. Simulations showed that Poisson statistics alone reduce the material discrimination performance to undesirable levels when the photon counts drop to 250. When additional, uncontrolled variation sources are considered, the photon count plays a less dominant role in detection performance, but limits the performance also for photon counts of 500 and higher. Experimental data confirmed the presence of such non-Poisson variation sources also in the XDi prototype system, which suggests that the present system can still be improved without necessarily increasing the photon flux, but by better controlling and accounting for these variation sources. When the classification algorithm was allowed to use spectral differences in the experimental data, the discrimination between the two materials improved significantly, proving the potential of X-ray diffraction also for liquid materials.
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. PMID:26991959
Technology Transfer Automated Retrieval System (TEKTRAN)
Statistically robust sampling strategies form an integral component of grain storage and handling activities throughout the world. Developing sampling strategies to target biological pests such as insects in stored grain is inherently difficult due to species biology and behavioral characteristics. ...
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
DAVIS, S.J.
2000-05-25
This document identifies critical characteristics of components to be dedicated for use in Safety Class (SC) or 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), in safety class, safety significant systems. System modifications are to be performed in accordance with the instructions provided on ECN 658230. 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.
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.
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.
Emery, C L; Weymouth, L A
1997-01-01
The prevalence of extended-spectrum beta-lactamase (ESBL)-mediated resistance remains unknown for most hospitals, and national guidelines for testing and reporting ESBL-mediated resistance have not yet been developed. We undertook a study to determine the prevalence of ESBLs and the clinical need for testing in our tertiary-care medical center. Members of the family Enterobacteriaceae isolated over a 6-month period for which ceftazidime or ceftriaxone MICs were greater than 1 microg/ml were tested for production of ESBLs by the double-disk synergy method. Approximately 1.5% of isolates of the family Enterobacteriaceae (50 of 3,273), which were isolated from 1.2% of patients (23 of 1,844), were found to express ESBLs. ESBL-producing strains included eight different species and were isolated from patients located throughout the hospital, including outpatient clinics. By using the interpretive guidelines of the National Committee for Clinical Laboratory Standards, 26 to 39% of the isolates would have been reported to be susceptible to ceftazidime, depending upon the routine susceptibility method used. However, tests with cefpodoxime found all of the ESBL-producing strains to be resistant or intermediate. Nine patients infected with ESBL-producing isolates were treated with therapy which included an expanded-spectrum cephalosporin. Seven were cured. The deaths of the other two patients were not attributed to bacterial resistance missed by routine susceptibility testing. These observations suggest that in our tertiary-care medical center, it may not be clinically necessary or cost-effective at this time to institute additional testing on a routine basis to detect ESBL production in all clinical isolates of the family Enterobacteriaceae. PMID:9230382
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.
Kolk, Daniel P.; Dockter, Janel; Linnen, Jeff; Ho-Sing-Loy, Marcy; Gillotte-Taylor, Kristin; McDonough, Sherrol H.; Mimms, Larry; Giachetti, Cristina
2002-01-01
While the present generation of serology-based assays has significantly decreased the number of human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV) infections acquired by transfusion, the possibility of infected donations escaping detection still exists. The average seronegative viremic window duration during which immunological assays are unable to detect the virus is estimated to be between 16 and 22 days for HIV-1 and approximately 70 days for HCV. Significant reduction of detection window duration was demonstrated using a nucleic acid amplification assay, the Procleix HIV-1/HCV Assay, which utilizes transcription-mediated amplification technology to simultaneously detect HIV-1 and HCV RNAs. For 26 commercially available HIV-1 seroconversion panels tested, specimens were reactive in the HIV-1/HCV assay at the same time as or earlier than in serological assays. Overall, the HIV-1/HCV assay was able to reduce the detection window duration by an average of 14 days and 6 days compared to tests relying on recognition of HIV-1 antibody and p24 antigen, respectively. For 24 commercially available HCV seroconversion panels tested, the specimens were reactive in the HIV-1/HCV assay at an earlier blood sampling date than in serological assays, reducing the detection window duration by an average of 26 days. Similar results were obtained in testing the HIV-1 and HCV seroconversion panels in the virus-specific HIV-1- and HCV-discriminatory assays, respectively. In conclusion, the HIV-1/HCV assay and corresponding discriminatory assays significantly reduced detection window durations compared to immunoassays. PMID:11980957
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. PMID:25427666
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...
Ponikowski, P; Chua, T P; Amadi, A A; Piepoli, M; Harrington, D; Volterrani, M; Colombo, R; Mazzuero, G; Giordano, A; Coats, A J
1996-06-15
controls, respectively). Patients with VLF had significantly increased hypoxic chemosensitivity, and hyperoxic conditions were able to decrease VLF power and abolish the VLF rhythm in 5 of 6 patients with CHF. Discrete VLF oscillations in RR variability are common in patients with advanced CHF and appear to be related to severely impaired autonomic regulation and suppression of baroreceptor function, with enhancement of hypoxic chemosensitivity. We hypothesize that this rhythm represents an enhanced chemoreflex harmonic oscillation in CHF patients, which may have application for arrhythmogenesis. PMID:8677873
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
NASA Astrophysics Data System (ADS)
Yuen, Peter WT; Blagg, A.; Bishop, G.
2005-10-01
In the literature of spectral unmixing (SU), particularly for remote sensing applications, there are claims that both geometric and statistical techniques using independency as cost functions1-4, are very applicable for analysing hyperspectral imagery. These claims are vigorously examined and verified in this paper, using sets of simulated and real data. The objective is to study how effective these two SU approaches are with respected to the modality and independency of the source data. The data sets are carefully designed such that only one parameter is varied at a time. The 'goodness' of the unmixed result is judged by using the well-known Amari index (AI), together with a 3D visualisation of the deduced simplex in eigenvector space. A total of seven different algorithms, of which one is geometric and the others are statistically independent based have been studied. Two of the statistical algorithms use non-negative constraint of modelling errors (NMF & NNICA) as cost functions and the other four employ the independent component analysis (ICA) principle to minimise mutual information (MI) as the objective function. The result has shown that, the ICA based statistical technique is very effective to find the correct endmember (EM) even for the highly intermixed imagery, provided that the sources are completely independent. Modality of the data source is found to only have a second order impact on the unmixing capabilities of ICA based algorithms. All ICA based algorithms are seen to fail when the MI of sources are above 1, and the NMF type of algorithms are found even more sensitive to the dependency of sources. Typical independency of species found in the natural environment is in the range of 15-30. This indicates that, conventional statistical ICA and matrix factorisation (MF) techniques, are really not very suitable for the spectral unmixing of hyperspectral (HSI) data. Future work is proposed to investigate the idea of a dependent component clustering
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. PMID:21045892
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.
Girard, Philippe
2011-01-01
Null alleles are common technical artifacts in genetic-based analysis. Powerful methods enabling their detection in either panmictic or inbred populations have been proposed. However, none of these methods appears unbiased in both types of mating systems, necessitating a priori knowledge of the inbreeding level of the population under study. To counter this problem, I propose to use the software FDist2 to detect the atypical fixation indices that characterize markers with null alleles. The rational behind this approach and the parameter settings are explained. The power of the method for various sample sizes, degrees of inbreeding and null allele frequencies is evaluated using simulated microsatellite and SNP datasets and then compared to two other null allele detection methods. The results clearly show the robustness of the method proposed here as well as its greater accuracy in both panmictic and inbred populations for both types of marker. By allowing a proper detection of null alleles for a wide range of mating systems and markers, this new method is particularly appealing for numerous genetic studies using co-dominant loci. PMID:21381434
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. PMID:26903208
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)
Pasmanik, Dmitry; Hayosh, Mykhaylo; Demekhov, Andrei; Santolík, Ondřej; Nemec, František; Parrot, Michel
2015-04-01
We present a statistical study of the quasi-periodic (QP) ELF/VLF emissions measured by the DEMETER spacecraft. Events with modulation period larger than 10 s and frequency bandwidth more than 200 Hz were visually selected among the six year of measurements. Selected QP-emissions events occur mostly at frequencies from about 750 Hz to 2 kHz, but they may be observed at frequencies as low as 500 Hz and as high as 8 kHz. The statistical analysis clearly shows that QP events with larger modulation periods have lower frequency drift and smaller wave amplitude. Intense QP events have higher frequency drifts and larger values of the frequency bandwiths. Numerical simulation of the QP emissions based on the theoretical model of the flow cyclotron maser is performed. Calculations were made for wide range of plasma parameters (i.e. cold plasma density, L-shell, energetic electron flux and etc.) The numerical results are in good agreement with the observed relationship between different parameters of the QP emissions. The comparison between theoretical results and observations allow us to estimate the typical properties of the source of the QP emissions observed by the DEMETER satellite.
Kim, Tae Sun; Ko, Kwang Jin; Shin, Seung Jea; Ryoo, Hyun Soo; Song, Wan; Sung, Hyun Hwan; Han, Deok Hyun; Jeong, Byong Chang; Seo, Seong Il; Jeon, Seong Soo; Lee, Kyu Sung; Lee, Sung Won; Lee, Hyun Moo; Choi, Han Yong
2015-01-01
Purpose To investigate the differences in the cancer detection rate and pathological findings on a second prostate biopsy according to benign diagnosis, high-grade prostatic intraepithelial neoplasia (HGPIN), and atypical small acinar proliferation (ASAP) on first biopsy. Materials and Methods We retrospectively reviewed the records of 1,323 patients who underwent a second prostate biopsy between March 1995 and November 2012. We divided the patients into three groups according to the pathologic findings on the first biopsy (benign diagnosis, HGPIN, and ASAP). We compared the cancer detection rate and Gleason scores on second biopsy and the unfavorable disease rate after radical prostatectomy among the three groups. Results A total of 214 patients (16.2%) were diagnosed with prostate cancer on a second biopsy. The rate of cancer detection was 14.6% in the benign diagnosis group, 22.1% in the HGPIN group, and 32.1% in the ASAP group, respectively (p<0.001). When patients were divided into subgroups according to the number of positive cores, the rate of cancer detection was 16.7%, 30.5%, 31.0%, and 36.4% in patients with a single core of HGPIN, more than one core of HGPIN, a single core of ASAP, and more than one core of ASAP, respectively. There were no significant differences in Gleason scores on second biopsy (p=0.324) or in the unfavorable disease rate after radical prostatectomy among the three groups (benign diagnosis vs. HGPIN, p=0.857, and benign diagnosis vs. ASAP, p=0.957, respectively). Conclusions Patients with multiple cores of HGPIN or any core number of ASAP on a first biopsy had a significantly higher cancer detection rate on a second biopsy. Repeat biopsy should be considered and not be delayed in those patients. PMID:26682019
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
Choi, Won-Tak; Emond, Mary J; Rabinovitch, Peter S; Ahn, Joseph; Upton, Melissa P; Westerhoff, Maria
2015-01-01
Background: Dysplasia arising from Barrett's esophagus precedes esophageal adenocarcinoma (EAC). Cases that are difficult to diagnose as dysplastic, especially in the setting of inflammation, may be designated “indefinite for dysplasia (IND).” Although flow cytometric analysis of DNA content has shown some promise in detecting EAC, there are few reports that have specifically evaluated the outcome of IND. Aims and methods: We analyzed a series of 96 IND patients seen at the University of Washington between 2005 and 2013 to determine the outcome of IND and to identify factors (including histologic features and DNA flow cytometric data) associated with subsequent detection of neoplasia. Results: Twenty-five percent of IND cases were found to have low-grade dysplasia, high-grade dysplasia (HGD), or EAC within 1 year, with 37% and 47% detected within 2 and 3 years, respectively. The 1-, 2-, and 3-year detection rates of HGD or EAC were 10%, 13%, and 20%, respectively. Active inflammation (hazard ratio (HR)=3.4, P=0.0005) and abnormal DNA content (HR=5.7, P=0.003) were significant risk factors of neoplasia. When active inflammation and DNA flow cytometric results were considered together, the HR for the combined markers was 18.8 (P<0.0001). The sensitivity and specificity of the combined markers for predicting detection of subsequent neoplasia within 3 years were 100% and 60%, respectively, with 100% negative and 89% positive predictive values. Conclusions: Histology with the support of DNA flow cytometry can identify a subset of IND patients who may have a higher risk for subsequent detection of neoplasia. PMID:25761942
Malachowski, M S; Levine, S P; Herrin, G; Spear, R C; Yost, M; Yi, Z
1994-05-01
Open path Fourier transform infrared (OP-FTIR) spectroscopy is a new air monitoring technique that can be used to measure concentrations of air contaminants in real or near-real time. OP-FTIR spectroscopy has been used to monitor workplace gas and vapor exposures, emissions from hazardous waste sites, and to track emissions along fence lines. This paper discusses a statistical process control technique that can be used with air monitoring data collected with an OP-FTIR spectrometer to detect departures from normal operating conditions in the workplace or along a fence line. Time series data, produced by plotting consecutive air sample concentrations in time, were analyzed. Autocorrelation in the time series data was removed by fitting dynamic models. Control charts were used with the residuals of the model fit data to determine if departures from defined normal operating conditions could be rapidly detected. Shewhart and exponentially weighted moving average (EWMA) control charts were evaluated for use with data collected under different room air flow and mixing conditions. Under rapidly changing conditions the Shewhart control chart was able to detect a leak in a simulated process area. The EWMA control chart was found to be more sensitive to drifts and slowly changing concentrations in air monitoring data. The time series and statistical process control techniques were also applied to data obtained during a field study at a chemical plant. A production area of an acrylonitrile, 1,3-butadiene, and styrene (ABS) polymer process was monitored in near-real time. Decision logics based on the time series and statistical process control technique introduced suggest several applications in workplace and environmental monitoring. These applications might include signaling of an alarm or warning, increasing levels of worker respiratory protection, or evacuation of a community, when gas and vapor concentrations are determined to be out-of-control. PMID:8012765
NASA Astrophysics Data System (ADS)
Gómez González, A.; Fassois, S. D.
2016-03-01
The problem of vibration-based damage detection under varying environmental conditions and uncertainty is considered, and a novel, supervised, PCA-type statistical methodology is postulated. The methodology employs vibration data records from the healthy and damaged states of a structure under various environmental conditions. Unlike standard PCA-type methods in which a feature vector corresponding to the least important eigenvalues is formed in a single step, the postulated methodology uses supervised learning in which damaged-state data records are employed to sequentially form a feature vector by appending a transformed scalar element at a time under the condition that it optimally, among all remaining elements, improves damage detectability. This leads to the formulation of feature vectors with optimized sensitivity to damage, and thus high damage detectability. Within this methodology three particular methods, two non-parametric and one parametric, are formulated. These are validated and comparatively assessed via a laboratory case study focusing on damage detection on a scale wind turbine blade under varying temperature and the potential presence of sprayed water. Damage detection performance is shown to be excellent based on a single vibration response sensor and a limited frequency bandwidth.
NASA Astrophysics Data System (ADS)
Wei, Xunxun; Liu, Rui; Zhang, Wenke; Zhu, Ming
2013-10-01
Statistics of the number of students in the classroom is very important for class surveillance. It can help teacher count the number of students and help students choose class for self-study. While as a canonical pattern recognition problem, it's very difficult due to various appearances of students and other outliers such as bags and books. We want to find a good solution to this problem. A novel method for texture feature extraction is now proposed based on that difference of Frequency spectrum image belongs to different seat image. Regarding frequency spectrum image as the texture image, the texture characteristics which can represent those differences are extracted using texture analysis's method. At the same time, we combine the Local binary patterns feature with the texture characteristics to describe the texture of seats. Experiments on a real classroom dataset demonstrate that the accuracy of the proposed method reaches 91.3%.
Ning, Lihua; Kan, Guizhen; Du, Wenkai; Guo, Shiwei; Wang, Qing; Zhang, Guozheng; Cheng, Hao; Yu, Deyue
2016-03-01
Tolerance to low-phosphorus soil is a desirable trait in soybean cultivars. Previous quantitative trait locus (QTL) studies for phosphorus-deficiency tolerance were mainly derived from bi-parental segregating populations and few reports from natural population. The objective of this study was to detect QTLs that regulate phosphorus-deficiency tolerance in soybean using association mapping approach. Phosphorus-deficiency tolerance was evaluated according to five traits (plant shoot height, shoot dry weight, phosphorus concentration, phosphorus acquisition efficiency and use efficiency) comprising a conditional phenotype at the seedling stage. Association mapping of the conditional phenotype detected 19 SNPs including 13 SNPs that were significantly associated with the five traits across two years. A novel cluster of SNPs, including three SNPs that consistently showed significant effects over two years, that associated with more than one trait was detected on chromosome 3. All favorable alleles, which were determined based on the mean of conditional phenotypic values of each trait over the two years, could be pyramided into one cultivar through parental cross combination. The best three cross combinations were predicted with the aim of simultaneously improving phosphorus acquisition efficiency and use efficiency. These results will provide a thorough understanding of the genetic basis of phosphorus deficiency tolerance in soybean. PMID:27162491
Ning, Lihua; Kan, Guizhen; Du, Wenkai; Guo, Shiwei; Wang, Qing; Zhang, Guozheng; Cheng, Hao; Yu, Deyue
2016-01-01
Tolerance to low-phosphorus soil is a desirable trait in soybean cultivars. Previous quantitative trait locus (QTL) studies for phosphorus-deficiency tolerance were mainly derived from bi-parental segregating populations and few reports from natural population. The objective of this study was to detect QTLs that regulate phosphorus-deficiency tolerance in soybean using association mapping approach. Phosphorus-deficiency tolerance was evaluated according to five traits (plant shoot height, shoot dry weight, phosphorus concentration, phosphorus acquisition efficiency and use efficiency) comprising a conditional phenotype at the seedling stage. Association mapping of the conditional phenotype detected 19 SNPs including 13 SNPs that were significantly associated with the five traits across two years. A novel cluster of SNPs, including three SNPs that consistently showed significant effects over two years, that associated with more than one trait was detected on chromosome 3. All favorable alleles, which were determined based on the mean of conditional phenotypic values of each trait over the two years, could be pyramided into one cultivar through parental cross combination. The best three cross combinations were predicted with the aim of simultaneously improving phosphorus acquisition efficiency and use efficiency. These results will provide a thorough understanding of the genetic basis of phosphorus deficiency tolerance in soybean. PMID:27162491
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
Nagelkerke, Leopold A J; van Densen, Wim L T
2007-02-01
We studied the effects of inter-annual variability and serial correlation on the statistical power of monitoring schemes to detect trends in biomass of bream (Abramis brama) in Lake Veluwemeer (The Netherlands). In order to distinguish between 'true' system variability and sampling variability we simulated the development of the bream population, using estimates for population structure and growth, and compared the resulting inter-annual variabilities and serial correlations with those from field data. In all cases the inter-annual variability in the field data was larger than in simulated data (e.g. for total biomass of all assessed bream sigma = 0.45 in field data, and sigma = 0.03-0.14 in simulated data) indicating that sampling variability decreased statistical power for detecting trends. Moreover, sampling variability obscured the inter-annual dependency (and thus the serial correlation) of biomass, which was expected because in this long-lived population biomass changes are buffered by the many year classes present. We did find the expected serial correlation in our simulation results and concluded that good survey data of long-lived fish populations should show low sampling variability and considerable inter-annual serial correlation. Since serial correlation decreases the power for detecting trends, this means that even when sampling variability would be greatly reduced, the number of sampling years to detect a change of 15%.year(-1) in bream populations (corresponding to a halving or doubling in a six-year period) would in most cases be more than six. This would imply that the six-year reporting periods that are required by the Water Framework Directive of the European Union are too short for the existing fish monitoring schemes. PMID:17219244
Lotterhos, Katie E; Whitlock, Michael C
2015-03-01
Although genome scans have become a popular approach towards understanding the genetic basis of local adaptation, the field still does not have a firm grasp on how sampling design and demographic history affect the performance of genome scans on complex landscapes. To explore these issues, we compared 20 different sampling designs in equilibrium (i.e. island model and isolation by distance) and nonequilibrium (i.e. range expansion from one or two refugia) demographic histories in spatially heterogeneous environments. We simulated spatially complex landscapes, which allowed us to exploit local maxima and minima in the environment in 'pair' and 'transect' sampling strategies. We compared F(ST) outlier and genetic-environment association (GEA) methods for each of two approaches that control for population structure: with a covariance matrix or with latent factors. We show that while the relative power of two methods in the same category (F(ST) or GEA) depended largely on the number of individuals sampled, overall GEA tests had higher power in the island model and F(ST) had higher power under isolation by distance. In the refugia models, however, these methods varied in their power to detect local adaptation at weakly selected loci. At weakly selected loci, paired sampling designs had equal or higher power than transect or random designs to detect local adaptation. Our results can inform sampling designs for studies of local adaptation and have important implications for the interpretation of genome scans based on landscape data. PMID:25648189
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.
NASA Astrophysics Data System (ADS)
Arismendi, I.; Johnson, S. L.; Dunham, J. B.
2015-03-01
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.
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).
NASA Astrophysics Data System (ADS)
Arismendi, I.; Johnson, S. L.; Dunham, J. B.
2014-05-01
Central tendency statistics may not capture relevant or desired characteristics about the variability of continuous phenomena and thus, they may not completely track temporal patterns of change. Here, we present two methodological approaches to identify long-term changes in environmental regimes. First, we use higher statistical moments (skewness and kurtosis) to examine potential changes of empirical distributions at decadal scale. Second, we adapt an outlier detection procedure combining a non-metric multidimensional scaling technique and higher density region plots to detect 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, patterns in variability through time, and 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 a differentiated vulnerability to both the 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.
NASA Astrophysics Data System (ADS)
Lee, Lopaka; Helsel, Dennis
2007-05-01
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 for the generation of empirical cumulative distribution functions, prediction or exceedance probabilities, and related confidence limits computation. Additionally, our software contains K-M-based routines for nonparametric hypothesis testing among an unlimited number of grouping variables. A primary characteristic of K-M methods is that they do not perform extrapolation and interpolation. Thus, these routines cannot be used to model statistics beyond the observed data range or when linear interpolation is desired. For such applications, the aforementioned parametric and semi-parametric methods must be used.
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 for the generation of empirical cumulative distribution functions, prediction or exceedance probabilities, and related confidence limits computation. Additionally, our software contains K-M-based routines for nonparametric hypothesis testing among an unlimited number of grouping variables. A primary characteristic of K-M methods is that they do not perform extrapolation and interpolation. Thus, these routines cannot be used to model statistics beyond the observed data range or when linear interpolation is desired. For such applications, the aforementioned parametric and semi-parametric methods must be used.
Lee, Dong Hoon; Nam, Jong Kil; Park, Sung Woo; Lee, Seung Soo; Han, Ji-Yeon; Lee, Sang Don; Lee, Joon Woo
2016-01-01
Purpose To compare prostate cancer detection rates between 12 cores transrectal ultrasound-guided prostate biopsy (TRUS-Bx) and visually estimated multiparametric magnetic resonance imaging (mp-MRI)-targeted prostate biopsy (MRI-visual-Bx) for patients with prostate specific antigen (PSA) level less than 10 ng/mL. Materials and Methods In total, 76 patients with PSA levels below 10 ng/mL underwent 3.0 Tesla mp-MRI and TRUS-Bx prospectively in 2014. In patients with abnormal lesions on mp-MRI, we performed additional MRI-visual-Bx. We compared pathologic results, including the rate of clinically significant prostate cancer cores (cancer length greater than 5 mm and/or any Gleason grade greater than 3 in the biopsy core). Results The mean PSA was 6.43 ng/mL. In total, 48 of 76 (63.2%) patients had abnormal lesions on mp-MRI, and 116 targeted biopsy cores, an average of 2.42 per patient, were taken. The overall detection rates of prostate cancer using TRUS-Bx and MRI-visual-Bx were 26/76 (34.2%) and 23/48 (47.9%), respectively. In comparing the pathologic results of TRUS-Bx and MRI-visual-Bx cores, the positive rates were 8.4% (77 of 912 cores) and 46.6% (54 of 116 cores), respectively (p<0.001). Mean cancer core lengths and mean cancer core percentages were 3.2 mm and 24.5%, respectively, in TRUS-Bx and 6.3 mm and 45.4% in MRI-visual-Bx (p<0.001). In addition, Gleason score ≥7 was noted more frequently using MRI-visual-Bx (p=0.028). The detection rate of clinically significant prostate cancer was 27/77 (35.1%) and 40/54 (74.1%) for TRUS-Bx and MRI-visual-Bx, respectively (p<0.001). Conclusion MRI-visual-Bx showed better performance in the detection of clinically significant prostate cancer, compared to TRUS-Bx among patients with a PSA level less than 10 ng/mL. PMID:26996553
NASA Astrophysics Data System (ADS)
Temme, F. P.
1992-12-01
Realisation of the invariance properties of the p ⩽ 2 number partitional inventory components of the 20-fold spin algebra associated with [A] 20 nuclear spin clusters under SU2 × L20 allows the mappings {[λ] → Γ} to be derived. In addition, recent general inner tensor product expressions under Ln, for n even (odd), also facilitates the evaluation of many higher [λ] ( L20; p = 3) correlative mappings onto SU3↓SO(3) × L↓20T A 5 subduced symmetry from SU2 duality, thus providing results that determine the nature of adapted NMR bases for both dodecahedrane and its d 20 analogue. The significance of this work lies in the pertinence of nuclear spin statistics to both selective MQ-NMR and to other spectroscopic aspects of cage clusters, e.g., [ 13C] n, n = 20, 60, fullerenes. Mappings onto Ln irreps sets of specific p ⩽ 3 number partitions arise in combinatorial treatment of {M iti} Rota fields, defining scalar invariants in the context of Cayley algebra. Inclusion of the Ln group in the specific Racah chain for NMR symmetry gives rise to significant further physical insight.
Zhang, Han; Zhao, Yang-Yu; Song, Jing; Zhu, Qi-Ying; Yang, Hua; Zheng, Mei-Ling; Xuan, Zhao-Ling; Wei, Yuan; Chen, Yang; Yuan, Peng-Bo; Yu, Yang; Li, Da-Wei; Liang, Jun-Bin; Fan, Ling; Chen, Chong-Jian; Qiao, Jie
2015-01-01
Analyses of cell-free fetal DNA (cff-DNA) from maternal plasma using massively parallel sequencing enable the noninvasive detection of feto-placental chromosome aneuploidy; this technique has been widely used in clinics worldwide. Noninvasive prenatal tests (NIPT) based on cff-DNA have achieved very high accuracy; however, they suffer from maternal copy-number variations (CNV) that may cause false positives and false negatives. In this study, we developed an algorithm to exclude the effect of maternal CNV and refined the Z-score that is used to determine fetal aneuploidy. The simulation results showed that the algorithm is robust against variations of fetal concentration and maternal CNV size. We also introduced a method based on the discrepancy between feto-placental concentrations to help reduce the false-positive ratio. A total of 6615 pregnant women were enrolled in a prospective study to validate the accuracy of our method. All 106 fetuses with T21, 20 with T18, and three with T13 were tested using our method, with sensitivity of 100% and specificity of 99.97%. In the results, two cases with maternal duplications in chromosome 21, which were falsely predicted as T21 by the previous NIPT method, were correctly classified as normal by our algorithm, which demonstrated the effectiveness of our approach. PMID:26534864
Zhang, Han; Zhao, Yang-Yu; Song, Jing; Zhu, Qi-Ying; Yang, Hua; Zheng, Mei-Ling; Xuan, Zhao-Ling; Wei, Yuan; Chen, Yang; Yuan, Peng-Bo; Yu, Yang; Li, Da-Wei; Liang, Jun-Bin; Fan, Ling; Chen, Chong-Jian; Qiao, Jie
2015-01-01
Analyses of cell-free fetal DNA (cff-DNA) from maternal plasma using massively parallel sequencing enable the noninvasive detection of feto-placental chromosome aneuploidy; this technique has been widely used in clinics worldwide. Noninvasive prenatal tests (NIPT) based on cff-DNA have achieved very high accuracy; however, they suffer from maternal copy-number variations (CNV) that may cause false positives and false negatives. In this study, we developed an algorithm to exclude the effect of maternal CNV and refined the Z-score that is used to determine fetal aneuploidy. The simulation results showed that the algorithm is robust against variations of fetal concentration and maternal CNV size. We also introduced a method based on the discrepancy between feto-placental concentrations to help reduce the false-positive ratio. A total of 6615 pregnant women were enrolled in a prospective study to validate the accuracy of our method. All 106 fetuses with T21, 20 with T18, and three with T13 were tested using our method, with sensitivity of 100% and specificity of 99.97%. In the results, two cases with maternal duplications in chromosome 21, which were falsely predicted as T21 by the previous NIPT method, were correctly classified as normal by our algorithm, which demonstrated the effectiveness of our approach. PMID:26534864
NASA Astrophysics Data System (ADS)
Hao, Q.; Fang, C.; Cao, W.; Chen, P. F.
2015-12-01
We improve our filament automated detection method which was proposed in our previous works. It is then applied to process the full disk Hα data mainly obtained by the Big Bear Solar Observatory from 1988 to 2013, spanning nearly three solar cycles. The butterfly diagrams of the filaments, showing the information of the filament area, spine length, tilt angle, and the barb number, are obtained. The variations of these features with the calendar year and the latitude band are analyzed. The drift velocities of the filaments in different latitude bands are calculated and studied. We also investigate the north-south (N-S) asymmetries of the filament numbers in total and in each subclass classified according to the filament area, spine length, and tilt angle. The latitudinal distribution of the filament number is found to be bimodal. About 80% of all the filaments have tilt angles within [0°, 60°]. For the filaments within latitudes lower (higher) than 50°, the northeast (northwest) direction is dominant in the northern hemisphere and the southeast (southwest) direction is dominant in the southern hemisphere. The latitudinal migrations of the filaments experience three stages with declining drift velocities in each of solar cycles 22 and 23, and it seems that the drift velocity is faster in shorter solar cycles. Most filaments in latitudes lower (higher) than 50° migrate toward the equator (polar region). The N-S asymmetry indices indicate that the southern hemisphere is the dominant hemisphere in solar cycle 22 and the northern hemisphere is the dominant one in solar cycle 23.
Pieralice, Francesca; Proietti, Raffaele; La Valle, Paola; Giorgi, Giordano; Mazzolena, Marco; Taramelli, Andrea; Nicoletti, Luisa
2014-12-01
The Marine Strategy Framework Directive (MSFD, 2008/56/EC) is focused on protection, preservation and restoration of the marine environment by achieving and maintaining Good Environmental Status (GES) by 2020. Within this context, this paper presents a methodological approach for a fast and repeatable monitoring that allows quantitative assessment of seabed abrasion pressure due to recreational boat anchoring. The methodology consists of two steps: a semi-automatic procedure based on an algorithm for the ship detection in SAR imagery and a statistical model to obtain maps of spatial and temporal distribution density of anchored boats. Ship detection processing has been performed on 36 ASAR VV-pol images of Liguria test site, for the three years 2008, 2009 and 2010. Starting from the pointwise distribution layer produced by ship detection in imagery, boats points have been subdivided into 4 areas where a constant distribution density has been assumed for the entire period 2008-2010. In the future, this methodology will be applied also to higher resolution data of Sentinel-1 mission, specifically designed for the operational needs of the European Programme Copernicus. PMID:25096752
Cho, Hyun-Deok; Kim, Unyong; Suh, Joon Hyuk; Eom, Han Young; Kim, Junghyun; Lee, Seul Gi; Choi, Yong Seok; Han, Sang Beom
2016-04-01
Analytical methods using high-performance liquid chromatography with diode array and tandem mass spectrometry detection were developed for the discrimination of the rhizomes of four Atractylodes medicinal plants: A. japonica, A. macrocephala, A. chinensis, and A. lancea. A quantitative study was performed, selecting five bioactive components, including atractylenolide I, II, III, eudesma-4(14),7(11)-dien-8-one and atractylodin, on twenty-six Atractylodes samples of various origins. Sample extraction was optimized to sonication with 80% methanol for 40 min at room temperature. High-performance liquid chromatography with diode array detection was established using a C18 column with a water/acetonitrile gradient system at a flow rate of 1.0 mL/min, and the detection wavelength was set at 236 nm. Liquid chromatography with tandem mass spectrometry was applied to certify the reliability of the quantitative results. The developed methods were validated by ensuring specificity, linearity, limit of quantification, accuracy, precision, recovery, robustness, and stability. Results showed that cangzhu contained higher amounts of atractylenolide I and atractylodin than baizhu, and especially atractylodin contents showed the greatest variation between baizhu and cangzhu. Multivariate statistical analysis, such as principal component analysis and hierarchical cluster analysis, were also employed for further classification of the Atractylodes plants. The established method was suitable for quality control of the Atractylodes plants. PMID:26888213
Cosmic statistics of statistics
NASA Astrophysics Data System (ADS)
Szapudi, István; Colombi, Stéphane; Bernardeau, Francis
1999-12-01
The errors on statistics measured in finite galaxy catalogues are exhaustively investigated. The theory of errors on factorial moments by Szapudi & Colombi is applied to cumulants via a series expansion method. All results are subsequently extended to the weakly non-linear regime. Together with previous investigations this yields an analytic theory of the errors for moments and connected moments of counts in cells from highly non-linear to weakly non-linear scales. For non-linear functions of unbiased estimators, such as the cumulants, the phenomenon of cosmic bias is identified and computed. Since it is subdued by the cosmic errors in the range of applicability of the theory, correction for it is inconsequential. In addition, the method of Colombi, Szapudi & Szalay concerning sampling effects is generalized, adapting the theory for inhomogeneous galaxy catalogues. While previous work focused on the variance only, the present article calculates the cross-correlations between moments and connected moments as well for a statistically complete description. The final analytic formulae representing the full theory are explicit but somewhat complicated. Therefore we have made available a fortran program capable of ca