Figure correction of a metallic ellipsoidal neutron focusing mirror

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

Guo, Jiang, E-mail: jiang.guo@riken.jp; Yamagata, Yutaka; Morita, Shin-ya

2015-06-15

An increasing number of neutron focusing mirrors is being adopted in neutron scattering experiments in order to provide high fluxes at sample positions, reduce measurement time, and/or increase statistical reliability. To realize a small focusing spot and high beam intensity, mirrors with both high form accuracy and low surface roughness are required. To achieve this, we propose a new figure correction technique to fabricate a two-dimensional neutron focusing mirror made with electroless nickel-phosphorus (NiP) by effectively combining ultraprecision shaper cutting and fine polishing. An arc envelope shaper cutting method is introduced to generate high form accuracy, while a fine polishingmore » method, in which the material is removed effectively without losing profile accuracy, is developed to reduce the surface roughness of the mirror. High form accuracy in the minor-axis and the major-axis is obtained through tool profile error compensation and corrective polishing, respectively, and low surface roughness is acquired under a low polishing load. As a result, an ellipsoidal neutron focusing mirror is successfully fabricated with high form accuracy of 0.5 μm peak-to-valley and low surface roughness of 0.2 nm root-mean-square.« less

[Road Extraction in Remote Sensing Images Based on Spectral and Edge Analysis].

PubMed

Zhao, Wen-zhi; Luo, Li-qun; Guo, Zhou; Yue, Jun; Yu, Xue-ying; Liu, Hui; Wei, Jing

2015-10-01

Roads are typically man-made objects in urban areas. Road extraction from high-resolution images has important applications for urban planning and transportation development. However, due to the confusion of spectral characteristic, it is difficult to distinguish roads from other objects by merely using traditional classification methods that mainly depend on spectral information. Edge is an important feature for the identification of linear objects (e. g. , roads). The distribution patterns of edges vary greatly among different objects. It is crucial to merge edge statistical information into spectral ones. In this study, a new method that combines spectral information and edge statistical features has been proposed. First, edge detection is conducted by using self-adaptive mean-shift algorithm on the panchromatic band, which can greatly reduce pseudo-edges and noise effects. Then, edge statistical features are obtained from the edge statistical model, which measures the length and angle distribution of edges. Finally, by integrating the spectral and edge statistical features, SVM algorithm is used to classify the image and roads are ultimately extracted. A series of experiments are conducted and the results show that the overall accuracy of proposed method is 93% comparing with only 78% overall accuracy of the traditional. The results demonstrate that the proposed method is efficient and valuable for road extraction, especially on high-resolution images.

Automated thematic mapping and change detection of ERTS-A images. [digital interpretation of Arizona imagery

NASA Technical Reports Server (NTRS)

Gramenopoulos, N. (Principal Investigator)

1973-01-01

The author has identified the following significant results. For the recognition of terrain types, spatial signatures are developed from the diffraction patterns of small areas of ERTS-1 images. This knowledge is exploited for the measurements of a small number of meaningful spatial features from the digital Fourier transforms of ERTS-1 image cells containing 32 x 32 picture elements. Using these spatial features and a heuristic algorithm, the terrain types in the vicinity of Phoenix, Arizona were recognized by the computer with a high accuracy. Then, the spatial features were combined with spectral features and using the maximum likelihood criterion the recognition accuracy of terrain types increased substantially. It was determined that the recognition accuracy with the maximum likelihood criterion depends on the statistics of the feature vectors. Nonlinear transformations of the feature vectors are required so that the terrain class statistics become approximately Gaussian. It was also determined that for a given geographic area the statistics of the classes remain invariable for a period of a month but vary substantially between seasons.

Treatment of control data in lunar phototriangulation. [application of statistical procedures and development of mathematical and computer techniques

NASA Technical Reports Server (NTRS)

Wong, K. W.

1974-01-01

In lunar phototriangulation, there is a complete lack of accurate ground control points. The accuracy analysis of the results of lunar phototriangulation must, therefore, be completely dependent on statistical procedure. It was the objective of this investigation to examine the validity of the commonly used statistical procedures, and to develop both mathematical techniques and computer softwares for evaluating (1) the accuracy of lunar phototriangulation; (2) the contribution of the different types of photo support data on the accuracy of lunar phototriangulation; (3) accuracy of absolute orientation as a function of the accuracy and distribution of both the ground and model points; and (4) the relative slope accuracy between any triangulated pass points.

Multi-fidelity stochastic collocation method for computation of statistical moments

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

Zhu, Xueyu, E-mail: xueyu-zhu@uiowa.edu; Linebarger, Erin M., E-mail: aerinline@sci.utah.edu; Xiu, Dongbin, E-mail: xiu.16@osu.edu

We present an efficient numerical algorithm to approximate the statistical moments of stochastic problems, in the presence of models with different fidelities. The method extends the multi-fidelity approximation method developed in . By combining the efficiency of low-fidelity models and the accuracy of high-fidelity models, our method exhibits fast convergence with a limited number of high-fidelity simulations. We establish an error bound of the method and present several numerical examples to demonstrate the efficiency and applicability of the multi-fidelity algorithm.

Accuracy of Shack-Hartmann wavefront sensor using a coherent wound fibre image bundle

NASA Astrophysics Data System (ADS)

Zheng, Jessica R.; Goodwin, Michael; Lawrence, Jon

2018-03-01

Shack-Hartmannwavefront sensors using wound fibre image bundles are desired for multi-object adaptive optical systems to provide large multiplex positioned by Starbugs. The use of a large-sized wound fibre image bundle provides the flexibility to use more sub-apertures wavefront sensor for ELTs. These compact wavefront sensors take advantage of large focal surfaces such as the Giant Magellan Telescope. The focus of this paper is to study the wound fibre image bundle structure defects effect on the centroid measurement accuracy of a Shack-Hartmann wavefront sensor. We use the first moment centroid method to estimate the centroid of a focused Gaussian beam sampled by a simulated bundle. Spot estimation accuracy with wound fibre image bundle and its structure impact on wavefront measurement accuracy statistics are addressed. Our results show that when the measurement signal-to-noise ratio is high, the centroid measurement accuracy is dominated by the wound fibre image bundle structure, e.g. tile angle and gap spacing. For the measurement with low signal-to-noise ratio, its accuracy is influenced by the read noise of the detector instead of the wound fibre image bundle structure defects. We demonstrate this both with simulation and experimentally. We provide a statistical model of the centroid and wavefront error of a wound fibre image bundle found through experiment.

Local indicators of geocoding accuracy (LIGA): theory and application

PubMed Central

Jacquez, Geoffrey M; Rommel, Robert

2009-01-01

Background Although sources of positional error in geographic locations (e.g. geocoding error) used for describing and modeling spatial patterns are widely acknowledged, research on how such error impacts the statistical results has been limited. In this paper we explore techniques for quantifying the perturbability of spatial weights to different specifications of positional error. Results We find that a family of curves describes the relationship between perturbability and positional error, and use these curves to evaluate sensitivity of alternative spatial weight specifications to positional error both globally (when all locations are considered simultaneously) and locally (to identify those locations that would benefit most from increased geocoding accuracy). We evaluate the approach in simulation studies, and demonstrate it using a case-control study of bladder cancer in south-eastern Michigan. Conclusion Three results are significant. First, the shape of the probability distributions of positional error (e.g. circular, elliptical, cross) has little impact on the perturbability of spatial weights, which instead depends on the mean positional error. Second, our methodology allows researchers to evaluate the sensitivity of spatial statistics to positional accuracy for specific geographies. This has substantial practical implications since it makes possible routine sensitivity analysis of spatial statistics to positional error arising in geocoded street addresses, global positioning systems, LIDAR and other geographic data. Third, those locations with high perturbability (most sensitive to positional error) and high leverage (that contribute the most to the spatial weight being considered) will benefit the most from increased positional accuracy. These are rapidly identified using a new visualization tool we call the LIGA scatterplot. Herein lies a paradox for spatial analysis: For a given level of positional error increasing sample density to more accurately follow the underlying population distribution increases perturbability and introduces error into the spatial weights matrix. In some studies positional error may not impact the statistical results, and in others it might invalidate the results. We therefore must understand the relationships between positional accuracy and the perturbability of the spatial weights in order to have confidence in a study's results. PMID:19863795

High Accuracy Transistor Compact Model Calibrations

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

Hembree, Charles E.; Mar, Alan; Robertson, Perry J.

2015-09-01

Typically, transistors are modeled by the application of calibrated nominal and range models. These models consists of differing parameter values that describe the location and the upper and lower limits of a distribution of some transistor characteristic such as current capacity. Correspond- ingly, when using this approach, high degrees of accuracy of the transistor models are not expected since the set of models is a surrogate for a statistical description of the devices. The use of these types of models describes expected performances considering the extremes of process or transistor deviations. In contrast, circuits that have very stringent accuracy requirementsmore » require modeling techniques with higher accuracy. Since these accurate models have low error in transistor descriptions, these models can be used to describe part to part variations as well as an accurate description of a single circuit instance. Thus, models that meet these stipulations also enable the calculation of quantifi- cation of margins with respect to a functional threshold and uncertainties in these margins. Given this need, new model high accuracy calibration techniques for bipolar junction transis- tors have been developed and are described in this report.« less

Thematic Accuracy Assessment of the 2011 National Land ...

EPA Pesticide Factsheets

Accuracy assessment is a standard protocol of National Land Cover Database (NLCD) mapping. Here we report agreement statistics between map and reference labels for NLCD 2011, which includes land cover for ca. 2001, ca. 2006, and ca. 2011. The two main objectives were assessment of agreement between map and reference labels for the three, single-date NLCD land cover products at Level II and Level I of the classification hierarchy, and agreement for 17 land cover change reporting themes based on Level I classes (e.g., forest loss; forest gain; forest, no change) for three change periods (2001–2006, 2006–2011, and 2001–2011). The single-date overall accuracies were 82%, 83%, and 83% at Level II and 88%, 89%, and 89% at Level I for 2011, 2006, and 2001, respectively. Many class-specific user's accuracies met or exceeded a previously established nominal accuracy benchmark of 85%. Overall accuracies for 2006 and 2001 land cover components of NLCD 2011 were approximately 4% higher (at Level II and Level I) than the overall accuracies for the same components of NLCD 2006. The high Level I overall, user's, and producer's accuracies for the single-date eras in NLCD 2011 did not translate into high class-specific user's and producer's accuracies for many of the 17 change reporting themes. User's accuracies were high for the no change reporting themes, commonly exceeding 85%, but were typically much lower for the reporting themes that represented change. Only forest l

Reference-based phasing using the Haplotype Reference Consortium panel.

PubMed

Loh, Po-Ru; Danecek, Petr; Palamara, Pier Francesco; Fuchsberger, Christian; A Reshef, Yakir; K Finucane, Hilary; Schoenherr, Sebastian; Forer, Lukas; McCarthy, Shane; Abecasis, Goncalo R; Durbin, Richard; L Price, Alkes

2016-11-01

Haplotype phasing is a fundamental problem in medical and population genetics. Phasing is generally performed via statistical phasing in a genotyped cohort, an approach that can yield high accuracy in very large cohorts but attains lower accuracy in smaller cohorts. Here we instead explore the paradigm of reference-based phasing. We introduce a new phasing algorithm, Eagle2, that attains high accuracy across a broad range of cohort sizes by efficiently leveraging information from large external reference panels (such as the Haplotype Reference Consortium; HRC) using a new data structure based on the positional Burrows-Wheeler transform. We demonstrate that Eagle2 attains a ∼20× speedup and ∼10% increase in accuracy compared to reference-based phasing using SHAPEIT2. On European-ancestry samples, Eagle2 with the HRC panel achieves >2× the accuracy of 1000 Genomes-based phasing. Eagle2 is open source and freely available for HRC-based phasing via the Sanger Imputation Service and the Michigan Imputation Server.

Exploring a Three-Level Model of Calibration Accuracy

ERIC Educational Resources Information Center

Schraw, Gregory; Kuch, Fred; Gutierrez, Antonio P.; Richmond, Aaron S.

2014-01-01

We compared 5 different statistics (i.e., G index, gamma, "d'", sensitivity, specificity) used in the social sciences and medical diagnosis literatures to assess calibration accuracy in order to examine the relationship among them and to explore whether one statistic provided a best fitting general measure of accuracy. College…

Predicting Statistical Response and Extreme Events in Uncertainty Quantification through Reduced-Order Models

NASA Astrophysics Data System (ADS)

Qi, D.; Majda, A.

2017-12-01

A low-dimensional reduced-order statistical closure model is developed for quantifying the uncertainty in statistical sensitivity and intermittency in principal model directions with largest variability in high-dimensional turbulent system and turbulent transport models. Imperfect model sensitivity is improved through a recent mathematical strategy for calibrating model errors in a training phase, where information theory and linear statistical response theory are combined in a systematic fashion to achieve the optimal model performance. The idea in the reduced-order method is from a self-consistent mathematical framework for general systems with quadratic nonlinearity, where crucial high-order statistics are approximated by a systematic model calibration procedure. Model efficiency is improved through additional damping and noise corrections to replace the expensive energy-conserving nonlinear interactions. Model errors due to the imperfect nonlinear approximation are corrected by tuning the model parameters using linear response theory with an information metric in a training phase before prediction. A statistical energy principle is adopted to introduce a global scaling factor in characterizing the higher-order moments in a consistent way to improve model sensitivity. Stringent models of barotropic and baroclinic turbulence are used to display the feasibility of the reduced-order methods. Principal statistical responses in mean and variance can be captured by the reduced-order models with accuracy and efficiency. Besides, the reduced-order models are also used to capture crucial passive tracer field that is advected by the baroclinic turbulent flow. It is demonstrated that crucial principal statistical quantities like the tracer spectrum and fat-tails in the tracer probability density functions in the most important large scales can be captured efficiently with accuracy using the reduced-order tracer model in various dynamical regimes of the flow field with distinct statistical structures.

MUSCLE: multiple sequence alignment with high accuracy and high throughput.

PubMed

Edgar, Robert C

2004-01-01

We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.

Image statistics for surface reflectance perception.

PubMed

Sharan, Lavanya; Li, Yuanzhen; Motoyoshi, Isamu; Nishida, Shin'ya; Adelson, Edward H

2008-04-01

Human observers can distinguish the albedo of real-world surfaces even when the surfaces are viewed in isolation, contrary to the Gelb effect. We sought to measure this ability and to understand the cues that might underlie it. We took photographs of complex surfaces such as stucco and asked observers to judge their diffuse reflectance by comparing them to a physical Munsell scale. Their judgments, while imperfect, were highly correlated with the true reflectance. The judgments were also highly correlated with certain image statistics, such as moment and percentile statistics of the luminance and subband histograms. When we digitally manipulated these statistics in an image, human judgments were correspondingly altered. Moreover, linear combinations of such statistics allow a machine vision system (operating within the constrained world of single surfaces) to estimate albedo with an accuracy similar to that of human observers. Taken together, these results indicate that some simple image statistics have a strong influence on the judgment of surface reflectance.

Modelling for Prediction vs. Modelling for Understanding: Commentary on Musso et al. (2013)

ERIC Educational Resources Information Center

Edelsbrunner, Peter; Schneider, Michael

2013-01-01

Musso et al. (2013) predict students' academic achievement with high accuracy one year in advance from cognitive and demographic variables, using artificial neural networks (ANNs). They conclude that ANNs have high potential for theoretical and practical improvements in learning sciences. ANNs are powerful statistical modelling tools but they can…

High-Reproducibility and High-Accuracy Method for Automated Topic Classification

NASA Astrophysics Data System (ADS)

Lancichinetti, Andrea; Sirer, M. Irmak; Wang, Jane X.; Acuna, Daniel; Körding, Konrad; Amaral, Luís A. Nunes

2015-01-01

Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent searching, statistical characterization, and meaningful classification. Latent Dirichlet allocation (LDA) is the state of the art in topic modeling. Here, we perform a systematic theoretical and numerical analysis that demonstrates that current optimization techniques for LDA often yield results that are not accurate in inferring the most suitable model parameters. Adapting approaches from community detection in networks, we propose a new algorithm that displays high reproducibility and high accuracy and also has high computational efficiency. We apply it to a large set of documents in the English Wikipedia and reveal its hierarchical structure.

Information filtering via biased heat conduction.

PubMed

Liu, Jian-Guo; Zhou, Tao; Guo, Qiang

2011-09-01

The process of heat conduction has recently found application in personalized recommendation [Zhou et al., Proc. Natl. Acad. Sci. USA 107, 4511 (2010)], which is of high diversity but low accuracy. By decreasing the temperatures of small-degree objects, we present an improved algorithm, called biased heat conduction, which could simultaneously enhance the accuracy and diversity. Extensive experimental analyses demonstrate that the accuracy on MovieLens, Netflix, and Delicious datasets could be improved by 43.5%, 55.4% and 19.2%, respectively, compared with the standard heat conduction algorithm and also the diversity is increased or approximately unchanged. Further statistical analyses suggest that the present algorithm could simultaneously identify users' mainstream and special tastes, resulting in better performance than the standard heat conduction algorithm. This work provides a creditable way for highly efficient information filtering.

Instrument for evaluation of sedentary lifestyle in patients with high blood pressure.

PubMed

Lopes, Marcos Venícios de Oliveira; da Silva, Viviane Martins; de Araujo, Thelma Leite; Guedes, Nirla Gomes; Martins, Larissa Castelo Guedes; Teixeira, Iane Ximenes

2015-01-01

this article describes the diagnostic accuracy of the International Physical Activity Questionnaire to identify the nursing diagnosis of sedentary lifestyle. a diagnostic accuracy study was developed with 240 individuals with established high blood pressure. The analysis of diagnostic accuracy was based on measures of sensitivity, specificity, predictive values, likelihood ratios, efficiency, diagnostic odds ratio, Youden index, and area under the receiver-operating characteristic curve. statistical differences between genders were observed for activities of moderate intensity and for total physical activity. Age was negatively correlated with activities of moderate intensity and total physical activity. the analysis of area under the receiver-operating characteristic curve for moderate intensity activities, walking, and total physical activity showed that the International Physical Activity Questionnaire present moderate capacity to correctly classify individuals with and without sedentary lifestyle.

Accuracy and precision of 3 intraoral scanners and accuracy of conventional impressions: A novel in vivo analysis method.

PubMed

Nedelcu, R; Olsson, P; Nyström, I; Rydén, J; Thor, A

2018-02-01

To evaluate a novel methodology using industrial scanners as a reference, and assess in vivo accuracy of 3 intraoral scanners (IOS) and conventional impressions. Further, to evaluate IOS precision in vivo. Four reference-bodies were bonded to the buccal surfaces of upper premolars and incisors in five subjects. After three reference-scans, ATOS Core 80 (ATOS), subjects were scanned three times with three IOS systems: 3M True Definition (3M), CEREC Omnicam (OMNI) and Trios 3 (TRIOS). One conventional impression (IMPR) was taken, 3M Impregum Penta Soft, and poured models were digitized with laboratory scanner 3shape D1000 (D1000). Best-fit alignment of reference-bodies and 3D Compare Analysis was performed. Precision of ATOS and D1000 was assessed for quantitative evaluation and comparison. Accuracy of IOS and IMPR were analyzed using ATOS as reference. Precision of IOS was evaluated through intra-system comparison. Precision of ATOS reference scanner (mean 0.6 μm) and D1000 (mean 0.5 μm) was high. Pairwise multiple comparisons of reference-bodies located in different tooth positions displayed a statistically significant difference of accuracy between two scanner-groups: 3M and TRIOS, over OMNI (p value range 0.0001 to 0.0006). IMPR did not show any statistically significant difference to IOS. However, deviations of IOS and IMPR were within a similar magnitude. No statistical difference was found for IOS precision. The methodology can be used for assessing accuracy of IOS and IMPR in vivo in up to five units bilaterally from midline. 3M and TRIOS had a higher accuracy than OMNI. IMPR overlapped both groups. Intraoral scanners can be used as a replacement for conventional impressions when restoring up to ten units without extended edentulous spans. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Seeing It All: Evaluating Supervised Machine Learning Methods for the Classification of Diverse Otariid Behaviours

PubMed Central

Slip, David J.; Hocking, David P.; Harcourt, Robert G.

2016-01-01

Constructing activity budgets for marine animals when they are at sea and cannot be directly observed is challenging, but recent advances in bio-logging technology offer solutions to this problem. Accelerometers can potentially identify a wide range of behaviours for animals based on unique patterns of acceleration. However, when analysing data derived from accelerometers, there are many statistical techniques available which when applied to different data sets produce different classification accuracies. We investigated a selection of supervised machine learning methods for interpreting behavioural data from captive otariids (fur seals and sea lions). We conducted controlled experiments with 12 seals, where their behaviours were filmed while they were wearing 3-axis accelerometers. From video we identified 26 behaviours that could be grouped into one of four categories (foraging, resting, travelling and grooming) representing key behaviour states for wild seals. We used data from 10 seals to train four predictive classification models: stochastic gradient boosting (GBM), random forests, support vector machine using four different kernels and a baseline model: penalised logistic regression. We then took the best parameters from each model and cross-validated the results on the two seals unseen so far. We also investigated the influence of feature statistics (describing some characteristic of the seal), testing the models both with and without these. Cross-validation accuracies were lower than training accuracy, but the SVM with a polynomial kernel was still able to classify seal behaviour with high accuracy (>70%). Adding feature statistics improved accuracies across all models tested. Most categories of behaviour -resting, grooming and feeding—were all predicted with reasonable accuracy (52–81%) by the SVM while travelling was poorly categorised (31–41%). These results show that model selection is important when classifying behaviour and that by using animal characteristics we can strengthen the overall accuracy. PMID:28002450

Person-Fit Statistics for Joint Models for Accuracy and Speed

ERIC Educational Resources Information Center

Fox, Jean-Paul; Marianti, Sukaesi

2017-01-01

Response accuracy and response time data can be analyzed with a joint model to measure ability and speed of working, while accounting for relationships between item and person characteristics. In this study, person-fit statistics are proposed for joint models to detect aberrant response accuracy and/or response time patterns. The person-fit tests…

Improving UWB-Based Localization in IoT Scenarios with Statistical Models of Distance Error.

PubMed

Monica, Stefania; Ferrari, Gianluigi

2018-05-17

Interest in the Internet of Things (IoT) is rapidly increasing, as the number of connected devices is exponentially growing. One of the application scenarios envisaged for IoT technologies involves indoor localization and context awareness. In this paper, we focus on a localization approach that relies on a particular type of communication technology, namely Ultra Wide Band (UWB). UWB technology is an attractive choice for indoor localization, owing to its high accuracy. Since localization algorithms typically rely on estimated inter-node distances, the goal of this paper is to evaluate the improvement brought by a simple (linear) statistical model of the distance error. On the basis of an extensive experimental measurement campaign, we propose a general analytical framework, based on a Least Square (LS) method, to derive a novel statistical model for the range estimation error between a pair of UWB nodes. The proposed statistical model is then applied to improve the performance of a few illustrative localization algorithms in various realistic scenarios. The obtained experimental results show that the use of the proposed statistical model improves the accuracy of the considered localization algorithms with a reduction of the localization error up to 66%.

Estimated Accuracy of Three Common Trajectory Statistical Methods

NASA Technical Reports Server (NTRS)

Kabashnikov, Vitaliy P.; Chaikovsky, Anatoli P.; Kucsera, Tom L.; Metelskaya, Natalia S.

2011-01-01

Three well-known trajectory statistical methods (TSMs), namely concentration field (CF), concentration weighted trajectory (CWT), and potential source contribution function (PSCF) methods were tested using known sources and artificially generated data sets to determine the ability of TSMs to reproduce spatial distribution of the sources. In the works by other authors, the accuracy of the trajectory statistical methods was estimated for particular species and at specified receptor locations. We have obtained a more general statistical estimation of the accuracy of source reconstruction and have found optimum conditions to reconstruct source distributions of atmospheric trace substances. Only virtual pollutants of the primary type were considered. In real world experiments, TSMs are intended for application to a priori unknown sources. Therefore, the accuracy of TSMs has to be tested with all possible spatial distributions of sources. An ensemble of geographical distributions of virtual sources was generated. Spearman s rank order correlation coefficient between spatial distributions of the known virtual and the reconstructed sources was taken to be a quantitative measure of the accuracy. Statistical estimates of the mean correlation coefficient and a range of the most probable values of correlation coefficients were obtained. All the TSMs that were considered here showed similar close results. The maximum of the ratio of the mean correlation to the width of the correlation interval containing the most probable correlation values determines the optimum conditions for reconstruction. An optimal geographical domain roughly coincides with the area supplying most of the substance to the receptor. The optimal domain s size is dependent on the substance decay time. Under optimum reconstruction conditions, the mean correlation coefficients can reach 0.70 0.75. The boundaries of the interval with the most probable correlation values are 0.6 0.9 for the decay time of 240 h and 0.5 0.95 for the decay time of 12 h. The best results of source reconstruction can be expected for the trace substances with a decay time on the order of several days. Although the methods considered in this paper do not guarantee high accuracy they are computationally simple and fast. Using the TSMs in optimum conditions and taking into account the range of uncertainties, one can obtain a first hint on potential source areas.

Which are the most useful scales for predicting repeat self-harm? A systematic review evaluating risk scales using measures of diagnostic accuracy

PubMed Central

Quinlivan, L; Cooper, J; Davies, L; Hawton, K; Gunnell, D; Kapur, N

2016-01-01

Objectives The aims of this review were to calculate the diagnostic accuracy statistics of risk scales following self-harm and consider which might be the most useful scales in clinical practice. Design Systematic review. Methods We based our search terms on those used in the systematic reviews carried out for the National Institute for Health and Care Excellence self-harm guidelines (2012) and evidence update (2013), and updated the searches through to February 2015 (CINAHL, EMBASE, MEDLINE, and PsychINFO). Methodological quality was assessed and three reviewers extracted data independently. We limited our analysis to cohort studies in adults using the outcome of repeat self-harm or attempted suicide. We calculated diagnostic accuracy statistics including measures of global accuracy. Statistical pooling was not possible due to heterogeneity. Results The eight papers included in the final analysis varied widely according to methodological quality and the content of scales employed. Overall, sensitivity of scales ranged from 6% (95% CI 5% to 6%) to 97% (CI 95% 94% to 98%). The positive predictive value (PPV) ranged from 5% (95% CI 3% to 9%) to 84% (95% CI 80% to 87%). The diagnostic OR ranged from 1.01 (95% CI 0.434 to 2.5) to 16.3 (95%CI 12.5 to 21.4). Scales with high sensitivity tended to have low PPVs. Conclusions It is difficult to be certain which, if any, are the most useful scales for self-harm risk assessment. No scales perform sufficiently well so as to be recommended for routine clinical use. Further robust prospective studies are warranted to evaluate risk scales following an episode of self-harm. Diagnostic accuracy statistics should be considered in relation to the specific service needs, and scales should only be used as an adjunct to assessment. PMID:26873046

solGS: a web-based tool for genomic selection

USDA-ARS?s Scientific Manuscript database

Genomic selection (GS) promises to improve accuracy in estimating breeding values and genetic gain for quantitative traits compared to traditional breeding methods. Its reliance on high-throughput genome-wide markers and statistical complexity, however, is a serious challenge in data management, ana...

Statistical validation of a solar wind propagation model from 1 to 10 AU

NASA Astrophysics Data System (ADS)

Zieger, Bertalan; Hansen, Kenneth C.

2008-08-01

A one-dimensional (1-D) numerical magnetohydrodynamic (MHD) code is applied to propagate the solar wind from 1 AU through 10 AU, i.e., beyond the heliocentric distance of Saturn's orbit, in a non-rotating frame of reference. The time-varying boundary conditions at 1 AU are obtained from hourly solar wind data observed near the Earth. Although similar MHD simulations have been carried out and used by several authors, very little work has been done to validate the statistical accuracy of such solar wind predictions. In this paper, we present an extensive analysis of the prediction efficiency, using 12 selected years of solar wind data from the major heliospheric missions Pioneer, Voyager, and Ulysses. We map the numerical solution to each spacecraft in space and time, and validate the simulation, comparing the propagated solar wind parameters with in-situ observations. We do not restrict our statistical analysis to the times of spacecraft alignment, as most of the earlier case studies do. Our superposed epoch analysis suggests that the prediction efficiency is significantly higher during periods with high recurrence index of solar wind speed, typically in the late declining phase of the solar cycle. Among the solar wind variables, the solar wind speed can be predicted to the highest accuracy, with a linear correlation of 0.75 on average close to the time of opposition. We estimate the accuracy of shock arrival times to be as high as 10-15 hours within ±75 d from apparent opposition during years with high recurrence index. During solar activity maximum, there is a clear bias for the model to predicted shocks arriving later than observed in the data, suggesting that during these periods, there is an additional acceleration mechanism in the solar wind that is not included in the model.

Discrimination of soft tissues using laser-induced breakdown spectroscopy in combination with k nearest neighbors (kNN) and support vector machine (SVM) classifiers

NASA Astrophysics Data System (ADS)

Li, Xiaohui; Yang, Sibo; Fan, Rongwei; Yu, Xin; Chen, Deying

2018-06-01

In this paper, discrimination of soft tissues using laser-induced breakdown spectroscopy (LIBS) in combination with multivariate statistical methods is presented. Fresh pork fat, skin, ham, loin and tenderloin muscle tissues are manually cut into slices and ablated using a 1064 nm pulsed Nd:YAG laser. Discrimination analyses between fat, skin and muscle tissues, and further between highly similar ham, loin and tenderloin muscle tissues, are performed based on the LIBS spectra in combination with multivariate statistical methods, including principal component analysis (PCA), k nearest neighbors (kNN) classification, and support vector machine (SVM) classification. Performances of the discrimination models, including accuracy, sensitivity and specificity, are evaluated using 10-fold cross validation. The classification models are optimized to achieve best discrimination performances. The fat, skin and muscle tissues can be definitely discriminated using both kNN and SVM classifiers, with accuracy of over 99.83%, sensitivity of over 0.995 and specificity of over 0.998. The highly similar ham, loin and tenderloin muscle tissues can also be discriminated with acceptable performances. The best performances are achieved with SVM classifier using Gaussian kernel function, with accuracy of 76.84%, sensitivity of over 0.742 and specificity of over 0.869. The results show that the LIBS technique assisted with multivariate statistical methods could be a powerful tool for online discrimination of soft tissues, even for tissues of high similarity, such as muscles from different parts of the animal body. This technique could be used for discrimination of tissues suffering minor clinical changes, thus may advance the diagnosis of early lesions and abnormalities.

Filter Tuning Using the Chi-Squared Statistic

NASA Technical Reports Server (NTRS)

Lilly-Salkowski, Tyler B.

2017-01-01

This paper examines the use of the Chi-square statistic as a means of evaluating filter performance. The goal of the process is to characterize the filter performance in the metric of covariance realism. The Chi-squared statistic is the value calculated to determine the realism of a covariance based on the prediction accuracy and the covariance values at a given point in time. Once calculated, it is the distribution of this statistic that provides insight on the accuracy of the covariance. The process of tuning an Extended Kalman Filter (EKF) for Aqua and Aura support is described, including examination of the measurement errors of available observation types, and methods of dealing with potentially volatile atmospheric drag modeling. Predictive accuracy and the distribution of the Chi-squared statistic, calculated from EKF solutions, are assessed.

Weighted Statistical Binning: Enabling Statistically Consistent Genome-Scale Phylogenetic Analyses

PubMed Central

Bayzid, Md Shamsuzzoha; Mirarab, Siavash; Boussau, Bastien; Warnow, Tandy

2015-01-01

Because biological processes can result in different loci having different evolutionary histories, species tree estimation requires multiple loci from across multiple genomes. While many processes can result in discord between gene trees and species trees, incomplete lineage sorting (ILS), modeled by the multi-species coalescent, is considered to be a dominant cause for gene tree heterogeneity. Coalescent-based methods have been developed to estimate species trees, many of which operate by combining estimated gene trees, and so are called "summary methods". Because summary methods are generally fast (and much faster than more complicated coalescent-based methods that co-estimate gene trees and species trees), they have become very popular techniques for estimating species trees from multiple loci. However, recent studies have established that summary methods can have reduced accuracy in the presence of gene tree estimation error, and also that many biological datasets have substantial gene tree estimation error, so that summary methods may not be highly accurate in biologically realistic conditions. Mirarab et al. (Science 2014) presented the "statistical binning" technique to improve gene tree estimation in multi-locus analyses, and showed that it improved the accuracy of MP-EST, one of the most popular coalescent-based summary methods. Statistical binning, which uses a simple heuristic to evaluate "combinability" and then uses the larger sets of genes to re-calculate gene trees, has good empirical performance, but using statistical binning within a phylogenomic pipeline does not have the desirable property of being statistically consistent. We show that weighting the re-calculated gene trees by the bin sizes makes statistical binning statistically consistent under the multispecies coalescent, and maintains the good empirical performance. Thus, "weighted statistical binning" enables highly accurate genome-scale species tree estimation, and is also statistically consistent under the multi-species coalescent model. New data used in this study are available at DOI: http://dx.doi.org/10.6084/m9.figshare.1411146, and the software is available at https://github.com/smirarab/binning. PMID:26086579

Statistical Method to Overcome Overfitting Issue in Rational Function Models

NASA Astrophysics Data System (ADS)

Alizadeh Moghaddam, S. H.; Mokhtarzade, M.; Alizadeh Naeini, A.; Alizadeh Moghaddam, S. A.

2017-09-01

Rational function models (RFMs) are known as one of the most appealing models which are extensively applied in geometric correction of satellite images and map production. Overfitting is a common issue, in the case of terrain dependent RFMs, that degrades the accuracy of RFMs-derived geospatial products. This issue, resulting from the high number of RFMs' parameters, leads to ill-posedness of the RFMs. To tackle this problem, in this study, a fast and robust statistical approach is proposed and compared to Tikhonov regularization (TR) method, as a frequently-used solution to RFMs' overfitting. In the proposed method, a statistical test, namely, significance test is applied to search for the RFMs' parameters that are resistant against overfitting issue. The performance of the proposed method was evaluated for two real data sets of Cartosat-1 satellite images. The obtained results demonstrate the efficiency of the proposed method in term of the achievable level of accuracy. This technique, indeed, shows an improvement of 50-80% over the TR.

Information filtering via biased heat conduction

NASA Astrophysics Data System (ADS)

Liu, Jian-Guo; Zhou, Tao; Guo, Qiang

2011-09-01

The process of heat conduction has recently found application in personalized recommendation [Zhou , Proc. Natl. Acad. Sci. USA PNASA60027-842410.1073/pnas.1000488107107, 4511 (2010)], which is of high diversity but low accuracy. By decreasing the temperatures of small-degree objects, we present an improved algorithm, called biased heat conduction, which could simultaneously enhance the accuracy and diversity. Extensive experimental analyses demonstrate that the accuracy on MovieLens, Netflix, and Delicious datasets could be improved by 43.5%, 55.4% and 19.2%, respectively, compared with the standard heat conduction algorithm and also the diversity is increased or approximately unchanged. Further statistical analyses suggest that the present algorithm could simultaneously identify users' mainstream and special tastes, resulting in better performance than the standard heat conduction algorithm. This work provides a creditable way for highly efficient information filtering.

The relationship between the C-statistic of a risk-adjustment model and the accuracy of hospital report cards: a Monte Carlo Study.

PubMed

Austin, Peter C; Reeves, Mathew J

2013-03-01

Hospital report cards, in which outcomes following the provision of medical or surgical care are compared across health care providers, are being published with increasing frequency. Essential to the production of these reports is risk-adjustment, which allows investigators to account for differences in the distribution of patient illness severity across different hospitals. Logistic regression models are frequently used for risk adjustment in hospital report cards. Many applied researchers use the c-statistic (equivalent to the area under the receiver operating characteristic curve) of the logistic regression model as a measure of the credibility and accuracy of hospital report cards. To determine the relationship between the c-statistic of a risk-adjustment model and the accuracy of hospital report cards. Monte Carlo simulations were used to examine this issue. We examined the influence of 3 factors on the accuracy of hospital report cards: the c-statistic of the logistic regression model used for risk adjustment, the number of hospitals, and the number of patients treated at each hospital. The parameters used to generate the simulated datasets came from analyses of patients hospitalized with a diagnosis of acute myocardial infarction in Ontario, Canada. The c-statistic of the risk-adjustment model had, at most, a very modest impact on the accuracy of hospital report cards, whereas the number of patients treated at each hospital had a much greater impact. The c-statistic of a risk-adjustment model should not be used to assess the accuracy of a hospital report card.

The relationship between the c-statistic of a risk-adjustment model and the accuracy of hospital report cards: A Monte Carlo study

PubMed Central

Austin, Peter C.; Reeves, Mathew J.

2015-01-01

Background Hospital report cards, in which outcomes following the provision of medical or surgical care are compared across health care providers, are being published with increasing frequency. Essential to the production of these reports is risk-adjustment, which allows investigators to account for differences in the distribution of patient illness severity across different hospitals. Logistic regression models are frequently used for risk-adjustment in hospital report cards. Many applied researchers use the c-statistic (equivalent to the area under the receiver operating characteristic curve) of the logistic regression model as a measure of the credibility and accuracy of hospital report cards. Objectives To determine the relationship between the c-statistic of a risk-adjustment model and the accuracy of hospital report cards. Research Design Monte Carlo simulations were used to examine this issue. We examined the influence of three factors on the accuracy of hospital report cards: the c-statistic of the logistic regression model used for risk-adjustment, the number of hospitals, and the number of patients treated at each hospital. The parameters used to generate the simulated datasets came from analyses of patients hospitalized with a diagnosis of acute myocardial infarction in Ontario, Canada. Results The c-statistic of the risk-adjustment model had, at most, a very modest impact on the accuracy of hospital report cards, whereas the number of patients treated at each hospital had a much greater impact. Conclusions The c-statistic of a risk-adjustment model should not be used to assess the accuracy of a hospital report card. PMID:23295579

Evaluation of the Eclipse eMC algorithm for bolus electron conformal therapy using a standard verification dataset.

PubMed

Carver, Robert L; Sprunger, Conrad P; Hogstrom, Kenneth R; Popple, Richard A; Antolak, John A

2016-05-08

The purpose of this study was to evaluate the accuracy and calculation speed of electron dose distributions calculated by the Eclipse electron Monte Carlo (eMC) algorithm for use with bolus electron conformal therapy (ECT). The recent com-mercial availability of bolus ECT technology requires further validation of the eMC dose calculation algorithm. eMC-calculated electron dose distributions for bolus ECT have been compared to previously measured TLD-dose points throughout patient-based cylindrical phantoms (retromolar trigone and nose), whose axial cross sections were based on the mid-PTV (planning treatment volume) CT anatomy. The phantoms consisted of SR4 muscle substitute, SR4 bone substitute, and air. The treatment plans were imported into the Eclipse treatment planning system, and electron dose distributions calculated using 1% and < 0.2% statistical uncertainties. The accuracy of the dose calculations using moderate smoothing and no smooth-ing were evaluated. Dose differences (eMC-calculated less measured dose) were evaluated in terms of absolute dose difference, where 100% equals the given dose, as well as distance to agreement (DTA). Dose calculations were also evaluated for calculation speed. Results from the eMC for the retromolar trigone phantom using 1% statistical uncertainty without smoothing showed calculated dose at 89% (41/46) of the measured TLD-dose points was within 3% dose difference or 3 mm DTA of the measured value. The average dose difference was -0.21%, and the net standard deviation was 2.32%. Differences as large as 3.7% occurred immediately distal to the mandible bone. Results for the nose phantom, using 1% statistical uncertainty without smoothing, showed calculated dose at 93% (53/57) of the measured TLD-dose points within 3% dose difference or 3 mm DTA. The average dose difference was 1.08%, and the net standard deviation was 3.17%. Differences as large as 10% occurred lateral to the nasal air cavities. Including smoothing had insignificant effects on the accuracy of the retromolar trigone phantom calculations, but reduced the accuracy of the nose phantom calculations in the high-gradient dose areas. Dose calculation times with 1% statistical uncertainty for the retromolar trigone and nose treatment plans were 30 s and 24 s, respectively, using 16 processors (Intel Xeon E5-2690, 2.9 GHz) on a framework agent server (FAS). In comparison, the eMC was significantly more accurate than the pencil beam algorithm (PBA). The eMC has comparable accuracy to the pencil beam redefinition algorithm (PBRA) used for bolus ECT planning and has acceptably low dose calculation times. The eMC accuracy decreased when smoothing was used in high-gradient dose regions. The eMC accuracy was consistent with that previously reported for accuracy of the eMC electron dose algorithm and shows that the algorithm is suitable for clinical implementation of bolus ECT.

A method of 2D/3D registration of a statistical mouse atlas with a planar X-ray projection and an optical photo.

PubMed

Wang, Hongkai; Stout, David B; Chatziioannou, Arion F

2013-05-01

The development of sophisticated and high throughput whole body small animal imaging technologies has created a need for improved image analysis and increased automation. The registration of a digital mouse atlas to individual images is a prerequisite for automated organ segmentation and uptake quantification. This paper presents a fully-automatic method for registering a statistical mouse atlas with individual subjects based on an anterior-posterior X-ray projection and a lateral optical photo of the mouse silhouette. The mouse atlas was trained as a statistical shape model based on 83 organ-segmented micro-CT images. For registration, a hierarchical approach is applied which first registers high contrast organs, and then estimates low contrast organs based on the registered high contrast organs. To register the high contrast organs, a 2D-registration-back-projection strategy is used that deforms the 3D atlas based on the 2D registrations of the atlas projections. For validation, this method was evaluated using 55 subjects of preclinical mouse studies. The results showed that this method can compensate for moderate variations of animal postures and organ anatomy. Two different metrics, the Dice coefficient and the average surface distance, were used to assess the registration accuracy of major organs. The Dice coefficients vary from 0.31 ± 0.16 for the spleen to 0.88 ± 0.03 for the whole body, and the average surface distance varies from 0.54 ± 0.06 mm for the lungs to 0.85 ± 0.10mm for the skin. The method was compared with a direct 3D deformation optimization (without 2D-registration-back-projection) and a single-subject atlas registration (instead of using the statistical atlas). The comparison revealed that the 2D-registration-back-projection strategy significantly improved the registration accuracy, and the use of the statistical mouse atlas led to more plausible organ shapes than the single-subject atlas. This method was also tested with shoulder xenograft tumor-bearing mice, and the results showed that the registration accuracy of most organs was not significantly affected by the presence of shoulder tumors, except for the lungs and the spleen. Copyright © 2013 Elsevier B.V. All rights reserved.

Systematic Review and Meta-Analysis of Studies Evaluating Diagnostic Test Accuracy: A Practical Review for Clinical Researchers-Part II. Statistical Methods of Meta-Analysis

PubMed Central

Lee, Juneyoung; Kim, Kyung Won; Choi, Sang Hyun; Huh, Jimi

2015-01-01

Meta-analysis of diagnostic test accuracy studies differs from the usual meta-analysis of therapeutic/interventional studies in that, it is required to simultaneously analyze a pair of two outcome measures such as sensitivity and specificity, instead of a single outcome. Since sensitivity and specificity are generally inversely correlated and could be affected by a threshold effect, more sophisticated statistical methods are required for the meta-analysis of diagnostic test accuracy. Hierarchical models including the bivariate model and the hierarchical summary receiver operating characteristic model are increasingly being accepted as standard methods for meta-analysis of diagnostic test accuracy studies. We provide a conceptual review of statistical methods currently used and recommended for meta-analysis of diagnostic test accuracy studies. This article could serve as a methodological reference for those who perform systematic review and meta-analysis of diagnostic test accuracy studies. PMID:26576107

Region based Brain Computer Interface for a home control application.

PubMed

Akman Aydin, Eda; Bay, Omer Faruk; Guler, Inan

2015-08-01

Environment control is one of the important challenges for disabled people who suffer from neuromuscular diseases. Brain Computer Interface (BCI) provides a communication channel between the human brain and the environment without requiring any muscular activation. The most important expectation for a home control application is high accuracy and reliable control. Region-based paradigm is a stimulus paradigm based on oddball principle and requires selection of a target at two levels. This paper presents an application of region based paradigm for a smart home control application for people with neuromuscular diseases. In this study, a region based stimulus interface containing 49 commands was designed. Five non-disabled subjects were attended to the experiments. Offline analysis results of the experiments yielded 95% accuracy for five flashes. This result showed that region based paradigm can be used to select commands of a smart home control application with high accuracy in the low number of repetitions successfully. Furthermore, a statistically significant difference was not observed between the level accuracies.

Child Effortful Control, Teacher-student Relationships, and Achievement in Academically At-risk Children: Additive and Interactive Effects

PubMed Central

Liew, Jeffrey; Chen, Qi; Hughes, Jan N.

2009-01-01

The joint contributions of child effortful control (using inhibitory control and task accuracy as behavioral indices) and positive teacher-student relationships at first grade on reading and mathematics achievement at second grade were examined in 761 children who were predominantly from low-income and ethnic minority backgrounds and assessed to be academically at-risk at entry to first grade. Analyses accounted for clustering effects, covariates, baselines of effortful control measures, and prior levels of achievement. Even with such conservative statistical controls, interactive effects were found for task accuracy and positive teacher-student relationships on future achievement. Results suggest that task accuracy served as a protective factor so that children with high task accuracy performed well academically despite not having positive teacher-student relationships. Further, positive teacher-student relationships served as a compensatory factor so that children with low task accuracy performed just as well as those with high task accuracy if they were paired with a positive and supportive teacher. Importantly, results indicate that the influence of positive teacher-student relationships on future achievement was most pronounced for students with low effortful control on tasks that require fine motor skills, accuracy, and attention-related skills. Study results have implications for narrowing achievement disparities for academically at-risk children. PMID:20161421

Child Effortful Control, Teacher-student Relationships, and Achievement in Academically At-risk Children: Additive and Interactive Effects.

PubMed

Liew, Jeffrey; Chen, Qi; Hughes, Jan N

2010-01-01

The joint contributions of child effortful control (using inhibitory control and task accuracy as behavioral indices) and positive teacher-student relationships at first grade on reading and mathematics achievement at second grade were examined in 761 children who were predominantly from low-income and ethnic minority backgrounds and assessed to be academically at-risk at entry to first grade. Analyses accounted for clustering effects, covariates, baselines of effortful control measures, and prior levels of achievement. Even with such conservative statistical controls, interactive effects were found for task accuracy and positive teacher-student relationships on future achievement. Results suggest that task accuracy served as a protective factor so that children with high task accuracy performed well academically despite not having positive teacher-student relationships. Further, positive teacher-student relationships served as a compensatory factor so that children with low task accuracy performed just as well as those with high task accuracy if they were paired with a positive and supportive teacher. Importantly, results indicate that the influence of positive teacher-student relationships on future achievement was most pronounced for students with low effortful control on tasks that require fine motor skills, accuracy, and attention-related skills. Study results have implications for narrowing achievement disparities for academically at-risk children.

Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD)

USGS Publications Warehouse

Wickham, James; Stehman, Stephen V.; Gass, Leila; Dewitz, Jon; Sorenson, Daniel G.; Granneman, Brian J.; Poss, Richard V.; Baer, Lori Anne

2017-01-01

Accuracy assessment is a standard protocol of National Land Cover Database (NLCD) mapping. Here we report agreement statistics between map and reference labels for NLCD 2011, which includes land cover for ca. 2001, ca. 2006, and ca. 2011. The two main objectives were assessment of agreement between map and reference labels for the three, single-date NLCD land cover products at Level II and Level I of the classification hierarchy, and agreement for 17 land cover change reporting themes based on Level I classes (e.g., forest loss; forest gain; forest, no change) for three change periods (2001–2006, 2006–2011, and 2001–2011). The single-date overall accuracies were 82%, 83%, and 83% at Level II and 88%, 89%, and 89% at Level I for 2011, 2006, and 2001, respectively. Many class-specific user's accuracies met or exceeded a previously established nominal accuracy benchmark of 85%. Overall accuracies for 2006 and 2001 land cover components of NLCD 2011 were approximately 4% higher (at Level II and Level I) than the overall accuracies for the same components of NLCD 2006. The high Level I overall, user's, and producer's accuracies for the single-date eras in NLCD 2011 did not translate into high class-specific user's and producer's accuracies for many of the 17 change reporting themes. User's accuracies were high for the no change reporting themes, commonly exceeding 85%, but were typically much lower for the reporting themes that represented change. Only forest loss, forest gain, and urban gain had user's accuracies that exceeded 70%. Lower user's accuracies for the other change reporting themes may be attributable to the difficulty in determining the context of grass (e.g., open urban, grassland, agriculture) and between the components of the forest-shrubland-grassland gradient at either the mapping phase, reference label assignment phase, or both. NLCD 2011 user's accuracies for forest loss, forest gain, and urban gain compare favorably with results from other land cover change accuracy assessments.

Perceptual statistical learning over one week in child speech production.

PubMed

Richtsmeier, Peter T; Goffman, Lisa

2017-07-01

What cognitive mechanisms account for the trajectory of speech sound development, in particular, gradually increasing accuracy during childhood? An intriguing potential contributor is statistical learning, a type of learning that has been studied frequently in infant perception but less often in child speech production. To assess the relevance of statistical learning to developing speech accuracy, we carried out a statistical learning experiment with four- and five-year-olds in which statistical learning was examined over one week. Children were familiarized with and tested on word-medial consonant sequences in novel words. There was only modest evidence for statistical learning, primarily in the first few productions of the first session. This initial learning effect nevertheless aligns with previous statistical learning research. Furthermore, the overall learning effect was similar to an estimate of weekly accuracy growth based on normative studies. The results implicate other important factors in speech sound development, particularly learning via production. Copyright © 2017 Elsevier Inc. All rights reserved.

Improvement of PM concentration predictability using WRF-CMAQ-DLM coupled system and its applications

NASA Astrophysics Data System (ADS)

Lee, Soon Hwan; Kim, Ji Sun; Lee, Kang Yeol; Shon, Keon Tae

2017-04-01

Air quality due to increasing Particulate Matter(PM) in Korea in Asia is getting worse. At present, the PM forecast is announced based on the PM concentration predicted from the air quality prediction numerical model. However, forecast accuracy is not as high as expected due to various uncertainties for PM physical and chemical characteristics. The purpose of this study was to develop a numerical-statistically ensemble models to improve the accuracy of prediction of PM10 concentration. Numerical models used in this study are the three dimensional atmospheric model Weather Research and Forecasting(WRF) and the community multiscale air quality model (CMAQ). The target areas for the PM forecast are Seoul, Busan, Daegu, and Daejeon metropolitan areas in Korea. The data used in the model development are PM concentration and CMAQ predictions and the data period is 3 months (March 1 - May 31, 2014). The dynamic-statistical technics for reducing the systematic error of the CMAQ predictions was applied to the dynamic linear model(DLM) based on the Baysian Kalman filter technic. As a result of applying the metrics generated from the dynamic linear model to the forecasting of PM concentrations accuracy was improved. Especially, at the high PM concentration where the damage is relatively large, excellent improvement results are shown.

Statistical and Machine Learning forecasting methods: Concerns and ways forward

PubMed Central

Makridakis, Spyros; Assimakopoulos, Vassilios

2018-01-01

Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions. PMID:29584784

High-accuracy user identification using EEG biometrics.

PubMed

Koike-Akino, Toshiaki; Mahajan, Ruhi; Marks, Tim K; Ye Wang; Watanabe, Shinji; Tuzel, Oncel; Orlik, Philip

2016-08-01

We analyze brain waves acquired through a consumer-grade EEG device to investigate its capabilities for user identification and authentication. First, we show the statistical significance of the P300 component in event-related potential (ERP) data from 14-channel EEGs across 25 subjects. We then apply a variety of machine learning techniques, comparing the user identification performance of various different combinations of a dimensionality reduction technique followed by a classification algorithm. Experimental results show that an identification accuracy of 72% can be achieved using only a single 800 ms ERP epoch. In addition, we demonstrate that the user identification accuracy can be significantly improved to more than 96.7% by joint classification of multiple epochs.

Automated breast tissue density assessment using high order regional texture descriptors in mammography

NASA Astrophysics Data System (ADS)

Law, Yan Nei; Lieng, Monica Keiko; Li, Jingmei; Khoo, David Aik-Aun

2014-03-01

Breast cancer is the most common cancer and second leading cause of cancer death among women in the US. The relative survival rate is lower among women with a more advanced stage at diagnosis. Early detection through screening is vital. Mammography is the most widely used and only proven screening method for reliably and effectively detecting abnormal breast tissues. In particular, mammographic density is one of the strongest breast cancer risk factors, after age and gender, and can be used to assess the future risk of disease before individuals become symptomatic. A reliable method for automatic density assessment would be beneficial and could assist radiologists in the evaluation of mammograms. To address this problem, we propose a density classification method which uses statistical features from different parts of the breast. Our method is composed of three parts: breast region identification, feature extraction and building ensemble classifiers for density assessment. It explores the potential of the features extracted from second and higher order statistical information for mammographic density classification. We further investigate the registration of bilateral pairs and time-series of mammograms. The experimental results on 322 mammograms demonstrate that (1) a classifier using features from dense regions has higher discriminative power than a classifier using only features from the whole breast region; (2) these high-order features can be effectively combined to boost the classification accuracy; (3) a classifier using these statistical features from dense regions achieves 75% accuracy, which is a significant improvement from 70% accuracy obtained by the existing approaches.

Evaluating model accuracy for model-based reasoning

NASA Technical Reports Server (NTRS)

Chien, Steve; Roden, Joseph

1992-01-01

Described here is an approach to automatically assessing the accuracy of various components of a model. In this approach, actual data from the operation of a target system is used to drive statistical measures to evaluate the prediction accuracy of various portions of the model. We describe how these statistical measures of model accuracy can be used in model-based reasoning for monitoring and design. We then describe the application of these techniques to the monitoring and design of the water recovery system of the Environmental Control and Life Support System (ECLSS) of Space Station Freedom.

The Search for Better Predictors of Incomes of High School and College Graduates. AIR Forum 1979 Paper.

ERIC Educational Resources Information Center

Witmer, David R.

A search for better predictors of incomes of high school and college graduates is described. The accuracy of the prediction, implicit in the work of John R. Walsh of Harvard University, that the income differences in a given year are good indicators of income differences in future years, was tested by applying standard statistical procedures to…

Multifactorial modelling of high-temperature treatment of timber in the saturated water steam medium

NASA Astrophysics Data System (ADS)

Prosvirnikov, D. B.; Safin, R. G.; Ziatdinova, D. F.; Timerbaev, N. F.; Lashkov, V. A.

2016-04-01

The paper analyses experimental data obtained in studies of high-temperature treatment of softwood and hardwood in an environment of saturated water steam. Data were processed in the Curve Expert software for the purpose of statistical modelling of processes and phenomena occurring during this process. The multifactorial modelling resulted in the empirical dependences, allowing determining the main parameters of this type of hydrothermal treatment with high accuracy.

Supervised classification in the presence of misclassified training data: a Monte Carlo simulation study in the three group case.

PubMed

Bolin, Jocelyn Holden; Finch, W Holmes

2014-01-01

Statistical classification of phenomena into observed groups is very common in the social and behavioral sciences. Statistical classification methods, however, are affected by the characteristics of the data under study. Statistical classification can be further complicated by initial misclassification of the observed groups. The purpose of this study is to investigate the impact of initial training data misclassification on several statistical classification and data mining techniques. Misclassification conditions in the three group case will be simulated and results will be presented in terms of overall as well as subgroup classification accuracy. Results show decreased classification accuracy as sample size, group separation and group size ratio decrease and as misclassification percentage increases with random forests demonstrating the highest accuracy across conditions.

Identifying Galactic Cosmic Ray Origins With Super-TIGER

NASA Technical Reports Server (NTRS)

deNolfo, Georgia; Binns, W. R.; Israel, M. H.; Christian, E. R.; Mitchell, J. W.; Hams, T.; Link, J. T.; Sasaki, M.; Labrador, A. W.; Mewaldt, R. A.;

Rectal cancer staging: Multidetector-row computed tomography diagnostic accuracy in assessment of mesorectal fascia invasion

PubMed Central

Ippolito, Davide; Drago, Silvia Girolama; Franzesi, Cammillo Talei; Fior, Davide; Sironi, Sandro

2016-01-01

AIM: To assess the diagnostic accuracy of multidetector-row computed tomography (MDCT) as compared with conventional magnetic resonance imaging (MRI), in identifying mesorectal fascia (MRF) invasion in rectal cancer patients. METHODS: Ninety-one patients with biopsy proven rectal adenocarcinoma referred for thoracic and abdominal CT staging were enrolled in this study. The contrast-enhanced MDCT scans were performed on a 256 row scanner (ICT, Philips) with the following acquisition parameters: tube voltage 120 KV, tube current 150-300 mAs. Imaging data were reviewed as axial and as multiplanar reconstructions (MPRs) images along the rectal tumor axis. MRI study, performed on 1.5 T with dedicated phased array multicoil, included multiplanar T2 and axial T1 sequences and diffusion weighted images (DWI). Axial and MPR CT images independently were compared to MRI and MRF involvement was determined. Diagnostic accuracy of both modalities was compared and statistically analyzed. RESULTS: According to MRI, the MRF was involved in 51 patients and not involved in 40 patients. DWI allowed to recognize the tumor as a focal mass with high signal intensity on high b-value images, compared with the signal of the normal adjacent rectal wall or with the lower tissue signal intensity background. The number of patients correctly staged by the native axial CT images was 71 out of 91 (41 with involved MRF; 30 with not involved MRF), while by using the MPR 80 patients were correctly staged (45 with involved MRF; 35 with not involved MRF). Local tumor staging suggested by MDCT agreed with those of MRI, obtaining for CT axial images sensitivity and specificity of 80.4% and 75%, positive predictive value (PPV) 80.4%, negative predictive value (NPV) 75% and accuracy 78%; while performing MPR the sensitivity and specificity increased to 88% and 87.5%, PPV was 90%, NPV 85.36% and accuracy 88%. MPR images showed higher diagnostic accuracy, in terms of MRF involvement, than native axial images, as compared to the reference magnetic resonance images. The difference in accuracy was statistically significant (P = 0.02). CONCLUSION: New generation CT scanner, using high resolution MPR images, represents a reliable diagnostic tool in assessment of loco-regional and whole body staging of advanced rectal cancer, especially in patients with MRI contraindications. PMID:27239115

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

PubMed Central

2011-01-01

Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing. PMID:21849043

A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

NASA Astrophysics Data System (ADS)

Zhao, Jianing; Gao, Wanlin; Liu, Zili; Mou, Guifen; Lu, Lin; Yu, Lina

The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.

Effect of data compression on diagnostic accuracy in digital hand and chest radiography

NASA Astrophysics Data System (ADS)

Sayre, James W.; Aberle, Denise R.; Boechat, Maria I.; Hall, Theodore R.; Huang, H. K.; Ho, Bruce K. T.; Kashfian, Payam; Rahbar, Guita

1992-05-01

Image compression is essential to handle a large volume of digital images including CT, MR, CR, and digitized films in a digital radiology operation. The full-frame bit allocation using the cosine transform technique developed during the last few years has been proven to be an excellent irreversible image compression method. This paper describes the effect of using the hardware compression module on diagnostic accuracy in hand radiographs with subperiosteal resorption and chest radiographs with interstitial disease. Receiver operating characteristic analysis using 71 hand radiographs and 52 chest radiographs with five observers each demonstrates that there is no statistical significant difference in diagnostic accuracy between the original films and the compressed images with a compression ratio as high as 20:1.

A segmentation editing framework based on shape change statistics

NASA Astrophysics Data System (ADS)

Mostapha, Mahmoud; Vicory, Jared; Styner, Martin; Pizer, Stephen

2017-02-01

Segmentation is a key task in medical image analysis because its accuracy significantly affects successive steps. Automatic segmentation methods often produce inadequate segmentations, which require the user to manually edit the produced segmentation slice by slice. Because editing is time-consuming, an editing tool that enables the user to produce accurate segmentations by only drawing a sparse set of contours would be needed. This paper describes such a framework as applied to a single object. Constrained by the additional information enabled by the manually segmented contours, the proposed framework utilizes object shape statistics to transform the failed automatic segmentation to a more accurate version. Instead of modeling the object shape, the proposed framework utilizes shape change statistics that were generated to capture the object deformation from the failed automatic segmentation to its corresponding correct segmentation. An optimization procedure was used to minimize an energy function that consists of two terms, an external contour match term and an internal shape change regularity term. The high accuracy of the proposed segmentation editing approach was confirmed by testing it on a simulated data set based on 10 in-vivo infant magnetic resonance brain data sets using four similarity metrics. Segmentation results indicated that our method can provide efficient and adequately accurate segmentations (Dice segmentation accuracy increase of 10%), with very sparse contours (only 10%), which is promising in greatly decreasing the work expected from the user.

High-precision X-ray spectroscopy of highly-charged ions at the experimental storage ring using silicon microcalorimeters

NASA Astrophysics Data System (ADS)

Scholz, Pascal A.; Andrianov, Victor; Echler, Artur; Egelhof, Peter; Kilbourne, Caroline; Kiselev, Oleg; Kraft-Bermuth, Saskia; McCammon, Dan

2017-10-01

X-ray spectroscopy on highly charged heavy ions provides a sensitive test of quantum electrodynamics in very strong Coulomb fields. One limitation of the current accuracy of such experiments is the energy resolution of available X-ray detectors for energies up to 100 keV. To improve this accuracy, a novel detector concept, namely the concept of microcalorimeters, is exploited for this kind of measurements. The microcalorimeters used in the present experiments consist of silicon thermometers, ensuring a high dynamic range, and of absorbers made of high-Z material to provide high X-ray absorption efficiency. Recently, besides an earlier used detector, a new compact detector design, housed in a new dry cryostat equipped with a pulse tube cooler, was applied at a test beamtime at the experimental storage ring (ESR) of the GSI facility in Darmstadt. A U89+ beam at 75 MeV/u and a 124Xe54+ beam at various beam energies, both interacting with an internal gas-jet target, were used in different cycles. This test was an important benchmark for designing a larger array with an improved lateral sensitivity and statistical accuracy.

Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves.

PubMed

Guyot, Patricia; Ades, A E; Ouwens, Mario J N M; Welton, Nicky J

2012-02-01

The results of Randomized Controlled Trials (RCTs) on time-to-event outcomes that are usually reported are median time to events and Cox Hazard Ratio. These do not constitute the sufficient statistics required for meta-analysis or cost-effectiveness analysis, and their use in secondary analyses requires strong assumptions that may not have been adequately tested. In order to enhance the quality of secondary data analyses, we propose a method which derives from the published Kaplan Meier survival curves a close approximation to the original individual patient time-to-event data from which they were generated. We develop an algorithm that maps from digitised curves back to KM data by finding numerical solutions to the inverted KM equations, using where available information on number of events and numbers at risk. The reproducibility and accuracy of survival probabilities, median survival times and hazard ratios based on reconstructed KM data was assessed by comparing published statistics (survival probabilities, medians and hazard ratios) with statistics based on repeated reconstructions by multiple observers. The validation exercise established there was no material systematic error and that there was a high degree of reproducibility for all statistics. Accuracy was excellent for survival probabilities and medians, for hazard ratios reasonable accuracy can only be obtained if at least numbers at risk or total number of events are reported. The algorithm is a reliable tool for meta-analysis and cost-effectiveness analyses of RCTs reporting time-to-event data. It is recommended that all RCTs should report information on numbers at risk and total number of events alongside KM curves.

Does clinical pretest probability influence image quality and diagnostic accuracy in dual-source coronary CT angiography?

PubMed

Thomas, Christoph; Brodoefel, Harald; Tsiflikas, Ilias; Bruckner, Friederike; Reimann, Anja; Ketelsen, Dominik; Drosch, Tanja; Claussen, Claus D; Kopp, Andreas; Heuschmid, Martin; Burgstahler, Christof

2010-02-01

To prospectively evaluate the influence of the clinical pretest probability assessed by the Morise score onto image quality and diagnostic accuracy in coronary dual-source computed tomography angiography (DSCTA). In 61 patients, DSCTA and invasive coronary angiography were performed. Subjective image quality and accuracy for stenosis detection (>50%) of DSCTA with invasive coronary angiography as gold standard were evaluated. The influence of pretest probability onto image quality and accuracy was assessed by logistic regression and chi-square testing. Correlations of image quality and accuracy with the Morise score were determined using linear regression. Thirty-eight patients were categorized into the high, 21 into the intermediate, and 2 into the low probability group. Accuracies for the detection of significant stenoses were 0.94, 0.97, and 1.00, respectively. Logistic regressions and chi-square tests showed statistically significant correlations between Morise score and image quality (P < .0001 and P < .001) and accuracy (P = .0049 and P = .027). Linear regression revealed a cutoff Morise score for a good image quality of 16 and a cutoff for a barely diagnostic image quality beyond the upper Morise scale. Pretest probability is a weak predictor of image quality and diagnostic accuracy in coronary DSCTA. A sufficient image quality for diagnostic images can be reached with all pretest probabilities. Therefore, coronary DSCTA might be suitable also for patients with a high pretest probability. Copyright 2010 AUR. Published by Elsevier Inc. All rights reserved.

Quantum mushroom billiards

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

Barnett, Alex H.; Betcke, Timo; School of Mathematics, University of Manchester, Manchester, M13 9PL

2007-12-15

We report the first large-scale statistical study of very high-lying eigenmodes (quantum states) of the mushroom billiard proposed by L. A. Bunimovich [Chaos 11, 802 (2001)]. The phase space of this mixed system is unusual in that it has a single regular region and a single chaotic region, and no KAM hierarchy. We verify Percival's conjecture to high accuracy (1.7%). We propose a model for dynamical tunneling and show that it predicts well the chaotic components of predominantly regular modes. Our model explains our observed density of such superpositions dying as E{sup -1/3} (E is the eigenvalue). We compare eigenvaluemore » spacing distributions against Random Matrix Theory expectations, using 16 000 odd modes (an order of magnitude more than any existing study). We outline new variants of mesh-free boundary collocation methods which enable us to achieve high accuracy and high mode numbers ({approx}10{sup 5}) orders of magnitude faster than with competing methods.« less

EFFICIENTLY ESTABLISHING CONCEPTS OF INFERENTIAL STATISTICS AND HYPOTHESIS DECISION MAKING THROUGH CONTEXTUALLY CONTROLLED EQUIVALENCE CLASSES

PubMed Central

Fienup, Daniel M; Critchfield, Thomas S

2010-01-01

Computerized lessons that reflect stimulus equivalence principles were used to teach college students concepts related to inferential statistics and hypothesis decision making. Lesson 1 taught participants concepts related to inferential statistics, and Lesson 2 taught them to base hypothesis decisions on a scientific hypothesis and the direction of an effect. Lesson 3 taught the conditional influence of inferential statistics over decisions regarding the scientific and null hypotheses. Participants entered the study with low scores on the targeted skills and left the study demonstrating a high level of accuracy on these skills, which involved mastering more relations than were taught formally. This study illustrates the efficiency of equivalence-based instruction in establishing academic skills in sophisticated learners. PMID:21358904

On the accuracy of the interdiffusion coefficient measurements of high-temperature binary mixtures under ISS conditions

NASA Astrophysics Data System (ADS)

Saez, Núria; Ruiz, Xavier; Pallarés, Jordi; Shevtsova, Valentina

2013-04-01

An accelerometric record from the IVIDIL experiment (ESA Columbus module) has exhaustively been studied. The analysis involved the determination of basic statistical properties as, for instance, the auto-correlation and the power spectrum (second-order statistical analyses). Also, and taking into account the shape of the associated histograms, we address another important question, the non-Gaussian nature of the time series using the bispectrum and the bicoherence of the signals. Extrapolating the above-mentioned results, a computational model of a high-temperature shear cell has been performed. A scalar indicator has been used to quantify the accuracy of the diffusion coefficient measurements in the case of binary mixtures involving photovoltaic silicon or liquid Al-Cu binary alloys. Three different initial arrangements have been considered, the so-called interdiffusion, centred thick layer and the lateral thick layer. Results allow us to conclude that, under the conditions of the present work, the diffusion coefficient is insensitive to the environmental conditions, that is to say, accelerometric disturbances and initial shear cell arrangement.

Predictive capacity of a non-radioisotopic local lymph node assay using flow cytometry, LLNA:BrdU-FCM: Comparison of a cutoff approach and inferential statistics.

PubMed

Kim, Da-Eun; Yang, Hyeri; Jang, Won-Hee; Jung, Kyoung-Mi; Park, Miyoung; Choi, Jin Kyu; Jung, Mi-Sook; Jeon, Eun-Young; Heo, Yong; Yeo, Kyung-Wook; Jo, Ji-Hoon; Park, Jung Eun; Sohn, Soo Jung; Kim, Tae Sung; Ahn, Il Young; Jeong, Tae-Cheon; Lim, Kyung-Min; Bae, SeungJin

2016-01-01

In order for a novel test method to be applied for regulatory purposes, its reliability and relevance, i.e., reproducibility and predictive capacity, must be demonstrated. Here, we examine the predictive capacity of a novel non-radioisotopic local lymph node assay, LLNA:BrdU-FCM (5-bromo-2'-deoxyuridine-flow cytometry), with a cutoff approach and inferential statistics as a prediction model. 22 reference substances in OECD TG429 were tested with a concurrent positive control, hexylcinnamaldehyde 25%(PC), and the stimulation index (SI) representing the fold increase in lymph node cells over the vehicle control was obtained. The optimal cutoff SI (2.7≤cutoff <3.5), with respect to predictive capacity, was obtained by a receiver operating characteristic curve, which produced 90.9% accuracy for the 22 substances. To address the inter-test variability in responsiveness, SI values standardized with PC were employed to obtain the optimal percentage cutoff (42.6≤cutoff <57.3% of PC), which produced 86.4% accuracy. A test substance may be diagnosed as a sensitizer if a statistically significant increase in SI is elicited. The parametric one-sided t-test and non-parametric Wilcoxon rank-sum test produced 77.3% accuracy. Similarly, a test substance could be defined as a sensitizer if the SI means of the vehicle control, and of the low, middle, and high concentrations were statistically significantly different, which was tested using ANOVA or Kruskal-Wallis, with post hoc analysis, Dunnett, or DSCF (Dwass-Steel-Critchlow-Fligner), respectively, depending on the equal variance test, producing 81.8% accuracy. The absolute SI-based cutoff approach produced the best predictive capacity, however the discordant decisions between prediction models need to be examined further. Copyright © 2015 Elsevier Inc. All rights reserved.

Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

PubMed Central

Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

2015-01-01

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

Determining the accuracy of maximum likelihood parameter estimates with colored residuals

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; Klein, Vladislav

1994-01-01

An important part of building high fidelity mathematical models based on measured data is calculating the accuracy associated with statistical estimates of the model parameters. Indeed, without some idea of the accuracy of parameter estimates, the estimates themselves have limited value. In this work, an expression based on theoretical analysis was developed to properly compute parameter accuracy measures for maximum likelihood estimates with colored residuals. This result is important because experience from the analysis of measured data reveals that the residuals from maximum likelihood estimation are almost always colored. The calculations involved can be appended to conventional maximum likelihood estimation algorithms. Simulated data runs were used to show that the parameter accuracy measures computed with this technique accurately reflect the quality of the parameter estimates from maximum likelihood estimation without the need for analysis of the output residuals in the frequency domain or heuristically determined multiplication factors. The result is general, although the application studied here is maximum likelihood estimation of aerodynamic model parameters from flight test data.

Improvement in the accuracy of flux measurement of radio sources by exploiting an arithmetic pattern in photon bunching noise

NASA Astrophysics Data System (ADS)

Lieu, Richard

2018-01-01

A hierarchy of statistics of increasing sophistication and accuracy is proposed, to exploit an interesting and fundamental arithmetic structure in the photon bunching noise of incoherent light of large photon occupation number, with the purpose of suppressing the noise and rendering a more reliable and unbiased measurement of the light intensity. The method does not require any new hardware, rather it operates at the software level, with the help of high precision computers, to reprocess the intensity time series of the incident light to create a new series with smaller bunching noise coherence length. The ultimate accuracy improvement of this method of flux measurement is limited by the timing resolution of the detector and the photon occupation number of the beam (the higher the photon number the better the performance). The principal application is accuracy improvement in the bolometric flux measurement of a radio source.

Variance estimates and confidence intervals for the Kappa measure of classification accuracy

Treesearch

M. A. Kalkhan; R. M. Reich; R. L. Czaplewski

1997-01-01

The Kappa statistic is frequently used to characterize the results of an accuracy assessment used to evaluate land use and land cover classifications obtained by remotely sensed data. This statistic allows comparisons of alternative sampling designs, classification algorithms, photo-interpreters, and so forth. In order to make these comparisons, it is...

Accuracy Evaluation of the Unified P-Value from Combining Correlated P-Values

PubMed Central

Alves, Gelio; Yu, Yi-Kuo

2014-01-01

Meta-analysis methods that combine -values into a single unified -value are frequently employed to improve confidence in hypothesis testing. An assumption made by most meta-analysis methods is that the -values to be combined are independent, which may not always be true. To investigate the accuracy of the unified -value from combining correlated -values, we have evaluated a family of statistical methods that combine: independent, weighted independent, correlated, and weighted correlated -values. Statistical accuracy evaluation by combining simulated correlated -values showed that correlation among -values can have a significant effect on the accuracy of the combined -value obtained. Among the statistical methods evaluated those that weight -values compute more accurate combined -values than those that do not. Also, statistical methods that utilize the correlation information have the best performance, producing significantly more accurate combined -values. In our study we have demonstrated that statistical methods that combine -values based on the assumption of independence can produce inaccurate -values when combining correlated -values, even when the -values are only weakly correlated. Therefore, to prevent from drawing false conclusions during hypothesis testing, our study advises caution be used when interpreting the -value obtained from combining -values of unknown correlation. However, when the correlation information is available, the weighting-capable statistical method, first introduced by Brown and recently modified by Hou, seems to perform the best amongst the methods investigated. PMID:24663491

A Synergy Cropland of China by Fusing Multiple Existing Maps and Statistics.

PubMed

Lu, Miao; Wu, Wenbin; You, Liangzhi; Chen, Di; Zhang, Li; Yang, Peng; Tang, Huajun

2017-07-12

Accurate information on cropland extent is critical for scientific research and resource management. Several cropland products from remotely sensed datasets are available. Nevertheless, significant inconsistency exists among these products and the cropland areas estimated from these products differ considerably from statistics. In this study, we propose a hierarchical optimization synergy approach (HOSA) to develop a hybrid cropland map of China, circa 2010, by fusing five existing cropland products, i.e., GlobeLand30, Climate Change Initiative Land Cover (CCI-LC), GlobCover 2009, MODIS Collection 5 (MODIS C5), and MODIS Cropland, and sub-national statistics of cropland area. HOSA simplifies the widely used method of score assignment into two steps, including determination of optimal agreement level and identification of the best product combination. The accuracy assessment indicates that the synergy map has higher accuracy of spatial locations and better consistency with statistics than the five existing datasets individually. This suggests that the synergy approach can improve the accuracy of cropland mapping and enhance consistency with statistics.

Combining Diffusion Tensor Metrics and DSC Perfusion Imaging: Can It Improve the Diagnostic Accuracy in Differentiating Tumefactive Demyelination from High-Grade Glioma?

PubMed

Hiremath, S B; Muraleedharan, A; Kumar, S; Nagesh, C; Kesavadas, C; Abraham, M; Kapilamoorthy, T R; Thomas, B

2017-04-01

Tumefactive demyelinating lesions with atypical features can mimic high-grade gliomas on conventional imaging sequences. The aim of this study was to assess the role of conventional imaging, DTI metrics ( p:q tensor decomposition), and DSC perfusion in differentiating tumefactive demyelinating lesions and high-grade gliomas. Fourteen patients with tumefactive demyelinating lesions and 21 patients with high-grade gliomas underwent brain MR imaging with conventional, DTI, and DSC perfusion imaging. Imaging sequences were assessed for differentiation of the lesions. DTI metrics in the enhancing areas and perilesional hyperintensity were obtained by ROI analysis, and the relative CBV values in enhancing areas were calculated on DSC perfusion imaging. Conventional imaging sequences had a sensitivity of 80.9% and specificity of 57.1% in differentiating high-grade gliomas ( P = .049) from tumefactive demyelinating lesions. DTI metrics ( p : q tensor decomposition) and DSC perfusion demonstrated a statistically significant difference in the mean values of ADC, the isotropic component of the diffusion tensor, the anisotropic component of the diffusion tensor, the total magnitude of the diffusion tensor, and rCBV among enhancing portions in tumefactive demyelinating lesions and high-grade gliomas ( P ≤ .02), with the highest specificity for ADC, the anisotropic component of the diffusion tensor, and relative CBV (92.9%). Mean fractional anisotropy values showed no significant statistical difference between tumefactive demyelinating lesions and high-grade gliomas. The combination of DTI and DSC parameters improved the diagnostic accuracy (area under the curve = 0.901). Addition of a heterogeneous enhancement pattern to DTI and DSC parameters improved it further (area under the curve = 0.966). The sensitivity increased from 71.4% to 85.7% after the addition of the enhancement pattern. DTI and DSC perfusion add profoundly to conventional imaging in differentiating tumefactive demyelinating lesions and high-grade gliomas. The combination of DTI metrics and DSC perfusion markedly improved diagnostic accuracy. © 2017 by American Journal of Neuroradiology.

The accuracy of Genomic Selection in Norwegian red cattle assessed by cross-validation.

PubMed

Luan, Tu; Woolliams, John A; Lien, Sigbjørn; Kent, Matthew; Svendsen, Morten; Meuwissen, Theo H E

2009-11-01

Genomic Selection (GS) is a newly developed tool for the estimation of breeding values for quantitative traits through the use of dense markers covering the whole genome. For a successful application of GS, accuracy of the prediction of genomewide breeding value (GW-EBV) is a key issue to consider. Here we investigated the accuracy and possible bias of GW-EBV prediction, using real bovine SNP genotyping (18,991 SNPs) and phenotypic data of 500 Norwegian Red bulls. The study was performed on milk yield, fat yield, protein yield, first lactation mastitis traits, and calving ease. Three methods, best linear unbiased prediction (G-BLUP), Bayesian statistics (BayesB), and a mixture model approach (MIXTURE), were used to estimate marker effects, and their accuracy and bias were estimated by using cross-validation. The accuracies of the GW-EBV prediction were found to vary widely between 0.12 and 0.62. G-BLUP gave overall the highest accuracy. We observed a strong relationship between the accuracy of the prediction and the heritability of the trait. GW-EBV prediction for production traits with high heritability achieved higher accuracy and also lower bias than health traits with low heritability. To achieve a similar accuracy for the health traits probably more records will be needed.

Designing image segmentation studies: Statistical power, sample size and reference standard quality.

PubMed

Gibson, Eli; Hu, Yipeng; Huisman, Henkjan J; Barratt, Dean C

2017-12-01

Segmentation algorithms are typically evaluated by comparison to an accepted reference standard. The cost of generating accurate reference standards for medical image segmentation can be substantial. Since the study cost and the likelihood of detecting a clinically meaningful difference in accuracy both depend on the size and on the quality of the study reference standard, balancing these trade-offs supports the efficient use of research resources. In this work, we derive a statistical power calculation that enables researchers to estimate the appropriate sample size to detect clinically meaningful differences in segmentation accuracy (i.e. the proportion of voxels matching the reference standard) between two algorithms. Furthermore, we derive a formula to relate reference standard errors to their effect on the sample sizes of studies using lower-quality (but potentially more affordable and practically available) reference standards. The accuracy of the derived sample size formula was estimated through Monte Carlo simulation, demonstrating, with 95% confidence, a predicted statistical power within 4% of simulated values across a range of model parameters. This corresponds to sample size errors of less than 4 subjects and errors in the detectable accuracy difference less than 0.6%. The applicability of the formula to real-world data was assessed using bootstrap resampling simulations for pairs of algorithms from the PROMISE12 prostate MR segmentation challenge data set. The model predicted the simulated power for the majority of algorithm pairs within 4% for simulated experiments using a high-quality reference standard and within 6% for simulated experiments using a low-quality reference standard. A case study, also based on the PROMISE12 data, illustrates using the formulae to evaluate whether to use a lower-quality reference standard in a prostate segmentation study. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

How Does One Assess the Accuracy of Academic Success Predictors? ROC Analysis Applied to University Entrance Factors

ERIC Educational Resources Information Center

Vivo, Juana-Maria; Franco, Manuel

2008-01-01

This article attempts to present a novel application of a method of measuring accuracy for academic success predictors that could be used as a standard. This procedure is known as the receiver operating characteristic (ROC) curve, which comes from statistical decision techniques. The statistical prediction techniques provide predictor models and…

High throughput single cell counting in droplet-based microfluidics.

PubMed

Lu, Heng; Caen, Ouriel; Vrignon, Jeremy; Zonta, Eleonora; El Harrak, Zakaria; Nizard, Philippe; Baret, Jean-Christophe; Taly, Valérie

2017-05-02

Droplet-based microfluidics is extensively and increasingly used for high-throughput single-cell studies. However, the accuracy of the cell counting method directly impacts the robustness of such studies. We describe here a simple and precise method to accurately count a large number of adherent and non-adherent human cells as well as bacteria. Our microfluidic hemocytometer provides statistically relevant data on large populations of cells at a high-throughput, used to characterize cell encapsulation and cell viability during incubation in droplets.

Cervical vertebral maturation as a biologic indicator of skeletal maturity.

PubMed

Santiago, Rodrigo César; de Miranda Costa, Luiz Felipe; Vitral, Robert Willer Farinazzo; Fraga, Marcelo Reis; Bolognese, Ana Maria; Maia, Lucianne Cople

2012-11-01

To identify and review the literature regarding the reliability of cervical vertebrae maturation (CVM) staging to predict the pubertal spurt. The selection criteria included cross-sectional and longitudinal descriptive studies in humans that evaluated qualitatively or quantitatively the accuracy and reproducibility of the CVM method on lateral cephalometric radiographs, as well as the correlation with a standard method established by hand-wrist radiographs. The searches retrieved 343 unique citations. Twenty-three studies met the inclusion criteria. Six articles had moderate to high scores, while 17 of 23 had low scores. Analysis also showed a moderate to high statistically significant correlation between CVM and hand-wrist maturation methods. There was a moderate to high reproducibility of the CVM method, and only one specific study investigated the accuracy of the CVM index in detecting peak pubertal growth. This systematic review has shown that the studies on CVM method for radiographic assessment of skeletal maturation stages suffer from serious methodological failures. Better-designed studies with adequate accuracy, reproducibility, and correlation analysis, including studies with appropriate sensitivity-specificity analysis, should be performed.

ECG Sensor Card with Evolving RBP Algorithms for Human Verification.

PubMed

Tseng, Kuo-Kun; Huang, Huang-Nan; Zeng, Fufu; Tu, Shu-Yi

2015-08-21

It is known that cardiac and respiratory rhythms in electrocardiograms (ECGs) are highly nonlinear and non-stationary. As a result, most traditional time-domain algorithms are inadequate for characterizing the complex dynamics of the ECG. This paper proposes a new ECG sensor card and a statistical-based ECG algorithm, with the aid of a reduced binary pattern (RBP), with the aim of achieving faster ECG human identity recognition with high accuracy. The proposed algorithm has one advantage that previous ECG algorithms lack-the waveform complex information and de-noising preprocessing can be bypassed; therefore, it is more suitable for non-stationary ECG signals. Experimental results tested on two public ECG databases (MIT-BIH) from MIT University confirm that the proposed scheme is feasible with excellent accuracy, low complexity, and speedy processing. To be more specific, the advanced RBP algorithm achieves high accuracy in human identity recognition and is executed at least nine times faster than previous algorithms. Moreover, based on the test results from a long-term ECG database, the evolving RBP algorithm also demonstrates superior capability in handling long-term and non-stationary ECG signals.

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

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

Diagnostic accuracy of imaging devices in glaucoma: A meta-analysis.

PubMed

Fallon, Monica; Valero, Oliver; Pazos, Marta; Antón, Alfonso

Imaging devices such as the Heidelberg retinal tomograph-3 (HRT3), scanning laser polarimetry (GDx), and optical coherence tomography (OCT) play an important role in glaucoma diagnosis. A systematic search for evidence-based data was performed for prospective studies evaluating the diagnostic accuracy of HRT3, GDx, and OCT. The diagnostic odds ratio (DOR) was calculated. To compare the accuracy among instruments and parameters, a meta-analysis considering the hierarchical summary receiver-operating characteristic model was performed. The risk of bias was assessed using quality assessment of diagnostic accuracy studies, version 2. Studies in the context of screening programs were used for qualitative analysis. Eighty-six articles were included. The DOR values were 29.5 for OCT, 18.6 for GDx, and 13.9 for HRT. The heterogeneity analysis demonstrated statistically a significant influence of degree of damage and ethnicity. Studies analyzing patients with earlier glaucoma showed poorer results. The risk of bias was high for patient selection. Screening studies showed lower sensitivity values and similar specificity values when compared with those included in the meta-analysis. The classification capabilities of GDx, HRT, and OCT were high and similar across the 3 instruments. The highest estimated DOR was obtained with OCT. Diagnostic accuracy could be overestimated in studies including prediagnosed groups of subjects. Copyright © 2017 Elsevier Inc. All rights reserved.

Overconfidence across the psychosis continuum: a calibration approach.

PubMed

Balzan, Ryan P; Woodward, Todd S; Delfabbro, Paul; Moritz, Steffen

2016-11-01

An 'overconfidence in errors' bias has been consistently observed in people with schizophrenia relative to healthy controls, however, the bias is seldom found to be associated with delusional ideation. Using a more precise confidence-accuracy calibration measure of overconfidence, the present study aimed to explore whether the overconfidence bias is greater in people with higher delusional ideation. A sample of 25 participants with schizophrenia and 50 non-clinical controls (25 high- and 25 low-delusion-prone) completed 30 difficult trivia questions (accuracy <75%); 15 'half-scale' items required participants to indicate their level of confidence for accuracy, and the remaining 'confidence-range' items asked participants to provide lower/upper bounds in which they were 80% confident the true answer lay within. There was a trend towards higher overconfidence for half-scale items in the schizophrenia and high-delusion-prone groups, which reached statistical significance for confidence-range items. However, accuracy was particularly low in the two delusional groups and a significant negative correlation between clinical delusional scores and overconfidence was observed for half-scale items within the schizophrenia group. Evidence in support of an association between overconfidence and delusional ideation was therefore mixed. Inflated confidence-accuracy miscalibration for the two delusional groups may be better explained by their greater unawareness of their underperformance, rather than representing genuinely inflated overconfidence in errors.

Alternative evaluation metrics for risk adjustment methods.

PubMed

Park, Sungchul; Basu, Anirban

2018-06-01

Risk adjustment is instituted to counter risk selection by accurately equating payments with expected expenditures. Traditional risk-adjustment methods are designed to estimate accurate payments at the group level. However, this generates residual risks at the individual level, especially for high-expenditure individuals, thereby inducing health plans to avoid those with high residual risks. To identify an optimal risk-adjustment method, we perform a comprehensive comparison of prediction accuracies at the group level, at the tail distributions, and at the individual level across 19 estimators: 9 parametric regression, 7 machine learning, and 3 distributional estimators. Using the 2013-2014 MarketScan database, we find that no one estimator performs best in all prediction accuracies. Generally, machine learning and distribution-based estimators achieve higher group-level prediction accuracy than parametric regression estimators. However, parametric regression estimators show higher tail distribution prediction accuracy and individual-level prediction accuracy, especially at the tails of the distribution. This suggests that there is a trade-off in selecting an appropriate risk-adjustment method between estimating accurate payments at the group level and lower residual risks at the individual level. Our results indicate that an optimal method cannot be determined solely on the basis of statistical metrics but rather needs to account for simulating plans' risk selective behaviors. Copyright © 2018 John Wiley & Sons, Ltd.

L-DOPA decarboxylase mRNA levels provide high diagnostic accuracy and discrimination between clear cell and non-clear cell subtypes in renal cell carcinoma.

PubMed

Papadopoulos, Emmanuel I; Petraki, Constantina; Gregorakis, Alkiviadis; Chra, Eleni; Fragoulis, Emmanuel G; Scorilas, Andreas

2015-06-01

Renal cell carcinoma (RCC) is the most frequent type of kidney cancer. RCC patients frequently present with arterial hypertension due to various causes, including intrarenal dopamine deficiency. L-DOPA decarboxylase (DDC) is the gene encoding the enzyme that catalyzes the biosynthesis of dopamine in humans. Several studies have shown that the expression levels of DDC are significantly deregulated in cancer. Thus, we herein sought to analyze the mRNA levels of DDC and evaluate their clinical significance in RCC. DDC levels were analyzed in 58 surgically resected RCC tumors and 44 adjacent non-cancerous renal tissue specimens via real-time PCR. Relative levels of DDC were estimated by applying the 2(-ΔΔC)T method, while their diagnostic accuracy and correlation with the clinicopathological features of RCC tumors were assessed by comprehensive statistical analysis. DDC mRNA levels were found to be dramatically downregulated (p<0.001) in RCC tumors, exhibiting remarkable diagnostic accuracy as assessed by ROC curve analysis (AUC: 0.910; p<0.001) and logistic regression (OR: 0.678; p=0.001). Likewise, DDC was found to be differentially expressed between clear cell RCC and the group of non-clear cell subtypes (p=0.001) consisted of papillary and chromophobe RCC specimens. Furthermore, a statistically significant inverse correlation was also observed when the mRNA levels of DDC were analyzed in relation to tumor grade (p=0.049). Our data showed that DDC constitutes a highly promising molecular marker for RCC, exhibiting remarkable diagnostic accuracy and potential to discriminate between clear cell and non-clear cell histological subtypes of RCC. Copyright © 2015. Published by Elsevier Inc.

A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform

PubMed Central

Yu, Xiao; Ding, Enjie; Chen, Chunxu; Liu, Xiaoming; Li, Li

2015-01-01

Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC) to select salient features from the marginal spectrum of vibration signals by Hilbert–Huang Transform (HHT). In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS) into window spectrums, following which Rand Index (RI) criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs). Next, a hybrid REBs fault diagnosis is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines). The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU). The said test results evidence three major advantages of the novel method. First, the fault classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, fault classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500–800 and a m range of 50–300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR) = 5. Experimental results indicate that the proposed WMSC method yields a high REBs fault classification accuracy and a good performance in Gauss white noise reduction. PMID:26540059

A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform.

PubMed

Yu, Xiao; Ding, Enjie; Chen, Chunxu; Liu, Xiaoming; Li, Li

2015-11-03

Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC) to select salient features from the marginal spectrum of vibration signals by Hilbert-Huang Transform (HHT). In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS) into window spectrums, following which Rand Index (RI) criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs). Next, a hybrid REBs fault diagnosis is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines). The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU). The said test results evidence three major advantages of the novel method. First, the fault classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, fault classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500-800 and a m range of 50-300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR) = 5. Experimental results indicate that the proposed WMSC method yields a high REBs fault classification accuracy and a good performance in Gauss white noise reduction.

Influence of Waveform Characteristics on LiDAR Ranging Accuracy and Precision

PubMed Central

Yang, Bingwei; Xie, Xinhao; Li, Duan

2018-01-01

Time of flight (TOF) based light detection and ranging (LiDAR) is a technology for calculating distance between start/stop signals of time of flight. In lab-built LiDAR, two ranging systems for measuring flying time between start/stop signals include time-to-digital converter (TDC) that counts time between trigger signals and analog-to-digital converter (ADC) that processes the sampled start/stop pulses waveform for time estimation. We study the influence of waveform characteristics on range accuracy and precision of two kinds of ranging system. Comparing waveform based ranging (WR) with analog discrete return system based ranging (AR), a peak detection method (WR-PK) shows the best ranging performance because of less execution time, high ranging accuracy, and stable precision. Based on a novel statistic mathematical method maximal information coefficient (MIC), WR-PK precision has a high linear relationship with the received pulse width standard deviation. Thus keeping the received pulse width of measuring a constant distance as stable as possible can improve ranging precision. PMID:29642639

Fuzzy membership functions for analysis of high-resolution CT images of diffuse pulmonary diseases.

PubMed

Almeida, Eliana; Rangayyan, Rangaraj M; Azevedo-Marques, Paulo M

2015-08-01

We propose the use of fuzzy membership functions to analyze images of diffuse pulmonary diseases (DPDs) based on fractal and texture features. The features were extracted from preprocessed regions of interest (ROIs) selected from high-resolution computed tomography images. The ROIs represent five different patterns of DPDs and normal lung tissue. A Gaussian mixture model (GMM) was constructed for each feature, with six Gaussians modeling the six patterns. Feature selection was performed and the GMMs of the five significant features were used. From the GMMs, fuzzy membership functions were obtained by a probability-possibility transformation and further statistical analysis was performed. An average classification accuracy of 63.5% was obtained for the six classes. For four of the six classes, the classification accuracy was superior to 65%, and the best classification accuracy was 75.5% for one class. The use of fuzzy membership functions to assist in pattern classification is an alternative to deterministic approaches to explore strategies for medical diagnosis.

Achieving Climate Change Absolute Accuracy in Orbit

NASA Technical Reports Server (NTRS)

Wielicki, Bruce A.; Young, D. F.; Mlynczak, M. G.; Thome, K. J; Leroy, S.; Corliss, J.; Anderson, J. G.; Ao, C. O.; Bantges, R.; Best, F.;

Artificial neural network modeling using clinical and knowledge independent variables predicts salt intake reduction behavior

PubMed Central

Isma’eel, Hussain A.; Sakr, George E.; Almedawar, Mohamad M.; Fathallah, Jihan; Garabedian, Torkom; Eddine, Savo Bou Zein

2015-01-01

Background High dietary salt intake is directly linked to hypertension and cardiovascular diseases (CVDs). Predicting behaviors regarding salt intake habits is vital to guide interventions and increase their effectiveness. We aim to compare the accuracy of an artificial neural network (ANN) based tool that predicts behavior from key knowledge questions along with clinical data in a high cardiovascular risk cohort relative to the least square models (LSM) method. Methods We collected knowledge, attitude and behavior data on 115 patients. A behavior score was calculated to classify patients’ behavior towards reducing salt intake. Accuracy comparison between ANN and regression analysis was calculated using the bootstrap technique with 200 iterations. Results Starting from a 69-item questionnaire, a reduced model was developed and included eight knowledge items found to result in the highest accuracy of 62% CI (58-67%). The best prediction accuracy in the full and reduced models was attained by ANN at 66% and 62%, respectively, compared to full and reduced LSM at 40% and 34%, respectively. The average relative increase in accuracy over all in the full and reduced models is 82% and 102%, respectively. Conclusions Using ANN modeling, we can predict salt reduction behaviors with 66% accuracy. The statistical model has been implemented in an online calculator and can be used in clinics to estimate the patient’s behavior. This will help implementation in future research to further prove clinical utility of this tool to guide therapeutic salt reduction interventions in high cardiovascular risk individuals. PMID:26090333

Combining accuracy assessment of land-cover maps with environmental monitoring programs

Treesearch

Stephen V. Stehman; Raymond L. Czaplewski; Sarah M. Nusser; Limin Yang; Zhiliang Zhu

2000-01-01

A scientifically valid accuracy assessment of a large-area, land-cover map is expensive. Environmental monitoring programs offer a potential source of data to partially defray the cost of accuracy assessment while still maintaining the statistical validity. In this article, three general strategies for combining accuracy assessment and environmental monitoring...

Accuracy of Person-Fit Statistics: A Monte Carlo Study of the Influence of Aberrance Rates

ERIC Educational Resources Information Center

St-Onge, Christina; Valois, Pierre; Abdous, Belkacem; Germain, Stephane

2011-01-01

Using a Monte Carlo experimental design, this research examined the relationship between answer patterns' aberrance rates and person-fit statistics (PFS) accuracy. It was observed that as the aberrance rate increased, the detection rates of PFS also increased until, in some situations, a peak was reached and then the detection rates of PFS…

Statistical Reference Datasets

National Institute of Standards and Technology Data Gateway

Statistical Reference Datasets (Web, free access)   The Statistical Reference Datasets is also supported by the Standard Reference Data Program. The purpose of this project is to improve the accuracy of statistical software by providing reference datasets with certified computational results that enable the objective evaluation of statistical software.

A Statistical Examination of Magnetic Field Model Accuracy for Mapping Geosynchronous Solar Energetic Particle Observations to Lower Earth Orbits

NASA Astrophysics Data System (ADS)

Young, S. L.; Kress, B. T.; Rodriguez, J. V.; McCollough, J. P.

2013-12-01

Operational specifications of space environmental hazards can be an important input used by decision makers. Ideally the specification would come from on-board sensors, but for satellites where that capability is not available another option is to map data from remote observations to the location of the satellite. This requires a model of the physical environment and an understanding of its accuracy for mapping applications. We present a statistical comparison between magnetic field model mappings of solar energetic particle observations made by NOAA's Geostationary Operational Environmental Satellites (GOES) to the location of the Combined Release and Radiation Effects Satellite (CRRES). Because CRRES followed a geosynchronous transfer orbit which precessed in local time this allows us to examine the model accuracy between LEO and GEO orbits across a range of local times. We examine the accuracy of multiple magnetic field models using a variety of statistics and examine their utility for operational purposes.

The spectral changes of deforestation in the Brazilian tropical savanna.

PubMed

Trancoso, Ralph; Sano, Edson E; Meneses, Paulo R

2015-01-01

The Cerrado is a biome in Brazil that is experiencing the most rapid loss in natural vegetation. The objective of this study was to analyze the changes in the spectral response in the red, near infrared (NIR), middle infrared (MIR), and normalized difference vegetation index (NDVI) when native vegetation in the Cerrado is deforested. The test sites were regions of the Cerrado located in the states of Bahia, Minas Gerais, and Mato Grosso. For each region, a pair of Landsat Thematic Mapper (TM) scenes from 2008 (before deforestation) and 2009 (after deforestation) was compared. A set of 1,380 samples of deforested polygons and an equal number of samples of native vegetation have their spectral properties statistically analyzed. The accuracy of deforestation detections was also evaluated using high spatial resolution imagery. Results showed that the spectral data of deforested areas and their corresponding native vegetation were statistically different. The red band showed the highest difference between the reflectance data from deforested areas and native vegetation, while the NIR band showed the lowest difference. A consistent pattern of spectral change when native vegetation in the Cerrado is deforested was identified regardless of the location in the biome. The overall accuracy of deforestation detections was 97.75%. Considering both the marked pattern of spectral changes and the high deforestation detection accuracy, this study suggests that deforestation in Cerrado can be accurately monitored, but a strong seasonal and spatial variability of spectral changes might be expected.

Accuracy, repeatability, and reproducibility of Artemis very high-frequency digital ultrasound arc-scan lateral dimension measurements

PubMed Central

Reinstein, Dan Z.; Archer, Timothy J.; Silverman, Ronald H.; Coleman, D. Jackson

2008-01-01

Purpose To determine the accuracy, repeatability, and reproducibility of measurement of lateral dimensions using the Artemis (Ultralink LLC) very high-frequency (VHF) digital ultrasound (US) arc scanner. Setting London Vision Clinic, London, United Kingdom. Methods A test object was measured first with a micrometer and then with the Artemis arc scanner. Five sets of 10 consecutive B-scans of the test object were performed with the scanner. The test object was removed from the system between each scan set. One expert observer and one newly trained observer separately measured the lateral dimension of the test object. Two-factor analysis of variance was performed. The accuracy was calculated as the average bias of the scan set averages. The repeatability and reproducibility coefficients were calculated. The coefficient of variation (CV) was calculated for repeatability and reproducibility. Results The test object was measured to be 10.80 mm wide. The mean lateral dimension bias was 0.00 mm. The repeatability coefficient was 0.114 mm. The reproducibility coefficient was 0.026 mm. The repeatability CV was 0.38%, and the reproducibility CV was 0.09%. There was no statistically significant variation between observers (P = .0965). There was a statistically significant variation between scan sets (P = .0036) attributed to minor vertical changes in the alignment of the test object between consecutive scan sets. Conclusion The Artemis VHF digital US arc scanner obtained accurate, repeatable, and reproducible measurements of lateral dimensions of the size commonly found in the anterior segment. PMID:17081860

Learning to Detect Deception from Evasive Answers and Inconsistencies across Repeated Interviews: A Study with Lay Respondents and Police Officers

PubMed Central

Masip, Jaume; Martínez, Carmen; Blandón-Gitlin, Iris; Sánchez, Nuria; Herrero, Carmen; Ibabe, Izaskun

2018-01-01

Previous research has shown that inconsistencies across repeated interviews do not indicate deception because liars deliberately tend to repeat the same story. However, when a strategic interview approach that makes it difficult for liars to use the repeat strategy is used, both consistency and evasive answers differ significantly between truth tellers and liars, and statistical software (binary logistic regression analyses) can reach high classification rates (Masip et al., 2016b). Yet, if the interview procedure is to be used in applied settings the decision process will be made by humans, not statistical software. To address this issue, in the current study, 475 college students (Experiment 1) and 142 police officers (Experiment 2) were instructed to code and use consistency, evasive answers, or a combination or both before judging the veracity of Masip et al.'s (2016b) interview transcripts. Accuracy rates were high (60% to over 90%). Evasive answers yielded higher rates than consistency, and the combination of both these cues produced the highest accuracy rates in identifying both truthful and deceptive statements. Uninstructed participants performed fairly well (around 75% accuracy), apparently because they spontaneously used consistency and evasive answers. The pattern of results was the same among students, all officers, and veteran officers only, and shows that inconsistencies between interviews and evasive answers reveal deception when a strategic interview approach that hinders the repeat strategy is used. PMID:29354078

Texture Classification by Texton: Statistical versus Binary

PubMed Central

Guo, Zhenhua; Zhang, Zhongcheng; Li, Xiu; Li, Qin; You, Jane

2014-01-01

Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training stage to build a texton library, thus the recognition accuracy will be highly depended on the training samples; second, during feature extraction, local feature is assigned to a texton by searching for the nearest texton in the whole library, which is time consuming when the library size is big and the dimension of feature is high. To address the above two issues, in this paper, three binary texton counterpart methods were proposed, Binary_MR8, Binary_Joint, and Binary_Fractal. These methods do not require any training step but encode local feature into binary representation directly. The experimental results on the CUReT, UIUC and KTH-TIPS databases show that binary texton could get sound results with fast feature extraction, especially when the image size is not big and the quality of image is not poor. PMID:24520346

Enhancing image classification models with multi-modal biomarkers

NASA Astrophysics Data System (ADS)

Caban, Jesus J.; Liao, David; Yao, Jianhua; Mollura, Daniel J.; Gochuico, Bernadette; Yoo, Terry

2011-03-01

Currently, most computer-aided diagnosis (CAD) systems rely on image analysis and statistical models to diagnose, quantify, and monitor the progression of a particular disease. In general, CAD systems have proven to be effective at providing quantitative measurements and assisting physicians during the decision-making process. As the need for more flexible and effective CADs continues to grow, questions about how to enhance their accuracy have surged. In this paper, we show how statistical image models can be augmented with multi-modal physiological values to create more robust, stable, and accurate CAD systems. In particular, this paper demonstrates how highly correlated blood and EKG features can be treated as biomarkers and used to enhance image classification models designed to automatically score subjects with pulmonary fibrosis. In our results, a 3-5% improvement was observed when comparing the accuracy of CADs that use multi-modal biomarkers with those that only used image features. Our results show that lab values such as Erythrocyte Sedimentation Rate and Fibrinogen, as well as EKG measurements such as QRS and I:40, are statistically significant and can provide valuable insights about the severity of the pulmonary fibrosis disease.

DESIGNA ND ANALYSIS FOR THEMATIC MAP ACCURACY ASSESSMENT: FUNDAMENTAL PRINCIPLES

EPA Science Inventory

Before being used in scientific investigations and policy decisions, thematic maps constructed from remotely sensed data should be subjected to a statistically rigorous accuracy assessment. The three basic components of an accuracy assessment are: 1) the sampling design used to s...

Limits on the Accuracy of Linking. Research Report. ETS RR-10-22

ERIC Educational Resources Information Center

Haberman, Shelby J.

2010-01-01

Sampling errors limit the accuracy with which forms can be linked. Limitations on accuracy are especially important in testing programs in which a very large number of forms are employed. Standard inequalities in mathematical statistics may be used to establish lower bounds on the achievable inking accuracy. To illustrate results, a variety of…

Speed-Accuracy Response Models: Scoring Rules Based on Response Time and Accuracy

ERIC Educational Resources Information Center

Maris, Gunter; van der Maas, Han

2012-01-01

Starting from an explicit scoring rule for time limit tasks incorporating both response time and accuracy, and a definite trade-off between speed and accuracy, a response model is derived. Since the scoring rule is interpreted as a sufficient statistic, the model belongs to the exponential family. The various marginal and conditional distributions…

A Monte Carlo Study of the Effect of Item Characteristic Curve Estimation on the Accuracy of Three Person-Fit Statistics

ERIC Educational Resources Information Center

St-Onge, Christina; Valois, Pierre; Abdous, Belkacem; Germain, Stephane

2009-01-01

To date, there have been no studies comparing parametric and nonparametric Item Characteristic Curve (ICC) estimation methods on the effectiveness of Person-Fit Statistics (PFS). The primary aim of this study was to determine if the use of ICCs estimated by nonparametric methods would increase the accuracy of item response theory-based PFS for…

Estimation of diagnostic test accuracy without full verification: a review of latent class methods

PubMed Central

Collins, John; Huynh, Minh

2014-01-01

The performance of a diagnostic test is best evaluated against a reference test that is without error. For many diseases, this is not possible, and an imperfect reference test must be used. However, diagnostic accuracy estimates may be biased if inaccurately verified status is used as the truth. Statistical models have been developed to handle this situation by treating disease as a latent variable. In this paper, we conduct a systematized review of statistical methods using latent class models for estimating test accuracy and disease prevalence in the absence of complete verification. PMID:24910172

Results of a joint NOAA/NASA sounder simulation study

NASA Technical Reports Server (NTRS)

Phillips, N.; Susskind, Joel; Mcmillin, L.

1988-01-01

This paper presents the results of a joint NOAA and NASA sounder simulation study in which the accuracies of atmospheric temperature profiles and surface skin temperature measuremnents retrieved from two sounders were compared: (1) the currently used IR temperature sounder HIRS2 (High-resolution Infrared Radiation Sounder 2); and (2) the recently proposed high-spectral-resolution IR sounder AMTS (Advanced Moisture and Temperature Sounder). Simulations were conducted for both clear and partial cloud conditions. Data were analyzed at NASA using a physical inversion technique and at NOAA using a statistical technique. Results show significant improvement of AMTS compared to HIRS2 for both clear and cloudy conditions. The improvements are indicated by both methods of data analysis, but the physical retrievals outperform the statistical retrievals.

Comparison of wheat classification accuracy using different classifiers of the image-100 system

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Chen, S. C.; Moreira, M. A.; Delima, A. M.

1981-01-01

Classification results using single-cell and multi-cell signature acquisition options, a point-by-point Gaussian maximum-likelihood classifier, and K-means clustering of the Image-100 system are presented. Conclusions reached are that: a better indication of correct classification can be provided by using a test area which contains various cover types of the study area; classification accuracy should be evaluated considering both the percentages of correct classification and error of commission; supervised classification approaches are better than K-means clustering; Gaussian distribution maximum likelihood classifier is better than Single-cell and Multi-cell Signature Acquisition Options of the Image-100 system; and in order to obtain a high classification accuracy in a large and heterogeneous crop area, using Gaussian maximum-likelihood classifier, homogeneous spectral subclasses of the study crop should be created to derive training statistics.

EEG Sleep Stages Classification Based on Time Domain Features and Structural Graph Similarity.

PubMed

Diykh, Mohammed; Li, Yan; Wen, Peng

2016-11-01

The electroencephalogram (EEG) signals are commonly used in diagnosing and treating sleep disorders. Many existing methods for sleep stages classification mainly depend on the analysis of EEG signals in time or frequency domain to obtain a high classification accuracy. In this paper, the statistical features in time domain, the structural graph similarity and the K-means (SGSKM) are combined to identify six sleep stages using single channel EEG signals. Firstly, each EEG segment is partitioned into sub-segments. The size of a sub-segment is determined empirically. Secondly, statistical features are extracted, sorted into different sets of features and forwarded to the SGSKM to classify EEG sleep stages. We have also investigated the relationships between sleep stages and the time domain features of the EEG data used in this paper. The experimental results show that the proposed method yields better classification results than other four existing methods and the support vector machine (SVM) classifier. A 95.93% average classification accuracy is achieved by using the proposed method.

Wang-Landau Reaction Ensemble Method: Simulation of Weak Polyelectrolytes and General Acid-Base Reactions.

PubMed

Landsgesell, Jonas; Holm, Christian; Smiatek, Jens

2017-02-14

We present a novel method for the study of weak polyelectrolytes and general acid-base reactions in molecular dynamics and Monte Carlo simulations. The approach combines the advantages of the reaction ensemble and the Wang-Landau sampling method. Deprotonation and protonation reactions are simulated explicitly with the help of the reaction ensemble method, while the accurate sampling of the corresponding phase space is achieved by the Wang-Landau approach. The combination of both techniques provides a sufficient statistical accuracy such that meaningful estimates for the density of states and the partition sum can be obtained. With regard to these estimates, several thermodynamic observables like the heat capacity or reaction free energies can be calculated. We demonstrate that the computation times for the calculation of titration curves with a high statistical accuracy can be significantly decreased when compared to the original reaction ensemble method. The applicability of our approach is validated by the study of weak polyelectrolytes and their thermodynamic properties.

Improvement in the Accuracy of Flux Measurement of Radio Sources by Exploiting an Arithmetic Pattern in Photon Bunching Noise

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

Lieu, Richard

A hierarchy of statistics of increasing sophistication and accuracy is proposed to exploit an interesting and fundamental arithmetic structure in the photon bunching noise of incoherent light of large photon occupation number, with the purpose of suppressing the noise and rendering a more reliable and unbiased measurement of the light intensity. The method does not require any new hardware, rather it operates at the software level with the help of high-precision computers to reprocess the intensity time series of the incident light to create a new series with smaller bunching noise coherence length. The ultimate accuracy improvement of this methodmore » of flux measurement is limited by the timing resolution of the detector and the photon occupation number of the beam (the higher the photon number the better the performance). The principal application is accuracy improvement in the signal-limited bolometric flux measurement of a radio source.« less

IMPROVING THE ACCURACY OF HISTORIC SATELLITE IMAGE CLASSIFICATION BY COMBINING LOW-RESOLUTION MULTISPECTRAL DATA WITH HIGH-RESOLUTION PANCHROMATIC DATA

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

Getman, Daniel J

2008-01-01

Many attempts to observe changes in terrestrial systems over time would be significantly enhanced if it were possible to improve the accuracy of classifications of low-resolution historic satellite data. In an effort to examine improving the accuracy of historic satellite image classification by combining satellite and air photo data, two experiments were undertaken in which low-resolution multispectral data and high-resolution panchromatic data were combined and then classified using the ECHO spectral-spatial image classification algorithm and the Maximum Likelihood technique. The multispectral data consisted of 6 multispectral channels (30-meter pixel resolution) from Landsat 7. These data were augmented with panchromatic datamore » (15m pixel resolution) from Landsat 7 in the first experiment, and with a mosaic of digital aerial photography (1m pixel resolution) in the second. The addition of the Landsat 7 panchromatic data provided a significant improvement in the accuracy of classifications made using the ECHO algorithm. Although the inclusion of aerial photography provided an improvement in accuracy, this improvement was only statistically significant at a 40-60% level. These results suggest that once error levels associated with combining aerial photography and multispectral satellite data are reduced, this approach has the potential to significantly enhance the precision and accuracy of classifications made using historic remotely sensed data, as a way to extend the time range of efforts to track temporal changes in terrestrial systems.« less

a Comparative Analysis of Five Cropland Datasets in Africa

NASA Astrophysics Data System (ADS)

Wei, Y.; Lu, M.; Wu, W.

2018-04-01

The food security, particularly in Africa, is a challenge to be resolved. The cropland area and spatial distribution obtained from remote sensing imagery are vital information. In this paper, according to cropland area and spatial location, we compare five global cropland datasets including CCI Land Cover, GlobCover, MODIS Collection 5, GlobeLand30 and Unified Cropland in circa 2010 of Africa in terms of cropland area and spatial location. The accuracy of cropland area calculated from five datasets was analyzed compared with statistic data. Based on validation samples, the accuracies of spatial location for the five cropland products were assessed by error matrix. The results show that GlobeLand30 has the best fitness with the statistics, followed by MODIS Collection 5 and Unified Cropland, GlobCover and CCI Land Cover have the lower accuracies. For the accuracy of spatial location of cropland, GlobeLand30 reaches the highest accuracy, followed by Unified Cropland, MODIS Collection 5 and GlobCover, CCI Land Cover has the lowest accuracy. The spatial location accuracy of five datasets in the Csa with suitable farming condition is generally higher than in the Bsk.

Survey methods for assessing land cover map accuracy

USGS Publications Warehouse

Nusser, S.M.; Klaas, E.E.

2003-01-01

The increasing availability of digital photographic materials has fueled efforts by agencies and organizations to generate land cover maps for states, regions, and the United States as a whole. Regardless of the information sources and classification methods used, land cover maps are subject to numerous sources of error. In order to understand the quality of the information contained in these maps, it is desirable to generate statistically valid estimates of accuracy rates describing misclassification errors. We explored a full sample survey framework for creating accuracy assessment study designs that balance statistical and operational considerations in relation to study objectives for a regional assessment of GAP land cover maps. We focused not only on appropriate sample designs and estimation approaches, but on aspects of the data collection process, such as gaining cooperation of land owners and using pixel clusters as an observation unit. The approach was tested in a pilot study to assess the accuracy of Iowa GAP land cover maps. A stratified two-stage cluster sampling design addressed sample size requirements for land covers and the need for geographic spread while minimizing operational effort. Recruitment methods used for private land owners yielded high response rates, minimizing a source of nonresponse error. Collecting data for a 9-pixel cluster centered on the sampled pixel was simple to implement, and provided better information on rarer vegetation classes as well as substantial gains in precision relative to observing data at a single-pixel.

A Hybrid Semi-supervised Classification Scheme for Mining Multisource Geospatial Data

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

Vatsavai, Raju; Bhaduri, Budhendra L

2011-01-01

Supervised learning methods such as Maximum Likelihood (ML) are often used in land cover (thematic) classification of remote sensing imagery. ML classifier relies exclusively on spectral characteristics of thematic classes whose statistical distributions (class conditional probability densities) are often overlapping. The spectral response distributions of thematic classes are dependent on many factors including elevation, soil types, and ecological zones. A second problem with statistical classifiers is the requirement of large number of accurate training samples (10 to 30 |dimensions|), which are often costly and time consuming to acquire over large geographic regions. With the increasing availability of geospatial databases, itmore » is possible to exploit the knowledge derived from these ancillary datasets to improve classification accuracies even when the class distributions are highly overlapping. Likewise newer semi-supervised techniques can be adopted to improve the parameter estimates of statistical model by utilizing a large number of easily available unlabeled training samples. Unfortunately there is no convenient multivariate statistical model that can be employed for mulitsource geospatial databases. In this paper we present a hybrid semi-supervised learning algorithm that effectively exploits freely available unlabeled training samples from multispectral remote sensing images and also incorporates ancillary geospatial databases. We have conducted several experiments on real datasets, and our new hybrid approach shows over 25 to 35% improvement in overall classification accuracy over conventional classification schemes.« less

Evaluation of a Performance-Based Expert Elicitation: WHO Global Attribution of Foodborne Diseases.

PubMed

Aspinall, W P; Cooke, R M; Havelaar, A H; Hoffmann, S; Hald, T

2016-01-01

For many societally important science-based decisions, data are inadequate, unreliable or non-existent, and expert advice is sought. In such cases, procedures for eliciting structured expert judgments (SEJ) are increasingly used. This raises questions regarding validity and reproducibility. This paper presents new findings from a large-scale international SEJ study intended to estimate the global burden of foodborne disease on behalf of WHO. The study involved 72 experts distributed over 134 expert panels, with panels comprising thirteen experts on average. Elicitations were conducted in five languages. Performance-based weighted solutions for target questions of interest were formed for each panel. These weights were based on individual expert's statistical accuracy and informativeness, determined using between ten and fifteen calibration variables from the experts' field with known values. Equal weights combinations were also calculated. The main conclusions on expert performance are: (1) SEJ does provide a science-based method for attribution of the global burden of foodborne diseases; (2) equal weighting of experts per panel increased statistical accuracy to acceptable levels, but at the cost of informativeness; (3) performance-based weighting increased informativeness, while retaining accuracy; (4) due to study constraints individual experts' accuracies were generally lower than in other SEJ studies, and (5) there was a negative correlation between experts' informativeness and statistical accuracy which attenuated as accuracy improved, revealing that the least accurate experts drive the negative correlation. It is shown, however, that performance-based weighting has the ability to yield statistically accurate and informative combinations of experts' judgments, thereby offsetting this contrary influence. The present findings suggest that application of SEJ on a large scale is feasible, and motivate the development of enhanced training and tools for remote elicitation of multiple, internationally-dispersed panels.

Evaluation of a Performance-Based Expert Elicitation: WHO Global Attribution of Foodborne Diseases

PubMed Central

Aspinall, W. P.; Cooke, R. M.; Havelaar, A. H.; Hoffmann, S.; Hald, T.

2016-01-01

For many societally important science-based decisions, data are inadequate, unreliable or non-existent, and expert advice is sought. In such cases, procedures for eliciting structured expert judgments (SEJ) are increasingly used. This raises questions regarding validity and reproducibility. This paper presents new findings from a large-scale international SEJ study intended to estimate the global burden of foodborne disease on behalf of WHO. The study involved 72 experts distributed over 134 expert panels, with panels comprising thirteen experts on average. Elicitations were conducted in five languages. Performance-based weighted solutions for target questions of interest were formed for each panel. These weights were based on individual expert’s statistical accuracy and informativeness, determined using between ten and fifteen calibration variables from the experts' field with known values. Equal weights combinations were also calculated. The main conclusions on expert performance are: (1) SEJ does provide a science-based method for attribution of the global burden of foodborne diseases; (2) equal weighting of experts per panel increased statistical accuracy to acceptable levels, but at the cost of informativeness; (3) performance-based weighting increased informativeness, while retaining accuracy; (4) due to study constraints individual experts’ accuracies were generally lower than in other SEJ studies, and (5) there was a negative correlation between experts' informativeness and statistical accuracy which attenuated as accuracy improved, revealing that the least accurate experts drive the negative correlation. It is shown, however, that performance-based weighting has the ability to yield statistically accurate and informative combinations of experts' judgments, thereby offsetting this contrary influence. The present findings suggest that application of SEJ on a large scale is feasible, and motivate the development of enhanced training and tools for remote elicitation of multiple, internationally-dispersed panels. PMID:26930595

Pixels, Blocks of Pixels, and Polygons: Choosing a Spatial Unit for Thematic Accuracy Assessment

EPA Science Inventory

Pixels, polygons, and blocks of pixels are all potentially viable spatial assessment units for conducting an accuracy assessment. We develop a statistical population-based framework to examine how the spatial unit chosen affects the outcome of an accuracy assessment. The populati...

Comparative effectiveness of i-SCAN™ and high-definition white light characterizing small colonic polyps.

PubMed

Chan, Johanna L; Lin, Li; Feiler, Michael; Wolf, Andrew I; Cardona, Diana M; Gellad, Ziad F

2012-11-07

To evaluate accuracy of in vivo diagnosis of adenomatous vs non-adenomatous polyps using i-SCAN digital chromoendoscopy compared with high-definition white light. This is a single-center comparative effectiveness pilot study. Polyps (n = 103) from 75 average-risk adult outpatients undergoing screening or surveillance colonoscopy between December 1, 2010 and April 1, 2011 were evaluated by two participating endoscopists in an academic outpatient endoscopy center. Polyps were evaluated both with high-definition white light and with i-SCAN to make an in vivo prediction of adenomatous vs non-adenomatous pathology. We determined diagnostic characteristics of i-SCAN and high-definition white light, including sensitivity, specificity, and accuracy, with regards to identifying adenomatous vs non-adenomatous polyps. Histopathologic diagnosis was the gold standard comparison. One hundred and three small polyps, detected from forty-three patients, were included in the analysis. The average size of the polyps evaluated in the analysis was 3.7 mm (SD 1.3 mm, range 2 mm to 8 mm). Formal histopathology revealed that 54/103 (52.4%) were adenomas, 26/103 (25.2%) were hyperplastic, and 23/103 (22.3%) were other diagnoses include "lymphoid aggregates", "non-specific colitis," and "no pathologic diagnosis." Overall, the combined accuracy of endoscopists for predicting adenomas was identical between i-SCAN (71.8%, 95%CI: 62.1%-80.3%) and high-definition white light (71.8%, 95%CI: 62.1%-80.3%). However, the accuracy of each endoscopist differed substantially, where endoscopist A demonstrated 63.0% overall accuracy (95%CI: 50.9%-74.0%) as compared with endoscopist B demonstrating 93.3% overall accuracy (95%CI: 77.9%-99.2%), irrespective of imaging modality. Neither endoscopist demonstrated a significant learning effect with i-SCAN during the study. Though endoscopist A increased accuracy using i-SCAN from 59% (95%CI: 42.1%-74.4%) in the first half to 67.6% (95%CI: 49.5%-82.6%) in the second half, and endoscopist B decreased accuracy using i-SCAN from 100% (95%CI: 80.5%-100.0%) in the first half to 84.6% (95%CI: 54.6%-98.1%) in the second half, neither of these differences were statistically significant. i-SCAN and high-definition white light had similar efficacy predicting polyp histology. Endoscopist training likely plays a critical role in diagnostic test characteristics and deserves further study.

Dimensional changes of acrylic resin denture bases: conventional versus injection-molding technique.

PubMed

Gharechahi, Jafar; Asadzadeh, Nafiseh; Shahabian, Foad; Gharechahi, Maryam

2014-07-01

Acrylic resin denture bases undergo dimensional changes during polymerization. Injection molding techniques are reported to reduce these changes and thereby improve physical properties of denture bases. The aim of this study was to compare dimensional changes of specimens processed by conventional and injection-molding techniques. SR-Ivocap Triplex Hot resin was used for conventional pressure-packed and SR-Ivocap High Impact was used for injection-molding techniques. After processing, all the specimens were stored in distilled water at room temperature until measured. For dimensional accuracy evaluation, measurements were recorded at 24-hour, 48-hour and 12-day intervals using a digital caliper with an accuracy of 0.01 mm. Statistical analysis was carried out by SPSS (SPSS Inc., Chicago, IL, USA) using t-test and repeated-measures ANOVA. Statistical significance was defined at P<0.05. After each water storage period, the acrylic specimens produced by injection exhibited less dimensional changes compared to those produced by the conventional technique. Curing shrinkage was compensated by water sorption with an increase in water storage time decreasing dimensional changes. Within the limitations of this study, dimensional changes of acrylic resin specimens were influenced by the molding technique used and SR-Ivocap injection procedure exhibited higher dimensional accuracy compared to conventional molding.

Predicting Sargassum blooms in the Caribbean Sea from MODIS observations

NASA Astrophysics Data System (ADS)

Wang, Mengqiu; Hu, Chuanmin

2017-04-01

Recurrent and significant Sargassum beaching events in the Caribbean Sea (CS) have caused serious environmental and economic problems, calling for a long-term prediction capacity of Sargassum blooms. Here we present predictions based on a hindcast of 2000-2016 observations from Moderate Resolution Imaging Spectroradiometer (MODIS), which showed Sargassum abundance in the CS and the Central West Atlantic (CWA), as well as connectivity between the two regions with time lags. This information was used to derive bloom and nonbloom probability matrices for each 1° square in the CS for the months of May-August, predicted from bloom conditions in a hotspot region in the CWA in February. A suite of standard statistical measures were used to gauge the prediction accuracy, among which the user's accuracy and kappa statistics showed high fidelity of the probability maps in predicting both blooms and nonblooms in the eastern CS with several months of lead time, with overall accuracy often exceeding 80%. The bloom probability maps from this hindcast analysis will provide early warnings to better study Sargassum blooms and prepare for beaching events near the study region. This approach may also be extendable to many other regions around the world that face similar challenges and opportunities of macroalgal blooms and beaching events.

Variation and Likeness in Ambient Artistic Portraiture.

PubMed

Hayes, Susan; Rheinberger, Nick; Powley, Meagan; Rawnsley, Tricia; Brown, Linda; Brown, Malcolm; Butler, Karen; Clarke, Ann; Crichton, Stephen; Henderson, Maggie; McCosker, Helen; Musgrave, Ann; Wilcock, Joyce; Williams, Darren; Yeaman, Karin; Zaracostas, T S; Taylor, Adam C; Wallace, Gordon

2018-06-01

An artist-led exploration of portrait accuracy and likeness involved 12 Artists producing 12 portraits referencing a life-size 3D print of the same Sitter. The works were assessed during a public exhibition, and the resulting likeness assessments were compared to portrait accuracy as measured using geometric morphometrics (statistical shape analysis). Our results are that, independently of the assessors' prior familiarity with the Sitter's face, the likeness judgements tended to be higher for less morphologically accurate portraits. The two highest rated were the portrait that most exaggerated the Sitter's distinctive features, and a portrait that was a more accurate (but not the most accurate) depiction. In keeping with research showing photograph likeness assessments involve recognition, we found familiar assessors rated the two highest ranked portraits even higher than those with some or no familiarity. In contrast, those lacking prior familiarity with the Sitter's face showed greater favour for the portrait with the highest morphological accuracy, and therefore most likely engaged in face-matching with the exhibited 3D print. Furthermore, our research indicates that abstraction in portraiture may not enhance likeness, and we found that when our 12 highly diverse portraits were statistically averaged, this resulted in a portrait that is more morphologically accurate than any of the individual artworks comprising the average.

Dimensional Changes of Acrylic Resin Denture Bases: Conventional Versus Injection-Molding Technique

PubMed Central

Gharechahi, Jafar; Asadzadeh, Nafiseh; Shahabian, Foad; Gharechahi, Maryam

2014-01-01

Objective: Acrylic resin denture bases undergo dimensional changes during polymerization. Injection molding techniques are reported to reduce these changes and thereby improve physical properties of denture bases. The aim of this study was to compare dimensional changes of specimens processed by conventional and injection-molding techniques. Materials and Methods: SR-Ivocap Triplex Hot resin was used for conventional pressure-packed and SR-Ivocap High Impact was used for injection-molding techniques. After processing, all the specimens were stored in distilled water at room temperature until measured. For dimensional accuracy evaluation, measurements were recorded at 24-hour, 48-hour and 12-day intervals using a digital caliper with an accuracy of 0.01 mm. Statistical analysis was carried out by SPSS (SPSS Inc., Chicago, IL, USA) using t-test and repeated-measures ANOVA. Statistical significance was defined at P<0.05. Results: After each water storage period, the acrylic specimens produced by injection exhibited less dimensional changes compared to those produced by the conventional technique. Curing shrinkage was compensated by water sorption with an increase in water storage time decreasing dimensional changes. Conclusion: Within the limitations of this study, dimensional changes of acrylic resin specimens were influenced by the molding technique used and SR-Ivocap injection procedure exhibited higher dimensional accuracy compared to conventional molding. PMID:25584050

A New Statistical Model of Electroencephalogram Noise Spectra for Real-Time Brain-Computer Interfaces.

PubMed

Paris, Alan; Atia, George K; Vosoughi, Azadeh; Berman, Stephen A

2017-08-01

A characteristic of neurological signal processing is high levels of noise from subcellular ion channels up to whole-brain processes. In this paper, we propose a new model of electroencephalogram (EEG) background periodograms, based on a family of functions which we call generalized van der Ziel-McWhorter (GVZM) power spectral densities (PSDs). To the best of our knowledge, the GVZM PSD function is the only EEG noise model that has relatively few parameters, matches recorded EEG PSD's with high accuracy from 0 to over 30 Hz, and has approximately 1/f θ behavior in the midfrequencies without infinities. We validate this model using three approaches. First, we show how GVZM PSDs can arise in a population of ion channels at maximum entropy equilibrium. Second, we present a class of mixed autoregressive models, which simulate brain background noise and whose periodograms are asymptotic to the GVZM PSD. Third, we present two real-time estimation algorithms for steady-state visual evoked potential (SSVEP) frequencies, and analyze their performance statistically. In pairwise comparisons, the GVZM-based algorithms showed statistically significant accuracy improvement over two well-known and widely used SSVEP estimators. The GVZM noise model can be a useful and reliable technique for EEG signal processing. Understanding EEG noise is essential for EEG-based neurology and applications such as real-time brain-computer interfaces, which must make accurate control decisions from very short data epochs. The GVZM approach represents a successful new paradigm for understanding and managing this neurological noise.

Prediction of peak response values of structures with and without TMD subjected to random pedestrian flows

NASA Astrophysics Data System (ADS)

Lievens, Klaus; Van Nimmen, Katrien; Lombaert, Geert; De Roeck, Guido; Van den Broeck, Peter

2016-09-01

In civil engineering and architecture, the availability of high strength materials and advanced calculation techniques enables the construction of slender footbridges, generally highly sensitive to human-induced excitation. Due to the inherent random character of the human-induced walking load, variability on the pedestrian characteristics must be considered in the response simulation. To assess the vibration serviceability of the footbridge, the statistics of the stochastic dynamic response are evaluated by considering the instantaneous peak responses in a time range. Therefore, a large number of time windows are needed to calculate the mean value and standard deviation of the instantaneous peak values. An alternative method to evaluate the statistics is based on the standard deviation of the response and a characteristic frequency as proposed in wind engineering applications. In this paper, the accuracy of this method is evaluated for human-induced vibrations. The methods are first compared for a group of pedestrians crossing a lightly damped footbridge. Small differences of the instantaneous peak value were found by the method using second order statistics. Afterwards, a TMD tuned to reduce the peak acceleration to a comfort value, was added to the structure. The comparison between both methods in made and the accuracy is verified. It is found that the TMD parameters are tuned sufficiently and good agreements between the two methods are found for the estimation of the instantaneous peak response for a strongly damped structure.

Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients.

PubMed

Kim, Yun Hak; Jeong, Dae Cheon; Pak, Kyoungjune; Goh, Tae Sik; Lee, Chi-Seung; Han, Myoung-Eun; Kim, Ji-Young; Liangwen, Liu; Kim, Chi Dae; Jang, Jeon Yeob; Cha, Wonjae; Oh, Sae-Ock

2017-09-29

Accurate prediction of prognosis is critical for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology such as microarray, next-generation sequencing, and signaling pathways. To develop a new prognostic scoring system for cancer patients, we used mRNA expression and clinical data in various independent breast cancer cohorts (n=1214) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). A new prognostic score that reflects gene network inherent in genomic big data was calculated using Network-Regularized high-dimensional Cox-regression (Net-score). We compared its discriminatory power with those of two previously used statistical methods: stepwise variable selection via univariate Cox regression (Uni-score) and Cox regression via Elastic net (Enet-score). The Net scoring system showed better discriminatory power in prediction of disease-specific survival (DSS) than other statistical methods (p=0 in METABRIC training cohort, p=0.000331, 4.58e-06 in two METABRIC validation cohorts) when accuracy was examined by log-rank test. Notably, comparison of C-index and AUC values in receiver operating characteristic analysis at 5 years showed fewer differences between training and validation cohorts with the Net scoring system than other statistical methods, suggesting minimal overfitting. The Net-based scoring system also successfully predicted prognosis in various independent GEO cohorts with high discriminatory power. In conclusion, the Net-based scoring system showed better discriminative power than previous statistical methods in prognostic prediction for breast cancer patients. This new system will mark a new era in prognosis prediction for cancer patients.

Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients

PubMed Central

Kim, Yun Hak; Jeong, Dae Cheon; Pak, Kyoungjune; Goh, Tae Sik; Lee, Chi-Seung; Han, Myoung-Eun; Kim, Ji-Young; Liangwen, Liu; Kim, Chi Dae; Jang, Jeon Yeob; Cha, Wonjae; Oh, Sae-Ock

2017-01-01

Accurate prediction of prognosis is critical for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology such as microarray, next-generation sequencing, and signaling pathways. To develop a new prognostic scoring system for cancer patients, we used mRNA expression and clinical data in various independent breast cancer cohorts (n=1214) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). A new prognostic score that reflects gene network inherent in genomic big data was calculated using Network-Regularized high-dimensional Cox-regression (Net-score). We compared its discriminatory power with those of two previously used statistical methods: stepwise variable selection via univariate Cox regression (Uni-score) and Cox regression via Elastic net (Enet-score). The Net scoring system showed better discriminatory power in prediction of disease-specific survival (DSS) than other statistical methods (p=0 in METABRIC training cohort, p=0.000331, 4.58e-06 in two METABRIC validation cohorts) when accuracy was examined by log-rank test. Notably, comparison of C-index and AUC values in receiver operating characteristic analysis at 5 years showed fewer differences between training and validation cohorts with the Net scoring system than other statistical methods, suggesting minimal overfitting. The Net-based scoring system also successfully predicted prognosis in various independent GEO cohorts with high discriminatory power. In conclusion, the Net-based scoring system showed better discriminative power than previous statistical methods in prognostic prediction for breast cancer patients. This new system will mark a new era in prognosis prediction for cancer patients. PMID:29100405

Random sampling technique for ultra-fast computations of molecular opacities for exoplanet atmospheres

NASA Astrophysics Data System (ADS)

Min, M.

2017-10-01

Context. Opacities of molecules in exoplanet atmospheres rely on increasingly detailed line-lists for these molecules. The line lists available today contain for many species up to several billions of lines. Computation of the spectral line profile created by pressure and temperature broadening, the Voigt profile, of all of these lines is becoming a computational challenge. Aims: We aim to create a method to compute the Voigt profile in a way that automatically focusses the computation time into the strongest lines, while still maintaining the continuum contribution of the high number of weaker lines. Methods: Here, we outline a statistical line sampling technique that samples the Voigt profile quickly and with high accuracy. The number of samples is adjusted to the strength of the line and the local spectral line density. This automatically provides high accuracy line shapes for strong lines or lines that are spectrally isolated. The line sampling technique automatically preserves the integrated line opacity for all lines, thereby also providing the continuum opacity created by the large number of weak lines at very low computational cost. Results: The line sampling technique is tested for accuracy when computing line spectra and correlated-k tables. Extremely fast computations ( 3.5 × 105 lines per second per core on a standard current day desktop computer) with high accuracy (≤1% almost everywhere) are obtained. A detailed recipe on how to perform the computations is given.

Accuracy assessment: The statistical approach to performance evaluation in LACIE. [Great Plains corridor, United States

NASA Technical Reports Server (NTRS)

Houston, A. G.; Feiveson, A. H.; Chhikara, R. S.; Hsu, E. M. (Principal Investigator)

1979-01-01

A statistical methodology was developed to check the accuracy of the products of the experimental operations throughout crop growth and to determine whether the procedures are adequate to accomplish the desired accuracy and reliability goals. It has allowed the identification and isolation of key problems in wheat area yield estimation, some of which have been corrected and some of which remain to be resolved. The major unresolved problem in accuracy assessment is that of precisely estimating the bias of the LACIE production estimator. Topics covered include: (1) evaluation techniques; (2) variance and bias estimation for the wheat production estimate; (3) the 90/90 evaluation; (4) comparison of the LACIE estimate with reference standards; and (5) first and second order error source investigations.

Error tolerance analysis of wave diagnostic based on coherent modulation imaging in high power laser system

NASA Astrophysics Data System (ADS)

Pan, Xingchen; Liu, Cheng; Zhu, Jianqiang

2018-02-01

Coherent modulation imaging providing fast convergence speed and high resolution with single diffraction pattern is a promising technique to satisfy the urgent demands for on-line multiple parameter diagnostics with single setup in high power laser facilities (HPLF). However, the influence of noise on the final calculated parameters concerned has not been investigated yet. According to a series of simulations with twenty different sampling beams generated based on the practical parameters and performance of HPLF, the quantitative analysis based on statistical results was first investigated after considering five different error sources. We found the background noise of detector and high quantization error will seriously affect the final accuracy and different parameters have different sensitivity to different noise sources. The simulation results and the corresponding analysis provide the potential directions to further improve the final accuracy of parameter diagnostics which is critically important to its formal applications in the daily routines of HPLF.

Robust continuous clustering

PubMed Central

Shah, Sohil Atul

2017-01-01

Clustering is a fundamental procedure in the analysis of scientific data. It is used ubiquitously across the sciences. Despite decades of research, existing clustering algorithms have limited effectiveness in high dimensions and often require tuning parameters for different domains and datasets. We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. The presented algorithm optimizes a smooth continuous objective, which is based on robust statistics and allows heavily mixed clusters to be untangled. The continuous nature of the objective also allows clustering to be integrated as a module in end-to-end feature learning pipelines. We demonstrate this by extending the algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a continuous global objective. The presented approach is evaluated on large datasets of faces, hand-written digits, objects, newswire articles, sensor readings from the Space Shuttle, and protein expression levels. Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank. PMID:28851838

Mammographic enhancement with combining local statistical measures and sliding band filter for improved mass segmentation in mammograms

NASA Astrophysics Data System (ADS)

Kim, Dae Hoe; Choi, Jae Young; Choi, Seon Hyeong; Ro, Yong Man

2012-03-01

In this study, a novel mammogram enhancement solution is proposed, aiming to improve the quality of subsequent mass segmentation in mammograms. It has been widely accepted that characteristics of masses are usually hyper-dense or uniform density with respect to its background. Also, their core parts are likely to have high-intensity values while the values of intensity tend to be decreased as the distance to core parts increases. Based on the aforementioned observations, we develop a new and effective mammogram enhancement method by combining local statistical measurements and Sliding Band Filtering (SBF). By effectively combining local statistical measurements and SBF, we are able to improve the contrast of the bright and smooth regions (which represent potential mass regions), as well as, at the same time, the regions where their surrounding gradients are converging to the centers of regions of interest. In this study, 89 mammograms were collected from the public MAIS database (DB) to demonstrate the effectiveness of the proposed enhancement solution in terms of improving mass segmentation. As for a segmentation method, widely used contour-based segmentation approach was employed. The contour-based method in conjunction with the proposed enhancement solution achieved overall detection accuracy of 92.4% with a total of 85 correct cases. On the other hand, without using our enhancement solution, overall detection accuracy of the contour-based method was only 78.3%. In addition, experimental results demonstrated the feasibility of our enhancement solution for the purpose of improving detection accuracy on mammograms containing dense parenchymal patterns.

Performance results of a digital test signal generator

NASA Technical Reports Server (NTRS)

Gutierrez-Luaces, B. O.; Marina, M.; Parham, B.

1993-01-01

Performance results of a digital test signal-generator hardware-demonstration unit are reported. Capabilities available include baseband and intermediate frequency (IF) spectrum generation, for which test results are provided. Repeatability in the setting of a given signal-to-noise ratio (SNR) when a baseband or an IF spectrum is being generated ranges from 0.01 dB at high SNR's or high data rates to 0.3 dB at low data rates or low SNR's. Baseband symbol SNR and carrier SNR (Pc/No) accuracies of 0.1 dB were verified with the built-in statistics circuitry. At low SNR's that accuracy remains to be fully verified. These results were confirmed with measurements from a demodulator synchronizer assembly for the baseband spectrum generation, and with a digital receiver (Pioneer 10 receiver) for the IF spectrum generation.

Alaska national hydrography dataset positional accuracy assessment study

USGS Publications Warehouse

Arundel, Samantha; Yamamoto, Kristina H.; Constance, Eric; Mantey, Kim; Vinyard-Houx, Jeremy

2013-01-01

Initial visual assessments Wide range in the quality of fit between features in NHD and these new image sources. No statistical analysis has been performed to actually quantify accuracy Determining absolute accuracy is cost prohibitive (must collect independent, well defined test points) Quantitative analysis of relative positional error is feasible.

Quantifying Safety Margin Using the Risk-Informed Safety Margin Characterization (RISMC)

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

Grabaskas, David; Bucknor, Matthew; Brunett, Acacia

2015-04-26

The Risk-Informed Safety Margin Characterization (RISMC), developed by Idaho National Laboratory as part of the Light-Water Reactor Sustainability Project, utilizes a probabilistic safety margin comparison between a load and capacity distribution, rather than a deterministic comparison between two values, as is usually done in best-estimate plus uncertainty analyses. The goal is to determine the failure probability, or in other words, the probability of the system load equaling or exceeding the system capacity. While this method has been used in pilot studies, there has been little work conducted investigating the statistical significance of the resulting failure probability. In particular, it ismore » difficult to determine how many simulations are necessary to properly characterize the failure probability. This work uses classical (frequentist) statistics and confidence intervals to examine the impact in statistical accuracy when the number of simulations is varied. Two methods are proposed to establish confidence intervals related to the failure probability established using a RISMC analysis. The confidence interval provides information about the statistical accuracy of the method utilized to explore the uncertainty space, and offers a quantitative method to gauge the increase in statistical accuracy due to performing additional simulations.« less

NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis.

PubMed

Markiewicz, Pawel J; Ehrhardt, Matthias J; Erlandsson, Kjell; Noonan, Philip J; Barnes, Anna; Schott, Jonathan M; Atkinson, David; Arridge, Simon R; Hutton, Brian F; Ourselin, Sebastien

2018-01-01

We present a standalone, scalable and high-throughput software platform for PET image reconstruction and analysis. We focus on high fidelity modelling of the acquisition processes to provide high accuracy and precision quantitative imaging, especially for large axial field of view scanners. All the core routines are implemented using parallel computing available from within the Python package NiftyPET, enabling easy access, manipulation and visualisation of data at any processing stage. The pipeline of the platform starts from MR and raw PET input data and is divided into the following processing stages: (1) list-mode data processing; (2) accurate attenuation coefficient map generation; (3) detector normalisation; (4) exact forward and back projection between sinogram and image space; (5) estimation of reduced-variance random events; (6) high accuracy fully 3D estimation of scatter events; (7) voxel-based partial volume correction; (8) region- and voxel-level image analysis. We demonstrate the advantages of this platform using an amyloid brain scan where all the processing is executed from a single and uniform computational environment in Python. The high accuracy acquisition modelling is achieved through span-1 (no axial compression) ray tracing for true, random and scatter events. Furthermore, the platform offers uncertainty estimation of any image derived statistic to facilitate robust tracking of subtle physiological changes in longitudinal studies. The platform also supports the development of new reconstruction and analysis algorithms through restricting the axial field of view to any set of rings covering a region of interest and thus performing fully 3D reconstruction and corrections using real data significantly faster. All the software is available as open source with the accompanying wiki-page and test data.

Manipulating measurement scales in medical statistical analysis and data mining: A review of methodologies

PubMed Central

Marateb, Hamid Reza; Mansourian, Marjan; Adibi, Peyman; Farina, Dario

2014-01-01

Background: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal–variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD). Ordinal-to-Interval scale conversion example: a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal-scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests. Results: the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable. Conclusion: by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables. PMID:24672565

Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure

Treesearch

Harold S.J. Zald; Janet L. Ohmann; Heather M. Roberts; Matthew J. Gregory; Emilie B. Henderson; Robert J. McGaughey; Justin Braaten

2014-01-01

This study investigated how lidar-derived vegetation indices, disturbance history from Landsat time series (LTS) imagery, plot location accuracy, and plot size influenced accuracy of statistical spatial models (nearest-neighbor imputation maps) of forest vegetation composition and structure. Nearest-neighbor (NN) imputation maps were developed for 539,000 ha in the...

Forest tree species discrimination in western Himalaya using EO-1 Hyperion

NASA Astrophysics Data System (ADS)

George, Rajee; Padalia, Hitendra; Kushwaha, S. P. S.

2014-05-01

The information acquired in the narrow bands of hyperspectral remote sensing data has potential to capture plant species spectral variability, thereby improving forest tree species mapping. This study assessed the utility of spaceborne EO-1 Hyperion data in discrimination and classification of broadleaved evergreen and conifer forest tree species in western Himalaya. The pre-processing of 242 bands of Hyperion data resulted into 160 noise-free and vertical stripe corrected reflectance bands. Of these, 29 bands were selected through step-wise exclusion of bands (Wilk's Lambda). Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) algorithms were applied to the selected bands to assess their effectiveness in classification. SVM was also applied to broadband data (Landsat TM) to compare the variation in classification accuracy. All commonly occurring six gregarious tree species, viz., white oak, brown oak, chir pine, blue pine, cedar and fir in western Himalaya could be effectively discriminated. SVM produced a better species classification (overall accuracy 82.27%, kappa statistic 0.79) than SAM (overall accuracy 74.68%, kappa statistic 0.70). It was noticed that classification accuracy achieved with Hyperion bands was significantly higher than Landsat TM bands (overall accuracy 69.62%, kappa statistic 0.65). Study demonstrated the potential utility of narrow spectral bands of Hyperion data in discriminating tree species in a hilly terrain.

Extracting fingerprint of wireless devices based on phase noise and multiple level wavelet decomposition

NASA Astrophysics Data System (ADS)

Zhao, Weichen; Sun, Zhuo; Kong, Song

2016-10-01

Wireless devices can be identified by the fingerprint extracted from the signal transmitted, which is useful in wireless communication security and other fields. This paper presents a method that extracts fingerprint based on phase noise of signal and multiple level wavelet decomposition. The phase of signal will be extracted first and then decomposed by multiple level wavelet decomposition. The statistic value of each wavelet coefficient vector is utilized for constructing fingerprint. Besides, the relationship between wavelet decomposition level and recognition accuracy is simulated. And advertised decomposition level is revealed as well. Compared with previous methods, our method is simpler and the accuracy of recognition remains high when Signal Noise Ratio (SNR) is low.

Validation of a physical anthropology methodology using mandibles for gender estimation in a Brazilian population

PubMed Central

CARVALHO, Suzana Papile Maciel; BRITO, Liz Magalhães; de PAIVA, Luiz Airton Saavedra; BICUDO, Lucilene Arilho Ribeiro; CROSATO, Edgard Michel; de OLIVEIRA, Rogério Nogueira

2013-01-01

Validation studies of physical anthropology methods in the different population groups are extremely important, especially in cases in which the population variations may cause problems in the identification of a native individual by the application of norms developed for different communities. Objective This study aimed to estimate the gender of skeletons by application of the method of Oliveira, et al. (1995), previously used in a population sample from Northeast Brazil. Material and Methods The accuracy of this method was assessed for a population from Southeast Brazil and validated by statistical tests. The method used two mandibular measurements, namely the bigonial distance and the mandibular ramus height. The sample was composed of 66 skulls and the method was applied by two examiners. The results were statistically analyzed by the paired t test, logistic discriminant analysis and logistic regression. Results The results demonstrated that the application of the method of Oliveira, et al. (1995) in this population achieved very different outcomes between genders, with 100% for females and only 11% for males, which may be explained by ethnic differences. However, statistical adjustment of measurement data for the population analyzed allowed accuracy of 76.47% for males and 78.13% for females, with the creation of a new discriminant formula. Conclusion It was concluded that methods involving physical anthropology present high rate of accuracy for human identification, easy application, low cost and simplicity; however, the methodologies must be validated for the different populations due to differences in ethnic patterns, which are directly related to the phenotypic aspects. In this specific case, the method of Oliveira, et al. (1995) presented good accuracy and may be used for gender estimation in Brazil in two geographic regions, namely Northeast and Southeast; however, for other regions of the country (North, Central West and South), previous methodological adjustment is recommended as demonstrated in this study. PMID:24037076

Validation of a physical anthropology methodology using mandibles for gender estimation in a Brazilian population.

PubMed

Carvalho, Suzana Papile Maciel; Brito, Liz Magalhães; Paiva, Luiz Airton Saavedra de; Bicudo, Lucilene Arilho Ribeiro; Crosato, Edgard Michel; Oliveira, Rogério Nogueira de

2013-01-01

Validation studies of physical anthropology methods in the different population groups are extremely important, especially in cases in which the population variations may cause problems in the identification of a native individual by the application of norms developed for different communities. This study aimed to estimate the gender of skeletons by application of the method of Oliveira, et al. (1995), previously used in a population sample from Northeast Brazil. The accuracy of this method was assessed for a population from Southeast Brazil and validated by statistical tests. The method used two mandibular measurements, namely the bigonial distance and the mandibular ramus height. The sample was composed of 66 skulls and the method was applied by two examiners. The results were statistically analyzed by the paired t test, logistic discriminant analysis and logistic regression. The results demonstrated that the application of the method of Oliveira, et al. (1995) in this population achieved very different outcomes between genders, with 100% for females and only 11% for males, which may be explained by ethnic differences. However, statistical adjustment of measurement data for the population analyzed allowed accuracy of 76.47% for males and 78.13% for females, with the creation of a new discriminant formula. It was concluded that methods involving physical anthropology present high rate of accuracy for human identification, easy application, low cost and simplicity; however, the methodologies must be validated for the different populations due to differences in ethnic patterns, which are directly related to the phenotypic aspects. In this specific case, the method of Oliveira, et al. (1995) presented good accuracy and may be used for gender estimation in Brazil in two geographic regions, namely Northeast and Southeast; however, for other regions of the country (North, Central West and South), previous methodological adjustment is recommended as demonstrated in this study.

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

PubMed

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

2017-09-15

Classification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classification accuracy but must be incorporated carefully into cross-validation to avoid overfitting. Recently, feature selection methods based on differential privacy, such as differentially private random forests and reusable holdout sets, have been proposed. However, for domains such as bioinformatics, where the number of features is much larger than the number of observations p≫n , these differential privacy methods are susceptible to overfitting. We introduce private Evaporative Cooling, a stochastic privacy-preserving machine learning algorithm that uses Relief-F for feature selection and random forest for privacy preserving classification that also prevents overfitting. We relate the privacy-preserving threshold mechanism to a thermodynamic Maxwell-Boltzmann distribution, where the temperature represents the privacy threshold. We use the thermal statistical physics concept of Evaporative Cooling of atomic gases to perform backward stepwise privacy-preserving feature selection. On simulated data with main effects and statistical interactions, we compare accuracies on holdout and validation sets for three privacy-preserving methods: the reusable holdout, reusable holdout with random forest, and private Evaporative Cooling, which uses Relief-F feature selection and random forest classification. In simulations where interactions exist between attributes, private Evaporative Cooling provides higher classification accuracy without overfitting based on an independent validation set. In simulations without interactions, thresholdout with random forest and private Evaporative Cooling give comparable accuracies. We also apply these privacy methods to human brain resting-state fMRI data from a study of major depressive disorder. Code available at http://insilico.utulsa.edu/software/privateEC . brett-mckinney@utulsa.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Identification of the Species of Origin for Meat Products by Rapid Evaporative Ionization Mass Spectrometry.

PubMed

Balog, Julia; Perenyi, Dora; Guallar-Hoyas, Cristina; Egri, Attila; Pringle, Steven D; Stead, Sara; Chevallier, Olivier P; Elliott, Chris T; Takats, Zoltan

2016-06-15

Increasingly abundant food fraud cases have brought food authenticity and safety into major focus. This study presents a fast and effective way to identify meat products using rapid evaporative ionization mass spectrometry (REIMS). The experimental setup was demonstrated to be able to record a mass spectrometric profile of meat specimens in a time frame of <5 s. A multivariate statistical algorithm was developed and successfully tested for the identification of animal tissue with different anatomical origin, breed, and species with 100% accuracy at species and 97% accuracy at breed level. Detection of the presence of meat originating from a different species (horse, cattle, and venison) has also been demonstrated with high accuracy using mixed patties with a 5% detection limit. REIMS technology was found to be a promising tool in food safety applications providing a reliable and simple method for the rapid characterization of food products.

Evaluating the decision accuracy and speed of clinical data visualizations.

PubMed

Pieczkiewicz, David S; Finkelstein, Stanley M

2010-01-01

Clinicians face an increasing volume of biomedical data. Assessing the efficacy of systems that enable accurate and timely clinical decision making merits corresponding attention. This paper discusses the multiple-reader multiple-case (MRMC) experimental design and linear mixed models as means of assessing and comparing decision accuracy and latency (time) for decision tasks in which clinician readers must interpret visual displays of data. These tools can assess and compare decision accuracy and latency (time). These experimental and statistical techniques, used extensively in radiology imaging studies, offer a number of practical and analytic advantages over more traditional quantitative methods such as percent-correct measurements and ANOVAs, and are recommended for their statistical efficiency and generalizability. An example analysis using readily available, free, and commercial statistical software is provided as an appendix. While these techniques are not appropriate for all evaluation questions, they can provide a valuable addition to the evaluative toolkit of medical informatics research.

Methods and statistics for combining motif match scores.

PubMed

Bailey, T L; Gribskov, M

1998-01-01

Position-specific scoring matrices are useful for representing and searching for protein sequence motifs. A sequence family can often be described by a group of one or more motifs, and an effective search must combine the scores for matching a sequence to each of the motifs in the group. We describe three methods for combining match scores and estimating the statistical significance of the combined scores and evaluate the search quality (classification accuracy) and the accuracy of the estimate of statistical significance of each. The three methods are: 1) sum of scores, 2) sum of reduced variates, 3) product of score p-values. We show that method 3) is superior to the other two methods in both regards, and that combining motif scores indeed gives better search accuracy. The MAST sequence homology search algorithm utilizing the product of p-values scoring method is available for interactive use and downloading at URL http:/(/)www.sdsc.edu/MEME.

Compositional Solution Space Quantification for Probabilistic Software Analysis

NASA Technical Reports Server (NTRS)

Borges, Mateus; Pasareanu, Corina S.; Filieri, Antonio; d'Amorim, Marcelo; Visser, Willem

2014-01-01

Probabilistic software analysis aims at quantifying how likely a target event is to occur during program execution. Current approaches rely on symbolic execution to identify the conditions to reach the target event and try to quantify the fraction of the input domain satisfying these conditions. Precise quantification is usually limited to linear constraints, while only approximate solutions can be provided in general through statistical approaches. However, statistical approaches may fail to converge to an acceptable accuracy within a reasonable time. We present a compositional statistical approach for the efficient quantification of solution spaces for arbitrarily complex constraints over bounded floating-point domains. The approach leverages interval constraint propagation to improve the accuracy of the estimation by focusing the sampling on the regions of the input domain containing the sought solutions. Preliminary experiments show significant improvement on previous approaches both in results accuracy and analysis time.

Wild Fire Risk Map in the Eastern Steppe of Mongolia Using Spatial Multi-Criteria Analysis

NASA Astrophysics Data System (ADS)

Nasanbat, Elbegjargal; Lkhamjav, Ochirkhuyag

2016-06-01

Grassland fire is a cause of major disturbance to ecosystems and economies throughout the world. This paper investigated to identify risk zone of wildfire distributions on the Eastern Steppe of Mongolia. The study selected variables for wildfire risk assessment using a combination of data collection, including Social Economic, Climate, Geographic Information Systems, Remotely sensed imagery, and statistical yearbook information. Moreover, an evaluation of the result is used field validation data and assessment. The data evaluation resulted divided by main three group factors Environmental, Social Economic factor, Climate factor and Fire information factor into eleven input variables, which were classified into five categories by risk levels important criteria and ranks. All of the explanatory variables were integrated into spatial a model and used to estimate the wildfire risk index. Within the index, five categories were created, based on spatial statistics, to adequately assess respective fire risk: very high risk, high risk, moderate risk, low and very low. Approximately more than half, 68 percent of the study area was predicted accuracy to good within the very high, high risk and moderate risk zones. The percentages of actual fires in each fire risk zone were as follows: very high risk, 42 percent; high risk, 26 percent; moderate risk, 13 percent; low risk, 8 percent; and very low risk, 11 percent. The main overall accuracy to correct prediction from the model was 62 percent. The model and results could be support in spatial decision making support system processes and in preventative wildfire management strategies. Also it could be help to improve ecological and biodiversity conservation management.

12 CFR 268.601 - EEO group statistics.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 3 2010-01-01 2010-01-01 false EEO group statistics. 268.601 Section 268.601... RULES REGARDING EQUAL OPPORTUNITY Matters of General Applicability § 268.601 EEO group statistics. (a... solely statistical purpose for which the data is being collected, the need for accuracy, the Board's...

Probabilistic performance estimators for computational chemistry methods: The empirical cumulative distribution function of absolute errors

NASA Astrophysics Data System (ADS)

Pernot, Pascal; Savin, Andreas

2018-06-01

Benchmarking studies in computational chemistry use reference datasets to assess the accuracy of a method through error statistics. The commonly used error statistics, such as the mean signed and mean unsigned errors, do not inform end-users on the expected amplitude of prediction errors attached to these methods. We show that, the distributions of model errors being neither normal nor zero-centered, these error statistics cannot be used to infer prediction error probabilities. To overcome this limitation, we advocate for the use of more informative statistics, based on the empirical cumulative distribution function of unsigned errors, namely, (1) the probability for a new calculation to have an absolute error below a chosen threshold and (2) the maximal amplitude of errors one can expect with a chosen high confidence level. Those statistics are also shown to be well suited for benchmarking and ranking studies. Moreover, the standard error on all benchmarking statistics depends on the size of the reference dataset. Systematic publication of these standard errors would be very helpful to assess the statistical reliability of benchmarking conclusions.

Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering.

PubMed

Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

2014-12-30

Understanding neural functions requires knowledge from analysing electrophysiological data. The process of assigning spikes of a multichannel signal into clusters, called spike sorting, is one of the important problems in such analysis. There have been various automated spike sorting techniques with both advantages and disadvantages regarding accuracy and computational costs. Therefore, developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice. An automatic unsupervised spike sorting method is proposed in this paper. The method uses features extracted by the locality preserving projection (LPP) algorithm. These features afterwards serve as inputs for the landmark-based spectral clustering (LSC) method. Gap statistics (GS) is employed to evaluate the number of clusters before the LSC can be performed. The proposed LPP-LSC is highly accurate and computationally inexpensive spike sorting approach. LPP spike features are very discriminative; thereby boost the performance of clustering methods. Furthermore, the LSC method exhibits its efficiency when integrated with the cluster evaluator GS. The proposed method's accuracy is approximately 13% superior to that of the benchmark combination between wavelet transformation and superparamagnetic clustering (WT-SPC). Additionally, LPP-LSC computing time is six times less than that of the WT-SPC. LPP-LSC obviously demonstrates a win-win spike sorting solution meeting both accuracy and computational cost criteria. LPP and LSC are linear algorithms that help reduce computational burden and thus their combination can be applied into real-time spike analysis. Copyright © 2014 Elsevier B.V. All rights reserved.

An analysis of I/O efficient order-statistic-based techniques for noise power estimation in the HRMS sky survey's operational system

NASA Technical Reports Server (NTRS)

Zimmerman, G. A.; Olsen, E. T.

1992-01-01

Noise power estimation in the High-Resolution Microwave Survey (HRMS) sky survey element is considered as an example of a constant false alarm rate (CFAR) signal detection problem. Order-statistic-based noise power estimators for CFAR detection are considered in terms of required estimator accuracy and estimator dynamic range. By limiting the dynamic range of the value to be estimated, the performance of an order-statistic estimator can be achieved by simpler techniques requiring only a single pass of the data. Simple threshold-and-count techniques are examined, and it is shown how several parallel threshold-and-count estimation devices can be used to expand the dynamic range to meet HRMS system requirements with minimal hardware complexity. An input/output (I/O) efficient limited-precision order-statistic estimator with wide but limited dynamic range is also examined.

TRAPR: R Package for Statistical Analysis and Visualization of RNA-Seq Data.

PubMed

Lim, Jae Hyun; Lee, Soo Youn; Kim, Ju Han

2017-03-01

High-throughput transcriptome sequencing, also known as RNA sequencing (RNA-Seq), is a standard technology for measuring gene expression with unprecedented accuracy. Numerous bioconductor packages have been developed for the statistical analysis of RNA-Seq data. However, these tools focus on specific aspects of the data analysis pipeline, and are difficult to appropriately integrate with one another due to their disparate data structures and processing methods. They also lack visualization methods to confirm the integrity of the data and the process. In this paper, we propose an R-based RNA-Seq analysis pipeline called TRAPR, an integrated tool that facilitates the statistical analysis and visualization of RNA-Seq expression data. TRAPR provides various functions for data management, the filtering of low-quality data, normalization, transformation, statistical analysis, data visualization, and result visualization that allow researchers to build customized analysis pipelines.

Directional statistics-based reflectance model for isotropic bidirectional reflectance distribution functions.

PubMed

Nishino, Ko; Lombardi, Stephen

2011-01-01

We introduce a novel parametric bidirectional reflectance distribution function (BRDF) model that can accurately encode a wide variety of real-world isotropic BRDFs with a small number of parameters. The key observation we make is that a BRDF may be viewed as a statistical distribution on a unit hemisphere. We derive a novel directional statistics distribution, which we refer to as the hemispherical exponential power distribution, and model real-world isotropic BRDFs as mixtures of it. We derive a canonical probabilistic method for estimating the parameters, including the number of components, of this novel directional statistics BRDF model. We show that the model captures the full spectrum of real-world isotropic BRDFs with high accuracy, but a small footprint. We also demonstrate the advantages of the novel BRDF model by showing its use for reflection component separation and for exploring the space of isotropic BRDFs.

Ultrasound detection of simulated intra-ocular foreign bodies by minimally trained personnel.

PubMed

Sargsyan, Ashot E; Dulchavsky, Alexandria G; Adams, James; Melton, Shannon; Hamilton, Douglas R; Dulchavsky, Scott A

2008-01-01

To test the ability of non-expert ultrasound operators of divergent backgrounds to detect the presence, size, location, and composition of foreign bodies in an ocular model. High school students (N = 10) and NASA astronauts (N = 4) completed a brief ultrasound training session which focused on basic ultrasound principles and the detection of foreign bodies. The operators used portable ultrasound devices to detect foreign objects of varying location, size (0.5-2 mm), and material (glass, plastic, metal) in a gelatinous ocular model. Operator findings were compared to known foreign object parameters and ultrasound experts (N = 2) to determine accuracy across and between groups. Ultrasound had high sensitivity (astronauts 85%, students 87%, and experts 100%) and specificity (astronauts 81%, students 83%, and experts 95%) for the detection of foreign bodies. All user groups were able to accurately detect the presence of foreign bodies in this model (astronauts 84%, students 81%, and experts 97%). Astronaut and student sensitivity results for material (64% vs. 48%), size (60% vs. 46%), and position (77% vs. 64%) were not statistically different. Experts' results for material (85%), size (90%), and position (98%) were higher; however, the small sample size precluded statistical conclusions. Ultrasound can be used by operators with varying training to detect the presence, location, and composition of intraocular foreign bodies with high sensitivity, specificity, and accuracy.

Building a knowledge-based statistical potential by capturing high-order inter-residue interactions and its applications in protein secondary structure assessment.

PubMed

Li, Yaohang; Liu, Hui; Rata, Ionel; Jakobsson, Eric

2013-02-25

The rapidly increasing number of protein crystal structures available in the Protein Data Bank (PDB) has naturally made statistical analyses feasible in studying complex high-order inter-residue correlations. In this paper, we report a context-based secondary structure potential (CSSP) for assessing the quality of predicted protein secondary structures generated by various prediction servers. CSSP is a sequence-position-specific knowledge-based potential generated based on the potentials of mean force approach, where high-order inter-residue interactions are taken into consideration. The CSSP potential is effective in identifying secondary structure predictions with good quality. In 56% of the targets in the CB513 benchmark, the optimal CSSP potential is able to recognize the native secondary structure or a prediction with Q3 accuracy higher than 90% as best scored in the predicted secondary structures generated by 10 popularly used secondary structure prediction servers. In more than 80% of the CB513 targets, the predicted secondary structures with the lowest CSSP potential values yield higher than 80% Q3 accuracy. Similar performance of CSSP is found on the CASP9 targets as well. Moreover, our computational results also show that the CSSP potential using triplets outperforms the CSSP potential using doublets and is currently better than the CSSP potential using quartets.

Land cover and land use mapping of the iSimangaliso Wetland Park, South Africa: comparison of oblique and orthogonal random forest algorithms

NASA Astrophysics Data System (ADS)

Bassa, Zaakirah; Bob, Urmilla; Szantoi, Zoltan; Ismail, Riyad

2016-01-01

In recent years, the popularity of tree-based ensemble methods for land cover classification has increased significantly. Using WorldView-2 image data, we evaluate the potential of the oblique random forest algorithm (oRF) to classify a highly heterogeneous protected area. In contrast to the random forest (RF) algorithm, the oRF algorithm builds multivariate trees by learning the optimal split using a supervised model. The oRF binary algorithm is adapted to a multiclass land cover and land use application using both the "one-against-one" and "one-against-all" combination approaches. Results show that the oRF algorithms are capable of achieving high classification accuracies (>80%). However, there was no statistical difference in classification accuracies obtained by the oRF algorithms and the more popular RF algorithm. For all the algorithms, user accuracies (UAs) and producer accuracies (PAs) >80% were recorded for most of the classes. Both the RF and oRF algorithms poorly classified the indigenous forest class as indicated by the low UAs and PAs. Finally, the results from this study advocate and support the utility of the oRF algorithm for land cover and land use mapping of protected areas using WorldView-2 image data.

Objective research of auscultation signals in Traditional Chinese Medicine based on wavelet packet energy and support vector machine.

PubMed

Yan, Jianjun; Shen, Xiaojing; Wang, Yiqin; Li, Fufeng; Xia, Chunming; Guo, Rui; Chen, Chunfeng; Shen, Qingwei

2010-01-01

This study aims at utilising Wavelet Packet Transform (WPT) and Support Vector Machine (SVM) algorithm to make objective analysis and quantitative research for the auscultation in Traditional Chinese Medicine (TCM) diagnosis. First, Wavelet Packet Decomposition (WPD) at level 6 was employed to split more elaborate frequency bands of the auscultation signals. Then statistic analysis was made based on the extracted Wavelet Packet Energy (WPE) features from WPD coefficients. Furthermore, the pattern recognition was used to distinguish mixed subjects' statistical feature values of sample groups through SVM. Finally, the experimental results showed that the classification accuracies were at a high level.

Effectiveness of feature and classifier algorithms in character recognition systems

NASA Astrophysics Data System (ADS)

Wilson, Charles L.

1993-04-01

At the first Census Optical Character Recognition Systems Conference, NIST generated accuracy data for more than character recognition systems. Most systems were tested on the recognition of isolated digits and upper and lower case alphabetic characters. The recognition experiments were performed on sample sizes of 58,000 digits, and 12,000 upper and lower case alphabetic characters. The algorithms used by the 26 conference participants included rule-based methods, image-based methods, statistical methods, and neural networks. The neural network methods included Multi-Layer Perceptron's, Learned Vector Quantitization, Neocognitrons, and cascaded neural networks. In this paper 11 different systems are compared using correlations between the answers of different systems, comparing the decrease in error rate as a function of confidence of recognition, and comparing the writer dependence of recognition. This comparison shows that methods that used different algorithms for feature extraction and recognition performed with very high levels of correlation. This is true for neural network systems, hybrid systems, and statistically based systems, and leads to the conclusion that neural networks have not yet demonstrated a clear superiority to more conventional statistical methods. Comparison of these results with the models of Vapnick (for estimation problems), MacKay (for Bayesian statistical models), Moody (for effective parameterization), and Boltzmann models (for information content) demonstrate that as the limits of training data variance are approached, all classifier systems have similar statistical properties. The limiting condition can only be approached for sufficiently rich feature sets because the accuracy limit is controlled by the available information content of the training set, which must pass through the feature extraction process prior to classification.

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

Shi, Xin, E-mail: xinshih86029@gmail.com; Zhao, Xiangmo, E-mail: xinshih86029@gmail.com; Hui, Fei, E-mail: xinshih86029@gmail.com

Clock synchronization in wireless sensor networks (WSNs) has been studied extensively in recent years and many protocols are put forward based on the point of statistical signal processing, which is an effective way to optimize accuracy. However, the accuracy derived from the statistical data can be improved mainly by sufficient packets exchange, which will consume the limited power resources greatly. In this paper, a reliable clock estimation using linear weighted fusion based on pairwise broadcast synchronization is proposed to optimize sync accuracy without expending additional sync packets. As a contribution, a linear weighted fusion scheme for multiple clock deviations ismore » constructed with the collaborative sensing of clock timestamp. And the fusion weight is defined by the covariance of sync errors for different clock deviations. Extensive simulation results show that the proposed approach can achieve better performance in terms of sync overhead and sync accuracy.« less

High-resolution sonography for distinguishing neoplastic gallbladder polyps and staging gallbladder cancer.

PubMed

Kim, Jung Hoon; Lee, Jae Young; Baek, Jee Hyun; Eun, Hyo Won; Kim, Young Jae; Han, Joon Koo; Choi, Byung Ihn

2015-02-01

OBJECTIVE. The purposes of this study were to compare staging accuracy of high-resolution sonography (HRUS) with combined low- and high-MHz transducers with that of conventional sonography for gallbladder cancer and to investigate the differences in the imaging findings of neoplastic and nonneoplastic gallbladder polyps. MATERIALS AND METHODS. Our study included 37 surgically proven gallbladder cancer (T1a = 7, T1b = 2, T2 = 22, T3 = 6), including 15 malignant neoplastic polyps and 73 surgically proven polyps (neoplastic = 31, nonneoplastic = 42) that underwent HRUS and conventional transabdominal sonography. Two radiologists assessed T-category and predefined polyp findings on HRUS and conventional transabdominal sonography. Statistical analyses were performed using chi-square and McNemar tests. RESULTS. The diagnostic accuracy for the T category was T1a = 92-95%, T1b = 89-95%, T2 = 78-86%, and T3 = 84-89%, all with good agreement (κ = 0.642) using HRUS. The diagnostic accuracy for differentiating T1 from T2 or greater than T2 was 92% and 89% on HRUS and 65% and 70% with conventional transabdominal sonography. Statistically common findings for neoplastic polyps included size greater than 1 cm, single lobular surface, vascular core, hypoechoic polyp, and hypoechoic foci (p < 0.05). The value of HRUS in the differential diagnosis of a gallbladder polyp was more clearly depicted internal echo foci than conventional transabdominal sonography (39 vs 21). A polyp size greater than 1 cm was independently associated with a neoplastic polyp (odds ratio = 7.5, p = 0.02). The AUC of a polyp size greater than 1 cm was 0.877. The sensitivity and specificity were 66.67% and 89.13%, respectively. CONCLUSION. HRUS is a simple method that enables accurate T categorization of gallbladder carcinoma. It provides high-resolution images of gallbladder polyps and may have a role in stratifying the risk for malignancy.

Comparison of pelvic phased-array versus endorectal coil magnetic resonance imaging at 3 Tesla for local staging of prostate cancer.

PubMed

Kim, Bum Soo; Kim, Tae-Hwan; Kwon, Tae Gyun; Yoo, Eun Sang

2012-05-01

Several studies have demonstrated the superiority of endorectal coil magnetic resonance imaging (MRI) over pelvic phased-array coil MRI at 1.5 Tesla for local staging of prostate cancer. However, few have studied which evaluation is more accurate at 3 Tesla MRI. In this study, we compared the accuracy of local staging of prostate cancer using pelvic phased-array coil or endorectal coil MRI at 3 Tesla. Between January 2005 and May 2010, 151 patients underwent radical prostatectomy. All patients were evaluated with either pelvic phased-array coil or endorectal coil prostate MRI prior to surgery (63 endorectal coils and 88 pelvic phased-array coils). Tumor stage based on MRI was compared with pathologic stage. We calculated the specificity, sensitivity and accuracy of each group in the evaluation of extracapsular extension and seminal vesicle invasion. Both endorectal coil and pelvic phased-array coil MRI achieved high specificity, low sensitivity and moderate accuracy for the detection of extracapsular extension and seminal vesicle invasion. There were statistically no differences in specificity, sensitivity and accuracy between the two groups. Overall staging accuracy, sensitivity and specificity were not significantly different between endorectal coil and pelvic phased-array coil MRI.

Examining Impulse-Variability Theory and the Speed-Accuracy Trade-Off in Children's Overarm Throwing Performance.

PubMed

Molina, Sergio L; Stodden, David F

2018-04-01

This study examined variability in throwing speed and spatial error to test the prediction of an inverted-U function (i.e., impulse-variability [IV] theory) and the speed-accuracy trade-off. Forty-five 9- to 11-year-old children were instructed to throw at a specified percentage of maximum speed (45%, 65%, 85%, and 100%) and hit the wall target. Results indicated no statistically significant differences in variable error across the target conditions (p = .72), failing to support the inverted-U hypothesis. Spatial accuracy results indicated no statistically significant differences with mean radial error (p = .18), centroid radial error (p = .13), and bivariate variable error (p = .08) also failing to support the speed-accuracy trade-off in overarm throwing. As neither throwing performance variability nor accuracy changed across percentages of maximum speed in this sample of children as well as in a previous adult sample, current policy and practices of practitioners may need to be reevaluated.

Nonalcoholic Fatty Liver Disease: Diagnostic and Fat-Grading Accuracy of Low-Flip-Angle Multiecho Gradient-Recalled-Echo MR Imaging at 1.5 T

PubMed Central

Yokoo, Takeshi; Bydder, Mark; Hamilton, Gavin; Middleton, Michael S.; Gamst, Anthony C.; Wolfson, Tanya; Hassanein, Tarek; Patton, Heather M.; Lavine, Joel E.; Schwimmer, Jeffrey B.; Sirlin, Claude B.

2009-01-01

Purpose: To assess the accuracy of four fat quantification methods at low-flip-angle multiecho gradient-recalled-echo (GRE) magnetic resonance (MR) imaging in nonalcoholic fatty liver disease (NAFLD) by using MR spectroscopy as the reference standard. Materials and Methods: In this institutional review board–approved, HIPAA-compliant prospective study, 110 subjects (29 with biopsy-confirmed NAFLD, 50 overweight and at risk for NAFLD, and 31 healthy volunteers) (mean age, 32.6 years ± 15.6 [standard deviation]; range, 8–66 years) gave informed consent and underwent MR spectroscopy and GRE MR imaging of the liver. Spectroscopy involved a long repetition time (to suppress T1 effects) and multiple echo times (to estimate T2 effects); the reference fat fraction (FF) was calculated from T2-corrected fat and water spectral peak areas. Imaging involved a low flip angle (to suppress T1 effects) and multiple echo times (to estimate T2* effects); imaging FF was calculated by using four analysis methods of progressive complexity: dual echo, triple echo, multiecho, and multiinterference. All methods except dual echo corrected for T2* effects. The multiinterference method corrected for multiple spectral interference effects of fat. For each method, the accuracy for diagnosis of fatty liver, as defined with a spectroscopic threshold, was assessed by estimating sensitivity and specificity; fat-grading accuracy was assessed by comparing imaging and spectroscopic FF values by using linear regression. Results: Dual-echo, triple-echo, multiecho, and multiinterference methods had a sensitivity of 0.817, 0.967, 0.950, and 0.983 and a specificity of 1.000, 0.880, 1.000, and 0.880, respectively. On the basis of regression slope and intercept, the multiinterference (slope, 0.98; intercept, 0.91%) method had high fat-grading accuracy without statistically significant error (P > .05). Dual-echo (slope, 0.98; intercept, −2.90%), triple-echo (slope, 0.94; intercept, 1.42%), and multiecho (slope, 0.85; intercept, −0.15%) methods had statistically significant error (P < .05). Conclusion: Relaxation- and interference-corrected fat quantification at low-flip-angle multiecho GRE MR imaging provides high diagnostic and fat-grading accuracy in NAFLD. © RSNA, 2009 PMID:19221054

Marginal adaptation of four inlay casting waxes on stone, titanium, and zirconia dies.

PubMed

Michalakis, Konstantinos X; Kapsampeli, Vassiliki; Kitsou, Aikaterini; Kirmanidou, Yvone; Fotiou, Anna; Pissiotis, Argirios L; Calvani, Pasquale Lino; Hirayama, Hiroshi; Kudara, Yukio

2014-07-01

Different inlay casting waxes do not produce copings with satisfactory marginal accuracy when used on different die materials. The purpose of this study was to evaluate the marginal accuracy of 4 inlay casting waxes on stone dies and titanium and zirconia abutments and to correlate the findings with the degree of wetting between the die specimens and the inlay casting waxes. The inlay casting waxes tested were Starwax (Dentaurum), Unterziehwachs (Bredent), SU Esthetic wax (Schuler), and Sculpturing wax (Renfert). The marginal opening of the waxes was measured with a stereomicroscope on high-strength stone dies and on titanium and zirconia abutments. Photographic images were obtained, and the mean marginal opening for each specimen was calculated. A total of 1440 measurements were made. Wetting between die materials and waxes was determined after fabricating stone, titanium, and zirconia rectangular specimens. A calibrated pipette was used to place a drop of molten wax onto each specimen. The contact angle was calculated with software after an image of each specimen had been made with a digital camera. Collected data were subjected to a 2-way analysis of variance (α=.05). Any association between marginal accuracy and wetting of different materials was found by using the Pearson correlation. The wax factor had a statistically significant effect both on the marginal discrepancy (F=158.31, P<.001) and contact angle values (F=68.09, P<.001). A statistically significant effect of the die material factor both on the marginal adaptation (F=503.47, P<.001) and contact angle values (F=585.02, P<.001) was detected. A significant correlation between the marginal accuracy and the contact angle values (Pearson=0.881, P=.01) was also found. Stone dies provided wax copings with the best marginal integrity, followed by titanium and zirconia abutments. Unterziehwachs (Bredent), wax produced the best marginal adaptation on different die materials. A significant correlation was found between the marginal accuracy and the contact angle values. As the contact angle value became smaller, the marginal accuracy improved. All combinations of waxes and stone and titanium dies presented a high wettability. Copyright © 2014 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Reproducible segmentation of white matter hyperintensities using a new statistical definition.

PubMed

Damangir, Soheil; Westman, Eric; Simmons, Andrew; Vrenken, Hugo; Wahlund, Lars-Olof; Spulber, Gabriela

2017-06-01

We present a method based on a proposed statistical definition of white matter hyperintensities (WMH), which can work with any combination of conventional magnetic resonance (MR) sequences without depending on manually delineated samples. T1-weighted, T2-weighted, FLAIR, and PD sequences acquired at 1.5 Tesla from 119 subjects from the Kings Health Partners-Dementia Case Register (healthy controls, mild cognitive impairment, Alzheimer's disease) were used. The segmentation was performed using a proposed definition for WMH based on the one-tailed Kolmogorov-Smirnov test. The presented method was verified, given all possible combinations of input sequences, against manual segmentations and a high similarity (Dice 0.85-0.91) was observed. Comparing segmentations with different input sequences to one another also yielded a high similarity (Dice 0.83-0.94) that exceeded intra-rater similarity (Dice 0.75-0.91). We compared the results with those of other available methods and showed that the segmentation based on the proposed definition has better accuracy and reproducibility in the test dataset used. Overall, the presented definition is shown to produce accurate results with higher reproducibility than manual delineation. This approach can be an alternative to other manual or automatic methods not only because of its accuracy, but also due to its good reproducibility.

GNSS global real-time augmentation positioning: Real-time precise satellite clock estimation, prototype system construction and performance analysis

NASA Astrophysics Data System (ADS)

Chen, Liang; Zhao, Qile; Hu, Zhigang; Jiang, Xinyuan; Geng, Changjiang; Ge, Maorong; Shi, Chuang

2018-01-01

Lots of ambiguities in un-differenced (UD) model lead to lower calculation efficiency, which isn't appropriate for the high-frequency real-time GNSS clock estimation, like 1 Hz. Mixed differenced model fusing UD pseudo-range and epoch-differenced (ED) phase observations has been introduced into real-time clock estimation. In this contribution, we extend the mixed differenced model for realizing multi-GNSS real-time clock high-frequency updating and a rigorous comparison and analysis on same conditions are performed to achieve the best real-time clock estimation performance taking the efficiency, accuracy, consistency and reliability into consideration. Based on the multi-GNSS real-time data streams provided by multi-GNSS Experiment (MGEX) and Wuhan University, GPS + BeiDou + Galileo global real-time augmentation positioning prototype system is designed and constructed, including real-time precise orbit determination, real-time precise clock estimation, real-time Precise Point Positioning (RT-PPP) and real-time Standard Point Positioning (RT-SPP). The statistical analysis of the 6 h-predicted real-time orbits shows that the root mean square (RMS) in radial direction is about 1-5 cm for GPS, Beidou MEO and Galileo satellites and about 10 cm for Beidou GEO and IGSO satellites. Using the mixed differenced estimation model, the prototype system can realize high-efficient real-time satellite absolute clock estimation with no constant clock-bias and can be used for high-frequency augmentation message updating (such as 1 Hz). The real-time augmentation message signal-in-space ranging error (SISRE), a comprehensive accuracy of orbit and clock and effecting the users' actual positioning performance, is introduced to evaluate and analyze the performance of GPS + BeiDou + Galileo global real-time augmentation positioning system. The statistical analysis of real-time augmentation message SISRE is about 4-7 cm for GPS, whlile 10 cm for Beidou IGSO/MEO, Galileo and about 30 cm for BeiDou GEO satellites. The real-time positioning results prove that the GPS + BeiDou + Galileo RT-PPP comparing to GPS-only can effectively accelerate convergence time by about 60%, improve the positioning accuracy by about 30% and obtain averaged RMS 4 cm in horizontal and 6 cm in vertical; additionally RT-SPP accuracy in the prototype system can realize positioning accuracy with about averaged RMS 1 m in horizontal and 1.5-2 m in vertical, which are improved by 60% and 70% to SPP based on broadcast ephemeris, respectively.

Improved Diagnostic Accuracy of SPECT Through Statistical Analysis and the Detection of Hot Spots at the Primary Sensorimotor Area for the Diagnosis of Alzheimer Disease in a Community-Based Study: "The Osaki-Tajiri Project".

PubMed

Kaneta, Tomohiro; Nakatsuka, Masahiro; Nakamura, Kei; Seki, Takashi; Yamaguchi, Satoshi; Tsuboi, Masahiro; Meguro, Kenichi

2016-01-01

SPECT is an important diagnostic tool for dementia. Recently, statistical analysis of SPECT has been commonly used for dementia research. In this study, we evaluated the accuracy of visual SPECT evaluation and/or statistical analysis for the diagnosis (Dx) of Alzheimer disease (AD) and other forms of dementia in our community-based study "The Osaki-Tajiri Project." Eighty-nine consecutive outpatients with dementia were enrolled and underwent brain perfusion SPECT with 99mTc-ECD. Diagnostic accuracy of SPECT was tested using 3 methods: visual inspection (SPECT Dx), automated diagnostic tool using statistical analysis with easy Z-score imaging system (eZIS Dx), and visual inspection plus eZIS (integrated Dx). Integrated Dx showed the highest sensitivity, specificity, and accuracy, whereas eZIS was the second most accurate method. We also observed that a higher than expected rate of SPECT images indicated false-negative cases of AD. Among these, 50% showed hypofrontality and were diagnosed as frontotemporal lobar degeneration. These cases typically showed regional "hot spots" in the primary sensorimotor cortex (ie, a sensorimotor hot spot sign), which we determined were associated with AD rather than frontotemporal lobar degeneration. We concluded that the diagnostic abilities were improved by the integrated use of visual assessment and statistical analysis. In addition, the detection of a sensorimotor hot spot sign was useful to detect AD when hypofrontality is present and improved the ability to properly diagnose AD.

Scout trajectory error propagation computer program

NASA Technical Reports Server (NTRS)

Myler, T. R.

1982-01-01

Since 1969, flight experience has been used as the basis for predicting Scout orbital accuracy. The data used for calculating the accuracy consists of errors in the trajectory parameters (altitude, velocity, etc.) at stage burnout as observed on Scout flights. Approximately 50 sets of errors are used in Monte Carlo analysis to generate error statistics in the trajectory parameters. A covariance matrix is formed which may be propagated in time. The mechanization of this process resulted in computer program Scout Trajectory Error Propagation (STEP) and is described herein. Computer program STEP may be used in conjunction with the Statistical Orbital Analysis Routine to generate accuracy in the orbit parameters (apogee, perigee, inclination, etc.) based upon flight experience.

Improved Statistical Sampling and Accuracy with Accelerated Molecular Dynamics on Rotatable Torsions.

PubMed

Doshi, Urmi; Hamelberg, Donald

2012-11-13

In enhanced sampling techniques, the precision of the reweighted ensemble properties is often decreased due to large variation in statistical weights and reduction in the effective sampling size. To abate this reweighting problem, here, we propose a general accelerated molecular dynamics (aMD) approach in which only the rotatable dihedrals are subjected to aMD (RaMD), unlike the typical implementation wherein all dihedrals are boosted (all-aMD). Nonrotatable and improper dihedrals are marginally important to conformational changes or the different rotameric states. Not accelerating them avoids the sharp increases in the potential energies due to small deviations from their minimum energy conformations and leads to improvement in the precision of RaMD. We present benchmark studies on two model dipeptides, Ace-Ala-Nme and Ace-Trp-Nme, simulated with normal MD, all-aMD, and RaMD. We carry out a systematic comparison between the performances of both forms of aMD using a theory that allows quantitative estimation of the effective number of sampled points and the associated uncertainty. Our results indicate that, for the same level of acceleration and simulation length, as used in all-aMD, RaMD results in significantly less loss in the effective sample size and, hence, increased accuracy in the sampling of φ-ψ space. RaMD yields an accuracy comparable to that of all-aMD, from simulation lengths 5 to 1000 times shorter, depending on the peptide and the acceleration level. Such improvement in speed and accuracy over all-aMD is highly remarkable, suggesting RaMD as a promising method for sampling larger biomolecules.

Multi-level methods and approximating distribution functions

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

Wilson, D., E-mail: daniel.wilson@dtc.ox.ac.uk; Baker, R. E.

2016-07-15

Biochemical reaction networks are often modelled using discrete-state, continuous-time Markov chains. System statistics of these Markov chains usually cannot be calculated analytically and therefore estimates must be generated via simulation techniques. There is a well documented class of simulation techniques known as exact stochastic simulation algorithms, an example of which is Gillespie’s direct method. These algorithms often come with high computational costs, therefore approximate stochastic simulation algorithms such as the tau-leap method are used. However, in order to minimise the bias in the estimates generated using them, a relatively small value of tau is needed, rendering the computational costs comparablemore » to Gillespie’s direct method. The multi-level Monte Carlo method (Anderson and Higham, Multiscale Model. Simul. 10:146–179, 2012) provides a reduction in computational costs whilst minimising or even eliminating the bias in the estimates of system statistics. This is achieved by first crudely approximating required statistics with many sample paths of low accuracy. Then correction terms are added until a required level of accuracy is reached. Recent literature has primarily focussed on implementing the multi-level method efficiently to estimate a single system statistic. However, it is clearly also of interest to be able to approximate entire probability distributions of species counts. We present two novel methods that combine known techniques for distribution reconstruction with the multi-level method. We demonstrate the potential of our methods using a number of examples.« less

Improving medical decisions for incapacitated persons: does focusing on "accurate predictions" lead to an inaccurate picture?

PubMed

Kim, Scott Y H

2014-04-01

The Patient Preference Predictor (PPP) proposal places a high priority on the accuracy of predicting patients' preferences and finds the performance of surrogates inadequate. However, the quest to develop a highly accurate, individualized statistical model has significant obstacles. First, it will be impossible to validate the PPP beyond the limit imposed by 60%-80% reliability of people's preferences for future medical decisions--a figure no better than the known average accuracy of surrogates. Second, evidence supports the view that a sizable minority of persons may not even have preferences to predict. Third, many, perhaps most, people express their autonomy just as much by entrusting their loved ones to exercise their judgment than by desiring to specifically control future decisions. Surrogate decision making faces none of these issues and, in fact, it may be more efficient, accurate, and authoritative than is commonly assumed.

Accuracy assessment for a multi-parameter optical calliper in on line automotive applications

NASA Astrophysics Data System (ADS)

D'Emilia, G.; Di Gasbarro, D.; Gaspari, A.; Natale, E.

2017-08-01

In this work, a methodological approach based on the evaluation of the measurement uncertainty is applied to an experimental test case, related to the automotive sector. The uncertainty model for different measurement procedures of a high-accuracy optical gauge is discussed in order to individuate the best measuring performances of the system for on-line applications and when the measurement requirements are becoming more stringent. In particular, with reference to the industrial production and control strategies of high-performing turbochargers, two uncertainty models are proposed, discussed and compared, to be used by the optical calliper. Models are based on an integrated approach between measurement methods and production best practices to emphasize their mutual coherence. The paper shows the possible advantages deriving from the considerations that the measurement uncertainty modelling provides, in order to keep control of the uncertainty propagation on all the indirect measurements useful for production statistical control, on which basing further improvements.

Comparison of citrus orchard inventory using LISS-III and LISS-IV data

NASA Astrophysics Data System (ADS)

Singh, Niti; Chaudhari, K. N.; Manjunath, K. R.

2016-04-01

In India, in terms of area under cultivation, citrus is the third most cultivated fruit crop after Banana and Mango. Among citrus group, lime is one of the most important horticultural crops in India as the demand for its consumption is very high. Hence, preparing citrus crop inventories using remote sensing techniques would help in maintaining a record of its area and production statistics. This study shows how accurately citrus orchard can be classified using both IRS Resourcesat-2 LISS-III and LISS-IV data and depicts the optimum bio-widow for procuring satellite data to achieve high classification accuracy required for maintaining inventory of crop. Findings of the study show classification accuracy increased from 55% (using LISS-III) to 77% (using LISS-IV). Also, according to classified outputs and NDVI values obtained, April and May months were identified as optimum bio-window for citrus crop identification.

Robust dynamic 3-D measurements with motion-compensated phase-shifting profilometry

NASA Astrophysics Data System (ADS)

Feng, Shijie; Zuo, Chao; Tao, Tianyang; Hu, Yan; Zhang, Minliang; Chen, Qian; Gu, Guohua

2018-04-01

Phase-shifting profilometry (PSP) is a widely used approach to high-accuracy three-dimensional shape measurements. However, when it comes to moving objects, phase errors induced by the movement often result in severe artifacts even though a high-speed camera is in use. From our observations, there are three kinds of motion artifacts: motion ripples, motion-induced phase unwrapping errors, and motion outliers. We present a novel motion-compensated PSP to remove the artifacts for dynamic measurements of rigid objects. The phase error of motion ripples is analyzed for the N-step phase-shifting algorithm and is compensated using the statistical nature of the fringes. The phase unwrapping errors are corrected exploiting adjacent reliable pixels, and the outliers are removed by comparing the original phase map with a smoothed phase map. Compared with the three-step PSP, our method can improve the accuracy by more than 95% for objects in motion.

Mapping rice areas of South Asia using MODIS multitemporal data

NASA Astrophysics Data System (ADS)

Gumma, Murali Krishna; Nelson, Andrew; Thenkabail, Prasad S.; Singh, Amrendra N.

2011-01-01

Our goal is to map the rice areas of six South Asian countries using moderate-resolution imaging spectroradiometer (MODIS) time-series data for the time period 2000 to 2001. South Asia accounts for almost 40% of the world's harvested rice area and is also home to 74% of the population that lives on less than $2.00 a day. The population of the region is growing faster than its ability to produce rice. Thus, accurate and timely assessment of where and how rice is cultivated is important to craft food security and poverty alleviation strategies. We used a time series of eight-day, 500-m spatial resolution composite images from the MODIS sensor to produce rice maps and rice characteristics (e.g., intensity of cropping, cropping calendar) taking data for the years 2000 to 2001 and by adopting a suite of methods that include spectral matching techniques, decision trees, and ideal temporal profile data banks to rapidly identify and classify rice areas over large spatial extents. These methods are used in conjunction with ancillary spatial data sets (e.g., elevation, precipitation), national statistics, and maps, and a large volume of field-plot data. The resulting rice maps and statistics are compared against a subset of independent field-plot points and the best available subnational statistics on rice areas for the main crop growing season (kharif season). A fuzzy classification accuracy assessment for the 2000 to 2001 rice-map product, based on field-plot data, demonstrated accuracies from 67% to 100% for individual rice classes, with an overall accuracy of 80% for all classes. Most of the mixing was within rice classes. The derived physical rice area was highly correlated with the subnational statistics with R2 values of 97% at the district level and 99% at the state level for 2000 to 2001. These results suggest that the methods, approaches, algorithms, and data sets we used are ideal for rapid, accurate, and large-scale mapping of paddy rice as well as for generating their statistics over large areas.

Mapping rice areas of South Asia using MODIS multitemporal data

USGS Publications Warehouse

Gumma, M.K.; Nelson, A.; Thenkabail, P.S.; Singh, A.N.

2011-01-01

Our goal is to map the rice areas of six South Asian countries using moderate-resolution imaging spectroradiometer (MODIS) time-series data for the time period 2000 to 2001. South Asia accounts for almost 40% of the world's harvested rice area and is also home to 74% of the population that lives on less than $2.00 a day. The population of the region is growing faster than its ability to produce rice. Thus, accurate and timely assessment of where and how rice is cultivated is important to craft food security and poverty alleviation strategies. We used a time series of eight-day, 500-m spatial resolution composite images from the MODIS sensor to produce rice maps and rice characteristics (e.g., intensity of cropping, cropping calendar) taking data for the years 2000 to 2001 and by adopting a suite of methods that include spectral matching techniques, decision trees, and ideal temporal profile data banks to rapidly identify and classify rice areas over large spatial extents. These methods are used in conjunction with ancillary spatial data sets (e.g., elevation, precipitation), national statistics, and maps, and a large volume of field-plot data. The resulting rice maps and statistics are compared against a subset of independent field-plot points and the best available subnational statistics on rice areas for the main crop growing season (kharif season). A fuzzy classification accuracy assessment for the 2000 to 2001 rice-map product, based on field-plot data, demonstrated accuracies from 67% to 100% for individual rice classes, with an overall accuracy of 80% for all classes. Most of the mixing was within rice classes. The derived physical rice area was highly correlated with the subnational statistics with R2 values of 97% at the district level and 99% at the state level for 2000 to 2001. These results suggest that the methods, approaches, algorithms, and data sets we used are ideal for rapid, accurate, and large-scale mapping of paddy rice as well as for generating their statistics over large areas. ?? 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

[The relationship between accommodative accuracy at different near-work distances and early-onset myopia].

PubMed

Yu, Q W; Zhang, P; Zhou, S B; Hu, Y; Ji, M X; Luo, Y C; You, H L; Yao, Z X

2016-07-01

To observe the accommodative accuracy of children with early-onset myopia at different near-work distances, and discuss the relationship between accommodative accuracy and early-onset myopia. This was a case-control study. Thirty-seven emmetropic children, 41 early-onset myopic children without correction, and 39 early-onset myopic children with spectacles, aged 7 to 13 years, were included. Measures of refractive errors and accommodative accuracy at four near-work distances, including 50 cm, 40 cm, 30 cm, and 20 cm, were made using the binocular fusion cross cylinder (FCC) of an automatic phoropter. Most candidates showed accommodative lags, including the children with emmetropia. The ratio of lags in all candidates at different near-work distances was 75.21% (50 cm), 87.18% (40 cm), 92.31% (30 cm), and 98.29% (20 cm), respectively. All accommodative accuracies became worse, and the accommodative lag ratio and values of FCC increased, along with the shortening of the distance. The difference in accommodative accuracy among groups was statistically significant at 30 cm (χ(2)=7.852, P= 0.020) and 20 cm (χ(2)=6.480, P=0.039). The values of FCC among groups were significantly different at 30 cm (F=3.626, P=0.030) and 20 cm (F=3.703, P=0.028), but not at 50 cm and 40 cm (P>0.05). In addition, the FCC values of 30 cm and 20 cm had a statistically significant difference between myopic children without correction [(1.25±0.44) D and (1.76±0.43) D] and emmetropic children [(0.95±0.52) D and (1.41±0.58) D] (P=0.012, 0.008). The correlation between diopters of myopia and accommodative accuracy at different nearwork distances was not statistically significant (P>0.05). However, the correlation between diopters of myopia and the accommodative lag value (FCC) at 20 cm was statistically significant (r=0.246, P=0.028). The closer the near-work distance is, the worse the accommodative accuracy is. This is more significant in early-onset myopia, especially myopia without correction, than emmetropia. Wearing spectacles may improve the threshold and sensitivity of accommodations, and the accommodative accuracy at near-work distances (<30 cm) to some extent. The poor accommodative accuracy at near-work distances may be not related to early-onset myopia, but the value of FCC (20 cm) is related to early-onset myopia. The higher the FCC value is, the higher the diopter is. (Chin J Ophthalmol, 2016, 52: 520-524).

Determination of sex from the patella in a contemporary Spanish population.

PubMed

Peckmann, Tanya R; Meek, Susan; Dilkie, Natasha; Rozendaal, Andrew

2016-11-01

The skull and pelvis have been used for the determination of sex for unknown human remains. However, in forensic cases where skeletal remains often exhibit postmortem damage and taphonomic changes the patella may be used for the determination of sex as it is a preservationally favoured bone. The goal of the present research was to derive discriminant function equations from the patella for estimation of sex from a contemporary Spanish population. Six parameters were measured on 106 individuals (55 males and 51 females), ranging in age from 22 to 85 years old, from the Granada Osteological Collection. The statistical analyses showed that all variables were sexually dimorphic. Discriminant function score equations were generated for use in sex determination. The overall accuracy of sex classification ranged from 75.2% to 84.8% for the direct method and 75.5%-83.8% for the stepwise method. When the South African White discriminant functions were applied to the Spanish sample they showed high accuracy rates for sexing female patellae (90%-95.9%) and low accuracy rates for sexing male patellae (52.7%-58.2%). When the South African Black discriminant functions were applied to the Spanish sample they showed high accuracy rates for sexing male patellae (90.9%) and low accuracy rates for sexing female patellae (70%-75.5%). The patella was shown to be useful for sex determination in the contemporary Spanish population. Copyright © 2016 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Diagnostic Accuracy of the Work Functioning Impairment Scale (WFun): A Method to Detect Workers Who Have Health Problems Affecting their Work and to Evaluate Fitness for Work.

PubMed

Nagata, Tomohisa; Fujino, Yoshihisa; Saito, Kumi; Uehara, Masamichi; Oyama, Ichiro; Izumi, Hiroyuki; Kubo, Tatsuhiko

2017-06-01

This study evaluated the diagnostic accuracy of the Work Functioning Impairment Scale (WFun), a questionnaire to detect workers with health problems which affect their work, using an assessment by an occupational health nurse as objective standard. The WFun was completed by 294 employees. The nurse interviewed to assess 1) health problems; 2) effects of health on their work; necessity for 3) treatment, 4) health care instruction, and 5) consideration of job accommodation. The odds ratio in the high work functioning impairment group compared with the low was highly statistically significant with 9.05, 10.26, 5.77, 9.37, and 14.70, respectively. The WFun demonstrated the high detectability with an area under the receiver operating characteristic of 0.75, 0.81, 0.72, 0.79, and 0.83, respectively. This study suggests that the WFun is useful in detecting those who have health problems affecting their work.

Extremal optimization for Sherrington-Kirkpatrick spin glasses

NASA Astrophysics Data System (ADS)

Boettcher, S.

2005-08-01

Extremal Optimization (EO), a new local search heuristic, is used to approximate ground states of the mean-field spin glass model introduced by Sherrington and Kirkpatrick. The implementation extends the applicability of EO to systems with highly connected variables. Approximate ground states of sufficient accuracy and with statistical significance are obtained for systems with more than N=1000 variables using ±J bonds. The data reproduces the well-known Parisi solution for the average ground state energy of the model to about 0.01%, providing a high degree of confidence in the heuristic. The results support to less than 1% accuracy rational values of ω=2/3 for the finite-size correction exponent, and of ρ=3/4 for the fluctuation exponent of the ground state energies, neither one of which has been obtained analytically yet. The probability density function for ground state energies is highly skewed and identical within numerical error to the one found for Gaussian bonds. But comparison with infinite-range models of finite connectivity shows that the skewness is connectivity-dependent.

High-order asynchrony-tolerant finite difference schemes for partial differential equations

NASA Astrophysics Data System (ADS)

Aditya, Konduri; Donzis, Diego A.

2017-12-01

Synchronizations of processing elements (PEs) in massively parallel simulations, which arise due to communication or load imbalances between PEs, significantly affect the scalability of scientific applications. We have recently proposed a method based on finite-difference schemes to solve partial differential equations in an asynchronous fashion - synchronization between PEs is relaxed at a mathematical level. While standard schemes can maintain their stability in the presence of asynchrony, their accuracy is drastically affected. In this work, we present a general methodology to derive asynchrony-tolerant (AT) finite difference schemes of arbitrary order of accuracy, which can maintain their accuracy when synchronizations are relaxed. We show that there are several choices available in selecting a stencil to derive these schemes and discuss their effect on numerical and computational performance. We provide a simple classification of schemes based on the stencil and derive schemes that are representative of different classes. Their numerical error is rigorously analyzed within a statistical framework to obtain the overall accuracy of the solution. Results from numerical experiments are used to validate the performance of the schemes.

A Statistical Evaluation of the Diagnostic Performance of MEDAS-The Medical Emergency Decision Assistance System

PubMed Central

Georgakis, D. Christine; Trace, David A.; Naeymi-Rad, Frank; Evens, Martha

1990-01-01

Medical expert systems require comprehensive evaluation of their diagnostic accuracy. The usefulness of these systems is limited without established evaluation methods. We propose a new methodology for evaluating the diagnostic accuracy and the predictive capacity of a medical expert system. We have adapted to the medical domain measures that have been used in the social sciences to examine the performance of human experts in the decision making process. Thus, in addition to the standard summary measures, we use measures of agreement and disagreement, and Goodman and Kruskal's λ and τ measures of predictive association. This methodology is illustrated by a detailed retrospective evaluation of the diagnostic accuracy of the MEDAS system. In a study using 270 patients admitted to the North Chicago Veterans Administration Hospital, diagnoses produced by MEDAS are compared with the discharge diagnoses of the attending physicians. The results of the analysis confirm the high diagnostic accuracy and predictive capacity of the MEDAS system. Overall, the agreement of the MEDAS system with the “gold standard” diagnosis of the attending physician has reached a 90% level.

Diagnostic Accuracy of Memory Measures in Alzheimer’s Dementia and Mild Cognitive Impairment: a Systematic Review and Meta-Analysis

PubMed Central

Weissberger, Gali H.; Strong, Jessica V.; Stefanidis, Kayla B.; Summers, Mathew J.; Bondi, Mark W.; Stricker, Nikki H.

2018-01-01

With an increasing focus on biomarkers in dementia research, illustrating the role of neuropsychological assessment in detecting mild cognitive impairment (MCI) and Alzheimer’s dementia (AD) is important. This systematic review and meta-analysis, conducted in accordance with PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) standards, summarizes the sensitivity and specificity of memory measures in individuals with MCI and AD. Both meta-analytic and qualitative examination of AD versus healthy control (HC) studies (n = 47) revealed generally high sensitivity and specificity (≥ 80% for AD comparisons) for measures of immediate (sensitivity = 87%, specificity = 88%) and delayed memory (sensitivity = 89%, specificity = 89%), especially those involving word-list recall. Examination of MCI versus HC studies (n = 38) revealed generally lower diagnostic accuracy for both immediate (sensitivity = 72%, specificity = 81%) and delayed memory (sensitivity = 75%, specificity = 81%). Measures that differentiated AD from other conditions (n = 10 studies) yielded mixed results, with generally high sensitivity in the context of low or variable specificity. Results confirm that memory measures have high diagnostic accuracy for identification of AD, are promising but require further refinement for identification of MCI, and provide support for ongoing investigation of neuropsychological assessment as a cognitive biomarker of preclinical AD. Emphasizing diagnostic test accuracy statistics over null hypothesis testing in future studies will promote the ongoing use of neuropsychological tests as Alzheimer’s disease research and clinical criteria increasingly rely upon cerebrospinal fluid (CSF) and neuroimaging biomarkers. PMID:28940127

Toward automated classification of consumers' cancer-related questions with a new taxonomy of expected answer types.

PubMed

McRoy, Susan; Jones, Sean; Kurmally, Adam

2016-09-01

This article examines methods for automated question classification applied to cancer-related questions that people have asked on the web. This work is part of a broader effort to provide automated question answering for health education. We created a new corpus of consumer-health questions related to cancer and a new taxonomy for those questions. We then compared the effectiveness of different statistical methods for developing classifiers, including weighted classification and resampling. Basic methods for building classifiers were limited by the high variability in the natural distribution of questions and typical refinement approaches of feature selection and merging categories achieved only small improvements to classifier accuracy. Best performance was achieved using weighted classification and resampling methods, the latter yielding an accuracy of F1 = 0.963. Thus, it would appear that statistical classifiers can be trained on natural data, but only if natural distributions of classes are smoothed. Such classifiers would be useful for automated question answering, for enriching web-based content, or assisting clinical professionals to answer questions. © The Author(s) 2015.

Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.

PubMed

Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru

2014-10-15

Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

Assessing artificial neural networks and statistical methods for infilling missing soil moisture records

NASA Astrophysics Data System (ADS)

Dumedah, Gift; Walker, Jeffrey P.; Chik, Li

2014-07-01

Soil moisture information is critically important for water management operations including flood forecasting, drought monitoring, and groundwater recharge estimation. While an accurate and continuous record of soil moisture is required for these applications, the available soil moisture data, in practice, is typically fraught with missing values. There are a wide range of methods available to infilling hydrologic variables, but a thorough inter-comparison between statistical methods and artificial neural networks has not been made. This study examines 5 statistical methods including monthly averages, weighted Pearson correlation coefficient, a method based on temporal stability of soil moisture, and a weighted merging of the three methods, together with a method based on the concept of rough sets. Additionally, 9 artificial neural networks are examined, broadly categorized into feedforward, dynamic, and radial basis networks. These 14 infilling methods were used to estimate missing soil moisture records and subsequently validated against known values for 13 soil moisture monitoring stations for three different soil layer depths in the Yanco region in southeast Australia. The evaluation results show that the top three highest performing methods are the nonlinear autoregressive neural network, rough sets method, and monthly replacement. A high estimation accuracy (root mean square error (RMSE) of about 0.03 m/m) was found in the nonlinear autoregressive network, due to its regression based dynamic network which allows feedback connections through discrete-time estimation. An equally high accuracy (0.05 m/m RMSE) in the rough sets procedure illustrates the important role of temporal persistence of soil moisture, with the capability to account for different soil moisture conditions.

Statistical properties of nonlinear one-dimensional wave fields

NASA Astrophysics Data System (ADS)

Chalikov, D.

2005-06-01

A numerical model for long-term simulation of gravity surface waves is described. The model is designed as a component of a coupled Wave Boundary Layer/Sea Waves model, for investigation of small-scale dynamic and thermodynamic interactions between the ocean and atmosphere. Statistical properties of nonlinear wave fields are investigated on a basis of direct hydrodynamical modeling of 1-D potential periodic surface waves. The method is based on a nonstationary conformal surface-following coordinate transformation; this approach reduces the principal equations of potential waves to two simple evolutionary equations for the elevation and the velocity potential on the surface. The numerical scheme is based on a Fourier transform method. High accuracy was confirmed by validation of the nonstationary model against known solutions, and by comparison between the results obtained with different resolutions in the horizontal. The scheme allows reproduction of the propagation of steep Stokes waves for thousands of periods with very high accuracy. The method here developed is applied to simulation of the evolution of wave fields with large number of modes for many periods of dominant waves. The statistical characteristics of nonlinear wave fields for waves of different steepness were investigated: spectra, curtosis and skewness, dispersion relation, life time. The prime result is that wave field may be presented as a superposition of linear waves is valid only for small amplitudes. It is shown as well, that nonlinear wave fields are rather a superposition of Stokes waves not linear waves. Potential flow, free surface, conformal mapping, numerical modeling of waves, gravity waves, Stokes waves, breaking waves, freak waves, wind-wave interaction.

Examining the effectiveness of discriminant function analysis and cluster analysis in species identification of male field crickets based on their calling songs.

PubMed

Jaiswara, Ranjana; Nandi, Diptarup; Balakrishnan, Rohini

2013-01-01

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

A comparative study of inter-abutment distance of dies made from full arch dual-arch impression trays with those made from full arch stock trays: an in vitro study.

PubMed

Reddy, Jagan Mohan; Prashanti, E; Kumar, G Vinay; Suresh Sajjan, M C; Mathew, Xavier

2009-01-01

The dual-arch impression technique is convenient in that it makes the required maxillary and mandibular impressions, as well as the inter-occlusal record in one procedure. The accuracy of inter-abutment distance in dies fabricated from dual-arch impression technique remains in question because there is little information available in the literature. This study was conducted to evaluate the accuracy of inter-abutment distance in dies obtained from full arch dual-arch trays with those obtained from full arch stock metal trays. The metal dual-arch trays showed better accuracy followed by the plastic dual-arch and stock dentulous trays, respectively, though statistically insignificant. The pouring sequence did not have any effect on the inter-abutment distance statistically, though pouring the non-working side of the dual-arch impression first showed better accuracy.

Assigning African elephant DNA to geographic region of origin: Applications to the ivory trade

PubMed Central

Wasser, Samuel K.; Shedlock, Andrew M.; Comstock, Kenine; Ostrander, Elaine A.; Mutayoba, Benezeth; Stephens, Matthew

2004-01-01

Resurgence of illicit trade in African elephant ivory is placing the elephant at renewed risk. Regulation of this trade could be vastly improved by the ability to verify the geographic origin of tusks. We address this need by developing a combined genetic and statistical method to determine the origin of poached ivory. Our statistical approach exploits a smoothing method to estimate geographic-specific allele frequencies over the entire African elephants' range for 16 microsatellite loci, using 315 tissue and 84 scat samples from forest (Loxodonta africana cyclotis) and savannah (Loxodonta africana africana) elephants at 28 locations. These geographic-specific allele frequency estimates are used to infer the geographic origin of DNA samples, such as could be obtained from tusks of unknown origin. We demonstrate that our method alleviates several problems associated with standard assignment methods in this context, and the absolute accuracy of our method is high. Continent-wide, 50% of samples were located within 500 km, and 80% within 932 km of their actual place of origin. Accuracy varied by region (median accuracies: West Africa, 135 km; Central Savannah, 286 km; Central Forest, 411 km; South, 535 km; and East, 697 km). In some cases, allele frequencies vary considerably over small geographic regions, making much finer discriminations possible and suggesting that resolution could be further improved by collection of samples from locations not represented in our study. PMID:15459317

Marginal Accuracy and Internal Fit of 3-D Printing Laser-Sintered Co-Cr Alloy Copings.

PubMed

Kim, Myung-Joo; Choi, Yun-Jung; Kim, Seong-Kyun; Heo, Seong-Joo; Koak, Jai-Young

2017-01-23

Laser sintered technology has been introduced for clinical use and can be utilized more widely, accompanied by the digitalization of dentistry and the development of direct oral scanning devices. This study was performed with the aim of comparing the marginal accuracy and internal fit of Co-Cr alloy copings fabricated by casting, CAD/CAM (Computer-aided design/Computer-assisted manufacture) milled, and 3-D laser sintered techniques. A total of 36 Co-Cr alloy crown-copings were fabricated from an implant abutment. The marginal and internal fit were evaluated by measuring the weight of the silicone material, the vertical marginal discrepancy using a microscope, and the internal gap in the sectioned specimens. The data were statistically analyzed by One-way ANOVA (analysis of variance), a Scheffe's test, and Pearson's correlation at the significance level of p = 0.05, using statistics software. The silicone weight was significantly low in the casting group. The 3-D laser sintered group showed the highest vertical discrepancy, and marginal-, occlusal-, and average- internal gaps ( p < 0.05). The CAD/CAM milled group revealed a significantly high axial internal gap. There are moderate correlations between the vertical marginal discrepancy and the internal gap variables ( r = 0.654), except for the silicone weight. In this study, the 3-D laser sintered group achieved clinically acceptable marginal accuracy and internal fit.

Marginal Accuracy and Internal Fit of 3-D Printing Laser-Sintered Co-Cr Alloy Copings

PubMed Central

Kim, Myung-Joo; Choi, Yun-Jung; Kim, Seong-Kyun; Heo, Seong-Joo; Koak, Jai-Young

2017-01-01

Laser sintered technology has been introduced for clinical use and can be utilized more widely, accompanied by the digitalization of dentistry and the development of direct oral scanning devices. This study was performed with the aim of comparing the marginal accuracy and internal fit of Co-Cr alloy copings fabricated by casting, CAD/CAM (Computer-aided design/Computer-assisted manufacture) milled, and 3-D laser sintered techniques. A total of 36 Co-Cr alloy crown-copings were fabricated from an implant abutment. The marginal and internal fit were evaluated by measuring the weight of the silicone material, the vertical marginal discrepancy using a microscope, and the internal gap in the sectioned specimens. The data were statistically analyzed by One-way ANOVA (analysis of variance), a Scheffe’s test, and Pearson’s correlation at the significance level of p = 0.05, using statistics software. The silicone weight was significantly low in the casting group. The 3-D laser sintered group showed the highest vertical discrepancy, and marginal-, occlusal-, and average- internal gaps (p < 0.05). The CAD/CAM milled group revealed a significantly high axial internal gap. There are moderate correlations between the vertical marginal discrepancy and the internal gap variables (r = 0.654), except for the silicone weight. In this study, the 3-D laser sintered group achieved clinically acceptable marginal accuracy and internal fit. PMID:28772451

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

Wilson, Eric J

The ResStock analysis tool is helping states, municipalities, utilities, and manufacturers identify which home upgrades save the most energy and money. Across the country there's a vast diversity in the age, size, construction practices, installed equipment, appliances, and resident behavior of the housing stock, not to mention the range of climates. These variations have hindered the accuracy of predicting savings for existing homes. Researchers at the National Renewable Energy Laboratory (NREL) developed ResStock. It's a versatile tool that takes a new approach to large-scale residential energy analysis by combining: large public and private data sources, statistical sampling, detailed subhourly buildingmore » simulations, high-performance computing. This combination achieves unprecedented granularity and most importantly - accuracy - in modeling the diversity of the single-family housing stock.« less

Optical coherence tomography in the diagnosis of dysplasia and adenocarcinoma in Barret's esophagus

NASA Astrophysics Data System (ADS)

Gladkova, N. D.; Zagaynova, E. V.; Zuccaro, G.; Kareta, M. V.; Feldchtein, F. I.; Balalaeva, I. V.; Balandina, E. B.

2007-02-01

Statistical analysis of endoscopic optical coherence tomography (EOCT) surveillance of 78 patients with Barrett's esophagus (BE) is presented in this study. The sensitivity of OCT device in retrospective open detection of early malignancy (including high grade dysplasia and intramucosal adenocarcinoma (IMAC)) was 75%, specificity 82%, diagnostic accuracy - 80%, positive predictive value- 60%, negative predictive value- 87%. In the open recognition of IMAC sensitivity was 81% and specificity were 85% each. Results of a blind recognition with the same material were similar: sensitivity - 77%, specificity 85%, diagnostic accuracy - 82%, positive predictive value- 70%, negative predictive value- 87%. As the endoscopic detection of early malignancy is problematic, OCT holds great promise in enhancing the diagnostic capability of clinical GI endoscopy.

A method for predicting DCT-based denoising efficiency for grayscale images corrupted by AWGN and additive spatially correlated noise

NASA Astrophysics Data System (ADS)

Rubel, Aleksey S.; Lukin, Vladimir V.; Egiazarian, Karen O.

2015-03-01

Results of denoising based on discrete cosine transform for a wide class of images corrupted by additive noise are obtained. Three types of noise are analyzed: additive white Gaussian noise and additive spatially correlated Gaussian noise with middle and high correlation levels. TID2013 image database and some additional images are taken as test images. Conventional DCT filter and BM3D are used as denoising techniques. Denoising efficiency is described by PSNR and PSNR-HVS-M metrics. Within hard-thresholding denoising mechanism, DCT-spectrum coefficient statistics are used to characterize images and, subsequently, denoising efficiency for them. Results of denoising efficiency are fitted for such statistics and efficient approximations are obtained. It is shown that the obtained approximations provide high accuracy of prediction of denoising efficiency.

Investigation of the {Fe}/{Si} interface and its phase transformations

NASA Astrophysics Data System (ADS)

Fanciulli, M.; Degroote, S.; Weyer, G.; Langouche, G.

1997-04-01

Thin 57Fe films (3-10 Å) have been grown by molecular beam epitaxy (MBE) on (7 × 7) reconstructed Si(111) and (2 × 1) reconstructed Si(001) surfaces and by e-gun evaporation on an H-terminated Si(111) surface. Conversion electron Mössbauer spectroscopy (CEMS) with high statistical accuracy and resolution allowed a detailed microscopic investigation of the silicide formation mechanism and of the structural phase transformations upon annealing.

What Makes a Message Stick? The Role of Content and Context in Social Media Epidemics

DTIC Science & Technology

2013-09-23

First, we propose visual memes , or frequently re-posted short video segments, for detecting and monitoring latent video interactions at scale. Content...interactions (such as quoting, or remixing, parts of a video). Visual memes are extracted by scalable detection algorithms that we develop, with...high accuracy. We further augment visual memes with text, via a statistical model of latent topics. We model content interactions on YouTube with

Accuracy assessment of maps of forest condition: Statistical design and methodological considerations [Chapter 5

Treesearch

Raymond L. Czaplewski

2003-01-01

No thematic map is perfect. Some pixels or polygons are not accurately classified, no matter how well the map is crafted. Therefore, thematic maps need metadata that sufficiently characterize the nature and degree of these imperfections. To decision-makers, an accuracy assessment helps judge the risks of using imperfect geospatial data. To analysts, an accuracy...

Development and evaluation of a digital dental modeling method based on grating projection and reverse engineering software.

PubMed

Zhou, Qin; Wang, Zhenzhen; Chen, Jun; Song, Jun; Chen, Lu; Lu, Yi

2016-01-01

For reasons of convenience and economy, attempts have been made to transform traditional dental gypsum casts into 3-dimensional (3D) digital casts. Different scanning devices have been developed to generate digital casts; however, each has its own limitations and disadvantages. The purpose of this study was to develop an advanced method for the 3D reproduction of dental casts by using a high-speed grating projection system and noncontact reverse engineering (RE) software and to evaluate the accuracy of the method. The methods consisted of 3 main steps: the scanning and acquisition of 3D dental cast data with a high-resolution grating projection system, the reconstruction and measurement of digital casts with RE software, and the evaluation of the accuracy of this method using 20 dental gypsum casts. The common anatomic landmarks were measured directly on the gypsum casts with a Vernier caliper and on the 3D digital casts with the Geomagic software measurement tool. Data were statistically assessed with the t test. The grating projection system had a rapid scanning speed, and smooth 3D dental casts were obtained. The mean differences between the gypsum and 3D measurements were approximately 0.05 mm, and no statistically significant differences were found between the 2 methods (P>.05), except for the measurements of the incisor tooth width and maxillary arch length. A method for the 3D reconstruction of dental casts was developed by using a grating projection system and RE software. The accuracy of the casts generated using the grating projection system was comparable with that of the gypsum casts. Copyright © 2016 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013⁻2014.

PubMed

Zanella-Calzada, Laura A; Galván-Tejada, Carlos E; Chávez-Lamas, Nubia M; Rivas-Gutierrez, Jesús; Magallanes-Quintanar, Rafael; Celaya-Padilla, Jose M; Galván-Tejada, Jorge I; Gamboa-Rosales, Hamurabi

2018-06-18

Oral health represents an essential component in the quality of life of people, being a determinant factor in general health since it may affect the risk of suffering other conditions, such as chronic diseases. Oral diseases have become one of the main public health problems, where dental caries is the condition that most affects oral health worldwide, occurring in about 90% of the global population. This condition has been considered a challenge because of its high prevalence, besides being a chronic but preventable disease which can be caused depending on the consumption of certain nutritional elements interacting simultaneously with different factors, such as socioeconomic factors. Based on this problem, an analysis of a set of 189 dietary and demographic determinants is performed in this work, in order to find the relationship between these factors and the oral situation of a set of subjects. The oral situation refers to the presence and absence/restorations of caries. The methodology is performed constructing a dense artificial neural network (ANN), as a computer-aided diagnosis tool, looking for a generalized model that allows for classifying subjects. As validation, the classification model was evaluated through a statistical analysis based on a cross validation, calculating the accuracy, loss function, receiving operating characteristic (ROC) curve and area under the curve (AUC) parameters. The results obtained were statistically significant, obtaining an accuracy ≃ 0.69 and AUC values of 0.69 and 0.75. Based on these results, it is possible to conclude that the classification model developed through the deep ANN is able to classify subjects with absence of caries from subjects with presence or restorations with high accuracy, according to their demographic and dietary factors.

GAPIT: genome association and prediction integrated tool.

PubMed

Lipka, Alexander E; Tian, Feng; Wang, Qishan; Peiffer, Jason; Li, Meng; Bradbury, Peter J; Gore, Michael A; Buckler, Edward S; Zhang, Zhiwu

2012-09-15

Software programs that conduct genome-wide association studies and genomic prediction and selection need to use methodologies that maximize statistical power, provide high prediction accuracy and run in a computationally efficient manner. We developed an R package called Genome Association and Prediction Integrated Tool (GAPIT) that implements advanced statistical methods including the compressed mixed linear model (CMLM) and CMLM-based genomic prediction and selection. The GAPIT package can handle large datasets in excess of 10 000 individuals and 1 million single-nucleotide polymorphisms with minimal computational time, while providing user-friendly access and concise tables and graphs to interpret results. http://www.maizegenetics.net/GAPIT. zhiwu.zhang@cornell.edu Supplementary data are available at Bioinformatics online.

Statistical Methods for Rapid Aerothermal Analysis and Design Technology: Validation

NASA Technical Reports Server (NTRS)

DePriest, Douglas; Morgan, Carolyn

2003-01-01

The cost and safety goals for NASA s next generation of reusable launch vehicle (RLV) will require that rapid high-fidelity aerothermodynamic design tools be used early in the design cycle. To meet these requirements, it is desirable to identify adequate statistical models that quantify and improve the accuracy, extend the applicability, and enable combined analyses using existing prediction tools. The initial research work focused on establishing suitable candidate models for these purposes. The second phase is focused on assessing the performance of these models to accurately predict the heat rate for a given candidate data set. This validation work compared models and methods that may be useful in predicting the heat rate.

Climatologies at high resolution for the earth’s land surface areas

PubMed Central

Karger, Dirk Nikolaus; Conrad, Olaf; Böhner, Jürgen; Kawohl, Tobias; Kreft, Holger; Soria-Auza, Rodrigo Wilber; Zimmermann, Niklaus E.; Linder, H. Peter; Kessler, Michael

2017-01-01

High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979–2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better. PMID:28872642

Climatologies at high resolution for the earth's land surface areas

NASA Astrophysics Data System (ADS)

Karger, Dirk Nikolaus; Conrad, Olaf; Böhner, Jürgen; Kawohl, Tobias; Kreft, Holger; Soria-Auza, Rodrigo Wilber; Zimmermann, Niklaus E.; Linder, H. Peter; Kessler, Michael

2017-09-01

High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth's land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979-2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better.

Volumetric quantification of lung nodules in CT with iterative reconstruction (ASiR and MBIR).

PubMed

Chen, Baiyu; Barnhart, Huiman; Richard, Samuel; Robins, Marthony; Colsher, James; Samei, Ehsan

2013-11-01

Volume quantifications of lung nodules with multidetector computed tomography (CT) images provide useful information for monitoring nodule developments. The accuracy and precision of the volume quantification, however, can be impacted by imaging and reconstruction parameters. This study aimed to investigate the impact of iterative reconstruction algorithms on the accuracy and precision of volume quantification with dose and slice thickness as additional variables. Repeated CT images were acquired from an anthropomorphic chest phantom with synthetic nodules (9.5 and 4.8 mm) at six dose levels, and reconstructed with three reconstruction algorithms [filtered backprojection (FBP), adaptive statistical iterative reconstruction (ASiR), and model based iterative reconstruction (MBIR)] into three slice thicknesses. The nodule volumes were measured with two clinical software (A: Lung VCAR, B: iNtuition), and analyzed for accuracy and precision. Precision was found to be generally comparable between FBP and iterative reconstruction with no statistically significant difference noted for different dose levels, slice thickness, and segmentation software. Accuracy was found to be more variable. For large nodules, the accuracy was significantly different between ASiR and FBP for all slice thicknesses with both software, and significantly different between MBIR and FBP for 0.625 mm slice thickness with Software A and for all slice thicknesses with Software B. For small nodules, the accuracy was more similar between FBP and iterative reconstruction, with the exception of ASIR vs FBP at 1.25 mm with Software A and MBIR vs FBP at 0.625 mm with Software A. The systematic difference between the accuracy of FBP and iterative reconstructions highlights the importance of extending current segmentation software to accommodate the image characteristics of iterative reconstructions. In addition, a calibration process may help reduce the dependency of accuracy on reconstruction algorithms, such that volumes quantified from scans of different reconstruction algorithms can be compared. The little difference found between the precision of FBP and iterative reconstructions could be a result of both iterative reconstruction's diminished noise reduction at the edge of the nodules as well as the loss of resolution at high noise levels with iterative reconstruction. The findings do not rule out potential advantage of IR that might be evident in a study that uses a larger number of nodules or repeated scans.

UWB pulse detection and TOA estimation using GLRT

NASA Astrophysics Data System (ADS)

Xie, Yan; Janssen, Gerard J. M.; Shakeri, Siavash; Tiberius, Christiaan C. J. M.

2017-12-01

In this paper, a novel statistical approach is presented for time-of-arrival (TOA) estimation based on first path (FP) pulse detection using a sub-Nyquist sampling ultra-wide band (UWB) receiver. The TOA measurement accuracy, which cannot be improved by averaging of the received signal, can be enhanced by the statistical processing of a number of TOA measurements. The TOA statistics are modeled and analyzed for a UWB receiver using threshold crossing detection of a pulse signal with noise. The detection and estimation scheme based on the Generalized Likelihood Ratio Test (GLRT) detector, which captures the full statistical information of the measurement data, is shown to achieve accurate TOA estimation and allows for a trade-off between the threshold level, the noise level, the amplitude and the arrival time of the first path pulse, and the accuracy of the obtained final TOA.

Mapping seabed sediments: Comparison of manual, geostatistical, object-based image analysis and machine learning approaches

NASA Astrophysics Data System (ADS)

Diesing, Markus; Green, Sophie L.; Stephens, David; Lark, R. Murray; Stewart, Heather A.; Dove, Dayton

2014-08-01

Marine spatial planning and conservation need underpinning with sufficiently detailed and accurate seabed substrate and habitat maps. Although multibeam echosounders enable us to map the seabed with high resolution and spatial accuracy, there is still a lack of fit-for-purpose seabed maps. This is due to the high costs involved in carrying out systematic seabed mapping programmes and the fact that the development of validated, repeatable, quantitative and objective methods of swath acoustic data interpretation is still in its infancy. We compared a wide spectrum of approaches including manual interpretation, geostatistics, object-based image analysis and machine-learning to gain further insights into the accuracy and comparability of acoustic data interpretation approaches based on multibeam echosounder data (bathymetry, backscatter and derivatives) and seabed samples with the aim to derive seabed substrate maps. Sample data were split into a training and validation data set to allow us to carry out an accuracy assessment. Overall thematic classification accuracy ranged from 67% to 76% and Cohen's kappa varied between 0.34 and 0.52. However, these differences were not statistically significant at the 5% level. Misclassifications were mainly associated with uncommon classes, which were rarely sampled. Map outputs were between 68% and 87% identical. To improve classification accuracy in seabed mapping, we suggest that more studies on the effects of factors affecting the classification performance as well as comparative studies testing the performance of different approaches need to be carried out with a view to developing guidelines for selecting an appropriate method for a given dataset. In the meantime, classification accuracy might be improved by combining different techniques to hybrid approaches and multi-method ensembles.

THE MIRA–TITAN UNIVERSE: PRECISION PREDICTIONS FOR DARK ENERGY SURVEYS

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

Heitmann, Katrin; Habib, Salman; Biswas, Rahul

2016-04-01

Large-scale simulations of cosmic structure formation play an important role in interpreting cosmological observations at high precision. The simulations must cover a parameter range beyond the standard six cosmological parameters and need to be run at high mass and force resolution. A key simulation-based task is the generation of accurate theoretical predictions for observables using a finite number of simulation runs, via the method of emulation. Using a new sampling technique, we explore an eight-dimensional parameter space including massive neutrinos and a variable equation of state of dark energy. We construct trial emulators using two surrogate models (the linear powermore » spectrum and an approximate halo mass function). The new sampling method allows us to build precision emulators from just 26 cosmological models and to systematically increase the emulator accuracy by adding new sets of simulations in a prescribed way. Emulator fidelity can now be continuously improved as new observational data sets become available and higher accuracy is required. Finally, using one ΛCDM cosmology as an example, we study the demands imposed on a simulation campaign to achieve the required statistics and accuracy when building emulators for investigations of dark energy.« less

The mira-titan universe. Precision predictions for dark energy surveys

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

Heitmann, Katrin; Bingham, Derek; Lawrence, Earl

2016-03-28

Large-scale simulations of cosmic structure formation play an important role in interpreting cosmological observations at high precision. The simulations must cover a parameter range beyond the standard six cosmological parameters and need to be run at high mass and force resolution. A key simulation-based task is the generation of accurate theoretical predictions for observables using a finite number of simulation runs, via the method of emulation. Using a new sampling technique, we explore an eight-dimensional parameter space including massive neutrinos and a variable equation of state of dark energy. We construct trial emulators using two surrogate models (the linear powermore » spectrum and an approximate halo mass function). The new sampling method allows us to build precision emulators from just 26 cosmological models and to systematically increase the emulator accuracy by adding new sets of simulations in a prescribed way. Emulator fidelity can now be continuously improved as new observational data sets become available and higher accuracy is required. Finally, using one ΛCDM cosmology as an example, we study the demands imposed on a simulation campaign to achieve the required statistics and accuracy when building emulators for investigations of dark energy.« less

Application of magnetic resonance imaging in diagnosis of Uterus Cervical Carcinoma.

PubMed

Peng, Jidong; Wang, Weiqiang; Zeng, Daohui

2017-01-01

Effective treatment of Uterus Cervical Carcinoma (UCC) rely heavily on the precise pre-surgical staging. The conventional International Federation of Gynecology and Obstetrics (FIGO) system based on clinical examination is being applied worldwide for UCC staging. Yet its performance just appears passable. Thus, this study aims to investigate the value of applying Magnetic Resonance Imaging (MRI) with clinical examination in staging of UCC. A retrospective dataset involving 164 patients diagnosed with UCC was enrolled in this study. The mean age of this study population was 46.1 years (range, 28-#x2013;75 years). All patients underwent operations and UCC types were confirmed by pathological examinations. The tumor stages were determined by two experienced Gynecologist independently based on FIGO examinations and MRI. The diagnostic results were also compared with the post-operative pathologic reports. Statistical data analysis on diagnostic performance was then done and reported. The study results showed that the overall accuracy of applying MRI in UCC staging was 82.32%, while using FIGO staging method, the staging accuracy was 59.15%. MRI is suitable to evaluate tumor extent with high accuracy, and it can offer more objective information for the diagnosis and staging of UCC. Compared with clinical examinations based on FIGO, MRI illustrated relatively high accuracy in evaluating UCC staging, and is worthwhile to be recommended in future clinical practice.

Filter Tuning Using the Chi-Squared Statistic

NASA Technical Reports Server (NTRS)

Lilly-Salkowski, Tyler

2017-01-01

The Goddard Space Flight Center (GSFC) Flight Dynamics Facility (FDF) performs orbit determination (OD) for the Aqua and Aura satellites. Both satellites are located in low Earth orbit (LEO), and are part of what is considered the A-Train satellite constellation. Both spacecraft are currently in the science phase of their respective missions. The FDF has recently been tasked with delivering definitive covariance for each satellite.The main source of orbit determination used for these missions is the Orbit Determination Toolkit developed by Analytical Graphics Inc. (AGI). This software uses an Extended Kalman Filter (EKF) to estimate the states of both spacecraft. The filter incorporates force modelling, ground station and space network measurements to determine spacecraft states. It also generates a covariance at each measurement. This covariance can be useful for evaluating the overall performance of the tracking data measurements and the filter itself. An accurate covariance is also useful for covariance propagation which is utilized in collision avoidance operations. It is also valuable when attempting to determine if the current orbital solution will meet mission requirements in the future.This paper examines the use of the Chi-square statistic as a means of evaluating filter performance. The Chi-square statistic is calculated to determine the realism of a covariance based on the prediction accuracy and the covariance values at a given point in time. Once calculated, it is the distribution of this statistic that provides insight on the accuracy of the covariance.For the EKF to correctly calculate the covariance, error models associated with tracking data measurements must be accurately tuned. Over estimating or under estimating these error values can have detrimental effects on the overall filter performance. The filter incorporates ground station measurements, which can be tuned based on the accuracy of the individual ground stations. It also includes measurements from the NASA space network (SN), which can be affected by the assumed accuracy of the TDRS satellite state at the time of the measurement.The force modelling in the EKF is also an important factor that affects the propagation accuracy and covariance sizing. The dominant force in the LEO orbit regime is the drag force caused by atmospheric drag. Accurate accounting of the drag force is especially important for the accuracy of the propagated state. The implementation of a box and wing model to improve drag estimation accuracy, and its overall effect on the covariance state is explored.The process of tuning the EKF for Aqua and Aura support is described, including examination of the measurement errors of available observation types (Doppler and range), and methods of dealing with potentially volatile atmospheric drag modeling. Predictive accuracy and the distribution of the Chi-square statistic, calculated based of the ODTK EKF solutions, are assessed versus accepted norms for the orbit regime.

Mapping irrigated lands at 250-m scale by merging MODIS data and National Agricultural Statistics

USGS Publications Warehouse

Pervez, Md Shahriar; Brown, Jesslyn F.

2010-01-01

Accurate geospatial information on the extent of irrigated land improves our understanding of agricultural water use, local land surface processes, conservation or depletion of water resources, and components of the hydrologic budget. We have developed a method in a geospatial modeling framework that assimilates irrigation statistics with remotely sensed parameters describing vegetation growth conditions in areas with agricultural land cover to spatially identify irrigated lands at 250-m cell size across the conterminous United States for 2002. The geospatial model result, known as the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset (MIrAD-US), identified irrigated lands with reasonable accuracy in California and semiarid Great Plains states with overall accuracies of 92% and 75% and kappa statistics of 0.75 and 0.51, respectively. A quantitative accuracy assessment of MIrAD-US for the eastern region has not yet been conducted, and qualitative assessment shows that model improvements are needed for the humid eastern regions where the distinction in annual peak NDVI between irrigated and non-irrigated crops is minimal and county sizes are relatively small. This modeling approach enables consistent mapping of irrigated lands based upon USDA irrigation statistics and should lead to better understanding of spatial trends in irrigated lands across the conterminous United States. An improved version of the model with revised datasets is planned and will employ 2007 USDA irrigation statistics.

Impact of genotyping errors on statistical power of association tests in genomic analyses: A case study

PubMed Central

Hou, Lin; Sun, Ning; Mane, Shrikant; Sayward, Fred; Rajeevan, Nallakkandi; Cheung, Kei-Hoi; Cho, Kelly; Pyarajan, Saiju; Aslan, Mihaela; Miller, Perry; Harvey, Philip D.; Gaziano, J. Michael; Concato, John; Zhao, Hongyu

2017-01-01

A key step in genomic studies is to assess high throughput measurements across millions of markers for each participant’s DNA, either using microarrays or sequencing techniques. Accurate genotype calling is essential for downstream statistical analysis of genotype-phenotype associations, and next generation sequencing (NGS) has recently become a more common approach in genomic studies. How the accuracy of variant calling in NGS-based studies affects downstream association analysis has not, however, been studied using empirical data in which both microarrays and NGS were available. In this article, we investigate the impact of variant calling errors on the statistical power to identify associations between single nucleotides and disease, and on associations between multiple rare variants and disease. Both differential and nondifferential genotyping errors are considered. Our results show that the power of burden tests for rare variants is strongly influenced by the specificity in variant calling, but is rather robust with regard to sensitivity. By using the variant calling accuracies estimated from a substudy of a Cooperative Studies Program project conducted by the Department of Veterans Affairs, we show that the power of association tests is mostly retained with commonly adopted variant calling pipelines. An R package, GWAS.PC, is provided to accommodate power analysis that takes account of genotyping errors (http://zhaocenter.org/software/). PMID:28019059

77 FR 10695 - Federal Housing Administration (FHA) Risk Management Initiatives: Revised Seller Concessions

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-23

... in Tables A and B. Table D--Borrower Closing Costs and Seller Concessions Descriptive Statistics by... accuracy of the statistical data illustrating the correlation between higher seller concessions and an...

Leuconostoc mesenteroides growth in food products: prediction and sensitivity analysis by adaptive-network-based fuzzy inference systems.

PubMed

Wang, Hue-Yu; Wen, Ching-Feng; Chiu, Yu-Hsien; Lee, I-Nong; Kao, Hao-Yun; Lee, I-Chen; Ho, Wen-Hsien

2013-01-01

An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. THE ANFIS AND ANN MODELS WERE COMPARED IN TERMS OF SIX STATISTICAL INDICES CALCULATED BY COMPARING THEIR PREDICTION RESULTS WITH ACTUAL DATA: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R (2)). Graphical plots were also used for model comparison. The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions.

Leuconostoc Mesenteroides Growth in Food Products: Prediction and Sensitivity Analysis by Adaptive-Network-Based Fuzzy Inference Systems

PubMed Central

Wang, Hue-Yu; Wen, Ching-Feng; Chiu, Yu-Hsien; Lee, I-Nong; Kao, Hao-Yun; Lee, I-Chen; Ho, Wen-Hsien

2013-01-01

Background An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. Methods The ANFIS and ANN models were compared in terms of six statistical indices calculated by comparing their prediction results with actual data: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R 2). Graphical plots were also used for model comparison. Conclusions The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. PMID:23705023

Improving accuracy of clinical coding in surgery: collaboration is key.

PubMed

Heywood, Nick A; Gill, Michael D; Charlwood, Natasha; Brindle, Rachel; Kirwan, Cliona C

2016-08-01

Clinical coding data provide the basis for Hospital Episode Statistics and Healthcare Resource Group codes. High accuracy of this information is required for payment by results, allocation of health and research resources, and public health data and planning. We sought to identify the level of accuracy of clinical coding in general surgical admissions across hospitals in the Northwest of England. Clinical coding departments identified a total of 208 emergency general surgical patients discharged between 1st March and 15th August 2013 from seven hospital trusts (median = 20, range = 16-60). Blinded re-coding was performed by a senior clinical coder and clinician, with results compared with the original coding outcome. Recorded codes were generated from OPCS-4 & ICD-10. Of all cases, 194 of 208 (93.3%) had at least one coding error and 9 of 208 (4.3%) had errors in both primary diagnosis and primary procedure. Errors were found in 64 of 208 (30.8%) of primary diagnoses and 30 of 137 (21.9%) of primary procedure codes. Median tariff using original codes was £1411.50 (range, £409-9138). Re-calculation using updated clinical codes showed a median tariff of £1387.50, P = 0.997 (range, £406-10,102). The most frequent reasons for incorrect coding were "coder error" and a requirement for "clinical interpretation of notes". Errors in clinical coding are multifactorial and have significant impact on primary diagnosis, potentially affecting the accuracy of Hospital Episode Statistics data and in turn the allocation of health care resources and public health planning. As we move toward surgeon specific outcomes, surgeons should increase collaboration with coding departments to ensure the system is robust. Copyright © 2016 Elsevier Inc. All rights reserved.

Predictors of Mortality in the Critically Ill Cirrhotic Patient: Is the Model for End-Stage Liver Disease Enough?

PubMed

Annamalai, Alagappan; Harada, Megan Y; Chen, Melissa; Tran, Tram; Ko, Ara; Ley, Eric J; Nuno, Miriam; Klein, Andrew; Nissen, Nicholas; Noureddin, Mazen

2017-03-01

Critically ill cirrhotics require liver transplantation urgently, but are at high risk for perioperative mortality. The Model for End-stage Liver Disease (MELD) score, recently updated to incorporate serum sodium, estimates survival probability in patients with cirrhosis, but needs additional evaluation in the critically ill. The purpose of this study was to evaluate the predictive power of ICU admission MELD scores and identify clinical risk factors associated with increased mortality. This was a retrospective review of cirrhotic patients admitted to the ICU between January 2011 and December 2014. Patients who were discharged or underwent transplantation (survivors) were compared with those who died (nonsurvivors). Demographic characteristics, admission MELD scores, and clinical risk factors were recorded. Multivariate regression was used to identify independent predictors of mortality, and measures of model performance were assessed to determine predictive accuracy. Of 276 patients who met inclusion criteria, 153 were considered survivors and 123 were nonsurvivors. Survivor and nonsurvivor cohorts had similar demographic characteristics. Nonsurvivors had increased MELD, gastrointestinal bleeding, infection, mechanical ventilation, encephalopathy, vasopressors, dialysis, renal replacement therapy, requirement of blood products, and ICU length of stay. The MELD demonstrated low predictive power (c-statistic 0.73). Multivariate analysis identified MELD score (adjusted odds ratio [AOR] = 1.05), mechanical ventilation (AOR = 4.55), vasopressors (AOR = 3.87), and continuous renal replacement therapy (AOR = 2.43) as independent predictors of mortality, with stronger predictive accuracy (c-statistic 0.87). The MELD demonstrated relatively poor predictive accuracy in critically ill patients with cirrhosis and might not be the best indicator for prognosis in the ICU population. Prognostic accuracy is significantly improved when variables indicating organ support (mechanical ventilation, vasopressors, and continuous renal replacement therapy) are included in the model. Copyright © 2016. Published by Elsevier Inc.

Comparison of Pelvic Phased-Array versus Endorectal Coil Magnetic Resonance Imaging at 3 Tesla for Local Staging of Prostate Cancer

PubMed Central

Kim, Bum Soo; Kim, Tae-Hwan; Kwon, Tae Gyun

2012-01-01

Purpose Several studies have demonstrated the superiority of endorectal coil magnetic resonance imaging (MRI) over pelvic phased-array coil MRI at 1.5 Tesla for local staging of prostate cancer. However, few have studied which evaluation is more accurate at 3 Tesla MRI. In this study, we compared the accuracy of local staging of prostate cancer using pelvic phased-array coil or endorectal coil MRI at 3 Tesla. Materials and Methods Between January 2005 and May 2010, 151 patients underwent radical prostatectomy. All patients were evaluated with either pelvic phased-array coil or endorectal coil prostate MRI prior to surgery (63 endorectal coils and 88 pelvic phased-array coils). Tumor stage based on MRI was compared with pathologic stage. We calculated the specificity, sensitivity and accuracy of each group in the evaluation of extracapsular extension and seminal vesicle invasion. Results Both endorectal coil and pelvic phased-array coil MRI achieved high specificity, low sensitivity and moderate accuracy for the detection of extracapsular extension and seminal vesicle invasion. There were statistically no differences in specificity, sensitivity and accuracy between the two groups. Conclusion Overall staging accuracy, sensitivity and specificity were not significantly different between endorectal coil and pelvic phased-array coil MRI. PMID:22476999

Diagnostic accuracy of liver fibrosis based on red cell distribution width (RDW) to platelet ratio with fibroscan in chronic hepatitis B

NASA Astrophysics Data System (ADS)

Sembiring, J.; Jones, F.

2018-03-01

Red cell Distribution Width (RDW) and platelet ratio (RPR) can predict liver fibrosis and cirrhosis in chronic hepatitis B with relatively high accuracy. RPR was superior to other non-invasive methods to predict liver fibrosis, such as AST and ALT ratio, AST and platelet ratio Index and FIB-4. The aim of this study was to assess diagnostic accuracy liver fibrosis by using RDW and platelets ratio in chronic hepatitis B patients based on compared with Fibroscan. This cross-sectional study was conducted at Adam Malik Hospital from January-June 2015. We examine 34 patients hepatitis B chronic, screen RDW, platelet, and fibroscan. Data were statistically analyzed. The result RPR with ROC procedure has an accuracy of 72.3% (95% CI: 84.1% - 97%). In this study, the RPR had a moderate ability to predict fibrosis degree (p = 0.029 with AUC> 70%). The cutoff value RPR was 0.0591, sensitivity and spesificity were 71.4% and 60%, Positive Prediction Value (PPV) was 55.6% and Negative Predictions Value (NPV) was 75%, positive likelihood ratio was 1.79 and negative likelihood ratio was 0.48. RPR have the ability to predict the degree of liver fibrosis in chronic hepatitis B patients with moderate accuracy.

Urban Land Cover Mapping Accuracy Assessment - A Cost-benefit Analysis Approach

NASA Astrophysics Data System (ADS)

Xiao, T.

2012-12-01

One of the most important components in urban land cover mapping is mapping accuracy assessment. Many statistical models have been developed to help design simple schemes based on both accuracy and confidence levels. It is intuitive that an increased number of samples increases the accuracy as well as the cost of an assessment. Understanding cost and sampling size is crucial in implementing efficient and effective of field data collection. Few studies have included a cost calculation component as part of the assessment. In this study, a cost-benefit sampling analysis model was created by combining sample size design and sampling cost calculation. The sampling cost included transportation cost, field data collection cost, and laboratory data analysis cost. Simple Random Sampling (SRS) and Modified Systematic Sampling (MSS) methods were used to design sample locations and to extract land cover data in ArcGIS. High resolution land cover data layers of Denver, CO and Sacramento, CA, street networks, and parcel GIS data layers were used in this study to test and verify the model. The relationship between the cost and accuracy was used to determine the effectiveness of each sample method. The results of this study can be applied to other environmental studies that require spatial sampling.

Automatic threshold optimization in nonlinear energy operator based spike detection.

PubMed

Malik, Muhammad H; Saeed, Maryam; Kamboh, Awais M

2016-08-01

In neural spike sorting systems, the performance of the spike detector has to be maximized because it affects the performance of all subsequent blocks. Non-linear energy operator (NEO), is a popular spike detector due to its detection accuracy and its hardware friendly architecture. However, it involves a thresholding stage, whose value is usually approximated and is thus not optimal. This approximation deteriorates the performance in real-time systems where signal to noise ratio (SNR) estimation is a challenge, especially at lower SNRs. In this paper, we propose an automatic and robust threshold calculation method using an empirical gradient technique. The method is tested on two different datasets. The results show that our optimized threshold improves the detection accuracy in both high SNR and low SNR signals. Boxplots are presented that provide a statistical analysis of improvements in accuracy, for instance, the 75th percentile was at 98.7% and 93.5% for the optimized NEO threshold and traditional NEO threshold, respectively.

Detection of neovascularization based on fractal and texture analysis with interaction effects in diabetic retinopathy.

PubMed

Lee, Jack; Zee, Benny Chung Ying; Li, Qing

2013-01-01

Diabetic retinopathy is a major cause of blindness. Proliferative diabetic retinopathy is a result of severe vascular complication and is visible as neovascularization of the retina. Automatic detection of such new vessels would be useful for the severity grading of diabetic retinopathy, and it is an important part of screening process to identify those who may require immediate treatment for their diabetic retinopathy. We proposed a novel new vessels detection method including statistical texture analysis (STA), high order spectrum analysis (HOS), fractal analysis (FA), and most importantly we have shown that by incorporating their associated interactions the accuracy of new vessels detection can be greatly improved. To assess its performance, the sensitivity, specificity and accuracy (AUC) are obtained. They are 96.3%, 99.1% and 98.5% (99.3%), respectively. It is found that the proposed method can improve the accuracy of new vessels detection significantly over previous methods. The algorithm can be automated and is valuable to detect relatively severe cases of diabetic retinopathy among diabetes patients.

Can laboratory findings on eyewitness testimony be generalized to the real world? An archival analysis of the influence of violence, weapon presence, and age on eyewitness accuracy.

PubMed

Wagstaff, Graham F; MacVeigh, Jo; Boston, Richard; Scott, Lisa; Brunas-Wagstaff, Jo; Cole, Jon

2003-01-01

The authors conducted 2 studies to assess the effects of levels of violence, the presence of a weapon, and the age of the witness on the accuracy of eyewitness testimony in real-life crime situations. Descriptions of offenders were taken from eyewitnesses' statements obtained by the police and were compared with the actual details of the same offenders obtained on arrest. The results showed that eyewitnesses tended to recall the offenders' hairstyle and hair color most accurately. None of the effects for the level of violence, the presence of a weapon, or age approached statistical significance, with the exception that, in the 1st study, accuracy in describing hair color was better when associated with high levels of violence and in cases of rape. It is argued that caution must be exercised in generalizing from laboratory studies of eyewitness testimony to actual crime situations.

Sensing multiple ligands with single receptor

NASA Astrophysics Data System (ADS)

Singh, Vijay; Nemenman, Ilya

2015-03-01

Cells use surface receptors to measure concentrations of external ligand molecules. Limits on the accuracy of such sensing are well-known for the scenario where concentration of one molecular species is being determined by one receptor [Endres]. However, in more realistic scenarios, a cognate (high-affinity) ligand competes with many non-cognate (low-affinity) ligands for binding to the receptor. We analyze effects of this competition on the accuracy of sensing. We show that maximum-likelihood statistical inference allows determination of concentrations of multiple ligands, cognate and non-cognate, by the same receptor concurrently. While it is unclear if traditional biochemical circuitry downstream of the receptor can implement such inference exactly, we show that an approximate inference can be performed by coupling the receptor to a kinetic proofreading cascade. We characterize the accuracy of such kinetic proofreading sensing in comparison to the exact maximum-likelihood approach. We acknowledge the support from the James S. McDonnell Foundation and the Human Frontier Science Program.

A comparison between Bayes discriminant analysis and logistic regression for prediction of debris flow in southwest Sichuan, China

NASA Astrophysics Data System (ADS)

Xu, Wenbo; Jing, Shaocai; Yu, Wenjuan; Wang, Zhaoxian; Zhang, Guoping; Huang, Jianxi

2013-11-01

In this study, the high risk areas of Sichuan Province with debris flow, Panzhihua and Liangshan Yi Autonomous Prefecture, were taken as the studied areas. By using rainfall and environmental factors as the predictors and based on the different prior probability combinations of debris flows, the prediction of debris flows was compared in the areas with statistical methods: logistic regression (LR) and Bayes discriminant analysis (BDA). The results through the comprehensive analysis show that (a) with the mid-range scale prior probability, the overall predicting accuracy of BDA is higher than those of LR; (b) with equal and extreme prior probabilities, the overall predicting accuracy of LR is higher than those of BDA; (c) the regional predicting models of debris flows with rainfall factors only have worse performance than those introduced environmental factors, and the predicting accuracies of occurrence and nonoccurrence of debris flows have been changed in the opposite direction as the supplemented information.

Comparison of marginal accuracy of castings fabricated by conventional casting technique and accelerated casting technique: an in vitro study.

PubMed

Reddy, S Srikanth; Revathi, Kakkirala; Reddy, S Kranthikumar

2013-01-01

Conventional casting technique is time consuming when compared to accelerated casting technique. In this study, marginal accuracy of castings fabricated using accelerated and conventional casting technique was compared. 20 wax patterns were fabricated and the marginal discrepancy between the die and patterns were measured using Optical stereomicroscope. Ten wax patterns were used for Conventional casting and the rest for Accelerated casting. A Nickel-Chromium alloy was used for the casting. The castings were measured for marginal discrepancies and compared. Castings fabricated using Conventional casting technique showed less vertical marginal discrepancy than the castings fabricated by Accelerated casting technique. The values were statistically highly significant. Conventional casting technique produced better marginal accuracy when compared to Accelerated casting. The vertical marginal discrepancy produced by the Accelerated casting technique was well within the maximum clinical tolerance limits. Accelerated casting technique can be used to save lab time to fabricate clinical crowns with acceptable vertical marginal discrepancy.

Post-mortem prediction of primal and selected retail cut weights of New Zealand lamb from carcass and animal characteristics.

PubMed

Ngo, L; Ho, H; Hunter, P; Quinn, K; Thomson, A; Pearson, G

2016-02-01

Post-mortem measurements (cold weight, grade and external carcass linear dimensions) as well as live animal data (age, breed, sex) were used to predict ovine primal and retail cut weights for 792 lamb carcases. Significant levels of variance could be explained using these predictors. The predictive power of those measurements on primal and retail cut weights was studied by using the results from principal component analysis and the absolute value of the t-statistics of the linear regression model. High prediction accuracy for primal cut weight was achieved (adjusted R(2) up to 0.95), as well as moderate accuracy for key retail cut weight: tenderloins (adj-R(2)=0.60), loin (adj-R(2)=0.62), French rack (adj-R(2)=0.76) and rump (adj-R(2)=0.75). The carcass cold weight had the best predictive power, with the accuracy increasing by around 10% after including the next three most significant variables. Copyright © 2015 Elsevier Ltd. All rights reserved.

Quantitative differentiation of breast lesions at 3T diffusion-weighted imaging (DWI) using the ratio of distributed diffusion coefficient (DDC).

PubMed

Ertas, Gokhan; Onaygil, Can; Akin, Yasin; Kaya, Handan; Aribal, Erkin

2016-12-01

To investigate the accuracy of diffusion coefficients and diffusion coefficient ratios of breast lesions and of glandular breast tissue from mono- and stretched-exponential models for quantitative diagnosis in diffusion-weighted magnetic resonance imaging (MRI). We analyzed pathologically confirmed 170 lesions (85 benign and 85 malignant) imaged using a 3.0T MR scanner. Small regions of interest (ROIs) focusing on the highest signal intensity for lesions and also for glandular tissue of contralateral breast were obtained. Apparent diffusion coefficient (ADC) and distributed diffusion coefficient (DDC) were estimated by performing nonlinear fittings using mono- and stretched-exponential models, respectively. Coefficient ratios were calculated by dividing the lesion coefficient by the glandular tissue coefficient. A stretched exponential model provides significantly better fits then the monoexponential model (P < 0.001): 65% of the better fits for glandular tissue and 71% of the better fits for lesion. High correlation was found in diffusion coefficients (0.99-0.81 and coefficient ratios (0.94) between the models. The highest diagnostic accuracy was found by the DDC ratio (area under the curve [AUC] = 0.93) when compared with lesion DDC, ADC ratio, and lesion ADC (AUC = 0.91, 0.90, 0.90) but with no statistically significant difference (P > 0.05). At optimal thresholds, the DDC ratio achieves 93% sensitivity, 80% specificity, and 87% overall diagnostic accuracy, while ADC ratio leads to 89% sensitivity, 78% specificity, and 83% overall diagnostic accuracy. The stretched exponential model fits better with signal intensity measurements from both lesion and glandular tissue ROIs. Although the DDC ratio estimated by using the model shows a higher diagnostic accuracy than the ADC ratio, lesion DDC, and ADC, it is not statistically significant. J. Magn. Reson. Imaging 2016;44:1633-1641. © 2016 International Society for Magnetic Resonance in Medicine.

Object-based random forest classification of Landsat ETM+ and WorldView-2 satellite imagery for mapping lowland native grassland communities in Tasmania, Australia

NASA Astrophysics Data System (ADS)

Melville, Bethany; Lucieer, Arko; Aryal, Jagannath

2018-04-01

This paper presents a random forest classification approach for identifying and mapping three types of lowland native grassland communities found in the Tasmanian Midlands region. Due to the high conservation priority assigned to these communities, there has been an increasing need to identify appropriate datasets that can be used to derive accurate and frequently updateable maps of community extent. Therefore, this paper proposes a method employing repeat classification and statistical significance testing as a means of identifying the most appropriate dataset for mapping these communities. Two datasets were acquired and analysed; a Landsat ETM+ scene, and a WorldView-2 scene, both from 2010. Training and validation data were randomly subset using a k-fold (k = 50) approach from a pre-existing field dataset. Poa labillardierei, Themeda triandra and lowland native grassland complex communities were identified in addition to dry woodland and agriculture. For each subset of randomly allocated points, a random forest model was trained based on each dataset, and then used to classify the corresponding imagery. Validation was performed using the reciprocal points from the independent subset that had not been used to train the model. Final training and classification accuracies were reported as per class means for each satellite dataset. Analysis of Variance (ANOVA) was undertaken to determine whether classification accuracy differed between the two datasets, as well as between classifications. Results showed mean class accuracies between 54% and 87%. Class accuracy only differed significantly between datasets for the dry woodland and Themeda grassland classes, with the WorldView-2 dataset showing higher mean classification accuracies. The results of this study indicate that remote sensing is a viable method for the identification of lowland native grassland communities in the Tasmanian Midlands, and that repeat classification and statistical significant testing can be used to identify optimal datasets for vegetation community mapping.

LP-search and its use in analysis of the accuracy of control systems with acoustical models

NASA Technical Reports Server (NTRS)

Sergeyev, V. I.; Sobol, I. M.; Statnikov, R. B.; Statnikov, I. N.

1973-01-01

The LP-search is proposed as an analog of the Monte Carlo method for finding values in nonlinear statistical systems. It is concluded that: To attain the required accuracy in solution to the problem of control for a statistical system in the LP-search, a considerably smaller number of tests is required than in the Monte Carlo method. The LP-search allows the possibility of multiple repetitions of tests under identical conditions and observability of the output variables of the system.

Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes.

PubMed

Nateghi, Roshanak; Guikema, Seth D; Quiring, Steven M

2011-12-01

This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy. © 2011 Society for Risk Analysis.

Sterile Basics of Compounding: Relationship Between Syringe Size and Dosing Accuracy.

PubMed

Kosinski, Tracy M; Brown, Michael C; Zavala, Pedro J

2018-01-01

The purpose of this study was to investigate the accuracy and reproducibility of a 2-mL volume injection using a 3-mL and 10-mL syringe with pharmacy student compounders. An exercise was designed to assess each student's accuracy in compounding a sterile preparation with the correct 4-mg strength using a 3-mL and 10-mL syringe. The average ondansetron dose when compounded with the 3-mL syringe was 4.03 mg (standard deviation ± 0.45 mg), which was not statistically significantly different than the intended 4-mg desired dose (P=0.497). The average ondansetron dose when compounded with the 10-mL syringe was 4.18 mg (standard deviation + 0.68 mg), which was statistically significantly different than the intended 4-mg desired dose (P=0.002). Additionally, there also was a statistically significant difference in the average ondansetron dose compounded using a 3-mL syringe (4.03 mg) and a 10-mL syringe (4.18 mg) (P=0.027). The accuracy and reproducibility of the 2-mL desired dose volume decreased as the compounding syringe size increased from 3 mL to 10 mL. Copyright© by International Journal of Pharmaceutical Compounding, Inc.

Analysis of variance to assess statistical significance of Laplacian estimation accuracy improvement due to novel variable inter-ring distances concentric ring electrodes.

PubMed

Makeyev, Oleksandr; Joe, Cody; Lee, Colin; Besio, Walter G

2017-07-01

Concentric ring electrodes have shown promise in non-invasive electrophysiological measurement demonstrating their superiority to conventional disc electrodes, in particular, in accuracy of Laplacian estimation. Recently, we have proposed novel variable inter-ring distances concentric ring electrodes. Analytic and finite element method modeling results for linearly increasing distances electrode configurations suggested they may decrease the truncation error resulting in more accurate Laplacian estimates compared to currently used constant inter-ring distances configurations. This study assesses statistical significance of Laplacian estimation accuracy improvement due to novel variable inter-ring distances concentric ring electrodes. Full factorial design of analysis of variance was used with one categorical and two numerical factors: the inter-ring distances, the electrode diameter, and the number of concentric rings in the electrode. The response variables were the Relative Error and the Maximum Error of Laplacian estimation computed using a finite element method model for each of the combinations of levels of three factors. Effects of the main factors and their interactions on Relative Error and Maximum Error were assessed and the obtained results suggest that all three factors have statistically significant effects in the model confirming the potential of using inter-ring distances as a means of improving accuracy of Laplacian estimation.

Lateral cephalometric analysis for treatment planning in orthodontics based on MRI compared with radiographs: A feasibility study in children and adolescents

PubMed Central

Lazo Gonzalez, Eduardo; Hilgenfeld, Tim; Kickingereder, Philipp; Bendszus, Martin; Heiland, Sabine; Ozga, Ann-Kathrin; Sommer, Andreas; Lux, Christopher J.; Zingler, Sebastian

2017-01-01

Objective The objective of this prospective study was to evaluate whether magnetic resonance imaging (MRI) is equivalent to lateral cephalometric radiographs (LCR, “gold standard”) in cephalometric analysis. Methods The applied MRI technique was optimized for short scanning time, high resolution, high contrast and geometric accuracy. Prior to orthodontic treatment, 20 patients (mean age ± SD, 13.95 years ± 5.34) received MRI and LCR. MRI datasets were postprocessed into lateral cephalograms. Cephalometric analysis was performed twice by two independent observers for both modalities with an interval of 4 weeks. Eight bilateral and 10 midsagittal landmarks were identified, and 24 widely used measurements (14 angles, 10 distances) were calculated. Statistical analysis was performed by using intraclass correlation coefficient (ICC), Bland-Altman analysis and two one-sided tests (TOST) within the predefined equivalence margin of ± 2°/mm. Results Geometric accuracy of the MRI technique was confirmed by phantom measurements. Mean intraobserver ICC were 0.977/0.975 for MRI and 0.975/0.961 for LCR. Average interobserver ICC were 0.980 for MRI and 0.929 for LCR. Bland-Altman analysis showed high levels of agreement between the two modalities, bias range (mean ± SD) was -0.66 to 0.61 mm (0.06 ± 0.44) for distances and -1.33 to 1.14° (0.06 ± 0.71) for angles. Except for the interincisal angle (p = 0.17) all measurements were statistically equivalent (p < 0.05). Conclusions This study demonstrates feasibility of orthodontic treatment planning without radiation exposure based on MRI. High-resolution isotropic MRI datasets can be transformed into lateral cephalograms allowing reliable measurements as applied in orthodontic routine with high concordance to the corresponding measurements on LCR. PMID:28334054

Lateral cephalometric analysis for treatment planning in orthodontics based on MRI compared with radiographs: A feasibility study in children and adolescents.

PubMed

Heil, Alexander; Lazo Gonzalez, Eduardo; Hilgenfeld, Tim; Kickingereder, Philipp; Bendszus, Martin; Heiland, Sabine; Ozga, Ann-Kathrin; Sommer, Andreas; Lux, Christopher J; Zingler, Sebastian

2017-01-01

The objective of this prospective study was to evaluate whether magnetic resonance imaging (MRI) is equivalent to lateral cephalometric radiographs (LCR, "gold standard") in cephalometric analysis. The applied MRI technique was optimized for short scanning time, high resolution, high contrast and geometric accuracy. Prior to orthodontic treatment, 20 patients (mean age ± SD, 13.95 years ± 5.34) received MRI and LCR. MRI datasets were postprocessed into lateral cephalograms. Cephalometric analysis was performed twice by two independent observers for both modalities with an interval of 4 weeks. Eight bilateral and 10 midsagittal landmarks were identified, and 24 widely used measurements (14 angles, 10 distances) were calculated. Statistical analysis was performed by using intraclass correlation coefficient (ICC), Bland-Altman analysis and two one-sided tests (TOST) within the predefined equivalence margin of ± 2°/mm. Geometric accuracy of the MRI technique was confirmed by phantom measurements. Mean intraobserver ICC were 0.977/0.975 for MRI and 0.975/0.961 for LCR. Average interobserver ICC were 0.980 for MRI and 0.929 for LCR. Bland-Altman analysis showed high levels of agreement between the two modalities, bias range (mean ± SD) was -0.66 to 0.61 mm (0.06 ± 0.44) for distances and -1.33 to 1.14° (0.06 ± 0.71) for angles. Except for the interincisal angle (p = 0.17) all measurements were statistically equivalent (p < 0.05). This study demonstrates feasibility of orthodontic treatment planning without radiation exposure based on MRI. High-resolution isotropic MRI datasets can be transformed into lateral cephalograms allowing reliable measurements as applied in orthodontic routine with high concordance to the corresponding measurements on LCR.

A very low noise, high accuracy, programmable voltage source for low frequency noise measurements.

PubMed

Scandurra, Graziella; Giusi, Gino; Ciofi, Carmine

2014-04-01

In this paper an approach for designing a programmable, very low noise, high accuracy voltage source for biasing devices under test in low frequency noise measurements is proposed. The core of the system is a supercapacitor based two pole low pass filter used for filtering out the noise produced by a standard DA converter down to 100 mHz with an attenuation in excess of 40 dB. The high leakage current of the supercapacitors, however, introduces large DC errors that need to be compensated in order to obtain high accuracy as well as very low output noise. To this end, a proper circuit topology has been developed that allows to considerably reduce the effect of the supercapacitor leakage current on the DC response of the system while maintaining a very low level of output noise. With a proper design an output noise as low as the equivalent input voltage noise of the OP27 operational amplifier, used as the output buffer of the system, can be obtained with DC accuracies better that 0.05% up to the maximum output of 8 V. The expected performances of the proposed voltage source have been confirmed both by means of SPICE simulations and by means of measurements on actual prototypes. Turn on and stabilization times for the system are of the order of a few hundred seconds. These times are fully compatible with noise measurements down to 100 mHz, since measurement times of the order of several tens of minutes are required in any case in order to reduce the statistical error in the measured spectra down to an acceptable level.

A very low noise, high accuracy, programmable voltage source for low frequency noise measurements

NASA Astrophysics Data System (ADS)

Scandurra, Graziella; Giusi, Gino; Ciofi, Carmine

2014-04-01

In this paper an approach for designing a programmable, very low noise, high accuracy voltage source for biasing devices under test in low frequency noise measurements is proposed. The core of the system is a supercapacitor based two pole low pass filter used for filtering out the noise produced by a standard DA converter down to 100 mHz with an attenuation in excess of 40 dB. The high leakage current of the supercapacitors, however, introduces large DC errors that need to be compensated in order to obtain high accuracy as well as very low output noise. To this end, a proper circuit topology has been developed that allows to considerably reduce the effect of the supercapacitor leakage current on the DC response of the system while maintaining a very low level of output noise. With a proper design an output noise as low as the equivalent input voltage noise of the OP27 operational amplifier, used as the output buffer of the system, can be obtained with DC accuracies better that 0.05% up to the maximum output of 8 V. The expected performances of the proposed voltage source have been confirmed both by means of SPICE simulations and by means of measurements on actual prototypes. Turn on and stabilization times for the system are of the order of a few hundred seconds. These times are fully compatible with noise measurements down to 100 mHz, since measurement times of the order of several tens of minutes are required in any case in order to reduce the statistical error in the measured spectra down to an acceptable level.

Testing the Potential of Vegetation Indices for Land Use/cover Classification Using High Resolution Data

NASA Astrophysics Data System (ADS)

Karakacan Kuzucu, A.; Bektas Balcik, F.

2017-11-01

Accurate and reliable land use/land cover (LULC) information obtained by remote sensing technology is necessary in many applications such as environmental monitoring, agricultural management, urban planning, hydrological applications, soil management, vegetation condition study and suitability analysis. But this information still remains a challenge especially in heterogeneous landscapes covering urban and rural areas due to spectrally similar LULC features. In parallel with technological developments, supplementary data such as satellite-derived spectral indices have begun to be used as additional bands in classification to produce data with high accuracy. The aim of this research is to test the potential of spectral vegetation indices combination with supervised classification methods and to extract reliable LULC information from SPOT 7 multispectral imagery. The Normalized Difference Vegetation Index (NDVI), the Ratio Vegetation Index (RATIO), the Soil Adjusted Vegetation Index (SAVI) were the three vegetation indices used in this study. The classical maximum likelihood classifier (MLC) and support vector machine (SVM) algorithm were applied to classify SPOT 7 image. Catalca is selected region located in the north west of the Istanbul in Turkey, which has complex landscape covering artificial surface, forest and natural area, agricultural field, quarry/mining area, pasture/scrubland and water body. Accuracy assessment of all classified images was performed through overall accuracy and kappa coefficient. The results indicated that the incorporation of these three different vegetation indices decrease the classification accuracy for the MLC and SVM classification. In addition, the maximum likelihood classification slightly outperformed the support vector machine classification approach in both overall accuracy and kappa statistics.

Determining the Statistical Power of the Kolmogorov-Smirnov and Anderson-Darling Goodness-of-Fit Tests via Monte Carlo Simulation

DTIC Science & Technology

2016-12-01

KS and AD Statistical Power via Monte Carlo Simulation Statistical power is the probability of correctly rejecting the null hypothesis when the...Select a caveat DISTRIBUTION STATEMENT A. Approved for public release: distribution unlimited. Determining the Statistical Power...real-world data to test the accuracy of the simulation. Statistical comparison of these metrics can be necessary when making such a determination

Deconstructing Statistical Analysis

ERIC Educational Resources Information Center

Snell, Joel

2014-01-01

Using a very complex statistical analysis and research method for the sake of enhancing the prestige of an article or making a new product or service legitimate needs to be monitored and questioned for accuracy. 1) The more complicated the statistical analysis, and research the fewer the number of learned readers can understand it. This adds a…

Prediction of Multiple-Trait and Multiple-Environment Genomic Data Using Recommender Systems.

PubMed

Montesinos-López, Osval A; Montesinos-López, Abelardo; Crossa, José; Montesinos-López, José C; Mota-Sanchez, David; Estrada-González, Fermín; Gillberg, Jussi; Singh, Ravi; Mondal, Suchismita; Juliana, Philomin

2018-01-04

In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: item-based collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment-trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets. Copyright © 2018 Montesinos-Lopez et al.

Prediction of Multiple-Trait and Multiple-Environment Genomic Data Using Recommender Systems

PubMed Central

Montesinos-López, Osval A.; Montesinos-López, Abelardo; Crossa, José; Montesinos-López, José C.; Mota-Sanchez, David; Estrada-González, Fermín; Gillberg, Jussi; Singh, Ravi; Mondal, Suchismita; Juliana, Philomin

2018-01-01

In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: item-based collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment–trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets. PMID:29097376

An adaptive state of charge estimation approach for lithium-ion series-connected battery system

NASA Astrophysics Data System (ADS)

Peng, Simin; Zhu, Xuelai; Xing, Yinjiao; Shi, Hongbing; Cai, Xu; Pecht, Michael

2018-07-01

Due to the incorrect or unknown noise statistics of a battery system and its cell-to-cell variations, state of charge (SOC) estimation of a lithium-ion series-connected battery system is usually inaccurate or even divergent using model-based methods, such as extended Kalman filter (EKF) and unscented Kalman filter (UKF). To resolve this problem, an adaptive unscented Kalman filter (AUKF) based on a noise statistics estimator and a model parameter regulator is developed to accurately estimate the SOC of a series-connected battery system. An equivalent circuit model is first built based on the model parameter regulator that illustrates the influence of cell-to-cell variation on the battery system. A noise statistics estimator is then used to attain adaptively the estimated noise statistics for the AUKF when its prior noise statistics are not accurate or exactly Gaussian. The accuracy and effectiveness of the SOC estimation method is validated by comparing the developed AUKF and UKF when model and measurement statistics noises are inaccurate, respectively. Compared with the UKF and EKF, the developed method shows the highest SOC estimation accuracy.

Checking the predictive accuracy of basic symptoms against ultra high-risk criteria and testing of a multivariable prediction model: Evidence from a prospective three-year observational study of persons at clinical high-risk for psychosis.

PubMed

Hengartner, M P; Heekeren, K; Dvorsky, D; Walitza, S; Rössler, W; Theodoridou, A

2017-09-01

The aim of this study was to critically examine the prognostic validity of various clinical high-risk (CHR) criteria alone and in combination with additional clinical characteristics. A total of 188 CHR positive persons from the region of Zurich, Switzerland (mean age 20.5 years; 60.2% male), meeting ultra high-risk (UHR) and/or basic symptoms (BS) criteria, were followed over three years. The test battery included the Structured Interview for Prodromal Syndromes (SIPS), verbal IQ and many other screening tools. Conversion to psychosis was defined according to ICD-10 criteria for schizophrenia (F20) or brief psychotic disorder (F23). Altogether n=24 persons developed manifest psychosis within three years and according to Kaplan-Meier survival analysis, the projected conversion rate was 17.5%. The predictive accuracy of UHR was statistically significant but poor (area under the curve [AUC]=0.65, P<.05), whereas BS did not predict psychosis beyond mere chance (AUC=0.52, P=.730). Sensitivity and specificity were 0.83 and 0.47 for UHR, and 0.96 and 0.09 for BS. UHR plus BS achieved an AUC=0.66, with sensitivity and specificity of 0.75 and 0.56. In comparison, baseline antipsychotic medication yielded a predictive accuracy of AUC=0.62 (sensitivity=0.42; specificity=0.82). A multivariable prediction model comprising continuous measures of positive symptoms and verbal IQ achieved a substantially improved prognostic accuracy (AUC=0.85; sensitivity=0.86; specificity=0.85; positive predictive value=0.54; negative predictive value=0.97). We showed that BS have no predictive accuracy beyond chance, while UHR criteria poorly predict conversion to psychosis. Combining BS with UHR criteria did not improve the predictive accuracy of UHR alone. In contrast, dimensional measures of both positive symptoms and verbal IQ showed excellent prognostic validity. A critical re-thinking of binary at-risk criteria is necessary in order to improve the prognosis of psychotic disorders. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

[Evaluation of the Performance of Two Kinds of Anti-TP Enzyme-Linked Immunosorbent Assay].

PubMed

Gao, Nan; Huang, Li-Qin; Wang, Rui; Jia, Jun-Jie; Wu, Shuo; Zhang, Jing; Ge, Hong-Wei

2018-06-01

To evaluate the accuracy and precision of 2 kinds of anti-treponema pallidum (anti-TP) ELISA reagents in our laboratory for detecting the anti-TP in voluntary blood donors, so as to provide the data support for use of ELISA reagents after introduction of chemiluminescene immunoassay (CLIA). The route detection of anti-TP was performed by using 2 kinds of ELISA reagents, then 546 responsive positive samples detected by anti-TP ELISA were collected, and the infections status of samples confirmed by treponema pallidum particle agglutination (TPPA) test was identified. The confirmed results of responsive samples detected by 2 kinds of anti-TP ELISA reagents were compared, the accuracy of 2 kinds of anti-TP ELISA reagents was analyzed by drawing ROC and comparing area under curve (AUC), and precision of 2 kinds of anti-TP ELISA reagents was compared by statistical analysis of quality control data from 7.1 2016 to 6.30 2017. There were no statistical difference in confirmed positive rate of responsive samples and weak positive samples between 2 kinds of anti-TP ELISA reagents. The responsive samples detected by 2 kinds of anti-TP ELISA reagents accounted for 85.53%(467/546) of all responsive samples, the positive rate confirmed by TPPA test was 82.87%. 44 responsive samples detected by anti-TP ELISA reagent A and 35 responsive samples detected by anti-TP ELISA reagent B were confirmed to be negative by TPPA test. Comparison of AUC showed that the accuracy of 2 kinds of anti-TP ELISA reagents was more high, the difference between 2 reagents was not statistically significant. The coefficient of variation (CV) of anti-TP ELISA reagent A and B was 14.98% and 18.04% respectively, which met the precision requirement of ELISA test. The accuracy and precision of 2 kinds of anti-TP ELISA reagents used in our laboratory are similar, and using any one of anti-TP ELISA reagents all can satisfy the requirements of blood screening.

Diagnostic Accuracy of Memory Measures in Alzheimer's Dementia and Mild Cognitive Impairment: a Systematic Review and Meta-Analysis.

PubMed

Weissberger, Gali H; Strong, Jessica V; Stefanidis, Kayla B; Summers, Mathew J; Bondi, Mark W; Stricker, Nikki H

2017-12-01

With an increasing focus on biomarkers in dementia research, illustrating the role of neuropsychological assessment in detecting mild cognitive impairment (MCI) and Alzheimer's dementia (AD) is important. This systematic review and meta-analysis, conducted in accordance with PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) standards, summarizes the sensitivity and specificity of memory measures in individuals with MCI and AD. Both meta-analytic and qualitative examination of AD versus healthy control (HC) studies (n = 47) revealed generally high sensitivity and specificity (≥ 80% for AD comparisons) for measures of immediate (sensitivity = 87%, specificity = 88%) and delayed memory (sensitivity = 89%, specificity = 89%), especially those involving word-list recall. Examination of MCI versus HC studies (n = 38) revealed generally lower diagnostic accuracy for both immediate (sensitivity = 72%, specificity = 81%) and delayed memory (sensitivity = 75%, specificity = 81%). Measures that differentiated AD from other conditions (n = 10 studies) yielded mixed results, with generally high sensitivity in the context of low or variable specificity. Results confirm that memory measures have high diagnostic accuracy for identification of AD, are promising but require further refinement for identification of MCI, and provide support for ongoing investigation of neuropsychological assessment as a cognitive biomarker of preclinical AD. Emphasizing diagnostic test accuracy statistics over null hypothesis testing in future studies will promote the ongoing use of neuropsychological tests as Alzheimer's disease research and clinical criteria increasingly rely upon cerebrospinal fluid (CSF) and neuroimaging biomarkers.

Potential accuracy of translation estimation between radar and optical images

NASA Astrophysics Data System (ADS)

Uss, M.; Vozel, B.; Lukin, V.; Chehdi, K.

2015-10-01

This paper investigates the potential accuracy achievable for optical to radar image registration by area-based approach. The analysis is carried out mainly based on the Cramér-Rao Lower Bound (CRLB) on translation estimation accuracy previously proposed by the authors and called CRLBfBm. This bound is now modified to take into account radar image speckle noise properties: spatial correlation and signal-dependency. The newly derived theoretical bound is fed with noise and texture parameters estimated for the co-registered pair of optical Landsat 8 and radar SIR-C images. It is found that difficulty of optical to radar image registration stems more from speckle noise influence than from dissimilarity of the considered kinds of images. At finer scales (and higher speckle noise level), probability of finding control fragments (CF) suitable for registration is low (1% or less) but overall number of such fragments is high thanks to image size. Conversely, at the coarse scale, where speckle noise level is reduced, probability of finding CFs suitable for registration can be as high as 40%, but overall number of such CFs is lower. Thus, the study confirms and supports area-based multiresolution approach for optical to radar registration where coarse scales are used for fast registration "lock" and finer scales for reaching higher registration accuracy. The CRLBfBm is found inaccurate for the main scale due to intensive speckle noise influence. For other scales, the validity of the CRLBfBm bound is confirmed by calculating statistical efficiency of area-based registration method based on normalized correlation coefficient (NCC) measure that takes high values of about 25%.

The better way to determine the validity, reliability, objectivity and accuracy of measuring devices.

PubMed

Pazira, Parvin; Rostami Haji-Abadi, Mahdi; Zolaktaf, Vahid; Sabahi, Mohammadfarzan; Pazira, Toomaj

2016-06-08

In relation to statistical analysis, studies to determine the validity, reliability, objectivity and precision of new measuring devices are usually incomplete, due in part to using only correlation coefficient and ignoring the data dispersion. The aim of this study was to demonstrate the best way to determine the validity, reliability, objectivity and accuracy of an electro-inclinometer or other measuring devices. Another purpose of this study is to answer the question of whether reliability and objectivity represent accuracy of measuring devices. The validity of an electro-inclinometer was examined by mechanical and geometric methods. The objectivity and reliability of the device was assessed by calculating Cronbach's alpha for repeated measurements by three raters and by measurements on the same person by mechanical goniometer and the electro-inclinometer. Measurements were performed on "hip flexion with the extended knee" and "shoulder abduction with the extended elbow." The raters measured every angle three times within an interval of two hours. The three-way ANOVA was used to determine accuracy. The results of mechanical and geometric analysis showed that validity of the electro-inclinometer was 1.00 and level of error was less than one degree. Objectivity and reliability of electro-inclinometer was 0.999, while objectivity of mechanical goniometer was in the range of 0.802 to 0.966 and the reliability was 0.760 to 0.961. For hip flexion, the difference between raters in joints angle measurement by electro-inclinometer and mechanical goniometer was 1.74 and 16.33 degree (P<0.05), respectively. The differences for shoulder abduction measurement by electro-inclinometer and goniometer were 0.35 and 4.40 degree (P<0.05). Although both the objectivity and reliability are acceptable, the results showed that measurement error was very high in the mechanical goniometer. Therefore, it can be concluded that objectivity and reliability alone cannot determine the accuracy of a device and it is preferable to use other statistical methods to compare and evaluate the accuracy of these two devices.

Upgrade Summer Severe Weather Tool

NASA Technical Reports Server (NTRS)

Watson, Leela

2011-01-01

The goal of this task was to upgrade to the existing severe weather database by adding observations from the 2010 warm season, update the verification dataset with results from the 2010 warm season, use statistical logistic regression analysis on the database and develop a new forecast tool. The AMU analyzed 7 stability parameters that showed the possibility of providing guidance in forecasting severe weather, calculated verification statistics for the Total Threat Score (TTS), and calculated warm season verification statistics for the 2010 season. The AMU also performed statistical logistic regression analysis on the 22-year severe weather database. The results indicated that the logistic regression equation did not show an increase in skill over the previously developed TTS. The equation showed less accuracy than TTS at predicting severe weather, little ability to distinguish between severe and non-severe weather days, and worse standard categorical accuracy measures and skill scores over TTS.

Enhancing predictive accuracy and reproducibility in clinical evaluation research: Commentary on the special section of the Journal of Evaluation in Clinical Practice.

PubMed

Bryant, Fred B

2016-12-01

This paper introduces a special section of the current issue of the Journal of Evaluation in Clinical Practice that includes a set of 6 empirical articles showcasing a versatile, new machine-learning statistical method, known as optimal data (or discriminant) analysis (ODA), specifically designed to produce statistical models that maximize predictive accuracy. As this set of papers clearly illustrates, ODA offers numerous important advantages over traditional statistical methods-advantages that enhance the validity and reproducibility of statistical conclusions in empirical research. This issue of the journal also includes a review of a recently published book that provides a comprehensive introduction to the logic, theory, and application of ODA in empirical research. It is argued that researchers have much to gain by using ODA to analyze their data. © 2016 John Wiley & Sons, Ltd.

Identification of natural images and computer-generated graphics based on statistical and textural features.

PubMed

Peng, Fei; Li, Jiao-ting; Long, Min

2015-03-01

To discriminate the acquisition pipelines of digital images, a novel scheme for the identification of natural images and computer-generated graphics is proposed based on statistical and textural features. First, the differences between them are investigated from the view of statistics and texture, and 31 dimensions of feature are acquired for identification. Then, LIBSVM is used for the classification. Finally, the experimental results are presented. The results show that it can achieve an identification accuracy of 97.89% for computer-generated graphics, and an identification accuracy of 97.75% for natural images. The analyses also demonstrate the proposed method has excellent performance, compared with some existing methods based only on statistical features or other features. The method has a great potential to be implemented for the identification of natural images and computer-generated graphics. © 2014 American Academy of Forensic Sciences.

Commissioning of the NPDGamma Detector Array: Counting Statistics in Current Mode Operation and Parity Violation in the Capture of Cold Neutrons on B 4 C and (27) Al.

PubMed

Gericke, M T; Bowman, J D; Carlini, R D; Chupp, T E; Coulter, K P; Dabaghyan, M; Desai, D; Freedman, S J; Gentile, T R; Gillis, R C; Greene, G L; Hersman, F W; Ino, T; Ishimoto, S; Jones, G L; Lauss, B; Leuschner, M B; Losowski, B; Mahurin, R; Masuda, Y; Mitchell, G S; Muto, S; Nann, H; Page, S A; Penttila, S I; Ramsay, W D; Santra, S; Seo, P-N; Sharapov, E I; Smith, T B; Snow, W M; Wilburn, W S; Yuan, V; Zhu, H

2005-01-01

The NPDGamma γ-ray detector has been built to measure, with high accuracy, the size of the small parity-violating asymmetry in the angular distribution of gamma rays from the capture of polarized cold neutrons by protons. The high cold neutron flux at the Los Alamos Neutron Scattering Center (LANSCE) spallation neutron source and control of systematic errors require the use of current mode detection with vacuum photodiodes and low-noise solid-state preamplifiers. We show that the detector array operates at counting statistics and that the asymmetries due to B4C and (27)Al are zero to with- in 2 × 10(-6) and 7 × 10(-7), respectively. Boron and aluminum are used throughout the experiment. The results presented here are preliminary.

Increased pulmonary alveolar-capillary permeability in patients at risk for adult respiratory distress syndrome

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

Tennenberg, S.D.; Jacobs, M.P.; Solomkin, J.S.

1987-04-01

Two methods for predicting adult respiratory distress syndrome (ARDS) were evaluated prospectively in a group of 81 multitrauma and sepsis patients considered at clinical high risk. A popular ARDS risk-scoring method, employing discriminant analysis equations (weighted risk criteria and oxygenation characteristics), yielded a predictive accuracy of 59% and a false-negative rate of 22%. Pulmonary alveolar-capillary permeability (PACP) was determined with a radioaerosol lung-scan technique in 23 of these 81 patients, representing a statistically similar subgroup. Lung scanning achieved a predictive accuracy of 71% (after excluding patients with unilateral pulmonary contusion) and gave no false-negatives. We propose a combination of clinicalmore » risk identification and functional determination of PACP to assess a patient's risk of developing ARDS.« less

Modeling of carbon dioxide condensation in the high pressure flows using the statistical BGK approach

NASA Astrophysics Data System (ADS)

Kumar, Rakesh; Li, Zheng; Levin, Deborah A.

2011-05-01

In this work, we propose a new heat accommodation model to simulate freely expanding homogeneous condensation flows of gaseous carbon dioxide using a new approach, the statistical Bhatnagar-Gross-Krook method. The motivation for the present work comes from the earlier work of Li et al. [J. Phys. Chem. 114, 5276 (2010)] in which condensation models were proposed and used in the direct simulation Monte Carlo method to simulate the flow of carbon dioxide from supersonic expansions of small nozzles into near-vacuum conditions. Simulations conducted for stagnation pressures of one and three bar were compared with the measurements of gas and cluster number densities, cluster size, and carbon dioxide rotational temperature obtained by Ramos et al. [Phys. Rev. A 72, 3204 (2005)]. Due to the high computational cost of direct simulation Monte Carlo method, comparison between simulations and data could only be performed for these stagnation pressures, with good agreement obtained beyond the condensation onset point, in the farfield. As the stagnation pressure increases, the degree of condensation also increases; therefore, to improve the modeling of condensation onset, one must be able to simulate higher stagnation pressures. In simulations of an expanding flow of argon through a nozzle, Kumar et al. [AIAA J. 48, 1531 (2010)] found that the statistical Bhatnagar-Gross-Krook method provides the same accuracy as direct simulation Monte Carlo method, but, at one half of the computational cost. In this work, the statistical Bhatnagar-Gross-Krook method was modified to account for internal degrees of freedom for multi-species polyatomic gases. With the computational approach in hand, we developed and tested a new heat accommodation model for a polyatomic system to properly account for the heat release of condensation. We then developed condensation models in the framework of the statistical Bhatnagar-Gross-Krook method. Simulations were found to agree well with the experiment for all stagnation pressure cases (1-5 bar), validating the accuracy of the Bhatnagar-Gross-Krook based condensation model in capturing the physics of condensation.

Statistical estimation of femur micro-architecture using optimal shape and density predictors.

PubMed

Lekadir, Karim; Hazrati-Marangalou, Javad; Hoogendoorn, Corné; Taylor, Zeike; van Rietbergen, Bert; Frangi, Alejandro F

2015-02-26

The personalization of trabecular micro-architecture has been recently shown to be important in patient-specific biomechanical models of the femur. However, high-resolution in vivo imaging of bone micro-architecture using existing modalities is still infeasible in practice due to the associated acquisition times, costs, and X-ray radiation exposure. In this study, we describe a statistical approach for the prediction of the femur micro-architecture based on the more easily extracted subject-specific bone shape and mineral density information. To this end, a training sample of ex vivo micro-CT images is used to learn the existing statistical relationships within the low and high resolution image data. More specifically, optimal bone shape and mineral density features are selected based on their predictive power and used within a partial least square regression model to estimate the unknown trabecular micro-architecture within the anatomical models of new subjects. The experimental results demonstrate the accuracy of the proposed approach, with average errors of 0.07 for both the degree of anisotropy and tensor norms. Copyright © 2015 Elsevier Ltd. All rights reserved.

Recognizing stationary and locomotion activities using combinational of spectral analysis with statistical descriptors features

NASA Astrophysics Data System (ADS)

Zainudin, M. N. Shah; Sulaiman, Md Nasir; Mustapha, Norwati; Perumal, Thinagaran

2017-10-01

Prior knowledge in pervasive computing recently garnered a lot of attention due to its high demand in various application domains. Human activity recognition (HAR) considered as the applications that are widely explored by the expertise that provides valuable information to the human. Accelerometer sensor-based approach is utilized as devices to undergo the research in HAR since their small in size and this sensor already build-in in the various type of smartphones. However, the existence of high inter-class similarities among the class tends to degrade the recognition performance. Hence, this work presents the method for activity recognition using our proposed features from combinational of spectral analysis with statistical descriptors that able to tackle the issue of differentiating stationary and locomotion activities. The noise signal is filtered using Fourier Transform before it will be extracted using two different groups of features, spectral frequency analysis, and statistical descriptors. Extracted signal later will be classified using random forest ensemble classifier models. The recognition results show the good accuracy performance for stationary and locomotion activities based on USC HAD datasets.

Appropriateness of the probability approach with a nutrient status biomarker to assess population inadequacy: a study using vitamin D123

PubMed Central

Carriquiry, Alicia L; Bailey, Regan L; Sempos, Christopher T; Yetley, Elizabeth A

2013-01-01

Background: There are questions about the appropriate method for the accurate estimation of the population prevalence of nutrient inadequacy on the basis of a biomarker of nutrient status (BNS). Objective: We determined the applicability of a statistical probability method to a BNS, specifically serum 25-hydroxyvitamin D [25(OH)D]. The ability to meet required statistical assumptions was the central focus. Design: Data on serum 25(OH)D concentrations in adults aged 19–70 y from the 2005–2006 NHANES were used (n = 3871). An Institute of Medicine report provided reference values. We analyzed key assumptions of symmetry, differences in variance, and the independence of distributions. We also corrected observed distributions for within-person variability (WPV). Estimates of vitamin D inadequacy were determined. Results: We showed that the BNS [serum 25(OH)D] met the criteria to use the method for the estimation of the prevalence of inadequacy. The difference between observations corrected compared with uncorrected for WPV was small for serum 25(OH)D but, nonetheless, showed enhanced accuracy because of correction. The method estimated a 19% prevalence of inadequacy in this sample, whereas misclassification inherent in the use of the more traditional 97.5th percentile high-end cutoff inflated the prevalence of inadequacy (36%). Conclusions: When the prevalence of nutrient inadequacy for a population is estimated by using serum 25(OH)D as an example of a BNS, a statistical probability method is appropriate and more accurate in comparison with a high-end cutoff. Contrary to a common misunderstanding, the method does not overlook segments of the population. The accuracy of population estimates of inadequacy is enhanced by the correction of observed measures for WPV. PMID:23097269

A novel metric that quantifies risk stratification for evaluating diagnostic tests: The example of evaluating cervical-cancer screening tests across populations.

PubMed

Katki, Hormuzd A; Schiffman, Mark

2018-05-01

Our work involves assessing whether new biomarkers might be useful for cervical-cancer screening across populations with different disease prevalences and biomarker distributions. When comparing across populations, we show that standard diagnostic accuracy statistics (predictive values, risk-differences, Youden's index and Area Under the Curve (AUC)) can easily be misinterpreted. We introduce an intuitively simple statistic for a 2 × 2 table, Mean Risk Stratification (MRS): the average change in risk (pre-test vs. post-test) revealed for tested individuals. High MRS implies better risk separation achieved by testing. MRS has 3 key advantages for comparing test performance across populations with different disease prevalences and biomarker distributions. First, MRS demonstrates that conventional predictive values and the risk-difference do not measure risk-stratification because they do not account for test-positivity rates. Second, Youden's index and AUC measure only multiplicative relative gains in risk-stratification: AUC = 0.6 achieves only 20% of maximum risk-stratification (AUC = 0.9 achieves 80%). Third, large relative gains in risk-stratification might not imply large absolute gains if disease is rare, demonstrating a "high-bar" to justify population-based screening for rare diseases such as cancer. We illustrate MRS by our experience comparing the performance of cervical-cancer screening tests in China vs. the USA. The test with the worst AUC = 0.72 in China (visual inspection with acetic acid) provides twice the risk-stratification (i.e. MRS) of the test with best AUC = 0.83 in the USA (human papillomavirus and Pap cotesting) because China has three times more cervical precancer/cancer. MRS could be routinely calculated to better understand the clinical/public-health implications of standard diagnostic accuracy statistics. Published by Elsevier Inc.

High-precision gauging of metal rings

NASA Astrophysics Data System (ADS)

Carlin, Mats; Lillekjendlie, Bjorn

1994-11-01

Raufoss AS designs and produces air brake fittings for trucks and buses on the international market. One of the critical components in the fittings is a small, circular metal ring, which is going through 100% dimension control. This article describes a low-price, high accuracy solution developed at SINTEF Instrumentation based on image metrology and a subpixel resolution algorithm. The measurement system consists of a PC-plugg-in transputer video board, a CCD camera, telecentric optics and a machine vision strobe. We describe the measurement technique in some detail, as well as the robust statistical techniques found to be essential in the real life environment.

Estimating Classification Consistency and Accuracy for Cognitive Diagnostic Assessment

ERIC Educational Resources Information Center

Cui, Ying; Gierl, Mark J.; Chang, Hua-Hua

2012-01-01

This article introduces procedures for the computation and asymptotic statistical inference for classification consistency and accuracy indices specifically designed for cognitive diagnostic assessments. The new classification indices can be used as important indicators of the reliability and validity of classification results produced by…

AMES Stereo Pipeline Derived DEM Accuracy Experiment Using LROC-NAC Stereopairs and Weighted Spatial Dependence Simulation for Lunar Site Selection

NASA Astrophysics Data System (ADS)

Laura, J. R.; Miller, D.; Paul, M. V.

2012-03-01

An accuracy assessment of AMES Stereo Pipeline derived DEMs for lunar site selection using weighted spatial dependence simulation and a call for outside AMES derived DEMs to facilitate a statistical precision analysis.

Annual Irrigation Dynamics in the U.S. Northern High Plains Derived from Landsat Satellite Data

NASA Astrophysics Data System (ADS)

Deines, Jillian M.; Kendall, Anthony D.; Hyndman, David W.

2017-09-01

Sustainable management of agricultural water resources requires improved understanding of irrigation patterns in space and time. We produced annual, high-resolution (30 m) irrigation maps for 1999-2016 by combining all available Landsat satellite imagery with climate and soil covariables in Google Earth Engine. Random forest classification had accuracies from 92 to 100% and generally agreed with county statistics (r2 = 0.88-0.96). Two novel indices that integrate plant greenness and moisture information show promise for improving satellite classification of irrigation. We found considerable interannual variability in irrigation location and extent, including a near doubling between 2002 and 2016. Statistical modeling suggested that precipitation and commodity price influenced irrigated extent through time. High prices incentivized expansion to increase crop yield and profit, but dry years required greater irrigation intensity, thus reducing area in this supply-limited region. Data sets produced with this approach can improve water sustainability by providing consistent, spatially explicit tracking of irrigation dynamics over time.

Feature extraction and classification algorithms for high dimensional data

NASA Technical Reports Server (NTRS)

Lee, Chulhee; Landgrebe, David

1993-01-01

Feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next an approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. The proposed feature extraction algorithm has several desirable properties: it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, some problems encountered in analyzing high dimensional data are studied and possible solutions are proposed. First, the increased importance of the second order statistics in analyzing high dimensional data is recognized. By investigating the characteristics of high dimensional data, the reason why the second order statistics must be taken into account in high dimensional data is suggested. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics. A method to visualize statistics using a color code is proposed. By representing statistics using color coding, one can easily extract and compare the first and the second statistics.

Optimizing the Terzaghi Estimator of the 3D Distribution of Rock Fracture Orientations

NASA Astrophysics Data System (ADS)

Tang, Huiming; Huang, Lei; Juang, C. Hsein; Zhang, Junrong

2017-08-01

Orientation statistics are prone to bias when surveyed with the scanline mapping technique in which the observed probabilities differ, depending on the intersection angle between the fracture and the scanline. This bias leads to 1D frequency statistical data that are poorly representative of the 3D distribution. A widely accessible estimator named after Terzaghi was developed to estimate 3D frequencies from 1D biased observations, but the estimation accuracy is limited for fractures at narrow intersection angles to scanlines (termed the blind zone). Although numerous works have concentrated on accuracy with respect to the blind zone, accuracy outside the blind zone has rarely been studied. This work contributes to the limited investigations of accuracy outside the blind zone through a qualitative assessment that deploys a mathematical derivation of the Terzaghi equation in conjunction with a quantitative evaluation that uses fractures simulation and verification of natural fractures. The results show that the estimator does not provide a precise estimate of 3D distributions and that the estimation accuracy is correlated with the grid size adopted by the estimator. To explore the potential for improving accuracy, the particular grid size producing maximum accuracy is identified from 168 combinations of grid sizes and two other parameters. The results demonstrate that the 2° × 2° grid size provides maximum accuracy for the estimator in most cases when applied outside the blind zone. However, if the global sample density exceeds 0.5°-2, then maximum accuracy occurs at a grid size of 1° × 1°.

Ensemble coding remains accurate under object and spatial visual working memory load.

PubMed

Epstein, Michael L; Emmanouil, Tatiana A

2017-10-01

A number of studies have provided evidence that the visual system statistically summarizes large amounts of information that would exceed the limitations of attention and working memory (ensemble coding). However the necessity of working memory resources for ensemble coding has not yet been tested directly. In the current study, we used a dual task design to test the effect of object and spatial visual working memory load on size averaging accuracy. In Experiment 1, we tested participants' accuracy in comparing the mean size of two sets under various levels of object visual working memory load. Although the accuracy of average size judgments depended on the difference in mean size between the two sets, we found no effect of working memory load. In Experiment 2, we tested the same average size judgment while participants were under spatial visual working memory load, again finding no effect of load on averaging accuracy. Overall our results reveal that ensemble coding can proceed unimpeded and highly accurately under both object and spatial visual working memory load, providing further evidence that ensemble coding reflects a basic perceptual process distinct from that of individual object processing.

Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network.

PubMed

Hwang, Yoo Na; Lee, Ju Hwan; Kim, Ga Young; Jiang, Yuan Yuan; Kim, Sung Min

2015-01-01

This paper focuses on the improvement of the diagnostic accuracy of focal liver lesions by quantifying the key features of cysts, hemangiomas, and malignant lesions on ultrasound images. The focal liver lesions were divided into 29 cysts, 37 hemangiomas, and 33 malignancies. A total of 42 hybrid textural features that composed of 5 first order statistics, 18 gray level co-occurrence matrices, 18 Law's, and echogenicity were extracted. A total of 29 key features that were selected by principal component analysis were used as a set of inputs for a feed-forward neural network. For each lesion, the performance of the diagnosis was evaluated by using the positive predictive value, negative predictive value, sensitivity, specificity, and accuracy. The results of the experiment indicate that the proposed method exhibits great performance, a high diagnosis accuracy of over 96% among all focal liver lesion groups (cyst vs. hemangioma, cyst vs. malignant, and hemangioma vs. malignant) on ultrasound images. The accuracy was slightly increased when echogenicity was included in the optimal feature set. These results indicate that it is possible for the proposed method to be applied clinically.

Using machine learning to assess covariate balance in matching studies.

PubMed

Linden, Ariel; Yarnold, Paul R

2016-12-01

In order to assess the effectiveness of matching approaches in observational studies, investigators typically present summary statistics for each observed pre-intervention covariate, with the objective of showing that matching reduces the difference in means (or proportions) between groups to as close to zero as possible. In this paper, we introduce a new approach to distinguish between study groups based on their distributions of the covariates using a machine-learning algorithm called optimal discriminant analysis (ODA). Assessing covariate balance using ODA as compared with the conventional method has several key advantages: the ability to ascertain how individuals self-select based on optimal (maximum-accuracy) cut-points on the covariates; the application to any variable metric and number of groups; its insensitivity to skewed data or outliers; and the use of accuracy measures that can be widely applied to all analyses. Moreover, ODA accepts analytic weights, thereby extending the assessment of covariate balance to any study design where weights are used for covariate adjustment. By comparing the two approaches using empirical data, we are able to demonstrate that using measures of classification accuracy as balance diagnostics produces highly consistent results to those obtained via the conventional approach (in our matched-pairs example, ODA revealed a weak statistically significant relationship not detected by the conventional approach). Thus, investigators should consider ODA as a robust complement, or perhaps alternative, to the conventional approach for assessing covariate balance in matching studies. © 2016 John Wiley & Sons, Ltd.

High order methods for the integration of the Bateman equations and other problems of the form of y‧ = F(y,t)y

NASA Astrophysics Data System (ADS)

Josey, C.; Forget, B.; Smith, K.

2017-12-01

This paper introduces two families of A-stable algorithms for the integration of y‧ = F (y , t) y: the extended predictor-corrector (EPC) and the exponential-linear (EL) methods. The structure of the algorithm families are described, and the method of derivation of the coefficients presented. The new algorithms are then tested on a simple deterministic problem and a Monte Carlo isotopic evolution problem. The EPC family is shown to be only second order for systems of ODEs. However, the EPC-RK45 algorithm had the highest accuracy on the Monte Carlo test, requiring at least a factor of 2 fewer function evaluations to achieve a given accuracy than a second order predictor-corrector method (center extrapolation / center midpoint method) with regards to Gd-157 concentration. Members of the EL family can be derived to at least fourth order. The EL3 and the EL4 algorithms presented are shown to be third and fourth order respectively on the systems of ODE test. In the Monte Carlo test, these methods did not overtake the accuracy of EPC methods before statistical uncertainty dominated the error. The statistical properties of the algorithms were also analyzed during the Monte Carlo problem. The new methods are shown to yield smaller standard deviations on final quantities as compared to the reference predictor-corrector method, by up to a factor of 1.4.

Modeling time-to-event (survival) data using classification tree analysis.

PubMed

Linden, Ariel; Yarnold, Paul R

2017-12-01

Time to the occurrence of an event is often studied in health research. Survival analysis differs from other designs in that follow-up times for individuals who do not experience the event by the end of the study (called censored) are accounted for in the analysis. Cox regression is the standard method for analysing censored data, but the assumptions required of these models are easily violated. In this paper, we introduce classification tree analysis (CTA) as a flexible alternative for modelling censored data. Classification tree analysis is a "decision-tree"-like classification model that provides parsimonious, transparent (ie, easy to visually display and interpret) decision rules that maximize predictive accuracy, derives exact P values via permutation tests, and evaluates model cross-generalizability. Using empirical data, we identify all statistically valid, reproducible, longitudinally consistent, and cross-generalizable CTA survival models and then compare their predictive accuracy to estimates derived via Cox regression and an unadjusted naïve model. Model performance is assessed using integrated Brier scores and a comparison between estimated survival curves. The Cox regression model best predicts average incidence of the outcome over time, whereas CTA survival models best predict either relatively high, or low, incidence of the outcome over time. Classification tree analysis survival models offer many advantages over Cox regression, such as explicit maximization of predictive accuracy, parsimony, statistical robustness, and transparency. Therefore, researchers interested in accurate prognoses and clear decision rules should consider developing models using the CTA-survival framework. © 2017 John Wiley & Sons, Ltd.

Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment

PubMed Central

Hashim, Mazlan

2015-01-01

This research presents the results of the GIS-based statistical models for generation of landslide susceptibility mapping using geographic information system (GIS) and remote-sensing data for Cameron Highlands area in Malaysia. Ten factors including slope, aspect, soil, lithology, NDVI, land cover, distance to drainage, precipitation, distance to fault, and distance to road were extracted from SAR data, SPOT 5 and WorldView-1 images. The relationships between the detected landslide locations and these ten related factors were identified by using GIS-based statistical models including analytical hierarchy process (AHP), weighted linear combination (WLC) and spatial multi-criteria evaluation (SMCE) models. The landslide inventory map which has a total of 92 landslide locations was created based on numerous resources such as digital aerial photographs, AIRSAR data, WorldView-1 images, and field surveys. Then, 80% of the landslide inventory was used for training the statistical models and the remaining 20% was used for validation purpose. The validation results using the Relative landslide density index (R-index) and Receiver operating characteristic (ROC) demonstrated that the SMCE model (accuracy is 96%) is better in prediction than AHP (accuracy is 91%) and WLC (accuracy is 89%) models. These landslide susceptibility maps would be useful for hazard mitigation purpose and regional planning. PMID:25898919

Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment.

PubMed

Shahabi, Himan; Hashim, Mazlan

2015-04-22

This research presents the results of the GIS-based statistical models for generation of landslide susceptibility mapping using geographic information system (GIS) and remote-sensing data for Cameron Highlands area in Malaysia. Ten factors including slope, aspect, soil, lithology, NDVI, land cover, distance to drainage, precipitation, distance to fault, and distance to road were extracted from SAR data, SPOT 5 and WorldView-1 images. The relationships between the detected landslide locations and these ten related factors were identified by using GIS-based statistical models including analytical hierarchy process (AHP), weighted linear combination (WLC) and spatial multi-criteria evaluation (SMCE) models. The landslide inventory map which has a total of 92 landslide locations was created based on numerous resources such as digital aerial photographs, AIRSAR data, WorldView-1 images, and field surveys. Then, 80% of the landslide inventory was used for training the statistical models and the remaining 20% was used for validation purpose. The validation results using the Relative landslide density index (R-index) and Receiver operating characteristic (ROC) demonstrated that the SMCE model (accuracy is 96%) is better in prediction than AHP (accuracy is 91%) and WLC (accuracy is 89%) models. These landslide susceptibility maps would be useful for hazard mitigation purpose and regional planning.

Weighted statistical parameters for irregularly sampled time series

NASA Astrophysics Data System (ADS)

Rimoldini, Lorenzo

2014-01-01

Unevenly spaced time series are common in astronomy because of the day-night cycle, weather conditions, dependence on the source position in the sky, allocated telescope time and corrupt measurements, for example, or inherent to the scanning law of satellites like Hipparcos and the forthcoming Gaia. Irregular sampling often causes clumps of measurements and gaps with no data which can severely disrupt the values of estimators. This paper aims at improving the accuracy of common statistical parameters when linear interpolation (in time or phase) can be considered an acceptable approximation of a deterministic signal. A pragmatic solution is formulated in terms of a simple weighting scheme, adapting to the sampling density and noise level, applicable to large data volumes at minimal computational cost. Tests on time series from the Hipparcos periodic catalogue led to significant improvements in the overall accuracy and precision of the estimators with respect to the unweighted counterparts and those weighted by inverse-squared uncertainties. Automated classification procedures employing statistical parameters weighted by the suggested scheme confirmed the benefits of the improved input attributes. The classification of eclipsing binaries, Mira, RR Lyrae, Delta Cephei and Alpha2 Canum Venaticorum stars employing exclusively weighted descriptive statistics achieved an overall accuracy of 92 per cent, about 6 per cent higher than with unweighted estimators.

Investigations of interference between electromagnetic transponders and wireless MOSFET dosimeters: a phantom study.

PubMed

Su, Zhong; Zhang, Lisha; Ramakrishnan, V; Hagan, Michael; Anscher, Mitchell

2011-05-01

To evaluate both the Calypso Systems' (Calypso Medical Technologies, Inc., Seattle, WA) localization accuracy in the presence of wireless metal-oxide-semiconductor field-effect transistor (MOSFET) dosimeters of dose verification system (DVS, Sicel Technologies, Inc., Morrisville, NC) and the dosimeters' reading accuracy in the presence of wireless electromagnetic transponders inside a phantom. A custom-made, solid-water phantom was fabricated with space for transponders and dosimeters. Two inserts were machined with positioning grooves precisely matching the dimensions of the transponders and dosimeters and were arranged in orthogonal and parallel orientations, respectively. To test the transponder localization accuracy with/without presence of dosimeters (hypothesis 1), multivariate analyses were performed on transponder-derived localization data with and without dosimeters at each preset distance to detect statistically significant localization differences between the control and test sets. To test dosimeter dose-reading accuracy with/without presence of transponders (hypothesis 2), an approach of alternating the transponder presence in seven identical fraction dose (100 cGy) deliveries and measurements was implemented. Two-way analysis of variance was performed to examine statistically significant dose-reading differences between the two groups and the different fractions. A relative-dose analysis method was also used to evaluate transponder impact on dose-reading accuracy after dose-fading effect was removed by a second-order polynomial fit. Multivariate analysis indicated that hypothesis 1 was false; there was a statistically significant difference between the localization data from the control and test sets. However, the upper and lower bounds of the 95% confidence intervals of the localized positional differences between the control and test sets were less than 0.1 mm, which was significantly smaller than the minimum clinical localization resolution of 0.5 mm. For hypothesis 2, analysis of variance indicated that there was no statistically significant difference between the dosimeter readings with and without the presence of transponders. Both orthogonal and parallel configurations had difference of polynomial-fit dose to measured dose values within 1.75%. The phantom study indicated that the Calypso System's localization accuracy was not affected clinically due to the presence of DVS wireless MOSFET dosimeters and the dosimeter-measured doses were not affected by the presence of transponders. Thus, the same patients could be implanted with both transponders and dosimeters to benefit from improved accuracy of radiotherapy treatments offered by conjunctional use of the two systems.

Analysis of model development strategies: predicting ventral hernia recurrence.

PubMed

Holihan, Julie L; Li, Linda T; Askenasy, Erik P; Greenberg, Jacob A; Keith, Jerrod N; Martindale, Robert G; Roth, J Scott; Liang, Mike K

2016-11-01

There have been many attempts to identify variables associated with ventral hernia recurrence; however, it is unclear which statistical modeling approach results in models with greatest internal and external validity. We aim to assess the predictive accuracy of models developed using five common variable selection strategies to determine variables associated with hernia recurrence. Two multicenter ventral hernia databases were used. Database 1 was randomly split into "development" and "internal validation" cohorts. Database 2 was designated "external validation". The dependent variable for model development was hernia recurrence. Five variable selection strategies were used: (1) "clinical"-variables considered clinically relevant, (2) "selective stepwise"-all variables with a P value <0.20 were assessed in a step-backward model, (3) "liberal stepwise"-all variables were included and step-backward regression was performed, (4) "restrictive internal resampling," and (5) "liberal internal resampling." Variables were included with P < 0.05 for the Restrictive model and P < 0.10 for the Liberal model. A time-to-event analysis using Cox regression was performed using these strategies. The predictive accuracy of the developed models was tested on the internal and external validation cohorts using Harrell's C-statistic where C > 0.70 was considered "reasonable". The recurrence rate was 32.9% (n = 173/526; median/range follow-up, 20/1-58 mo) for the development cohort, 36.0% (n = 95/264, median/range follow-up 20/1-61 mo) for the internal validation cohort, and 12.7% (n = 155/1224, median/range follow-up 9/1-50 mo) for the external validation cohort. Internal validation demonstrated reasonable predictive accuracy (C-statistics = 0.772, 0.760, 0.767, 0.757, 0.763), while on external validation, predictive accuracy dipped precipitously (C-statistic = 0.561, 0.557, 0.562, 0.553, 0.560). Predictive accuracy was equally adequate on internal validation among models; however, on external validation, all five models failed to demonstrate utility. Future studies should report multiple variable selection techniques and demonstrate predictive accuracy on external data sets for model validation. Copyright © 2016 Elsevier Inc. All rights reserved.

Algorithms for tensor network renormalization

NASA Astrophysics Data System (ADS)

Evenbly, G.

2017-01-01

We discuss in detail algorithms for implementing tensor network renormalization (TNR) for the study of classical statistical and quantum many-body systems. First, we recall established techniques for how the partition function of a 2 D classical many-body system or the Euclidean path integral of a 1 D quantum system can be represented as a network of tensors, before describing how TNR can be implemented to efficiently contract the network via a sequence of coarse-graining transformations. The efficacy of the TNR approach is then benchmarked for the 2 D classical statistical and 1 D quantum Ising models; in particular the ability of TNR to maintain a high level of accuracy over sustained coarse-graining transformations, even at a critical point, is demonstrated.

Method and system of Jones-matrix mapping of blood plasma films with "fuzzy" analysis in differentiation of breast pathology changes

NASA Astrophysics Data System (ADS)

Zabolotna, Natalia I.; Radchenko, Kostiantyn O.; Karas, Oleksandr V.

2018-01-01

A fibroadenoma diagnosing of breast using statistical analysis (determination and analysis of statistical moments of the 1st-4th order) of the obtained polarization images of Jones matrix imaginary elements of the optically thin (attenuation coefficient τ <= 0,1 ) blood plasma films with further intellectual differentiation based on the method of "fuzzy" logic and discriminant analysis were proposed. The accuracy of the intellectual differentiation of blood plasma samples to the "norm" and "fibroadenoma" of breast was 82.7% by the method of linear discriminant analysis, and by the "fuzzy" logic method is 95.3%. The obtained results allow to confirm the potentially high level of reliability of the method of differentiation by "fuzzy" analysis.

Accuracy of Prediction Instruments for Diagnosing Large Vessel Occlusion in Individuals With Suspected Stroke: A Systematic Review for the 2018 Guidelines for the Early Management of Patients With Acute Ischemic Stroke.

PubMed

Smith, Eric E; Kent, David M; Bulsara, Ketan R; Leung, Lester Y; Lichtman, Judith H; Reeves, Mathew J; Towfighi, Amytis; Whiteley, William N; Zahuranec, Darin B

2018-03-01

Endovascular thrombectomy is a highly efficacious treatment for large vessel occlusion (LVO). LVO prediction instruments, based on stroke signs and symptoms, have been proposed to identify stroke patients with LVO for rapid transport to endovascular thrombectomy-capable hospitals. This evidence review committee was commissioned by the American Heart Association/American Stroke Association to systematically review evidence for the accuracy of LVO prediction instruments. Medline, Embase, and Cochrane databases were searched on October 27, 2016. Study quality was assessed with the Quality Assessment of Diagnostic Accuracy-2 tool. Thirty-six relevant studies were identified. Most studies (21 of 36) recruited patients with ischemic stroke, with few studies in the prehospital setting (4 of 36) and in populations that included hemorrhagic stroke or stroke mimics (12 of 36). The most frequently studied prediction instrument was the National Institutes of Health Stroke Scale. Most studies had either some risk of bias or unclear risk of bias. Reported discrimination of LVO mostly ranged from 0.70 to 0.85, as measured by the C statistic. In meta-analysis, sensitivity was as high as 87% and specificity was as high as 90%, but no threshold on any instruments predicted LVO with both high sensitivity and specificity. With a positive LVO prediction test, the probability of LVO could be 50% to 60% (depending on the LVO prevalence in the population), but the probability of LVO with a negative test could still be ≥10%. No scale predicted LVO with both high sensitivity and high specificity. Systems that use LVO prediction instruments for triage will miss some patients with LVO and milder stroke. More prospective studies are needed to assess the accuracy of LVO prediction instruments in the prehospital setting in all patients with suspected stroke, including patients with hemorrhagic stroke and stroke mimics. © 2018 American Heart Association, Inc.

Pre-Then-Post Testing: A Tool To Improve the Accuracy of Management Training Program Evaluation.

ERIC Educational Resources Information Center

Mezoff, Bob

1981-01-01

Explains a procedure to avoid the detrimental biases of conventional self-reports of training outcomes. The evaluation format provided is a method for using statistical procedures to increase the accuracy of self-reports by overcoming response-shift-bias. (Author/MER)

A New Chemotherapeutic Investigation: Piracetam Effects on Dyslexia.

ERIC Educational Resources Information Center

Chase, Christopher H.; Schmitt, R. Larry

1984-01-01

Compared to placebo controls, 28 individuals treated with Piracetam (a new drug thought to enhance learning and memory consolidation) showed statistically significant improvements above baseline scores on measures of effective reading accuracy and comprehension, reading speed, and writing accuracy. The medication was well tolerated and showed no…

Achieving a Linear Dose Rate Response in Pulse-Mode Silicon Photodiode Scintillation Detectors Over a Wide Range of Excitations

NASA Astrophysics Data System (ADS)

Carroll, Lewis

2014-02-01

We are developing a new dose calibrator for nuclear pharmacies that can measure radioactivity in a vial or syringe without handling it directly or removing it from its transport shield “pig”. The calibrator's detector comprises twin opposing scintillating crystals coupled to Si photodiodes and current-amplifying trans-resistance amplifiers. Such a scheme is inherently linear with respect to dose rate over a wide range of radiation intensities, but accuracy at low activity levels may be impaired, beyond the effects of meager photon statistics, by baseline fluctuation and drift inevitably present in high-gain, current-mode photodiode amplifiers. The work described here is motivated by our desire to enhance accuracy at low excitations while maintaining linearity at high excitations. Thus, we are also evaluating a novel “pulse-mode” analog signal processing scheme that employs a linear threshold discriminator to virtually eliminate baseline fluctuation and drift. We will show the results of a side-by-side comparison of current-mode versus pulse-mode signal processing schemes, including perturbing factors affecting linearity and accuracy at very low and very high excitations. Bench testing over a wide range of excitations is done using a Poisson random pulse generator plus an LED light source to simulate excitations up to ˜106 detected counts per second without the need to handle and store large amounts of radioactive material.

Analyzing thematic maps and mapping for accuracy

USGS Publications Warehouse

Rosenfield, G.H.

1982-01-01

Two problems which exist while attempting to test the accuracy of thematic maps and mapping are: (1) evaluating the accuracy of thematic content, and (2) evaluating the effects of the variables on thematic mapping. Statistical analysis techniques are applicable to both these problems and include techniques for sampling the data and determining their accuracy. In addition, techniques for hypothesis testing, or inferential statistics, are used when comparing the effects of variables. A comprehensive and valid accuracy test of a classification project, such as thematic mapping from remotely sensed data, includes the following components of statistical analysis: (1) sample design, including the sample distribution, sample size, size of the sample unit, and sampling procedure; and (2) accuracy estimation, including estimation of the variance and confidence limits. Careful consideration must be given to the minimum sample size necessary to validate the accuracy of a given. classification category. The results of an accuracy test are presented in a contingency table sometimes called a classification error matrix. Usually the rows represent the interpretation, and the columns represent the verification. The diagonal elements represent the correct classifications. The remaining elements of the rows represent errors by commission, and the remaining elements of the columns represent the errors of omission. For tests of hypothesis that compare variables, the general practice has been to use only the diagonal elements from several related classification error matrices. These data are arranged in the form of another contingency table. The columns of the table represent the different variables being compared, such as different scales of mapping. The rows represent the blocking characteristics, such as the various categories of classification. The values in the cells of the tables might be the counts of correct classification or the binomial proportions of these counts divided by either the row totals or the column totals from the original classification error matrices. In hypothesis testing, when the results of tests of multiple sample cases prove to be significant, some form of statistical test must be used to separate any results that differ significantly from the others. In the past, many analyses of the data in this error matrix were made by comparing the relative magnitudes of the percentage of correct classifications, for either individual categories, the entire map or both. More rigorous analyses have used data transformations and (or) two-way classification analysis of variance. A more sophisticated step of data analysis techniques would be to use the entire classification error matrices using the methods of discrete multivariate analysis or of multiviariate analysis of variance.

Estimation of critical behavior from the density of states in classical statistical models

NASA Astrophysics Data System (ADS)

Malakis, A.; Peratzakis, A.; Fytas, N. G.

2004-12-01

We present a simple and efficient approximation scheme which greatly facilitates the extension of Wang-Landau sampling (or similar techniques) in large systems for the estimation of critical behavior. The method, presented in an algorithmic approach, is based on a very simple idea, familiar in statistical mechanics from the notion of thermodynamic equivalence of ensembles and the central limit theorem. It is illustrated that we can predict with high accuracy the critical part of the energy space and by using this restricted part we can extend our simulations to larger systems and improve the accuracy of critical parameters. It is proposed that the extensions of the finite-size critical part of the energy space, determining the specific heat, satisfy a scaling law involving the thermal critical exponent. The method is applied successfully for the estimation of the scaling behavior of specific heat of both square and simple cubic Ising lattices. The proposed scaling law is verified by estimating the thermal critical exponent from the finite-size behavior of the critical part of the energy space. The density of states of the zero-field Ising model on these lattices is obtained via a multirange Wang-Landau sampling.

In-line calibration of Raman systems for analysis of gas mixtures of hydrogen isotopologues with sub-percent accuracy.

PubMed

Schlösser, Magnus; Seitz, Hendrik; Rupp, Simone; Herwig, Philipp; Alecu, Catalin Gabriel; Sturm, Michael; Bornschein, Beate

2013-03-05

Highly accurate, in-line, and real-time composition measurements of gases are mandatory in many processing applications. The quantitative analysis of mixtures of hydrogen isotopologues (H2, D2, T2, HD, HT, and DT) is of high importance in such fields as DT fusion, neutrino mass measurements using tritium β-decay or photonuclear experiments where HD targets are used. Raman spectroscopy is a favorable method for these tasks. In this publication we present a method for the in-line calibration of Raman systems for the nonradioactive hydrogen isotopologues. It is based on precise volumetric gas mixing of the homonuclear species H2/D2 and a controlled catalytic production of the heteronuclear species HD. Systematic effects like spurious exchange reactions with wall materials and others are considered with care during the procedure. A detailed discussion of statistical and systematic uncertainties is presented which finally yields a calibration accuracy of better than 0.4%.

A novel spectrofluorimetric method for the assay of pseudoephedrine hydrochloride in pharmaceutical formulations via derivatization with 4-chloro-7-nitrobenzofurazan.

PubMed

El-Didamony, Akram M; Gouda, Ayman A

2011-01-01

A new highly sensitive and specific spectrofluorimetric method has been developed to determine a sympathomimetic drug pseudoephedrine hydrochloride. The present method was based on derivatization with 4-chloro-7-nitrobenzofurazan in phosphate buffer at pH 7.8 to produce a highly fluorescent product which was measured at 532 nm (excitation at 475 nm). Under the optimized conditions a linear relationship and good correlation was found between the fluorescence intensity and pseudoephedrine hydrochloride concentration in the range of 0.5-5 µg mL(-1). The proposed method was successfully applied to the assay of pseudoephedrine hydrochloride in commercial pharmaceutical formulations with good accuracy and precision and without interferences from common additives. Statistical comparison of the results with a well-established method showed excellent agreement and proved that there was no significant difference in the accuracy and precision. The stoichiometry of the reaction was determined and the reaction pathway was postulated. Copyright © 2010 John Wiley & Sons, Ltd.

Humans make efficient use of natural image statistics when performing spatial interpolation.

PubMed

D'Antona, Anthony D; Perry, Jeffrey S; Geisler, Wilson S

2013-12-16

Visual systems learn through evolution and experience over the lifespan to exploit the statistical structure of natural images when performing visual tasks. Understanding which aspects of this statistical structure are incorporated into the human nervous system is a fundamental goal in vision science. To address this goal, we measured human ability to estimate the intensity of missing image pixels in natural images. Human estimation accuracy is compared with various simple heuristics (e.g., local mean) and with optimal observers that have nearly complete knowledge of the local statistical structure of natural images. Human estimates are more accurate than those of simple heuristics, and they match the performance of an optimal observer that knows the local statistical structure of relative intensities (contrasts). This optimal observer predicts the detailed pattern of human estimation errors and hence the results place strong constraints on the underlying neural mechanisms. However, humans do not reach the performance of an optimal observer that knows the local statistical structure of the absolute intensities, which reflect both local relative intensities and local mean intensity. As predicted from a statistical analysis of natural images, human estimation accuracy is negligibly improved by expanding the context from a local patch to the whole image. Our results demonstrate that the human visual system exploits efficiently the statistical structure of natural images.

Accuracy of Digital vs. Conventional Implant Impressions

PubMed Central

Lee, Sang J.; Betensky, Rebecca A.; Gianneschi, Grace E.; Gallucci, German O.

2015-01-01

The accuracy of digital impressions greatly influences the clinical viability in implant restorations. The aim of this study is to compare the accuracy of gypsum models acquired from the conventional implant impression to digitally milled models created from direct digitalization by three-dimensional analysis. Thirty gypsum and 30 digitally milled models impressed directly from a reference model were prepared. The models were scanned by a laboratory scanner and 30 STL datasets from each group were imported to an inspection software. The datasets were aligned to the reference dataset by a repeated best fit algorithm and 10 specified contact locations of interest were measured in mean volumetric deviations. The areas were pooled by cusps, fossae, interproximal contacts, horizontal and vertical axes of implant position and angulation. The pooled areas were statistically analysed by comparing each group to the reference model to investigate the mean volumetric deviations accounting for accuracy and standard deviations for precision. Milled models from digital impressions had comparable accuracy to gypsum models from conventional impressions. However, differences in fossae and vertical displacement of the implant position from the gypsum and digitally milled models compared to the reference model, exhibited statistical significance (p<0.001, p=0.020 respectively). PMID:24720423

Spatial Pattern Classification for More Accurate Forecasting of Variable Energy Resources

NASA Astrophysics Data System (ADS)

Novakovskaia, E.; Hayes, C.; Collier, C.

2014-12-01

The accuracy of solar and wind forecasts is becoming increasingly essential as grid operators continue to integrate additional renewable generation onto the electric grid. Forecast errors affect rate payers, grid operators, wind and solar plant maintenance crews and energy traders through increases in prices, project down time or lost revenue. While extensive and beneficial efforts were undertaken in recent years to improve physical weather models for a broad spectrum of applications these improvements have generally not been sufficient to meet the accuracy demands of system planners. For renewables, these models are often used in conjunction with additional statistical models utilizing both meteorological observations and the power generation data. Forecast accuracy can be dependent on specific weather regimes for a given location. To account for these dependencies it is important that parameterizations used in statistical models change as the regime changes. An automated tool, based on an artificial neural network model, has been developed to identify different weather regimes as they impact power output forecast accuracy at wind or solar farms. In this study, improvements in forecast accuracy were analyzed for varying time horizons for wind farms and utility-scale PV plants located in different geographical regions.

Testing high SPF sunscreens: a demonstration of the accuracy and reproducibility of the results of testing high SPF formulations by two methods and at different testing sites.

PubMed

Agin, Patricia Poh; Edmonds, Susan H

2002-08-01

The goals of this study were (i) to demonstrate that existing and widely used sun protection factor (SPF) test methodologies can produce accurate and reproducible results for high SPF formulations and (ii) to provide data on the number of test-subjects needed, the variability of the data, and the appropriate exposure increments needed for testing high SPF formulations. Three high SPF formulations were tested, according to the Food and Drug Administration's (FDA) 1993 tentative final monograph (TFM) 'very water resistant' test method and/or the 1978 proposed monograph 'waterproof' test method, within one laboratory. A fourth high SPF formulation was tested at four independent SPF testing laboratories, using the 1978 waterproof SPF test method. All laboratories utilized xenon arc solar simulators. The data illustrate that the testing conducted within one laboratory, following either the 1978 proposed or the 1993 TFM SPF test method, was able to reproducibly determine the SPFs of the formulations tested, using either the statistical analysis method in the proposed monograph or the statistical method described in the TFM. When one formulation was tested at four different laboratories, the anticipated variation in the data owing to the equipment and other operational differences was minimized through the use of the statistical method described in the 1993 monograph. The data illustrate that either the 1978 proposed monograph SPF test method or the 1993 TFM SPF test method can provide accurate and reproducible results for high SPF formulations. Further, these results can be achieved with panels of 20-25 subjects with an acceptable level of variability. Utilization of the statistical controls from the 1993 sunscreen monograph can help to minimize lab-to-lab variability for well-formulated products.

On the control of brain-computer interfaces by users with cerebral palsy.

PubMed

Daly, Ian; Billinger, Martin; Laparra-Hernández, José; Aloise, Fabio; García, Mariano Lloria; Faller, Josef; Scherer, Reinhold; Müller-Putz, Gernot

2013-09-01

Brain-computer interfaces (BCIs) have been proposed as a potential assistive device for individuals with cerebral palsy (CP) to assist with their communication needs. However, it is unclear how well-suited BCIs are to individuals with CP. Therefore, this study aims to investigate to what extent these users are able to gain control of BCIs. This study is conducted with 14 individuals with CP attempting to control two standard online BCIs (1) based upon sensorimotor rhythm modulations, and (2) based upon steady state visual evoked potentials. Of the 14 users, 8 are able to use one or other of the BCIs, online, with a statistically significant level of accuracy, without prior training. Classification results are driven by neurophysiological activity and not seen to correlate with occurrences of artifacts. However, many of these users' accuracies, while statistically significant, would require either more training or more advanced methods before practical BCI control would be possible. The results indicate that BCIs may be controlled by individuals with CP but that many issues need to be overcome before practical application use may be achieved. This is the first study to assess the ability of a large group of different individuals with CP to gain control of an online BCI system. The results indicate that six users could control a sensorimotor rhythm BCI and three a steady state visual evoked potential BCI at statistically significant levels of accuracy (SMR accuracies; mean ± STD, 0.821 ± 0.116, SSVEP accuracies; 0.422 ± 0.069). Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

High Accuracy Verification of a Correlated-Photon-Based Method for Determining Photon-Counting Detection Efficiency

DTIC Science & Technology

2007-01-01

Metrology; (270.5290) Photon statistics. References and links 1. W. H. Louisell, A. Yariv, and A. E. Siegman , “Quantum Fluctuations and Noise in...939–941 (1981). 7. S. R. Bowman, Y. H. Shih, and C. O. Alley, “The use of Geiger mode avalanche photodiodes for precise laser ranging at very low...light levels: An experimental evaluation”, in Laser Radar Technology and Applications I, James M. Cruickshank, Robert C. Harney eds., Proc. SPIE 663

21 CFR 211.165 - Testing and release for distribution.

Code of Federal Regulations, 2010 CFR

2010-04-01

... products meet each appropriate specification and appropriate statistical quality control criteria as a condition for their approval and release. The statistical quality control criteria shall include appropriate acceptance levels and/or appropriate rejection levels. (e) The accuracy, sensitivity, specificity, and...

Radiological interpretation of images displayed on tablet computers: a systematic review.

PubMed

Caffery, L J; Armfield, N R; Smith, A C

2015-06-01

To review the published evidence and to determine if radiological diagnostic accuracy is compromised when images are displayed on a tablet computer and thereby inform practice on using tablet computers for radiological interpretation by on-call radiologists. We searched the PubMed and EMBASE databases for studies on the diagnostic accuracy or diagnostic reliability of images interpreted on tablet computers. Studies were screened for inclusion based on pre-determined inclusion and exclusion criteria. Studies were assessed for quality and risk of bias using Quality Appraisal of Diagnostic Reliability Studies or the revised Quality Assessment of Diagnostic Accuracy Studies tool. Treatment of studies was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). 11 studies met the inclusion criteria. 10 of these studies tested the Apple iPad(®) (Apple, Cupertino, CA). The included studies reported high sensitivity (84-98%), specificity (74-100%) and accuracy rates (98-100%) for radiological diagnosis. There was no statistically significant difference in accuracy between a tablet computer and a digital imaging and communication in medicine-calibrated control display. There was a near complete consensus from authors on the non-inferiority of diagnostic accuracy of images displayed on a tablet computer. All of the included studies were judged to be at risk of bias. Our findings suggest that the diagnostic accuracy of radiological interpretation is not compromised by using a tablet computer. This result is only relevant to the Apple iPad and to the modalities of CT, MRI and plain radiography. The iPad may be appropriate for an on-call radiologist to use for radiological interpretation.

Thematic Accuracy Assessment of the 2011 National Land Cover Database (NLCD)

EPA Science Inventory

Accuracy assessment is a standard protocol of National Land Cover Database (NLCD) mapping. Here we report agreement statistics between map and reference labels for NLCD 2011, which includes land cover for ca. 2001, ca. 2006, and ca. 2011. The two main objectives were assessment o...

A comprehensive assessment of different evapotranspiration products using the site-level FLUXNET database

NASA Astrophysics Data System (ADS)

Liu, J.

2017-12-01

Accurately estimate of ET is crucial for studies of land-atmosphere interactions. A series of ET products have been developed recently relying on various simulation methods, however, uncertainties in accuracy of products limit their implications. In this study, accuracies of total 8 popular global ET products simulated based on satellite retrieves (ETMODIS and ETZhang), reanalysis (ETJRA55), machine learning method (ETJung) and land surface models (ETCLM, ETMOS, ETNoah and ETVIC) forcing by Global Land Data Assimilation System (GLDAS), respectively, were comprehensively evaluated against observations from eddy covariance FLUXNET sites by yearly, land cover and climate zones. The result shows that all simulated ET products tend to underestimate in the lower ET ranges or overestimate in higher ET ranges compared with ET observations. Through the examining of four statistic criterias, the root mean square error (RMSE), mean bias error (MBE), R2, and Taylor skill score (TSS), ETJung provided a high performance whether yearly or land cover or climatic zones. Satellite based ET products also have impressive performance. ETMODIS and ETZhang present comparable accuracy, while were skilled for different land cover and climate zones, respectively. Generally, the ET products from GLDAS show reasonable accuracy, despite ETCLM has relative higher RMSE and MBE for yearly, land cover and climate zones comparisons. Although the ETJRA55 shows comparable R2 with other products, its performance was constraint by the high RMSE and MBE. Knowledge from this study is crucial for ET products improvement and selection when they were used.

Effect of high altitude on blood glucose meter performance.

PubMed

Fink, Kenneth S; Christensen, Dale B; Ellsworth, Allan

2002-01-01

Participation in high-altitude wilderness activities may expose persons to extreme environmental conditions, and for those with diabetes mellitus, euglycemia is important to ensure safe travel. We conducted a field assessment of the precision and accuracy of seven commonly used blood glucose meters while mountaineering on Mount Rainier, located in Washington State (elevation 14,410 ft). At various elevations each climber-subject used the randomly assigned device to measure the glucose level of capillary blood and three different concentrations of standardized control solutions, and a venous sample was also collected for later glucose analysis. Ordinary least squares regression was used to assess the effect of elevation and of other environmental potential covariates on the precision and accuracy of blood glucose meters. Elevation affects glucometer precision (p = 0.08), but becomes less significant (p = 0.21) when adjusted for temperature and relative humidity. The overall effect of elevation was to underestimate glucose levels by approximately 1-2% (unadjusted) for each 1,000 ft gain in elevation. Blood glucose meter accuracy was affected by elevation (p = 0.03), temperature (p < 0.01), and relative humidity (p = 0.04) after adjustment for the other variables. The interaction between elevation and relative humidity had a meaningful but not statistically significant effect on accuracy (p = 0.07). Thus, elevation, temperature, and relative humidity affect blood glucose meter performance, and elevated glucose levels are more greatly underestimated at higher elevations. Further research will help to identify which blood glucose meters are best suited for specific environments.

Towards equation of state of dark energy from quasar monitoring: Reverberation strategy

NASA Astrophysics Data System (ADS)

Czerny, B.; Hryniewicz, K.; Maity, I.; Schwarzenberg-Czerny, A.; Życki, P. T.; Bilicki, M.

2013-08-01

Context. High-redshift quasars can be used to constrain the equation of state of dark energy. They can serve as a complementary tool to supernovae Type Ia, especially at z > 1. Aims: The method is based on the determination of the size of the broad line region (BLR) from the emission line delay, the determination of the absolute monochromatic luminosity either from the observed statistical relation or from a model of the formation of the BLR, and the determination of the observed monochromatic flux from photometry. This allows the luminosity distance to a quasar to be obtained, independently from its redshift. The accuracy of the measurements is, however, a key issue. Methods: We modeled the expected accuracy of the measurements by creating artificial quasar monochromatic lightcurves and responses from the BLR under various assumptions about the variability of a quasar, BLR extension, distribution of the measurements in time, accuracy of the measurements, and the intrinsic line variability. Results: We show that the five-year monitoring of a single quasar based on the Mg II line should give an accuracy of 0.06-0.32 mag in the distance modulus which will allow new constraints to be put on the expansion rate of the Universe at high redshifts. Successful monitoring of higher redshift quasars based on C IV lines requires proper selection of the objects to avoid sources with much higher levels of the intrinsic variability of C IV compared to Mg II.

Statistical retrieval of thin liquid cloud microphysical properties using ground-based infrared and microwave observations

NASA Astrophysics Data System (ADS)

Marke, Tobias; Ebell, Kerstin; Löhnert, Ulrich; Turner, David D.

2016-12-01

In this article, liquid water cloud microphysical properties are retrieved by a combination of microwave and infrared ground-based observations. Clouds containing liquid water are frequently occurring in most climate regimes and play a significant role in terms of interaction with radiation. Small perturbations in the amount of liquid water contained in the cloud can cause large variations in the radiative fluxes. This effect is enhanced for thin clouds (liquid water path, LWP <100 g/m2), which makes accurate retrieval information of the cloud properties crucial. Due to large relative errors in retrieving low LWP values from observations in the microwave domain and a high sensitivity for infrared methods when the LWP is low, a synergistic retrieval based on a neural network approach is built to estimate both LWP and cloud effective radius (reff). These statistical retrievals can be applied without high computational demand but imply constraints like prior information on cloud phase and cloud layering. The neural network retrievals are able to retrieve LWP and reff for thin clouds with a mean relative error of 9% and 17%, respectively. This is demonstrated using synthetic observations of a microwave radiometer (MWR) and a spectrally highly resolved infrared interferometer. The accuracy and robustness of the synergistic retrievals is confirmed by a low bias in a radiative closure study for the downwelling shortwave flux, even for marginally invalid scenes. Also, broadband infrared radiance observations, in combination with the MWR, have the potential to retrieve LWP with a higher accuracy than a MWR-only retrieval.

Stochastic analysis of 1D and 2D surface topography of x-ray mirrors

NASA Astrophysics Data System (ADS)

Tyurina, Anastasia Y.; Tyurin, Yury N.; Yashchuk, Valeriy V.

2017-08-01

The design and evaluation of the expected performance of new optical systems requires sophisticated and reliable information about the surface topography for planned optical elements before they are fabricated. The problem is especially complex in the case of x-ray optics, particularly for the X-ray Surveyor under development and other missions. Modern x-ray source facilities are reliant upon the availability of optics with unprecedented quality (surface slope accuracy < 0.1μrad). The high angular resolution and throughput of future x-ray space observatories requires hundreds of square meters of high quality optics. The uniqueness of the optics and limited number of proficient vendors makes the fabrication extremely time consuming and expensive, mostly due to the limitations in accuracy and measurement rate of metrology used in fabrication. We discuss improvements in metrology efficiency via comprehensive statistical analysis of a compact volume of metrology data. The data is considered stochastic and a new statistical model called Invertible Time Invariant Linear Filter (InTILF) is developed now for 2D surface profiles to provide compact description of the 2D data additionally to 1D data treated so far. The model captures faint patterns in the data and serves as a quality metric and feedback to polishing processes, avoiding high resolution metrology measurements over the entire optical surface. The modeling, implemented in our Beatmark software, allows simulating metrology data for optics made by the same vendor and technology. The forecast data is vital for reliable specification for optical fabrication, to be exactly adequate for the required system performance.

Diagnostic accuracy of a bayesian latent group analysis for the detection of malingering-related poor effort.

PubMed

Ortega, Alonso; Labrenz, Stephan; Markowitsch, Hans J; Piefke, Martina

2013-01-01

In the last decade, different statistical techniques have been introduced to improve assessment of malingering-related poor effort. In this context, we have recently shown preliminary evidence that a Bayesian latent group model may help to optimize classification accuracy using a simulation research design. In the present study, we conducted two analyses. Firstly, we evaluated how accurately this Bayesian approach can distinguish between participants answering in an honest way (honest response group) and participants feigning cognitive impairment (experimental malingering group). Secondly, we tested the accuracy of our model in the differentiation between patients who had real cognitive deficits (cognitively impaired group) and participants who belonged to the experimental malingering group. All Bayesian analyses were conducted using the raw scores of a visual recognition forced-choice task (2AFC), the Test of Memory Malingering (TOMM, Trial 2), and the Word Memory Test (WMT, primary effort subtests). The first analysis showed 100% accuracy for the Bayesian model in distinguishing participants of both groups with all effort measures. The second analysis showed outstanding overall accuracy of the Bayesian model when estimates were obtained from the 2AFC and the TOMM raw scores. Diagnostic accuracy of the Bayesian model diminished when using the WMT total raw scores. Despite, overall diagnostic accuracy can still be considered excellent. The most plausible explanation for this decrement is the low performance in verbal recognition and fluency tasks of some patients of the cognitively impaired group. Additionally, the Bayesian model provides individual estimates, p(zi |D), of examinees' effort levels. In conclusion, both high classification accuracy levels and Bayesian individual estimates of effort may be very useful for clinicians when assessing for effort in medico-legal settings.

Implementation of statistical process control for proteomic experiments via LC MS/MS.

PubMed

Bereman, Michael S; Johnson, Richard; Bollinger, James; Boss, Yuval; Shulman, Nick; MacLean, Brendan; Hoofnagle, Andrew N; MacCoss, Michael J

2014-04-01

Statistical process control (SPC) is a robust set of tools that aids in the visualization, detection, and identification of assignable causes of variation in any process that creates products, services, or information. A tool has been developed termed Statistical Process Control in Proteomics (SProCoP) which implements aspects of SPC (e.g., control charts and Pareto analysis) into the Skyline proteomics software. It monitors five quality control metrics in a shotgun or targeted proteomic workflow. None of these metrics require peptide identification. The source code, written in the R statistical language, runs directly from the Skyline interface, which supports the use of raw data files from several of the mass spectrometry vendors. It provides real time evaluation of the chromatographic performance (e.g., retention time reproducibility, peak asymmetry, and resolution), and mass spectrometric performance (targeted peptide ion intensity and mass measurement accuracy for high resolving power instruments) via control charts. Thresholds are experiment- and instrument-specific and are determined empirically from user-defined quality control standards that enable the separation of random noise and systematic error. Finally, Pareto analysis provides a summary of performance metrics and guides the user to metrics with high variance. The utility of these charts to evaluate proteomic experiments is illustrated in two case studies.

Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

PubMed

Sadeghi, Zahra; Testolin, Alberto

2017-08-01

In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Persian character recognition based on deep belief networks, where increasingly more complex visual features emerge in a completely unsupervised manner by fitting a hierarchical generative model to the sensory data. Crucially, high-level internal representations emerging from unsupervised deep learning can be easily read out by a linear classifier, achieving state-of-the-art recognition accuracy. Furthermore, we tested the hypothesis that handwritten digits and letters share many common visual features: A generative model that captures the statistical structure of the letters distribution should therefore also support the recognition of written digits. To this aim, deep networks trained on Persian letters were used to build high-level representations of Persian digits, which were indeed read out with high accuracy. Our simulations show that complex visual features, such as those mediating the identification of Persian symbols, can emerge from unsupervised learning in multilayered neural networks and can support knowledge transfer across related domains.

Biomarker Development for Intraductal Papillary Mucinous Neoplasms Using Multiple Reaction Monitoring Mass Spectrometry.

PubMed

Kim, Yikwon; Kang, MeeJoo; Han, Dohyun; Kim, Hyunsoo; Lee, KyoungBun; Kim, Sun-Whe; Kim, Yongkang; Park, Taesung; Jang, Jin-Young; Kim, Youngsoo

2016-01-04

Intraductal papillary mucinous neoplasm (IPMN) is a common precursor of pancreatic cancer (PC). Much clinical attention has been directed toward IPMNs due to the increase in the prevalence of PC. The diagnosis of IPMN depends primarily on a radiological examination, but the diagnostic accuracy of this tool is not satisfactory, necessitating the development of accurate diagnostic biomarkers for IPMN to prevent PC. Recently, high-throughput targeted proteomic quantification methods have accelerated the discovery of biomarkers, rendering them powerful platforms for the evolution of IPMN diagnostic biomarkers. In this study, a robust multiple reaction monitoring (MRM) pipeline was applied to discovery and verify IPMN biomarker candidates in a large cohort of plasma samples. Through highly reproducible MRM assays and a stringent statistical analysis, 11 proteins were selected as IPMN marker candidates with high confidence in 184 plasma samples, comprising a training (n = 84) and test set (n = 100). To improve the discriminatory power, we constructed a six-protein panel by combining marker candidates. The multimarker panel had high discriminatory power in distinguishing between IPMN and controls, including other benign diseases. Consequently, the diagnostic accuracy of IPMN can be improved dramatically with this novel plasma-based panel in combination with a radiological examination.

Commercial enzyme-linked immunosorbent assay versuspolymerase chain reaction for the diagnosis of chronic Chagas disease: a systematic review and meta-analysis.

PubMed

Brasil, Pedro Emmanuel Alvarenga Americano do; Castro, Rodolfo; Castro, Liane de

2016-01-01

Chronic Chagas disease diagnosis relies on laboratory tests due to its clinical characteristics. The aim of this research was to review commercial enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) diagnostic test performance. Performance of commercial ELISA or PCR for the diagnosis of chronic Chagas disease were systematically searched in PubMed, Scopus, Embase, ISI Web, and LILACS through the bibliography from 1980-2014 and by contact with the manufacturers. The risk of bias was assessed with QUADAS-2. Heterogeneity was estimated with the I2 statistic. Accuracies provided by the manufacturers usually overestimate the accuracy provided by academia. The risk of bias is high in most tests and in most QUADAS dimensions. Heterogeneity is high in either sensitivity, specificity, or both. The evidence regarding commercial ELISA and ELISA-rec sensitivity and specificity indicates that there is overestimation. The current recommendation to use two simultaneous serological tests can be supported by the risk of bias analysis and the amount of heterogeneity but not by the observed accuracies. The usefulness of PCR tests are debatable and health care providers should not order them on a routine basis. PCR may be used in selected cases due to its potential to detect seronegative subjects.

Assessing Videogrammetry for Static Aeroelastic Testing of a Wind-Tunnel Model

NASA Technical Reports Server (NTRS)

Spain, Charles V.; Heeg, Jennifer; Ivanco, Thomas G.; Barrows, Danny A.; Florance, James R.; Burner, Alpheus W.; DeMoss, Joshua; Lively, Peter S.

2004-01-01

The Videogrammetric Model Deformation (VMD) technique, developed at NASA Langley Research Center, was recently used to measure displacements and local surface angle changes on a static aeroelastic wind-tunnel model. The results were assessed for consistency, accuracy and usefulness. Vertical displacement measurements and surface angular deflections (derived from vertical displacements) taken at no-wind/no-load conditions were analyzed. For accuracy assessment, angular measurements were compared to those from a highly accurate accelerometer. Shewhart's Variables Control Charts were used in the assessment of consistency and uncertainty. Some bad data points were discovered, and it is shown that the measurement results at certain targets were more consistent than at other targets. Physical explanations for this lack of consistency have not been determined. However, overall the measurements were sufficiently accurate to be very useful in monitoring wind-tunnel model aeroelastic deformation and determining flexible stability and control derivatives. After a structural model component failed during a highly loaded condition, analysis of VMD data clearly indicated progressive structural deterioration as the wind-tunnel condition where failure occurred was approached. As a result, subsequent testing successfully incorporated near- real-time monitoring of VMD data in order to ensure structural integrity. The potential for higher levels of consistency and accuracy through the use of statistical quality control practices are discussed and recommended for future applications.

Commercial enzyme-linked immunosorbent assay versus polymerase chain reaction for the diagnosis of chronic Chagas disease: a systematic review and meta-analysis

PubMed Central

do Brasil, Pedro Emmanuel Alvarenga Americano; Castro, Rodolfo; de Castro, Liane

2016-01-01

Chronic Chagas disease diagnosis relies on laboratory tests due to its clinical characteristics. The aim of this research was to review commercial enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) diagnostic test performance. Performance of commercial ELISA or PCR for the diagnosis of chronic Chagas disease were systematically searched in PubMed, Scopus, Embase, ISI Web, and LILACS through the bibliography from 1980-2014 and by contact with the manufacturers. The risk of bias was assessed with QUADAS-2. Heterogeneity was estimated with the I2 statistic. Accuracies provided by the manufacturers usually overestimate the accuracy provided by academia. The risk of bias is high in most tests and in most QUADAS dimensions. Heterogeneity is high in either sensitivity, specificity, or both. The evidence regarding commercial ELISA and ELISA-rec sensitivity and specificity indicates that there is overestimation. The current recommendation to use two simultaneous serological tests can be supported by the risk of bias analysis and the amount of heterogeneity but not by the observed accuracies. The usefulness of PCR tests are debatable and health care providers should not order them on a routine basis. PCR may be used in selected cases due to its potential to detect seronegative subjects. PMID:26814640

Statistics Refresher for Molecular Imaging Technologists, Part 2: Accuracy of Interpretation, Significance, and Variance.

PubMed

Farrell, Mary Beth

2018-06-01

This article is the second part of a continuing education series reviewing basic statistics that nuclear medicine and molecular imaging technologists should understand. In this article, the statistics for evaluating interpretation accuracy, significance, and variance are discussed. Throughout the article, actual statistics are pulled from the published literature. We begin by explaining 2 methods for quantifying interpretive accuracy: interreader and intrareader reliability. Agreement among readers can be expressed simply as a percentage. However, the Cohen κ-statistic is a more robust measure of agreement that accounts for chance. The higher the κ-statistic is, the higher is the agreement between readers. When 3 or more readers are being compared, the Fleiss κ-statistic is used. Significance testing determines whether the difference between 2 conditions or interventions is meaningful. Statistical significance is usually expressed using a number called a probability ( P ) value. Calculation of P value is beyond the scope of this review. However, knowing how to interpret P values is important for understanding the scientific literature. Generally, a P value of less than 0.05 is considered significant and indicates that the results of the experiment are due to more than just chance. Variance, standard deviation (SD), confidence interval, and standard error (SE) explain the dispersion of data around a mean of a sample drawn from a population. SD is commonly reported in the literature. A small SD indicates that there is not much variation in the sample data. Many biologic measurements fall into what is referred to as a normal distribution taking the shape of a bell curve. In a normal distribution, 68% of the data will fall within 1 SD, 95% will fall within 2 SDs, and 99.7% will fall within 3 SDs. Confidence interval defines the range of possible values within which the population parameter is likely to lie and gives an idea of the precision of the statistic being measured. A wide confidence interval indicates that if the experiment were repeated multiple times on other samples, the measured statistic would lie within a wide range of possibilities. The confidence interval relies on the SE. © 2018 by the Society of Nuclear Medicine and Molecular Imaging.

The Theory and Practice of Estimating the Accuracy of Dynamic Flight-Determined Coefficients

NASA Technical Reports Server (NTRS)

Maine, R. E.; Iliff, K. W.

1981-01-01

Means of assessing the accuracy of maximum likelihood parameter estimates obtained from dynamic flight data are discussed. The most commonly used analytical predictors of accuracy are derived and compared from both statistical and simplified geometrics standpoints. The accuracy predictions are evaluated with real and simulated data, with an emphasis on practical considerations, such as modeling error. Improved computations of the Cramer-Rao bound to correct large discrepancies due to colored noise and modeling error are presented. The corrected Cramer-Rao bound is shown to be the best available analytical predictor of accuracy, and several practical examples of the use of the Cramer-Rao bound are given. Engineering judgement, aided by such analytical tools, is the final arbiter of accuracy estimation.

Accuracy Evaluation of a Stereolithographic Surgical Template for Dental Implant Insertion Using 3D Superimposition Protocol.

PubMed

Cristache, Corina Marilena; Gurbanescu, Silviu

2017-01-01

of this study was to evaluate the accuracy of a stereolithographic template, with sleeve structure incorporated into the design, for computer-guided dental implant insertion in partially edentulous patients. Sixty-five implants were placed in twenty-five consecutive patients with a stereolithographic surgical template. After surgery, digital impression was taken and 3D inaccuracy of implants position at entry point, apex, and angle deviation was measured using an inspection tool software. Mann-Whitney U test was used to compare accuracy between maxillary and mandibular surgical guides. A p value < .05 was considered significant. Mean (and standard deviation) of 3D error at the entry point was 0.798 mm (±0.52), at the implant apex it was 1.17 mm (±0.63), and mean angular deviation was 2.34 (±0.85). A statistically significant reduced 3D error was observed at entry point p = .037, at implant apex p = .008, and also in angular deviation p = .030 in mandible when comparing to maxilla. The surgical template used has proved high accuracy for implant insertion. Within the limitations of the present study, the protocol for comparing a digital file (treatment plan) with postinsertion digital impression may be considered a useful procedure for assessing surgical template accuracy, avoiding radiation exposure, during postoperative CBCT scanning.

Sensitivity, Specificity, Predictive Values, and Accuracy of Three Diagnostic Tests to Predict Inferior Alveolar Nerve Blockade Failure in Symptomatic Irreversible Pulpitis

PubMed Central

Rodríguez-Wong, Laura; Noguera-González, Danny; Esparza-Villalpando, Vicente; Montero-Aguilar, Mauricio

2017-01-01

Introduction The inferior alveolar nerve block (IANB) is the most common anesthetic technique used on mandibular teeth during root canal treatment. Its success in the presence of preoperative inflammation is still controversial. The aim of this study was to evaluate the sensitivity, specificity, predictive values, and accuracy of three diagnostic tests used to predict IANB failure in symptomatic irreversible pulpitis (SIP). Methodology A cross-sectional study was carried out on the mandibular molars of 53 patients with SIP. All patients received a single cartridge of mepivacaine 2% with 1 : 100000 epinephrine using the IANB technique. Three diagnostic clinical tests were performed to detect anesthetic failure. Anesthetic failure was defined as a positive painful response to any of the three tests. Sensitivity, specificity, predictive values, accuracy, and ROC curves were calculated and compared and significant differences were analyzed. Results IANB failure was determined in 71.7% of the patients. The sensitivity scores for the three tests (lip numbness, the cold stimuli test, and responsiveness during endodontic access) were 0.03, 0.35, and 0.55, respectively, and the specificity score was determined as 1 for all of the tests. Clinically, none of the evaluated tests demonstrated a high enough accuracy (0.30, 0.53, and 0.68 for lip numbness, the cold stimuli test, and responsiveness during endodontic access, resp.). A comparison of the areas under the curve in the ROC analyses showed statistically significant differences between the three tests (p < 0.05). Conclusion None of the analyzed tests demonstrated a high enough accuracy to be considered a reliable diagnostic tool for the prediction of anesthetic failure. PMID:28694714

Downlink Probability Density Functions for EOS-McMurdo Sound

NASA Technical Reports Server (NTRS)

Christopher, P.; Jackson, A. H.

1996-01-01

The visibility times and communication link dynamics for the Earth Observations Satellite (EOS)-McMurdo Sound direct downlinks have been studied. The 16 day EOS periodicity may be shown with the Goddard Trajectory Determination System (GTDS) and the entire 16 day period should be simulated for representative link statistics. We desire many attributes of the downlink, however, and a faster orbital determination method is desirable. We use the method of osculating elements for speed and accuracy in simulating the EOS orbit. The accuracy of the method of osculating elements is demonstrated by closely reproducing the observed 16 day Landsat periodicity. An autocorrelation function method is used to show the correlation spike at 16 days. The entire 16 day record of passes over McMurdo Sound is then used to generate statistics for innage time, outage time, elevation angle, antenna angle rates, and propagation loss. The levation angle probability density function is compared with 1967 analytic approximation which has been used for medium to high altitude satellites. One practical result of this comparison is seen to be the rare occurrence of zenith passes. The new result is functionally different than the earlier result, with a heavy emphasis on low elevation angles. EOS is one of a large class of sun synchronous satellites which may be downlinked to McMurdo Sound. We examine delay statistics for an entire group of sun synchronous satellites ranging from 400 km to 1000 km altitude. Outage probability density function results are presented three dimensionally.

Statistical models for fever forecasting based on advanced body temperature monitoring.

PubMed

Jordan, Jorge; Miro-Martinez, Pau; Vargas, Borja; Varela-Entrecanales, Manuel; Cuesta-Frau, David

2017-02-01

Body temperature monitoring provides health carers with key clinical information about the physiological status of patients. Temperature readings are taken periodically to detect febrile episodes and consequently implement the appropriate medical countermeasures. However, fever is often difficult to assess at early stages, or remains undetected until the next reading, probably a few hours later. The objective of this article is to develop a statistical model to forecast fever before a temperature threshold is exceeded to improve the therapeutic approach to the subjects involved. To this end, temperature series of 9 patients admitted to a general internal medicine ward were obtained with a continuous monitoring Holter device, collecting measurements of peripheral and core temperature once per minute. These series were used to develop different statistical models that could quantify the probability of having a fever spike in the following 60 minutes. A validation series was collected to assess the accuracy of the models. Finally, the results were compared with the analysis of some series by experienced clinicians. Two different models were developed: a logistic regression model and a linear discrimination analysis model. Both of them exhibited a fever peak forecasting accuracy greater than 84%. When compared with experts' assessment, both models identified 35 (97.2%) of 36 fever spikes. The models proposed are highly accurate in forecasting the appearance of fever spikes within a short period in patients with suspected or confirmed febrile-related illnesses. Copyright © 2016 Elsevier Inc. All rights reserved.

Incorporating Functional Annotations for Fine-Mapping Causal Variants in a Bayesian Framework Using Summary Statistics.

PubMed

Chen, Wenan; McDonnell, Shannon K; Thibodeau, Stephen N; Tillmans, Lori S; Schaid, Daniel J

2016-11-01

Functional annotations have been shown to improve both the discovery power and fine-mapping accuracy in genome-wide association studies. However, the optimal strategy to incorporate the large number of existing annotations is still not clear. In this study, we propose a Bayesian framework to incorporate functional annotations in a systematic manner. We compute the maximum a posteriori solution and use cross validation to find the optimal penalty parameters. By extending our previous fine-mapping method CAVIARBF into this framework, we require only summary statistics as input. We also derived an exact calculation of Bayes factors using summary statistics for quantitative traits, which is necessary when a large proportion of trait variance is explained by the variants of interest, such as in fine mapping expression quantitative trait loci (eQTL). We compared the proposed method with PAINTOR using different strategies to combine annotations. Simulation results show that the proposed method achieves the best accuracy in identifying causal variants among the different strategies and methods compared. We also find that for annotations with moderate effects from a large annotation pool, screening annotations individually and then combining the top annotations can produce overly optimistic results. We applied these methods on two real data sets: a meta-analysis result of lipid traits and a cis-eQTL study of normal prostate tissues. For the eQTL data, incorporating annotations significantly increased the number of potential causal variants with high probabilities. Copyright © 2016 by the Genetics Society of America.

Characteristics of genomic signatures derived using univariate methods and mechanistically anchored functional descriptors for predicting drug- and xenobiotic-induced nephrotoxicity.

PubMed

Shi, Weiwei; Bugrim, Andrej; Nikolsky, Yuri; Nikolskya, Tatiana; Brennan, Richard J

2008-01-01

ABSTRACT The ideal toxicity biomarker is composed of the properties of prediction (is detected prior to traditional pathological signs of injury), accuracy (high sensitivity and specificity), and mechanistic relationships to the endpoint measured (biological relevance). Gene expression-based toxicity biomarkers ("signatures") have shown good predictive power and accuracy, but are difficult to interpret biologically. We have compared different statistical methods of feature selection with knowledge-based approaches, using GeneGo's database of canonical pathway maps, to generate gene sets for the classification of renal tubule toxicity. The gene set selection algorithms include four univariate analyses: t-statistics, fold-change, B-statistics, and RankProd, and their combination and overlap for the identification of differentially expressed probes. Enrichment analysis following the results of the four univariate analyses, Hotelling T-square test, and, finally out-of-bag selection, a variant of cross-validation, were used to identify canonical pathway maps-sets of genes coordinately involved in key biological processes-with classification power. Differentially expressed genes identified by the different statistical univariate analyses all generated reasonably performing classifiers of tubule toxicity. Maps identified by enrichment analysis or Hotelling T-square had lower classification power, but highlighted perturbed lipid homeostasis as a common discriminator of nephrotoxic treatments. The out-of-bag method yielded the best functionally integrated classifier. The map "ephrins signaling" performed comparably to a classifier derived using sparse linear programming, a machine learning algorithm, and represents a signaling network specifically involved in renal tubule development and integrity. Such functional descriptors of toxicity promise to better integrate predictive toxicogenomics with mechanistic analysis, facilitating the interpretation and risk assessment of predictive genomic investigations.

Ex vivo accuracy of an apex locator using digital signal processing in primary teeth.

PubMed

Leonardo, Mário Roberto; da Silva, Lea Assed Bezerra; Nelson-Filho, Paulo; da Silva, Raquel Assed Bezerra; Lucisano, Marília Pacífico

2009-01-01

The purpose of this study was to evaluate ex vivo the accuracy an electronic apex locator during root canal length determination in primary molars. One calibrated examiner determined the root canal length in 15 primary molars (total=34 root canals) with different stages of root resorption. Root canal length was measured both visually with the placement of a K-file 1 mm short of the apical foramen or the apical resorption bevel, and electronically using an electronic apex locator (Digital Signal Processing). Data were analyzed statistically using the intraclass correlation (ICC) test. Comparing the actual and electronic root canal length measurements in the primary teeth showed a high correlation (ICC=0.95). The Digital Signal Processing apex locator is useful and accurate for apex foramen location during root canal length measurement in primary molars.

Android Malware Classification Using K-Means Clustering Algorithm

NASA Astrophysics Data System (ADS)

Hamid, Isredza Rahmi A.; Syafiqah Khalid, Nur; Azma Abdullah, Nurul; Rahman, Nurul Hidayah Ab; Chai Wen, Chuah

2017-08-01

Malware was designed to gain access or damage a computer system without user notice. Besides, attacker exploits malware to commit crime or fraud. This paper proposed Android malware classification approach based on K-Means clustering algorithm. We evaluate the proposed model in terms of accuracy using machine learning algorithms. Two datasets were selected to demonstrate the practicing of K-Means clustering algorithms that are Virus Total and Malgenome dataset. We classify the Android malware into three clusters which are ransomware, scareware and goodware. Nine features were considered for each types of dataset such as Lock Detected, Text Detected, Text Score, Encryption Detected, Threat, Porn, Law, Copyright and Moneypak. We used IBM SPSS Statistic software for data classification and WEKA tools to evaluate the built cluster. The proposed K-Means clustering algorithm shows promising result with high accuracy when tested using Random Forest algorithm.

Examining the Effectiveness of Discriminant Function Analysis and Cluster Analysis in Species Identification of Male Field Crickets Based on Their Calling Songs

PubMed Central

Jaiswara, Ranjana; Nandi, Diptarup; Balakrishnan, Rohini

2013-01-01

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6–7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification. PMID:24086666

Measurement of the parity violating 6S-7S transition amplitude in cesium achieved within 2×10-13 atomic-unit accuracy by stimulated-emission detection

NASA Astrophysics Data System (ADS)

Guéna, J.; Lintz, M.; Bouchiat, M. A.

2005-04-01

We exploit the process of asymmetry amplification by stimulated emission which provides an original method for parity violation (PV) measurements in a highly forbidden atomic transition. The method involves measurements of a chiral, transient, optical gain of a cesium vapor on the 7S-6P3/2 transition, probed after it is excited by an intense, linearly polarized, collinear laser, tuned to resonance for one hyperfine line of the forbidden 6S-7S transition in a longitudinal electric field. We report here a 3.5-fold increase of the one-second-measurement sensitivity and subsequent reduction by a factor of 3.5 of the statistical accuracy compared with our previous result [J. Guéna , Phys. Rev. Lett. 90, 143001 (2003)]. Decisive improvements to the setup include an increased repetition rate, better extinction of the probe beam at the end of the probe pulse, and, for the first time to our knowledge, the following: a polarization-tilt magnifier, quasisuppression of beam reflections at the cell windows, and a Cs cell with electrically conductive windows. We also present real-time tests of systematic effects and consistency checks on the data, as well as a 1% accurate measurement of the electric field seen by the atoms, from atomic signals. PV measurements performed in seven different vapor cells agree within the statistical error. Our present result is compatible with the more precise result of Wood within our present relative statistical accuracy of 2.6%, corresponding to a 2×10-13 atomic-unit uncertainty in E1pv . Theoretical motivations for further measurements are emphasized and we give a brief overview of a recent proposal that would allow the uncertainty to be reduced to the 0.1% level by creating conditions where asymmetry amplification is much greater.

Paroxysmal atrial fibrillation prediction method with shorter HRV sequences.

PubMed

Boon, K H; Khalil-Hani, M; Malarvili, M B; Sia, C W

2016-10-01

This paper proposes a method that predicts the onset of paroxysmal atrial fibrillation (PAF), using heart rate variability (HRV) segments that are shorter than those applied in existing methods, while maintaining good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to stabilize (electrically) and prevent the onset of atrial arrhythmias with different pacing techniques. We investigate the effect of HRV features extracted from different lengths of HRV segments prior to PAF onset with the proposed PAF prediction method. The pre-processing stage of the predictor includes QRS detection, HRV quantification and ectopic beat correction. Time-domain, frequency-domain, non-linear and bispectrum features are then extracted from the quantified HRV. In the feature selection, the HRV feature set and classifier parameters are optimized simultaneously using an optimization procedure based on genetic algorithm (GA). Both full feature set and statistically significant feature subset are optimized by GA respectively. For the statistically significant feature subset, Mann-Whitney U test is used to filter non-statistical significance features that cannot pass the statistical test at 20% significant level. The final stage of our predictor is the classifier that is based on support vector machine (SVM). A 10-fold cross-validation is applied in performance evaluation, and the proposed method achieves 79.3% prediction accuracy using 15-minutes HRV segment. This accuracy is comparable to that achieved by existing methods that use 30-minutes HRV segments, most of which achieves accuracy of around 80%. More importantly, our method significantly outperforms those that applied segments shorter than 30 minutes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

[Statistical approach to evaluate the occurrence of out-of acceptable ranges and accuracy for antimicrobial susceptibility tests in inter-laboratory quality control program].

PubMed

Ueno, Tamio; Matuda, Junichi; Yamane, Nobuhisa

2013-03-01

To evaluate the occurrence of out-of acceptable ranges and accuracy of antimicrobial susceptibility tests, we applied a new statistical tool to the Inter-Laboratory Quality Control Program established by the Kyushu Quality Control Research Group. First, we defined acceptable ranges of minimum inhibitory concentration (MIC) for broth microdilution tests and inhibitory zone diameter for disk diffusion tests on the basis of Clinical and Laboratory Standards Institute (CLSI) M100-S21. In the analysis, more than two out-of acceptable range results in the 20 tests were considered as not allowable according to the CLSI document. Of the 90 participating laboratories, 46 (51%) experienced one or more occurrences of out-of acceptable range results. Then, a binomial test was applied to each participating laboratory. The results indicated that the occurrences of out-of acceptable range results in the 11 laboratories were significantly higher when compared to the CLSI recommendation (allowable rate < or = 0.05). The standard deviation indices(SDI) were calculated by using reported results, mean and standard deviation values for the respective antimicrobial agents tested. In the evaluation of accuracy, mean value from each laboratory was statistically compared with zero using a Student's t-test. The results revealed that 5 of the 11 above laboratories reported erroneous test results that systematically drifted to the side of resistance. In conclusion, our statistical approach has enabled us to detect significantly higher occurrences and source of interpretive errors in antimicrobial susceptibility tests; therefore, this approach can provide us with additional information that can improve the accuracy of the test results in clinical microbiology laboratories.

The effect of search term on the quality and accuracy of online information regarding distal radius fractures.

PubMed

Dy, Christopher J; Taylor, Samuel A; Patel, Ronak M; Kitay, Alison; Roberts, Timothy R; Daluiski, Aaron

2012-09-01

Recent emphasis on shared decision making and patient-centered research has increased the importance of patient education and health literacy. The internet is rapidly growing as a source of self-education for patients. However, concern exists over the quality, accuracy, and readability of the information. Our objective was to determine whether the quality, accuracy, and readability of information online about distal radius fractures vary with the search term. This was a prospective evaluation of 3 search engines using 3 different search terms of varying sophistication ("distal radius fracture," "wrist fracture," and "broken wrist"). We evaluated 70 unique Web sites for quality, accuracy, and readability. We used comparative statistics to determine whether the search term affected the quality, accuracy, and readability of the Web sites found. Three orthopedic surgeons independently gauged quality and accuracy of information using a set of predetermined scoring criteria. We evaluated the readability of the Web site using the Fleisch-Kincaid score for reading grade level. There were significant differences in the quality, accuracy, and readability of information found, depending on the search term. We found higher quality and accuracy resulted from the search term "distal radius fracture," particularly compared with Web sites resulting from the term "broken wrist." The reading level was higher than recommended in 65 of the 70 Web sites and was significantly higher when searching with "distal radius fracture" than "wrist fracture" or "broken wrist." There was no correlation between Web site reading level and quality or accuracy. The readability of information about distal radius fractures in most Web sites was higher than the recommended reading level for the general public. The quality and accuracy of the information found significantly varied with the sophistication of the search term used. Physicians, professional societies, and search engines should consider efforts to improve internet access to high-quality information at an understandable level. Copyright © 2012 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.

Influence of time of placement of investments for burnout and the type of rings being used on the casting accuracy.

PubMed

Shah, Shabir A; Naqash, Talib Amin; Padmanabhan, T V; Subramanium; Lambodaran; Nazir, Shazana

2014-03-01

The sole objective of casting procedure is to provide a metallic duplication of missing tooth structure, with as great accuracy as possible. The ability to produce well fitting castings require strict adherence to certain fundamentals. A study was undertaken to comparatively evaluate the effect on casting accuracy by subjecting the invested wax patterns to burnout after different time intervals. The effect on casting accuracy using metal ring into a pre heated burnout furnace and using split ring was also carried. The readings obtained were tabulated and subjected to statistical analysis.

Measurements of experimental precision for trials with cowpea (Vigna unguiculata L. Walp.) genotypes.

PubMed

Teodoro, P E; Torres, F E; Santos, A D; Corrêa, A M; Nascimento, M; Barroso, L M A; Ceccon, G

2016-05-09

The aim of this study was to evaluate the suitability of statistics as experimental precision degree measures for trials with cowpea (Vigna unguiculata L. Walp.) genotypes. Cowpea genotype yields were evaluated in 29 trials conducted in Brazil between 2005 and 2012. The genotypes were evaluated with a randomized block design with four replications. Ten statistics that were estimated for each trial were compared using descriptive statistics, Pearson correlations, and path analysis. According to the class limits established, selective accuracy and F-test values for genotype, heritability, and the coefficient of determination adequately estimated the degree of experimental precision. Using these statistics, 86.21% of the trials had adequate experimental precision. Selective accuracy and the F-test values for genotype, heritability, and the coefficient of determination were directly related to each other, and were more suitable than the coefficient of variation and the least significant difference (by the Tukey test) to evaluate experimental precision in trials with cowpea genotypes.

Blind image quality assessment based on aesthetic and statistical quality-aware features

NASA Astrophysics Data System (ADS)

Jenadeleh, Mohsen; Masaeli, Mohammad Masood; Moghaddam, Mohsen Ebrahimi

2017-07-01

The main goal of image quality assessment (IQA) methods is the emulation of human perceptual image quality judgments. Therefore, the correlation between objective scores of these methods with human perceptual scores is considered as their performance metric. Human judgment of the image quality implicitly includes many factors when assessing perceptual image qualities such as aesthetics, semantics, context, and various types of visual distortions. The main idea of this paper is to use a host of features that are commonly employed in image aesthetics assessment in order to improve blind image quality assessment (BIQA) methods accuracy. We propose an approach that enriches the features of BIQA methods by integrating a host of aesthetics image features with the features of natural image statistics derived from multiple domains. The proposed features have been used for augmenting five different state-of-the-art BIQA methods, which use statistical natural scene statistics features. Experiments were performed on seven benchmark image quality databases. The experimental results showed significant improvement of the accuracy of the methods.

The control of manual entry accuracy in management/engineering information systems, phase 1

NASA Technical Reports Server (NTRS)

Hays, Daniel; Nocke, Henry; Wilson, Harold; Woo, John, Jr.; Woo, June

1987-01-01

It was shown that clerical personnel can be tested for proofreading performance under simulated industrial conditions. A statistical study showed that errors in proofreading follow an extreme value probability theory. The study showed that innovative man/machine interfaces can be developed to improve and control accuracy during data entry.

An improved method for determining force balance calibration accuracy

NASA Technical Reports Server (NTRS)

Ferris, Alice T.

1993-01-01

The results of an improved statistical method used at Langley Research Center for determining and stating the accuracy of a force balance calibration are presented. The application of the method for initial loads, initial load determination, auxiliary loads, primary loads, and proof loads is described. The data analysis is briefly addressed.

Assessing the Accuracy and Consistency of Language Proficiency Classification under Competing Measurement Models

ERIC Educational Resources Information Center

Zhang, Bo

2010-01-01

This article investigates how measurement models and statistical procedures can be applied to estimate the accuracy of proficiency classification in language testing. The paper starts with a concise introduction of four measurement models: the classical test theory (CTT) model, the dichotomous item response theory (IRT) model, the testlet response…

Comparison of the accuracy of radiography and ultrasonography for detection of articular lesions in horses.

PubMed

Hinz, Antje; Fischer, Andrew T

2011-10-01

To compare the accuracy of ultrasonographic and radiographic examination for evaluation of articular lesions in horses. Cross-sectional study. Horses (n = 137) with articular lesions. Radiographic and ultrasonographic examinations of the affected joint(s) were performed before diagnostic or therapeutic arthroscopic surgery. Findings were recorded and compared to lesions identified during arthroscopy. In 254 joints, 432 lesions were identified by arthroscopy. The overall accuracy was 82.9% for ultrasonography and 62.2% for radiography (P < .0001) with a sensitivity of 91.4% for ultrasonography and 66.7% for radiography (P < .0001). The difference in specificity was not statistically significant (P = .2628). The negative predictive value for ultrasonography was 31.5% and 13.2% for radiography (P = .0022), the difference for the positive predictive value was not statistically significant (P = .3898). The accuracy for ultrasonography and radiography for left versus right joints was equal and corresponded with the overall results. Ultrasonographic evaluation of articular lesions was more accurate than radiographic evaluation. © Copyright 2011 by The American College of Veterinary Surgeons.

Approximate Single-Diode Photovoltaic Model for Efficient I-V Characteristics Estimation

PubMed Central

Ting, T. O.; Zhang, Nan; Guan, Sheng-Uei; Wong, Prudence W. H.

2013-01-01

Precise photovoltaic (PV) behavior models are normally described by nonlinear analytical equations. To solve such equations, it is necessary to use iterative procedures. Aiming to make the computation easier, this paper proposes an approximate single-diode PV model that enables high-speed predictions for the electrical characteristics of commercial PV modules. Based on the experimental data, statistical analysis is conducted to validate the approximate model. Simulation results show that the calculated current-voltage (I-V) characteristics fit the measured data with high accuracy. Furthermore, compared with the existing modeling methods, the proposed model reduces the simulation time by approximately 30% in this work. PMID:24298205

Thermocouple Calibration and Accuracy in a Materials Testing Laboratory

NASA Technical Reports Server (NTRS)

Lerch, B. A.; Nathal, M. V.; Keller, D. J.

2002-01-01

A consolidation of information has been provided that can be used to define procedures for enhancing and maintaining accuracy in temperature measurements in materials testing laboratories. These studies were restricted to type R and K thermocouples (TCs) tested in air. Thermocouple accuracies, as influenced by calibration methods, thermocouple stability, and manufacturer's tolerances were all quantified in terms of statistical confidence intervals. By calibrating specific TCs the benefits in accuracy can be as great as 6 C or 5X better compared to relying on manufacturer's tolerances. The results emphasize strict reliance on the defined testing protocol and on the need to establish recalibration frequencies in order to maintain these levels of accuracy.

Characterizing the D2 statistic: word matches in biological sequences.

PubMed

Forêt, Sylvain; Wilson, Susan R; Burden, Conrad J

2009-01-01

Word matches are often used in sequence comparison methods, either as a measure of sequence similarity or in the first search steps of algorithms such as BLAST or BLAT. The D2 statistic is the number of matches of words of k letters between two sequences. Recent advances have been made in the characterization of this statistic and in the approximation of its distribution. Here, these results are extended to the case of approximate word matches. We compute the exact value of the variance of the D2 statistic for the case of a uniform letter distribution, and introduce a method to provide accurate approximations of the variance in the remaining cases. This enables the distribution of D2 to be approximated for typical situations arising in biological research. We apply these results to the identification of cis-regulatory modules, and show that this method detects such sequences with a high accuracy. The ability to approximate the distribution of D2 for both exact and approximate word matches will enable the use of this statistic in a more precise manner for sequence comparison, database searches, and identification of transcription factor binding sites.

Assessing Statistically Significant Heavy-Metal Concentrations in Abandoned Mine Areas via Hot Spot Analysis of Portable XRF Data

PubMed Central

Kim, Sung-Min; Choi, Yosoon

2017-01-01

To develop appropriate measures to prevent soil contamination in abandoned mining areas, an understanding of the spatial variation of the potentially toxic trace elements (PTEs) in the soil is necessary. For the purpose of effective soil sampling, this study uses hot spot analysis, which calculates a z-score based on the Getis-Ord Gi* statistic to identify a statistically significant hot spot sample. To constitute a statistically significant hot spot, a feature with a high value should also be surrounded by other features with high values. Using relatively cost- and time-effective portable X-ray fluorescence (PXRF) analysis, sufficient input data are acquired from the Busan abandoned mine and used for hot spot analysis. To calibrate the PXRF data, which have a relatively low accuracy, the PXRF analysis data are transformed using the inductively coupled plasma atomic emission spectrometry (ICP-AES) data. The transformed PXRF data of the Busan abandoned mine are classified into four groups according to their normalized content and z-scores: high content with a high z-score (HH), high content with a low z-score (HL), low content with a high z-score (LH), and low content with a low z-score (LL). The HL and LH cases may be due to measurement errors. Additional or complementary surveys are required for the areas surrounding these suspect samples or for significant hot spot areas. The soil sampling is conducted according to a four-phase procedure in which the hot spot analysis and proposed group classification method are employed to support the development of a sampling plan for the following phase. Overall, 30, 50, 80, and 100 samples are investigated and analyzed in phases 1–4, respectively. The method implemented in this case study may be utilized in the field for the assessment of statistically significant soil contamination and the identification of areas for which an additional survey is required. PMID:28629168

Assessing Statistically Significant Heavy-Metal Concentrations in Abandoned Mine Areas via Hot Spot Analysis of Portable XRF Data.

PubMed

Kim, Sung-Min; Choi, Yosoon

2017-06-18

To develop appropriate measures to prevent soil contamination in abandoned mining areas, an understanding of the spatial variation of the potentially toxic trace elements (PTEs) in the soil is necessary. For the purpose of effective soil sampling, this study uses hot spot analysis, which calculates a z -score based on the Getis-Ord Gi* statistic to identify a statistically significant hot spot sample. To constitute a statistically significant hot spot, a feature with a high value should also be surrounded by other features with high values. Using relatively cost- and time-effective portable X-ray fluorescence (PXRF) analysis, sufficient input data are acquired from the Busan abandoned mine and used for hot spot analysis. To calibrate the PXRF data, which have a relatively low accuracy, the PXRF analysis data are transformed using the inductively coupled plasma atomic emission spectrometry (ICP-AES) data. The transformed PXRF data of the Busan abandoned mine are classified into four groups according to their normalized content and z -scores: high content with a high z -score (HH), high content with a low z -score (HL), low content with a high z -score (LH), and low content with a low z -score (LL). The HL and LH cases may be due to measurement errors. Additional or complementary surveys are required for the areas surrounding these suspect samples or for significant hot spot areas. The soil sampling is conducted according to a four-phase procedure in which the hot spot analysis and proposed group classification method are employed to support the development of a sampling plan for the following phase. Overall, 30, 50, 80, and 100 samples are investigated and analyzed in phases 1-4, respectively. The method implemented in this case study may be utilized in the field for the assessment of statistically significant soil contamination and the identification of areas for which an additional survey is required.

Modeling the Lyα Forest in Collisionless Simulations

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

Sorini, Daniele; Oñorbe, José; Lukić, Zarija

2016-08-11

Cosmological hydrodynamic simulations can accurately predict the properties of the intergalactic medium (IGM), but only under the condition of retaining the high spatial resolution necessary to resolve density fluctuations in the IGM. This resolution constraint prohibits simulating large volumes, such as those probed by BOSS and future surveys, like DESI and 4MOST. To overcome this limitation, we present in this paper "Iteratively Matched Statistics" (IMS), a novel method to accurately model the Lyα forest with collisionless N-body simulations, where the relevant density fluctuations are unresolved. We use a small-box, high-resolution hydrodynamic simulation to obtain the probability distribution function (PDF) andmore » the power spectrum of the real-space Lyα forest flux. These two statistics are iteratively mapped onto a pseudo-flux field of an N-body simulation, which we construct from the matter density. We demonstrate that our method can reproduce the PDF, line of sight and 3D power spectra of the Lyα forest with good accuracy (7%, 4%, and 7% respectively). We quantify the performance of the commonly used Gaussian smoothing technique and show that it has significantly lower accuracy (20%–80%), especially for N-body simulations with achievable mean inter-particle separations in large-volume simulations. Finally, in addition, we show that IMS produces reasonable and smooth spectra, making it a powerful tool for modeling the IGM in large cosmological volumes and for producing realistic "mock" skies for Lyα forest surveys.« less

An algorithm for synchronizing a clock when the data are received over a network with an unstable delay

PubMed Central

Levine, Judah

2016-01-01

A method is presented for synchronizing the time of a clock to a remote time standard when the channel connecting the two has significant delay variation that can be described only statistically. The method compares the Allan deviation of the channel fluctuations to the free-running stability of the local clock, and computes the optimum interval between requests based on one of three selectable requirements: (1) choosing the highest possible accuracy, (2) choosing the best tradeoff of cost vs. accuracy, or (3) minimizing the number of requests to realize a specific accuracy. Once the interval between requests is chosen, the final step is to steer the local clock based on the received data. A typical adjustment algorithm, which supports both the statistical considerations based on the Allan deviation comparison and the timely detection of errors is included as an example. PMID:26529759

Screening of 485 Pesticide Residues in Fruits and Vegetables by Liquid Chromatography-Quadrupole-Time-of-Flight Mass Spectrometry Based on TOF Accurate Mass Database and QTOF Spectrum Library.

PubMed

Pang, Guo-Fang; Fan, Chun-Lin; Chang, Qiao-Ying; Li, Jian-Xun; Kang, Jian; Lu, Mei-Ling

2018-03-22

This paper uses the LC-quadrupole-time-of-flight MS technique to evaluate the behavioral characteristics of MSof 485 pesticides under different conditions and has developed an accurate mass database and spectra library. A high-throughput screening and confirmation method has been developed for the 485 pesticides in fruits and vegetables. Through the optimization of parameters such as accurate mass number, time of retention window, ionization forms, etc., the method has improved the accuracy of pesticide screening, thus avoiding the occurrence of false-positive and false-negative results. The method features a full scan of fragments, with 80% of pesticide qualitative points over 10, which helps increase pesticide qualitative accuracy. The abundant differences of fragment categories help realize the effective separation and qualitative identification of isomer pesticides. Four different fruits and vegetables-apples, grapes, celery, and tomatoes-were chosen to evaluate the efficiency of the method at three fortification levels of 5, 10, and 20 μg/kg, and satisfactory results were obtained. With this method, a national survey of pesticide residues was conducted between 2012 and 2015 for 12 551 samples of 146 different fruits and vegetables collected from 638 sampling points in 284 counties across 31 provincial capitals/cities directly under the central government, which provided scientific data backup for ensuring pesticide residue safety of the fruits and vegetables consumed daily by the public. Meanwhile, the big data statistical analysis of the new technique also further proves it to be of high speed, high throughput, high accuracy, high reliability, and high informatization.

Fluency and belief bias in deductive reasoning: new indices for old effects

PubMed Central

Trippas, Dries; Handley, Simon J.; Verde, Michael F.

2014-01-01

Models based on signal detection theory (SDT) have occupied a prominent role in domains such as perception, categorization, and memory. Recent work by Dube et al. (2010) suggests that the framework may also offer important insights in the domain of deductive reasoning. Belief bias in reasoning has traditionally been examined using indices based on raw endorsement rates—indices that critics have claimed are highly problematic. We discuss a new set of SDT indices fit for the investigation belief bias and apply them to new data examining the effect of perceptual disfluency on belief bias in syllogisms. In contrast to the traditional approach, the SDT indices do not violate important statistical assumptions, resulting in a decreased Type 1 error rate. Based on analyses using these novel indices we demonstrate that perceptual disfluency leads to decreased reasoning accuracy, contrary to predictions. Disfluency also appears to eliminate the typical link found between cognitive ability and the effect of beliefs on accuracy. Finally, replicating previous work, we demonstrate that cognitive ability leads to an increase in reasoning accuracy and a decrease in the response bias component of belief bias. PMID:25009515

Utilization of the Deep Space Atomic Clock for Europa Gravitational Tide Recovery

NASA Technical Reports Server (NTRS)

Seubert, Jill; Ely, Todd

2015-01-01

Estimation of Europa's gravitational tide can provide strong evidence of the existence of a subsurface liquid ocean. Due to limited close approach tracking data, a Europa flyby mission suffers strong coupling between the gravity solution quality and tracking data quantity and quality. This work explores utilizing Low Gain Antennas with the Deep Space Atomic Clock (DSAC) to provide abundant high accuracy uplink-only radiometric tracking data. DSAC's performance, expected to exhibit an Allan Deviation of less than 3e-15 at one day, provides long-term stability and accuracy on par with the Deep Space Network ground clocks, enabling one-way radiometric tracking data with accuracy equivalent to that of its two-way counterpart. The feasibility of uplink-only Doppler tracking via the coupling of LGAs and DSAC and the expected Doppler data quality are presented. Violations of the Kalman filter's linearization assumptions when state perturbations are included in the flyby analysis results in poor determination of the Europa gravitational tide parameters. B-plane targeting constraints are statistically determined, and a solution to the linearization issues via pre-flyby approach orbit determination is proposed and demonstrated.

New machine-learning algorithms for prediction of Parkinson's disease

NASA Astrophysics Data System (ADS)

Mandal, Indrajit; Sairam, N.

2014-03-01

This article presents an enhanced prediction accuracy of diagnosis of Parkinson's disease (PD) to prevent the delay and misdiagnosis of patients using the proposed robust inference system. New machine-learning methods are proposed and performance comparisons are based on specificity, sensitivity, accuracy and other measurable parameters. The robust methods of treating Parkinson's disease (PD) includes sparse multinomial logistic regression, rotation forest ensemble with support vector machines and principal components analysis, artificial neural networks, boosting methods. A new ensemble method comprising of the Bayesian network optimised by Tabu search algorithm as classifier and Haar wavelets as projection filter is used for relevant feature selection and ranking. The highest accuracy obtained by linear logistic regression and sparse multinomial logistic regression is 100% and sensitivity, specificity of 0.983 and 0.996, respectively. All the experiments are conducted over 95% and 99% confidence levels and establish the results with corrected t-tests. This work shows a high degree of advancement in software reliability and quality of the computer-aided diagnosis system and experimentally shows best results with supportive statistical inference.

Parkinson's disease detection based on dysphonia measurements

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2017-04-01

Assessing dysphonic symptoms is a noninvasive and effective approach to detect Parkinson's disease (PD) in patients. The main purpose of this study is to investigate the effect of different dysphonia measurements on PD detection by support vector machine (SVM). Seven categories of dysphonia measurements are considered. Experimental results from ten-fold cross-validation technique demonstrate that vocal fundamental frequency statistics yield the highest accuracy of 88 % ± 0.04. When all dysphonia measurements are employed, the SVM classifier achieves 94 % ± 0.03 accuracy. A refinement of the original patterns space by removing dysphonia measurements with similar variation across healthy and PD subjects allows achieving 97.03 % ± 0.03 accuracy. The latter performance is larger than what is reported in the literature on the same dataset with ten-fold cross-validation technique. Finally, it was found that measures of ratio of noise to tonal components in the voice are the most suitable dysphonic symptoms to detect PD subjects as they achieve 99.64 % ± 0.01 specificity. This finding is highly promising for understanding PD symptoms.

Camera Calibration with Radial Variance Component Estimation

NASA Astrophysics Data System (ADS)

Mélykuti, B.; Kruck, E. J.

2014-11-01

Camera calibration plays a more and more important role in recent times. Beside real digital aerial survey cameras the photogrammetric market is dominated by a big number of non-metric digital cameras mounted on UAVs or other low-weight flying platforms. The in-flight calibration of those systems has a significant role to enhance the geometric accuracy of survey photos considerably. It is expected to have a better precision of photo measurements in the center of images then along the edges or in the corners. With statistical methods the accuracy of photo measurements in dependency of the distance of points from image center has been analyzed. This test provides a curve for the measurement precision as function of the photo radius. A high number of camera types have been tested with well penetrated point measurements in image space. The result of the tests led to a general consequence to show a functional connection between accuracy and radial distance and to give a method how to check and enhance the geometrical capability of the cameras in respect to these results.

Computer aided manual validation of mass spectrometry-based proteomic data.

PubMed

Curran, Timothy G; Bryson, Bryan D; Reigelhaupt, Michael; Johnson, Hannah; White, Forest M

2013-06-15

Advances in mass spectrometry-based proteomic technologies have increased the speed of analysis and the depth provided by a single analysis. Computational tools to evaluate the accuracy of peptide identifications from these high-throughput analyses have not kept pace with technological advances; currently the most common quality evaluation methods are based on statistical analysis of the likelihood of false positive identifications in large-scale data sets. While helpful, these calculations do not consider the accuracy of each identification, thus creating a precarious situation for biologists relying on the data to inform experimental design. Manual validation is the gold standard approach to confirm accuracy of database identifications, but is extremely time-intensive. To palliate the increasing time required to manually validate large proteomic datasets, we provide computer aided manual validation software (CAMV) to expedite the process. Relevant spectra are collected, catalogued, and pre-labeled, allowing users to efficiently judge the quality of each identification and summarize applicable quantitative information. CAMV significantly reduces the burden associated with manual validation and will hopefully encourage broader adoption of manual validation in mass spectrometry-based proteomics. Copyright © 2013 Elsevier Inc. All rights reserved.

Creation of a virtual cutaneous tissue bank

NASA Astrophysics Data System (ADS)

LaFramboise, William A.; Shah, Sujal; Hoy, R. W.; Letbetter, D.; Petrosko, P.; Vennare, R.; Johnson, Peter C.

2000-04-01

Cellular and non-cellular constituents of skin contain fundamental morphometric features and structural patterns that correlate with tissue function. High resolution digital image acquisitions performed using an automated system and proprietary software to assemble adjacent images and create a contiguous, lossless, digital representation of individual microscope slide specimens. Serial extraction, evaluation and statistical analysis of cutaneous feature is performed utilizing an automated analysis system, to derive normal cutaneous parameters comprising essential structural skin components. Automated digital cutaneous analysis allows for fast extraction of microanatomic dat with accuracy approximating manual measurement. The process provides rapid assessment of feature both within individual specimens and across sample populations. The images, component data, and statistical analysis comprise a bioinformatics database to serve as an architectural blueprint for skin tissue engineering and as a diagnostic standard of comparison for pathologic specimens.

A method for classification of multisource data using interval-valued probabilities and its application to HIRIS data

NASA Technical Reports Server (NTRS)

Kim, H.; Swain, P. H.

1991-01-01

A method of classifying multisource data in remote sensing is presented. The proposed method considers each data source as an information source providing a body of evidence, represents statistical evidence by interval-valued probabilities, and uses Dempster's rule to integrate information based on multiple data source. The method is applied to the problems of ground-cover classification of multispectral data combined with digital terrain data such as elevation, slope, and aspect. Then this method is applied to simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data by dividing the dimensionally huge data source into smaller and more manageable pieces based on the global statistical correlation information. It produces higher classification accuracy than the Maximum Likelihood (ML) classification method when the Hughes phenomenon is apparent.

Identification of USSR Indicator Regions

NASA Technical Reports Server (NTRS)

Disler, J.; Breigh, H. (Principal Investigator)

1980-01-01

Potential indicator regions were determined by comparing the statistics for barley and wheat at the lowest administrative levels for which published statistics were available. Fourteen were selected for review based on their relative abundances of wheat and barely. These potential indicator regions were grouped according to three conditions that could affect labeling and classification accuracies: (1) high-barley content; (2) presence of barley and spring wheat; and (3) presence of barley and winter wheat. Each region was further evaluated based on the availability of crop calendars, LANDSAT acquisitions, and ancillary data. Based on the relative abundance of wheat and barley and the availability of data, three indicator regions were recommended. Within each region, individual oblasts and/or krays were selected according to segment availability and segment acquisition histories for potential barley separation.

Angular velocity estimation based on star vector with improved current statistical model Kalman filter.

PubMed

Zhang, Hao; Niu, Yanxiong; Lu, Jiazhen; Zhang, He

2016-11-20

Angular velocity information is a requisite for a spacecraft guidance, navigation, and control system. In this paper, an approach for angular velocity estimation based merely on star vector measurement with an improved current statistical model Kalman filter is proposed. High-precision angular velocity estimation can be achieved under dynamic conditions. The amount of calculation is also reduced compared to a Kalman filter. Different trajectories are simulated to test this approach, and experiments with real starry sky observation are implemented for further confirmation. The estimation accuracy is proved to be better than 10^-4  rad/s under various conditions. Both the simulation and the experiment demonstrate that the described approach is effective and shows an excellent performance under both static and dynamic conditions.

Use of model calibration to achieve high accuracy in analysis of computer networks

DOEpatents

Frogner, Bjorn; Guarro, Sergio; Scharf, Guy

2004-05-11

A system and method are provided for creating a network performance prediction model, and calibrating the prediction model, through application of network load statistical analyses. The method includes characterizing the measured load on the network, which may include background load data obtained over time, and may further include directed load data representative of a transaction-level event. Probabilistic representations of load data are derived to characterize the statistical persistence of the network performance variability and to determine delays throughout the network. The probabilistic representations are applied to the network performance prediction model to adapt the model for accurate prediction of network performance. Certain embodiments of the method and system may be used for analysis of the performance of a distributed application characterized as data packet streams.

Power flow as a complement to statistical energy analysis and finite element analysis

NASA Technical Reports Server (NTRS)

Cuschieri, J. M.

1987-01-01

Present methods of analysis of the structural response and the structure-borne transmission of vibrational energy use either finite element (FE) techniques or statistical energy analysis (SEA) methods. The FE methods are a very useful tool at low frequencies where the number of resonances involved in the analysis is rather small. On the other hand SEA methods can predict with acceptable accuracy the response and energy transmission between coupled structures at relatively high frequencies where the structural modal density is high and a statistical approach is the appropriate solution. In the mid-frequency range, a relatively large number of resonances exist which make finite element method too costly. On the other hand SEA methods can only predict an average level form. In this mid-frequency range a possible alternative is to use power flow techniques, where the input and flow of vibrational energy to excited and coupled structural components can be expressed in terms of input and transfer mobilities. This power flow technique can be extended from low to high frequencies and this can be integrated with established FE models at low frequencies and SEA models at high frequencies to form a verification of the method. This method of structural analysis using power flo and mobility methods, and its integration with SEA and FE analysis is applied to the case of two thin beams joined together at right angles.

Diagnostic accuracy of transabdominal high-resolution US for staging gallbladder cancer and differential diagnosis of neoplastic polyps compared with EUS.

PubMed

Lee, Jeong Sub; Kim, Jung Hoon; Kim, Yong Jae; Ryu, Ji Kon; Kim, Yong-Tae; Lee, Jae Young; Han, Joon Koo

2017-07-01

To compare the diagnostic accuracy of transabdominal high-resolution ultrasound (HRUS) for staging gallbladder cancer and differential diagnosis of neoplastic polyps compared with endoscopic ultrasound (EUS) and pathology. Among 125 patients who underwent both HRUS and EUS, we included 29 pathologically proven cancers (T1 = 7, T2 = 19, T3 = 3) including 15 polypoid cancers and 50 surgically proven polyps (neoplastic = 30, non-neoplastic = 20). We reviewed formal reports and assessed the accuracy of HRUS and EUS for diagnosing cancer as well as the differential diagnosis of neoplastic polyps. Statistical analyses were performed using chi-square tests. The sensitivity, specificity, PPV, and NPV for gallbladder cancer were 82.7 %, 44.4 %, 82.7 %, and 44 % using HRUS and 86.2 %, 22.2 %, 78.1 %, and 33.3 % using EUS. HRUS and EUS correctly diagnosed the stage in 13 and 12 patients. The sensitivity, specificity, PPV, and NPV for neoplastic polyps were 80 %, 80 %, 86 %, and 73 % using HRUS and 73 %, 85 %, 88 %, and 69 % using EUS. Single polyps (8/20 vs. 21/30), larger (1.0 ± 0.28 cm vs. 1.9 ± 0.85 cm) polyps, and older age (52.5 ± 13.2 vs. 66.1 ± 10.3 years) were common in neoplastic polyps (p < 0.05). Transabdominal HRUS showed comparable accuracy for diagnosing gallbladder cancer and differentiating neoplastic polyps compared with EUS. HRUS is also easy to use during our routine ultrasound examinations. • HRUS showed comparable diagnostic accuracy for GB cancer compared with EUS. • HRUS and EUS showed similar diagnostic accuracy for differentiating neoplastic polyps. • Single, larger polyps and older age were common in neoplastic polyps. • HRUS is less invasive compared with EUS.

Mesial Temporal Sclerosis: Accuracy of NeuroQuant versus Neuroradiologist.

PubMed

Azab, M; Carone, M; Ying, S H; Yousem, D M

2015-08-01

We sought to compare the accuracy of a volumetric fully automated computer assessment of hippocampal volume asymmetry versus neuroradiologists' interpretations of the temporal lobes for mesial temporal sclerosis. Detecting mesial temporal sclerosis (MTS) is important for the evaluation of patients with temporal lobe epilepsy as it often guides surgical intervention. One feature of MTS is hippocampal volume loss. Electronic medical record and researcher reports of scans of patients with proved mesial temporal sclerosis were compared with volumetric assessment with an FDA-approved software package, NeuroQuant, for detection of mesial temporal sclerosis in 63 patients. The degree of volumetric asymmetry was analyzed to determine the neuroradiologists' threshold for detecting right-left asymmetry in temporal lobe volumes. Thirty-six patients had left-lateralized MTS, 25 had right-lateralized MTS, and 2 had bilateral MTS. The estimated accuracy of the neuroradiologist was 72.6% with a κ statistic of 0.512 (95% CI, 0.315-0.710) [moderate agreement, P < 3 × 10(-6)]), whereas the estimated accuracy of NeuroQuant was 79.4% with a κ statistic of 0.588 (95% CI, 0.388-0.787) [moderate agreement, P < 2 × 10(-6)]). This discrepancy in accuracy was not statistically significant. When at least a 5%-10% volume discrepancy between temporal lobes was present, the neuroradiologists detected it 75%-80% of the time. As a stand-alone fully automated software program that can process temporal lobe volume in 5-10 minutes, NeuroQuant compares favorably with trained neuroradiologists in predicting the side of mesial temporal sclerosis. Neuroradiologists can often detect even small temporal lobe volumetric changes visually. © 2015 by American Journal of Neuroradiology.

Relationship between cutoff frequency and accuracy in time-interval photon statistics applied to oscillating signals

NASA Astrophysics Data System (ADS)

Rebolledo, M. A.; Martinez-Betorz, J. A.

1989-04-01

In this paper the accuracy in the determination of the period of an oscillating signal, when obtained from the photon statistics time-interval probability, is studied as a function of the precision (the inverse of the cutoff frequency of the photon counting system) with which time intervals are measured. The results are obtained by means of an experiment with a square-wave signal, where the Fourier or square-wave transforms of the time-interval probability are measured. It is found that for values of the frequency of the signal near the cutoff frequency the errors in the period are small.

Technical evaluation of Virtual Touch™ tissue quantification and elastography in benign and malignant breast tumors

PubMed Central

JIANG, QUAN; ZHANG, YUAN; CHEN, JIAN; ZHANG, YUN-XIAO; HE, ZHU

2014-01-01

The aim of this study was to investigate the diagnostic value of the Virtual Touch™ tissue quantification (VTQ) and elastosonography technologies in benign and malignant breast tumors. Routine preoperative ultrasound, elastosonography and VTQ examinations were performed on 86 patients with breast lesions. The elastosonography score and VTQ speed grouping of each lesion were measured and compared with the pathological findings. The difference in the elastosonography score between the benign and malignant breast tumors was statistically significant (P<0.05). The detection rate for an elastosonography score of 1–3 points in benign tumors was 68.09% and that for an elastosonography score of 4–5 points in malignant tumors was 82.05%. The difference in VTQ speed values between the benign and malignant tumors was also statistically significant (P<0.05). In addition, the diagnostic accuracy of conventional ultrasound, elastosonography, VTQ technology and the combined methods showed statistically significant differences (P<0.05). The use of the three technologies in combination significantly improved the diagnostic accuracy to 91.86%. In conclusion, the combination of conventional ultrasound, elastosonography and VTQ technology can significantly improve accuracy in the diagnosis of breast cancer. PMID:25187797

Comparison of methods for calculating conditional expectations of sufficient statistics for continuous time Markov chains.

PubMed

Tataru, Paula; Hobolth, Asger

2011-12-05

Continuous time Markov chains (CTMCs) is a widely used model for describing the evolution of DNA sequences on the nucleotide, amino acid or codon level. The sufficient statistics for CTMCs are the time spent in a state and the number of changes between any two states. In applications past evolutionary events (exact times and types of changes) are unaccessible and the past must be inferred from DNA sequence data observed in the present. We describe and implement three algorithms for computing linear combinations of expected values of the sufficient statistics, conditioned on the end-points of the chain, and compare their performance with respect to accuracy and running time. The first algorithm is based on an eigenvalue decomposition of the rate matrix (EVD), the second on uniformization (UNI), and the third on integrals of matrix exponentials (EXPM). The implementation in R of the algorithms is available at http://www.birc.au.dk/~paula/. We use two different models to analyze the accuracy and eight experiments to investigate the speed of the three algorithms. We find that they have similar accuracy and that EXPM is the slowest method. Furthermore we find that UNI is usually faster than EVD.

Application of classification trees for the qualitative differentiation of focal liver lesions suspicious for metastasis in gadolinium-EOB-DTPA-enhanced liver MR imaging.

PubMed

Schelhorn, J; Benndorf, M; Dietzel, M; Burmeister, H P; Kaiser, W A; Baltzer, P A T

2012-09-01

To evaluate the diagnostic accuracy of qualitative descriptors alone and in combination for the classification of focal liver lesions (FLLs) suspicious for metastasis in gadolinium-EOB-DTPA-enhanced liver MR imaging. Consecutive patients with clinically suspected liver metastases were eligible for this retrospective investigation. 50 patients met the inclusion criteria. All underwent Gd-EOB-DTPA-enhanced liver MRI (T2w, chemical shift T1w, dynamic T1w). Primary liver malignancies or treated lesions were excluded. All investigations were read by two blinded observers (O1, O2). Both independently identified the presence of lesions and evaluated predefined qualitative lesion descriptors (signal intensities, enhancement pattern and morphology). A reference standard was determined under consideration of all clinical and follow-up information. Statistical analysis besides contingency tables (chi square, kappa statistics) included descriptor combinations using classification trees (CHAID methodology) as well as ROC analysis. In 38 patients, 120 FLLs (52 benign, 68 malignant) were present. 115 (48 benign, 67 malignant) were identified by the observers. The enhancement pattern, relative SI upon T2w and late enhanced T1w images contributed significantly to the differentiation of FLLs. The overall classification accuracy was 91.3 % (O1) and 88.7 % (O2), kappa = 0.902. The combination of qualitative lesion descriptors proposed in this work revealed high diagnostic accuracy and interobserver agreement in the differentiation of focal liver lesions suspicious for metastases using Gd-EOB-DTPA-enhanced liver MRI. © Georg Thieme Verlag KG Stuttgart · New York.

Split-mouth comparison of the accuracy of computer-generated and conventional surgical guides.

PubMed

Farley, Nathaniel E; Kennedy, Kelly; McGlumphy, Edwin A; Clelland, Nancy L

2013-01-01

Recent clinical studies have shown that implant placement is highly predictable with computer-generated surgical guides; however, the reliability of these guides has not been compared to that of conventional guides clinically. This study aimed to compare the accuracy of reproducing planned implant positions with computer-generated and conventional surgical guides using a split-mouth design. Ten patients received two implants each in symmetric locations. All implants were planned virtually using a software program and information from cone beam computed tomographic scans taken with scan appliances in place. Patients were randomly selected for computer-aided design/computer-assisted manufacture (CAD/CAM)-guided implant placement on their right or left side. Conventional guides were used on the contralateral side. Patients underwent operative cone beam computed tomography postoperatively. Planned and actual implant positions were compared using three-dimensional analyses capable of measuring volume overlap as well as differences in angles and coronal and apical positions. Results were compared using a mixed-model repeated-measures analysis of variance and were further analyzed using a Bartlett test for unequal variance (α = .05). Implants placed with CAD/CAM guides were closer to the planned positions in all eight categories examined. However, statistically significant differences were shown only for coronal horizontal distances. It was also shown that CAD/CAM guides had less variability than conventional guides, which was statistically significant for apical distance. Implants placed using CAD/CAM surgical guides provided greater accuracy in a lateral direction than conventional guides. In addition, CAD/CAM guides were more consistent in their deviation from the planned locations than conventional guides.

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part I: Effects of Random Error

NASA Technical Reports Server (NTRS)

Duda, David P.; Minnis, Patrick

2009-01-01

Straightforward application of the Schmidt-Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy, the percent correct (PC) and the Hanssen-Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher when the climatological frequency of contrail occurrence is used as the critical threshold, while the PC scores are higher when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85 percent for both the prediction of contrail occurrence and non-occurrence, although in practice, larger errors would be anticipated.

Influence of LCD color reproduction accuracy on observer performance using virtual pathology slides

NASA Astrophysics Data System (ADS)

Krupinski, Elizabeth A.; Silverstein, Louis D.; Hashmi, Syed F.; Graham, Anna R.; Weinstein, Ronald S.; Roehrig, Hans

2012-02-01

The use of color LCDs in medical imaging is growing as more clinical specialties use digital images as a resource in diagnosis and treatment decisions. Telemedicine applications such as telepathology, teledermatology and teleophthalmology rely heavily on color images. However, standard methods for calibrating, characterizing and profiling color displays do not exist, resulting in inconsistent presentation. To address this, we developed a calibration, characterization and profiling protocol for color-critical medical imaging applications. Physical characterization of displays calibrated with and without the protocol revealed high color reproduction accuracy with the protocol. The present study assessed the impact of this protocol on observer performance. A set of 250 breast biopsy virtual slide regions of interest (half malignant, half benign) were shown to 6 pathologists, once using the calibration protocol and once using the same display in its "native" off-the-shelf uncalibrated state. Diagnostic accuracy and time to render a decision were measured. In terms of ROC performance, Az (area under the curve) calibrated = 0.8640; uncalibrated = 0.8558. No statistically significant difference (p = 0.2719) was observed. In terms of interpretation speed, mean calibrated = 4.895 sec, mean uncalibrated = 6.304 sec which is statistically significant (p = 0.0460). Early results suggest a slight advantage diagnostically for a properly calibrated and color-managed display and a significant potential advantage in terms of improved workflow. Future work should be conducted using different types of color images that may be more dependent on accurate color rendering and a wider range of LCDs with varying characteristics.

Early prediction of lung cancer recurrence after stereotactic radiotherapy using second order texture statistics

NASA Astrophysics Data System (ADS)

Mattonen, Sarah A.; Palma, David A.; Haasbeek, Cornelis J. A.; Senan, Suresh; Ward, Aaron D.

2014-03-01

Benign radiation-induced lung injury is a common finding following stereotactic ablative radiotherapy (SABR) for lung cancer, and is often difficult to differentiate from a recurring tumour due to the ablative doses and highly conformal treatment with SABR. Current approaches to treatment response assessment have shown limited ability to predict recurrence within 6 months of treatment. The purpose of our study was to evaluate the accuracy of second order texture statistics for prediction of eventual recurrence based on computed tomography (CT) images acquired within 6 months of treatment, and compare with the performance of first order appearance and lesion size measures. Consolidative and ground-glass opacity (GGO) regions were manually delineated on post-SABR CT images. Automatic consolidation expansion was also investigated to act as a surrogate for GGO position. The top features for prediction of recurrence were all texture features within the GGO and included energy, entropy, correlation, inertia, and first order texture (standard deviation of density). These predicted recurrence with 2-fold cross validation (CV) accuracies of 70-77% at 2- 5 months post-SABR, with energy, entropy, and first order texture having leave-one-out CV accuracies greater than 80%. Our results also suggest that automatic expansion of the consolidation region could eliminate the need for manual delineation, and produced reproducible results when compared to manually delineated GGO. If validated on a larger data set, this could lead to a clinically useful computer-aided diagnosis system for prediction of recurrence within 6 months of SABR and allow for early salvage therapy for patients with recurrence.

Investigations of interference between electromagnetic transponders and wireless MOSFET dosimeters: A phantom study

PubMed Central

Su, Zhong; Zhang, Lisha; Ramakrishnan, V.; Hagan, Michael; Anscher, Mitchell

2011-01-01

Purpose: To evaluate both the Calypso Systems’ (Calypso Medical Technologies, Inc., Seattle, WA) localization accuracy in the presence of wireless metal–oxide–semiconductor field-effect transistor (MOSFET) dosimeters of dose verification system (DVS, Sicel Technologies, Inc., Morrisville, NC) and the dosimeters’ reading accuracy in the presence of wireless electromagnetic transponders inside a phantom.Methods: A custom-made, solid-water phantom was fabricated with space for transponders and dosimeters. Two inserts were machined with positioning grooves precisely matching the dimensions of the transponders and dosimeters and were arranged in orthogonal and parallel orientations, respectively. To test the transponder localization accuracy with∕without presence of dosimeters (hypothesis 1), multivariate analyses were performed on transponder-derived localization data with and without dosimeters at each preset distance to detect statistically significant localization differences between the control and test sets. To test dosimeter dose-reading accuracy with∕without presence of transponders (hypothesis 2), an approach of alternating the transponder presence in seven identical fraction dose (100 cGy) deliveries and measurements was implemented. Two-way analysis of variance was performed to examine statistically significant dose-reading differences between the two groups and the different fractions. A relative-dose analysis method was also used to evaluate transponder impact on dose-reading accuracy after dose-fading effect was removed by a second-order polynomial fit.Results: Multivariate analysis indicated that hypothesis 1 was false; there was a statistically significant difference between the localization data from the control and test sets. However, the upper and lower bounds of the 95% confidence intervals of the localized positional differences between the control and test sets were less than 0.1 mm, which was significantly smaller than the minimum clinical localization resolution of 0.5 mm. For hypothesis 2, analysis of variance indicated that there was no statistically significant difference between the dosimeter readings with and without the presence of transponders. Both orthogonal and parallel configurations had difference of polynomial-fit dose to measured dose values within 1.75%.Conclusions: The phantom study indicated that the Calypso System’s localization accuracy was not affected clinically due to the presence of DVS wireless MOSFET dosimeters and the dosimeter-measured doses were not affected by the presence of transponders. Thus, the same patients could be implanted with both transponders and dosimeters to benefit from improved accuracy of radiotherapy treatments offered by conjunctional use of the two systems. PMID:21776780

Predicting juvenile recidivism: new method, old problems.

PubMed

Benda, B B

1987-01-01

This prediction study compared three statistical procedures for accuracy using two assessment methods. The criterion is return to a juvenile prison after the first release, and the models tested are logit analysis, predictive attribute analysis, and a Burgess procedure. No significant differences are found between statistics in prediction.

Statistical label fusion with hierarchical performance models

PubMed Central

Asman, Andrew J.; Dagley, Alexander S.; Landman, Bennett A.

2014-01-01

Label fusion is a critical step in many image segmentation frameworks (e.g., multi-atlas segmentation) as it provides a mechanism for generalizing a collection of labeled examples into a single estimate of the underlying segmentation. In the multi-label case, typical label fusion algorithms treat all labels equally – fully neglecting the known, yet complex, anatomical relationships exhibited in the data. To address this problem, we propose a generalized statistical fusion framework using hierarchical models of rater performance. Building on the seminal work in statistical fusion, we reformulate the traditional rater performance model from a multi-tiered hierarchical perspective. This new approach provides a natural framework for leveraging known anatomical relationships and accurately modeling the types of errors that raters (or atlases) make within a hierarchically consistent formulation. Herein, we describe several contributions. First, we derive a theoretical advancement to the statistical fusion framework that enables the simultaneous estimation of multiple (hierarchical) performance models within the statistical fusion context. Second, we demonstrate that the proposed hierarchical formulation is highly amenable to the state-of-the-art advancements that have been made to the statistical fusion framework. Lastly, in an empirical whole-brain segmentation task we demonstrate substantial qualitative and significant quantitative improvement in overall segmentation accuracy. PMID:24817809

A large-area, spatially continuous assessment of land cover map error and its impact on downstream analyses.

PubMed

Estes, Lyndon; Chen, Peng; Debats, Stephanie; Evans, Tom; Ferreira, Stefanus; Kuemmerle, Tobias; Ragazzo, Gabrielle; Sheffield, Justin; Wolf, Adam; Wood, Eric; Caylor, Kelly

2018-01-01

Land cover maps increasingly underlie research into socioeconomic and environmental patterns and processes, including global change. It is known that map errors impact our understanding of these phenomena, but quantifying these impacts is difficult because many areas lack adequate reference data. We used a highly accurate, high-resolution map of South African cropland to assess (1) the magnitude of error in several current generation land cover maps, and (2) how these errors propagate in downstream studies. We first quantified pixel-wise errors in the cropland classes of four widely used land cover maps at resolutions ranging from 1 to 100 km, and then calculated errors in several representative "downstream" (map-based) analyses, including assessments of vegetative carbon stocks, evapotranspiration, crop production, and household food security. We also evaluated maps' spatial accuracy based on how precisely they could be used to locate specific landscape features. We found that cropland maps can have substantial biases and poor accuracy at all resolutions (e.g., at 1 km resolution, up to ∼45% underestimates of cropland (bias) and nearly 50% mean absolute error (MAE, describing accuracy); at 100 km, up to 15% underestimates and nearly 20% MAE). National-scale maps derived from higher-resolution imagery were most accurate, followed by multi-map fusion products. Constraining mapped values to match survey statistics may be effective at minimizing bias (provided the statistics are accurate). Errors in downstream analyses could be substantially amplified or muted, depending on the values ascribed to cropland-adjacent covers (e.g., with forest as adjacent cover, carbon map error was 200%-500% greater than in input cropland maps, but ∼40% less for sparse cover types). The average locational error was 6 km (600%). These findings provide deeper insight into the causes and potential consequences of land cover map error, and suggest several recommendations for land cover map users. © 2017 John Wiley & Sons Ltd.

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils

PubMed Central

Shi, Tiezhu; Liu, Huizeng; Chen, Yiyun; Fei, Teng; Wang, Junjie; Wu, Guofeng

2017-01-01

This study investigated the abilities of pre-processing, feature selection and machine-learning methods for the spectroscopic diagnosis of soil arsenic contamination. The spectral data were pre-processed by using Savitzky-Golay smoothing, first and second derivatives, multiplicative scatter correction, standard normal variate, and mean centering. Principle component analysis (PCA) and the RELIEF algorithm were used to extract spectral features. Machine-learning methods, including random forests (RF), artificial neural network (ANN), radial basis function- and linear function- based support vector machine (RBF- and LF-SVM) were employed for establishing diagnosis models. The model accuracies were evaluated and compared by using overall accuracies (OAs). The statistical significance of the difference between models was evaluated by using McNemar’s test (Z value). The results showed that the OAs varied with the different combinations of pre-processing, feature selection, and classification methods. Feature selection methods could improve the modeling efficiencies and diagnosis accuracies, and RELIEF often outperformed PCA. The optimal models established by RF (OA = 86%), ANN (OA = 89%), RBF- (OA = 89%) and LF-SVM (OA = 87%) had no statistical difference in diagnosis accuracies (Z < 1.96, p < 0.05). These results indicated that it was feasible to diagnose soil arsenic contamination using reflectance spectroscopy. The appropriate combination of multivariate methods was important to improve diagnosis accuracies. PMID:28471412

Parallel solution of the symmetric tridiagonal eigenproblem. Research report

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

Jessup, E.R.

1989-10-01

This thesis discusses methods for computing all eigenvalues and eigenvectors of a symmetric tridiagonal matrix on a distributed-memory Multiple Instruction, Multiple Data multiprocessor. Only those techniques having the potential for both high numerical accuracy and significant large-grained parallelism are investigated. These include the QL method or Cuppen's divide and conquer method based on rank-one updating to compute both eigenvalues and eigenvectors, bisection to determine eigenvalues and inverse iteration to compute eigenvectors. To begin, the methods are compared with respect to computation time, communication time, parallel speed up, and accuracy. Experiments on an IPSC hypercube multiprocessor reveal that Cuppen's method ismore » the most accurate approach, but bisection with inverse iteration is the fastest and most parallel. Because the accuracy of the latter combination is determined by the quality of the computed eigenvectors, the factors influencing the accuracy of inverse iteration are examined. This includes, in part, statistical analysis of the effect of a starting vector with random components. These results are used to develop an implementation of inverse iteration producing eigenvectors with lower residual error and better orthogonality than those generated by the EISPACK routine TINVIT. This thesis concludes with adaptions of methods for the symmetric tridiagonal eigenproblem to the related problem of computing the singular value decomposition (SVD) of a bidiagonal matrix.« less

Parallel solution of the symmetric tridiagonal eigenproblem

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

Jessup, E.R.

1989-01-01

This thesis discusses methods for computing all eigenvalues and eigenvectors of a symmetric tridiagonal matrix on a distributed memory MIMD multiprocessor. Only those techniques having the potential for both high numerical accuracy and significant large-grained parallelism are investigated. These include the QL method or Cuppen's divide and conquer method based on rank-one updating to compute both eigenvalues and eigenvectors, bisection to determine eigenvalues, and inverse iteration to compute eigenvectors. To begin, the methods are compared with respect to computation time, communication time, parallel speedup, and accuracy. Experiments on an iPSC hyper-cube multiprocessor reveal that Cuppen's method is the most accuratemore » approach, but bisection with inverse iteration is the fastest and most parallel. Because the accuracy of the latter combination is determined by the quality of the computed eigenvectors, the factors influencing the accuracy of inverse iteration are examined. This includes, in part, statistical analysis of the effects of a starting vector with random components. These results are used to develop an implementation of inverse iteration producing eigenvectors with lower residual error and better orthogonality than those generated by the EISPACK routine TINVIT. This thesis concludes with adaptations of methods for the symmetric tridiagonal eigenproblem to the related problem of computing the singular value decomposition (SVD) of a bidiagonal matrix.« less

Statistical Analysis of the Indus Script Using n-Grams

PubMed Central

Yadav, Nisha; Joglekar, Hrishikesh; Rao, Rajesh P. N.; Vahia, Mayank N.; Adhikari, Ronojoy; Mahadevan, Iravatham

2010-01-01

The Indus script is one of the major undeciphered scripts of the ancient world. The small size of the corpus, the absence of bilingual texts, and the lack of definite knowledge of the underlying language has frustrated efforts at decipherment since the discovery of the remains of the Indus civilization. Building on previous statistical approaches, we apply the tools of statistical language processing, specifically n-gram Markov chains, to analyze the syntax of the Indus script. We find that unigrams follow a Zipf-Mandelbrot distribution. Text beginner and ender distributions are unequal, providing internal evidence for syntax. We see clear evidence of strong bigram correlations and extract significant pairs and triplets using a log-likelihood measure of association. Highly frequent pairs and triplets are not always highly significant. The model performance is evaluated using information-theoretic measures and cross-validation. The model can restore doubtfully read texts with an accuracy of about 75%. We find that a quadrigram Markov chain saturates information theoretic measures against a held-out corpus. Our work forms the basis for the development of a stochastic grammar which may be used to explore the syntax of the Indus script in greater detail. PMID:20333254

Resident accuracy of joint line palpation using ultrasound verification.

PubMed

Rho, Monica E; Chu, Samuel K; Yang, Aaron; Hameed, Farah; Lin, Cindy Yuchin; Hurh, Peter J

2014-10-01

To determine the accuracy of knee and acromioclavicular (AC) joint line palpation in Physical Medicine and Rehabilitation (PM&R) residents using ultrasound (US) verification. Cohort study. PM&R residency program at an academic institution. Twenty-four PM&R residents participating in a musculoskeletal US course (7 PGY-2, 8 PGY-3, and 9 PGY4 residents). Twenty-four PM&R residents participating in an US course were asked to palpate the AC joint and lateral joint line of the knee in a female and male model before the start of the course. Once the presumed joint line was localized, the residents were asked to tape an 18-gauge, 1.5-inch, blunt-tip needle parallel to the joint line on the overlying skin. The accuracy of needle placement over the joint line was verified using US. US verification of correct needle placement over the joint line. Overall AC joint palpation accuracy was 16.7%, and knee lateral joint line palpation accuracy was 58.3%. Based on the resident level of education, using a value of P < .05, there were no statistically significant differences in the accuracy of joint line palpation. Residents in this study demonstrate poor accuracy of AC joint and lateral knee joint line identification by palpation, using US as the criterion standard for verification. There were no statistically significant differences in the accuracy rates of joint line palpation based on resident level of education. US may be a useful tool to use to advance the current methods of teaching the physical examination in medical education. Copyright © 2014 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

MR thermometry analysis of sonication accuracy and safety margin of volumetric MR imaging-guided high-intensity focused ultrasound ablation of symptomatic uterine fibroids.

PubMed

Kim, Young-sun; Trillaud, Hervé; Rhim, Hyunchul; Lim, Hyo K; Mali, Willem; Voogt, Marianne; Barkhausen, Jörg; Eckey, Thomas; Köhler, Max O; Keserci, Bilgin; Mougenot, Charles; Sokka, Shunmugavelu D; Soini, Jouko; Nieminen, Heikki J

2012-11-01

To evaluate the accuracy of the size and location of the ablation zone produced by volumetric magnetic resonance (MR) imaging-guided high-intensity focused ultrasound ablation of uterine fibroids on the basis of MR thermometric analysis and to assess the effects of a feedback control technique. This prospective study was approved by the institutional review board, and written informed consent was obtained. Thirty-three women with 38 uterine fibroids were treated with an MR imaging-guided high-intensity focused ultrasound system capable of volumetric feedback ablation. Size (diameter times length) and location (three-dimensional displacements) of each ablation zone induced by 527 sonications (with [n=471] and without [n=56] feedback) were analyzed according to the thermal dose obtained with MR thermometry. Prospectively defined acceptance ranges of targeting accuracy were ±5 mm in left-right (LR) and craniocaudal (CC) directions and ±12 mm in anteroposterior (AP) direction. Effects of feedback control in 8- and 12-mm treatment cells were evaluated by using a mixed model with repeated observations within patients. Overall mean sizes of ablation zones produced by 4-, 8-, 12-, and 16-mm treatment cells (with and without feedback) were 4.6 mm±1.4 (standard deviation)×4.4 mm±4.8 (n=13), 8.9 mm±1.9×20.2 mm±6.5 (n=248), 13.0 mm±1.2×29.1 mm±5.6 (n=234), and 18.1 mm±1.4×38.2 mm±7.6 (n=32), respectively. Targeting accuracy values (displacements in absolute values) were 0.9 mm±0.7, 1.2 mm±0.9, and 2.8 mm±2.2 in LR, CC, and AP directions, respectively. Of 527 sonications, 99.8% (526 of 527) were within acceptance ranges. Feedback control had no statistically significant effect on targeting accuracy or ablation zone size. However, variations in ablation zone size were smaller in the feedback control group. Sonication accuracy of volumetric MR imaging-guided high-intensity focused ultrasound ablation of uterine fibroids appears clinically acceptable and may be further improved by feedback control to produce more consistent ablation zones. © RSNA, 2012

Proposal for a New Predictive Model of Short-Term Mortality After Living Donor Liver Transplantation due to Acute Liver Failure.

PubMed

Chung, Hyun Sik; Lee, Yu Jung; Jo, Yun Sung

2017-02-21

BACKGROUND Acute liver failure (ALF) is known to be a rapidly progressive and fatal disease. Various models which could help to estimate the post-transplant outcome for ALF have been developed; however, none of them have been proved to be the definitive predictive model of accuracy. We suggest a new predictive model, and investigated which model has the highest predictive accuracy for the short-term outcome in patients who underwent living donor liver transplantation (LDLT) due to ALF. MATERIAL AND METHODS Data from a total 88 patients were collected retrospectively. King's College Hospital criteria (KCH), Child-Turcotte-Pugh (CTP) classification, and model for end-stage liver disease (MELD) score were calculated. Univariate analysis was performed, and then multivariate statistical adjustment for preoperative variables of ALF prognosis was performed. A new predictive model was developed, called the MELD conjugated serum phosphorus model (MELD-p). The individual diagnostic accuracy and cut-off value of models in predicting 3-month post-transplant mortality were evaluated using the area under the receiver operating characteristic curve (AUC). The difference in AUC between MELD-p and the other models was analyzed. The diagnostic improvement in MELD-p was assessed using the net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS The MELD-p and MELD scores had high predictive accuracy (AUC >0.9). KCH and serum phosphorus had an acceptable predictive ability (AUC >0.7). The CTP classification failed to show discriminative accuracy in predicting 3-month post-transplant mortality. The difference in AUC between MELD-p and the other models had statistically significant associations with CTP and KCH. The cut-off value of MELD-p was 3.98 for predicting 3-month post-transplant mortality. The NRI was 9.9% and the IDI was 2.9%. CONCLUSIONS MELD-p score can predict 3-month post-transplant mortality better than other scoring systems after LDLT due to ALF. The recommended cut-off value of MELD-p is 3.98.

Radiological interpretation of images displayed on tablet computers: a systematic review

PubMed Central

Armfield, N R; Smith, A C

2015-01-01

Objective: To review the published evidence and to determine if radiological diagnostic accuracy is compromised when images are displayed on a tablet computer and thereby inform practice on using tablet computers for radiological interpretation by on-call radiologists. Methods: We searched the PubMed and EMBASE databases for studies on the diagnostic accuracy or diagnostic reliability of images interpreted on tablet computers. Studies were screened for inclusion based on pre-determined inclusion and exclusion criteria. Studies were assessed for quality and risk of bias using Quality Appraisal of Diagnostic Reliability Studies or the revised Quality Assessment of Diagnostic Accuracy Studies tool. Treatment of studies was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results: 11 studies met the inclusion criteria. 10 of these studies tested the Apple iPad® (Apple, Cupertino, CA). The included studies reported high sensitivity (84–98%), specificity (74–100%) and accuracy rates (98–100%) for radiological diagnosis. There was no statistically significant difference in accuracy between a tablet computer and a digital imaging and communication in medicine-calibrated control display. There was a near complete consensus from authors on the non-inferiority of diagnostic accuracy of images displayed on a tablet computer. All of the included studies were judged to be at risk of bias. Conclusion: Our findings suggest that the diagnostic accuracy of radiological interpretation is not compromised by using a tablet computer. This result is only relevant to the Apple iPad and to the modalities of CT, MRI and plain radiography. Advances in knowledge: The iPad may be appropriate for an on-call radiologist to use for radiological interpretation. PMID:25882691

Evaluating the Quality, Accuracy, and Readability of Online Resources Pertaining to Hallux Valgus.

PubMed

Tartaglione, Jason P; Rosenbaum, Andrew J; Abousayed, Mostafa; Hushmendy, Shazaan F; DiPreta, John A

2016-02-01

The Internet is one of the most widely utilized resources for health-related information. Evaluation of the medical literature suggests that the quality and accuracy of these resources are poor and written at inappropriately high reading levels. The purpose of our study was to evaluate the quality, accuracy, and readability of online resources pertaining to hallux valgus. Two search terms ("hallux valgus" and "bunion") were entered into Google, Yahoo, and Bing. With the use of scoring criteria specific to hallux valgus, the quality and accuracy of online information related to hallux valgus was evaluated by 3 reviewers. The Flesch-Kincaid score was used to determine readability. Statistical analysis was performed with t tests and significance was determined by P values <.05. Sixty-two unique websites were evaluated. Quality was significantly higher with use of the search term "bunion" as compared to "hallux valgus" (P = .045). Quality and accuracy were significantly higher in resources authored by physicians as compared to nonphysicians (quality, P = .04; accuracy, P < .001) and websites without commercial bias (quality, P = .038; accuracy, P = .011). However, the reading level was significantly more advanced for websites authored by physicians (P = .035). Websites written above an eighth-grade reading level were significantly more accurate than those written at or below an eighth-grade reading level (P = .032). The overall quality of online information related to hallux valgus is poor and written at inappropriate reading levels. Furthermore, the search term used, authorship, and presence of commercial bias influence the value of these materials. It is important for orthopaedic surgeons to become familiar with patient education materials, so that appropriate recommendations can be made regarding valuable resources. Level IV. © 2015 The Author(s).

How to select electrical end-use meters for proper measurement of DSM impact estimates

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

Bowman, M.

1994-12-31

Does metering actually provide higher accuracy impact estimates? The answer is sometimes yes, sometimes no. It depends on how the metered data will be used. DSM impact estimates can be achieved in a variety of ways, including engineering algorithms, modeling and statistical methods. Yet for all of these methods, impacts can be calculated as the difference in pre- and post-installation annual load shapes. Increasingly, end-use metering is being used to either adjust and calibrate a particular estimate method, or measure load shapes directly. It is therefore not surprising that metering has become synonymous with higher accuracy impact estimates. If meteredmore » data is used as a component in an estimating methodology, its relative contribution to accuracy can be analyzed through propagation of error or {open_quotes}POE{close_quotes} analysis. POE analysis is a framework which can be used to evaluate different metering options and their relative effects on cost and accuracy. If metered data is used to directly measure pre- and post-installation load shapes to calculate energy and demand impacts, then the accuracy of the whole metering process directly affects the accuracy of the impact estimate. This paper is devoted to the latter case, where the decision has been made to collect high-accuracy metered data of electrical energy and demand. The underlying assumption is that all meters can yield good results if applied within the scope of their limitations. The objective is to know the application, understand what meters are actually doing to measure and record power, and decide with confidence when a sophisticated meter is required, and when a less expensive type will suffice.« less

The non-equilibrium and energetic cost of sensory adaptation

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

Lan, G.; Sartori, Pablo; Tu, Y.

2011-03-24

Biological sensory systems respond to external signals in short time and adapt to permanent environmental changes over a longer timescale to maintain high sensitivity in widely varying environments. In this work we have shown how all adaptation dynamics are intrinsically non-equilibrium and free energy is dissipated. We show that the dissipated energy is utilized to maintain adaptation accuracy. A universal relation between the energy dissipation and the optimum adaptation accuracy is established by both a general continuum model and a discrete model i n the specific case of the well-known E. coli chemo-sensory adaptation. Our study suggests that cellular levelmore » adaptations are fueled by hydrolysis of high energy biomolecules, such as ATP. The relevance of this work lies on linking the functionality of a biological system (sensory adaptation) with a concept rooted in statistical physics (energy dissipation), by a mathematical law. This has been made possible by identifying a general sensory system with a non-equilibrium steady state (a stationary state in which the probability current is not zero, but its divergence is, see figure), and then numerically and analytically solving the Fokker-Planck and Master Equations which describe the sensory adaptive system. The application of our general results to the case of E. Coli has shed light on why this system uses the high energy SAM molecule to perform adaptation, since using the more common ATP would not suffice to obtain the required adaptation accuracy.« less

Evaluating statistical cloud schemes: What can we gain from ground-based remote sensing?

NASA Astrophysics Data System (ADS)

Grützun, V.; Quaas, J.; Morcrette, C. J.; Ament, F.

2013-09-01

Statistical cloud schemes with prognostic probability distribution functions have become more important in atmospheric modeling, especially since they are in principle scale adaptive and capture cloud physics in more detail. While in theory the schemes have a great potential, their accuracy is still questionable. High-resolution three-dimensional observational data of water vapor and cloud water, which could be used for testing them, are missing. We explore the potential of ground-based remote sensing such as lidar, microwave, and radar to evaluate prognostic distribution moments using the "perfect model approach." This means that we employ a high-resolution weather model as virtual reality and retrieve full three-dimensional atmospheric quantities and virtual ground-based observations. We then use statistics from the virtual observation to validate the modeled 3-D statistics. Since the data are entirely consistent, any discrepancy occurring is due to the method. Focusing on total water mixing ratio, we find that the mean ratio can be evaluated decently but that it strongly depends on the meteorological conditions as to whether the variance and skewness are reliable. Using some simple schematic description of different synoptic conditions, we show how statistics obtained from point or line measurements can be poor at representing the full three-dimensional distribution of water in the atmosphere. We argue that a careful analysis of measurement data and detailed knowledge of the meteorological situation is necessary to judge whether we can use the data for an evaluation of higher moments of the humidity distribution used by a statistical cloud scheme.

Textural Analysis and Substrate Classification in the Nearshore Region of Lake Superior Using High-Resolution Multibeam Bathymetry

NASA Astrophysics Data System (ADS)

Dennison, Andrew G.

Classification of the seafloor substrate can be done with a variety of methods. These methods include Visual (dives, drop cameras); mechanical (cores, grab samples); acoustic (statistical analysis of echosounder returns). Acoustic methods offer a more powerful and efficient means of collecting useful information about the bottom type. Due to the nature of an acoustic survey, larger areas can be sampled, and by combining the collected data with visual and mechanical survey methods provide greater confidence in the classification of a mapped region. During a multibeam sonar survey, both bathymetric and backscatter data is collected. It is well documented that the statistical characteristic of a sonar backscatter mosaic is dependent on bottom type. While classifying the bottom-type on the basis on backscatter alone can accurately predict and map bottom-type, i.e a muddy area from a rocky area, it lacks the ability to resolve and capture fine textural details, an important factor in many habitat mapping studies. Statistical processing of high-resolution multibeam data can capture the pertinent details about the bottom-type that are rich in textural information. Further multivariate statistical processing can then isolate characteristic features, and provide the basis for an accurate classification scheme. The development of a new classification method is described here. It is based upon the analysis of textural features in conjunction with ground truth sampling. The processing and classification result of two geologically distinct areas in nearshore regions of Lake Superior; off the Lester River,MN and Amnicon River, WI are presented here, using the Minnesota Supercomputer Institute's Mesabi computing cluster for initial processing. Processed data is then calibrated using ground truth samples to conduct an accuracy assessment of the surveyed areas. From analysis of high-resolution bathymetry data collected at both survey sites is was possible to successfully calculate a series of measures that describe textural information about the lake floor. Further processing suggests that the features calculated capture a significant amount of statistical information about the lake floor terrain as well. Two sources of error, an anomalous heave and refraction error significantly deteriorated the quality of the processed data and resulting validate results. Ground truth samples used to validate the classification methods utilized for both survey sites, however, resulted in accuracy values ranging from 5 -30 percent at the Amnicon River, and between 60-70 percent for the Lester River. The final results suggest that this new processing methodology does adequately capture textural information about the lake floor and does provide an acceptable classification in the absence of significant data quality issues.

Apolipoprotein M can discriminate HNF1A-MODY from Type 1 diabetes.

PubMed

Mughal, S A; Park, R; Nowak, N; Gloyn, A L; Karpe, F; Matile, H; Malecki, M T; McCarthy, M I; Stoffel, M; Owen, K R

2013-02-01

Missed diagnosis of maturity-onset diabetes of the young (MODY) has led to an interest in biomarkers that enable efficient prioritization of patients for definitive molecular testing. Apolipoprotein M (apoM) was suggested as a biomarker for hepatocyte nuclear factor 1 alpha (HNF1A)-MODY because of its reduced expression in Hnf1a(-/-) mice. However, subsequent human studies examining apoM as a biomarker have yielded conflicting results. We aimed to evaluate apoM as a biomarker for HNF1A-MODY using a highly specific and sensitive ELISA. ApoM concentration was measured in subjects with HNF1A-MODY (n = 69), Type 1 diabetes (n = 50), Type 2 diabetes (n = 120) and healthy control subjects (n = 100). The discriminative accuracy of apoM and of the apoM/HDL ratio for diabetes aetiology was evaluated. Mean (standard deviation) serum apoM concentration (μmol/l) was significantly lower for subjects with HNF1A-MODY [0.86 (0.29)], than for those with Type 1 diabetes [1.37 (0.26), P = 3.1 × 10(-18) ) and control subjects [1.34 (0.22), P = 7.2 × 10(-19) ). There was no significant difference in apoM concentration between subjects with HNF1A-MODY and Type 2 diabetes [0.89 (0.28), P = 0.13]. The C-statistic measure of discriminative accuracy for apoM was 0.91 for HNF1A-MODY vs. Type 1 diabetes, indicating high discriminative accuracy. The apoM/HDL ratio was significantly lower in HNF1A-MODY than other study groups. However, this ratio did not perform well in discriminating HNF1A-MODY from either Type 1 diabetes (C-statistic = 0.79) or Type 2 diabetes (C-statistic = 0.68). We confirm an earlier report that serum apoM levels are lower in HNF1A-MODY than in controls. Serum apoM provides good discrimination between HNF1A-MODY and Type 1 diabetes and warrants further investigation for clinical utility in diabetes diagnostics. © 2012 The Authors. Diabetic Medicine © 2012 Diabetes UK.

Applications of Principled Search Methods in Climate Influences and Mechanisms

NASA Technical Reports Server (NTRS)

Glymour, Clark

2005-01-01

Forest and grass fires cause economic losses in the billions of dollars in the U.S. alone. In addition, boreal forests constitute a large carbon store; it has been estimated that, were no burning to occur, an additional 7 gigatons of carbon would be sequestered in boreal soils each century. Effective wildfire suppression requires anticipation of locales and times for which wildfire is most probable, preferably with a two to four week forecast, so that limited resources can be efficiently deployed. The United States Forest Service (USFS), and other experts and agencies have developed several measures of fire risk combining physical principles and expert judgment, and have used them in automated procedures for forecasting fire risk. Forecasting accuracies for some fire risk indices in combination with climate and other variables have been estimated for specific locations, with the value of fire risk index variables assessed by their statistical significance in regressions. In other cases, the MAPSS forecasts [23, 241 for example, forecasting accuracy has been estimated only by simulated data. We describe alternative forecasting methods that predict fire probability by locale and time using statistical or machine learning procedures trained on historical data, and we give comparative assessments of their forecasting accuracy for one fire season year, April- October, 2003, for all U.S. Forest Service lands. Aside from providing an accuracy baseline for other forecasting methods, the results illustrate the interdependence between the statistical significance of prediction variables and the forecasting method used.

Predictive capabilities of statistical learning methods for lung nodule malignancy classification using diagnostic image features: an investigation using the Lung Image Database Consortium dataset

NASA Astrophysics Data System (ADS)

Hancock, Matthew C.; Magnan, Jerry F.

2017-03-01

To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capabilities of statistical learning methods for classifying nodule malignancy, utilizing the Lung Image Database Consortium (LIDC) dataset, and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists' annotations. We calculate theoretical upper bounds on the classification accuracy that is achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 (+/-1.14)% which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 (+/-0.012), which increases to 0.949 (+/-0.007) when diameter and volume features are included, along with the accuracy to 88.08 (+/-1.11)%. Our results are comparable to those in the literature that use algorithmically-derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features, and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification.

Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks.

PubMed

Catto, James W F; Linkens, Derek A; Abbod, Maysam F; Chen, Minyou; Burton, Julian L; Feeley, Kenneth M; Hamdy, Freddie C

2003-09-15

New techniques for the prediction of tumor behavior are needed, because statistical analysis has a poor accuracy and is not applicable to the individual. Artificial intelligence (AI) may provide these suitable methods. Whereas artificial neural networks (ANN), the best-studied form of AI, have been used successfully, its hidden networks remain an obstacle to its acceptance. Neuro-fuzzy modeling (NFM), another AI method, has a transparent functional layer and is without many of the drawbacks of ANN. We have compared the predictive accuracies of NFM, ANN, and traditional statistical methods, for the behavior of bladder cancer. Experimental molecular biomarkers, including p53 and the mismatch repair proteins, and conventional clinicopathological data were studied in a cohort of 109 patients with bladder cancer. For all three of the methods, models were produced to predict the presence and timing of a tumor relapse. Both methods of AI predicted relapse with an accuracy ranging from 88% to 95%. This was superior to statistical methods (71-77%; P < 0.0006). NFM appeared better than ANN at predicting the timing of relapse (P = 0.073). The use of AI can accurately predict cancer behavior. NFM has a similar or superior predictive accuracy to ANN. However, unlike the impenetrable "black-box" of a neural network, the rules of NFM are transparent, enabling validation from clinical knowledge and the manipulation of input variables to allow exploratory predictions. This technique could be used widely in a variety of areas of medicine.

A high-frequency warm shallow water acoustic communications channel model and measurements.

PubMed

Chitre, Mandar

2007-11-01

Underwater acoustic communication is a core enabling technology with applications in ocean monitoring using remote sensors and autonomous underwater vehicles. One of the more challenging underwater acoustic communication channels is the medium-range very shallow warm-water channel, common in tropical coastal regions. This channel exhibits two key features-extensive time-varying multipath and high levels of non-Gaussian ambient noise due to snapping shrimp-both of which limit the performance of traditional communication techniques. A good understanding of the communications channel is key to the design of communication systems. It aids in the development of signal processing techniques as well as in the testing of the techniques via simulation. In this article, a physics-based channel model for the very shallow warm-water acoustic channel at high frequencies is developed, which are of interest to medium-range communication system developers. The model is based on ray acoustics and includes time-varying statistical effects as well as non-Gaussian ambient noise statistics observed during channel studies. The model is calibrated and its accuracy validated using measurements made at sea.

Prognostic factors in patients with advanced cancer: use of the patient-generated subjective global assessment in survival prediction.

PubMed

Martin, Lisa; Watanabe, Sharon; Fainsinger, Robin; Lau, Francis; Ghosh, Sunita; Quan, Hue; Atkins, Marlis; Fassbender, Konrad; Downing, G Michael; Baracos, Vickie

2010-10-01

To determine whether elements of a standard nutritional screening assessment are independently prognostic of survival in patients with advanced cancer. A prospective nested cohort of patients with metastatic cancer were accrued from different units of a Regional Palliative Care Program. Patients completed a nutritional screen on admission. Data included age, sex, cancer site, height, weight history, dietary intake, 13 nutrition impact symptoms, and patient- and physician-reported performance status (PS). Univariate and multivariate survival analyses were conducted. Concordance statistics (c-statistics) were used to test the predictive accuracy of models based on training and validation sets; a c-statistic of 0.5 indicates the model predicts the outcome as well as chance; perfect prediction has a c-statistic of 1.0. A training set of patients in palliative home care (n = 1,164) was used to identify prognostic variables. Primary disease site, PS, short-term weight change (either gain or loss), dietary intake, and dysphagia predicted survival in multivariate analysis (P < .05). A model including only patients separated by disease site and PS with high c-statistics between predicted and observed responses for survival in the training set (0.90) and validation set (0.88; n = 603). The addition of weight change, dietary intake, and dysphagia did not further improve the c-statistic of the model. The c-statistic was also not altered by substituting physician-rated palliative PS for patient-reported PS. We demonstrate a high probability of concordance between predicted and observed survival for patients in distinct palliative care settings (home care, tertiary inpatient, ambulatory outpatient) based on patient-reported information.

PLASS: Protein-ligand affinity statistical score a knowledge-based force-field model of interaction derived from the PDB

NASA Astrophysics Data System (ADS)

Ozrin, V. D.; Subbotin, M. V.; Nikitin, S. M.

2004-04-01

We have developed PLASS (Protein-Ligand Affinity Statistical Score), a pair-wise potential of mean-force for rapid estimation of the binding affinity of a ligand molecule to a protein active site. This scoring function is derived from the frequency of occurrence of atom-type pairs in crystallographic complexes taken from the Protein Data Bank (PDB). Statistical distributions are converted into distance-dependent contributions to the Gibbs free interaction energy for 10 atomic types using the Boltzmann hypothesis, with only one adjustable parameter. For a representative set of 72 protein-ligand structures, PLASS scores correlate well with the experimentally measured dissociation constants: a correlation coefficient R of 0.82 and RMS error of 2.0 kcal/mol. Such high accuracy results from our novel treatment of the volume correction term, which takes into account the inhomogeneous properties of the protein-ligand complexes. PLASS is able to rank reliably the affinity of complexes which have as much diversity as in the PDB.

Statistical process control using optimized neural networks: a case study.

PubMed

Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid

2014-09-01

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Accuracy assessment of percent canopy cover, cover type, and size class

Treesearch

H. T. Schreuder; S. Bain; R. C. Czaplewski

2003-01-01

Truth for vegetation cover percent and type is obtained from very large-scale photography (VLSP), stand structure as measured by size classes, and vegetation types from a combination of VLSP and ground sampling. We recommend using the Kappa statistic with bootstrap confidence intervals for overall accuracy, and similarly bootstrap confidence intervals for percent...

THEMATIC ACCURACY OF THE 1992 NATIONAL LAND-COVER DATA (NLCD) FOR THE EASTERN UNITED STATES: STATISTICAL METHODOLOGY AND REGIONAL RESULTS

EPA Science Inventory

The accuracy of the National Land Cover Data (NLCD) map is assessed via a probability sampling design incorporating three levels of stratification and two stages of selection. Agreement between the map and reference land-cover labels is defined as a match between the primary or a...

Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model

ERIC Educational Resources Information Center

de la Torre, Jimmy; Hong, Yuan; Deng, Weiling

2010-01-01

To better understand the statistical properties of the deterministic inputs, noisy "and" gate cognitive diagnosis (DINA) model, the impact of several factors on the quality of the item parameter estimates and classification accuracy was investigated. Results of the simulation study indicate that the fully Bayes approach is most accurate when the…

3D source localization of interictal spikes in epilepsy patients with MRI lesions

NASA Astrophysics Data System (ADS)

Ding, Lei; Worrell, Gregory A.; Lagerlund, Terrence D.; He, Bin

2006-08-01

The present study aims to accurately localize epileptogenic regions which are responsible for epileptic activities in epilepsy patients by means of a new subspace source localization approach, i.e. first principle vectors (FINE), using scalp EEG recordings. Computer simulations were first performed to assess source localization accuracy of FINE in the clinical electrode set-up. The source localization results from FINE were compared with the results from a classic subspace source localization approach, i.e. MUSIC, and their differences were tested statistically using the paired t-test. Other factors influencing the source localization accuracy were assessed statistically by ANOVA. The interictal epileptiform spike data from three adult epilepsy patients with medically intractable partial epilepsy and well-defined symptomatic MRI lesions were then studied using both FINE and MUSIC. The comparison between the electrical sources estimated by the subspace source localization approaches and MRI lesions was made through the coregistration between the EEG recordings and MRI scans. The accuracy of estimations made by FINE and MUSIC was also evaluated and compared by R2 statistic, which was used to indicate the goodness-of-fit of the estimated sources to the scalp EEG recordings. The three-concentric-spheres head volume conductor model was built for each patient with three spheres of different radii which takes the individual head size and skull thickness into consideration. The results from computer simulations indicate that the improvement of source spatial resolvability and localization accuracy of FINE as compared with MUSIC is significant when simulated sources are closely spaced, deep, or signal-to-noise ratio is low in a clinical electrode set-up. The interictal electrical generators estimated by FINE and MUSIC are in concordance with the patients' structural abnormality, i.e. MRI lesions, in all three patients. The higher R2 values achieved by FINE than MUSIC indicate that FINE provides a more satisfactory fitting of the scalp potential measurements than MUSIC in all patients. The present results suggest that FINE provides a useful brain source imaging technique, from clinical EEG recordings, for identifying and localizing epileptogenic regions in epilepsy patients with focal partial seizures. The present study may lead to the establishment of a high-resolution source localization technique from scalp-recorded EEGs for aiding presurgical planning in epilepsy patients.

Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography.

PubMed

Yilmaz, E; Kayikcioglu, T; Kayipmaz, S

2017-07-01

In this article, we propose a decision support system for effective classification of dental periapical cyst and keratocystic odontogenic tumor (KCOT) lesions obtained via cone beam computed tomography (CBCT). CBCT has been effectively used in recent years for diagnosing dental pathologies and determining their boundaries and content. Unlike other imaging techniques, CBCT provides detailed and distinctive information about the pathologies by enabling a three-dimensional (3D) image of the region to be displayed. We employed 50 CBCT 3D image dataset files as the full dataset of our study. These datasets were identified by experts as periapical cyst and KCOT lesions according to the clinical, radiographic and histopathologic features. Segmentation operations were performed on the CBCT images using viewer software that we developed. Using the tools of this software, we marked the lesional volume of interest and calculated and applied the order statistics and 3D gray-level co-occurrence matrix for each CBCT dataset. A feature vector of the lesional region, including 636 different feature items, was created from those statistics. Six classifiers were used for the classification experiments. The Support Vector Machine (SVM) classifier achieved the best classification performance with 100% accuracy, and 100% F-score (F1) scores as a result of the experiments in which a ten-fold cross validation method was used with a forward feature selection algorithm. SVM achieved the best classification performance with 96.00% accuracy, and 96.00% F1 scores in the experiments in which a split sample validation method was used with a forward feature selection algorithm. SVM additionally achieved the best performance of 94.00% accuracy, and 93.88% F1 in which a leave-one-out (LOOCV) method was used with a forward feature selection algorithm. Based on the results, we determined that periapical cyst and KCOT lesions can be classified with a high accuracy with the models that we built using the new dataset selected for this study. The studies mentioned in this article, along with the selected 3D dataset, 3D statistics calculated from the dataset, and performance results of the different classifiers, comprise an important contribution to the field of computer-aided diagnosis of dental apical lesions. Copyright © 2017 Elsevier B.V. All rights reserved.

Statistical classifiers on multifractal parameters for optical diagnosis of cervical cancer

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Sabyasachi; Pratiher, Sawon; Kumar, Rajeev; Krishnamoorthy, Vigneshram; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2017-06-01

An augmented set of multifractal parameters with physical interpretations have been proposed to quantify the varying distribution and shape of the multifractal spectrum. The statistical classifier with accuracy of 84.17% validates the adequacy of multi-feature MFDFA characterization of elastic scattering spectroscopy for optical diagnosis of cancer.

Fast numerical methods for simulating large-scale integrate-and-fire neuronal networks.

PubMed

Rangan, Aaditya V; Cai, David

2007-02-01

We discuss numerical methods for simulating large-scale, integrate-and-fire (I&F) neuronal networks. Important elements in our numerical methods are (i) a neurophysiologically inspired integrating factor which casts the solution as a numerically tractable integral equation, and allows us to obtain stable and accurate individual neuronal trajectories (i.e., voltage and conductance time-courses) even when the I&F neuronal equations are stiff, such as in strongly fluctuating, high-conductance states; (ii) an iterated process of spike-spike corrections within groups of strongly coupled neurons to account for spike-spike interactions within a single large numerical time-step; and (iii) a clustering procedure of firing events in the network to take advantage of localized architectures, such as spatial scales of strong local interactions, which are often present in large-scale computational models-for example, those of the primary visual cortex. (We note that the spike-spike corrections in our methods are more involved than the correction of single neuron spike-time via a polynomial interpolation as in the modified Runge-Kutta methods commonly used in simulations of I&F neuronal networks.) Our methods can evolve networks with relatively strong local interactions in an asymptotically optimal way such that each neuron fires approximately once in [Formula: see text] operations, where N is the number of neurons in the system. We note that quantifications used in computational modeling are often statistical, since measurements in a real experiment to characterize physiological systems are typically statistical, such as firing rate, interspike interval distributions, and spike-triggered voltage distributions. We emphasize that it takes much less computational effort to resolve statistical properties of certain I&F neuronal networks than to fully resolve trajectories of each and every neuron within the system. For networks operating in realistic dynamical regimes, such as strongly fluctuating, high-conductance states, our methods are designed to achieve statistical accuracy when very large time-steps are used. Moreover, our methods can also achieve trajectory-wise accuracy when small time-steps are used.

Transvaginal ultrasound versus magnetic resonance imaging for preoperative assessment of myometrial infiltration in patients with endometrial cancer: a systematic review and meta-analysis.

PubMed

Alcázar, Juan Luis; Gastón, Begoña; Navarro, Beatriz; Salas, Rocío; Aranda, Juana; Guerriero, Stefano

2017-11-01

To compare the diagnostic accuracy of transvaginal ultrasound (TVS) and magnetic resonance imaging (MRI) for detecting myometrial infiltration (MI) in endometrial carcinoma. An extensive search of papers comparing TVS and MRI in assessing MI in endometrial cancer was performed in MEDLINE (PubMed), Web of Science, and Cochrane Database from January 1989 to January 2017. Quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Our extended search identified 747 citations but after exclusions we finally included in the meta-analysis 8 articles. The risk of bias for most studies was low for most 4 domains assessed in QUADAS-2. Overall, pooled estimated sensitivity and specificity for diagnosing deep MI were 75% (95% confidence interval [CI]=67%-82%) and 82% (95% CI=75%-93%) for TVS, and 83% (95% CI=76%-89%) and 82% (95% CI=72%-89%) for MRI, respectively. No statistical differences were found when comparing both methods (p=0.314). Heterogeneity was low for sensitivity and high for specificity for TVS and MRI. MRI showed a better sensitivity than TVS for detecting deep MI in women with endometrial cancer. However, the difference observed was not statistically significant. Copyright © 2017. Asian Society of Gynecologic Oncology, Korean Society of Gynecologic Oncology

Transvaginal ultrasound versus magnetic resonance imaging for preoperative assessment of myometrial infiltration in patients with endometrial cancer: a systematic review and meta-analysis

PubMed Central

2017-01-01

Objective To compare the diagnostic accuracy of transvaginal ultrasound (TVS) and magnetic resonance imaging (MRI) for detecting myometrial infiltration (MI) in endometrial carcinoma. Methods An extensive search of papers comparing TVS and MRI in assessing MI in endometrial cancer was performed in MEDLINE (PubMed), Web of Science, and Cochrane Database from January 1989 to January 2017. Quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Results Our extended search identified 747 citations but after exclusions we finally included in the meta-analysis 8 articles. The risk of bias for most studies was low for most 4 domains assessed in QUADAS-2. Overall, pooled estimated sensitivity and specificity for diagnosing deep MI were 75% (95% confidence interval [CI]=67%–82%) and 82% (95% CI=75%–93%) for TVS, and 83% (95% CI=76%–89%) and 82% (95% CI=72%–89%) for MRI, respectively. No statistical differences were found when comparing both methods (p=0.314). Heterogeneity was low for sensitivity and high for specificity for TVS and MRI. Conclusion MRI showed a better sensitivity than TVS for detecting deep MI in women with endometrial cancer. However, the difference observed was not statistically significant. PMID:29027404

Empirical best linear unbiased prediction method for small areas with restricted maximum likelihood and bootstrap procedure to estimate the average of household expenditure per capita in Banjar Regency

NASA Astrophysics Data System (ADS)

Aminah, Agustin Siti; Pawitan, Gandhi; Tantular, Bertho

2017-03-01

So far, most of the data published by Statistics Indonesia (BPS) as data providers for national statistics are still limited to the district level. Less sufficient sample size for smaller area levels to make the measurement of poverty indicators with direct estimation produced high standard error. Therefore, the analysis based on it is unreliable. To solve this problem, the estimation method which can provide a better accuracy by combining survey data and other auxiliary data is required. One method often used for the estimation is the Small Area Estimation (SAE). There are many methods used in SAE, one of them is Empirical Best Linear Unbiased Prediction (EBLUP). EBLUP method of maximum likelihood (ML) procedures does not consider the loss of degrees of freedom due to estimating β with β ^. This drawback motivates the use of the restricted maximum likelihood (REML) procedure. This paper proposed EBLUP with REML procedure for estimating poverty indicators by modeling the average of household expenditures per capita and implemented bootstrap procedure to calculate MSE (Mean Square Error) to compare the accuracy EBLUP method with the direct estimation method. Results show that EBLUP method reduced MSE in small area estimation.

Improved Bayesian Infrasonic Source Localization for regional infrasound

DOE PAGES

Blom, Philip S.; Marcillo, Omar; Arrowsmith, Stephen J.

2015-10-20

The Bayesian Infrasonic Source Localization (BISL) methodology is examined and simplified providing a generalized method of estimating the source location and time for an infrasonic event and the mathematical framework is used therein. The likelihood function describing an infrasonic detection used in BISL has been redefined to include the von Mises distribution developed in directional statistics and propagation-based, physically derived celerity-range and azimuth deviation models. Frameworks for constructing propagation-based celerity-range and azimuth deviation statistics are presented to demonstrate how stochastic propagation modelling methods can be used to improve the precision and accuracy of the posterior probability density function describing themore » source localization. Infrasonic signals recorded at a number of arrays in the western United States produced by rocket motor detonations at the Utah Test and Training Range are used to demonstrate the application of the new mathematical framework and to quantify the improvement obtained by using the stochastic propagation modelling methods. Moreover, using propagation-based priors, the spatial and temporal confidence bounds of the source decreased by more than 40 per cent in all cases and by as much as 80 per cent in one case. Further, the accuracy of the estimates remained high, keeping the ground truth within the 99 per cent confidence bounds for all cases.« less

Automated brain volumetrics in multiple sclerosis: a step closer to clinical application

PubMed Central

Beadnall, H N; Hatton, S N; Bader, G; Tomic, D; Silva, D G

2016-01-01

Background Whole brain volume (WBV) estimates in patients with multiple sclerosis (MS) correlate more robustly with clinical disability than traditional, lesion-based metrics. Numerous algorithms to measure WBV have been developed over the past two decades. We compare Structural Image Evaluation using Normalisation of Atrophy-Cross-sectional (SIENAX) to NeuroQuant and MSmetrix, for assessment of cross-sectional WBV in patients with MS. Methods MRIs from 61 patients with relapsing-remitting MS and 2 patients with clinically isolated syndrome were analysed. WBV measurements were calculated using SIENAX, NeuroQuant and MSmetrix. Statistical agreement between the methods was evaluated using linear regression and Bland-Altman plots. Precision and accuracy of WBV measurement was calculated for (1) NeuroQuant versus SIENAX and (2) MSmetrix versus SIENAX. Results Precision (Pearson's r) of WBV estimation for NeuroQuant and MSmetrix versus SIENAX was 0.983 and 0.992, respectively. Accuracy (Cb) was 0.871 and 0.994, respectively. NeuroQuant and MSmetrix showed a 5.5% and 1.0% volume difference compared with SIENAX, respectively, that was consistent across low and high values. Conclusions In the analysed population, NeuroQuant and MSmetrix both quantified cross-sectional WBV with comparable statistical agreement to SIENAX, a well-validated cross-sectional tool that has been used extensively in MS clinical studies. PMID:27071647

Genetic algorithm for the optimization of features and neural networks in ECG signals classification

NASA Astrophysics Data System (ADS)

Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

2017-01-01

Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

Application of Intra-Oral Dental Scanners in the Digital Workflow of Implantology

PubMed Central

van der Meer, Wicher J.; Andriessen, Frank S.; Wismeijer, Daniel; Ren, Yijin

2012-01-01

Intra-oral scanners will play a central role in digital dentistry in the near future. In this study the accuracy of three intra-oral scanners was compared. Materials and methods: A master model made of stone was fitted with three high precision manufactured PEEK cylinders and scanned with three intra-oral scanners: the CEREC (Sirona), the iTero (Cadent) and the Lava COS (3M). In software the digital files were imported and the distance between the centres of the cylinders and the angulation between the cylinders was assessed. These values were compared to the measurements made on a high accuracy 3D scan of the master model. Results: The distance errors were the smallest and most consistent for the Lava COS. The distance errors for the Cerec were the largest and least consistent. All the angulation errors were small. Conclusions: The Lava COS in combination with a high accuracy scanning protocol resulted in the smallest and most consistent errors of all three scanners tested when considering mean distance errors in full arch impressions both in absolute values and in consistency for both measured distances. For the mean angulation errors, the Lava COS had the smallest errors between cylinders 1–2 and the largest errors between cylinders 1–3, although the absolute difference with the smallest mean value (iTero) was very small (0,0529°). An expected increase in distance and/or angular errors over the length of the arch due to an accumulation of registration errors of the patched 3D surfaces could be observed in this study design, but the effects were statistically not significant. Clinical relevance For making impressions of implant cases for digital workflows, the most accurate scanner with the scanning protocol that will ensure the most accurate digital impression should be used. In our study model that was the Lava COS with the high accuracy scanning protocol. PMID:22937030

Continuous monitoring bed-level dynamics on an intertidal flat: introducing novel stand-alone high-resolution SED-sensors

NASA Astrophysics Data System (ADS)

Hu, Zhan; Lenting, Walther; van der Wal, Daphne; Bouma, Tjeerd

2015-04-01

Tidal flat morphology is continuously shaped by hydrodynamic force, resulting in highly dynamic bed elevations. The knowledge of short-term bed-level changes is important both for understanding sediment transport processes as well as for assessing critical ecological processes such as e.g. vegetation recruitment chances on tidal flats. Due to the labour involved, manual discontinuous measurements lack the ability to continuously monitor bed-elevation changes. Existing methods for automated continuous monitoring of bed-level changes lack vertical accuracy (e.g., Photo-Electronic Erosion Pin sensor and resistive rod) or limited in spatial application by using expensive technology (e.g., acoustic bed level sensors). A method provides sufficient accuracy with a reasonable cost is needed. In light of this, a high-accuracy sensor (2 mm) for continuously measuring short-term Surface-Elevation Dynamics (SED-sensor) was developed. This SED-sensor makes use of photovoltaic cells and operates stand-alone using internal power supply and data logging system. The unit cost and the labour in deployments is therefore reduced, which facilitates monitoring with a number of units. In this study, the performance of a group of SED-sensors is tested against data obtained with precise manual measurements using traditional Sediment Erosion Bars (SEB). An excellent agreement between the two methods was obtained, indicating the accuracy and precision of the SED-sensors. Furthermore, to demonstrate how the SED-sensors can be used for measuring short-term bed-level dynamics, two SED-sensors were deployed for 1 month at two sites with contrasting wave exposure conditions. Daily bed-level changes were obtained including a severe storm erosion event. The difference in observed bed-level dynamics at both sites was statistically explained by their different hydrodynamic conditions. Thus, the stand-alone SED-sensor can be applied to monitor sediment surface dynamics with high vertical and temporal resolutions, which provides opportunities to pinpoint morphological responses to various forces in a number of environments (e.g. tidal flats, beaches, rivers and dunes).

Articular cartilage degeneration classification by means of high-frequency ultrasound.

PubMed

Männicke, N; Schöne, M; Oelze, M; Raum, K

2014-10-01

To date only single ultrasound parameters were regarded in statistical analyses to characterize osteoarthritic changes in articular cartilage and the potential benefit of using parameter combinations for characterization remains unclear. Therefore, the aim of this work was to utilize feature selection and classification of a Mankin subset score (i.e., cartilage surface and cell sub-scores) using ultrasound-based parameter pairs and investigate both classification accuracy and the sensitivity towards different degeneration stages. 40 punch biopsies of human cartilage were previously scanned ex vivo with a 40-MHz transducer. Ultrasound-based surface parameters, as well as backscatter and envelope statistics parameters were available. Logistic regression was performed with each unique US parameter pair as predictor and different degeneration stages as response variables. The best ultrasound-based parameter pair for each Mankin subset score value was assessed by highest classification accuracy and utilized in receiver operating characteristics (ROC) analysis. The classifications discriminating between early degenerations yielded area under the ROC curve (AUC) values of 0.94-0.99 (mean ± SD: 0.97 ± 0.03). In contrast, classifications among higher Mankin subset scores resulted in lower AUC values: 0.75-0.91 (mean ± SD: 0.84 ± 0.08). Variable sensitivities of the different ultrasound features were observed with respect to different degeneration stages. Our results strongly suggest that combinations of high-frequency ultrasound-based parameters exhibit potential to characterize different, particularly very early, degeneration stages of hyaline cartilage. Variable sensitivities towards different degeneration stages suggest that a concurrent estimation of multiple ultrasound-based parameters is diagnostically valuable. In-vivo application of the present findings is conceivable in both minimally invasive arthroscopic ultrasound and high-frequency transcutaneous ultrasound. Copyright © 2014 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

An Examination of a Proposed DSM-IV Pathological Gambling Hierarchy in a Treatment Seeking Population: Similarities with Substance Dependence and Evidence for Three Classification Systems.

PubMed

Christensen, Darren R; Jackson, Alun C; Dowling, Nicki A; Volberg, Rachel A; Thomas, Shane A

2015-09-01

Toce-Gerstein et al. (Addiction 98:1661-1672, 2003) investigated the distribution of Diagnostic and Statistical Manual for Mental Disorders, 4th edition (DSM-IV) pathological gambling criteria endorsement in a U.S. community sample for those people endorsing a least one of the DSM-IV criteria (n = 399). They proposed a hierarchy of gambling disorders where endorsement of 1-2 criteria were deemed 'At-Risk', 3-4 'Problem gamblers', 5-7 'Low Pathological', and 8-10 'High Pathological' gamblers. This article examines these claims in a larger Australian treatment seeking population. Data from 4,349 clients attending specialist problem gambling services were assessed for meeting the ten DSM-IV pathological gambling criteria. Results found higher overall criteria endorsement frequencies, three components, a direct relationship between criteria endorsement and gambling severity, clustering of criteria similar to the Toce-Gerstein et al. taxonomy, high accuracy scores for numerical and criteria specific taxonomies, and also high accuracy scores for dichotomous pathological gambling diagnoses. These results suggest significant complexities in the frequencies of criteria reports and relationships between criteria.

Patient Populations, Clinical Associations, and System Efficiency in Healthcare Delivery System

NASA Astrophysics Data System (ADS)

Liu, Yazhuo

The efforts to improve health care delivery usually involve studies and analysis of patient populations and healthcare systems. In this dissertation, I present the research conducted in the following areas: identifying patient groups, improving treatments for specific conditions by using statistical as well as data mining techniques, and developing new operation research models to increase system efficiency from the health institutes' perspective. The results provide better understanding of high risk patient groups, more accuracy in detecting disease' correlations and practical scheduling tools that consider uncertain operation durations and real-life constraints.

Estrogens in seminal plasma of human and animal species: identification and quantitative estimation by gas chromatography-mass spectrometry associated with stable isotope dilution.

PubMed

Reiffsteck, A; Dehennin, L; Scholler, R

1982-11-01

Estrone, 2-methoxyestrone and estradiol-17 beta have been definitely identified in seminal plasma of man, bull, boar and stallion by high resolution gas chromatography associated with selective monitoring of characteristic ions of suitable derivatives. Quantitative estimations were performed by isotope dilution with deuterated analogues and by monitoring molecular ions of trimethylsilyl ethers of labelled and unlabelled compounds. Concentrations of unconjugated and total estrogens are reported together with the statistical evaluation of accuracy and precision.

Constraints of Melting, Sea-Level and the Paleoclimate from GRACE

NASA Technical Reports Server (NTRS)

Davis, James L.

2005-01-01

To gauge the accuracy of the GRACE data, we have undertaken a study to compare deformations predicted by GRACE inferences of seasonal water loading to crustal position variations determined from GRACE data. Two manuscripts that resulted from this study are attached. We found a very high correlation between the GRACE and GPS determinations for South America [Duvis et al., 2004]. We also developed a statistical approach for choosing which Stokes coefficients to include. This approach proves to be somewhat more accurate than the traditional Gaussian filter [Duvis et al., 2005].

Precise determination of neutron binding energy of 64Cu

NASA Astrophysics Data System (ADS)

Telezhnikov, S. A.; Granja, C.; Honzatko, J.; Pospisil, S.; Tomandl, I.

2016-05-01

The neutron binding energy in 64Cu has been accurately measured in thermal neutron capture. A composite target of natural Cu and NaCl was used on a high flux neutron beam using a large measuring time. The γ-ray spectrum emitted in the ( n, γ) reaction was measured with a HPGe detector in large statistics (up to 106 events per channel). Intrinsic limitations of HPGe detectors, which restrict the accuracy of energy calibration, were determined. The value B n of 64Cu was determined as 7915.867(24) keV.

SPIPS: Spectro-Photo-Interferometry of Pulsating Stars

NASA Astrophysics Data System (ADS)

Mérand, Antoine

2017-10-01

SPIPS (Spectro-Photo-Interferometry of Pulsating Stars) combines radial velocimetry, interferometry, and photometry to estimate physical parameters of pulsating stars, including presence of infrared excess, color excess, Teff, and ratio distance/p-factor. The global model-based parallax-of-pulsation method is implemented in Python. Derived parameters have a high level of confidence; statistical precision is improved (compared to other methods) due to the large number of data taken into account, accuracy is improved by using consistent physical modeling and reliability of the derived parameters is strengthened by redundancy in the data.

A method of online quantitative interpretation of diffuse reflection profiles of biological tissues

NASA Astrophysics Data System (ADS)

Lisenko, S. A.; Kugeiko, M. M.

2013-02-01

We have developed a method of combined interpretation of spectral and spatial characteristics of diffuse reflection of biological tissues, which makes it possible to determine biophysical parameters of the tissue with a high accuracy in real time under conditions of their general variability. Using the Monte Carlo method, we have modeled a statistical ensemble of profiles of diffuse reflection coefficients of skin, which corresponds to a wave variation of its biophysical parameters. On its basis, we have estimated the retrieval accuracy of biophysical parameters using the developed method and investigated the stability of the method to errors of optical measurements. We have showed that it is possible to determine online the concentrations of melanin, hemoglobin, bilirubin, oxygen saturation of blood, and structural parameters of skin from measurements of its diffuse reflection in the spectral range 450-800 nm at three distances between the radiation source and detector.

Polychromatic wave-optics models for image-plane speckle. 2. Unresolved objects.

PubMed

Van Zandt, Noah R; Spencer, Mark F; Steinbock, Michael J; Anderson, Brian M; Hyde, Milo W; Fiorino, Steven T

2018-05-20

Polychromatic laser light can reduce speckle noise in many wavefront-sensing and imaging applications. To help quantify the achievable reduction in speckle noise, this study investigates the accuracy of three polychromatic wave-optics models under the specific conditions of an unresolved object. Because existing theory assumes a well-resolved object, laboratory experiments are used to evaluate model accuracy. The three models use Monte-Carlo averaging, depth slicing, and spectral slicing, respectively, to simulate the laser-object interaction. The experiments involve spoiling the temporal coherence of laser light via a fiber-based, electro-optic modulator. After the light scatters off of the rough object, speckle statistics are measured. The Monte-Carlo method is found to be highly inaccurate, while depth-slicing error peaks at 7.8% but is generally much lower in comparison. The spectral-slicing method is the most accurate, always producing results within the error bounds of the experiment.

EUV local CDU healing performance and modeling capability towards 5nm node

NASA Astrophysics Data System (ADS)

Jee, Tae Kwon; Timoshkov, Vadim; Choi, Peter; Rio, David; Tsai, Yu-Cheng; Yaegashi, Hidetami; Koike, Kyohei; Fonseca, Carlos; Schoofs, Stijn

2017-10-01

Both local variability and optical proximity correction (OPC) errors are big contributors to the edge placement error (EPE) budget which is closely related to the device yield. The post-litho contact hole healing will be demonstrated to meet after-etch local variability specifications using a low dose, 30mJ/cm2 dose-to-size, positive tone developed (PTD) resist with relevant throughput in high volume manufacturing (HVM). The total local variability of the node 5nm (N5) contact holes will be characterized in terms of local CD uniformity (LCDU), local placement error (LPE), and contact edge roughness (CER) using a statistical methodology. The CD healing process has complex etch proximity effects, so the OPC prediction accuracy is challenging to meet EPE requirements for the N5. Thus, the prediction accuracy of an after-etch model will be investigated and discussed using ASML Tachyon OPC model.

Digital test signal generation: An accurate SNR calibration approach for the DSN

NASA Technical Reports Server (NTRS)

Gutierrez-Luaces, Benito O.

1993-01-01

In support of the on-going automation of the Deep Space Network (DSN) a new method of generating analog test signals with accurate signal-to-noise ratio (SNR) is described. High accuracy is obtained by simultaneous generation of digital noise and signal spectra at the desired bandwidth (base-band or bandpass). The digital synthesis provides a test signal embedded in noise with the statistical properties of a stationary random process. Accuracy is dependent on test integration time and limited only by the system quantization noise (0.02 dB). The monitor and control as well as signal-processing programs reside in a personal computer (PC). Commands are transmitted to properly configure the specially designed high-speed digital hardware. The prototype can generate either two data channels modulated or not on a subcarrier, or one QPSK channel, or a residual carrier with one biphase data channel. The analog spectrum generated is on the DC to 10 MHz frequency range. These spectra may be up-converted to any desired frequency without loss on the characteristics of the SNR provided. Test results are presented.

Modelling soil salinity in Oued El Abid watershed, Morocco

NASA Astrophysics Data System (ADS)

Mouatassime Sabri, El; Boukdir, Ahmed; Karaoui, Ismail; Arioua, Abdelkrim; Messlouhi, Rachid; El Amrani Idrissi, Abdelkhalek

2018-05-01

Soil salinisation is a phenomenon considered to be a real threat to natural resources in semi-arid climates. The phenomenon is controlled by soil (texture, depth, slope etc.), anthropogenic factors (drainage system, irrigation, crops types, etc.), and climate factors. This study was conducted in the watershed of Oued El Abid in the region of Beni Mellal-Khenifra, aimed at localising saline soil using remote sensing and a regression model. The spectral indices were extracted from Landsat imagery (30 m resolution). A linear correlation of electrical conductivity, which was calculated based on soil samples (ECs), and the values extracted based on spectral bands showed a high accuracy with an R2 (Root square) of 0.80. This study proposes a new spectral salinity index using Landsat bands B1 and B4. This hydro-chemical and statistical study, based on a yearlong survey, showed a moderate amount of salinity, which threatens dam water quality. The results present an improved ability to use remote sensing and regression model integration to detect soil salinity with high accuracy and low cost, and permit intervention at an early stage of salinisation.

Real-time and wearable functional electrical stimulation system for volitional hand motor function control using the electromyography bridge method

PubMed Central

Wang, Hai-peng; Bi, Zheng-yang; Zhou, Yang; Zhou, Yu-xuan; Wang, Zhi-gong; Lv, Xiao-ying

2017-01-01

Voluntary participation of hemiplegic patients is crucial for functional electrical stimulation therapy. A wearable functional electrical stimulation system has been proposed for real-time volitional hand motor function control using the electromyography bridge method. Through a series of novel design concepts, including the integration of a detecting circuit and an analog-to-digital converter, a miniaturized functional electrical stimulation circuit technique, a low-power super-regeneration chip for wireless receiving, and two wearable armbands, a prototype system has been established with reduced size, power, and overall cost. Based on wrist joint torque reproduction and classification experiments performed on six healthy subjects, the optimized surface electromyography thresholds and trained logistic regression classifier parameters were statistically chosen to establish wrist and hand motion control with high accuracy. Test results showed that wrist flexion/extension, hand grasp, and finger extension could be reproduced with high accuracy and low latency. This system can build a bridge of information transmission between healthy limbs and paralyzed limbs, effectively improve voluntary participation of hemiplegic patients, and elevate efficiency of rehabilitation training. PMID:28250759

Interactive lesion segmentation with shape priors from offline and online learning.

PubMed

Shepherd, Tony; Prince, Simon J D; Alexander, Daniel C

2012-09-01

In medical image segmentation, tumors and other lesions demand the highest levels of accuracy but still call for the highest levels of manual delineation. One factor holding back automatic segmentation is the exemption of pathological regions from shape modelling techniques that rely on high-level shape information not offered by lesions. This paper introduces two new statistical shape models (SSMs) that combine radial shape parameterization with machine learning techniques from the field of nonlinear time series analysis. We then develop two dynamic contour models (DCMs) using the new SSMs as shape priors for tumor and lesion segmentation. From training data, the SSMs learn the lower level shape information of boundary fluctuations, which we prove to be nevertheless highly discriminant. One of the new DCMs also uses online learning to refine the shape prior for the lesion of interest based on user interactions. Classification experiments reveal superior sensitivity and specificity of the new shape priors over those previously used to constrain DCMs. User trials with the new interactive algorithms show that the shape priors are directly responsible for improvements in accuracy and reductions in user demand.

Segmentation of Brain Lesions in MRI and CT Scan Images: A Hybrid Approach Using k-Means Clustering and Image Morphology

NASA Astrophysics Data System (ADS)

Agrawal, Ritu; Sharma, Manisha; Singh, Bikesh Kumar

2018-04-01

Manual segmentation and analysis of lesions in medical images is time consuming and subjected to human errors. Automated segmentation has thus gained significant attention in recent years. This article presents a hybrid approach for brain lesion segmentation in different imaging modalities by combining median filter, k means clustering, Sobel edge detection and morphological operations. Median filter is an essential pre-processing step and is used to remove impulsive noise from the acquired brain images followed by k-means segmentation, Sobel edge detection and morphological processing. The performance of proposed automated system is tested on standard datasets using performance measures such as segmentation accuracy and execution time. The proposed method achieves a high accuracy of 94% when compared with manual delineation performed by an expert radiologist. Furthermore, the statistical significance test between lesion segmented using automated approach and that by expert delineation using ANOVA and correlation coefficient achieved high significance values of 0.986 and 1 respectively. The experimental results obtained are discussed in lieu of some recently reported studies.

Bivariate empirical mode decomposition for ECG-based biometric identification with emotional data.

PubMed

Ferdinando, Hany; Seppanen, Tapio; Alasaarela, Esko

2017-07-01

Emotions modulate ECG signals such that they might affect ECG-based biometric identification in real life application. It motivated in finding good feature extraction methods where the emotional state of the subjects has minimum impacts. This paper evaluates feature extraction based on bivariate empirical mode decomposition (BEMD) for biometric identification when emotion is considered. Using the ECG signal from the Mahnob-HCI database for affect recognition, the features were statistical distributions of dominant frequency after applying BEMD analysis to ECG signals. The achieved accuracy was 99.5% with high consistency using kNN classifier in 10-fold cross validation to identify 26 subjects when the emotional states of the subjects were ignored. When the emotional states of the subject were considered, the proposed method also delivered high accuracy, around 99.4%. We concluded that the proposed method offers emotion-independent features for ECG-based biometric identification. The proposed method needs more evaluation related to testing with other classifier and variation in ECG signals, e.g. normal ECG vs. ECG with arrhythmias, ECG from various ages, and ECG from other affective databases.

Gleason Score Determination with Transrectal Ultrasound-Magnetic Resonance Imaging Fusion Guided Prostate Biopsies--Are We Gaining in Accuracy?

PubMed

Lanz, Camille; Cornud, François; Beuvon, Frédéric; Lefèvre, Arnaud; Legmann, Paul; Zerbib, Marc; Delongchamps, Nicolas Barry

2016-01-01

We evaluated the accuracy of prostate magnetic resonance imaging- transrectal ultrasound targeted biopsy for Gleason score determination. We selected 125 consecutive patients treated with radical prostatectomy for a clinically localized prostate cancer diagnosed on magnetic resonance imaging-transrectal ultrasound targeted biopsy and/or systematic biopsy. On multiparametric magnetic resonance imaging each suspicious area was graded according to PI-RADS™ score. A correlation analysis between multiparametric magnetic resonance imaging and pathological findings was performed. Factors associated with determining the accuracy of Gleason score on targeted biopsy were statistically assessed. Pathological analysis of radical prostatectomy specimens detected 230 tumor foci. Multiparametric magnetic resonance imaging detected 151 suspicious areas. Of these areas targeted biopsy showed 126 cancer foci in 115 patients, and detected the index lesion in all of them. The primary Gleason grade, secondary Gleason grade and Gleason score of the 126 individual tumors were determined accurately in 114 (90%), 75 (59%) and 85 (67%) cases, respectively. Maximal Gleason score was determined accurately in 80 (70%) patients. Gleason score determination accuracy on targeted biopsy was significantly higher for low Gleason and high PI-RADS score tumors. Magnetic resonance imaging-transrectal ultrasound targeted biopsy allowed for an accurate estimation of Gleason score in more than two-thirds of patients. Gleason score misclassification was mostly due to a lack of accuracy in the determination of the secondary Gleason grade. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Accuracy of two digital implant impression systems based on confocal microscopy with variations in customized software and clinical parameters.

PubMed

Giménez, Beatriz; Pradíes, Guillermo; Martínez-Rus, Francisco; Özcan, Mutlu

2015-01-01

To evaluate the accuracy of two digital impression systems based on the same technology but different postprocessing correction modes of customized software, with consideration of several clinical parameters. A maxillary master model with six implants located in the second molar, second premolar, and lateral incisor positions was fitted with six cylindrical scan bodies. Scan bodies were placed at different angulations or depths apical to the gingiva. Two experienced and two inexperienced operators performed scans with either 3D Progress (MHT) or ZFX Intrascan (Zimmer Dental). Five different distances between implants (scan bodies) were measured, yielding five data points per impression and 100 per impression system. Measurements made with a high-accuracy three-dimensional coordinate measuring machine (CMM) of the master model acted as the true values. The values obtained from the digital impressions were subtracted from the CMM values to identify the deviations. The differences between experienced and inexperienced operators and implant angulation and depth were compared statistically. Experience of the operator, implant angulation, and implant depth were not associated with significant differences in deviation from the true values with both 3D Progress and ZFX Intrascan. Accuracy in the first scanned quadrant was significantly better with 3D Progress, but ZFX Intrascan presented better accuracy in the full arch. Neither of the two systems tested would be suitable for digital impression of multiple-implant prostheses. Because of the errors, further development of both systems is required.

Combining accuracy assessment of land-cover maps with environmental monitoring programs

USGS Publications Warehouse

Stehman, S.V.; Czaplewski, R.L.; Nusser, S.M.; Yang, L.; Zhu, Z.

2000-01-01

A scientifically valid accuracy assessment of a large-area, land-cover map is expensive. Environmental monitoring programs offer a potential source of data to partially defray the cost of accuracy assessment while still maintaining the statistical validity. In this article, three general strategies for combining accuracy assessment and environmental monitoring protocols are described. These strategies range from a fully integrated accuracy assessment and environmental monitoring protocol, to one in which the protocols operate nearly independently. For all three strategies, features critical to using monitoring data for accuracy assessment include compatibility of the land-cover classification schemes, precisely co-registered sample data, and spatial and temporal compatibility of the map and reference data. Two monitoring programs, the National Resources Inventory (NRI) and the Forest Inventory and Monitoring (FIM), are used to illustrate important features for implementing a combined protocol.

Satellite Remote Sensing of Cropland Characteristics in 30m Resolution: The First North American Continental-Scale Classification on High Performance Computing Platforms

NASA Astrophysics Data System (ADS)

Massey, Richard

Cropland characteristics and accurate maps of their spatial distribution are required to develop strategies for global food security by continental-scale assessments and agricultural land use policies. North America is the major producer and exporter of coarse grains, wheat, and other crops. While cropland characteristics such as crop types are available at country-scales in North America, however, at continental-scale cropland products are lacking at fine sufficient resolution such as 30m. Additionally, applications of automated, open, and rapid methods to map cropland characteristics over large areas without the need of ground samples are needed on efficient high performance computing platforms for timely and long-term cropland monitoring. In this study, I developed novel, automated, and open methods to map cropland extent, crop intensity, and crop types in the North American continent using large remote sensing datasets on high-performance computing platforms. First, a novel method was developed in this study to fuse pixel-based classification of continental-scale Landsat data using Random Forest algorithm available on Google Earth Engine cloud computing platform with an object-based classification approach, recursive hierarchical segmentation (RHSeg) to map cropland extent at continental scale. Using the fusion method, a continental-scale cropland extent map for North America at 30m spatial resolution for the nominal year 2010 was produced. In this map, the total cropland area for North America was estimated at 275.2 million hectares (Mha). This map was assessed for accuracy using randomly distributed samples derived from United States Department of Agriculture (USDA) cropland data layer (CDL), Agriculture and Agri-Food Canada (AAFC) annual crop inventory (ACI), Servicio de Informacion Agroalimentaria y Pesquera (SIAP), Mexico's agricultural boundaries, and photo-interpretation of high-resolution imagery. The overall accuracies of the map are 93.4% with a producer's accuracy for crop class at 85.4% and user's accuracy of 74.5% across the continent. The sub-country statistics including state-wise and county-wise cropland statistics derived from this map compared well in regression models resulting in R2 > 0.84. Secondly, an automated phenological pattern matching (PPM) method to efficiently map cropping intensity was also developed in this study. This study presents a continental-scale cropping intensity map for the North American continent at 250m spatial resolution for 2010. In this map, the total areas for single crop, double crop, continuous crop, and fallow were estimated to be 123.5 Mha, 11.1 Mha, 64.0 Mha, and 83.4 Mha, respectively. This map was assessed using limited country-level reference datasets derived from United States Department of Agriculture cropland data layer and Agriculture and Agri-Food Canada annual crop inventory with overall accuracies of 79.8% and 80.2%, respectively. Third, two novel and automated decision tree classification approaches to map crop types across the conterminous United States (U.S.) using MODIS 250 m resolution data: 1) generalized, and 2) year-specific classification were developed. The classification approaches use similarities and dissimilarities in crop type phenology derived from NDVI time-series data for the two approaches. Annual crop type maps were produced for 8 major crop types in the United States using the generalized classification approach for 2001-2014 and the year-specific approach for 2008, 2010, 2011 and 2012. The year-specific classification had overall accuracies greater than 78%, while the generalized classifier had accuracies greater than 75% for the conterminous U.S. for 2008, 2010, 2011, and 2012. The generalized classifier enables automated and routine crop type mapping without repeated and expensive ground sample collection year after year with overall accuracies > 70% across all independent years. Taken together, these cropland products of extent, cropping intensity, and crop types, are significantly beneficial in agricultural and water use planning and monitoring to formulate policies towards global and North American food security issues.

Effects of disease severity distribution on the performance of quantitative diagnostic methods and proposal of a novel 'V-plot' methodology to display accuracy values.

PubMed

Petraco, Ricardo; Dehbi, Hakim-Moulay; Howard, James P; Shun-Shin, Matthew J; Sen, Sayan; Nijjer, Sukhjinder S; Mayet, Jamil; Davies, Justin E; Francis, Darrel P

2018-01-01

Diagnostic accuracy is widely accepted by researchers and clinicians as an optimal expression of a test's performance. The aim of this study was to evaluate the effects of disease severity distribution on values of diagnostic accuracy as well as propose a sample-independent methodology to calculate and display accuracy of diagnostic tests. We evaluated the diagnostic relationship between two hypothetical methods to measure serum cholesterol (Chol rapid and Chol gold ) by generating samples with statistical software and (1) keeping the numerical relationship between methods unchanged and (2) changing the distribution of cholesterol values. Metrics of categorical agreement were calculated (accuracy, sensitivity and specificity). Finally, a novel methodology to display and calculate accuracy values was presented (the V-plot of accuracies). No single value of diagnostic accuracy can be used to describe the relationship between tests, as accuracy is a metric heavily affected by the underlying sample distribution. Our novel proposed methodology, the V-plot of accuracies, can be used as a sample-independent measure of a test performance against a reference gold standard.

Tonopen XL assessment of intraocular pressure through silicone hydrogel contact lenses.

PubMed

Schornack, Muriel; Rice, Melissa; Hodge, David

2012-09-01

To assess the accuracy of Tonopen XL measurement of intraocular pressure (IOP) through low-power (-0.25 to -3.00) and high power (-3.25 to -6.00) silicone hydrogel lenses of 3 different materials (galyfilcon A, senofilcon A, and lotrafilcon B). Seventy-eight patients were recruited for participation in this study. All were habitual wearers of silicone hydrogel contact lenses, and none had been diagnosed with glaucoma, ocular hypertension, or anterior surface disease. IOP was measured with and without lenses in place in the right eye only. Patients were randomized to initial measurement either with or without the lens in place. A single examiner collected all data. No statistically significant differences were noted between IOP measured without lenses and IOP measured through low-power lotrafilcon B lenses or high-power or low-power galyfilcon A and senofilcon A lenses. However, we did find a statistically significant difference between IOP measured without lenses and IOP measured through high-power lotrafilcon B lenses. In general, Tonopen XL measurement of IOP through silicone hydrogel lenses may be sufficiently accurate for clinical purposes. However, Tonopen XL may overestimate IOP if performed through a silicone hydrogel lens of relatively high modulus.

Diagnostic accuracy of fine-needle aspiration cytology of thyroid gland lesions: A study of 200 cases in Himalayan belt.

PubMed

Sharma, Reetika; Verma, Neelam; Kaushal, Vijay; Sharma, Dev Raj; Sharma, Dhruv

2017-01-01

The study is undertaken to correlate the fine-needle aspiration cytology (FNAC) findings with histopathology in a spectrum of thyroid lesions and to find the diagnostic accuracy of fine-needle aspiration (FNA) so that unnecessary thyroidectomies can be avoided in benign lesions. This study was carried out over the period of 1-year (May 1, 2012-April, 30 2013). FNA specimens obtained from 200 patients were analyzed. Of these, only 40 patients underwent surgery and their thyroid specimens were subjected to histopathological examination. The age of the patients ranged from 9 to 82 years with mean age being 43 years. There was female preponderance, with male to female ratio being 1:7. On cytology out of 200 cases, 148 (74%) were benign, 25 (12.5%) were malignant, 16 (8%) were indeterminate, and 11 (5.5%) were nondiagnostic. Only 40 patients underwent surgery. On histopathology, 21 (52.5%) cases were benign and 19 (47.5%) were malignant. The statistical analysis of cytohistological correlation for both benign and malignant lesions revealed sensitivity, specificity, and diagnostic accuracy of 84%, 100% and 90%, respectively. FNAC is a minimally invasive, highly accurate and cost-effective procedure for the assessment of patients with thyroid lesions and has high -sensitivity and specificity. It acts as a good screening test and avoids unnecessary thyroidectomies.

Evaluation of HER-2/neu gene amplification and overexpression: comparison of frequently used assay methods in a molecularly characterized cohort of breast cancer specimens.

PubMed

Press, Michael F; Slamon, Dennis J; Flom, Kerry J; Park, Jinha; Zhou, Jian-Yuan; Bernstein, Leslie

2002-07-15

To compare and evaluate HER-2/neu clinical assay methods. One hundred seventeen breast cancer specimens with known HER-2/neu amplification and overexpression status were assayed with four different immunohistochemical assays and two different fluorescence in situ hybridization (FISH) assays. The accuracy of the FISH assays for HER-2/neu gene amplification was high, 97.4% for the Vysis PathVision assay (Vysis, Inc, Downers Grove, IL) and 95.7% for the the Ventana INFORM assay (Ventana, Medical Systems, Inc, Tucson, AZ). The immunohistochemical assay with the highest accuracy for HER-2/neu overexpression was obtained with R60 polyclonal antibody (96.6%), followed by immunohistochemical assays performed with 10H8 monoclonal antibody (95.7%), the Ventana CB11 monoclonal antibody (89.7%), and the DAKO HercepTest (88.9%; Dako, Corp, Carpinteria, CA). Only the sensitivities, and therefore, overall accuracy, of the DAKO Herceptest and Ventana CB11 immunohistochemical assays were significantly different from the more sensitive FISH assay. Based on these findings, the FISH assays were highly accurate, with immunohistochemical assays performed with R60 and 10H8 nearly as accurate. The DAKO HercepTest and the Ventana CB11 immunohistochemical assay were statistically significantly different from the Vysis FISH assay in evaluating these previously molecularly characterized breast cancer specimens.

Hybrid cochlear implantation: quality of life, quality of hearing, and working performance compared to patients with conventional unilateral or bilateral cochlear implantation.

PubMed

Härkönen, Kati; Kivekäs, Ilkka; Kotti, Voitto; Sivonen, Ville; Vasama, Juha-Pekka

2017-10-01

The objective of the present study is to evaluate the effect of hybrid cochlear implantation (hCI) on quality of life (QoL), quality of hearing (QoH), and working performance in adult patients, and to compare the long-term results of patients with hCI to those of patients with conventional unilateral cochlear implantation (CI), bilateral CI, and single-sided deafness (SSD) with CI. Sound localization accuracy and speech-in-noise test were also compared between these groups. Eight patients with high-frequency sensorineural hearing loss of unknown etiology were selected in the study. Patients with hCI had better long-term speech perception in noise than uni- or bilateral CI patients, but the difference was not statistically significant. The sound localization accuracy was equal in the hCI, bilateral CI, and SSD patients. QoH was statistically significantly better in bilateral CI patients than in the others. In hCI patients, residual hearing was preserved in all patients after the surgery. During the 3.6-year follow-up, the mean hearing threshold at 125-500 Hz decreased on average by 15 dB HL in the implanted ear. QoL and working performance improved significantly in all CI patients. Hearing outcomes with hCI are comparable to the results of bilateral CI or CI with SSD, but hearing in noise and sound localization are statistically significantly better than with unilateral CI. Interestingly, the impact of CI on QoL, QoH, and working performance was similar in all groups.

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Salivary progesterone and cervical length measurement as predictors of spontaneous preterm birth.

PubMed

Maged, Ahmed M; Mohesen, Mohamed; Elhalwagy, Ahmed; Abdelhafiz, Ali

2015-07-01

To evaluate the efficacy of salivary progesterone, cervical length measurement in predicting preterm birth (PTB). Prospective observational study included 240 pregnant women with gestational age (GA) 26-34 weeks classified into two equal groups; group one are high risk for PTB (those with symptoms of uterine contractions or history of one or more spontaneous preterm delivery or second trimester abortion) and group 2 are controls. There was a highly significant difference between the two study groups regarding GA at delivery (31.3 ± 3.75 in high risk versus 38.5 ± 1.3 in control), cervical length measured by transvaginal ultrasound (24.7 ± 8.6 in high risk versus 40.1 ± 4.67 in control) and salivary progesterone level (728.9 ± 222.3 in high risk versus 1099.9 ± 189.4 in control; p < 0.001). There was a statistically significant difference between levels of salivary progesterone at different GA among the high risk group (p value 0.035) but not in low risk group (p value 0.492). CL measurement showed a sensitivity of 71.5% with 100% specificity, 100% PPV, 69.97% NPV and accuracy of 83%, while salivary progesterone showed a sensitivity of 84% with 90% specificity, 89.8% PPV, 85.9% NPV and accuracy of 92.2%. The measurement of both salivary progesterone and cervical length are good predictors for development of PTB.

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

PubMed

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

2015-01-01

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

Storage-induced changes of the cytosolic red blood cell proteome analyzed by 2D DIGE and high-resolution/high-accuracy MS.

PubMed

Walpurgis, Katja; Kohler, Maxie; Thomas, Andreas; Wenzel, Folker; Geyer, Hans; Schänzer, Wilhelm; Thevis, Mario

2012-11-01

The storage of packed red blood cells (RBCs) is associated with the development of morphological and biochemical changes leading to a reduced posttransfusion functionality and viability of the cells. Within this study, 2D DIGE and high-resolution/high-accuracy Orbitrap MS were used to analyze the storage-induced changes of the cytosolic RBC proteome and identify characteristic protein patterns and potential marker proteins for the assessment of RBC storage lesions. Leukodepleted RBC concentrates were stored according to standard blood bank conditions for 0, 7, 14, 28, and 42 days and analyzed by using a characterized and validated protocol. Following statistical evaluation, a total of 14 protein spots were found to be significantly altered after 42 days of ex vivo storage. Protein identification was accomplished by tryptic digestion and LC-MS/MS and three proteins potentially useful as biomarkers for RBC aging comprising transglutaminase 2, beta actin, and copper chaperone for superoxide dismutase were selected and validated by western blot analysis. These can serve as a basis for the development of a screening assay to detect RBC storage lesions and autologous blood doping in sports. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

TH-CD-207A-07: Prediction of High Dimensional State Subject to Respiratory Motion: A Manifold Learning Approach

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

Liu, W; Sawant, A; Ruan, D

Purpose: The development of high dimensional imaging systems (e.g. volumetric MRI, CBCT, photogrammetry systems) in image-guided radiotherapy provides important pathways to the ultimate goal of real-time volumetric/surface motion monitoring. This study aims to develop a prediction method for the high dimensional state subject to respiratory motion. Compared to conventional linear dimension reduction based approaches, our method utilizes manifold learning to construct a descriptive feature submanifold, where more efficient and accurate prediction can be performed. Methods: We developed a prediction framework for high-dimensional state subject to respiratory motion. The proposed method performs dimension reduction in a nonlinear setting to permit moremore » descriptive features compared to its linear counterparts (e.g., classic PCA). Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where low-dimensional prediction is performed. A fixed-point iterative pre-image estimation method is applied subsequently to recover the predicted value in the original state space. We evaluated and compared the proposed method with PCA-based method on 200 level-set surfaces reconstructed from surface point clouds captured by the VisionRT system. The prediction accuracy was evaluated with respect to root-mean-squared-error (RMSE) for both 200ms and 600ms lookahead lengths. Results: The proposed method outperformed PCA-based approach with statistically higher prediction accuracy. In one-dimensional feature subspace, our method achieved mean prediction accuracy of 0.86mm and 0.89mm for 200ms and 600ms lookahead lengths respectively, compared to 0.95mm and 1.04mm from PCA-based method. The paired t-tests further demonstrated the statistical significance of the superiority of our method, with p-values of 6.33e-3 and 5.78e-5, respectively. Conclusion: The proposed approach benefits from the descriptiveness of a nonlinear manifold and the prediction reliability in such low dimensional manifold. The fixed-point iterative approach turns out to work well practically for the pre-image recovery. Our approach is particularly suitable to facilitate managing respiratory motion in image-guide radiotherapy. This work is supported in part by NIH grant R01 CA169102-02.« less

Accuracy and Variability of Item Parameter Estimates from Marginal Maximum a Posteriori Estimation and Bayesian Inference via Gibbs Samplers

ERIC Educational Resources Information Center

Wu, Yi-Fang

2015-01-01

Item response theory (IRT) uses a family of statistical models for estimating stable characteristics of items and examinees and defining how these characteristics interact in describing item and test performance. With a focus on the three-parameter logistic IRT (Birnbaum, 1968; Lord, 1980) model, the current study examines the accuracy and…

High accuracy in knee alignment and implant placement in unicompartmental medial knee replacement when using patient-specific instrumentation.

PubMed

Volpi, P; Prospero, E; Bait, C; Cervellin, M; Quaglia, A; Redaelli, A; Denti, M

2015-05-01

The influence of patient-specific instrumentations on the accuracy of unicompartmental medial knee replacement remains unclear. The goal of this study was to examine the ability of patient-specific instrumentation to accurately reproduce postoperatively what the surgeon had planned preoperatively. Twenty consecutive patients (20 knees) who suffered from isolated unicompartmental medial osteoarthritis of the knee and underwent medial knee replacement using newly introduced magnetic resonance imaging-based patient-specific instrumentation were assessed. This assessment recorded the following parameters: (1) the planned and the postoperative mechanical axis acquired through long-leg AP view radiographies; (2) the planned and the postoperative tibial slope acquired by means of standard AP and lateral view radiographies; and (3) the postoperative fit of the implanted components to the bone in coronal and sagittal planes. The hypothesis of the study was that there was no statistically significant difference between postoperative results and preoperatively planned values. The study showed that (1) the difference between the postoperative mechanical axis (mean 1.9° varus ± 1.2° SD) and the planned mechanical axis (mean 1.8° varus ± 1.2° SD) was not statistically significant; (2) the difference between the postoperative tibial slope (mean 5.2° ± 0.6° SD) and the planned tibial slope (mean 5.4° ± 0.6° SD) was statistically significant (p = 0.008); and (3) the postoperative component fit to bone in the coronal and sagittal planes was accurate in all cases; nevertheless, in one knee, all components were implanted one size smaller than preoperatively planned. Moreover, in two additional cases, one size thinner and one size thicker of the polyethylene insert were used. This study suggests that overall patient-specific instrumentation was highly accurate in reproducing postoperatively what the surgeon had planned preoperatively in terms of mechanical axis, tibial slope and component fit to bone. IV.

A GATE evaluation of the sources of error in quantitative {sup 90}Y PET

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

Strydhorst, Jared, E-mail: jared.strydhorst@gmail.

Purpose: Accurate reconstruction of the dose delivered by {sup 90}Y microspheres using a postembolization PET scan would permit the establishment of more accurate dose–response relationships for treatment of hepatocellular carcinoma with {sup 90}Y. However, the quality of the PET data obtained is compromised by several factors, including poor count statistics and a very high random fraction. This work uses Monte Carlo simulations to investigate what impact factors other than low count statistics have on the quantification of {sup 90}Y PET. Methods: PET acquisitions of two phantoms—a NEMA PET phantom and the NEMA IEC PET body phantom-containing either {sup 90}Y ormore » {sup 18}F were simulated using GATE. Simulated projections were created with subsets of the simulation data allowing the contributions of random, scatter, and LSO background to be independently evaluated. The simulated projections were reconstructed using the commercial software for the simulated scanner, and the quantitative accuracy of the reconstruction and the contrast recovery of the reconstructed images were evaluated. Results: The quantitative accuracy of the {sup 90}Y reconstructions were not strongly influenced by the high random fraction present in the projection data, and the activity concentration was recovered to within 5% of the known value. The contrast recovery measured for simulated {sup 90}Y data was slightly poorer than that for simulated {sup 18}F data with similar count statistics. However, the degradation was not strongly linked to any particular factor. Using a more restricted energy range to reduce the random fraction in the projections had no significant effect. Conclusions: Simulations of {sup 90}Y PET confirm that quantitative {sup 90}Y is achievable with the same approach as that used for {sup 18}F, and that there is likely very little margin for improvement by attempting to model aspects unique to {sup 90}Y, such as the much higher random fraction or the presence of bremsstrahlung in the singles data.« less

Optimizing a Sensor Network with Data from Hazard Mapping Demonstrated in a Heavy-Vehicle Manufacturing Facility.

PubMed

Berman, Jesse D; Peters, Thomas M; Koehler, Kirsten A

2018-05-28

To design a method that uses preliminary hazard mapping data to optimize the number and location of sensors within a network for a long-term assessment of occupational concentrations, while preserving temporal variability, accuracy, and precision of predicted hazards. Particle number concentrations (PNCs) and respirable mass concentrations (RMCs) were measured with direct-reading instruments in a large heavy-vehicle manufacturing facility at 80-82 locations during 7 mapping events, stratified by day and season. Using kriged hazard mapping, a statistical approach identified optimal orders for removing locations to capture temporal variability and high prediction precision of PNC and RMC concentrations. We compared optimal-removal, random-removal, and least-optimal-removal orders to bound prediction performance. The temporal variability of PNC was found to be higher than RMC with low correlation between the two particulate metrics (ρ = 0.30). Optimal-removal orders resulted in more accurate PNC kriged estimates (root mean square error [RMSE] = 49.2) at sample locations compared with random-removal order (RMSE = 55.7). For estimates at locations having concentrations in the upper 10th percentile, the optimal-removal order preserved average estimated concentrations better than random- or least-optimal-removal orders (P < 0.01). However, estimated average concentrations using an optimal-removal were not statistically different than random-removal when averaged over the entire facility. No statistical difference was observed for optimal- and random-removal methods for RMCs that were less variable in time and space than PNCs. Optimized removal performed better than random-removal in preserving high temporal variability and accuracy of hazard map for PNC, but not for the more spatially homogeneous RMC. These results can be used to reduce the number of locations used in a network of static sensors for long-term monitoring of hazards in the workplace, without sacrificing prediction performance.

Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach.

PubMed

Mete, Mutlu; Sakoglu, Unal; Spence, Jeffrey S; Devous, Michael D; Harris, Thomas S; Adinoff, Bryon

2016-10-06

Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2-4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations. The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively. Sensitivity and specificity were 0.90 and 0.89 for LOO; 0.83 and 0.83 for 10xCV. Many of the 30 selected clusters are highly relevant to the addictive process, including regions relevant to cognitive control, default mode network related self-referential thought, behavioral inhibition, and contextual memories. Relative hyperactivity and hypoactivity of regional cerebral blood flow in brain regions in cocaine-dependent participants are presented with corresponding level of significance. The SVM-based approach successfully classified cocaine-dependent and healthy control participants using voxels selected with information theoretic-based and statistical methods from participants' SPECT data. The regions found in this study align with brain regions reported in the literature. These findings support the future use of brain imaging and SVM-based classifier in the diagnosis of substance use disorders and furthering an understanding of their underlying pathology.

Man Versus Machine: Comparing Double Data Entry and Optical Mark Recognition for Processing CAHPS Survey Data.

PubMed

Fifolt, Matthew; Blackburn, Justin; Rhodes, David J; Gillespie, Shemeka; Bennett, Aleena; Wolff, Paul; Rucks, Andrew

Historically, double data entry (DDE) has been considered the criterion standard for minimizing data entry errors. However, previous studies considered data entry alternatives through the limited lens of data accuracy. This study supplies information regarding data accuracy, operational efficiency, and cost for DDE and Optical Mark Recognition (OMR) for processing the Consumer Assessment of Healthcare Providers and Systems 5.0 survey. To assess data accuracy, we compared error rates for DDE and OMR by dividing the number of surveys that were arbitrated by the total number of surveys processed for each method. To assess operational efficiency, we tallied the cost of data entry for DDE and OMR after survey receipt. Costs were calculated on the basis of personnel, depreciation for capital equipment, and costs of noncapital equipment. The cost savings attributed to this method were negated by the operational efficiency of OMR. There was a statistical significance between rates of arbitration between DDE and OMR; however, this statistical significance did not create a practical significance. The potential benefits of DDE in terms of data accuracy did not outweigh the operational efficiency and thereby financial savings of OMR.

Additional studies of forest classification accuracy as influenced by multispectral scanner spatial resolution

NASA Technical Reports Server (NTRS)

Sadowski, F. E.; Sarno, J. E.

1976-01-01

First, an analysis of forest feature signatures was used to help explain the large variation in classification accuracy that can occur among individual forest features for any one case of spatial resolution and the inconsistent changes in classification accuracy that were demonstrated among features as spatial resolution was degraded. Second, the classification rejection threshold was varied in an effort to reduce the large proportion of unclassified resolution elements that previously appeared in the processing of coarse resolution data when a constant rejection threshold was used for all cases of spatial resolution. For the signature analysis, two-channel ellipse plots showing the feature signature distributions for several cases of spatial resolution indicated that the capability of signatures to correctly identify their respective features is dependent on the amount of statistical overlap among signatures. Reductions in signature variance that occur in data of degraded spatial resolution may not necessarily decrease the amount of statistical overlap among signatures having large variance and small mean separations. Features classified by such signatures may thus continue to have similar amounts of misclassified elements in coarser resolution data, and thus, not necessarily improve in classification accuracy.

Interval Timing Accuracy and Scalar Timing in C57BL/6 Mice

PubMed Central

Buhusi, Catalin V.; Aziz, Dyana; Winslow, David; Carter, Rickey E.; Swearingen, Joshua E.; Buhusi, Mona C.

2010-01-01

In many species, interval timing behavior is accurate—appropriate estimated durations—and scalar—errors vary linearly with estimated durations. While accuracy has been previously examined, scalar timing has not been yet clearly demonstrated in house mice (Mus musculus), raising concerns about mouse models of human disease. We estimated timing accuracy and precision in C57BL/6 mice, the most used background strain for genetic models of human disease, in a peak-interval procedure with multiple intervals. Both when timing two intervals (Experiment 1) or three intervals (Experiment 2), C57BL/6 mice demonstrated varying degrees of timing accuracy. Importantly, both at individual and group level, their precision varied linearly with the subjective estimated duration. Further evidence for scalar timing was obtained using an intraclass correlation statistic. This is the first report of consistent, reliable scalar timing in a sizable sample of house mice, thus validating the PI procedure as a valuable technique, the intraclass correlation statistic as a powerful test of the scalar property, and the C57BL/6 strain as a suitable background for behavioral investigations of genetically engineered mice modeling disorders of interval timing. PMID:19824777

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

PubMed Central

Ramirez-Villegas, Juan F.; Lam-Espinosa, Eric; Ramirez-Moreno, David F.; Calvo-Echeverry, Paulo C.; Agredo-Rodriguez, Wilfredo

2011-01-01

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis. PMID:21386966

State Estimation Using Dependent Evidence Fusion: Application to Acoustic Resonance-Based Liquid Level Measurement.

PubMed

Xu, Xiaobin; Li, Zhenghui; Li, Guo; Zhou, Zhe

2017-04-21

Estimating the state of a dynamic system via noisy sensor measurement is a common problem in sensor methods and applications. Most state estimation methods assume that measurement noise and state perturbations can be modeled as random variables with known statistical properties. However in some practical applications, engineers can only get the range of noises, instead of the precise statistical distributions. Hence, in the framework of Dempster-Shafer (DS) evidence theory, a novel state estimatation method by fusing dependent evidence generated from state equation, observation equation and the actual observations of the system states considering bounded noises is presented. It can be iteratively implemented to provide state estimation values calculated from fusion results at every time step. Finally, the proposed method is applied to a low-frequency acoustic resonance level gauge to obtain high-accuracy measurement results.

Characterizing challenged Minnesota ballots

NASA Astrophysics Data System (ADS)

Nagy, George; Lopresti, Daniel; Barney Smith, Elisa H.; Wu, Ziyan

2011-01-01

Photocopies of the ballots challenged in the 2008 Minnesota elections, which constitute a public record, were scanned on a high-speed scanner and made available on a public radio website. The PDF files were downloaded, converted to TIF images, and posted on the PERFECT website. Based on a review of relevant image-processing aspects of paper-based election machinery and on additional statistics and observations on the posted sample data, robust tools were developed for determining the underlying grid of the targets on these ballots regardless of skew, clipping, and other degradations caused by high-speed copying and digitization. The accuracy and robustness of a method based on both index-marks and oval targets are demonstrated on 13,435 challenged ballot page images.

A Mechanical Power Flow Capability for the Finite Element Code NASTRAN

DTIC Science & Technology

1989-07-01

perimental methods. statistical energy analysis , the finite element method, and a finite element analog-,y using heat conduction equations. Experimental...weights and inertias of the transducers attached to an experimental structure may produce accuracy problems. Statistical energy analysis (SEA) is a...405-422 (1987). 8. Lyon, R.L., Statistical Energy Analysis of Dynamical Sistems, The M.I.T. Press, (1975). 9. Mickol, J.D., and R.J. Bernhard, "An

Pitfalls of national routine death statistics for maternal mortality study.

PubMed

Saucedo, Monica; Bouvier-Colle, Marie-Hélène; Chantry, Anne A; Lamarche-Vadel, Agathe; Rey, Grégoire; Deneux-Tharaux, Catherine

2014-11-01

The lessons learned from the study of maternal deaths depend on the accuracy of data. Our objective was to assess time trends in the underestimation of maternal mortality (MM) in the national routine death statistics in France and to evaluate their current accuracy for the selection and causes of maternal deaths. National data obtained by enhanced methods in 1989, 1999, and 2007-09 were used as the gold standard to assess time trends in the underestimation of MM ratios (MMRs) in death statistics. Enhanced data and death statistics for 2007-09 were further compared by characterising false negatives (FNs) and false positives (FPs). The distribution of cause-specific MMRs, as assessed by each system, was described. Underestimation of MM in death statistics decreased from 55.6% in 1989 to 11.4% in 2007-09 (P < 0.001). In 2007-09, of 787 pregnancy-associated deaths, 254 were classified as maternal by the enhanced system and 211 by the death statistics; 34% of maternal deaths in the enhanced system were FNs in the death statistics, and 20% of maternal deaths in the death statistics were FPs. The hierarchy of causes of MM differed between the two systems. The discordances were mainly explained by the lack of precision in the drafting of death certificates by clinicians. Although the underestimation of MM in routine death statistics has decreased substantially over time, one third of maternal deaths remain unidentified, and the main causes of death are incorrectly identified in these data. Defining relevant priorities in maternal health requires the use of enhanced methods for MM study. © 2014 John Wiley & Sons Ltd.

Integrated Wind Power Planning Tool

NASA Astrophysics Data System (ADS)

Rosgaard, M. H.; Giebel, G.; Nielsen, T. S.; Hahmann, A.; Sørensen, P.; Madsen, H.

2012-04-01

This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the working title "Integrated Wind Power Planning Tool". The project commenced October 1, 2011, and the goal is to integrate a numerical weather prediction (NWP) model with purely statistical tools in order to assess wind power fluctuations, with focus on long term power system planning for future wind farms as well as short term forecasting for existing wind farms. Currently, wind power fluctuation models are either purely statistical or integrated with NWP models of limited resolution. With regard to the latter, one such simulation tool has been developed at the Wind Energy Division, Risø DTU, intended for long term power system planning. As part of the PSO project the inferior NWP model used at present will be replaced by the state-of-the-art Weather Research & Forecasting (WRF) model. Furthermore, the integrated simulation tool will be improved so it can handle simultaneously 10-50 times more turbines than the present ~ 300, as well as additional atmospheric parameters will be included in the model. The WRF data will also be input for a statistical short term prediction model to be developed in collaboration with ENFOR A/S; a danish company that specialises in forecasting and optimisation for the energy sector. This integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting for its spatio-temporal dependencies, and depending on the prevailing weather conditions defined by the WRF output. The output from the integrated prediction tool constitute scenario forecasts for the coming period, which can then be fed into any type of system model or decision making problem to be solved. The high resolution of the WRF results loaded into the integrated prediction model will ensure a high accuracy data basis is available for use in the decision making process of the Danish transmission system operator, and the need for high accuracy predictions will only increase over the next decade as Denmark approaches the goal of 50% wind power based electricity in 2020, from the current 20%.

Integrated Wind Power Planning Tool

NASA Astrophysics Data System (ADS)

Rosgaard, Martin; Giebel, Gregor; Skov Nielsen, Torben; Hahmann, Andrea; Sørensen, Poul; Madsen, Henrik

2013-04-01

This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the title "Integrated Wind Power Planning Tool". The goal is to integrate a mesoscale numerical weather prediction (NWP) model with purely statistical tools in order to assess wind power fluctuations, with focus on long term power system planning for future wind farms as well as short term forecasting for existing wind farms. Currently, wind power fluctuation models are either purely statistical or integrated with NWP models of limited resolution. Using the state-of-the-art mesoscale NWP model Weather Research & Forecasting model (WRF) the forecast error is sought quantified in dependence of the time scale involved. This task constitutes a preparative study for later implementation of features accounting for NWP forecast errors in the DTU Wind Energy maintained Corwind code - a long term wind power planning tool. Within the framework of PSO 10464 research related to operational short term wind power prediction will be carried out, including a comparison of forecast quality at different mesoscale NWP model resolutions and development of a statistical wind power prediction tool taking input from WRF. The short term prediction part of the project is carried out in collaboration with ENFOR A/S; a Danish company that specialises in forecasting and optimisation for the energy sector. The integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting for its spatio-temporal dependencies, and depending on the prevailing weather conditions defined by the WRF output. The output from the integrated short term prediction tool constitutes scenario forecasts for the coming period, which can then be fed into any type of system model or decision making problem to be solved. The high resolution of the WRF results loaded into the integrated prediction model will ensure a high accuracy data basis is available for use in the decision making process of the Danish transmission system operator. The need for high accuracy predictions will only increase over the next decade as Denmark approaches the goal of 50% wind power based electricity in 2025 from the current 20%.

Computerised tomography vs magnetic resonance imaging for modeling of patient-specific instrumentation in total knee arthroplasty.

PubMed

Stirling, Paul; Valsalan Mannambeth, Rejith; Soler, Agustin; Batta, Vineet; Malhotra, Rajeev Kumar; Kalairajah, Yegappan

2015-03-18

To summarise and compare currently available evidence regarding accuracy of pre-operative imaging, which is one of the key choices for surgeons contemplating patient-specific instrumentation (PSI) surgery. The MEDLINE and EMBASE medical literature databases were searched, from January 1990 to December 2013, to identify relevant studies. The data from several clinical studies was assimilated to allow appreciation and comparison of the accuracy of each modality. The overall accuracy of each modality was calculated as proportion of outliers > 3% in the coronal plane of both computerised tomography (CT) or magnetic resonance imaging (MRI). Seven clinical studies matched our inclusion criteria for comparison and were included in our study for statistical analysis. Three of these reported series using MRI and four with CT. Overall percentage of outliers > 3% in patients with CT-based PSI systems was 12.5% vs 16.9% for MRI-based systems. These results were not statistically significant. Although many studies have been undertaken to determine the ideal pre-operative imaging modality, conclusions remain speculative in the absence of long term data. Ultimately, information regarding accuracy of CT and MRI will be the main determining factor. Increased accuracy of pre-operative imaging could result in longer-term savings, and reduced accumulated dose of radiation by eliminating the need for post-operative imaging and revision surgery.

Computerised tomography vs magnetic resonance imaging for modeling of patient-specific instrumentation in total knee arthroplasty

PubMed Central

Stirling, Paul; Valsalan Mannambeth, Rejith; Soler, Agustin; Batta, Vineet; Malhotra, Rajeev Kumar; Kalairajah, Yegappan

2015-01-01

AIM: To summarise and compare currently available evidence regarding accuracy of pre-operative imaging, which is one of the key choices for surgeons contemplating patient-specific instrumentation (PSI) surgery. METHODS: The MEDLINE and EMBASE medical literature databases were searched, from January 1990 to December 2013, to identify relevant studies. The data from several clinical studies was assimilated to allow appreciation and comparison of the accuracy of each modality. The overall accuracy of each modality was calculated as proportion of outliers > 3% in the coronal plane of both computerised tomography (CT) or magnetic resonance imaging (MRI). RESULTS: Seven clinical studies matched our inclusion criteria for comparison and were included in our study for statistical analysis. Three of these reported series using MRI and four with CT. Overall percentage of outliers > 3% in patients with CT-based PSI systems was 12.5% vs 16.9% for MRI-based systems. These results were not statistically significant. CONCLUSION: Although many studies have been undertaken to determine the ideal pre-operative imaging modality, conclusions remain speculative in the absence of long term data. Ultimately, information regarding accuracy of CT and MRI will be the main determining factor. Increased accuracy of pre-operative imaging could result in longer-term savings, and reduced accumulated dose of radiation by eliminating the need for post-operative imaging and revision surgery. PMID:25793170

Machine learning approach for automated screening of malaria parasite using light microscopic images.

PubMed

Das, Dev Kumar; Ghosh, Madhumala; Pal, Mallika; Maiti, Asok K; Chakraborty, Chandan

2013-02-01

The aim of this paper is to address the development of computer assisted malaria parasite characterization and classification using machine learning approach based on light microscopic images of peripheral blood smears. In doing this, microscopic image acquisition from stained slides, illumination correction and noise reduction, erythrocyte segmentation, feature extraction, feature selection and finally classification of different stages of malaria (Plasmodium vivax and Plasmodium falciparum) have been investigated. The erythrocytes are segmented using marker controlled watershed transformation and subsequently total ninety six features describing shape-size and texture of erythrocytes are extracted in respect to the parasitemia infected versus non-infected cells. Ninety four features are found to be statistically significant in discriminating six classes. Here a feature selection-cum-classification scheme has been devised by combining F-statistic, statistical learning techniques i.e., Bayesian learning and support vector machine (SVM) in order to provide the higher classification accuracy using best set of discriminating features. Results show that Bayesian approach provides the highest accuracy i.e., 84% for malaria classification by selecting 19 most significant features while SVM provides highest accuracy i.e., 83.5% with 9 most significant features. Finally, the performance of these two classifiers under feature selection framework has been compared toward malaria parasite classification. Copyright © 2012 Elsevier Ltd. All rights reserved.

Random noise effects in pulse-mode digital multilayer neural networks.

PubMed

Kim, Y C; Shanblatt, M A

1995-01-01

A pulse-mode digital multilayer neural network (DMNN) based on stochastic computing techniques is implemented with simple logic gates as basic computing elements. The pulse-mode signal representation and the use of simple logic gates for neural operations lead to a massively parallel yet compact and flexible network architecture, well suited for VLSI implementation. Algebraic neural operations are replaced by stochastic processes using pseudorandom pulse sequences. The distributions of the results from the stochastic processes are approximated using the hypergeometric distribution. Synaptic weights and neuron states are represented as probabilities and estimated as average pulse occurrence rates in corresponding pulse sequences. A statistical model of the noise (error) is developed to estimate the relative accuracy associated with stochastic computing in terms of mean and variance. Computational differences are then explained by comparison to deterministic neural computations. DMNN feedforward architectures are modeled in VHDL using character recognition problems as testbeds. Computational accuracy is analyzed, and the results of the statistical model are compared with the actual simulation results. Experiments show that the calculations performed in the DMNN are more accurate than those anticipated when Bernoulli sequences are assumed, as is common in the literature. Furthermore, the statistical model successfully predicts the accuracy of the operations performed in the DMNN.

Detection of prostate cancer with multiparametric MRI (mpMRI): effect of dedicated reader education on accuracy and confidence of index and anterior cancer diagnosis

PubMed Central

Garcia-Reyes, Kirema; Passoni, Niccolò M.; Palmeri, Mark L.; Kauffman, Christopher R.; Choudhury, Kingshuk Roy; Polascik, Thomas J.; Gupta, Rajan T.

2015-01-01

Purpose To evaluate the impact of dedicated reader education on accuracy/confidence of peripheral zone index cancer and anterior prostate cancer (PCa) diagnosis with mpMRI; secondary aim was to assess the ability of readers to differentiate low-grade cancer (Gleason 6 or below) from high-grade cancer (Gleason 7+). Materials and methods Five blinded radiology fellows evaluated 31 total prostate mpMRIs in this IRB-approved, HIPAA-compliant, retrospective study for index lesion detection, confidence in lesion diagnosis (1–5 scale), and Gleason grade (Gleason 6 or lower vs. Gleason 7+). Following a dedicated education program, readers reinterpreted cases after a memory extinction period, blinded to initial reads. Reference standard was established combining whole mount histopathology with mpMRI findings by a board-certified radiologist with 5 years of prostate mpMRI experience. Results Index cancer detection: pre-education accuracy 74.2%; post-education accuracy 87.7% (p = 0.003). Confidence in index lesion diagnosis: pre-education 4.22 ± 1.04; post-education 3.75 ± 1.41 (p = 0.0004). Anterior PCa detection: pre-education accuracy 54.3%; post-education accuracy 94.3% (p = 0.001). Confidence in anterior PCa diagnosis: pre-education 3.22 ± 1.54; post-education 4.29 ± 0.83 (p = 0.0003). Gleason score accuracy: pre-education 54.8%; post-education 73.5% (p = 0.0005). Conclusions A dedicated reader education program on PCa detection with mpMRI was associated with a statistically significant increase in diagnostic accuracy of index cancer and anterior cancer detection as well as Gleason grade identification as compared to pre-education values. This was also associated with a significant increase in reader diagnostic confidence. This suggests that substantial interobserver variability in mpMRI interpretation can potentially be reduced with a focus on education and that this can occur over a fellowship training year. PMID:25034558

A comparison of the accuracy of intraoral scanners using an intraoral environment simulator.

PubMed

Park, Hye-Nan; Lim, Young-Jun; Yi, Won-Jin; Han, Jung-Suk; Lee, Seung-Pyo

2018-02-01

The aim of this study was to design an intraoral environment simulator and to assess the accuracy of two intraoral scanners using the simulator. A box-shaped intraoral environment simulator was designed to simulate two specific intraoral environments. The cast was scanned 10 times by Identica Blue (MEDIT, Seoul, South Korea), TRIOS (3Shape, Copenhagen, Denmark), and CS3500 (Carestream Dental, Georgia, USA) scanners in the two simulated groups. The distances between the left and right canines (D3), first molars (D6), second molars (D7), and the left canine and left second molar (D37) were measured. The distance data were analyzed by the Kruskal-Wallis test. The differences in intraoral environments were not statistically significant ( P >.05). Between intraoral scanners, statistically significant differences ( P <.05) were revealed by the Kruskal-Wallis test with regard to D3 and D6. No difference due to the intraoral environment was revealed. The simulator will contribute to the higher accuracy of intraoral scanners in the future.

Towards a Statistical Model of Tropical Cyclone Genesis

NASA Astrophysics Data System (ADS)

Fernandez, A.; Kashinath, K.; McAuliffe, J.; Prabhat, M.; Stark, P. B.; Wehner, M. F.

2017-12-01

Tropical Cyclones (TCs) are important extreme weather phenomena that have a strong impact on humans. TC forecasts are largely based on global numerical models that produce TC-like features. Aspects of Tropical Cyclones such as their formation/genesis, evolution, intensification and dissipation over land are important and challenging problems in climate science. This study investigates the environmental conditions associated with Tropical Cyclone Genesis (TCG) by testing how accurately a statistical model can predict TCG in the CAM5.1 climate model. TCG events are defined using TECA software @inproceedings{Prabhat2015teca, title={TECA: Petascale Pattern Recognition for Climate Science}, author={Prabhat and Byna, Surendra and Vishwanath, Venkatram and Dart, Eli and Wehner, Michael and Collins, William D}, booktitle={Computer Analysis of Images and Patterns}, pages={426-436}, year={2015}, organization={Springer}} to extract TC trajectories from CAM5.1. L1-regularized logistic regression (L1LR) is applied to the CAM5.1 output. The predictions have nearly perfect accuracy for data not associated with TC tracks and high accuracy differentiating between high vorticity and low vorticity systems. The model's active variables largely correspond to current hypotheses about important factors for TCG, such as wind field patterns and local pressure minima, and suggests new routes for investigation. Furthermore, our model's predictions of TC activity are competitive with the output of an instantaneous version of Emanuel and Nolan's Genesis Potential Index (GPI) @inproceedings{eman04, title = "Tropical cyclone activity and the global climate system", author = "Kerry Emanuel and Nolan, {David S.}", year = "2004", pages = "240-241", booktitle = "26th Conference on Hurricanes and Tropical Meteorology"}.

Three-dimensional virtual planning in orthognathic surgery enhances the accuracy of soft tissue prediction.

PubMed

Van Hemelen, Geert; Van Genechten, Maarten; Renier, Lieven; Desmedt, Maria; Verbruggen, Elric; Nadjmi, Nasser

2015-07-01

Throughout the history of computing, shortening the gap between the physical and digital world behind the screen has always been strived for. Recent advances in three-dimensional (3D) virtual surgery programs have reduced this gap significantly. Although 3D assisted surgery is now widely available for orthognathic surgery, one might still argue whether a 3D virtual planning approach is a better alternative to a conventional two-dimensional (2D) planning technique. The purpose of this study was to compare the accuracy of a traditional 2D technique and a 3D computer-aided prediction method. A double blind randomised prospective study was performed to compare the prediction accuracy of a traditional 2D planning technique versus a 3D computer-aided planning approach. The accuracy of the hard and soft tissue profile predictions using both planning methods was investigated. There was a statistically significant difference between 2D and 3D soft tissue planning (p < 0.05). The statistically significant difference found between 2D and 3D planning and the actual soft tissue outcome was not confirmed by a statistically significant difference between methods. The 3D planning approach provides more accurate soft tissue planning. However, the 2D orthognathic planning is comparable to 3D planning when it comes to hard tissue planning. This study provides relevant results for choosing between 3D and 2D planning in clinical practice. Copyright © 2015 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

Accuracy comparison of guided surgery for dental implants according to the tissue of support: a systematic review and meta-analysis.

PubMed

Raico Gallardo, Yolanda Natali; da Silva-Olivio, Isabela Rodrigues Teixeira; Mukai, Eduardo; Morimoto, Susana; Sesma, Newton; Cordaro, Luca

2017-05-01

To systematically assess the current dental literature comparing the accuracy of computer-aided implant surgery when using different supporting tissues (tooth, mucosa, or bone). Two reviewers searched PubMed (1972 to January 2015) and the Cochrane Central Register of Controlled Trials (Central) (2002 to January 2015). For the assessment of accuracy, studies were included with the following outcome measures: (i) angle deviation, (ii) deviation at the entry point, and (iii) deviation at the apex. Eight clinical studies from the 1602 articles initially identified met the inclusion criteria for the qualitative analysis. Four studies (n = 599 implants) were evaluated using meta-analysis. The bone-supported guides showed a statistically significant greater deviation in angle (P < 0.001), entry point (P = 0.01), and the apex (P = 0.001) when compared to the tooth-supported guides. Conversely, when only retrospective studies were analyzed, not significant differences are revealed in the deviation of the entry point and apex. The mucosa-supported guides indicated a statistically significant greater reduction in angle deviation (P = 0.02), deviation at the entry point (P = 0.002), and deviation at the apex (P = 0.04) when compared to the bone-supported guides. Between the mucosa- and tooth-supported guides, there were no statistically significant differences for any of the outcome measures. It can be concluded that the tissue of the guide support influences the accuracy of computer-aided implant surgery. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine

NASA Astrophysics Data System (ADS)

Obuchowski, Nancy A.; Bullen, Jennifer A.

2018-04-01

Receiver operating characteristic (ROC) analysis is a tool used to describe the discrimination accuracy of a diagnostic test or prediction model. While sensitivity and specificity are the basic metrics of accuracy, they have many limitations when characterizing test accuracy, particularly when comparing the accuracies of competing tests. In this article we review the basic study design features of ROC studies, illustrate sample size calculations, present statistical methods for measuring and comparing accuracy, and highlight commonly used ROC software. We include descriptions of multi-reader ROC study design and analysis, address frequently seen problems of verification and location bias, discuss clustered data, and provide strategies for testing endpoints in ROC studies. The methods are illustrated with a study of transmission ultrasound for diagnosing breast lesions.

Representation of Probability Density Functions from Orbit Determination using the Particle Filter

NASA Technical Reports Server (NTRS)

Mashiku, Alinda K.; Garrison, James; Carpenter, J. Russell

2012-01-01

Statistical orbit determination enables us to obtain estimates of the state and the statistical information of its region of uncertainty. In order to obtain an accurate representation of the probability density function (PDF) that incorporates higher order statistical information, we propose the use of nonlinear estimation methods such as the Particle Filter. The Particle Filter (PF) is capable of providing a PDF representation of the state estimates whose accuracy is dependent on the number of particles or samples used. For this method to be applicable to real case scenarios, we need a way of accurately representing the PDF in a compressed manner with little information loss. Hence we propose using the Independent Component Analysis (ICA) as a non-Gaussian dimensional reduction method that is capable of maintaining higher order statistical information obtained using the PF. Methods such as the Principal Component Analysis (PCA) are based on utilizing up to second order statistics, hence will not suffice in maintaining maximum information content. Both the PCA and the ICA are applied to two scenarios that involve a highly eccentric orbit with a lower apriori uncertainty covariance and a less eccentric orbit with a higher a priori uncertainty covariance, to illustrate the capability of the ICA in relation to the PCA.

Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods

PubMed Central

Hancock, Matthew C.; Magnan, Jerry F.

2016-01-01

Abstract. In the assessment of nodules in CT scans of the lungs, a number of image-derived features are diagnostically relevant. Currently, many of these features are defined only qualitatively, so they are difficult to quantify from first principles. Nevertheless, these features (through their qualitative definitions and interpretations thereof) are often quantified via a variety of mathematical methods for the purpose of computer-aided diagnosis (CAD). To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capability of statistical learning methods for classifying nodule malignancy. We utilize the Lung Image Database Consortium dataset and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists’ annotations. We calculate theoretical upper bounds on the classification accuracy that are achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 (±1.14)%, which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 (±0.012), which increases to 0.949 (±0.007) when diameter and volume features are included and has an accuracy of 88.08 (±1.11)%. Our results are comparable to those in the literature that use algorithmically derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification. PMID:27990453

Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods.

PubMed

Hancock, Matthew C; Magnan, Jerry F

2016-10-01

In the assessment of nodules in CT scans of the lungs, a number of image-derived features are diagnostically relevant. Currently, many of these features are defined only qualitatively, so they are difficult to quantify from first principles. Nevertheless, these features (through their qualitative definitions and interpretations thereof) are often quantified via a variety of mathematical methods for the purpose of computer-aided diagnosis (CAD). To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capability of statistical learning methods for classifying nodule malignancy. We utilize the Lung Image Database Consortium dataset and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists' annotations. We calculate theoretical upper bounds on the classification accuracy that are achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 [Formula: see text], which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 ([Formula: see text]), which increases to 0.949 ([Formula: see text]) when diameter and volume features are included and has an accuracy of 88.08 [Formula: see text]. Our results are comparable to those in the literature that use algorithmically derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification.

Exploiting Measurement Uncertainty Estimation in Evaluation of GOES-R ABI Image Navigation Accuracy Using Image Registration Techniques

NASA Technical Reports Server (NTRS)

Haas, Evan; DeLuccia, Frank

2016-01-01

In evaluating GOES-R Advanced Baseline Imager (ABI) image navigation quality, upsampled sub-images of ABI images are translated against downsampled Landsat 8 images of localized, high contrast earth scenes to determine the translations in the East-West and North-South directions that provide maximum correlation. The native Landsat resolution is much finer than that of ABI, and Landsat navigation accuracy is much better than ABI required navigation accuracy and expected performance. Therefore, Landsat images are considered to provide ground truth for comparison with ABI images, and the translations of ABI sub-images that produce maximum correlation with Landsat localized images are interpreted as ABI navigation errors. The measured local navigation errors from registration of numerous sub-images with the Landsat images are averaged to provide a statistically reliable measurement of the overall navigation error of the ABI image. The dispersion of the local navigation errors is also of great interest, since ABI navigation requirements are specified as bounds on the 99.73rd percentile of the magnitudes of per pixel navigation errors. However, the measurement uncertainty inherent in the use of image registration techniques tends to broaden the dispersion in measured local navigation errors, masking the true navigation performance of the ABI system. We have devised a novel and simple method for estimating the magnitude of the measurement uncertainty in registration error for any pair of images of the same earth scene. We use these measurement uncertainty estimates to filter out the higher quality measurements of local navigation error for inclusion in statistics. In so doing, we substantially reduce the dispersion in measured local navigation errors, thereby better approximating the true navigation performance of the ABI system.

Learning discriminative functional network features of schizophrenia

NASA Astrophysics Data System (ADS)

Gheiratmand, Mina; Rish, Irina; Cecchi, Guillermo; Brown, Matthew; Greiner, Russell; Bashivan, Pouya; Polosecki, Pablo; Dursun, Serdar

2017-03-01

Associating schizophrenia with disrupted functional connectivity is a central idea in schizophrenia research. However, identifying neuroimaging-based features that can serve as reliable "statistical biomarkers" of the disease remains a challenging open problem. We argue that generalization accuracy and stability of candidate features ("biomarkers") must be used as additional criteria on top of standard significance tests in order to discover more robust biomarkers. Generalization accuracy refers to the utility of biomarkers for making predictions about individuals, for example discriminating between patients and controls, in novel datasets. Feature stability refers to the reproducibility of the candidate features across different datasets. Here, we extracted functional connectivity network features from fMRI data at both high-resolution (voxel-level) and a spatially down-sampled lower-resolution ("supervoxel" level). At the supervoxel level, we used whole-brain network links, while at the voxel level, due to the intractably large number of features, we sampled a subset of them. We compared statistical significance, stability and discriminative utility of both feature types in a multi-site fMRI dataset, composed of schizophrenia patients and healthy controls. For both feature types, a considerable fraction of features showed significant differences between the two groups. Also, both feature types were similarly stable across multiple data subsets. However, the whole-brain supervoxel functional connectivity features showed a higher cross-validation classification accuracy of 78.7% vs. 72.4% for the voxel-level features. Cross-site variability and heterogeneity in the patient samples in the multi-site FBIRN dataset made the task more challenging compared to single-site studies. The use of the above methodology in combination with the fully data-driven approach using the whole brain information have the potential to shed light on "biomarker discovery" in schizophrenia.

Optimized statistical parametric mapping for partial-volume-corrected amyloid positron emission tomography in patients with Alzheimer's disease and Lewy body dementia

NASA Astrophysics Data System (ADS)

Oh, Jungsu S.; Kim, Jae Seung; Chae, Sun Young; Oh, Minyoung; Oh, Seung Jun; Cha, Seung Nam; Chang, Ho-Jong; Lee, Chong Sik; Lee, Jae Hong

2017-03-01

We present an optimized voxelwise statistical parametric mapping (SPM) of partial-volume (PV)-corrected positron emission tomography (PET) of 11C Pittsburgh Compound B (PiB), incorporating the anatomical precision of magnetic resonance image (MRI) and amyloid β (A β) burden-specificity of PiB PET. First, we applied region-based partial-volume correction (PVC), termed the geometric transfer matrix (GTM) method, to PiB PET, creating MRI-based lobar parcels filled with mean PiB uptakes. Then, we conducted a voxelwise PVC by multiplying the original PET by the ratio of a GTM-based PV-corrected PET to a 6-mm-smoothed PV-corrected PET. Finally, we conducted spatial normalizations of the PV-corrected PETs onto the study-specific template. As such, we increased the accuracy of the SPM normalization and the tissue specificity of SPM results. Moreover, lobar smoothing (instead of whole-brain smoothing) was applied to increase the signal-to-noise ratio in the image without degrading the tissue specificity. Thereby, we could optimize a voxelwise group comparison between subjects with high and normal A β burdens (from 10 patients with Alzheimer's disease, 30 patients with Lewy body dementia, and 9 normal controls). Our SPM framework outperformed than the conventional one in terms of the accuracy of the spatial normalization (85% of maximum likelihood tissue classification volume) and the tissue specificity (larger gray matter, and smaller cerebrospinal fluid volume fraction from the SPM results). Our SPM framework optimized the SPM of a PV-corrected A β PET in terms of anatomical precision, normalization accuracy, and tissue specificity, resulting in better detection and localization of A β burdens in patients with Alzheimer's disease and Lewy body dementia.

Automating Electronic Clinical Data Capture for Quality Improvement and Research: The CERTAIN Validation Project of Real World Evidence.

PubMed

Devine, Emily Beth; Van Eaton, Erik; Zadworny, Megan E; Symons, Rebecca; Devlin, Allison; Yanez, David; Yetisgen, Meliha; Keyloun, Katelyn R; Capurro, Daniel; Alfonso-Cristancho, Rafael; Flum, David R; Tarczy-Hornoch, Peter

2018-05-22

The availability of high fidelity electronic health record (EHR) data is a hallmark of the learning health care system. Washington State's Surgical Care Outcomes and Assessment Program (SCOAP) is a network of hospitals participating in quality improvement (QI) registries wherein data are manually abstracted from EHRs. To create the Comparative Effectiveness Research and Translation Network (CERTAIN), we semi-automated SCOAP data abstraction using a centralized federated data model, created a central data repository (CDR), and assessed whether these data could be used as real world evidence for QI and research. Describe the validation processes and complexities involved and lessons learned. Investigators installed a commercial CDR to retrieve and store data from disparate EHRs. Manual and automated abstraction systems were conducted in parallel (10/2012-7/2013) and validated in three phases using the EHR as the gold standard: 1) ingestion, 2) standardization, and 3) concordance of automated versus manually abstracted cases. Information retrieval statistics were calculated. Four unaffiliated health systems provided data. Between 6 and 15 percent of data elements were abstracted: 51 to 86 percent from structured data; the remainder using natural language processing (NLP). In phase 1, data ingestion from 12 out of 20 feeds reached 95 percent accuracy. In phase 2, 55 percent of structured data elements performed with 96 to 100 percent accuracy; NLP with 89 to 91 percent accuracy. In phase 3, concordance ranged from 69 to 89 percent. Information retrieval statistics were consistently above 90 percent. Semi-automated data abstraction may be useful, although raw data collected as a byproduct of health care delivery is not immediately available for use as real world evidence. New approaches to gathering and analyzing extant data are required.

Accuracy Rates of Ancestry Estimation by Forensic Anthropologists Using Identified Forensic Cases.

PubMed

Thomas, Richard M; Parks, Connie L; Richard, Adam H

2017-07-01

A common task in forensic anthropology involves the estimation of the ancestry of a decedent by comparing their skeletal morphology and measurements to skeletons of individuals from known geographic groups. However, the accuracy rates of ancestry estimation methods in actual forensic casework have rarely been studied. This article uses 99 forensic cases with identified skeletal remains to develop accuracy rates for ancestry estimations conducted by forensic anthropologists. The overall rate of correct ancestry estimation from these cases is 90.9%, which is comparable to most research-derived rates and those reported by individual practitioners. Statistical tests showed no significant difference in accuracy rates depending on examiner education level or on the estimated or identified ancestry. More recent cases showed a significantly higher accuracy rate. The incorporation of metric analyses into the ancestry estimate in these cases led to a higher accuracy rate. © 2017 American Academy of Forensic Sciences.

Accuracy of digital impressions of multiple dental implants: an in vitro study.

PubMed

Vandeweghe, Stefan; Vervack, Valentin; Dierens, Melissa; De Bruyn, Hugo

2017-06-01

Studies demonstrated that the accuracy of intra-oral scanners can be compared with conventional impressions for most indications. However, little is known about their applicability to take impressions of multiple implants. The aim of this study was to evaluate the accuracy of four intra-oral scanners when applied for implant impressions in the edentulous jaw. An acrylic mandibular cast containing six external connection implants (region 36, 34, 32, 42, 44 and 46) with PEEK scanbodies was scanned using four intra-oral scanners: the Lava C.O.S. and the 3M True Definition, Cerec Omnicam and 3Shape Trios. Each model was scanned 10 times with every intra-oral scanner. As a reference, a highly accurate laboratory scanner (104i, Imetric, Courgenay, Switzerland) was used. The scans were imported into metrology software (Geomagic Qualify 12) for analyses. Accuracy was measured in terms of trueness (comparing test and reference) and precision (determining the deviation between different test scans). Mann-Whitney U-test and Wilcoxon signed rank test were used to detect statistically significant differences in trueness and precision respectively. The mean trueness was 0.112 mm for Lava COS, 0.035 mm for 3M TrueDef, 0.028 mm for Trios and 0.061 mm for Cerec Omnicam. There was no statistically significant difference between 3M TrueDef and Trios (P = 0.262). Cerec Omnicam was less accurate than 3M TrueDef (P = 0.013) and Trios (P = 0.005), but more accurate compared to Lava COS (P = 0.007). Lava COS was also less accurate compared to 3M TrueDef (P = 0.005) and Trios (P = 0.005). The mean precision was 0.066 mm for Lava COS, 0.030 mm for 3M TrueDef, 0.033 mm for Trios and 0.059 mm for Cerec Omnicam. There was no statistically significant difference between 3M TrueDef and Trios (P = 0.119). Cerec Omnicam was less accurate compared to 3M TrueDef (P < 0.001) and Trios (P < 0.001), but no difference was found with Lava COS (P = 0.169). Lava COS was also less accurate compared to 3M TrueDef (P < 0.001) and Trios (P < 0.001). Based on the findings of this in vitro study, the 3M True Definition and Trios scanner demonstrated the highest accuracy. The Lava COS was found not suitable for taking implant impressions for a cross-arch bridge in the edentulous jaw. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

How Do Different Ways of Measuring Individual Differences in Zero-Acquaintance Personality Judgment Accuracy Correlate With Each Other?

PubMed

Hall, Judith A; Back, Mitja D; Nestler, Steffen; Frauendorfer, Denise; Schmid Mast, Marianne; Ruben, Mollie A

2018-04-01

This research compares two different approaches that are commonly used to measure accuracy of personality judgment: the trait accuracy approach wherein participants discriminate among targets on a given trait, thus making intertarget comparisons, and the profile accuracy approach wherein participants discriminate between traits for a given target, thus making intratarget comparisons. We examined correlations between these methods as well as correlations among accuracies for judging specific traits. The present article documents relations among these approaches based on meta-analysis of five studies of zero-acquaintance impressions of the Big Five traits. Trait accuracies correlated only weakly with overall and normative profile accuracy. Substantial convergence between the trait and profile accuracy methods was only found when an aggregate of all five trait accuracies was correlated with distinctive profile accuracy. Importantly, however, correlations between the trait and profile accuracy approaches were reduced to negligibility when statistical overlap was corrected by removing the respective trait from the profile correlations. Moreover, correlations of the separate trait accuracies with each other were very weak. Different ways of measuring individual differences in personality judgment accuracy are not conceptually and empirically the same, but rather represent distinct abilities that rely on different judgment processes. © 2017 Wiley Periodicals, Inc.

The Development of Statistical Models for Predicting Surgical Site Infections in Japan: Toward a Statistical Model-Based Standardized Infection Ratio.

PubMed

Fukuda, Haruhisa; Kuroki, Manabu

2016-03-01

To develop and internally validate a surgical site infection (SSI) prediction model for Japan. Retrospective observational cohort study. We analyzed surveillance data submitted to the Japan Nosocomial Infections Surveillance system for patients who had undergone target surgical procedures from January 1, 2010, through December 31, 2012. Logistic regression analyses were used to develop statistical models for predicting SSIs. An SSI prediction model was constructed for each of the procedure categories by statistically selecting the appropriate risk factors from among the collected surveillance data and determining their optimal categorization. Standard bootstrapping techniques were applied to assess potential overfitting. The C-index was used to compare the predictive performances of the new statistical models with those of models based on conventional risk index variables. The study sample comprised 349,987 cases from 428 participant hospitals throughout Japan, and the overall SSI incidence was 7.0%. The C-indices of the new statistical models were significantly higher than those of the conventional risk index models in 21 (67.7%) of the 31 procedure categories (P<.05). No significant overfitting was detected. Japan-specific SSI prediction models were shown to generally have higher accuracy than conventional risk index models. These new models may have applications in assessing hospital performance and identifying high-risk patients in specific procedure categories.

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments

PubMed Central

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

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

PubMed

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.

Role of endoscopic ultrasound-guided fine needle aspiration and ultrasound-guided fine-needle aspiration in diagnosis of cystic pancreatic lesions

PubMed Central

Okasha, Hussein Hassan; Ashry, Mahmoud; Imam, Hala M. K.; Ezzat, Reem; Naguib, Mohamed; Farag, Ali H.; Gemeie, Emad H.; Khattab, Hani M.

2015-01-01

Background and Objective: The addition of fine-needle aspiration (FNA) to different imaging modalities has raised the accuracy for diagnosis of cystic pancreatic lesions. We aim to differentiate benign from neoplastic pancreatic cysts by evaluating cyst fluid carcinoembryonic antigen (CEA), carbohydrate antigen (CA19-9), and amylase levels and cytopathological examination, including mucin stain. Patients and Methods: This prospective study included 77 patients with pancreatic cystic lesions. Ultrasound-FNA (US-FNA) or endoscopic ultrasound-FNA (EUS-FNA) was done according to the accessibility of the lesion. The aspirated specimens were subjected to cytopathological examination (including mucin staining), tumor markers (CEA, CA19-9), and amylase level. Results: Cyst CEA value of 279 or more showed high statistical significance in differentiating mucinous from nonmucinous lesions with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of 73%, 60%, 50%, 80%, and 65%, respectively. Cyst amylase could differentiate between neoplastic and nonneoplastic cysts at a level of 1043 with sensitivity of 58%, specificity of 75%, PPV of 73%, NPV of 60%, and accuracy of 66%. CA19-9 could not differentiate between neoplastic and nonneoplastic cysts. Mucin examination showed a sensitivity of 85%, specificity of 95%, PPV of 92%, NPV of 91%, and accuracy of 91% in differentiating mucinous from non-mucinous lesions. Cytopathological examination showed a sensitivity of 81%, specificity of 94%, PPV of 94%, NPV of 83%, and accuracy of 88%. Conclusion: US or EUS-FNA with analysis of cyst CEA level, CA19-9, amylase, mucin stain, and cytopathological examination increases the diagnostic accuracy of cystic pancreatic lesions. PMID:26020048

Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: impacts of the genetic architecture.

PubMed

Mehrban, Hossein; Lee, Deuk Hwan; Moradi, Mohammad Hossein; IlCho, Chung; Naserkheil, Masoumeh; Ibáñez-Escriche, Noelia

2017-01-04

Hanwoo beef is known for its marbled fat, tenderness, juiciness and characteristic flavor, as well as for its low cholesterol and high omega 3 fatty acid contents. As yet, there has been no comprehensive investigation to estimate genomic selection accuracy for carcass traits in Hanwoo cattle using dense markers. This study aimed at evaluating the accuracy of alternative statistical methods that differed in assumptions about the underlying genetic model for various carcass traits: backfat thickness (BT), carcass weight (CW), eye muscle area (EMA), and marbling score (MS). Accuracies of direct genomic breeding values (DGV) for carcass traits were estimated by applying fivefold cross-validation to a dataset including 1183 animals and approximately 34,000 single nucleotide polymorphisms (SNPs). Accuracies of BayesC, Bayesian LASSO (BayesL) and genomic best linear unbiased prediction (GBLUP) methods were similar for BT, EMA and MS. However, for CW, DGV accuracy was 7% higher with BayesC than with BayesL and GBLUP. The increased accuracy of BayesC, compared to GBLUP and BayesL, was maintained for CW, regardless of the training sample size, but not for BT, EMA, and MS. Genome-wide association studies detected consistent large effects for SNPs on chromosomes 6 and 14 for CW. The predictive performance of the models depended on the trait analyzed. For CW, the results showed a clear superiority of BayesC compared to GBLUP and BayesL. These findings indicate the importance of using a proper variable selection method for genomic selection of traits and also suggest that the genetic architecture that underlies CW differs from that of the other carcass traits analyzed. Thus, our study provides significant new insights into the carcass traits of Hanwoo cattle.

Predictors of nutrition information comprehension in adulthood.

PubMed

Miller, Lisa M Soederberg; Gibson, Tanja N; Applegate, Elizabeth A

2010-07-01

The goal of the present study was to examine relationships among several predictors of nutrition comprehension. We were particularly interested in exploring whether nutrition knowledge or motivation moderated the effects of attention on comprehension across a wide age range of adults. Ninety-three participants, ages 18-80, completed measures of nutrition knowledge and motivation and then read nutrition information (from which attention allocation was derived) and answered comprehension questions. In general, predictor variables were highly intercorrelated. However, knowledge, but not motivation, had direct effects on comprehension accuracy. In contrast, motivation influenced attention, which in turn influenced accuracy. Results also showed that comprehension accuracy decreased-and knowledge increased-with age. When knowledge was statistically controlled, age declines in comprehension increased. Knowledge is an important predictor of nutrition information comprehension and its role increases in later life. Motivation is also important; however, its effects on comprehension differ from knowledge. Health educators and clinicians should consider cognitive skills such as knowledge as well as motivation and age of patients when deciding how to best convey health information. The increased role of knowledge among older adults suggests that lifelong educational efforts may have important payoffs in later life. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

Predictors of Nutrition Information Comprehension in Adulthood

PubMed Central

Miller, Lisa M. Soederberg; Gibson, Tanja N.; Applegate, Elizabeth A.

2009-01-01

Objective The goal of the present study was to examine relationships among several predictors of nutrition comprehension. We were particularly interested in exploring whether nutrition knowledge or motivation moderated the effects of attention on comprehension across a wide age range of adults. Methods Ninety-three participants, ages 18 to 80, completed measures of nutrition knowledge and motivation and then read nutrition information (from which attention allocation was derived) and answered comprehension questions. Results In general, predictor variables were highly intercorrelated. However, knowledge, but not motivation, had direct effects on comprehension accuracy. In contrast, motivation influenced attention, which in turn influenced accuracy. Results also showed that comprehension accuracy decreased- and knowledge increased -with age. When knowledge was statistically controlled, age declines in comprehension increased. Conclusion Knowledge is an important predictor of nutrition information comprehension and its role increases in later life. Motivation is also important; however, its effects on comprehension differ from knowledge. Practice Implications Health educators and clinicians should consider cognitive skills such as knowledge as well as motivation and age of patients when deciding how to best convey health information. The increased role of knowledge among older adults suggests that lifelong educational efforts may have important payoffs in later life. PMID:19854605

[Fabrication and accuracy research on 3D printing dental model based on cone beam computed tomography digital modeling].

PubMed

Zhang, Hui-Rong; Yin, Le-Feng; Liu, Yan-Li; Yan, Li-Yi; Wang, Ning; Liu, Gang; An, Xiao-Li; Liu, Bin

2018-04-01

The aim of this study is to build a digital dental model with cone beam computed tomography (CBCT), to fabricate a virtual model via 3D printing, and to determine the accuracy of 3D printing dental model by comparing the result with a traditional dental cast. CBCT of orthodontic patients was obtained to build a digital dental model by using Mimics 10.01 and Geomagic studio software. The 3D virtual models were fabricated via fused deposition modeling technique (FDM). The 3D virtual models were compared with the traditional cast models by using a Vernier caliper. The measurements used for comparison included the width of each tooth, the length and width of the maxillary and mandibular arches, and the length of the posterior dental crest. 3D printing models had higher accuracy compared with the traditional cast models. The results of the paired t-test of all data showed that no statistically significant difference was observed between the two groups (P>0.05). Dental digital models built with CBCT realize the digital storage of patients' dental condition. The virtual dental model fabricated via 3D printing avoids traditional impression and simplifies the clinical examination process. The 3D printing dental models produced via FDM show a high degree of accuracy. Thus, these models are appropriate for clinical practice.

The use of evidence-based guidance to enable reliable and accurate measurements of the home environment

PubMed Central

Atwal, Anita; McIntyre, Anne

2017-01-01

Introduction High quality guidance in home strategies is needed to enable older people to measure their home environment and become involved in the provision of assistive devices and to promote consistency among professionals. This study aims to investigate the reliability of such guidance and its ability to promote accuracy of results when measurements are taken by both older people and professionals. Method Twenty-five health professionals and 26 older people participated in a within-group design to test the accuracy of measurements taken (that is, person’s popliteal height, baths, toilets, beds, stairs and chairs). Data were analysed with descriptive analysis and the Wilcoxon test. The intra-rater reliability was assessed by correlating measurements taken at two different times with guidance use. Results The intra-rater reliability analysis revealed statistical significance (P < 0.05) for all measurements except for the bath internal width. The guidance enabled participants to take 90% of measurements that they were not able to complete otherwise, 80.55% of which lay within the acceptable suggested margin of variation. Accuracy was supported by the significant reduction in the standard deviation of the actual measurements and accuracy scores. Conclusion This evidence-based guidance can be used in its current format by older people and professionals to facilitate appropriate measurements. Yet, some users might need help from carers or specialists depending on their impairments. PMID:29386701

The use of evidence-based guidance to enable reliable and accurate measurements of the home environment.

PubMed

Spiliotopoulou, Georgia; Atwal, Anita; McIntyre, Anne

2018-01-01

High quality guidance in home strategies is needed to enable older people to measure their home environment and become involved in the provision of assistive devices and to promote consistency among professionals. This study aims to investigate the reliability of such guidance and its ability to promote accuracy of results when measurements are taken by both older people and professionals. Twenty-five health professionals and 26 older people participated in a within-group design to test the accuracy of measurements taken (that is, person's popliteal height, baths, toilets, beds, stairs and chairs). Data were analysed with descriptive analysis and the Wilcoxon test. The intra-rater reliability was assessed by correlating measurements taken at two different times with guidance use. The intra-rater reliability analysis revealed statistical significance ( P  < 0.05) for all measurements except for the bath internal width. The guidance enabled participants to take 90% of measurements that they were not able to complete otherwise, 80.55% of which lay within the acceptable suggested margin of variation. Accuracy was supported by the significant reduction in the standard deviation of the actual measurements and accuracy scores. This evidence-based guidance can be used in its current format by older people and professionals to facilitate appropriate measurements. Yet, some users might need help from carers or specialists depending on their impairments.

Iodine-123 metaiodobenzylguanidine scintigraphy and iodine-123 ioflupane single photon emission computed tomography in Lewy body diseases: complementary or alternative techniques?

PubMed

Treglia, Giorgio; Cason, Ernesto; Cortelli, Pietro; Gabellini, Anna; Liguori, Rocco; Bagnato, Antonio; Giordano, Alessandro; Fagioli, Giorgio

2014-01-01

To compare myocardial sympathetic imaging using (123)I-Metaiodobenzylguanidine (MIBG) scintigraphy and striatal dopaminergic imaging using (123)I-Ioflupane (FP-CIT) single photon emission computed tomography (SPECT) in patients with suspected Lewy body diseases (LBD). Ninety-nine patients who performed both methods within 2 months for differential diagnosis between Parkinson's disease (PD) and other parkinsonism (n = 68) or between dementia with Lewy bodies (DLB) and other dementia (n = 31) were enrolled. Sensitivity, specificity, accuracy, positive and negative predictive values of both methods were calculated. For (123) I-MIBG scintigraphy, the overall sensitivity, specificity, accuracy, positive and negative predictive values in LBD were 83%, 79%, 82%, 86%, and 76%, respectively. For (123)I-FP-CIT SPECT, the overall sensitivity, specificity, accuracy, positive and negative predictive values in LBD were 93%, 41%, 73%, 71%, and 80%, respectively. There was a statistically significant difference between these two methods in patients without LBD, but not in patients with LBD. LBD usually present both myocardial sympathetic and striatal dopaminergic impairments. (123)I-FP-CIT SPECT presents high sensitivity in the diagnosis of LBD; (123)I-MIBG scintigraphy may have a complementary role in differential diagnosis between PD and other parkinsonism. These scintigraphic methods showed similar diagnostic accuracy in differential diagnosis between DLB and other dementia. Copyright © 2012 by the American Society of Neuroimaging.

Free Global Dsm Assessment on Large Scale Areas Exploiting the Potentialities of the Innovative Google Earth Engine Platform

NASA Astrophysics Data System (ADS)

Nascetti, A.; Di Rita, M.; Ravanelli, R.; Amicuzi, M.; Esposito, S.; Crespi, M.

2017-05-01

The high-performance cloud-computing platform Google Earth Engine has been developed for global-scale analysis based on the Earth observation data. In particular, in this work, the geometric accuracy of the two most used nearly-global free DSMs (SRTM and ASTER) has been evaluated on the territories of four American States (Colorado, Michigan, Nevada, Utah) and one Italian Region (Trentino Alto- Adige, Northern Italy) exploiting the potentiality of this platform. These are large areas characterized by different terrain morphology, land covers and slopes. The assessment has been performed using two different reference DSMs: the USGS National Elevation Dataset (NED) and a LiDAR acquisition. The DSMs accuracy has been evaluated through computation of standard statistic parameters, both at global scale (considering the whole State/Region) and in function of the terrain morphology using several slope classes. The geometric accuracy in terms of Standard deviation and NMAD, for SRTM range from 2-3 meters in the first slope class to about 45 meters in the last one, whereas for ASTER, the values range from 5-6 to 30 meters. In general, the performed analysis shows a better accuracy for the SRTM in the flat areas whereas the ASTER GDEM is more reliable in the steep areas, where the slopes increase. These preliminary results highlight the GEE potentialities to perform DSM assessment on a global scale.

Comparison modeling for alpine vegetation distribution in an arid area.

PubMed

Zhou, Jihua; Lai, Liming; Guan, Tianyu; Cai, Wetao; Gao, Nannan; Zhang, Xiaolong; Yang, Dawen; Cong, Zhentao; Zheng, Yuanrun

2016-07-01

Mapping and modeling vegetation distribution are fundamental topics in vegetation ecology. With the rise of powerful new statistical techniques and GIS tools, the development of predictive vegetation distribution models has increased rapidly. However, modeling alpine vegetation with high accuracy in arid areas is still a challenge because of the complexity and heterogeneity of the environment. Here, we used a set of 70 variables from ASTER GDEM, WorldClim, and Landsat-8 OLI (land surface albedo and spectral vegetation indices) data with decision tree (DT), maximum likelihood classification (MLC), and random forest (RF) models to discriminate the eight vegetation groups and 19 vegetation formations in the upper reaches of the Heihe River Basin in the Qilian Mountains, northwest China. The combination of variables clearly discriminated vegetation groups but failed to discriminate vegetation formations. Different variable combinations performed differently in each type of model, but the most consistently important parameter in alpine vegetation modeling was elevation. The best RF model was more accurate for vegetation modeling compared with the DT and MLC models for this alpine region, with an overall accuracy of 75 % and a kappa coefficient of 0.64 verified against field point data and an overall accuracy of 65 % and a kappa of 0.52 verified against vegetation map data. The accuracy of regional vegetation modeling differed depending on the variable combinations and models, resulting in different classifications for specific vegetation groups.

Automatic Fault Characterization via Abnormality-Enhanced Classification

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

Bronevetsky, G; Laguna, I; de Supinski, B R

Enterprise and high-performance computing systems are growing extremely large and complex, employing hundreds to hundreds of thousands of processors and software/hardware stacks built by many people across many organizations. As the growing scale of these machines increases the frequency of faults, system complexity makes these faults difficult to detect and to diagnose. Current system management techniques, which focus primarily on efficient data access and query mechanisms, require system administrators to examine the behavior of various system services manually. Growing system complexity is making this manual process unmanageable: administrators require more effective management tools that can detect faults and help tomore » identify their root causes. System administrators need timely notification when a fault is manifested that includes the type of fault, the time period in which it occurred and the processor on which it originated. Statistical modeling approaches can accurately characterize system behavior. However, the complex effects of system faults make these tools difficult to apply effectively. This paper investigates the application of classification and clustering algorithms to fault detection and characterization. We show experimentally that naively applying these methods achieves poor accuracy. Further, we design novel techniques that combine classification algorithms with information on the abnormality of application behavior to improve detection and characterization accuracy. Our experiments demonstrate that these techniques can detect and characterize faults with 65% accuracy, compared to just 5% accuracy for naive approaches.« less

Accuracy of optical spectroscopy for the detection of cervical intraepithelial neoplasia without colposcopic tissue information; a step toward automation for low resource settings

PubMed Central

Zewdie, Getie A.; Cox, Dennis D.; Neely Atkinson, E.; Cantor, Scott B.; MacAulay, Calum; Davies, Kalatu; Adewole, Isaac; Buys, Timon P. H.; Follen, Michele

2012-01-01

Abstract. Optical spectroscopy has been proposed as an accurate and low-cost alternative for detection of cervical intraepithelial neoplasia. We previously published an algorithm using optical spectroscopy as an adjunct to colposcopy and found good accuracy (sensitivity=1.00 [95% confidence interval (CI)=0.92 to 1.00], specificity=0.71 [95% CI=0.62 to 0.79]). Those results used measurements taken by expert colposcopists as well as the colposcopy diagnosis. In this study, we trained and tested an algorithm for the detection of cervical intraepithelial neoplasia (i.e., identifying those patients who had histology reading CIN 2 or worse) that did not include the colposcopic diagnosis. Furthermore, we explored the interaction between spectroscopy and colposcopy, examining the importance of probe placement expertise. The colposcopic diagnosis-independent spectroscopy algorithm had a sensitivity of 0.98 (95% CI=0.89 to 1.00) and a specificity of 0.62 (95% CI=0.52 to 0.71). The difference in the partial area under the ROC curves between spectroscopy with and without the colposcopic diagnosis was statistically significant at the patient level (p=0.05) but not the site level (p=0.13). The results suggest that the device has high accuracy over a wide range of provider accuracy and hence could plausibly be implemented by providers with limited training. PMID:22559693

Staging of colorectal liver metastases after preoperative chemotherapy. Diffusion-weighted imaging in combination with Gd-EOB-DTPA MRI sequences increases sensitivity and diagnostic accuracy.

PubMed

Macera, Annalisa; Lario, Chiara; Petracchini, Massimo; Gallo, Teresa; Regge, Daniele; Floriani, Irene; Ribero, Dario; Capussotti, Lorenzo; Cirillo, Stefano

2013-03-01

To compare the diagnostic accuracy and sensitivity of Gd-EOB-DTPA MRI and diffusion-weighted (DWI) imaging alone and in combination for detecting colorectal liver metastases in patients who had undergone preoperative chemotherapy. Thirty-two consecutive patients with a total of 166 liver lesions were retrospectively enrolled. Of the lesions, 144 (86.8 %) were metastatic at pathology. Three image sets (1, Gd-EOB-DTPA; 2, DWI; 3, combined Gd-EOB-DTPA and DWI) were independently reviewed by two observers. Statistical analysis was performed on a per-lesion basis. Evaluation of image set 1 correctly identified 127/166 lesions (accuracy 76.5 %; 95 % CI 69.3-82.7) and 106/144 metastases (sensitivity 73.6 %, 95 % CI 65.6-80.6). Evaluation of image set 2 correctly identified 108/166 (accuracy 65.1 %, 95 % CI 57.3-72.3) and 87/144 metastases (sensitivity of 60.4 %, 95 % CI 51.9-68.5). Evaluation of image set 3 correctly identified 148/166 (accuracy 89.2 %, 95 % CI 83.4-93.4) and 131/144 metastases (sensitivity 91 %, 95 % CI 85.1-95.1). Differences were statistically significant (P < 0.001). Notably, similar results were obtained analysing only small lesions (<1 cm). The combination of DWI with Gd-EOB-DTPA-enhanced MRI imaging significantly increases the diagnostic accuracy and sensitivity in patients with colorectal liver metastases treated with preoperative chemotherapy, and it is particularly effective in the detection of small lesions.

Demand Forecasting: An Evaluation of DODs Accuracy Metric and Navys Procedures

DTIC Science & Technology

2016-06-01

inventory management improvement plan, mean of absolute scaled error, lead time adjusted squared error, forecast accuracy, benchmarking, naïve method...Manager JASA Journal of the American Statistical Association LASE Lead-time Adjusted Squared Error LCI Life Cycle Indicator MA Moving Average MAE...Mean Squared Error xvi NAVSUP Naval Supply Systems Command NDAA National Defense Authorization Act NIIN National Individual Identification Number

A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments

NASA Astrophysics Data System (ADS)

Li, Manchun; Ma, Lei; Blaschke, Thomas; Cheng, Liang; Tiede, Dirk

2016-07-01

Geographic Object-Based Image Analysis (GEOBIA) is becoming more prevalent in remote sensing classification, especially for high-resolution imagery. Many supervised classification approaches are applied to objects rather than pixels, and several studies have been conducted to evaluate the performance of such supervised classification techniques in GEOBIA. However, these studies did not systematically investigate all relevant factors affecting the classification (segmentation scale, training set size, feature selection and mixed objects). In this study, statistical methods and visual inspection were used to compare these factors systematically in two agricultural case studies in China. The results indicate that Random Forest (RF) and Support Vector Machines (SVM) are highly suitable for GEOBIA classifications in agricultural areas and confirm the expected general tendency, namely that the overall accuracies decline with increasing segmentation scale. All other investigated methods except for RF and SVM are more prone to obtain a lower accuracy due to the broken objects at fine scales. In contrast to some previous studies, the RF classifiers yielded the best results and the k-nearest neighbor classifier were the worst results, in most cases. Likewise, the RF and Decision Tree classifiers are the most robust with or without feature selection. The results of training sample analyses indicated that the RF and adaboost. M1 possess a superior generalization capability, except when dealing with small training sample sizes. Furthermore, the classification accuracies were directly related to the homogeneity/heterogeneity of the segmented objects for all classifiers. Finally, it was suggested that RF should be considered in most cases for agricultural mapping.

Discrimination of benign and neoplastic mucosa with a high-resolution microendoscope (HRME) in head and neck cancer.

PubMed

Vila, Peter M; Park, Chan W; Pierce, Mark C; Goldstein, Gregg H; Levy, Lauren; Gurudutt, Vivek V; Polydorides, Alexandros D; Godbold, James H; Teng, Marita S; Genden, Eric M; Miles, Brett A; Anandasabapathy, Sharmila; Gillenwater, Ann M; Richards-Kortum, Rebecca; Sikora, Andrew G

2012-10-01

The efficacy of ablative surgery for head and neck squamous cell carcinoma (HNSCC) depends critically on obtaining negative margins. Although intraoperative "frozen section" analysis of margins is a valuable adjunct, it is expensive, time-consuming, and highly dependent on pathologist expertise. Optical imaging has potential to improve the accuracy of margins by identifying cancerous tissue in real time. Our goal was to determine the accuracy and inter-rater reliability of head and neck cancer specialists using high-resolution microendoscopic (HRME) images to discriminate between cancerous and benign mucosa. Thirty-eight patients diagnosed with head and neck squamous cell carcinoma (HNSCC) were enrolled in this single-center study. HRME was used to image each specimen after application of proflavine, with concurrent standard histopathologic analysis. Images were evaluated for quality control, and a training set containing representative images of benign and neoplastic tissue was assembled. After viewing training images, seven head and neck cancer specialists with no previous HRME experience reviewed 36 test images and were asked to classify each. The mean accuracy of all reviewers in correctly diagnosing neoplastic mucosa was 97% (95% confidence interval (CI), 94-99%). The mean sensitivity and specificity were 98% (97-100%) and 92% (87-98%), respectively. The Fleiss kappa statistic for inter-rater reliability was 0.84 (0.77-0.91). Medical professionals can be quickly trained to use HRME to discriminate between benign and neoplastic mucosa in the head and neck. With further development, the HRME shows promise as a method of real-time margin determination at the point of care.

Discrimination of Benign and Neoplastic Mucosa with a High-Resolution Microendoscope (HRME) in Head and Neck Cancer

PubMed Central

Vila, Peter M.; Park, Chan W.; Pierce, Mark C.; Goldstein, Gregg H.; Levy, Lauren; Gurudutt, Vivek V.; Polydorides, Alexandras D.; Godbold, James H.; Teng, Marita S.; Genden, Eric M.; Miles, Brett A.; Anandasabapathy, Sharmila; Gillenwater, Ann M.; Richards-Kortum, Rebecca; Sikora, Andrew G.

2012-01-01

Background The efficacy of ablative surgery for head and neck squamous cell carcinoma (HNSCC) depends critically on obtaining negative margins. While intraoperative "frozen section" analysis of margins is a valuable adjunct, it is expensive, time-consuming, and highly dependent on pathologist expertise. Optical imaging has potential to improve the accuracy of margins by identifying cancerous tissue in real time. Our aim was to determine the accuracy and inter-rater reliability of head and neck cancer specialists using high-resolution microendoscopic (HRME) images to discriminate between cancerous and benign mucosa. Methods Thirty-eight patients diagnosed with HNSCC were enrolled in this single-center study. HRME was used to image each specimen after application of proflavine, with concurrent standard histopathologic analysis. Images were evaluated for quality control, and a training set containing representative images of benign and neoplastic tissue was assembled. After viewing training images, seven head and neck cancer specialists with no prior HRME experience reviewed 37 test images and were asked to classify each. Results The mean accuracy of all reviewers in correctly diagnosing neoplastic mucosa was 97 percent (95% Cl = 94–99%). The mean sensitivity and specificity were 98 percent (97–100%) and 92 percent (87–98%), respectively. The Fleiss kappa statistic for inter-rater reliability was 0.84 (0.77–0.91). Conclusions Medical professionals can be quickly trained to use HRME to discriminate between benign and neoplastic mucosa in the head and neck. With further development, the HRME shows promise as a method of real-time margin determination at the point of care. PMID:22492225

Analysis of Ultra High Resolution Sea Surface Temperature Level 4 Datasets

NASA Technical Reports Server (NTRS)

Wagner, Grant

2011-01-01

Sea surface temperature (SST) studies are often focused on improving accuracy, or understanding and quantifying uncertainties in the measurement, as SST is a leading indicator of climate change and represents the longest time series of any ocean variable observed from space. Over the past several decades SST has been studied with the use of satellite data. This allows a larger area to be studied with much more frequent measurements being taken than direct measurements collected aboard ship or buoys. The Group for High Resolution Sea Surface Temperature (GHRSST) is an international project that distributes satellite derived sea surface temperatures (SST) data from multiple platforms and sensors. The goal of the project is to distribute these SSTs for operational uses such as ocean model assimilation and decision support applications, as well as support fundamental SST research and climate studies. Examples of near real time applications include hurricane and fisheries studies and numerical weather forecasting. The JPL group has produced a new 1 km daily global Level 4 SST product, the Multiscale Ultrahigh Resolution (MUR), that blends SST data from 3 distinct NASA radiometers: the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Very High Resolution Radiometer (AVHRR), and the Advanced Microwave Scanning Radiometer ? Earth Observing System(AMSRE). This new product requires further validation and accuracy assessment, especially in coastal regions.We examined the accuracy of the new MUR SST product by comparing the high resolution version and a lower resolution version that has been smoothed to 19 km (but still gridded to 1 km). Both versions were compared to the same data set of in situ buoy temperature measurements with a focus on study regions of the oceans surrounding North and Central America as well as two smaller regions around the Gulf Stream and California coast. Ocean fronts exhibit high temperature gradients (Roden, 1976), and thus satellite data of SST can be used in the detection of these fronts. In this case, accuracy is less of a concern because the primary focus is on the spatial derivative of SST. We calculated the gradients for both versions of the MUR data set and did statistical comparisons focusing on the same regions.

ArcticDEM Validation and Accuracy Assessment

NASA Astrophysics Data System (ADS)

Candela, S. G.; Howat, I.; Noh, M. J.; Porter, C. C.; Morin, P. J.

2017-12-01

ArcticDEM comprises a growing inventory Digital Elevation Models (DEMs) covering all land above 60°N. As of August, 2017, ArcticDEM had openly released 2-m resolution, individual DEM covering over 51 million km2, which includes areas of repeat coverage for change detection, as well as over 15 million km2 of 5-m resolution seamless mosaics. By the end of the project, over 80 million km2 of 2-m DEMs will be produced, averaging four repeats of the 20 million km2 Arctic landmass. ArcticDEM is produced from sub-meter resolution, stereoscopic imagery using open source software (SETSM) on the NCSA Blue Waters supercomputer. These DEMs have known biases of several meters due to errors in the sensor models generated from satellite positioning. These systematic errors are removed through three-dimensional registration to high-precision Lidar or other control datasets. ArcticDEM is registered to seasonally-subsetted ICESat elevations due its global coverage and high report accuracy ( 10 cm). The vertical accuracy of ArcticDEM is then obtained from the statistics of the fit to the ICESat point cloud, which averages -0.01 m ± 0.07 m. ICESat, however, has a relatively coarse measurement footprint ( 70 m) which may impact the precision of the registration. Further, the ICESat data predates the ArcticDEM imagery by a decade, so that temporal changes in the surface may also impact the registration. Finally, biases may exist between different the different sensors in the ArcticDEM constellation. Here we assess the accuracy of ArcticDEM and the ICESat registration through comparison to multiple high-resolution airborne lidar datasets that were acquired within one year of the imagery used in ArcticDEM. We find the ICESat dataset is performing as anticipated, introducing no systematic bias during the coregistration process, and reducing vertical errors to within the uncertainty of the airborne Lidars. Preliminary sensor comparisons show no significant difference post coregistration, suggesting that there is no sensor bias between platforms, and all data is suitable for analysis without further correction. Here we will present accuracy assessments, observations and comparisons over diverse terrain in parts of Alaska and Greenland.