Modeling Error Distributions of Growth Curve Models through Bayesian Methods

ERIC Educational Resources Information Center

Zhang, Zhiyong

2016-01-01

Growth curve models are widely used in social and behavioral sciences. However, typical growth curve models often assume that the errors are normally distributed although non-normal data may be even more common than normal data. In order to avoid possible statistical inference problems in blindly assuming normality, a general Bayesian framework is…

A Framework for Assessing High School Students' Statistical Reasoning.

PubMed

Chan, Shiau Wei; Ismail, Zaleha; Sumintono, Bambang

2016-01-01

Based on a synthesis of literature, earlier studies, analyses and observations on high school students, this study developed an initial framework for assessing students' statistical reasoning about descriptive statistics. Framework descriptors were established across five levels of statistical reasoning and four key constructs. The former consisted of idiosyncratic reasoning, verbal reasoning, transitional reasoning, procedural reasoning, and integrated process reasoning. The latter include describing data, organizing and reducing data, representing data, and analyzing and interpreting data. In contrast to earlier studies, this initial framework formulated a complete and coherent statistical reasoning framework. A statistical reasoning assessment tool was then constructed from this initial framework. The tool was administered to 10 tenth-grade students in a task-based interview. The initial framework was refined, and the statistical reasoning assessment tool was revised. The ten students then participated in the second task-based interview, and the data obtained were used to validate the framework. The findings showed that the students' statistical reasoning levels were consistent across the four constructs, and this result confirmed the framework's cohesion. Developed to contribute to statistics education, this newly developed statistical reasoning framework provides a guide for planning learning goals and designing instruction and assessments.

Graph embedding and extensions: a general framework for dimensionality reduction.

PubMed

Yan, Shuicheng; Xu, Dong; Zhang, Benyu; Zhang, Hong-Jiang; Yang, Qiang; Lin, Stephen

2007-01-01

Over the past few decades, a large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to provide different solutions to the problem of dimensionality reduction. Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework. In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algorithm called Marginal Fisher Analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. We show that MFA effectively overcomes the limitations of the traditional Linear Discriminant Analysis algorithm due to data distribution assumptions and available projection directions. Real face recognition experiments show the superiority of our proposed MFA in comparison to LDA, also for corresponding kernel and tensor extensions.

When mechanism matters: Bayesian forecasting using models of ecological diffusion

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.

2017-01-01

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

A Framework for Assessing High School Students' Statistical Reasoning

PubMed Central

2016-01-01

Based on a synthesis of literature, earlier studies, analyses and observations on high school students, this study developed an initial framework for assessing students’ statistical reasoning about descriptive statistics. Framework descriptors were established across five levels of statistical reasoning and four key constructs. The former consisted of idiosyncratic reasoning, verbal reasoning, transitional reasoning, procedural reasoning, and integrated process reasoning. The latter include describing data, organizing and reducing data, representing data, and analyzing and interpreting data. In contrast to earlier studies, this initial framework formulated a complete and coherent statistical reasoning framework. A statistical reasoning assessment tool was then constructed from this initial framework. The tool was administered to 10 tenth-grade students in a task-based interview. The initial framework was refined, and the statistical reasoning assessment tool was revised. The ten students then participated in the second task-based interview, and the data obtained were used to validate the framework. The findings showed that the students’ statistical reasoning levels were consistent across the four constructs, and this result confirmed the framework’s cohesion. Developed to contribute to statistics education, this newly developed statistical reasoning framework provides a guide for planning learning goals and designing instruction and assessments. PMID:27812091

Heuristic Identification of Biological Architectures for Simulating Complex Hierarchical Genetic Interactions

PubMed Central

Moore, Jason H; Amos, Ryan; Kiralis, Jeff; Andrews, Peter C

2015-01-01

Simulation plays an essential role in the development of new computational and statistical methods for the genetic analysis of complex traits. Most simulations start with a statistical model using methods such as linear or logistic regression that specify the relationship between genotype and phenotype. This is appealing due to its simplicity and because these statistical methods are commonly used in genetic analysis. It is our working hypothesis that simulations need to move beyond simple statistical models to more realistically represent the biological complexity of genetic architecture. The goal of the present study was to develop a prototype genotype–phenotype simulation method and software that are capable of simulating complex genetic effects within the context of a hierarchical biology-based framework. Specifically, our goal is to simulate multilocus epistasis or gene–gene interaction where the genetic variants are organized within the framework of one or more genes, their regulatory regions and other regulatory loci. We introduce here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating data in this manner. This approach combines a biological hierarchy, a flexible mathematical framework, a liability threshold model for defining disease endpoints, and a heuristic search strategy for identifying high-order epistatic models of disease susceptibility. We provide several simulation examples using genetic models exhibiting independent main effects and three-way epistatic effects. PMID:25395175

Geometry of behavioral spaces: A computational approach to analysis and understanding of agent based models and agent behaviors

NASA Astrophysics Data System (ADS)

Cenek, Martin; Dahl, Spencer K.

2016-11-01

Systems with non-linear dynamics frequently exhibit emergent system behavior, which is important to find and specify rigorously to understand the nature of the modeled phenomena. Through this analysis, it is possible to characterize phenomena such as how systems assemble or dissipate and what behaviors lead to specific final system configurations. Agent Based Modeling (ABM) is one of the modeling techniques used to study the interaction dynamics between a system's agents and its environment. Although the methodology of ABM construction is well understood and practiced, there are no computational, statistically rigorous, comprehensive tools to evaluate an ABM's execution. Often, a human has to observe an ABM's execution in order to analyze how the ABM functions, identify the emergent processes in the agent's behavior, or study a parameter's effect on the system-wide behavior. This paper introduces a new statistically based framework to automatically analyze agents' behavior, identify common system-wide patterns, and record the probability of agents changing their behavior from one pattern of behavior to another. We use network based techniques to analyze the landscape of common behaviors in an ABM's execution. Finally, we test the proposed framework with a series of experiments featuring increasingly emergent behavior. The proposed framework will allow computational comparison of ABM executions, exploration of a model's parameter configuration space, and identification of the behavioral building blocks in a model's dynamics.

Geometry of behavioral spaces: A computational approach to analysis and understanding of agent based models and agent behaviors.

PubMed

Cenek, Martin; Dahl, Spencer K

2016-11-01

Systems with non-linear dynamics frequently exhibit emergent system behavior, which is important to find and specify rigorously to understand the nature of the modeled phenomena. Through this analysis, it is possible to characterize phenomena such as how systems assemble or dissipate and what behaviors lead to specific final system configurations. Agent Based Modeling (ABM) is one of the modeling techniques used to study the interaction dynamics between a system's agents and its environment. Although the methodology of ABM construction is well understood and practiced, there are no computational, statistically rigorous, comprehensive tools to evaluate an ABM's execution. Often, a human has to observe an ABM's execution in order to analyze how the ABM functions, identify the emergent processes in the agent's behavior, or study a parameter's effect on the system-wide behavior. This paper introduces a new statistically based framework to automatically analyze agents' behavior, identify common system-wide patterns, and record the probability of agents changing their behavior from one pattern of behavior to another. We use network based techniques to analyze the landscape of common behaviors in an ABM's execution. Finally, we test the proposed framework with a series of experiments featuring increasingly emergent behavior. The proposed framework will allow computational comparison of ABM executions, exploration of a model's parameter configuration space, and identification of the behavioral building blocks in a model's dynamics.

Mapping older forests: satellites, statistics, and boots on the ground

Treesearch

Paul Meznarich; Janet Ohmann; Warren Cohen

2011-01-01

The 1994 Northwest Forest Plan (NWFP) established a common management approach across federal land within the range of the northern spotted owl. It also established a monitoring framework to track, among other things, the plan’s effectiveness at maintaining and restoring late-successional and old-growth forests.Station scientists Janet Ohmann and Warren...

A UNIFIED FRAMEWORK FOR VARIANCE COMPONENT ESTIMATION WITH SUMMARY STATISTICS IN GENOME-WIDE ASSOCIATION STUDIES.

PubMed

Zhou, Xiang

2017-12-01

Linear mixed models (LMMs) are among the most commonly used tools for genetic association studies. However, the standard method for estimating variance components in LMMs-the restricted maximum likelihood estimation method (REML)-suffers from several important drawbacks: REML requires individual-level genotypes and phenotypes from all samples in the study, is computationally slow, and produces downward-biased estimates in case control studies. To remedy these drawbacks, we present an alternative framework for variance component estimation, which we refer to as MQS. MQS is based on the method of moments (MoM) and the minimal norm quadratic unbiased estimation (MINQUE) criterion, and brings two seemingly unrelated methods-the renowned Haseman-Elston (HE) regression and the recent LD score regression (LDSC)-into the same unified statistical framework. With this new framework, we provide an alternative but mathematically equivalent form of HE that allows for the use of summary statistics. We provide an exact estimation form of LDSC to yield unbiased and statistically more efficient estimates. A key feature of our method is its ability to pair marginal z -scores computed using all samples with SNP correlation information computed using a small random subset of individuals (or individuals from a proper reference panel), while capable of producing estimates that can be almost as accurate as if both quantities are computed using the full data. As a result, our method produces unbiased and statistically efficient estimates, and makes use of summary statistics, while it is computationally efficient for large data sets. Using simulations and applications to 37 phenotypes from 8 real data sets, we illustrate the benefits of our method for estimating and partitioning SNP heritability in population studies as well as for heritability estimation in family studies. Our method is implemented in the GEMMA software package, freely available at www.xzlab.org/software.html.

A scan statistic to extract causal gene clusters from case-control genome-wide rare CNV data.

PubMed

Nishiyama, Takeshi; Takahashi, Kunihiko; Tango, Toshiro; Pinto, Dalila; Scherer, Stephen W; Takami, Satoshi; Kishino, Hirohisa

2011-05-26

Several statistical tests have been developed for analyzing genome-wide association data by incorporating gene pathway information in terms of gene sets. Using these methods, hundreds of gene sets are typically tested, and the tested gene sets often overlap. This overlapping greatly increases the probability of generating false positives, and the results obtained are difficult to interpret, particularly when many gene sets show statistical significance. We propose a flexible statistical framework to circumvent these problems. Inspired by spatial scan statistics for detecting clustering of disease occurrence in the field of epidemiology, we developed a scan statistic to extract disease-associated gene clusters from a whole gene pathway. Extracting one or a few significant gene clusters from a global pathway limits the overall false positive probability, which results in increased statistical power, and facilitates the interpretation of test results. In the present study, we applied our method to genome-wide association data for rare copy-number variations, which have been strongly implicated in common diseases. Application of our method to a simulated dataset demonstrated the high accuracy of this method in detecting disease-associated gene clusters in a whole gene pathway. The scan statistic approach proposed here shows a high level of accuracy in detecting gene clusters in a whole gene pathway. This study has provided a sound statistical framework for analyzing genome-wide rare CNV data by incorporating topological information on the gene pathway.

The Role of Discrete Global Grid Systems in the Global Statistical Geospatial Framework

NASA Astrophysics Data System (ADS)

Purss, M. B. J.; Peterson, P.; Minchin, S. A.; Bermudez, L. E.

2016-12-01

The United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM) has proposed the development of a Global Statistical Geospatial Framework (GSGF) as a mechanism for the establishment of common analytical systems that enable the integration of statistical and geospatial information. Conventional coordinate reference systems address the globe with a continuous field of points suitable for repeatable navigation and analytical geometry. While this continuous field is represented on a computer in a digitized and discrete fashion by tuples of fixed-precision floating point values, it is a non-trivial exercise to relate point observations spatially referenced in this way to areal coverages on the surface of the Earth. The GSGF states the need to move to gridded data delivery and the importance of using common geographies and geocoding. The challenges associated with meeting these goals are not new and there has been a significant effort within the geospatial community to develop nested gridding standards to tackle these issues over many years. These efforts have recently culminated in the development of a Discrete Global Grid Systems (DGGS) standard which has been developed under the auspices of Open Geospatial Consortium (OGC). DGGS provide a fixed areal based geospatial reference frame for the persistent location of measured Earth observations, feature interpretations, and modelled predictions. DGGS address the entire planet by partitioning it into a discrete hierarchical tessellation of progressively finer resolution cells, which are referenced by a unique index that facilitates rapid computation, query and analysis. The geometry and location of the cell is the principle aspect of a DGGS. Data integration, decomposition, and aggregation is optimised in the DGGS hierarchical structure and can be exploited for efficient multi-source data processing, storage, discovery, transmission, visualization, computation, analysis, and modelling. During the 6th Session of the UN-GGIM in August 2016 the role of DGGS in the context of the GSGF was formally acknowledged. This paper proposes to highlight the synergies and role of DGGS in the Global Statistical Geospatial Framework and to show examples of the use of DGGS to combine geospatial statistics with traditional geoscientific data.

Incorporating imperfect detection into joint models of communites: A response to Warton et al.

USGS Publications Warehouse

Beissinger, Steven R.; Iknayan, Kelly J.; Guillera-Arroita, Gurutzeta; Zipkin, Elise; Dorazio, Robert; Royle, Andy; Kery, Marc

2016-01-01

Warton et al. [1] advance community ecology by describing a statistical framework that can jointly model abundances (or distributions) across many taxa to quantify how community properties respond to environmental variables. This framework specifies the effects of both measured and unmeasured (latent) variables on the abundance (or occurrence) of each species. Latent variables are random effects that capture the effects of both missing environmental predictors and correlations in parameter values among different species. As presented in Warton et al., however, the joint modeling framework fails to account for the common problem of detection or measurement errors that always accompany field sampling of abundance or occupancy, and are well known to obscure species- and community-level inferences.

A Tutorial in Bayesian Potential Outcomes Mediation Analysis.

PubMed

Miočević, Milica; Gonzalez, Oscar; Valente, Matthew J; MacKinnon, David P

2018-01-01

Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.

Statistically Controlling for Confounding Constructs Is Harder than You Think

PubMed Central

Westfall, Jacob; Yarkoni, Tal

2016-01-01

Social scientists often seek to demonstrate that a construct has incremental validity over and above other related constructs. However, these claims are typically supported by measurement-level models that fail to consider the effects of measurement (un)reliability. We use intuitive examples, Monte Carlo simulations, and a novel analytical framework to demonstrate that common strategies for establishing incremental construct validity using multiple regression analysis exhibit extremely high Type I error rates under parameter regimes common in many psychological domains. Counterintuitively, we find that error rates are highest—in some cases approaching 100%—when sample sizes are large and reliability is moderate. Our findings suggest that a potentially large proportion of incremental validity claims made in the literature are spurious. We present a web application (http://jakewestfall.org/ivy/) that readers can use to explore the statistical properties of these and other incremental validity arguments. We conclude by reviewing SEM-based statistical approaches that appropriately control the Type I error rate when attempting to establish incremental validity. PMID:27031707

A comparison of linear and nonlinear statistical techniques in performance attribution.

PubMed

Chan, N H; Genovese, C R

2001-01-01

Performance attribution is usually conducted under the linear framework of multifactor models. Although commonly used by practitioners in finance, linear multifactor models are known to be less than satisfactory in many situations. After a brief survey of nonlinear methods, nonlinear statistical techniques are applied to performance attribution of a portfolio constructed from a fixed universe of stocks using factors derived from some commonly used cross sectional linear multifactor models. By rebalancing this portfolio monthly, the cumulative returns for procedures based on standard linear multifactor model and three nonlinear techniques-model selection, additive models, and neural networks-are calculated and compared. It is found that the first two nonlinear techniques, especially in combination, outperform the standard linear model. The results in the neural-network case are inconclusive because of the great variety of possible models. Although these methods are more complicated and may require some tuning, toolboxes are developed and suggestions on calibration are proposed. This paper demonstrates the usefulness of modern nonlinear statistical techniques in performance attribution.

Robustness of movement models: can models bridge the gap between temporal scales of data sets and behavioural processes?

PubMed

Schlägel, Ulrike E; Lewis, Mark A

2016-12-01

Discrete-time random walks and their extensions are common tools for analyzing animal movement data. In these analyses, resolution of temporal discretization is a critical feature. Ideally, a model both mirrors the relevant temporal scale of the biological process of interest and matches the data sampling rate. Challenges arise when resolution of data is too coarse due to technological constraints, or when we wish to extrapolate results or compare results obtained from data with different resolutions. Drawing loosely on the concept of robustness in statistics, we propose a rigorous mathematical framework for studying movement models' robustness against changes in temporal resolution. In this framework, we define varying levels of robustness as formal model properties, focusing on random walk models with spatially-explicit component. With the new framework, we can investigate whether models can validly be applied to data across varying temporal resolutions and how we can account for these different resolutions in statistical inference results. We apply the new framework to movement-based resource selection models, demonstrating both analytical and numerical calculations, as well as a Monte Carlo simulation approach. While exact robustness is rare, the concept of approximate robustness provides a promising new direction for analyzing movement models.

Evaluating the use of diversity indices to distinguish between microbial communities with different traits.

PubMed

Feranchuk, Sergey; Belkova, Natalia; Potapova, Ulyana; Kuzmin, Dmitry; Belikov, Sergei

2018-05-23

Several measures of biodiversity are commonly used to describe microbial communities, analyzed using 16S gene sequencing. A wide range of available experiments on 16S gene sequencing allows us to present a framework for a comparison of various diversity indices. The criterion for the comparison is the statistical significance of the difference in index values for microbial communities with different traits, within the same experiment. The results of the evaluation indicate that Shannon diversity is the most effective measure among the commonly used diversity indices. The results also indicate that, within the present framework, the Gini coefficient as a diversity index is comparable to Shannon diversity, despite the fact that the Gini coefficient, as a diversity estimator, is far less popular in microbiology than several other measures. Copyright © 2018 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

MIDAS: Regionally linear multivariate discriminative statistical mapping.

PubMed

Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos

2018-07-01

Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data. Copyright © 2018. Published by Elsevier Inc.

Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework.

PubMed

Yin, X-X; Zhang, Y; Cao, J; Wu, J-L; Hadjiloucas, S

2016-12-01

We provide a comprehensive account of recent advances in biomedical image analysis and classification from two complementary imaging modalities: terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The work aims to highlight underlining commonalities in both data structures so that a common multi-channel data fusion framework can be developed. Signal pre-processing in both datasets is discussed briefly taking into consideration advances in multi-resolution analysis and model based fractional order calculus system identification. Developments in statistical signal processing using principal component and independent component analysis are also considered. These algorithms have been developed independently by the THz-pulse imaging and DCE-MRI communities, and there is scope to place them in a common multi-channel framework to provide better software standardization at the pre-processing de-noising stage. A comprehensive discussion of feature selection strategies is also provided and the importance of preserving textural information is highlighted. Feature extraction and classification methods taking into consideration recent advances in support vector machine (SVM) and extreme learning machine (ELM) classifiers and their complex extensions are presented. An outlook on Clifford algebra classifiers and deep learning techniques suitable to both types of datasets is also provided. The work points toward the direction of developing a new unified multi-channel signal processing framework for biomedical image analysis that will explore synergies from both sensing modalities for inferring disease proliferation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The extraction and integration framework: a two-process account of statistical learning.

PubMed

Thiessen, Erik D; Kronstein, Alexandra T; Hufnagle, Daniel G

2013-07-01

The term statistical learning in infancy research originally referred to sensitivity to transitional probabilities. Subsequent research has demonstrated that statistical learning contributes to infant development in a wide array of domains. The range of statistical learning phenomena necessitates a broader view of the processes underlying statistical learning. Learners are sensitive to a much wider range of statistical information than the conditional relations indexed by transitional probabilities, including distributional and cue-based statistics. We propose a novel framework that unifies learning about all of these kinds of statistical structure. From our perspective, learning about conditional relations outputs discrete representations (such as words). Integration across these discrete representations yields sensitivity to cues and distributional information. To achieve sensitivity to all of these kinds of statistical structure, our framework combines processes that extract segments of the input with processes that compare across these extracted items. In this framework, the items extracted from the input serve as exemplars in long-term memory. The similarity structure of those exemplars in long-term memory leads to the discovery of cues and categorical structure, which guides subsequent extraction. The extraction and integration framework provides a way to explain sensitivity to both conditional statistical structure (such as transitional probabilities) and distributional statistical structure (such as item frequency and variability), and also a framework for thinking about how these different aspects of statistical learning influence each other. 2013 APA, all rights reserved

Using greenhouse gas fluxes to define soil functional types

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

Petrakis, Sandra; Barba, Josep; Bond-Lamberty, Ben

Soils provide key ecosystem services and directly control ecosystem functions; thus, there is a need to define the reference state of soil functionality. Most common functional classifications of ecosystems are vegetation-centered and neglect soil characteristics and processes. We propose Soil Functional Types (SFTs) as a conceptual approach to represent and describe the functionality of soils based on characteristics of their greenhouse gas (GHG) flux dynamics. We used automated measurements of CO2, CH4 and N2O in a forested area to define SFTs following a simple statistical framework. This study supports the hypothesis that SFTs provide additional insights on the spatial variabilitymore » of soil functionality beyond information represented by commonly measured soil parameters (e.g., soil moisture, soil temperature, litter biomass). We discuss the implications of this framework at the plot-scale and the potential of this approach at larger scales. This approach is a first step to provide a framework to define SFTs, but a community effort is necessary to harmonize any global classification for soil functionality. A global application of the proposed SFT framework will only be possible if there is a community-wide effort to share data and create a global database of GHG emissions from soils.« less

Crystal structure of dimanganese(II) zinc bis­[ortho­phosphate(V)] monohydrate

PubMed Central

Alhakmi, Ghaleb; Assani, Abderrazzak; Saadi, Mohamed; El Ammari, Lahcen

2015-01-01

The title compound, Mn2Zn(PO4)2·H2O, was obtained under hydro­thermal conditions. The structure is isotypic with other transition metal phosphates of the type M 3− xM′x(PO4)2·H2O, but shows no statistical disorder of the three metallic sites. The principal building units are distorted [MnO6] and [MnO5(H2O)] octa­hedra, a distorted [ZnO5] square pyramid and two regular PO4 tetra­hedra. The connection of the polyhedra leads to a framework structure. Two types of layers parallel to (-101) can be distinguished in this framework. One layer contains [Zn2O8] dimers linked to PO4 tetra­hedra via common edges. The other layer is more corrugated and contains [Mn2O8(H2O)2] dimers and [MnO6] octa­hedra linked together by common edges. The PO4 tetra­hedra link the two types of layers into a framework structure with channels parallel to [101]. The H atoms of the water mol­ecules point into the channels and form O—H⋯O hydrogen bonds (one of which is bifurcated) with framework O atoms across the channels. PMID:25878806

Framework for performance evaluation of face, text, and vehicle detection and tracking in video: data, metrics, and protocol.

PubMed

Kasturi, Rangachar; Goldgof, Dmitry; Soundararajan, Padmanabhan; Manohar, Vasant; Garofolo, John; Bowers, Rachel; Boonstra, Matthew; Korzhova, Valentina; Zhang, Jing

2009-02-01

Common benchmark data sets, standardized performance metrics, and baseline algorithms have demonstrated considerable impact on research and development in a variety of application domains. These resources provide both consumers and developers of technology with a common framework to objectively compare the performance of different algorithms and algorithmic improvements. In this paper, we present such a framework for evaluating object detection and tracking in video: specifically for face, text, and vehicle objects. This framework includes the source video data, ground-truth annotations (along with guidelines for annotation), performance metrics, evaluation protocols, and tools including scoring software and baseline algorithms. For each detection and tracking task and supported domain, we developed a 50-clip training set and a 50-clip test set. Each data clip is approximately 2.5 minutes long and has been completely spatially/temporally annotated at the I-frame level. Each task/domain, therefore, has an associated annotated corpus of approximately 450,000 frames. The scope of such annotation is unprecedented and was designed to begin to support the necessary quantities of data for robust machine learning approaches, as well as a statistically significant comparison of the performance of algorithms. The goal of this work was to systematically address the challenges of object detection and tracking through a common evaluation framework that permits a meaningful objective comparison of techniques, provides the research community with sufficient data for the exploration of automatic modeling techniques, encourages the incorporation of objective evaluation into the development process, and contributes useful lasting resources of a scale and magnitude that will prove to be extremely useful to the computer vision research community for years to come.

Complexities and potential pitfalls of clinical study design and data analysis in assisted reproduction.

PubMed

Patounakis, George; Hill, Micah J

2018-06-01

The purpose of the current review is to describe the common pitfalls in design and statistical analysis of reproductive medicine studies. It serves to guide both authors and reviewers toward reducing the incidence of spurious statistical results and erroneous conclusions. The large amount of data gathered in IVF cycles leads to problems with multiplicity, multicollinearity, and over fitting of regression models. Furthermore, the use of the word 'trend' to describe nonsignificant results has increased in recent years. Finally, methods to accurately account for female age in infertility research models are becoming more common and necessary. The pitfalls of study design and analysis reviewed provide a framework for authors and reviewers to approach clinical research in the field of reproductive medicine. By providing a more rigorous approach to study design and analysis, the literature in reproductive medicine will have more reliable conclusions that can stand the test of time.

General Framework for Meta-analysis of Rare Variants in Sequencing Association Studies

PubMed Central

Lee, Seunggeun; Teslovich, Tanya M.; Boehnke, Michael; Lin, Xihong

2013-01-01

We propose a general statistical framework for meta-analysis of gene- or region-based multimarker rare variant association tests in sequencing association studies. In genome-wide association studies, single-marker meta-analysis has been widely used to increase statistical power by combining results via regression coefficients and standard errors from different studies. In analysis of rare variants in sequencing studies, region-based multimarker tests are often used to increase power. We propose meta-analysis methods for commonly used gene- or region-based rare variants tests, such as burden tests and variance component tests. Because estimation of regression coefficients of individual rare variants is often unstable or not feasible, the proposed method avoids this difficulty by calculating score statistics instead that only require fitting the null model for each study and then aggregating these score statistics across studies. Our proposed meta-analysis rare variant association tests are conducted based on study-specific summary statistics, specifically score statistics for each variant and between-variant covariance-type (linkage disequilibrium) relationship statistics for each gene or region. The proposed methods are able to incorporate different levels of heterogeneity of genetic effects across studies and are applicable to meta-analysis of multiple ancestry groups. We show that the proposed methods are essentially as powerful as joint analysis by directly pooling individual level genotype data. We conduct extensive simulations to evaluate the performance of our methods by varying levels of heterogeneity across studies, and we apply the proposed methods to meta-analysis of rare variant effects in a multicohort study of the genetics of blood lipid levels. PMID:23768515

Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis.

PubMed

Moore, Jason H; Williams, Scott M

2005-06-01

Epistasis plays an important role in the genetic architecture of common human diseases and can be viewed from two perspectives, biological and statistical, each derived from and leading to different assumptions and research strategies. Biological epistasis is the result of physical interactions among biomolecules within gene regulatory networks and biochemical pathways in an individual such that the effect of a gene on a phenotype is dependent on one or more other genes. In contrast, statistical epistasis is defined as deviation from additivity in a mathematical model summarizing the relationship between multilocus genotypes and phenotypic variation in a population. The goal of this essay is to review definitions and examples of biological and statistical epistasis and to explore the relationship between the two. Specifically, we present and discuss the following two questions in the context of human health and disease. First, when does statistical evidence of epistasis in human populations imply underlying biomolecular interactions in the etiology of disease? Second, when do biomolecular interactions produce patterns of statistical epistasis in human populations? Answers to these two reciprocal questions will provide an important framework for using genetic information to improve our ability to diagnose, prevent and treat common human diseases. We propose that systems biology will provide the necessary information for addressing these questions and that model systems such as bacteria, yeast and digital organisms will be a useful place to start.

Optimal moment determination in POME-copula based hydrometeorological dependence modelling

NASA Astrophysics Data System (ADS)

Liu, Dengfeng; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi

2017-07-01

Copula has been commonly applied in multivariate modelling in various fields where marginal distribution inference is a key element. To develop a flexible, unbiased mathematical inference framework in hydrometeorological multivariate applications, the principle of maximum entropy (POME) is being increasingly coupled with copula. However, in previous POME-based studies, determination of optimal moment constraints has generally not been considered. The main contribution of this study is the determination of optimal moments for POME for developing a coupled optimal moment-POME-copula framework to model hydrometeorological multivariate events. In this framework, margins (marginals, or marginal distributions) are derived with the use of POME, subject to optimal moment constraints. Then, various candidate copulas are constructed according to the derived margins, and finally the most probable one is determined, based on goodness-of-fit statistics. This optimal moment-POME-copula framework is applied to model the dependence patterns of three types of hydrometeorological events: (i) single-site streamflow-water level; (ii) multi-site streamflow; and (iii) multi-site precipitation, with data collected from Yichang and Hankou in the Yangtze River basin, China. Results indicate that the optimal-moment POME is more accurate in margin fitting and the corresponding copulas reflect a good statistical performance in correlation simulation. Also, the derived copulas, capturing more patterns which traditional correlation coefficients cannot reflect, provide an efficient way in other applied scenarios concerning hydrometeorological multivariate modelling.

Spatial Modeling for Resources Framework (SMRF): A modular framework for developing spatial forcing data in mountainous terrain

NASA Astrophysics Data System (ADS)

Havens, S.; Marks, D. G.; Kormos, P.; Hedrick, A. R.; Johnson, M.; Robertson, M.; Sandusky, M.

2017-12-01

In the Western US, operational water supply managers rely on statistical techniques to forecast the volume of water left to enter the reservoirs. As the climate changes and the demand increases for stored water utilized for irrigation, flood control, power generation, and ecosystem services, water managers have begun to move from statistical techniques towards using physically based models. To assist with the transition, a new open source framework was developed, the Spatial Modeling for Resources Framework (SMRF), to automate and simplify the most common forcing data distribution methods. SMRF is computationally efficient and can be implemented for both research and operational applications. Currently, SMRF is able to generate all of the forcing data required to run physically based snow or hydrologic models at 50-100 m resolution over regions of 500-10,000 km2, and has been successfully applied in real time and historical applications for the Boise River Basin in Idaho, USA, the Tuolumne River Basin and San Joaquin in California, USA, and Reynolds Creek Experimental Watershed in Idaho, USA. These applications use meteorological station measurements and numerical weather prediction model outputs as input data. SMRF has significantly streamlined the modeling workflow, decreased model set up time from weeks to days, and made near real-time application of physics-based snow and hydrologic models possible.

Mean template for tensor-based morphometry using deformation tensors.

PubMed

Leporé, Natasha; Brun, Caroline; Pennec, Xavier; Chou, Yi-Yu; Lopez, Oscar L; Aizenstein, Howard J; Becker, James T; Toga, Arthur W; Thompson, Paul M

2007-01-01

Tensor-based morphometry (TBM) studies anatomical differences between brain images statistically, to identify regions that differ between groups, over time, or correlate with cognitive or clinical measures. Using a nonlinear registration algorithm, all images are mapped to a common space, and statistics are most commonly performed on the Jacobian determinant (local expansion factor) of the deformation fields. In, it was shown that the detection sensitivity of the standard TBM approach could be increased by using the full deformation tensors in a multivariate statistical analysis. Here we set out to improve the common space itself, by choosing the shape that minimizes a natural metric on the deformation tensors from that space to the population of control subjects. This method avoids statistical bias and should ease nonlinear registration of new subjects data to a template that is 'closest' to all subjects' anatomies. As deformation tensors are symmetric positive-definite matrices and do not form a vector space, all computations are performed in the log-Euclidean framework. The control brain B that is already the closest to 'average' is found. A gradient descent algorithm is then used to perform the minimization that iteratively deforms this template and obtains the mean shape. We apply our method to map the profile of anatomical differences in a dataset of 26 HIV/AIDS patients and 14 controls, via a log-Euclidean Hotelling's T2 test on the deformation tensors. These results are compared to the ones found using the 'best' control, B. Statistics on both shapes are evaluated using cumulative distribution functions of the p-values in maps of inter-group differences.

Individual behavioral phenotypes: an integrative meta-theoretical framework. Why "behavioral syndromes" are not analogs of "personality".

PubMed

Uher, Jana

2011-09-01

Animal researchers are increasingly interested in individual differences in behavior. Their interpretation as meaningful differences in behavioral strategies stable over time and across contexts, adaptive, heritable, and acted upon by natural selection has triggered new theoretical developments. However, the analytical approaches used to explore behavioral data still address population-level phenomena, and statistical methods suitable to analyze individual behavior are rarely applied. I discuss fundamental investigative principles and analytical approaches to explore whether, in what ways, and under which conditions individual behavioral differences are actually meaningful. I elaborate the meta-theoretical ideas underlying common theoretical concepts and integrate them into an overarching meta-theoretical and methodological framework. This unravels commonalities and differences, and shows that assumptions of analogy to concepts of human personality are not always warranted and that some theoretical developments may be based on methodological artifacts. Yet, my results also highlight possible directions for new theoretical developments in animal behavior research. Copyright © 2011 Wiley Periodicals, Inc.

Toward a unified approach to dose-response modeling in ecotoxicology.

PubMed

Ritz, Christian

2010-01-01

This study reviews dose-response models that are used in ecotoxicology. The focus lies on clarification of differences and similarities between models, and as a side effect, their different guises in ecotoxicology are unravelled. A look at frequently used dose-response models reveals major discrepancies, among other things in naming conventions. Therefore, there is a need for a unified view on dose-response modeling in order to improve the understanding of it and to facilitate communication and comparison of findings across studies, thus realizing its full potential. This study attempts to establish a general framework that encompasses most dose-response models that are of interest to ecotoxicologists in practice. The framework includes commonly used models such as the log-logistic and Weibull models, but also features entire suites of models as found in various guidance documents. An outline on how the proposed framework can be implemented in statistical software systems is also provided.

Confounding in statistical mediation analysis: What it is and how to address it.

PubMed

Valente, Matthew J; Pelham, William E; Smyth, Heather; MacKinnon, David P

2017-11-01

Psychology researchers are often interested in mechanisms underlying how randomized interventions affect outcomes such as substance use and mental health. Mediation analysis is a common statistical method for investigating psychological mechanisms that has benefited from exciting new methodological improvements over the last 2 decades. One of the most important new developments is methodology for estimating causal mediated effects using the potential outcomes framework for causal inference. Potential outcomes-based methods developed in epidemiology and statistics have important implications for understanding psychological mechanisms. We aim to provide a concise introduction to and illustration of these new methods and emphasize the importance of confounder adjustment. First, we review the traditional regression approach for estimating mediated effects. Second, we describe the potential outcomes framework. Third, we define what a confounder is and how the presence of a confounder can provide misleading evidence regarding mechanisms of interventions. Fourth, we describe experimental designs that can help rule out confounder bias. Fifth, we describe new statistical approaches to adjust for measured confounders of the mediator-outcome relation and sensitivity analyses to probe effects of unmeasured confounders on the mediated effect. All approaches are illustrated with application to a real counseling intervention dataset. Counseling psychologists interested in understanding the causal mechanisms of their interventions can benefit from incorporating the most up-to-date techniques into their mediation analyses. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A theoretical Gaussian framework for anomalous change detection in hyperspectral images

NASA Astrophysics Data System (ADS)

Acito, Nicola; Diani, Marco; Corsini, Giovanni

2017-10-01

Exploitation of temporal series of hyperspectral images is a relatively new discipline that has a wide variety of possible applications in fields like remote sensing, area surveillance, defense and security, search and rescue and so on. In this work, we discuss how images taken at two different times can be processed to detect changes caused by insertion, deletion or displacement of small objects in the monitored scene. This problem is known in the literature as anomalous change detection (ACD) and it can be viewed as the extension, to the multitemporal case, of the well-known anomaly detection problem in a single image. In fact, in both cases, the hyperspectral images are processed blindly in an unsupervised manner and without a-priori knowledge about the target spectrum. We introduce the ACD problem using an approach based on the statistical decision theory and we derive a common framework including different ACD approaches. Particularly, we clearly define the observation space, the data statistical distribution conditioned to the two competing hypotheses and the procedure followed to come with the solution. The proposed overview places emphasis on techniques based on the multivariate Gaussian model that allows a formal presentation of the ACD problem and the rigorous derivation of the possible solutions in a way that is both mathematically more tractable and easier to interpret. We also discuss practical problems related to the application of the detectors in the real world and present affordable solutions. Namely, we describe the ACD processing chain including the strategies that are commonly adopted to compensate pervasive radiometric changes, caused by the different illumination/atmospheric conditions, and to mitigate the residual geometric image co-registration errors. Results obtained on real freely available data are discussed in order to test and compare the methods within the proposed general framework.

Time Scale Optimization and the Hunt for Astronomical Cycles in Deep Time Strata

NASA Astrophysics Data System (ADS)

Meyers, Stephen R.

2016-04-01

A valuable attribute of astrochronology is the direct link between chronometer and climate change, providing a remarkable opportunity to constrain the evolution of the surficial Earth System. Consequently, the hunt for astronomical cycles in strata has spurred the development of a rich conceptual framework for climatic/oceanographic change, and has allowed exploration of the geologic record with unprecedented temporal resolution. Accompanying these successes, however, has been a persistent skepticism about appropriate astrochronologic testing and circular reasoning: how does one reliably test for astronomical cycles in stratigraphic data, especially when time is poorly constrained? From this perspective, it would seem that the merits and promise of astrochronology (e.g., a geologic time scale measured in ≤400 kyr increments) also serves as its Achilles heel, if the confirmation of such short rhythms defies rigorous statistical testing. To address these statistical challenges in astrochronologic testing, a new approach has been developed that (1) explicitly evaluates time scale uncertainty, (2) is resilient to common problems associated with spectrum confidence level assessment and 'multiple testing', and (3) achieves high statistical power under a wide range of conditions (it can identify astronomical cycles when present in data). Designated TimeOpt (for "time scale optimization"; Meyers 2015), the method employs a probabilistic linear regression model framework to investigate amplitude modulation and frequency ratios (bundling) in stratigraphic data, while simultaneously determining the optimal time scale. This presentation will review the TimeOpt method, and demonstrate how the flexible statistical framework can be further extended to evaluate (and optimize upon) complex sedimentation rate models, enhancing the statistical power of the approach, and addressing the challenge of unsteady sedimentation. Meyers, S. R. (2015), The evaluation of eccentricity-related amplitude modulation and bundling in paleoclimate data: An inverse approach for astrochronologic testing and time scale optimization, Paleoceanography, 30, doi:10.1002/ 2015PA002850.

The dimensional salience solution to the expectancy-value muddle: an extension.

PubMed

Newton, Joshua D; Newton, Fiona J; Ewing, Michael T

2014-01-01

The theory of reasoned action (TRA) specifies a set of expectancy-value, belief-based frameworks that underpin attitude (behavioural beliefs × outcome evaluations) and subjective norm (normative beliefs × motivation to comply). Unfortunately, the most common method for analysing these frameworks generates statistically uninterpretable findings, resulting in what has been termed the 'expectancy-value muddle'. Recently, however, a dimensional salience approach was found to resolve this muddle for the belief-based framework underpinning attitude. An online survey of 262 participants was therefore conducted to determine whether the dimensional salience approach could also be applied to the belief-based framework underpinning subjective norm. Results revealed that motivations to comply were greater for salient, as opposed to non-salient, social referents. The belief-based framework underpinning subjective norm was therefore represented by evaluating normative belief ratings for salient social referents. This modified framework was found to predict subjective norm, although predictions were greater when participants were forced to select five salient social referents rather than being free to select any number of social referents. These findings validate the use of the dimensional salience approach for examining the belief-based frameworks underpinning subjective norm. As such, this approach provides a complete solution to addressing the expectancy-value muddle in the TRA.

Toward Global Comparability of Sexual Orientation Data in Official Statistics: A Conceptual Framework of Sexual Orientation for Health Data Collection in New Zealand's Official Statistics System

PubMed Central

Gray, Alistair; Veale, Jaimie F.; Binson, Diane; Sell, Randell L.

2013-01-01

Objective. Effectively addressing health disparities experienced by sexual minority populations requires high-quality official data on sexual orientation. We developed a conceptual framework of sexual orientation to improve the quality of sexual orientation data in New Zealand's Official Statistics System. Methods. We reviewed conceptual and methodological literature, culminating in a draft framework. To improve the framework, we held focus groups and key-informant interviews with sexual minority stakeholders and producers and consumers of official statistics. An advisory board of experts provided additional guidance. Results. The framework proposes working definitions of the sexual orientation topic and measurement concepts, describes dimensions of the measurement concepts, discusses variables framing the measurement concepts, and outlines conceptual grey areas. Conclusion. The framework proposes standard definitions and concepts for the collection of official sexual orientation data in New Zealand. It presents a model for producers of official statistics in other countries, who wish to improve the quality of health data on their citizens. PMID:23840231

A Statistical Framework for Analyzing Cyber Threats

DTIC Science & Technology

defender cares most about the attacks against certain ports or services). The grey-box statistical framework formulates a new methodology of Cybersecurity ...the design of prediction models. Our research showed that the grey-box framework is effective in predicting cybersecurity situational awareness.

Identification of genetic loci shared between schizophrenia and the Big Five personality traits.

PubMed

Smeland, Olav B; Wang, Yunpeng; Lo, Min-Tzu; Li, Wen; Frei, Oleksandr; Witoelar, Aree; Tesli, Martin; Hinds, David A; Tung, Joyce Y; Djurovic, Srdjan; Chen, Chi-Hua; Dale, Anders M; Andreassen, Ole A

2017-05-22

Schizophrenia is associated with differences in personality traits, and recent studies suggest that personality traits and schizophrenia share a genetic basis. Here we aimed to identify specific genetic loci shared between schizophrenia and the Big Five personality traits using a Bayesian statistical framework. Using summary statistics from genome-wide association studies (GWAS) on personality traits in the 23andMe cohort (n = 59,225) and schizophrenia in the Psychiatric Genomics Consortium cohort (n = 82,315), we evaluated overlap in common genetic variants. The Big Five personality traits neuroticism, extraversion, openness, agreeableness and conscientiousness were measured using a web implementation of the Big Five Inventory. Applying the conditional false discovery rate approach, we increased discovery of genetic loci and identified two loci shared between neuroticism and schizophrenia and six loci shared between openness and schizophrenia. The study provides new insights into the relationship between personality traits and schizophrenia by highlighting genetic loci involved in their common genetic etiology.

Performance metrics for the assessment of satellite data products: an ocean color case study

PubMed Central

Seegers, Bridget N.; Stumpf, Richard P.; Schaeffer, Blake A.; Loftin, Keith A.; Werdell, P. Jeremy

2018-01-01

Performance assessment of ocean color satellite data has generally relied on statistical metrics chosen for their common usage and the rationale for selecting certain metrics is infrequently explained. Commonly reported statistics based on mean squared errors, such as the coefficient of determination (r2), root mean square error, and regression slopes, are most appropriate for Gaussian distributions without outliers and, therefore, are often not ideal for ocean color algorithm performance assessment, which is often limited by sample availability. In contrast, metrics based on simple deviations, such as bias and mean absolute error, as well as pair-wise comparisons, often provide more robust and straightforward quantities for evaluating ocean color algorithms with non-Gaussian distributions and outliers. This study uses a SeaWiFS chlorophyll-a validation data set to demonstrate a framework for satellite data product assessment and recommends a multi-metric and user-dependent approach that can be applied within science, modeling, and resource management communities. PMID:29609296

The biometric menagerie.

PubMed

Yager, Neil; Dunstone, Ted

2010-02-01

It is commonly accepted that users of a biometric system may have differing degrees of accuracy within the system. Some people may have trouble authenticating, while others may be particularly vulnerable to impersonation. Goats, wolves, and lambs are labels commonly applied to these problem users. These user types are defined in terms of verification performance when users are matched against themselves (goats) or when matched against others (lambs and wolves). The relationship between a user's genuine and impostor match results suggests four new user groups: worms, doves, chameleons, and phantoms. We establish formal definitions for these animals and a statistical test for their existence. A thorough investigation is conducted using a broad range of biometric modalities, including 2D and 3D faces, fingerprints, iris, speech, and keystroke dynamics. Patterns that emerge from the results expose novel, important, and encouraging insights into the nature of biometric match results. A new framework for the evaluation of biometric systems based on the biometric menagerie, as opposed to collective statistics, is proposed.

Using high-resolution variant frequencies to empower clinical genome interpretation.

PubMed

Whiffin, Nicola; Minikel, Eric; Walsh, Roddy; O'Donnell-Luria, Anne H; Karczewski, Konrad; Ing, Alexander Y; Barton, Paul J R; Funke, Birgit; Cook, Stuart A; MacArthur, Daniel; Ware, James S

2017-10-01

PurposeWhole-exome and whole-genome sequencing have transformed the discovery of genetic variants that cause human Mendelian disease, but discriminating pathogenic from benign variants remains a daunting challenge. Rarity is recognized as a necessary, although not sufficient, criterion for pathogenicity, but frequency cutoffs used in Mendelian analysis are often arbitrary and overly lenient. Recent very large reference datasets, such as the Exome Aggregation Consortium (ExAC), provide an unprecedented opportunity to obtain robust frequency estimates even for very rare variants.MethodsWe present a statistical framework for the frequency-based filtering of candidate disease-causing variants, accounting for disease prevalence, genetic and allelic heterogeneity, inheritance mode, penetrance, and sampling variance in reference datasets.ResultsUsing the example of cardiomyopathy, we show that our approach reduces by two-thirds the number of candidate variants under consideration in the average exome, without removing true pathogenic variants (false-positive rate<0.001).ConclusionWe outline a statistically robust framework for assessing whether a variant is "too common" to be causative for a Mendelian disorder of interest. We present precomputed allele frequency cutoffs for all variants in the ExAC dataset.

Conceptualizing a Framework for Advanced Placement Statistics Teaching Knowledge

ERIC Educational Resources Information Center

Haines, Brenna

2015-01-01

The purpose of this article is to sketch a conceptualization of a framework for Advanced Placement (AP) Statistics Teaching Knowledge. Recent research continues to problematize the lack of knowledge and preparation among secondary level statistics teachers. The College Board's AP Statistics course continues to grow and gain popularity, but is a…

Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning.

PubMed

Rohrmeier, Martin A; Cross, Ian

2014-07-01

Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.

New software for statistical analysis of Cambridge Structural Database data

PubMed Central

Sykes, Richard A.; McCabe, Patrick; Allen, Frank H.; Battle, Gary M.; Bruno, Ian J.; Wood, Peter A.

2011-01-01

A collection of new software tools is presented for the analysis of geometrical, chemical and crystallographic data from the Cambridge Structural Database (CSD). This software supersedes the program Vista. The new functionality is integrated into the program Mercury in order to provide statistical, charting and plotting options alongside three-dimensional structural visualization and analysis. The integration also permits immediate access to other information about specific CSD entries through the Mercury framework, a common requirement in CSD data analyses. In addition, the new software includes a range of more advanced features focused towards structural analysis such as principal components analysis, cone-angle correction in hydrogen-bond analyses and the ability to deal with topological symmetry that may be exhibited in molecular search fragments. PMID:22477784

Collective behaviours: from biochemical kinetics to electronic circuits.

PubMed

Agliari, Elena; Barra, Adriano; Burioni, Raffaella; Di Biasio, Aldo; Uguzzoni, Guido

2013-12-10

In this work we aim to highlight a close analogy between cooperative behaviors in chemical kinetics and cybernetics; this is realized by using a common language for their description, that is mean-field statistical mechanics. First, we perform a one-to-one mapping between paradigmatic behaviors in chemical kinetics (i.e., non-cooperative, cooperative, ultra-sensitive, anti-cooperative) and in mean-field statistical mechanics (i.e., paramagnetic, high and low temperature ferromagnetic, anti-ferromagnetic). Interestingly, the statistical mechanics approach allows a unified, broad theory for all scenarios and, in particular, Michaelis-Menten, Hill and Adair equations are consistently recovered. This framework is then tested against experimental biological data with an overall excellent agreement. One step forward, we consistently read the whole mapping from a cybernetic perspective, highlighting deep structural analogies between the above-mentioned kinetics and fundamental bricks in electronics (i.e. operational amplifiers, flashes, flip-flops), so to build a clear bridge linking biochemical kinetics and cybernetics.

Collective behaviours: from biochemical kinetics to electronic circuits

NASA Astrophysics Data System (ADS)

Agliari, Elena; Barra, Adriano; Burioni, Raffaella; di Biasio, Aldo; Uguzzoni, Guido

2013-12-01

In this work we aim to highlight a close analogy between cooperative behaviors in chemical kinetics and cybernetics; this is realized by using a common language for their description, that is mean-field statistical mechanics. First, we perform a one-to-one mapping between paradigmatic behaviors in chemical kinetics (i.e., non-cooperative, cooperative, ultra-sensitive, anti-cooperative) and in mean-field statistical mechanics (i.e., paramagnetic, high and low temperature ferromagnetic, anti-ferromagnetic). Interestingly, the statistical mechanics approach allows a unified, broad theory for all scenarios and, in particular, Michaelis-Menten, Hill and Adair equations are consistently recovered. This framework is then tested against experimental biological data with an overall excellent agreement. One step forward, we consistently read the whole mapping from a cybernetic perspective, highlighting deep structural analogies between the above-mentioned kinetics and fundamental bricks in electronics (i.e. operational amplifiers, flashes, flip-flops), so to build a clear bridge linking biochemical kinetics and cybernetics.

Using expert knowledge to incorporate uncertainty in cause-of-death assignments for modeling of cause-specific mortality

USGS Publications Warehouse

Walsh, Daniel P.; Norton, Andrew S.; Storm, Daniel J.; Van Deelen, Timothy R.; Heisy, Dennis M.

2018-01-01

Implicit and explicit use of expert knowledge to inform ecological analyses is becoming increasingly common because it often represents the sole source of information in many circumstances. Thus, there is a need to develop statistical methods that explicitly incorporate expert knowledge, and can successfully leverage this information while properly accounting for associated uncertainty during analysis. Studies of cause-specific mortality provide an example of implicit use of expert knowledge when causes-of-death are uncertain and assigned based on the observer's knowledge of the most likely cause. To explicitly incorporate this use of expert knowledge and the associated uncertainty, we developed a statistical model for estimating cause-specific mortality using a data augmentation approach within a Bayesian hierarchical framework. Specifically, for each mortality event, we elicited the observer's belief of cause-of-death by having them specify the probability that the death was due to each potential cause. These probabilities were then used as prior predictive values within our framework. This hierarchical framework permitted a simple and rigorous estimation method that was easily modified to include covariate effects and regularizing terms. Although applied to survival analysis, this method can be extended to any event-time analysis with multiple event types, for which there is uncertainty regarding the true outcome. We conducted simulations to determine how our framework compared to traditional approaches that use expert knowledge implicitly and assume that cause-of-death is specified accurately. Simulation results supported the inclusion of observer uncertainty in cause-of-death assignment in modeling of cause-specific mortality to improve model performance and inference. Finally, we applied the statistical model we developed and a traditional method to cause-specific survival data for white-tailed deer, and compared results. We demonstrate that model selection results changed between the two approaches, and incorporating observer knowledge in cause-of-death increased the variability associated with parameter estimates when compared to the traditional approach. These differences between the two approaches can impact reported results, and therefore, it is critical to explicitly incorporate expert knowledge in statistical methods to ensure rigorous inference.

Estimating the Diets of Animals Using Stable Isotopes and a Comprehensive Bayesian Mixing Model

PubMed Central

Hopkins, John B.; Ferguson, Jake M.

2012-01-01

Using stable isotope mixing models (SIMMs) as a tool to investigate the foraging ecology of animals is gaining popularity among researchers. As a result, statistical methods are rapidly evolving and numerous models have been produced to estimate the diets of animals—each with their benefits and their limitations. Deciding which SIMM to use is contingent on factors such as the consumer of interest, its food sources, sample size, the familiarity a user has with a particular framework for statistical analysis, or the level of inference the researcher desires to make (e.g., population- or individual-level). In this paper, we provide a review of commonly used SIMM models and describe a comprehensive SIMM that includes all features commonly used in SIMM analysis and two new features. We used data collected in Yosemite National Park to demonstrate IsotopeR's ability to estimate dietary parameters. We then examined the importance of each feature in the model and compared our results to inferences from commonly used SIMMs. IsotopeR's user interface (in R) will provide researchers a user-friendly tool for SIMM analysis. The model is also applicable for use in paleontology, archaeology, and forensic studies as well as estimating pollution inputs. PMID:22235246

Open Source Tools for Seismicity Analysis

NASA Astrophysics Data System (ADS)

Powers, P.

2010-12-01

The spatio-temporal analysis of seismicity plays an important role in earthquake forecasting and is integral to research on earthquake interactions and triggering. For instance, the third version of the Uniform California Earthquake Rupture Forecast (UCERF), currently under development, will use Epidemic Type Aftershock Sequences (ETAS) as a model for earthquake triggering. UCERF will be a "living" model and therefore requires robust, tested, and well-documented ETAS algorithms to ensure transparency and reproducibility. Likewise, as earthquake aftershock sequences unfold, real-time access to high quality hypocenter data makes it possible to monitor the temporal variability of statistical properties such as the parameters of the Omori Law and the Gutenberg Richter b-value. Such statistical properties are valuable as they provide a measure of how much a particular sequence deviates from expected behavior and can be used when assigning probabilities of aftershock occurrence. To address these demands and provide public access to standard methods employed in statistical seismology, we present well-documented, open-source JavaScript and Java software libraries for the on- and off-line analysis of seismicity. The Javascript classes facilitate web-based asynchronous access to earthquake catalog data and provide a framework for in-browser display, analysis, and manipulation of catalog statistics; implementations of this framework will be made available on the USGS Earthquake Hazards website. The Java classes, in addition to providing tools for seismicity analysis, provide tools for modeling seismicity and generating synthetic catalogs. These tools are extensible and will be released as part of the open-source OpenSHA Commons library.

On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs

PubMed Central

Truccolo, Wilson

2017-01-01

Point process generalized linear models (PP-GLMs) provide an important statistical framework for modeling spiking activity in single-neurons and neuronal networks. Stochastic stability is essential when sampling from these models, as done in computational neuroscience to analyze statistical properties of neuronal dynamics and in neuro-engineering to implement closed-loop applications. Here we show, however, that despite passing common goodness-of-fit tests, PP-GLMs estimated from data are often unstable, leading to divergent firing rates. The inclusion of absolute refractory periods is not a satisfactory solution since the activity then typically settles into unphysiological rates. To address these issues, we derive a framework for determining the existence and stability of fixed points of the expected conditional intensity function (CIF) for general PP-GLMs. Specifically, in nonlinear Hawkes PP-GLMs, the CIF is expressed as a function of the previous spike history and exogenous inputs. We use a mean-field quasi-renewal (QR) approximation that decomposes spike history effects into the contribution of the last spike and an average of the CIF over all spike histories prior to the last spike. Fixed points for stationary rates are derived as self-consistent solutions of integral equations. Bifurcation analysis and the number of fixed points predict that the original models can show stable, divergent, and metastable (fragile) dynamics. For fragile models, fluctuations of the single-neuron dynamics predict expected divergence times after which rates approach unphysiologically high values. This metric can be used to estimate the probability of rates to remain physiological for given time periods, e.g., for simulation purposes. We demonstrate the use of the stability framework using simulated single-neuron examples and neurophysiological recordings. Finally, we show how to adapt PP-GLM estimation procedures to guarantee model stability. Overall, our results provide a stability framework for data-driven PP-GLMs and shed new light on the stochastic dynamics of state-of-the-art statistical models of neuronal spiking activity. PMID:28234899

On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs.

PubMed

Gerhard, Felipe; Deger, Moritz; Truccolo, Wilson

2017-02-01

Point process generalized linear models (PP-GLMs) provide an important statistical framework for modeling spiking activity in single-neurons and neuronal networks. Stochastic stability is essential when sampling from these models, as done in computational neuroscience to analyze statistical properties of neuronal dynamics and in neuro-engineering to implement closed-loop applications. Here we show, however, that despite passing common goodness-of-fit tests, PP-GLMs estimated from data are often unstable, leading to divergent firing rates. The inclusion of absolute refractory periods is not a satisfactory solution since the activity then typically settles into unphysiological rates. To address these issues, we derive a framework for determining the existence and stability of fixed points of the expected conditional intensity function (CIF) for general PP-GLMs. Specifically, in nonlinear Hawkes PP-GLMs, the CIF is expressed as a function of the previous spike history and exogenous inputs. We use a mean-field quasi-renewal (QR) approximation that decomposes spike history effects into the contribution of the last spike and an average of the CIF over all spike histories prior to the last spike. Fixed points for stationary rates are derived as self-consistent solutions of integral equations. Bifurcation analysis and the number of fixed points predict that the original models can show stable, divergent, and metastable (fragile) dynamics. For fragile models, fluctuations of the single-neuron dynamics predict expected divergence times after which rates approach unphysiologically high values. This metric can be used to estimate the probability of rates to remain physiological for given time periods, e.g., for simulation purposes. We demonstrate the use of the stability framework using simulated single-neuron examples and neurophysiological recordings. Finally, we show how to adapt PP-GLM estimation procedures to guarantee model stability. Overall, our results provide a stability framework for data-driven PP-GLMs and shed new light on the stochastic dynamics of state-of-the-art statistical models of neuronal spiking activity.

The APA Task Force on Statistical Inference (TFSI) Report as a Framework for Teaching and Evaluating Students' Understandings of Study Validity.

ERIC Educational Resources Information Center

Thompson, Bruce

Web-based statistical instruction, like all statistical instruction, ought to focus on teaching the essence of the research endeavor: the exercise of reflective judgment. Using the framework of the recent report of the American Psychological Association (APA) Task Force on Statistical Inference (Wilkinson and the APA Task Force on Statistical…

Probabilistic Common Spatial Patterns for Multichannel EEG Analysis

PubMed Central

Chen, Zhe; Gao, Xiaorong; Li, Yuanqing; Brown, Emery N.; Gao, Shangkai

2015-01-01

Common spatial patterns (CSP) is a well-known spatial filtering algorithm for multichannel electroencephalogram (EEG) analysis. In this paper, we cast the CSP algorithm in a probabilistic modeling setting. Specifically, probabilistic CSP (P-CSP) is proposed as a generic EEG spatio-temporal modeling framework that subsumes the CSP and regularized CSP algorithms. The proposed framework enables us to resolve the overfitting issue of CSP in a principled manner. We derive statistical inference algorithms that can alleviate the issue of local optima. In particular, an efficient algorithm based on eigendecomposition is developed for maximum a posteriori (MAP) estimation in the case of isotropic noise. For more general cases, a variational algorithm is developed for group-wise sparse Bayesian learning for the P-CSP model and for automatically determining the model size. The two proposed algorithms are validated on a simulated data set. Their practical efficacy is also demonstrated by successful applications to single-trial classifications of three motor imagery EEG data sets and by the spatio-temporal pattern analysis of one EEG data set recorded in a Stroop color naming task. PMID:26005228

A statistical parts-based appearance model of inter-subject variability.

PubMed

Toews, Matthew; Collins, D Louis; Arbel, Tal

2006-01-01

In this article, we present a general statistical parts-based model for representing the appearance of an image set, applied to the problem of inter-subject MR brain image matching. In contrast with global image representations such as active appearance models, the parts-based model consists of a collection of localized image parts whose appearance, geometry and occurrence frequency are quantified statistically. The parts-based approach explicitly addresses the case where one-to-one correspondence does not exist between subjects due to anatomical differences, as parts are not expected to occur in all subjects. The model can be learned automatically, discovering structures that appear with statistical regularity in a large set of subject images, and can be robustly fit to new images, all in the presence of significant inter-subject variability. As parts are derived from generic scale-invariant features, the framework can be applied in a wide variety of image contexts, in order to study the commonality of anatomical parts or to group subjects according to the parts they share. Experimentation shows that a parts-based model can be learned from a large set of MR brain images, and used to determine parts that are common within the group of subjects. Preliminary results indicate that the model can be used to automatically identify distinctive features for inter-subject image registration despite large changes in appearance.

Shiny FHIR: An Integrated Framework Leveraging Shiny R and HL7 FHIR to Empower Standards-Based Clinical Data Applications.

PubMed

Hong, Na; Prodduturi, Naresh; Wang, Chen; Jiang, Guoqian

2017-01-01

In this study, we describe our efforts in building a clinical statistics and analysis application platform using an emerging clinical data standard, HL7 FHIR, and an open source web application framework, Shiny. We designed two primary workflows that integrate a series of R packages to enable both patient-centered and cohort-based interactive analyses. We leveraged Shiny with R to develop interactive interfaces on FHIR-based data and used ovarian cancer study datasets as a use case to implement a prototype. Specifically, we implemented patient index, patient-centered data report and analysis, and cohort analysis. The evaluation of our study was performed by testing the adaptability of the framework on two public FHIR servers. We identify common research requirements and current outstanding issues, and discuss future enhancement work of the current studies. Overall, our study demonstrated that it is feasible to use Shiny for implementing interactive analysis on FHIR-based standardized clinical data.

Automatising the analysis of stochastic biochemical time-series

PubMed Central

2015-01-01

Background Mathematical and computational modelling of biochemical systems has seen a lot of effort devoted to the definition and implementation of high-performance mechanistic simulation frameworks. Within these frameworks it is possible to analyse complex models under a variety of configurations, eventually selecting the best setting of, e.g., parameters for a target system. Motivation This operational pipeline relies on the ability to interpret the predictions of a model, often represented as simulation time-series. Thus, an efficient data analysis pipeline is crucial to automatise time-series analyses, bearing in mind that errors in this phase might mislead the modeller's conclusions. Results For this reason we have developed an intuitive framework-independent Python tool to automate analyses common to a variety of modelling approaches. These include assessment of useful non-trivial statistics for simulation ensembles, e.g., estimation of master equations. Intuitive and domain-independent batch scripts will allow the researcher to automatically prepare reports, thus speeding up the usual model-definition, testing and refinement pipeline. PMID:26051821

A new framework for interactive quality assessment with application to light field coding

NASA Astrophysics Data System (ADS)

Viola, Irene; Ebrahimi, Touradj

2017-09-01

In recent years, light field has experienced a surge of popularity, mainly due to the recent advances in acquisition and rendering technologies that have made it more accessible to the public. Thanks to image-based rendering techniques, light field contents can be rendered in real time on common 2D screens, allowing virtual navigation through the captured scenes in an interactive fashion. However, this richer representation of the scene poses the problem of reliable quality assessments for light field contents. In particular, while subjective methodologies that enable interaction have already been proposed, no work has been done on assessing how users interact with light field contents. In this paper, we propose a new framework to subjectively assess the quality of light field contents in an interactive manner and simultaneously track users behaviour. The framework is successfully used to perform subjective assessment of two coding solutions. Moreover, statistical analysis performed on the results shows interesting correlation between subjective scores and average interaction time.

A unified framework for physical print quality

NASA Astrophysics Data System (ADS)

Eid, Ahmed; Cooper, Brian; Rippetoe, Ed

2007-01-01

In this paper we present a unified framework for physical print quality. This framework includes a design for a testbed, testing methodologies and quality measures of physical print characteristics. An automatic belt-fed flatbed scanning system is calibrated to acquire L* data for a wide range of flat field imagery. Testing methodologies based on wavelet pre-processing and spectral/statistical analysis are designed. We apply the proposed framework to three common printing artifacts: banding, jitter, and streaking. Since these artifacts are directional, wavelet based approaches are used to extract one artifact at a time and filter out other artifacts. Banding is characterized as a medium-to-low frequency, vertical periodic variation down the page. The same definition is applied to the jitter artifact, except that the jitter signal is characterized as a high-frequency signal above the banding frequency range. However, streaking is characterized as a horizontal aperiodic variation in the high-to-medium frequency range. Wavelets at different levels are applied to the input images in different directions to extract each artifact within specified frequency bands. Following wavelet reconstruction, images are converted into 1-D signals describing the artifact under concern. Accurate spectral analysis using a DFT with Blackman-Harris windowing technique is used to extract the power (strength) of periodic signals (banding and jitter). Since streaking is an aperiodic signal, a statistical measure is used to quantify the streaking strength. Experiments on 100 print samples scanned at 600 dpi from 10 different printers show high correlation (75% to 88%) between the ranking of these samples by the proposed metrologies and experts' visual ranking.

Feasibility Study on the Use of On-line Multivariate Statistical Process Control for Safeguards Applications in Natural Uranium Conversion Plants

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

Ladd-Lively, Jennifer L

2014-01-01

The objective of this work was to determine the feasibility of using on-line multivariate statistical process control (MSPC) for safeguards applications in natural uranium conversion plants. Multivariate statistical process control is commonly used throughout industry for the detection of faults. For safeguards applications in uranium conversion plants, faults could include the diversion of intermediate products such as uranium dioxide, uranium tetrafluoride, and uranium hexafluoride. This study was limited to a 100 metric ton of uranium (MTU) per year natural uranium conversion plant (NUCP) using the wet solvent extraction method for the purification of uranium ore concentrate. A key component inmore » the multivariate statistical methodology is the Principal Component Analysis (PCA) approach for the analysis of data, development of the base case model, and evaluation of future operations. The PCA approach was implemented through the use of singular value decomposition of the data matrix where the data matrix represents normal operation of the plant. Component mole balances were used to model each of the process units in the NUCP. However, this approach could be applied to any data set. The monitoring framework developed in this research could be used to determine whether or not a diversion of material has occurred at an NUCP as part of an International Atomic Energy Agency (IAEA) safeguards system. This approach can be used to identify the key monitoring locations, as well as locations where monitoring is unimportant. Detection limits at the key monitoring locations can also be established using this technique. Several faulty scenarios were developed to test the monitoring framework after the base case or normal operating conditions of the PCA model were established. In all of the scenarios, the monitoring framework was able to detect the fault. Overall this study was successful at meeting the stated objective.« less

A statistical framework for the validation of a population exposure model based on personal exposure data

NASA Astrophysics Data System (ADS)

Rodriguez, Delphy; Valari, Myrto; Markakis, Konstantinos; Payan, Sébastien

2016-04-01

Currently, ambient pollutant concentrations at monitoring sites are routinely measured by local networks, such as AIRPARIF in Paris, France. Pollutant concentration fields are also simulated with regional-scale chemistry transport models such as CHIMERE (http://www.lmd.polytechnique.fr/chimere) under air-quality forecasting platforms (e.g. Prev'Air http://www.prevair.org) or research projects. These data may be combined with more or less sophisticated techniques to provide a fairly good representation of pollutant concentration spatial gradients over urban areas. Here we focus on human exposure to atmospheric contaminants. Based on census data on population dynamics and demographics, modeled outdoor concentrations and infiltration of outdoor air-pollution indoors we have developed a population exposure model for ozone and PM2.5. A critical challenge in the field of population exposure modeling is model validation since personal exposure data are expensive and therefore, rare. However, recent research has made low cost mobile sensors fairly common and therefore personal exposure data should become more and more accessible. In view of planned cohort field-campaigns where such data will be available over the Paris region, we propose in the present study a statistical framework that makes the comparison between modeled and measured exposures meaningful. Our ultimate goal is to evaluate the exposure model by comparing modeled exposures to monitor data. The scientific question we address here is how to downscale modeled data that are estimated on the county population scale at the individual scale which is appropriate to the available measurements. To assess this question we developed a Bayesian hierarchical framework that assimilates actual individual data into population statistics and updates the probability estimate.

Capturing farm diversity with hypothesis-based typologies: An innovative methodological framework for farming system typology development

PubMed Central

Alvarez, Stéphanie; Timler, Carl J.; Michalscheck, Mirja; Paas, Wim; Descheemaeker, Katrien; Tittonell, Pablo; Andersson, Jens A.; Groot, Jeroen C. J.

2018-01-01

Creating typologies is a way to summarize the large heterogeneity of smallholder farming systems into a few farm types. Various methods exist, commonly using statistical analysis, to create these typologies. We demonstrate that the methodological decisions on data collection, variable selection, data-reduction and clustering techniques can bear a large impact on the typology results. We illustrate the effects of analysing the diversity from different angles, using different typology objectives and different hypotheses, on typology creation by using an example from Zambia’s Eastern Province. Five separate typologies were created with principal component analysis (PCA) and hierarchical clustering analysis (HCA), based on three different expert-informed hypotheses. The greatest overlap between typologies was observed for the larger, wealthier farm types but for the remainder of the farms there were no clear overlaps between typologies. Based on these results, we argue that the typology development should be guided by a hypothesis on the local agriculture features and the drivers and mechanisms of differentiation among farming systems, such as biophysical and socio-economic conditions. That hypothesis is based both on the typology objective and on prior expert knowledge and theories of the farm diversity in the study area. We present a methodological framework that aims to integrate participatory and statistical methods for hypothesis-based typology construction. This is an iterative process whereby the results of the statistical analysis are compared with the reality of the target population as hypothesized by the local experts. Using a well-defined hypothesis and the presented methodological framework, which consolidates the hypothesis through local expert knowledge for the creation of typologies, warrants development of less subjective and more contextualized quantitative farm typologies. PMID:29763422

Capturing farm diversity with hypothesis-based typologies: An innovative methodological framework for farming system typology development.

PubMed

Alvarez, Stéphanie; Timler, Carl J; Michalscheck, Mirja; Paas, Wim; Descheemaeker, Katrien; Tittonell, Pablo; Andersson, Jens A; Groot, Jeroen C J

2018-01-01

Creating typologies is a way to summarize the large heterogeneity of smallholder farming systems into a few farm types. Various methods exist, commonly using statistical analysis, to create these typologies. We demonstrate that the methodological decisions on data collection, variable selection, data-reduction and clustering techniques can bear a large impact on the typology results. We illustrate the effects of analysing the diversity from different angles, using different typology objectives and different hypotheses, on typology creation by using an example from Zambia's Eastern Province. Five separate typologies were created with principal component analysis (PCA) and hierarchical clustering analysis (HCA), based on three different expert-informed hypotheses. The greatest overlap between typologies was observed for the larger, wealthier farm types but for the remainder of the farms there were no clear overlaps between typologies. Based on these results, we argue that the typology development should be guided by a hypothesis on the local agriculture features and the drivers and mechanisms of differentiation among farming systems, such as biophysical and socio-economic conditions. That hypothesis is based both on the typology objective and on prior expert knowledge and theories of the farm diversity in the study area. We present a methodological framework that aims to integrate participatory and statistical methods for hypothesis-based typology construction. This is an iterative process whereby the results of the statistical analysis are compared with the reality of the target population as hypothesized by the local experts. Using a well-defined hypothesis and the presented methodological framework, which consolidates the hypothesis through local expert knowledge for the creation of typologies, warrants development of less subjective and more contextualized quantitative farm typologies.

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

Sexual dysfunctions in women.

PubMed

Meston, Cindy M; Bradford, Andrea

2007-01-01

In this article, we summarize the definition, etiology, assessment, and treatment of sexual dysfunctions in women. Although the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV-TR) is our guiding framework for classifying and defining women's sexual dysfunctions, we draw special attention to recent discussion in the literature criticizing the DSM-IV-TR diagnostic criteria and their underlying assumptions. Our review of clinical research on sexual dysfunction summarizes psychosocial and biomedical management approaches, with a critical examination of the empirical support for commonly prescribed therapies and limitations of recent clinical trials.

Statistical analysis of water-quality data containing multiple detection limits II: S-language software for nonparametric distribution modeling and hypothesis testing

USGS Publications Warehouse

Lee, L.; Helsel, D.

2007-01-01

Analysis of low concentrations of trace contaminants in environmental media often results in left-censored data that are below some limit of analytical precision. Interpretation of values becomes complicated when there are multiple detection limits in the data-perhaps as a result of changing analytical precision over time. Parametric and semi-parametric methods, such as maximum likelihood estimation and robust regression on order statistics, can be employed to model distributions of multiply censored data and provide estimates of summary statistics. However, these methods are based on assumptions about the underlying distribution of data. Nonparametric methods provide an alternative that does not require such assumptions. A standard nonparametric method for estimating summary statistics of multiply-censored data is the Kaplan-Meier (K-M) method. This method has seen widespread usage in the medical sciences within a general framework termed "survival analysis" where it is employed with right-censored time-to-failure data. However, K-M methods are equally valid for the left-censored data common in the geosciences. Our S-language software provides an analytical framework based on K-M methods that is tailored to the needs of the earth and environmental sciences community. This includes routines for the generation of empirical cumulative distribution functions, prediction or exceedance probabilities, and related confidence limits computation. Additionally, our software contains K-M-based routines for nonparametric hypothesis testing among an unlimited number of grouping variables. A primary characteristic of K-M methods is that they do not perform extrapolation and interpolation. Thus, these routines cannot be used to model statistics beyond the observed data range or when linear interpolation is desired. For such applications, the aforementioned parametric and semi-parametric methods must be used.

Item Analysis Appropriate for Domain-Referenced Classroom Testing. (Project Technical Report Number 1).

ERIC Educational Resources Information Center

Nitko, Anthony J.; Hsu, Tse-chi

Item analysis procedures appropriate for domain-referenced classroom testing are described. A conceptual framework within which item statistics can be considered and promising statistics in light of this framework are presented. The sampling fluctuations of the more promising item statistics for sample sizes comparable to the typical classroom…

Abstinence education for urban youth.

PubMed

Carter-Jessop, L; Franklin, L N; Heath, J W; Jimenez-Irizarry, G; Peace, M D

2000-08-01

Teen sexual problems in the U.S. are reaching enormous proportions. Attempts to prevent common problems, pregnancy and sexually transmitted diseases, are underway through the persistent efforts of community, health, academic and government organizations. Abstinence education is one of the current attempts. However, the lack of well defined theoretical frameworks and analyses of outcomes have limited progress in the study of abstinence education. This article describes a pilot program in abstinence-only education provided to six groups of young teens within an urban middle school. The framework for the program, cognitive social learning theory, is described and operationalized. Student pretest-posttest attitudes, open-ended written comments about the program and the researchers' anecdotes about behavioral changes in the students are the outcome measures. Positive attitudes about premarital abstinence increased for all six groups; for four of the six groups the increase was statistically significant.

From data to the decision: A software architecture to integrate predictive modelling in clinical settings.

PubMed

Martinez-Millana, A; Fernandez-Llatas, C; Sacchi, L; Segagni, D; Guillen, S; Bellazzi, R; Traver, V

2015-08-01

The application of statistics and mathematics over large amounts of data is providing healthcare systems with new tools for screening and managing multiple diseases. Nonetheless, these tools have many technical and clinical limitations as they are based on datasets with concrete characteristics. This proposition paper describes a novel architecture focused on providing a validation framework for discrimination and prediction models in the screening of Type 2 diabetes. For that, the architecture has been designed to gather different data sources under a common data structure and, furthermore, to be controlled by a centralized component (Orchestrator) in charge of directing the interaction flows among data sources, models and graphical user interfaces. This innovative approach aims to overcome the data-dependency of the models by providing a validation framework for the models as they are used within clinical settings.

CEOS WGISS Common Data Framework for WGISS Connected Data Assets

NASA Technical Reports Server (NTRS)

Enloe, Yonsook; Mitchell, Andrew; Albani, Mirko; Yapur, Martin

2016-01-01

This session will explore the benefits of having such a policy framework and future steps both domestically and internationally. Speakers can highlight current work being done to improve data interoperability, how the Common Framework is relevant for other data types, other countries and multinational organizations, and considerations for data management that have yet to be addressed in the Common Framework.

Examining statewide capacity for school health and mental health promotion: a post hoc application of a district capacity-building framework.

PubMed

Maras, Melissa A; Weston, Karen J; Blacksmith, Jennifer; Brophy, Chelsey

2015-03-01

Schools must possess a variety of capacities to effectively support comprehensive and coordinated school health promotion activities, and researchers have developed a district-level capacity-building framework specific to school health promotion. State-level school health coalitions often support such capacity-building efforts and should embed this work within a data-based, decision-making model. However, there is a lack of guidance for state school health coalitions on how they should collect and use data. This article uses a district-level capacity-building framework to interpret findings from a statewide coordinated school health needs/resource assessment in order to examine statewide capacity for school health promotion. Participants included school personnel (N = 643) from one state. Descriptive statistics were calculated for survey items, with further examination of subgroup differences among school administrators and nurses. Results were then interpreted via a post hoc application of a district-level capacity-building framework. Findings across districts revealed statewide strengths and gaps with regard to leadership and management capacities, internal and external supports, and an indicator of global capacity. Findings support the utility of using a common framework across local and state levels to align efforts and embed capacity-building activities within a data-driven, continuous improvement model. © 2014 Society for Public Health Education.

A flexible data-driven comorbidity feature extraction framework.

PubMed

Sideris, Costas; Pourhomayoun, Mohammad; Kalantarian, Haik; Sarrafzadeh, Majid

2016-06-01

Disease and symptom diagnostic codes are a valuable resource for classifying and predicting patient outcomes. In this paper, we propose a novel methodology for utilizing disease diagnostic information in a predictive machine learning framework. Our methodology relies on a novel, clustering-based feature extraction framework using disease diagnostic information. To reduce the data dimensionality, we identify disease clusters using co-occurrence statistics. We optimize the number of generated clusters in the training set and then utilize these clusters as features to predict patient severity of condition and patient readmission risk. We build our clustering and feature extraction algorithm using the 2012 National Inpatient Sample (NIS), Healthcare Cost and Utilization Project (HCUP) which contains 7 million hospital discharge records and ICD-9-CM codes. The proposed framework is tested on Ronald Reagan UCLA Medical Center Electronic Health Records (EHR) from 3041 Congestive Heart Failure (CHF) patients and the UCI 130-US diabetes dataset that includes admissions from 69,980 diabetic patients. We compare our cluster-based feature set with the commonly used comorbidity frameworks including Charlson's index, Elixhauser's comorbidities and their variations. The proposed approach was shown to have significant gains between 10.7-22.1% in predictive accuracy for CHF severity of condition prediction and 4.65-5.75% in diabetes readmission prediction. Copyright © 2016 Elsevier Ltd. All rights reserved.

Is there a need for a universal benefit-risk assessment framework for medicines? Regulatory and industry perspectives.

PubMed

Leong, James; McAuslane, Neil; Walker, Stuart; Salek, Sam

2013-09-01

To explore the current status and need for a universal benefit-risk framework for medicines in regulatory agencies and pharmaceutical companies. A questionnaire was developed and sent to 14 mature regulatory agencies and 24 major companies. The data were analysed using descriptive statistics, for a minority of questions preceded by manual grouping of the responses. Overall response rate was 82%, and study participants included key decision makers from agencies and companies. None used a fully quantitative system, most companies preferring a qualitative method. The major reasons for this group not using semi-quantitative or quantitative systems were lack of a universal and scientifically validated framework. The main advantages of a benefit-risk framework were that it provided a systematic standardised approach to decision-making and that it acted as a tool to enhance quality of communication. It was also reported that a framework should be of value to both agencies and companies throughout the life cycle of a product. They believed that it is possible to develop an overarching benefit-risk framework that should involve relevant stakeholders in the development, validation and application of a universal framework. The entire cohort indicated common barriers to implementing a framework were resource limitations, a lack of knowledge and a scientifically validated and acceptable framework. Stakeholders prefer a semi-quantitative, overarching framework that incorporates a toolbox of different methodologies. A coordinating committee of relevant stakeholders should be formed to guide its development and implementation. Through engaging the stakeholders, these outcomes confirm sentiments and need for developing a universal benefit-risk assessment framework. Copyright © 2013 John Wiley & Sons, Ltd.

A general framework for parametric survival analysis.

PubMed

Crowther, Michael J; Lambert, Paul C

2014-12-30

Parametric survival models are being increasingly used as an alternative to the Cox model in biomedical research. Through direct modelling of the baseline hazard function, we can gain greater understanding of the risk profile of patients over time, obtaining absolute measures of risk. Commonly used parametric survival models, such as the Weibull, make restrictive assumptions of the baseline hazard function, such as monotonicity, which is often violated in clinical datasets. In this article, we extend the general framework of parametric survival models proposed by Crowther and Lambert (Journal of Statistical Software 53:12, 2013), to incorporate relative survival, and robust and cluster robust standard errors. We describe the general framework through three applications to clinical datasets, in particular, illustrating the use of restricted cubic splines, modelled on the log hazard scale, to provide a highly flexible survival modelling framework. Through the use of restricted cubic splines, we can derive the cumulative hazard function analytically beyond the boundary knots, resulting in a combined analytic/numerical approach, which substantially improves the estimation process compared with only using numerical integration. User-friendly Stata software is provided, which significantly extends parametric survival models available in standard software. Copyright © 2014 John Wiley & Sons, Ltd.

The effects of informal carers' characteristics on their information needs: The information needs state approach.

PubMed

Alzougool, Basil; Chang, Shanton; Gray, Kathleen

2017-09-01

There has been little research that provides a comprehensive account of the nature and aspects of information needs of informal carers. The authors have previously developed and validated a framework that accounts for major underlying states of information need. This paper aims to apply this framework to explore whether there are common demographic and socioeconomic characteristics that affect the information needs states of carers. A questionnaire about the information needs states was completed by 198 carers above 18 years old. We use statistical methods to look for similarities and differences in respondents' information needs states, in terms of the demographic and socioeconomic variables. At least one information needs state varies among carers, in terms of seven demographic and socioeconomic variables: the age of the patient(s) that they are caring for; the condition(s) of the patient(s) that they are caring for; the number of patients that they are caring for; their length of time as a carer; their gender; the country that they live in; and the population of the area that they live in. The findings demonstrate the utility of the information needs state framework. We outline some practical implications of the framework.

UNITY: Confronting Supernova Cosmology's Statistical and Systematic Uncertainties in a Unified Bayesian Framework

NASA Astrophysics Data System (ADS)

Rubin, D.; Aldering, G.; Barbary, K.; Boone, K.; Chappell, G.; Currie, M.; Deustua, S.; Fagrelius, P.; Fruchter, A.; Hayden, B.; Lidman, C.; Nordin, J.; Perlmutter, S.; Saunders, C.; Sofiatti, C.; Supernova Cosmology Project, The

2015-11-01

While recent supernova (SN) cosmology research has benefited from improved measurements, current analysis approaches are not statistically optimal and will prove insufficient for future surveys. This paper discusses the limitations of current SN cosmological analyses in treating outliers, selection effects, shape- and color-standardization relations, unexplained dispersion, and heterogeneous observations. We present a new Bayesian framework, called UNITY (Unified Nonlinear Inference for Type-Ia cosmologY), that incorporates significant improvements in our ability to confront these effects. We apply the framework to real SN observations and demonstrate smaller statistical and systematic uncertainties. We verify earlier results that SNe Ia require nonlinear shape and color standardizations, but we now include these nonlinear relations in a statistically well-justified way. This analysis was primarily performed blinded, in that the basic framework was first validated on simulated data before transitioning to real data. We also discuss possible extensions of the method.

Functional annotation of regulatory pathways.

PubMed

Pandey, Jayesh; Koyutürk, Mehmet; Kim, Yohan; Szpankowski, Wojciech; Subramaniam, Shankar; Grama, Ananth

2007-07-01

Standardized annotations of biomolecules in interaction networks (e.g. Gene Ontology) provide comprehensive understanding of the function of individual molecules. Extending such annotations to pathways is a critical component of functional characterization of cellular signaling at the systems level. We propose a framework for projecting gene regulatory networks onto the space of functional attributes using multigraph models, with the objective of deriving statistically significant pathway annotations. We first demonstrate that annotations of pairwise interactions do not generalize to indirect relationships between processes. Motivated by this result, we formalize the problem of identifying statistically overrepresented pathways of functional attributes. We establish the hardness of this problem by demonstrating the non-monotonicity of common statistical significance measures. We propose a statistical model that emphasizes the modularity of a pathway, evaluating its significance based on the coupling of its building blocks. We complement the statistical model by an efficient algorithm and software, Narada, for computing significant pathways in large regulatory networks. Comprehensive results from our methods applied to the Escherichia coli transcription network demonstrate that our approach is effective in identifying known, as well as novel biological pathway annotations. Narada is implemented in Java and is available at http://www.cs.purdue.edu/homes/jpandey/narada/.

Post-fire debris flow prediction in Western United States: Advancements based on a nonparametric statistical technique

NASA Astrophysics Data System (ADS)

Nikolopoulos, E. I.; Destro, E.; Bhuiyan, M. A. E.; Borga, M., Sr.; Anagnostou, E. N.

2017-12-01

Fire disasters affect modern societies at global scale inducing significant economic losses and human casualties. In addition to their direct impacts they have various adverse effects on hydrologic and geomorphologic processes of a region due to the tremendous alteration of the landscape characteristics (vegetation, soil properties etc). As a consequence, wildfires often initiate a cascade of hazards such as flash floods and debris flows that usually follow the occurrence of a wildfire thus magnifying the overall impact in a region. Post-fire debris flows (PFDF) is one such type of hazards frequently occurring in Western United States where wildfires are a common natural disaster. Prediction of PDFD is therefore of high importance in this region and over the last years a number of efforts from United States Geological Survey (USGS) and National Weather Service (NWS) have been focused on the development of early warning systems that will help mitigate PFDF risk. This work proposes a prediction framework that is based on a nonparametric statistical technique (random forests) that allows predicting the occurrence of PFDF at regional scale with a higher degree of accuracy than the commonly used approaches that are based on power-law thresholds and logistic regression procedures. The work presented is based on a recently released database from USGS that reports a total of 1500 storms that triggered and did not trigger PFDF in a number of fire affected catchments in Western United States. The database includes information on storm characteristics (duration, accumulation, max intensity etc) and other auxiliary information of land surface properties (soil erodibility index, local slope etc). Results show that the proposed model is able to achieve a satisfactory prediction accuracy (threat score > 0.6) superior of previously published prediction frameworks highlighting the potential of nonparametric statistical techniques for development of PFDF prediction systems.

A two-dimensional statistical framework connecting thermodynamic profiles with filaments in the scrape off layer and application to experiments

NASA Astrophysics Data System (ADS)

Militello, F.; Farley, T.; Mukhi, K.; Walkden, N.; Omotani, J. T.

2018-05-01

A statistical framework was introduced in Militello and Omotani [Nucl. Fusion 56, 104004 (2016)] to correlate the dynamics and statistics of L-mode and inter-ELM plasma filaments with the radial profiles of thermodynamic quantities they generate in the Scrape Off Layer. This paper extends the framework to cases in which the filaments are emitted from the separatrix at different toroidal positions and with a finite toroidal velocity. It is found that the toroidal velocity does not affect the profiles, while the toroidal distribution of filament emission renormalises the waiting time between two events. Experimental data collected by visual camera imaging are used to evaluate the statistics of the fluctuations, to inform the choice of the probability distribution functions used in the application of the framework. It is found that the toroidal separation of the filaments is exponentially distributed, thus suggesting the lack of a toroidal modal structure. Finally, using these measurements, the framework is applied to an experimental case and good agreement is found.

Aligning ESP Courses with the "Common European Framework of Reference for Languages"

ERIC Educational Resources Information Center

Athanasiou, Androulla; Constantinou, Elis Kakoulli; Neophytou, Maro; Nicolaou, Anna; Papadima Sophocleous, Salomi; Yerou, Christina

2016-01-01

This article explains how the "Common European Framework of References for Languages" (CEFR; Council of Europe 2001, "Common European Framework of Reference for Languages: Learning, teaching, assessment." Cambridge: Cambridge University Press) has been applied in language courses at the Language Centre (LC) of the Cyprus…

Collective behaviours: from biochemical kinetics to electronic circuits

PubMed Central

Agliari, Elena; Barra, Adriano; Burioni, Raffaella; Di Biasio, Aldo; Uguzzoni, Guido

2013-01-01

In this work we aim to highlight a close analogy between cooperative behaviors in chemical kinetics and cybernetics; this is realized by using a common language for their description, that is mean-field statistical mechanics. First, we perform a one-to-one mapping between paradigmatic behaviors in chemical kinetics (i.e., non-cooperative, cooperative, ultra-sensitive, anti-cooperative) and in mean-field statistical mechanics (i.e., paramagnetic, high and low temperature ferromagnetic, anti-ferromagnetic). Interestingly, the statistical mechanics approach allows a unified, broad theory for all scenarios and, in particular, Michaelis-Menten, Hill and Adair equations are consistently recovered. This framework is then tested against experimental biological data with an overall excellent agreement. One step forward, we consistently read the whole mapping from a cybernetic perspective, highlighting deep structural analogies between the above-mentioned kinetics and fundamental bricks in electronics (i.e. operational amplifiers, flashes, flip-flops), so to build a clear bridge linking biochemical kinetics and cybernetics. PMID:24322327

Model Fit and Item Factor Analysis: Overfactoring, Underfactoring, and a Program to Guide Interpretation.

PubMed

Clark, D Angus; Bowles, Ryan P

2018-04-23

In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present study used Monte Carlo simulation methods to investigate the ability of popular model fit statistics (chi-square, root mean square error of approximation, the comparative fit index, and the Tucker-Lewis index) and their standard cutoff values to detect the optimal number of latent dimensions underlying sets of dichotomous items. Models were fit to data generated from three-factor population structures that varied in factor loading magnitude, factor intercorrelation magnitude, number of indicators, and whether cross loadings or minor factors were included. The effectiveness of the thresholds varied across fit statistics, and was conditional on many features of the underlying model. Together, results suggest that conventional fit thresholds offer questionable utility in the context of IFA.

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.

INFORMATION: THEORY, BRAIN, AND BEHAVIOR

PubMed Central

Jensen, Greg; Ward, Ryan D.; Balsam, Peter D.

2016-01-01

In the 65 years since its formal specification, information theory has become an established statistical paradigm, providing powerful tools for quantifying probabilistic relationships. Behavior analysis has begun to adopt these tools as a novel means of measuring the interrelations between behavior, stimuli, and contingent outcomes. This approach holds great promise for making more precise determinations about the causes of behavior and the forms in which conditioning may be encoded by organisms. In addition to providing an introduction to the basics of information theory, we review some of the ways that information theory has informed the studies of Pavlovian conditioning, operant conditioning, and behavioral neuroscience. In addition to enriching each of these empirical domains, information theory has the potential to act as a common statistical framework by which results from different domains may be integrated, compared, and ultimately unified. PMID:24122456

The European Federation of Clinical Chemistry and Laboratory Medicine syllabus for postgraduate education and training for Specialists in Laboratory Medicine: version 5 - 2018.

PubMed

Jassam, Nuthar; Lake, Jennifer; Dabrowska, Milena; Queralto, Jose; Rizos, Demetrios; Lichtinghagen, Ralf; Baum, Hannsjörg; Ceriotti, Ferruccio; O'Mullane, John; Homšak, Evgenija; Charilaou, Charis; Ohlson, Mats; Rako, Ivana; Vitkus, Dalius; Kovac, Gustav; Verschuure, Pauline; Racek, Jaroslav; Chifiriuc, Mariana Carmen; Wieringa, Gilbert

2018-06-05

Although laboratory medicine practise varies across the European Union's (EU) member states, the extent of overlap in scope is such that a common syllabus describing the education and training associated with high-quality, specialist practise can be identified. In turn, such a syllabus can help define the common set of skills, knowledge and competence in a Common Training Framework (CTF) for non-medical Specialists in Laboratory Medicine under EU Directive 2013/55/EU (The recognition of Professional Qualifications). In meeting the requirements of the directive's CTF patient safety is particularly enhanced when specialists seek to capitalise on opportunities for free professional migration across EU borders. In updating the fourth syllabus, the fifth expands on individual discipline requirements, new analytical techniques and use of statistics. An outline structure for a training programme is proposed together with expected responsibilities of trainees and trainers; reference is provided to a trainee's log book. In updating the syllabus, it continues to support national programmes and the aims of EU Directive 2013/55/EU in providing safeguards to professional mobility across European borders at a time when the demand for highly qualified professionals is increasing in the face of a disparity in their distribution across Europe. In support of achieving a CTF, the syllabus represents EFLM's position statement for the education and training that underpins the framework.

An Argument Framework for the Application of Null Hypothesis Statistical Testing in Support of Research

ERIC Educational Resources Information Center

LeMire, Steven D.

2010-01-01

This paper proposes an argument framework for the teaching of null hypothesis statistical testing and its application in support of research. Elements of the Toulmin (1958) model of argument are used to illustrate the use of p values and Type I and Type II error rates in support of claims about statistical parameters and subject matter research…

A Regression Framework for Effect Size Assessments in Longitudinal Modeling of Group Differences

PubMed Central

Feingold, Alan

2013-01-01

The use of growth modeling analysis (GMA)--particularly multilevel analysis and latent growth modeling--to test the significance of intervention effects has increased exponentially in prevention science, clinical psychology, and psychiatry over the past 15 years. Model-based effect sizes for differences in means between two independent groups in GMA can be expressed in the same metric (Cohen’s d) commonly used in classical analysis and meta-analysis. This article first reviews conceptual issues regarding calculation of d for findings from GMA and then introduces an integrative framework for effect size assessments that subsumes GMA. The new approach uses the structure of the linear regression model, from which effect sizes for findings from diverse cross-sectional and longitudinal analyses can be calculated with familiar statistics, such as the regression coefficient, the standard deviation of the dependent measure, and study duration. PMID:23956615

A weighted generalized score statistic for comparison of predictive values of diagnostic tests.

PubMed

Kosinski, Andrzej S

2013-03-15

Positive and negative predictive values are important measures of a medical diagnostic test performance. We consider testing equality of two positive or two negative predictive values within a paired design in which all patients receive two diagnostic tests. The existing statistical tests for testing equality of predictive values are either Wald tests based on the multinomial distribution or the empirical Wald and generalized score tests within the generalized estimating equations (GEE) framework. As presented in the literature, these test statistics have considerably complex formulas without clear intuitive insight. We propose their re-formulations that are mathematically equivalent but algebraically simple and intuitive. As is clearly seen with a new re-formulation we presented, the generalized score statistic does not always reduce to the commonly used score statistic in the independent samples case. To alleviate this, we introduce a weighted generalized score (WGS) test statistic that incorporates empirical covariance matrix with newly proposed weights. This statistic is simple to compute, always reduces to the score statistic in the independent samples situation, and preserves type I error better than the other statistics as demonstrated by simulations. Thus, we believe that the proposed WGS statistic is the preferred statistic for testing equality of two predictive values and for corresponding sample size computations. The new formulas of the Wald statistics may be useful for easy computation of confidence intervals for difference of predictive values. The introduced concepts have potential to lead to development of the WGS test statistic in a general GEE setting. Copyright © 2012 John Wiley & Sons, Ltd.

A weighted generalized score statistic for comparison of predictive values of diagnostic tests

PubMed Central

Kosinski, Andrzej S.

2013-01-01

Positive and negative predictive values are important measures of a medical diagnostic test performance. We consider testing equality of two positive or two negative predictive values within a paired design in which all patients receive two diagnostic tests. The existing statistical tests for testing equality of predictive values are either Wald tests based on the multinomial distribution or the empirical Wald and generalized score tests within the generalized estimating equations (GEE) framework. As presented in the literature, these test statistics have considerably complex formulas without clear intuitive insight. We propose their re-formulations which are mathematically equivalent but algebraically simple and intuitive. As is clearly seen with a new re-formulation we present, the generalized score statistic does not always reduce to the commonly used score statistic in the independent samples case. To alleviate this, we introduce a weighted generalized score (WGS) test statistic which incorporates empirical covariance matrix with newly proposed weights. This statistic is simple to compute, it always reduces to the score statistic in the independent samples situation, and it preserves type I error better than the other statistics as demonstrated by simulations. Thus, we believe the proposed WGS statistic is the preferred statistic for testing equality of two predictive values and for corresponding sample size computations. The new formulas of the Wald statistics may be useful for easy computation of confidence intervals for difference of predictive values. The introduced concepts have potential to lead to development of the weighted generalized score test statistic in a general GEE setting. PMID:22912343

Statistical Irreversible Thermodynamics in the Framework of Zubarev's Nonequilibrium Statistical Operator Method

NASA Astrophysics Data System (ADS)

Luzzi, R.; Vasconcellos, A. R.; Ramos, J. G.; Rodrigues, C. G.

2018-01-01

We describe the formalism of statistical irreversible thermodynamics constructed based on Zubarev's nonequilibrium statistical operator (NSO) method, which is a powerful and universal tool for investigating the most varied physical phenomena. We present brief overviews of the statistical ensemble formalism and statistical irreversible thermodynamics. The first can be constructed either based on a heuristic approach or in the framework of information theory in the Jeffreys-Jaynes scheme of scientific inference; Zubarev and his school used both approaches in formulating the NSO method. We describe the main characteristics of statistical irreversible thermodynamics and discuss some particular considerations of several authors. We briefly describe how Rosenfeld, Bohr, and Prigogine proposed to derive a thermodynamic uncertainty principle.

Resonance decay dynamics and their effects on pT spectra of pions in heavy-ion collisions

NASA Astrophysics Data System (ADS)

Lo, Pok Man

2018-03-01

The influence of resonance decay dynamics on the momentum spectra of pions in heavy-ion collisions is examined. Taking the decay processes ω →3 π and ρ →2 π as examples, I demonstrate how the resonance width and details of decay dynamics (via the decay matrix element) can modify the physical observables. The latter effect is commonly neglected in statistical models. To remedy the situation, a theoretical framework for incorporating hadron dynamics into the analysis is formulated, which can be straightforwardly extended to describe general N -body decays.

A Framework for Thinking about Informal Statistical Inference

ERIC Educational Resources Information Center

Makar, Katie; Rubin, Andee

2009-01-01

Informal inferential reasoning has shown some promise in developing students' deeper understanding of statistical processes. This paper presents a framework to think about three key principles of informal inference--generalizations "beyond the data," probabilistic language, and data as evidence. The authors use primary school classroom…

Statistical Analysis of Zebrafish Locomotor Response.

PubMed

Liu, Yiwen; Carmer, Robert; Zhang, Gaonan; Venkatraman, Prahatha; Brown, Skye Ashton; Pang, Chi-Pui; Zhang, Mingzhi; Ma, Ping; Leung, Yuk Fai

2015-01-01

Zebrafish larvae display rich locomotor behaviour upon external stimulation. The movement can be simultaneously tracked from many larvae arranged in multi-well plates. The resulting time-series locomotor data have been used to reveal new insights into neurobiology and pharmacology. However, the data are of large scale, and the corresponding locomotor behavior is affected by multiple factors. These issues pose a statistical challenge for comparing larval activities. To address this gap, this study has analyzed a visually-driven locomotor behaviour named the visual motor response (VMR) by the Hotelling's T-squared test. This test is congruent with comparing locomotor profiles from a time period. Different wild-type (WT) strains were compared using the test, which shows that they responded differently to light change at different developmental stages. The performance of this test was evaluated by a power analysis, which shows that the test was sensitive for detecting differences between experimental groups with sample numbers that were commonly used in various studies. In addition, this study investigated the effects of various factors that might affect the VMR by multivariate analysis of variance (MANOVA). The results indicate that the larval activity was generally affected by stage, light stimulus, their interaction, and location in the plate. Nonetheless, different factors affected larval activity differently over time, as indicated by a dynamical analysis of the activity at each second. Intriguingly, this analysis also shows that biological and technical repeats had negligible effect on larval activity. This finding is consistent with that from the Hotelling's T-squared test, and suggests that experimental repeats can be combined to enhance statistical power. Together, these investigations have established a statistical framework for analyzing VMR data, a framework that should be generally applicable to other locomotor data with similar structure.

Statistical Analysis of Zebrafish Locomotor Response

PubMed Central

Zhang, Gaonan; Venkatraman, Prahatha; Brown, Skye Ashton; Pang, Chi-Pui; Zhang, Mingzhi; Ma, Ping; Leung, Yuk Fai

2015-01-01

Zebrafish larvae display rich locomotor behaviour upon external stimulation. The movement can be simultaneously tracked from many larvae arranged in multi-well plates. The resulting time-series locomotor data have been used to reveal new insights into neurobiology and pharmacology. However, the data are of large scale, and the corresponding locomotor behavior is affected by multiple factors. These issues pose a statistical challenge for comparing larval activities. To address this gap, this study has analyzed a visually-driven locomotor behaviour named the visual motor response (VMR) by the Hotelling’s T-squared test. This test is congruent with comparing locomotor profiles from a time period. Different wild-type (WT) strains were compared using the test, which shows that they responded differently to light change at different developmental stages. The performance of this test was evaluated by a power analysis, which shows that the test was sensitive for detecting differences between experimental groups with sample numbers that were commonly used in various studies. In addition, this study investigated the effects of various factors that might affect the VMR by multivariate analysis of variance (MANOVA). The results indicate that the larval activity was generally affected by stage, light stimulus, their interaction, and location in the plate. Nonetheless, different factors affected larval activity differently over time, as indicated by a dynamical analysis of the activity at each second. Intriguingly, this analysis also shows that biological and technical repeats had negligible effect on larval activity. This finding is consistent with that from the Hotelling’s T-squared test, and suggests that experimental repeats can be combined to enhance statistical power. Together, these investigations have established a statistical framework for analyzing VMR data, a framework that should be generally applicable to other locomotor data with similar structure. PMID:26437184

ON MODEL SELECTION STRATEGIES TO IDENTIFY GENES UNDERLYING BINARY TRAITS USING GENOME-WIDE ASSOCIATION DATA.

PubMed

Wu, Zheyang; Zhao, Hongyu

2012-01-01

For more fruitful discoveries of genetic variants associated with diseases in genome-wide association studies, it is important to know whether joint analysis of multiple markers is more powerful than the commonly used single-marker analysis, especially in the presence of gene-gene interactions. This article provides a statistical framework to rigorously address this question through analytical power calculations for common model search strategies to detect binary trait loci: marginal search, exhaustive search, forward search, and two-stage screening search. Our approach incorporates linkage disequilibrium, random genotypes, and correlations among score test statistics of logistic regressions. We derive analytical results under two power definitions: the power of finding all the associated markers and the power of finding at least one associated marker. We also consider two types of error controls: the discovery number control and the Bonferroni type I error rate control. After demonstrating the accuracy of our analytical results by simulations, we apply them to consider a broad genetic model space to investigate the relative performances of different model search strategies. Our analytical study provides rapid computation as well as insights into the statistical mechanism of capturing genetic signals under different genetic models including gene-gene interactions. Even though we focus on genetic association analysis, our results on the power of model selection procedures are clearly very general and applicable to other studies.

ON MODEL SELECTION STRATEGIES TO IDENTIFY GENES UNDERLYING BINARY TRAITS USING GENOME-WIDE ASSOCIATION DATA

PubMed Central

Wu, Zheyang; Zhao, Hongyu

2013-01-01

For more fruitful discoveries of genetic variants associated with diseases in genome-wide association studies, it is important to know whether joint analysis of multiple markers is more powerful than the commonly used single-marker analysis, especially in the presence of gene-gene interactions. This article provides a statistical framework to rigorously address this question through analytical power calculations for common model search strategies to detect binary trait loci: marginal search, exhaustive search, forward search, and two-stage screening search. Our approach incorporates linkage disequilibrium, random genotypes, and correlations among score test statistics of logistic regressions. We derive analytical results under two power definitions: the power of finding all the associated markers and the power of finding at least one associated marker. We also consider two types of error controls: the discovery number control and the Bonferroni type I error rate control. After demonstrating the accuracy of our analytical results by simulations, we apply them to consider a broad genetic model space to investigate the relative performances of different model search strategies. Our analytical study provides rapid computation as well as insights into the statistical mechanism of capturing genetic signals under different genetic models including gene-gene interactions. Even though we focus on genetic association analysis, our results on the power of model selection procedures are clearly very general and applicable to other studies. PMID:23956610

Structure-Specific Statistical Mapping of White Matter Tracts

PubMed Central

Yushkevich, Paul A.; Zhang, Hui; Simon, Tony; Gee, James C.

2008-01-01

We present a new model-based framework for the statistical analysis of diffusion imaging data associated with specific white matter tracts. The framework takes advantage of the fact that several of the major white matter tracts are thin sheet-like structures that can be effectively modeled by medial representations. The approach involves segmenting major tracts and fitting them with deformable geometric medial models. The medial representation makes it possible to average and combine tensor-based features along directions locally perpendicular to the tracts, thus reducing data dimensionality and accounting for errors in normalization. The framework enables the analysis of individual white matter structures, and provides a range of possibilities for computing statistics and visualizing differences between cohorts. The framework is demonstrated in a study of white matter differences in pediatric chromosome 22q11.2 deletion syndrome. PMID:18407524

A Survey of Statistical Models for Reverse Engineering Gene Regulatory Networks

PubMed Central

Huang, Yufei; Tienda-Luna, Isabel M.; Wang, Yufeng

2009-01-01

Statistical models for reverse engineering gene regulatory networks are surveyed in this article. To provide readers with a system-level view of the modeling issues in this research, a graphical modeling framework is proposed. This framework serves as the scaffolding on which the review of different models can be systematically assembled. Based on the framework, we review many existing models for many aspects of gene regulation; the pros and cons of each model are discussed. In addition, network inference algorithms are also surveyed under the graphical modeling framework by the categories of point solutions and probabilistic solutions and the connections and differences among the algorithms are provided. This survey has the potential to elucidate the development and future of reverse engineering GRNs and bring statistical signal processing closer to the core of this research. PMID:20046885

A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis.

PubMed

Tucker, George; Loh, Po-Ru; Berger, Bonnie

2013-10-04

Comprehensive protein-protein interaction (PPI) maps are a powerful resource for uncovering the molecular basis of genetic interactions and providing mechanistic insights. Over the past decade, high-throughput experimental techniques have been developed to generate PPI maps at proteome scale, first using yeast two-hybrid approaches and more recently via affinity purification combined with mass spectrometry (AP-MS). Unfortunately, data from both protocols are prone to both high false positive and false negative rates. To address these issues, many methods have been developed to post-process raw PPI data. However, with few exceptions, these methods only analyze binary experimental data (in which each potential interaction tested is deemed either observed or unobserved), neglecting quantitative information available from AP-MS such as spectral counts. We propose a novel method for incorporating quantitative information from AP-MS data into existing PPI inference methods that analyze binary interaction data. Our approach introduces a probabilistic framework that models the statistical noise inherent in observations of co-purifications. Using a sampling-based approach, we model the uncertainty of interactions with low spectral counts by generating an ensemble of possible alternative experimental outcomes. We then apply the existing method of choice to each alternative outcome and aggregate results over the ensemble. We validate our approach on three recent AP-MS data sets and demonstrate performance comparable to or better than state-of-the-art methods. Additionally, we provide an in-depth discussion comparing the theoretical bases of existing approaches and identify common aspects that may be key to their performance. Our sampling framework extends the existing body of work on PPI analysis using binary interaction data to apply to the richer quantitative data now commonly available through AP-MS assays. This framework is quite general, and many enhancements are likely possible. Fruitful future directions may include investigating more sophisticated schemes for converting spectral counts to probabilities and applying the framework to direct protein complex prediction methods.

Product plots.

PubMed

Wickham, Hadley; Hofmann, Heike

2011-12-01

We propose a new framework for visualising tables of counts, proportions and probabilities. We call our framework product plots, alluding to the computation of area as a product of height and width, and the statistical concept of generating a joint distribution from the product of conditional and marginal distributions. The framework, with extensions, is sufficient to encompass over 20 visualisations previously described in fields of statistical graphics and infovis, including bar charts, mosaic plots, treemaps, equal area plots and fluctuation diagrams. © 2011 IEEE

A network-base analysis of CMIP5 "historical" experiments

NASA Astrophysics Data System (ADS)

Bracco, A.; Foudalis, I.; Dovrolis, C.

2012-12-01

In computer science, "complex network analysis" refers to a set of metrics, modeling tools and algorithms commonly used in the study of complex nonlinear dynamical systems. Its main premise is that the underlying topology or network structure of a system has a strong impact on its dynamics and evolution. By allowing to investigate local and non-local statistical interaction, network analysis provides a powerful, but only marginally explored, framework to validate climate models and investigate teleconnections, assessing their strength, range, and impacts on the climate system. In this work we propose a new, fast, robust and scalable methodology to examine, quantify, and visualize climate sensitivity, while constraining general circulation models (GCMs) outputs with observations. The goal of our novel approach is to uncover relations in the climate system that are not (or not fully) captured by more traditional methodologies used in climate science and often adopted from nonlinear dynamical systems analysis, and to explain known climate phenomena in terms of the network structure or its metrics. Our methodology is based on a solid theoretical framework and employs mathematical and statistical tools, exploited only tentatively in climate research so far. Suitably adapted to the climate problem, these tools can assist in visualizing the trade-offs in representing global links and teleconnections among different data sets. Here we present the methodology, and compare network properties for different reanalysis data sets and a suite of CMIP5 coupled GCM outputs. With an extensive model intercomparison in terms of the climate network that each model leads to, we quantify how each model reproduces major teleconnections, rank model performances, and identify common or specific errors in comparing model outputs and observations.

Statistical atlas based extrapolation of CT data

NASA Astrophysics Data System (ADS)

Chintalapani, Gouthami; Murphy, Ryan; Armiger, Robert S.; Lepisto, Jyri; Otake, Yoshito; Sugano, Nobuhiko; Taylor, Russell H.; Armand, Mehran

2010-02-01

We present a framework to estimate the missing anatomical details from a partial CT scan with the help of statistical shape models. The motivating application is periacetabular osteotomy (PAO), a technique for treating developmental hip dysplasia, an abnormal condition of the hip socket that, if untreated, may lead to osteoarthritis. The common goals of PAO are to reduce pain, joint subluxation and improve contact pressure distribution by increasing the coverage of the femoral head by the hip socket. While current diagnosis and planning is based on radiological measurements, because of significant structural variations in dysplastic hips, a computer-assisted geometrical and biomechanical planning based on CT data is desirable to help the surgeon achieve optimal joint realignments. Most of the patients undergoing PAO are young females, hence it is usually desirable to minimize the radiation dose by scanning only the joint portion of the hip anatomy. These partial scans, however, do not provide enough information for biomechanical analysis due to missing iliac region. A statistical shape model of full pelvis anatomy is constructed from a database of CT scans. The partial volume is first aligned with the statistical atlas using an iterative affine registration, followed by a deformable registration step and the missing information is inferred from the atlas. The atlas inferences are further enhanced by the use of X-ray images of the patient, which are very common in an osteotomy procedure. The proposed method is validated with a leave-one-out analysis method. Osteotomy cuts are simulated and the effect of atlas predicted models on the actual procedure is evaluated.

Rethinking dependent personality disorder: comparing different human relatedness in cultural contexts.

PubMed

Chen, YuJu; Nettles, Margaret E; Chen, Shun-Wen

2009-11-01

We argue that the Diagnostic and Statistical Manual of Mental Disorders dependent personality disorder is a culturally related concept reflecting deeply rooted values, beliefs, and assumptions of American individualistic convictions about self and interpersonal relationship. This article integrates social psychology concepts into the exploration of psychopathology. Beginning with the construct of individualism and collectivism, we demonstrate the limitations of this commonly used framework. The indigenous Chinese concept of Confucianism and Chinese Relationalism is introduced to highlight that a well-differentiated self is not a universal premise of human beings, healthy existence. In East Asian Confucianism the manifestation of dependence and submission may be considered individuals' proper behavior and required for their social obligation, rather than a direct display of individuals' personality. Thus, the complexity of dependent personality disorder is beyond the neo-Kraepelinian approach assumed by the Diagnostic and Statistical Manual of Mental Disorders system.

regioneR: an R/Bioconductor package for the association analysis of genomic regions based on permutation tests.

PubMed

Gel, Bernat; Díez-Villanueva, Anna; Serra, Eduard; Buschbeck, Marcus; Peinado, Miguel A; Malinverni, Roberto

2016-01-15

Statistically assessing the relation between a set of genomic regions and other genomic features is a common challenging task in genomic and epigenomic analyses. Randomization based approaches implicitly take into account the complexity of the genome without the need of assuming an underlying statistical model. regioneR is an R package that implements a permutation test framework specifically designed to work with genomic regions. In addition to the predefined randomization and evaluation strategies, regioneR is fully customizable allowing the use of custom strategies to adapt it to specific questions. Finally, it also implements a novel function to evaluate the local specificity of the detected association. regioneR is an R package released under Artistic-2.0 License. The source code and documents are freely available through Bioconductor (http://www.bioconductor.org/packages/regioneR). rmalinverni@carrerasresearch.org. © The Author 2015. Published by Oxford University Press.

Assessment of Surface Air Temperature over China Using Multi-criterion Model Ensemble Framework

NASA Astrophysics Data System (ADS)

Li, J.; Zhu, Q.; Su, L.; He, X.; Zhang, X.

2017-12-01

The General Circulation Models (GCMs) are designed to simulate the present climate and project future trends. It has been noticed that the performances of GCMs are not always in agreement with each other over different regions. Model ensemble techniques have been developed to post-process the GCMs' outputs and improve their prediction reliabilities. To evaluate the performances of GCMs, root-mean-square error, correlation coefficient, and uncertainty are commonly used statistical measures. However, the simultaneous achievements of these satisfactory statistics cannot be guaranteed when using many model ensemble techniques. Meanwhile, uncertainties and future scenarios are critical for Water-Energy management and operation. In this study, a new multi-model ensemble framework was proposed. It uses a state-of-art evolutionary multi-objective optimization algorithm, termed Multi-Objective Complex Evolution Global Optimization with Principle Component Analysis and Crowding Distance (MOSPD), to derive optimal GCM ensembles and demonstrate the trade-offs among various solutions. Such trade-off information was further analyzed with a robust Pareto front with respect to different statistical measures. A case study was conducted to optimize the surface air temperature (SAT) ensemble solutions over seven geographical regions of China for the historical period (1900-2005) and future projection (2006-2100). The results showed that the ensemble solutions derived with MOSPD algorithm are superior over the simple model average and any single model output during the historical simulation period. For the future prediction, the proposed ensemble framework identified that the largest SAT change would occur in the South Central China under RCP 2.6 scenario, North Eastern China under RCP 4.5 scenario, and North Western China under RCP 8.5 scenario, while the smallest SAT change would occur in the Inner Mongolia under RCP 2.6 scenario, South Central China under RCP 4.5 scenario, and South Central China under RCP 8.5 scenario.

Using structural equation modeling for network meta-analysis.

PubMed

Tu, Yu-Kang; Wu, Yun-Chun

2017-07-14

Network meta-analysis overcomes the limitations of traditional pair-wise meta-analysis by incorporating all available evidence into a general statistical framework for simultaneous comparisons of several treatments. Currently, network meta-analyses are undertaken either within the Bayesian hierarchical linear models or frequentist generalized linear mixed models. Structural equation modeling (SEM) is a statistical method originally developed for modeling causal relations among observed and latent variables. As random effect is explicitly modeled as a latent variable in SEM, it is very flexible for analysts to specify complex random effect structure and to make linear and nonlinear constraints on parameters. The aim of this article is to show how to undertake a network meta-analysis within the statistical framework of SEM. We used an example dataset to demonstrate the standard fixed and random effect network meta-analysis models can be easily implemented in SEM. It contains results of 26 studies that directly compared three treatment groups A, B and C for prevention of first bleeding in patients with liver cirrhosis. We also showed that a new approach to network meta-analysis based on the technique of unrestricted weighted least squares (UWLS) method can also be undertaken using SEM. For both the fixed and random effect network meta-analysis, SEM yielded similar coefficients and confidence intervals to those reported in the previous literature. The point estimates of two UWLS models were identical to those in the fixed effect model but the confidence intervals were greater. This is consistent with results from the traditional pairwise meta-analyses. Comparing to UWLS model with common variance adjusted factor, UWLS model with unique variance adjusted factor has greater confidence intervals when the heterogeneity was larger in the pairwise comparison. The UWLS model with unique variance adjusted factor reflects the difference in heterogeneity within each comparison. SEM provides a very flexible framework for univariate and multivariate meta-analysis, and its potential as a powerful tool for advanced meta-analysis is still to be explored.

The Defense Science Board Task Force on Tactical Battlefield Communications

DTIC Science & Technology

1999-12-01

impact of the system is clearly under appreciated. It could be the foundation for a common- user , QoS, Internet and could integrate legacy systems...into a common- user framework as is occurring in the private sector. Unfortunately, the networking aspects of the system are being lost; the focus...system-centric framework to a common- user , internetwork framework . Recommendation V—Information Security

Combining synthetic controls and interrupted time series analysis to improve causal inference in program evaluation.

PubMed

Linden, Ariel

2018-04-01

Interrupted time series analysis (ITSA) is an evaluation methodology in which a single treatment unit's outcome is studied over time and the intervention is expected to "interrupt" the level and/or trend of the outcome. The internal validity is strengthened considerably when the treated unit is contrasted with a comparable control group. In this paper, we introduce a robust evaluation framework that combines the synthetic controls method (SYNTH) to generate a comparable control group and ITSA regression to assess covariate balance and estimate treatment effects. We evaluate the effect of California's Proposition 99 for reducing cigarette sales, by comparing California to other states not exposed to smoking reduction initiatives. SYNTH is used to reweight nontreated units to make them comparable to the treated unit. These weights are then used in ITSA regression models to assess covariate balance and estimate treatment effects. Covariate balance was achieved for all but one covariate. While California experienced a significant decrease in the annual trend of cigarette sales after Proposition 99, there was no statistically significant treatment effect when compared to synthetic controls. The advantage of using this framework over regression alone is that it ensures that a comparable control group is generated. Additionally, it offers a common set of statistical measures familiar to investigators, the capability for assessing covariate balance, and enhancement of the evaluation with a comprehensive set of postestimation measures. Therefore, this robust framework should be considered as a primary approach for evaluating treatment effects in multiple group time series analysis. © 2018 John Wiley & Sons, Ltd.

Interrelationships Between Receiver/Relative Operating Characteristics Display, Binomial, Logit, and Bayes' Rule Probability of Detection Methodologies

NASA Technical Reports Server (NTRS)

Generazio, Edward R.

2014-01-01

Unknown risks are introduced into failure critical systems when probability of detection (POD) capabilities are accepted without a complete understanding of the statistical method applied and the interpretation of the statistical results. The presence of this risk in the nondestructive evaluation (NDE) community is revealed in common statements about POD. These statements are often interpreted in a variety of ways and therefore, the very existence of the statements identifies the need for a more comprehensive understanding of POD methodologies. Statistical methodologies have data requirements to be met, procedures to be followed, and requirements for validation or demonstration of adequacy of the POD estimates. Risks are further enhanced due to the wide range of statistical methodologies used for determining the POD capability. Receiver/Relative Operating Characteristics (ROC) Display, simple binomial, logistic regression, and Bayes' rule POD methodologies are widely used in determining POD capability. This work focuses on Hit-Miss data to reveal the framework of the interrelationships between Receiver/Relative Operating Characteristics Display, simple binomial, logistic regression, and Bayes' Rule methodologies as they are applied to POD. Knowledge of these interrelationships leads to an intuitive and global understanding of the statistical data, procedural and validation requirements for establishing credible POD estimates.

Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium - Part 1: Theory

NASA Astrophysics Data System (ADS)

Sundberg, R.; Moberg, A.; Hind, A.

2012-08-01

A statistical framework for comparing the output of ensemble simulations from global climate models with networks of climate proxy and instrumental records has been developed, focusing on near-surface temperatures for the last millennium. This framework includes the formulation of a joint statistical model for proxy data, instrumental data and simulation data, which is used to optimize a quadratic distance measure for ranking climate model simulations. An essential underlying assumption is that the simulations and the proxy/instrumental series have a shared component of variability that is due to temporal changes in external forcing, such as volcanic aerosol load, solar irradiance or greenhouse gas concentrations. Two statistical tests have been formulated. Firstly, a preliminary test establishes whether a significant temporal correlation exists between instrumental/proxy and simulation data. Secondly, the distance measure is expressed in the form of a test statistic of whether a forced simulation is closer to the instrumental/proxy series than unforced simulations. The proposed framework allows any number of proxy locations to be used jointly, with different seasons, record lengths and statistical precision. The goal is to objectively rank several competing climate model simulations (e.g. with alternative model parameterizations or alternative forcing histories) by means of their goodness of fit to the unobservable true past climate variations, as estimated from noisy proxy data and instrumental observations.

Improved analyses using function datasets and statistical modeling

Treesearch

John S. Hogland; Nathaniel M. Anderson

2014-01-01

Raster modeling is an integral component of spatial analysis. However, conventional raster modeling techniques can require a substantial amount of processing time and storage space and have limited statistical functionality and machine learning algorithms. To address this issue, we developed a new modeling framework using C# and ArcObjects and integrated that framework...

How Does Teacher Knowledge in Statistics Impact on Teacher Listening?

ERIC Educational Resources Information Center

Burgess, Tim

2012-01-01

For teaching statistics investigations at primary school level, teacher knowledge has been identified using a framework developed from a classroom based study. Through development of the framework, three types of teacher listening problems were identified, each of which had potential impact on the students' learning. The three types of problems…

General Aviation Avionics Statistics : 1975

DOT National Transportation Integrated Search

1978-06-01

This report presents avionics statistics for the 1975 general aviation (GA) aircraft fleet and updates a previous publication, General Aviation Avionics Statistics: 1974. The statistics are presented in a capability group framework which enables one ...

The development of a digital logic concept inventory

NASA Astrophysics Data System (ADS)

Herman, Geoffrey Lindsay

Instructors in electrical and computer engineering and in computer science have developed innovative methods to teach digital logic circuits. These methods attempt to increase student learning, satisfaction, and retention. Although there are readily accessible and accepted means for measuring satisfaction and retention, there are no widely accepted means for assessing student learning. Rigorous assessment of learning is elusive because differences in topic coverage, curriculum and course goals, and exam content prevent direct comparison of two teaching methods when using tools such as final exam scores or course grades. Because of these difficulties, computing educators have issued a general call for the adoption of assessment tools to critically evaluate and compare the various teaching methods. Science, Technology, Engineering, and Mathematics (STEM) education researchers commonly measure students' conceptual learning to compare how much different pedagogies improve learning. Conceptual knowledge is often preferred because all engineering courses should teach a fundamental set of concepts even if they emphasize design or analysis to different degrees. Increasing conceptual learning is also important, because students who can organize facts and ideas within a consistent conceptual framework are able to learn new information quickly and can apply what they know in new situations. If instructors can accurately assess their students' conceptual knowledge, they can target instructional interventions to remedy common problems. To properly assess conceptual learning, several researchers have developed concept inventories (CIs) for core subjects in engineering sciences. CIs are multiple-choice assessment tools that evaluate how well a student's conceptual framework matches the accepted conceptual framework of a discipline or common faulty conceptual frameworks. We present how we created and evaluated the digital logic concept inventory (DLCI).We used a Delphi process to identify the important and difficult concepts to include on the DLCI. To discover and describe common student misconceptions, we interviewed students who had completed a digital logic course. Students vocalized their thoughts as they solved digital logic problems. We analyzed the interview data using a qualitative grounded theory approach. We have administered the DLCI at several institutions and have checked the validity, reliability, and bias of the DLCI with classical testing theory procedures. These procedures consisted of follow-up interviews with students, analysis of administration results with statistical procedures, and expert feedback. We discuss these results and present the DLCI's potential for providing a meaningful tool for comparing student learning at different institutions.

Effect of geometry on deformation of anterior implant-supported zirconia frameworks: An in vitro study using digital image correlation.

PubMed

Calha, Nuno; Messias, Ana; Guerra, Fernando; Martinho, Beatriz; Neto, Maria Augusta; Nicolau, Pedro

2017-04-01

To evaluate the effect of geometry on the displacement and the strain distribution of anterior implant-supported zirconia frameworks under static load using the 3D digital image correlation method. Two groups (n=5) of 4-unit zirconia frameworks were produced by CAD/CAM for the implant-abutment assembly. Group 1 comprised five straight configuration frameworks and group 2 consisted of five curved configuration frameworks. Specimens were cemented and submitted to static load up to 200N. Displacements were captured with two high-speed photographic cameras and analyzed with video correlation system in three spacial axes U, V, W. Statistical analysis was made using the nonparametric Mann-Whitney test. Up to 150N loads, the vertical displacements (V axis) were statistically higher for curved frameworks (-267.83±23.76μm), when compared to the straight frameworks (-120.73±36.17μm) (p=0.008), as well as anterior displacements in the W transformed axis (589.55±64.51μm vs 224.29±50.38μm for the curved and straight frameworks), respectively (p=0.008). The mean von Mises strains over the surface frameworks were statistically higher for the curved frameworks under any load. Within the limitations of this in vitro study, it is possible to conclude that the geometric configuration influences the deformation of 4-unit anterior frameworks under static load. The higher strain distribution and micro-movements of the curved frameworks reflect less rigidity and increased risk of fractures associated to FPDs. Copyright © 2016 Japan Prosthodontic Society. Published by Elsevier Ltd. All rights reserved.

Combining statistical inference and decisions in ecology

USGS Publications Warehouse

Williams, Perry J.; Hooten, Mevin B.

2016-01-01

Statistical decision theory (SDT) is a sub-field of decision theory that formally incorporates statistical investigation into a decision-theoretic framework to account for uncertainties in a decision problem. SDT provides a unifying analysis of three types of information: statistical results from a data set, knowledge of the consequences of potential choices (i.e., loss), and prior beliefs about a system. SDT links the theoretical development of a large body of statistical methods including point estimation, hypothesis testing, and confidence interval estimation. The theory and application of SDT have mainly been developed and published in the fields of mathematics, statistics, operations research, and other decision sciences, but have had limited exposure in ecology. Thus, we provide an introduction to SDT for ecologists and describe its utility for linking the conventionally separate tasks of statistical investigation and decision making in a single framework. We describe the basic framework of both Bayesian and frequentist SDT, its traditional use in statistics, and discuss its application to decision problems that occur in ecology. We demonstrate SDT with two types of decisions: Bayesian point estimation, and an applied management problem of selecting a prescribed fire rotation for managing a grassland bird species. Central to SDT, and decision theory in general, are loss functions. Thus, we also provide basic guidance and references for constructing loss functions for an SDT problem.

Genetic markers as instrumental variables.

PubMed

von Hinke, Stephanie; Davey Smith, George; Lawlor, Debbie A; Propper, Carol; Windmeijer, Frank

2016-01-01

The use of genetic markers as instrumental variables (IV) is receiving increasing attention from economists, statisticians, epidemiologists and social scientists. Although IV is commonly used in economics, the appropriate conditions for the use of genetic variants as instruments have not been well defined. The increasing availability of biomedical data, however, makes understanding of these conditions crucial to the successful use of genotypes as instruments. We combine the econometric IV literature with that from genetic epidemiology, and discuss the biological conditions and IV assumptions within the statistical potential outcomes framework. We review this in the context of two illustrative applications. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

"Epidemiological criminology": coming full circle.

PubMed

Akers, Timothy A; Lanier, Mark M

2009-03-01

Members of the public health and criminal justice disciplines often work with marginalized populations: people at high risk of drug use, health problems, incarceration, and other difficulties. As these fields increasingly overlap, distinctions between them are blurred, as numerous research reports and funding trends document. However, explicit theoretical and methodological linkages between the 2 disciplines remain rare. A new paradigm that links methods and statistical models of public health with those of their criminal justice counterparts is needed, as are increased linkages between epidemiological analogies, theories, and models and the corresponding tools of criminology. We outline disciplinary commonalities and distinctions, present policy examples that integrate similarities, and propose "epidemiological criminology" as a bridging framework.

An integrated data model to estimate spatiotemporal occupancy, abundance, and colonization dynamics

USGS Publications Warehouse

Williams, Perry J.; Hooten, Mevin B.; Womble, Jamie N.; Esslinger, George G.; Bower, Michael R.; Hefley, Trevor J.

2017-01-01

Ecological invasions and colonizations occur dynamically through space and time. Estimating the distribution and abundance of colonizing species is critical for efficient management or conservation. We describe a statistical framework for simultaneously estimating spatiotemporal occupancy and abundance dynamics of a colonizing species. Our method accounts for several issues that are common when modeling spatiotemporal ecological data including multiple levels of detection probability, multiple data sources, and computational limitations that occur when making fine-scale inference over a large spatiotemporal domain. We apply the model to estimate the colonization dynamics of sea otters (Enhydra lutris) in Glacier Bay, in southeastern Alaska.

Weighted analysis of composite endpoints with simultaneous inference for flexible weight constraints.

PubMed

Duc, Anh Nguyen; Wolbers, Marcel

2017-02-10

Composite endpoints are widely used as primary endpoints of randomized controlled trials across clinical disciplines. A common critique of the conventional analysis of composite endpoints is that all disease events are weighted equally, whereas their clinical relevance may differ substantially. We address this by introducing a framework for the weighted analysis of composite endpoints and interpretable test statistics, which are applicable to both binary and time-to-event data. To cope with the difficulty of selecting an exact set of weights, we propose a method for constructing simultaneous confidence intervals and tests that asymptotically preserve the family-wise type I error in the strong sense across families of weights satisfying flexible inequality or order constraints based on the theory of χ¯2-distributions. We show that the method achieves the nominal simultaneous coverage rate with substantial efficiency gains over Scheffé's procedure in a simulation study and apply it to trials in cardiovascular disease and enteric fever. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Dynamic causal modelling: a critical review of the biophysical and statistical foundations.

PubMed

Daunizeau, J; David, O; Stephan, K E

2011-09-15

The goal of dynamic causal modelling (DCM) of neuroimaging data is to study experimentally induced changes in functional integration among brain regions. This requires (i) biophysically plausible and physiologically interpretable models of neuronal network dynamics that can predict distributed brain responses to experimental stimuli and (ii) efficient statistical methods for parameter estimation and model comparison. These two key components of DCM have been the focus of more than thirty methodological articles since the seminal work of Friston and colleagues published in 2003. In this paper, we provide a critical review of the current state-of-the-art of DCM. We inspect the properties of DCM in relation to the most common neuroimaging modalities (fMRI and EEG/MEG) and the specificity of inference on neural systems that can be made from these data. We then discuss both the plausibility of the underlying biophysical models and the robustness of the statistical inversion techniques. Finally, we discuss potential extensions of the current DCM framework, such as stochastic DCMs, plastic DCMs and field DCMs. Copyright © 2009 Elsevier Inc. All rights reserved.

Exact results in nonequilibrium statistical mechanics: Formalism and applications in chemical kinetics and single-molecule free energy estimation

NASA Astrophysics Data System (ADS)

Adib, Artur B.

In the last two decades or so, a collection of results in nonequilibrium statistical mechanics that departs from the traditional near-equilibrium framework introduced by Lars Onsager in 1931 has been derived, yielding new fundamental insights into far-from-equilibrium processes in general. Apart from offering a more quantitative statement of the second law of thermodynamics, some of these results---typified by the so-called "Jarzynski equality"---have also offered novel means of estimating equilibrium quantities from nonequilibrium processes, such as free energy differences from single-molecule "pulling" experiments. This thesis contributes to such efforts by offering three novel results in nonequilibrium statistical mechanics: (a) The entropic analog of the Jarzynski equality; (b) A methodology for estimating free energies from "clamp-and-release" nonequilibrium processes; and (c) A directly measurable symmetry relation in chemical kinetics similar to (but more general than) chemical detailed balance. These results share in common the feature of remaining valid outside Onsager's near-equilibrium regime, and bear direct applicability in protein folding kinetics as well as in single-molecule free energy estimation.

[Legal medicine specialists within the framework of acute care : Analysis of legal medicine consultations in relation to the victims' statistics of the state office of criminal investigation in Saxony-Anhalt].

PubMed

Pliske, G; Heide, S; Lucas, B; Brandstädter, K; Walcher, F; Kropf, S; Lessig, R; Piatek, S

2018-05-01

In acute medical care, there are patients who have been injured by the influence of others. The aim of this study was to analyze all cases which were presented to the Institute for Legal Medicine of the University Halle (Saale). The cases where analyzed in relation to the victims' statistics of the state office of criminal investigation in Saxony-Anhalt. The consultations of the Institute for Legal Medicine Halle-Wittenberg for 2012-2015 were evaluated with regard to the age and gender distribution, the reasons for the consultation and time until the request for consultations. These cases were statistically compared to the victims' statistics of the state office of criminal investigation in Saxony-Anhalt 2014-2015. A total of 536 cases (55.6% male and 44.4% female patients) were evaluated. In all, 62.1% of patients were under 18 years of age; 43.5% of all consultations were requested by pediatric (surgery) clinics. The most common reasons for consultation were sexual child abuse or violence against children (50.7%). Compared to the victims' statistics, significantly more children were examined by legal medicine specialists than could have been expected (p < 0.001). In adult patients, the most common causes for consultation were acts of violence (20.4%) and domestic violence (10.1%). Among adults, significantly more women and fewer men were presented than expected (p = 0.001). There were only a small number of consultations of legal medicine specialists in relation to the victims' statistics. Most of them were children and women. The temporal latency between the act of violence and the consultations was one day and more. The latency and the renunciation of the consultation of the legal medicine specialists can lead to loss of evidence.

Health systems strengthening: a common classification and framework for investment analysis

PubMed Central

Shakarishvili, George; Lansang, Mary Ann; Mitta, Vinod; Bornemisza, Olga; Blakley, Matthew; Kley, Nicole; Burgess, Craig; Atun, Rifat

2011-01-01

Significant scale-up of donors’ investments in health systems strengthening (HSS), and the increased application of harmonization mechanisms for jointly channelling donor resources in countries, necessitate the development of a common framework for tracking donors’ HSS expenditures. Such a framework would make it possible to comparatively analyse donors’ contributions to strengthening specific aspects of countries’ health systems in multi-donor-supported HSS environments. Four pre-requisite factors are required for developing such a framework: (i) harmonization of conceptual and operational understanding of what constitutes HSS; (ii) development of a common set of criteria to define health expenditures as contributors to HSS; (iii) development of a common HSS classification system; and (iv) harmonization of HSS programmatic and financial data to allow for inter-agency comparative analyses. Building on the analysis of these aspects, the paper proposes a framework for tracking donors’ investments in HSS, as a departure point for further discussions aimed at developing a commonly agreed approach. Comparative analysis of financial allocations by the Global Fund to Fight AIDS, Tuberculosis and Malaria and the GAVI Alliance for HSS, as an illustrative example of applying the proposed framework in practice, is also presented. PMID:20952397

Functional Path Analysis as a Multivariate Technique in Developing a Theory of Participation in Adult Education.

ERIC Educational Resources Information Center

Martin, James L.

This paper reports on attempts by the author to construct a theoretical framework of adult education participation using a theory development process and the corresponding multivariate statistical techniques. Two problems are identified: the lack of theoretical framework in studying problems, and the limiting of statistical analysis to univariate…

Teaching Introductory Business Statistics Using the DCOVA Framework

ERIC Educational Resources Information Center

Levine, David M.; Stephan, David F.

2011-01-01

Introductory business statistics students often receive little guidance on how to apply the methods they learn to further business objectives they may one day face. And those students may fail to see the continuity among the topics taught in an introductory course if they learn those methods outside a context that provides a unifying framework.…

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

Vrugt, Jasper A; Robinson, Bruce A; Ter Braak, Cajo J F

In recent years, a strong debate has emerged in the hydrologic literature regarding what constitutes an appropriate framework for uncertainty estimation. Particularly, there is strong disagreement whether an uncertainty framework should have its roots within a proper statistical (Bayesian) context, or whether such a framework should be based on a different philosophy and implement informal measures and weaker inference to summarize parameter and predictive distributions. In this paper, we compare a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE) for assessing uncertainty in conceptual watershed modeling. Our formal Bayesian approach is implemented usingmore » the recently developed differential evolution adaptive metropolis (DREAM) MCMC scheme with a likelihood function that explicitly considers model structural, input and parameter uncertainty. Our results demonstrate that DREAM and GLUE can generate very similar estimates of total streamflow uncertainty. This suggests that formal and informal Bayesian approaches have more common ground than the hydrologic literature and ongoing debate might suggest. The main advantage of formal approaches is, however, that they attempt to disentangle the effect of forcing, parameter and model structural error on total predictive uncertainty. This is key to improving hydrologic theory and to better understand and predict the flow of water through catchments.« less

Extending information retrieval methods to personalized genomic-based studies of disease.

PubMed

Ye, Shuyun; Dawson, John A; Kendziorski, Christina

2014-01-01

Genomic-based studies of disease now involve diverse types of data collected on large groups of patients. A major challenge facing statistical scientists is how best to combine the data, extract important features, and comprehensively characterize the ways in which they affect an individual's disease course and likelihood of response to treatment. We have developed a survival-supervised latent Dirichlet allocation (survLDA) modeling framework to address these challenges. Latent Dirichlet allocation (LDA) models have proven extremely effective at identifying themes common across large collections of text, but applications to genomics have been limited. Our framework extends LDA to the genome by considering each patient as a "document" with "text" detailing his/her clinical events and genomic state. We then further extend the framework to allow for supervision by a time-to-event response. The model enables the efficient identification of collections of clinical and genomic features that co-occur within patient subgroups, and then characterizes each patient by those features. An application of survLDA to The Cancer Genome Atlas ovarian project identifies informative patient subgroups showing differential response to treatment, and validation in an independent cohort demonstrates the potential for patient-specific inference.

The Alberta K-9 Mathematics Program of Studies with Achievement Indicators

ERIC Educational Resources Information Center

Alberta Education, 2007

2007-01-01

The "Alberta K-9 Mathematics Program of Studies with Achievement Indicators" has been derived from "The Common Curriculum Framework for K-9 Mathematics: Western and Northern Canadian Protocol," May 2006 (the Common Curriculum Framework). The program of studies incorporates the conceptual framework for Kindergarten to Grade 9…

General Aviation Avionics Statistics : 1976

DOT National Transportation Integrated Search

1979-11-01

This report presents avionics statistics for the 1976 general aviation (GA) aircraft fleet and is the third in a series titled "General Aviation Avionics Statistics." The statistics are presented in a capability group framework which enables one to r...

General Aviation Avionics Statistics : 1978 Data

DOT National Transportation Integrated Search

1980-12-01

The report presents avionics statistics for the 1978 general aviation (GA) aircraft fleet and is the fifth in a series titled "General Aviation Statistics." The statistics are presented in a capability group framework which enables one to relate airb...

General Aviation Avionics Statistics : 1979 Data

DOT National Transportation Integrated Search

1981-04-01

This report presents avionics statistics for the 1979 general aviation (GA) aircraft fleet and is the sixth in a series titled General Aviation Avionics Statistics. The statistics preseneted in a capability group framework which enables one to relate...

Causal inference as an emerging statistical approach in neurology: an example for epilepsy in the elderly.

PubMed

Moura, Lidia Mvr; Westover, M Brandon; Kwasnik, David; Cole, Andrew J; Hsu, John

2017-01-01

The elderly population faces an increasing number of cases of chronic neurological conditions, such as epilepsy and Alzheimer's disease. Because the elderly with epilepsy are commonly excluded from randomized controlled clinical trials, there are few rigorous studies to guide clinical practice. When the elderly are eligible for trials, they either rarely participate or frequently have poor adherence to therapy, thus limiting both generalizability and validity. In contrast, large observational data sets are increasingly available, but are susceptible to bias when using common analytic approaches. Recent developments in causal inference-analytic approaches also introduce the possibility of emulating randomized controlled trials to yield valid estimates. We provide a practical example of the application of the principles of causal inference to a large observational data set of patients with epilepsy. This review also provides a framework for comparative-effectiveness research in chronic neurological conditions.

A proposed framework for assessing risk from less-than-lifetime exposures to carcinogens.

PubMed

Felter, Susan P; Conolly, Rory B; Bercu, Joel P; Bolger, P Michael; Boobis, Alan R; Bos, Peter M J; Carthew, Philip; Doerrer, Nancy G; Goodman, Jay I; Harrouk, Wafa A; Kirkland, David J; Lau, Serrine S; Llewellyn, G Craig; Preston, R Julian; Schoeny, Rita; Schnatter, A Robert; Tritscher, Angelika; van Velsen, Frans; Williams, Gary M

2011-07-01

Quantitative methods for estimation of cancer risk have been developed for daily, lifetime human exposures. There are a variety of studies or methodologies available to address less-than-lifetime exposures. However, a common framework for evaluating risk from less-than-lifetime exposures (including short-term and/or intermittent exposures) does not exist, which could result in inconsistencies in risk assessment practice. To address this risk assessment need, a committee of the International Life Sciences Institute (ILSI) Health and Environmental Sciences Institute conducted a multisector workshop in late 2009 to discuss available literature, different methodologies, and a proposed framework. The proposed framework provides a decision tree and guidance for cancer risk assessments for less-than-lifetime exposures based on current knowledge of mode of action and dose-response. Available data from rodent studies and epidemiological studies involving less-than-lifetime exposures are considered, in addition to statistical approaches described in the literature for evaluating the impact of changing the dose rate and exposure duration for exposure to carcinogens. The decision tree also provides for scenarios in which an assumption of potential carcinogenicity is appropriate (e.g., based on structural alerts or genotoxicity data), but bioassay or other data are lacking from which a chemical-specific cancer potency can be determined. This paper presents an overview of the rationale for the workshop, reviews historical background, describes the proposed framework for assessing less-than-lifetime exposures to potential human carcinogens, and suggests next steps.

Directions for new developments on statistical design and analysis of small population group trials.

PubMed

Hilgers, Ralf-Dieter; Roes, Kit; Stallard, Nigel

2016-06-14

Most statistical design and analysis methods for clinical trials have been developed and evaluated where at least several hundreds of patients could be recruited. These methods may not be suitable to evaluate therapies if the sample size is unavoidably small, which is usually termed by small populations. The specific sample size cut off, where the standard methods fail, needs to be investigated. In this paper, the authors present their view on new developments for design and analysis of clinical trials in small population groups, where conventional statistical methods may be inappropriate, e.g., because of lack of power or poor adherence to asymptotic approximations due to sample size restrictions. Following the EMA/CHMP guideline on clinical trials in small populations, we consider directions for new developments in the area of statistical methodology for design and analysis of small population clinical trials. We relate the findings to the research activities of three projects, Asterix, IDeAl, and InSPiRe, which have received funding since 2013 within the FP7-HEALTH-2013-INNOVATION-1 framework of the EU. As not all aspects of the wide research area of small population clinical trials can be addressed, we focus on areas where we feel advances are needed and feasible. The general framework of the EMA/CHMP guideline on small population clinical trials stimulates a number of research areas. These serve as the basis for the three projects, Asterix, IDeAl, and InSPiRe, which use various approaches to develop new statistical methodology for design and analysis of small population clinical trials. Small population clinical trials refer to trials with a limited number of patients. Small populations may result form rare diseases or specific subtypes of more common diseases. New statistical methodology needs to be tailored to these specific situations. The main results from the three projects will constitute a useful toolbox for improved design and analysis of small population clinical trials. They address various challenges presented by the EMA/CHMP guideline as well as recent discussions about extrapolation. There is a need for involvement of the patients' perspective in the planning and conduct of small population clinical trials for a successful therapy evaluation.

Spatial Modeling of Agricultural Land-Use Change at Global Scale

NASA Astrophysics Data System (ADS)

Meiyappan, Prasanth; Dalton, Michael; O'Neill, Brian C.; Jain, Atul K.

2013-12-01

Land use is both a source and consequence of climate change. Long-term modeling of land use is central in global scale assessments using Integrated Assessment Models (IAMs) to explore policy alternatives; especially because adaptation and mitigation of climate change requires long-term commitment. We present a land-use change modeling framework that can reproduce the past 100 years of evolution of global cropland and pastureland patterns to a reasonable accuracy. The novelty of our approach underlies in integrating knowledge from both the observed behavior and economic rationale behind land-use decisions, thereby making up for the intrinsic deficits in both the disciplines. The underlying economic rationale is profit maximization of individual landowners that implicitly reflects local-level decisions-making process at a larger scale. Observed behavior based on examining the relationships between contemporary land-use patterns and its socioeconomic and biophysical drivers, enters as an explicit factor into the economic framework. The land-use allocation is modified by autonomous developments and competition between land-use types. The framework accounts for spatial heterogeneity in the nature of driving factors across geographic regions. The model is currently configured to downscale continental-scale aggregate land-use information to region specific changes in land-use patterns (0.5-deg spatial resolution). The temporal resolution is one year. The historical validation experiment is facilitated by synthesizing gridded maps of a wide range of potential biophysical and socioeconomic driving factors for the 20th century. To our knowledge, this is the first retrospective analysis that has been successful in reproducing the historical experience at a global scale. We apply the method to gain useful insights on two questions: (1) what are the dominant socioeconomic and biophysical driving factors of contemporary cropland and pastureland patterns, across geographic regions, and (2) the impacts of various driving factors on shaping the cropland and pastureland patterns over the 20th century. Specifically, we focus on the causes of changes in land-use patterns in certain key regions of the world, such as the abandonment of cropland in the eastern US and a subsequent expansion to the mid-west US. This presentation will focus on the scientific basis behind the developed framework and motivations behind selecting specific statistical techniques to implement the scientific theory. Specifically, we will highlight the application of recently developed statistical techniques that are highly efficient in dealing with problems such as spatial autocorrelation and multicollinearity that are common in land-change studies. However, these statistical techniques have largely been confined to medical literature. We will present the validation results and an example application of the developed framework within an IAM. The presented framework provides a benchmark for long-term spatial modeling of land use that will benefit the IAM, land use and the Earth system modeling communities.

Evaluating aggregate effects of rare and common variants in the 1000 Genomes Project exon sequencing data using latent variable structural equation modeling.

PubMed

Nock, Nl; Zhang, Lx

2011-11-29

Methods that can evaluate aggregate effects of rare and common variants are limited. Therefore, we applied a two-stage approach to evaluate aggregate gene effects in the 1000 Genomes Project data, which contain 24,487 single-nucleotide polymorphisms (SNPs) in 697 unrelated individuals from 7 populations. In stage 1, we identified potentially interesting genes (PIGs) as those having at least one SNP meeting Bonferroni correction using univariate, multiple regression models. In stage 2, we evaluate aggregate PIG effects on trait, Q1, by modeling each gene as a latent construct, which is defined by multiple common and rare variants, using the multivariate statistical framework of structural equation modeling (SEM). In stage 1, we found that PIGs varied markedly between a randomly selected replicate (replicate 137) and 100 other replicates, with the exception of FLT1. In stage 1, collapsing rare variants decreased false positives but increased false negatives. In stage 2, we developed a good-fitting SEM model that included all nine genes simulated to affect Q1 (FLT1, KDR, ARNT, ELAV4, FLT4, HIF1A, HIF3A, VEGFA, VEGFC) and found that FLT1 had the largest effect on Q1 (βstd = 0.33 ± 0.05). Using replicate 137 estimates as population values, we found that the mean relative bias in the parameters (loadings, paths, residuals) and their standard errors across 100 replicates was on average, less than 5%. Our latent variable SEM approach provides a viable framework for modeling aggregate effects of rare and common variants in multiple genes, but more elegant methods are needed in stage 1 to minimize type I and type II error.

Remote visual analysis of large turbulence databases at multiple scales

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

Pulido, Jesus; Livescu, Daniel; Kanov, Kalin

The remote analysis and visualization of raw large turbulence datasets is challenging. Current accurate direct numerical simulations (DNS) of turbulent flows generate datasets with billions of points per time-step and several thousand time-steps per simulation. Until recently, the analysis and visualization of such datasets was restricted to scientists with access to large supercomputers. The public Johns Hopkins Turbulence database simplifies access to multi-terabyte turbulence datasets and facilitates the computation of statistics and extraction of features through the use of commodity hardware. In this paper, we present a framework designed around wavelet-based compression for high-speed visualization of large datasets and methodsmore » supporting multi-resolution analysis of turbulence. By integrating common technologies, this framework enables remote access to tools available on supercomputers and over 230 terabytes of DNS data over the Web. Finally, the database toolset is expanded by providing access to exploratory data analysis tools, such as wavelet decomposition capabilities and coherent feature extraction.« less

Remote visual analysis of large turbulence databases at multiple scales

DOE PAGES

Pulido, Jesus; Livescu, Daniel; Kanov, Kalin; ...

2018-06-15

The remote analysis and visualization of raw large turbulence datasets is challenging. Current accurate direct numerical simulations (DNS) of turbulent flows generate datasets with billions of points per time-step and several thousand time-steps per simulation. Until recently, the analysis and visualization of such datasets was restricted to scientists with access to large supercomputers. The public Johns Hopkins Turbulence database simplifies access to multi-terabyte turbulence datasets and facilitates the computation of statistics and extraction of features through the use of commodity hardware. In this paper, we present a framework designed around wavelet-based compression for high-speed visualization of large datasets and methodsmore » supporting multi-resolution analysis of turbulence. By integrating common technologies, this framework enables remote access to tools available on supercomputers and over 230 terabytes of DNS data over the Web. Finally, the database toolset is expanded by providing access to exploratory data analysis tools, such as wavelet decomposition capabilities and coherent feature extraction.« less

An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder.

PubMed

Werling, Donna M; Brand, Harrison; An, Joon-Yong; Stone, Matthew R; Zhu, Lingxue; Glessner, Joseph T; Collins, Ryan L; Dong, Shan; Layer, Ryan M; Markenscoff-Papadimitriou, Eirene; Farrell, Andrew; Schwartz, Grace B; Wang, Harold Z; Currall, Benjamin B; Zhao, Xuefang; Dea, Jeanselle; Duhn, Clif; Erdman, Carolyn A; Gilson, Michael C; Yadav, Rachita; Handsaker, Robert E; Kashin, Seva; Klei, Lambertus; Mandell, Jeffrey D; Nowakowski, Tomasz J; Liu, Yuwen; Pochareddy, Sirisha; Smith, Louw; Walker, Michael F; Waterman, Matthew J; He, Xin; Kriegstein, Arnold R; Rubenstein, John L; Sestan, Nenad; McCarroll, Steven A; Neale, Benjamin M; Coon, Hilary; Willsey, A Jeremy; Buxbaum, Joseph D; Daly, Mark J; State, Matthew W; Quinlan, Aaron R; Marth, Gabor T; Roeder, Kathryn; Devlin, Bernie; Talkowski, Michael E; Sanders, Stephan J

2018-05-01

Genomic association studies of common or rare protein-coding variation have established robust statistical approaches to account for multiple testing. Here we present a comparable framework to evaluate rare and de novo noncoding single-nucleotide variants, insertion/deletions, and all classes of structural variation from whole-genome sequencing (WGS). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions, we define 51,801 annotation categories. Analyses of 519 autism spectrum disorder families did not identify association with any categories after correction for 4,123 effective tests. Without appropriate correction, biologically plausible associations are observed in both cases and controls. Despite excluding previously identified gene-disrupting mutations, coding regions still exhibited the strongest associations. Thus, in autism, the contribution of de novo noncoding variation is probably modest in comparison to that of de novo coding variants. Robust results from future WGS studies will require large cohorts and comprehensive analytical strategies that consider the substantial multiple-testing burden.

A consistent framework for Horton regression statistics that leads to a modified Hack's law

USGS Publications Warehouse

Furey, P.R.; Troutman, B.M.

2008-01-01

A statistical framework is introduced that resolves important problems with the interpretation and use of traditional Horton regression statistics. The framework is based on a univariate regression model that leads to an alternative expression for Horton ratio, connects Horton regression statistics to distributional simple scaling, and improves the accuracy in estimating Horton plot parameters. The model is used to examine data for drainage area A and mainstream length L from two groups of basins located in different physiographic settings. Results show that confidence intervals for the Horton plot regression statistics are quite wide. Nonetheless, an analysis of covariance shows that regression intercepts, but not regression slopes, can be used to distinguish between basin groups. The univariate model is generalized to include n > 1 dependent variables. For the case where the dependent variables represent ln A and ln L, the generalized model performs somewhat better at distinguishing between basin groups than two separate univariate models. The generalized model leads to a modification of Hack's law where L depends on both A and Strahler order ??. Data show that ?? plays a statistically significant role in the modified Hack's law expression. ?? 2008 Elsevier B.V.

A General Framework for Power Analysis to Detect the Moderator Effects in Two- and Three-Level Cluster Randomized Trials

ERIC Educational Resources Information Center

Dong, Nianbo; Spybrook, Jessaca; Kelcey, Ben

2016-01-01

The purpose of this study is to propose a general framework for power analyses to detect the moderator effects in two- and three-level cluster randomized trials (CRTs). The study specifically aims to: (1) develop the statistical formulations for calculating statistical power, minimum detectable effect size (MDES) and its confidence interval to…

Reducing Anxiety and Increasing Self-Efficacy within an Advanced Graduate Psychology Statistics Course

ERIC Educational Resources Information Center

McGrath, April L.; Ferns, Alyssa; Greiner, Leigh; Wanamaker, Kayla; Brown, Shelley

2015-01-01

In this study we assessed the usefulness of a multifaceted teaching framework in an advanced statistics course. We sought to expand on past findings by using this framework to assess changes in anxiety and self-efficacy, and we collected focus group data to ascertain whether students attribute such changes to a multifaceted teaching approach.…

A data fusion framework for meta-evaluation of intelligent transportation system effectiveness

DOT National Transportation Integrated Search

This study presents a framework for the meta-evaluation of Intelligent Transportation System effectiveness. The framework is based on data fusion approaches that adjust for data biases and violations of other standard statistical assumptions. Operati...

Applications of statistics to medical science (1) Fundamental concepts.

PubMed

Watanabe, Hiroshi

2011-01-01

The conceptual framework of statistical tests and statistical inferences are discussed, and the epidemiological background of statistics is briefly reviewed. This study is one of a series in which we survey the basics of statistics and practical methods used in medical statistics. Arguments related to actual statistical analysis procedures will be made in subsequent papers.

Combining statistical inference and decisions in ecology.

PubMed

Williams, Perry J; Hooten, Mevin B

2016-09-01

Statistical decision theory (SDT) is a sub-field of decision theory that formally incorporates statistical investigation into a decision-theoretic framework to account for uncertainties in a decision problem. SDT provides a unifying analysis of three types of information: statistical results from a data set, knowledge of the consequences of potential choices (i.e., loss), and prior beliefs about a system. SDT links the theoretical development of a large body of statistical methods, including point estimation, hypothesis testing, and confidence interval estimation. The theory and application of SDT have mainly been developed and published in the fields of mathematics, statistics, operations research, and other decision sciences, but have had limited exposure in ecology. Thus, we provide an introduction to SDT for ecologists and describe its utility for linking the conventionally separate tasks of statistical investigation and decision making in a single framework. We describe the basic framework of both Bayesian and frequentist SDT, its traditional use in statistics, and discuss its application to decision problems that occur in ecology. We demonstrate SDT with two types of decisions: Bayesian point estimation and an applied management problem of selecting a prescribed fire rotation for managing a grassland bird species. Central to SDT, and decision theory in general, are loss functions. Thus, we also provide basic guidance and references for constructing loss functions for an SDT problem. © 2016 by the Ecological Society of America.

New model framework and structure and the commonality evaluation model. [concerning unmanned spacecraft projects

NASA Technical Reports Server (NTRS)

1977-01-01

The development of a framework and structure for shuttle era unmanned spacecraft projects and the development of a commonality evaluation model is documented. The methodology developed for model utilization in performing cost trades and comparative evaluations for commonality studies is discussed. The model framework consists of categories of activities associated with the spacecraft system's development process. The model structure describes the physical elements to be treated as separate identifiable entities. Cost estimating relationships for subsystem and program-level components were calculated.

When is Chemical Similarity Significant? The Statistical Distribution of Chemical Similarity Scores and Its Extreme Values

PubMed Central

Baldi, Pierre

2010-01-01

As repositories of chemical molecules continue to expand and become more open, it becomes increasingly important to develop tools to search them efficiently and assess the statistical significance of chemical similarity scores. Here we develop a general framework for understanding, modeling, predicting, and approximating the distribution of chemical similarity scores and its extreme values in large databases. The framework can be applied to different chemical representations and similarity measures but is demonstrated here using the most common binary fingerprints with the Tanimoto similarity measure. After introducing several probabilistic models of fingerprints, including the Conditional Gaussian Uniform model, we show that the distribution of Tanimoto scores can be approximated by the distribution of the ratio of two correlated Normal random variables associated with the corresponding unions and intersections. This remains true also when the distribution of similarity scores is conditioned on the size of the query molecules in order to derive more fine-grained results and improve chemical retrieval. The corresponding extreme value distributions for the maximum scores are approximated by Weibull distributions. From these various distributions and their analytical forms, Z-scores, E-values, and p-values are derived to assess the significance of similarity scores. In addition, the framework allows one to predict also the value of standard chemical retrieval metrics, such as Sensitivity and Specificity at fixed thresholds, or ROC (Receiver Operating Characteristic) curves at multiple thresholds, and to detect outliers in the form of atypical molecules. Numerous and diverse experiments carried in part with large sets of molecules from the ChemDB show remarkable agreement between theory and empirical results. PMID:20540577

Social significance of community structure: Statistical view

NASA Astrophysics Data System (ADS)

Li, Hui-Jia; Daniels, Jasmine J.

2015-01-01

Community structure analysis is a powerful tool for social networks that can simplify their topological and functional analysis considerably. However, since community detection methods have random factors and real social networks obtained from complex systems always contain error edges, evaluating the significance of a partitioned community structure is an urgent and important question. In this paper, integrating the specific characteristics of real society, we present a framework to analyze the significance of a social community. The dynamics of social interactions are modeled by identifying social leaders and corresponding hierarchical structures. Instead of a direct comparison with the average outcome of a random model, we compute the similarity of a given node with the leader by the number of common neighbors. To determine the membership vector, an efficient community detection algorithm is proposed based on the position of the nodes and their corresponding leaders. Then, using a log-likelihood score, the tightness of the community can be derived. Based on the distribution of community tightness, we establish a connection between p -value theory and network analysis, and then we obtain a significance measure of statistical form . Finally, the framework is applied to both benchmark networks and real social networks. Experimental results show that our work can be used in many fields, such as determining the optimal number of communities, analyzing the social significance of a given community, comparing the performance among various algorithms, etc.

Sub-grid scale models for discontinuous Galerkin methods based on the Mori-Zwanzig formalism

NASA Astrophysics Data System (ADS)

Parish, Eric; Duraisamy, Karthk

2017-11-01

The optimal prediction framework of Chorin et al., which is a reformulation of the Mori-Zwanzig (M-Z) formalism of non-equilibrium statistical mechanics, provides a framework for the development of mathematically-derived closure models. The M-Z formalism provides a methodology to reformulate a high-dimensional Markovian dynamical system as a lower-dimensional, non-Markovian (non-local) system. In this lower-dimensional system, the effects of the unresolved scales on the resolved scales are non-local and appear as a convolution integral. The non-Markovian system is an exact statement of the original dynamics and is used as a starting point for model development. In this work, we investigate the development of M-Z-based closures model within the context of the Variational Multiscale Method (VMS). The method relies on a decomposition of the solution space into two orthogonal subspaces. The impact of the unresolved subspace on the resolved subspace is shown to be non-local in time and is modeled through the M-Z-formalism. The models are applied to hierarchical discontinuous Galerkin discretizations. Commonalities between the M-Z closures and conventional flux schemes are explored. This work was supported in part by AFOSR under the project ''LES Modeling of Non-local effects using Statistical Coarse-graining'' with Dr. Jean-Luc Cambier as the technical monitor.

Detecting higher-order interactions among the spiking events in a group of neurons.

PubMed

Martignon, L; Von Hasseln, H; Grün, S; Aertsen, A; Palm, G

1995-06-01

We propose a formal framework for the description of interactions among groups of neurons. This framework is not restricted to the common case of pair interactions, but also incorporates higher-order interactions, which cannot be reduced to lower-order ones. We derive quantitative measures to detect the presence of such interactions in experimental data, by statistical analysis of the frequency distribution of higher-order correlations in multiple neuron spike train data. Our first step is to represent a frequency distribution as a Markov field on the minimal graph it induces. We then show the invariance of this graph with regard to changes of state. Clearly, only linear Markov fields can be adequately represented by graphs. Higher-order interdependencies, which are reflected by the energy expansion of the distribution, require more complex graphical schemes, like constellations or assembly diagrams, which we introduce and discuss. The coefficients of the energy expansion not only point to the interactions among neurons but are also a measure of their strength. We investigate the statistical meaning of detected interactions in an information theoretic sense and propose minimum relative entropy approximations as null hypotheses for significance tests. We demonstrate the various steps of our method in the situation of an empirical frequency distribution on six neurons, extracted from data on simultaneous multineuron recordings from the frontal cortex of a behaving monkey and close with a brief outlook on future work.

Magnetic storms and solar flares: can be analysed within similar mathematical framework with other extreme events?

NASA Astrophysics Data System (ADS)

Balasis, Georgios; Potirakis, Stelios M.; Papadimitriou, Constantinos; Zitis, Pavlos I.; Eftaxias, Konstantinos

2015-04-01

The field of study of complex systems considers that the dynamics of complex systems are founded on universal principles that may be used to describe a great variety of scientific and technological approaches of different types of natural, artificial, and social systems. We apply concepts of the nonextensive statistical physics, on time-series data of observable manifestations of the underlying complex processes ending up to different extreme events, in order to support the suggestion that a dynamical analogy characterizes the generation of a single magnetic storm, solar flare, earthquake (in terms of pre-seismic electromagnetic signals) , epileptic seizure, and economic crisis. The analysis reveals that all the above mentioned different extreme events can be analyzed within similar mathematical framework. More precisely, we show that the populations of magnitudes of fluctuations included in all the above mentioned pulse-like-type time series follow the traditional Gutenberg-Richter law as well as a nonextensive model for earthquake dynamics, with similar nonextensive q-parameter values. Moreover, based on a multidisciplinary statistical analysis we show that the extreme events are characterized by crucial common symptoms, namely: (i) high organization, high compressibility, low complexity, high information content; (ii) strong persistency; and (iii) existence of clear preferred direction of emerged activities. These symptoms clearly discriminate the appearance of the extreme events under study from the corresponding background noise.

A Classification of Statistics Courses (A Framework for Studying Statistical Education)

ERIC Educational Resources Information Center

Turner, J. C.

1976-01-01

A classification of statistics courses in presented, with main categories of "course type,""methods of presentation,""objectives," and "syllabus." Examples and suggestions for uses of the classification are given. (DT)

Comparative analysis of the fit of 3-unit implant-supported frameworks cast in nickel-chromium and cobalt-chromium alloys and commercially pure titanium after casting, laser welding, and simulated porcelain firings.

PubMed

Tiossi, Rodrigo; Rodrigues, Renata Cristina Silveira; de Mattos, Maria da Glória Chiarello; Ribeiro, Ricardo Faria

2008-01-01

This study compared the vertical misfit of 3-unit implant-supported nickel-chromium (Ni-Cr) and cobalt-chromium (Co-Cr) alloy and commercially pure titanium (cpTi) frameworks after casting as 1 piece, after sectioning and laser welding, and after simulated porcelain firings. The results on the tightened side showed no statistically significant differences. On the opposite side, statistically significant differences were found for Co-Cr alloy (118.64 microm [SD: 91.48] to 39.90 microm [SD: 27.13]) and cpTi (118.56 microm [51.35] to 27.87 microm [12.71]) when comparing 1-piece to laser-welded frameworks. With both sides tightened, only Co-Cr alloy showed statistically significant differences after laser welding. Ni-Cr alloy showed the lowest misfit values, though the differences were not statistically significantly different. Simulated porcelain firings revealed no significant differences.

graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture.

PubMed

Chung, Dongjun; Kim, Hang J; Zhao, Hongyu

2017-02-01

Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. However, identification of risk variants associated with complex diseases remains challenging as they are often affected by many genetic variants with small or moderate effects. There has been accumulating evidence suggesting that different complex traits share common risk basis, namely pleiotropy. Recently, several statistical methods have been developed to improve statistical power to identify risk variants for complex traits through a joint analysis of multiple GWAS datasets by leveraging pleiotropy. While these methods were shown to improve statistical power for association mapping compared to separate analyses, they are still limited in the number of phenotypes that can be integrated. In order to address this challenge, in this paper, we propose a novel statistical framework, graph-GPA, to integrate a large number of GWAS datasets for multiple phenotypes using a hidden Markov random field approach. Application of graph-GPA to a joint analysis of GWAS datasets for 12 phenotypes shows that graph-GPA improves statistical power to identify risk variants compared to statistical methods based on smaller number of GWAS datasets. In addition, graph-GPA also promotes better understanding of genetic mechanisms shared among phenotypes, which can potentially be useful for the development of improved diagnosis and therapeutics. The R implementation of graph-GPA is currently available at https://dongjunchung.github.io/GGPA/.

Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics

PubMed Central

Brusniak, Mi-Youn; Bodenmiller, Bernd; Campbell, David; Cooke, Kelly; Eddes, James; Garbutt, Andrew; Lau, Hollis; Letarte, Simon; Mueller, Lukas N; Sharma, Vagisha; Vitek, Olga; Zhang, Ning; Aebersold, Ruedi; Watts, Julian D

2008-01-01

Background Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics. Results We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling. Conclusion The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field. PMID:19087345

Assessing the effects of cocaine dependence and pathological gambling using group-wise sparse representation of natural stimulus FMRI data.

PubMed

Ren, Yudan; Fang, Jun; Lv, Jinglei; Hu, Xintao; Guo, Cong Christine; Guo, Lei; Xu, Jiansong; Potenza, Marc N; Liu, Tianming

2017-08-01

Assessing functional brain activation patterns in neuropsychiatric disorders such as cocaine dependence (CD) or pathological gambling (PG) under naturalistic stimuli has received rising interest in recent years. In this paper, we propose and apply a novel group-wise sparse representation framework to assess differences in neural responses to naturalistic stimuli across multiple groups of participants (healthy control, cocaine dependence, pathological gambling). Specifically, natural stimulus fMRI (N-fMRI) signals from all three groups of subjects are aggregated into a big data matrix, which is then decomposed into a common signal basis dictionary and associated weight coefficient matrices via an effective online dictionary learning and sparse coding method. The coefficient matrices associated with each common dictionary atom are statistically assessed for each group separately. With the inter-group comparisons based on the group-wise correspondence established by the common dictionary, our experimental results demonstrated that the group-wise sparse coding and representation strategy can effectively and specifically detect brain networks/regions affected by different pathological conditions of the brain under naturalistic stimuli.

A High Performance Bayesian Computing Framework for Spatiotemporal Uncertainty Modeling

NASA Astrophysics Data System (ADS)

Cao, G.

2015-12-01

All types of spatiotemporal measurements are subject to uncertainty. With spatiotemporal data becomes increasingly involved in scientific research and decision making, it is important to appropriately model the impact of uncertainty. Quantitatively modeling spatiotemporal uncertainty, however, is a challenging problem considering the complex dependence and dataheterogeneities.State-space models provide a unifying and intuitive framework for dynamic systems modeling. In this paper, we aim to extend the conventional state-space models for uncertainty modeling in space-time contexts while accounting for spatiotemporal effects and data heterogeneities. Gaussian Markov Random Field (GMRF) models, also known as conditional autoregressive models, are arguably the most commonly used methods for modeling of spatially dependent data. GMRF models basically assume that a geo-referenced variable primarily depends on its neighborhood (Markov property), and the spatial dependence structure is described via a precision matrix. Recent study has shown that GMRFs are efficient approximation to the commonly used Gaussian fields (e.g., Kriging), and compared with Gaussian fields, GMRFs enjoy a series of appealing features, such as fast computation and easily accounting for heterogeneities in spatial data (e.g, point and areal). This paper represents each spatial dataset as a GMRF and integrates them into a state-space form to statistically model the temporal dynamics. Different types of spatial measurements (e.g., categorical, count or continuous), can be accounted for by according link functions. A fast alternative to MCMC framework, so-called Integrated Nested Laplace Approximation (INLA), was adopted for model inference.Preliminary case studies will be conducted to showcase the advantages of the described framework. In the first case, we apply the proposed method for modeling the water table elevation of Ogallala aquifer over the past decades. In the second case, we analyze the drought impacts in Texas counties in the past years, where the spatiotemporal dynamics are represented in areal data.

An integrated data model to estimate spatiotemporal occupancy, abundance, and colonization dynamics.

PubMed

Williams, Perry J; Hooten, Mevin B; Womble, Jamie N; Esslinger, George G; Bower, Michael R; Hefley, Trevor J

2017-02-01

Ecological invasions and colonizations occur dynamically through space and time. Estimating the distribution and abundance of colonizing species is critical for efficient management or conservation. We describe a statistical framework for simultaneously estimating spatiotemporal occupancy and abundance dynamics of a colonizing species. Our method accounts for several issues that are common when modeling spatiotemporal ecological data including multiple levels of detection probability, multiple data sources, and computational limitations that occur when making fine-scale inference over a large spatiotemporal domain. We apply the model to estimate the colonization dynamics of sea otters (Enhydra lutris) in Glacier Bay, in southeastern Alaska. © 2016 by the Ecological Society of America.

“Epidemiological Criminology”: Coming Full Circle

PubMed Central

Lanier, Mark M.

2009-01-01

Members of the public health and criminal justice disciplines often work with marginalized populations: people at high risk of drug use, health problems, incarceration, and other difficulties. As these fields increasingly overlap, distinctions between them are blurred, as numerous research reports and funding trends document. However, explicit theoretical and methodological linkages between the 2 disciplines remain rare. A new paradigm that links methods and statistical models of public health with those of their criminal justice counterparts is needed, as are increased linkages between epidemiological analogies, theories, and models and the corresponding tools of criminology. We outline disciplinary commonalities and distinctions, present policy examples that integrate similarities, and propose “epidemiological criminology” as a bridging framework. PMID:19150901

Zubarev's Nonequilibrium Statistical Operator Method in the Generalized Statistics of Multiparticle Systems

NASA Astrophysics Data System (ADS)

Glushak, P. A.; Markiv, B. B.; Tokarchuk, M. V.

2018-01-01

We present a generalization of Zubarev's nonequilibrium statistical operator method based on the principle of maximum Renyi entropy. In the framework of this approach, we obtain transport equations for the basic set of parameters of the reduced description of nonequilibrium processes in a classical system of interacting particles using Liouville equations with fractional derivatives. For a classical systems of particles in a medium with a fractal structure, we obtain a non-Markovian diffusion equation with fractional spatial derivatives. For a concrete model of the frequency dependence of a memory function, we obtain generalized Kettano-type diffusion equation with the spatial and temporal fractality taken into account. We present a generalization of nonequilibrium thermofield dynamics in Zubarev's nonequilibrium statistical operator method in the framework of Renyi statistics.

Understanding Statistical Concepts and Terms in Context: The GovStat Ontology and the Statistical Interactive Glossary.

ERIC Educational Resources Information Center

Haas, Stephanie W.; Pattuelli, Maria Cristina; Brown, Ron T.

2003-01-01

Describes the Statistical Interactive Glossary (SIG), an enhanced glossary of statistical terms supported by the GovStat ontology of statistical concepts. Presents a conceptual framework whose components articulate different aspects of a term's basic explanation that can be manipulated to produce a variety of presentations. The overarching…

Active contours on statistical manifolds and texture segmentation

Treesearch

Sang-Mook Lee; A. Lynn Abbott; Neil A. Clark; Philip A. Araman

2005-01-01

A new approach to active contours on statistical manifolds is presented. The statistical manifolds are 2- dimensional Riemannian manifolds that are statistically defined by maps that transform a parameter domain onto a set of probability density functions. In this novel framework, color or texture features are measured at each image point and their statistical...

Active contours on statistical manifolds and texture segmentaiton

Treesearch

Sang-Mook Lee; A. Lynn Abbott; Neil A. Clark; Philip A. Araman

2005-01-01

A new approach to active contours on statistical manifolds is presented. The statistical manifolds are 2- dimensional Riemannian manifolds that are statistically defined by maps that transform a parameter domain onto-a set of probability density functions. In this novel framework, color or texture features are measured at each Image point and their statistical...

SOCR: Statistics Online Computational Resource

PubMed Central

Dinov, Ivo D.

2011-01-01

The need for hands-on computer laboratory experience in undergraduate and graduate statistics education has been firmly established in the past decade. As a result a number of attempts have been undertaken to develop novel approaches for problem-driven statistical thinking, data analysis and result interpretation. In this paper we describe an integrated educational web-based framework for: interactive distribution modeling, virtual online probability experimentation, statistical data analysis, visualization and integration. Following years of experience in statistical teaching at all college levels using established licensed statistical software packages, like STATA, S-PLUS, R, SPSS, SAS, Systat, etc., we have attempted to engineer a new statistics education environment, the Statistics Online Computational Resource (SOCR). This resource performs many of the standard types of statistical analysis, much like other classical tools. In addition, it is designed in a plug-in object-oriented architecture and is completely platform independent, web-based, interactive, extensible and secure. Over the past 4 years we have tested, fine-tuned and reanalyzed the SOCR framework in many of our undergraduate and graduate probability and statistics courses and have evidence that SOCR resources build student’s intuition and enhance their learning. PMID:21451741

47 CFR 54.807 - Interstate access universal service support.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Common Carrier Bureau Report, Statistics of Communications Common Carriers, Table 6.10—Selected Operating Statistics. Interested parties may obtain this report from the U.S. Government Printing Office or by... Bureau Report, Statistics of Communications Common Carriers, Table 6.10—Selected Operating Statistics...

A normative inference approach for optimal sample sizes in decisions from experience

PubMed Central

Ostwald, Dirk; Starke, Ludger; Hertwig, Ralph

2015-01-01

“Decisions from experience” (DFE) refers to a body of work that emerged in research on behavioral decision making over the last decade. One of the major experimental paradigms employed to study experience-based choice is the “sampling paradigm,” which serves as a model of decision making under limited knowledge about the statistical structure of the world. In this paradigm respondents are presented with two payoff distributions, which, in contrast to standard approaches in behavioral economics, are specified not in terms of explicit outcome-probability information, but by the opportunity to sample outcomes from each distribution without economic consequences. Participants are encouraged to explore the distributions until they feel confident enough to decide from which they would prefer to draw from in a final trial involving real monetary payoffs. One commonly employed measure to characterize the behavior of participants in the sampling paradigm is the sample size, that is, the number of outcome draws which participants choose to obtain from each distribution prior to terminating sampling. A natural question that arises in this context concerns the “optimal” sample size, which could be used as a normative benchmark to evaluate human sampling behavior in DFE. In this theoretical study, we relate the DFE sampling paradigm to the classical statistical decision theoretic literature and, under a probabilistic inference assumption, evaluate optimal sample sizes for DFE. In our treatment we go beyond analytically established results by showing how the classical statistical decision theoretic framework can be used to derive optimal sample sizes under arbitrary, but numerically evaluable, constraints. Finally, we critically evaluate the value of deriving optimal sample sizes under this framework as testable predictions for the experimental study of sampling behavior in DFE. PMID:26441720

Structured statistical models of inductive reasoning.

PubMed

Kemp, Charles; Tenenbaum, Joshua B

2009-01-01

Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. This article presents a Bayesian framework that attempts to meet both goals and describes [corrected] 4 applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the 4 models are defined over different kinds of structures that capture different relationships between the categories in a domain. The framework therefore shows how statistical inference can operate over structured background knowledge, and the authors argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning.

Multiresolution multiscale active mask segmentation of fluorescence microscope images

NASA Astrophysics Data System (ADS)

Srinivasa, Gowri; Fickus, Matthew; Kovačević, Jelena

2009-08-01

We propose an active mask segmentation framework that combines the advantages of statistical modeling, smoothing, speed and flexibility offered by the traditional methods of region-growing, multiscale, multiresolution and active contours respectively. At the crux of this framework is a paradigm shift from evolving contours in the continuous domain to evolving multiple masks in the discrete domain. Thus, the active mask framework is particularly suited to segment digital images. We demonstrate the use of the framework in practice through the segmentation of punctate patterns in fluorescence microscope images. Experiments reveal that statistical modeling helps the multiple masks converge from a random initial configuration to a meaningful one. This obviates the need for an involved initialization procedure germane to most of the traditional methods used to segment fluorescence microscope images. While we provide the mathematical details of the functions used to segment fluorescence microscope images, this is only an instantiation of the active mask framework. We suggest some other instantiations of the framework to segment different types of images.

Impacts of crop rotations on soil organic carbon sequestration

NASA Astrophysics Data System (ADS)

Gobin, Anne; Vos, Johan; Joris, Ingeborg; Van De Vreken, Philippe

2013-04-01

Agricultural land use and crop rotations can greatly affect the amount of carbon sequestered in the soil. We developed a framework for modelling the impacts of crop rotations on soil carbon sequestration at the field scale with test case Flanders. A crop rotation geo-database was constructed covering 10 years of crop rotation in Flanders using the IACS parcel registration (Integrated Administration and Control System) to elicit the most common crop rotation on major soil types in Flanders. In order to simulate the impact of crop cover on carbon sequestration, the Roth-C model was adapted to Flanders' environment and coupled to common crop rotations extracted from the IACS geodatabases and statistical databases on crop yield. Crop allometric models were used to calculate crop residues from common crops in Flanders and subsequently derive stable organic matter fluxes to the soil (REGSOM). The REGSOM model was coupled to Roth-C model was run for 30 years and for all combinations of seven main arable crops, two common catch crops and two common dosages of organic manure. The common crops are winter wheat, winter barley, sugar beet, potato, grain maize, silage maize and winter rapeseed; the catch crops are yellow mustard and Italian ryegrass; the manure dosages are 35 ton/ha cattle slurry and 22 ton/ha pig slurry. Four common soils were simulated: sand, loam, sandy loam and clay. In total more than 2.4 million simulations were made with monthly output of carbon content for 30 years. Results demonstrate that crop cover dynamics influence carbon sequestration for a very large percentage. For the same rotations carbon sequestration is highest on clay soils and lowest on sandy soils. Crop residues of grain maize and winter wheat followed by catch crops contribute largely to the total carbon sequestered. This implies that agricultural policies that impact on agricultural land management influence soil carbon sequestration for a large percentage. The framework is therefore suited for further scenario analysis and impact assessment in order to support agri-environmental policy decisions.

General aviation avionics statistics : 1977.

DOT National Transportation Integrated Search

1980-06-01

This report presents avionics statistics for the 1977 general aviation (GA) aircraft fleet and is the fourth in a series. The statistics are presented in a capability group framework which enables one to relate airborne avionics equipment to the capa...

Matter-wave diffraction approaching limits predicted by Feynman path integrals for multipath interference

NASA Astrophysics Data System (ADS)

Barnea, A. Ronny; Cheshnovsky, Ori; Even, Uzi

2018-02-01

Interference experiments have been paramount in our understanding of quantum mechanics and are frequently the basis of testing the superposition principle in the framework of quantum theory. In recent years, several studies have challenged the nature of wave-function interference from the perspective of Born's rule—namely, the manifestation of so-called high-order interference terms in a superposition generated by diffraction of the wave functions. Here we present an experimental test of multipath interference in the diffraction of metastable helium atoms, with large-number counting statistics, comparable to photon-based experiments. We use a variation of the original triple-slit experiment and accurate single-event counting techniques to provide a new experimental bound of 2.9 ×10-5 on the statistical deviation from the commonly approximated null third-order interference term in Born's rule for matter waves. Our value is on the order of the maximal contribution predicted for multipath trajectories by Feynman path integrals.

The Common Framework for Earth Observation Data

NASA Astrophysics Data System (ADS)

Gallo, J.; Stryker, T. S.; Sherman, R.

2016-12-01

Each year, the Federal government records petabytes of data about our home planet. That massive amount of data in turn provides enormous benefits to society through weather reports, agricultural forecasts, air and water quality warnings, and countless other applications. To maximize the ease of transforming the data into useful information for research and for public services, the U.S. Group on Earth Observations released the first Common Framework for Earth Observation Data in March 2016. The Common Framework recommends practices for Federal agencies to adopt in order to improve the ability of all users to discover, access, and use Federal Earth observations data. The U.S. Government is committed to making data from civil Earth observation assets freely available to all users. Building on the Administration's commitment to promoting open data, open science, and open government, the Common Framework goes beyond removing financial barriers to data access, and attempts to minimize the technical impediments that limit data utility. While Earth observation systems typically collect data for a specific purpose, these data are often also useful in applications unforeseen during development of the systems. Managing and preserving these data with a common approach makes it easier for a wide range of users to find, evaluate, understand, and utilize the data, which in turn leads to the development of a wide range of innovative applications. The Common Framework provides Federal agencies with a recommended set of standards and practices to follow in order to achieve this goal. Federal agencies can follow these best practices as they develop new observing systems or modernize their existing collections of data. This presentation will give a brief on the context and content of the Common Framework, along with future directions for implementation and keeping its recommendations up-to-date with developing technology.

Studying Weather and Climate Extremes in a Non-stationary Framework

NASA Astrophysics Data System (ADS)

Wu, Z.

2010-12-01

The study of weather and climate extremes often uses the theory of extreme values. Such a detection method has a major problem: to obtain the probability distribution of extremes, one has to implicitly assume the Earth’s climate is stationary over a long period within which the climatology is defined. While such detection makes some sense in a purely statistical view of stationary processes, it can lead to misleading statistical properties of weather and climate extremes caused by long term climate variability and change, and may also cause enormous difficulty in attributing and predicting these extremes. To alleviate this problem, here we report a novel non-stationary framework for studying weather and climate extremes in a non-stationary framework. In this new framework, the weather and climate extremes will be defined as timescale-dependent quantities derived from the anomalies with respect to non-stationary climatologies of different timescales. With this non-stationary framework, the non-stationary and nonlinear nature of climate system will be taken into account; and the attribution and the prediction of weather and climate extremes can then be separated into 1) the change of the statistical properties of the weather and climate extremes themselves and 2) the background climate variability and change. The new non-stationary framework will use the ensemble empirical mode decomposition (EEMD) method, which is a recent major improvement of the Hilbert-Huang Transform for time-frequency analysis. Using this tool, we will adaptively decompose various weather and climate data from observation and climate models in terms of the components of the various natural timescales contained in the data. With such decompositions, the non-stationary statistical properties (both spatial and temporal) of weather and climate anomalies and of their corresponding climatologies will be analyzed and documented.

Epidemiology of multiple chronic conditions: an international perspective.

PubMed

Schellevis, François G

2013-01-01

The epidemiology of multimorbidity, or multiple chronic conditions (MCCs), is one of the research priority areas of the U.S. Department of Health and Human Services (HHS) by its Strategic Framework on MCCs. A conceptual model addressing methodological issues leading to a valid measurement of the prevalence rates of MCCs has been developed and applied in descriptive epidemiological studies. Comparing these results with those from prevalence studies performed earlier and in other countries is hampered by methodological limitations. Therefore, this paper aims to put the size and patterns of MCCs in the USA, as established within the HHS Strategic Framework on MCCs, in perspective of the findings on the prevalence of MCCs in other countries. General common trends can be observed: increasing prevalence rates with increasing age, and multimorbidity being the rule rather than the exception at old age. Most frequent combinations of chronic diseases include the most frequently occurring single chronic diseases. New descriptive epidemiological studies will probably not provide new results; therefore, future descriptive studies should focus on the prevalence rates of MCCs in subpopulations, statistical clustering of chronic conditions, and the development of the prevalence rates of MCCs over time. The finding of common trends also indicates the necessary transition to a next phase of MCC research, addressing the quality of care of patients with MCCs from an organizational perspective and with respect to the content of care. Journal of Comorbidity 2013;3:36-40.

Estimating individual benefits of medical or behavioral treatments in severely ill patients.

PubMed

Diaz, Francisco J

2017-01-01

There is a need for statistical methods appropriate for the analysis of clinical trials from a personalized-medicine viewpoint as opposed to the common statistical practice that simply examines average treatment effects. This article proposes an approach to quantifying, reporting and analyzing individual benefits of medical or behavioral treatments to severely ill patients with chronic conditions, using data from clinical trials. The approach is a new development of a published framework for measuring the severity of a chronic disease and the benefits treatments provide to individuals, which utilizes regression models with random coefficients. Here, a patient is considered to be severely ill if the patient's basal severity is close to one. This allows the derivation of a very flexible family of probability distributions of individual benefits that depend on treatment duration and the covariates included in the regression model. Our approach may enrich the statistical analysis of clinical trials of severely ill patients because it allows investigating the probability distribution of individual benefits in the patient population and the variables that influence it, and we can also measure the benefits achieved in specific patients including new patients. We illustrate our approach using data from a clinical trial of the anti-depressant imipramine.

Relationships among Classical Test Theory and Item Response Theory Frameworks via Factor Analytic Models

ERIC Educational Resources Information Center

Kohli, Nidhi; Koran, Jennifer; Henn, Lisa

2015-01-01

There are well-defined theoretical differences between the classical test theory (CTT) and item response theory (IRT) frameworks. It is understood that in the CTT framework, person and item statistics are test- and sample-dependent. This is not the perception with IRT. For this reason, the IRT framework is considered to be theoretically superior…

Defense Resource Management Studies: Introduction to Capability and Acquisition Planning Processes

DTIC Science & Technology

2010-08-01

interchangeable and useful in a common contextual framework . Currently, both simulations use a common scenario, the same fictitious country, and...culture, legal framework , and institutions. • Incorporate Principles of Good Governance and Respect for Human Rights: Stress accountability and...Preparing for the assessments requires defining the missions to be analyzed; subdividing the mission definitions to provide a framework for analytic work

Quality Evaluation of Zirconium Dioxide Frameworks Produced in Five Dental Laboratories from Different Countries.

PubMed

Schneebeli, Esther; Brägger, Urs; Scherrer, Susanne S; Keller, Andrea; Wittneben, Julia G; Hicklin, Stefan P

2017-07-01

The aim of this study was to assess and compare quality as well as economic aspects of CAD/CAM high strength ceramic three-unit FDP frameworks ordered from dental laboratories located in emerging countries and Switzerland. The master casts of six cases were sent to five dental laboratories located in Thailand (Bangkok), China (Peking and Shenzhen), Turkey (Izmir), and Switzerland (Bern). Each laboratory was using a different CAD/CAM system. The clinical fit of the frameworks was qualitatively assessed, and the thickness of the framework material, the connector height, the width, and the diameter were evaluated using a measuring sensor. The analysis of the internal fit of the frameworks was performed by means of a replica technique, whereas the inner and outer surfaces of the frameworks were evaluated for traces of postprocessing and damage to the intaglio surface with light and electronic microscopes. Groups (dental laboratories and cases) were compared for statistically significant differences using Mann-Whitney U-tests after Bonferroni correction. An acceptable clinical fit was found at 97.9% of the margins produced in laboratory E, 87.5% in B, 93.7% in C, 79.2% in A, and 62.5% in D. The mean framework thicknesses were not statistically significantly different for the premolar regions; however, for the molar area 4/8 of the evaluated sites were statistically significantly different. Circumference, surface, and width of the connectors produced in the different laboratories were statistically significantly different but not the height. There were great differences in the designs for the pontic and connector regions, and some of the frameworks would not be recommended for clinical use. Traces of heavy postprocessing were found in frameworks from some of the laboratories. The prices per framework ranged from US$177 to US$896. By ordering laboratory work in developing countries, a considerable price reduction was obtained compared to the price level in Switzerland. Despite the use of the standardized CAD/CAM chains of production in all laboratories, a large variability in the quality aspects, such as clinical marginal fit, connector and pontic design, as well as postprocessing traces was noted. Recommended sound handling of postprocessing was not applied in all laboratories. Dentists should be aware of the true and factitious advantages of CAD/CAM production chains and not lose control over the process. © 2015 by the American College of Prosthodontists.

Crime Scenes and Mystery Players! Using Driving Questions to Support the Development of Statistical Literacy

ERIC Educational Resources Information Center

Leavy, Aisling; Hourigan, Mairead

2016-01-01

We argue that the development of statistical literacy is greatly supported by engaging students in carrying out statistical investigations. We describe the use of driving questions and interesting contexts to motivate two statistical investigations. The PPDAC cycle is use as an organizing framework to support the process statistical investigation.

Detecting Anomalous Insiders in Collaborative Information Systems

PubMed Central

Chen, You; Nyemba, Steve; Malin, Bradley

2012-01-01

Collaborative information systems (CISs) are deployed within a diverse array of environments that manage sensitive information. Current security mechanisms detect insider threats, but they are ill-suited to monitor systems in which users function in dynamic teams. In this paper, we introduce the community anomaly detection system (CADS), an unsupervised learning framework to detect insider threats based on the access logs of collaborative environments. The framework is based on the observation that typical CIS users tend to form community structures based on the subjects accessed (e.g., patients’ records viewed by healthcare providers). CADS consists of two components: 1) relational pattern extraction, which derives community structures and 2) anomaly prediction, which leverages a statistical model to determine when users have sufficiently deviated from communities. We further extend CADS into MetaCADS to account for the semantics of subjects (e.g., patients’ diagnoses). To empirically evaluate the framework, we perform an assessment with three months of access logs from a real electronic health record (EHR) system in a large medical center. The results illustrate our models exhibit significant performance gains over state-of-the-art competitors. When the number of illicit users is low, MetaCADS is the best model, but as the number grows, commonly accessed semantics lead to hiding in a crowd, such that CADS is more prudent. PMID:24489520

An Optimal Parameterization Framework for Infrasonic Tomography of the Stratospheric Winds Using Non-Local Sources

DOE PAGES

Blom, Philip Stephen; Marcillo, Omar Eduardo

2016-12-05

A method is developed to apply acoustic tomography methods to a localized network of infrasound arrays with intention of monitoring the atmosphere state in the region around the network using non-local sources without requiring knowledge of the precise source location or non-local atmosphere state. Closely spaced arrays provide a means to estimate phase velocities of signals that can provide limiting bounds on certain characteristics of the atmosphere. Larger spacing between such clusters provide a means to estimate celerity from propagation times along multiple unique stratospherically or thermospherically ducted propagation paths and compute more precise estimates of the atmosphere state. Inmore » order to avoid the commonly encountered complex, multimodal distributions for parametric atmosphere descriptions and to maximize the computational efficiency of the method, an optimal parametrization framework is constructed. This framework identifies the ideal combination of parameters for tomography studies in specific regions of the atmosphere and statistical model selection analysis shows that high quality corrections to the middle atmosphere winds can be obtained using as few as three parameters. Lastly, comparison of the resulting estimates for synthetic data sets shows qualitative agreement between the middle atmosphere winds and those estimated from infrasonic traveltime observations.« less

Mapping the Energy Cascade in the North Atlantic Ocean: The Coarse-graining Approach

DOE PAGES

Aluie, Hussein; Hecht, Matthew; Vallis, Geoffrey K.

2017-11-14

A coarse-graining framework is implemented to analyze nonlinear processes, measure energy transfer rates and map out the energy pathways from simulated global ocean data. Traditional tools to measure the energy cascade from turbulence theory, such as spectral flux or spectral transfer rely on the assumption of statistical homogeneity, or at least a large separation between the scales of motion and the scales of statistical inhomogeneity. The coarse-graining framework allows for probing the fully nonlinear dynamics simultaneously in scale and in space, and is not restricted by those assumptions. This study describes how the framework can be applied to ocean flows.

Mapping the Energy Cascade in the North Atlantic Ocean: The Coarse-graining Approach

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

Aluie, Hussein; Hecht, Matthew; Vallis, Geoffrey K.

A coarse-graining framework is implemented to analyze nonlinear processes, measure energy transfer rates and map out the energy pathways from simulated global ocean data. Traditional tools to measure the energy cascade from turbulence theory, such as spectral flux or spectral transfer rely on the assumption of statistical homogeneity, or at least a large separation between the scales of motion and the scales of statistical inhomogeneity. The coarse-graining framework allows for probing the fully nonlinear dynamics simultaneously in scale and in space, and is not restricted by those assumptions. This study describes how the framework can be applied to ocean flows.

Software for Data Analysis with Graphical Models

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Roy, H. Scott

1994-01-01

Probabilistic graphical models are being used widely in artificial intelligence and statistics, for instance, in diagnosis and expert systems, as a framework for representing and reasoning with probabilities and independencies. They come with corresponding algorithms for performing statistical inference. This offers a unifying framework for prototyping and/or generating data analysis algorithms from graphical specifications. This paper illustrates the framework with an example and then presents some basic techniques for the task: problem decomposition and the calculation of exact Bayes factors. Other tools already developed, such as automatic differentiation, Gibbs sampling, and use of the EM algorithm, make this a broad basis for the generation of data analysis software.

Exploratory Visual Analysis of Statistical Results from Microarray Experiments Comparing High and Low Grade Glioma

PubMed Central

Reif, David M.; Israel, Mark A.; Moore, Jason H.

2007-01-01

The biological interpretation of gene expression microarray results is a daunting challenge. For complex diseases such as cancer, wherein the body of published research is extensive, the incorporation of expert knowledge provides a useful analytical framework. We have previously developed the Exploratory Visual Analysis (EVA) software for exploring data analysis results in the context of annotation information about each gene, as well as biologically relevant groups of genes. We present EVA as a flexible combination of statistics and biological annotation that provides a straightforward visual interface for the interpretation of microarray analyses of gene expression in the most commonly occuring class of brain tumors, glioma. We demonstrate the utility of EVA for the biological interpretation of statistical results by analyzing publicly available gene expression profiles of two important glial tumors. The results of a statistical comparison between 21 malignant, high-grade glioblastoma multiforme (GBM) tumors and 19 indolent, low-grade pilocytic astrocytomas were analyzed using EVA. By using EVA to examine the results of a relatively simple statistical analysis, we were able to identify tumor class-specific gene expression patterns having both statistical and biological significance. Our interactive analysis highlighted the potential importance of genes involved in cell cycle progression, proliferation, signaling, adhesion, migration, motility, and structure, as well as candidate gene loci on a region of Chromosome 7 that has been implicated in glioma. Because EVA does not require statistical or computational expertise and has the flexibility to accommodate any type of statistical analysis, we anticipate EVA will prove a useful addition to the repertoire of computational methods used for microarray data analysis. EVA is available at no charge to academic users and can be found at http://www.epistasis.org. PMID:19390666

The role of ensemble-based statistics in variational assimilation of cloud-affected observations from infrared imagers

NASA Astrophysics Data System (ADS)

Hacker, Joshua; Vandenberghe, Francois; Jung, Byoung-Jo; Snyder, Chris

2017-04-01

Effective assimilation of cloud-affected radiance observations from space-borne imagers, with the aim of improving cloud analysis and forecasting, has proven to be difficult. Large observation biases, nonlinear observation operators, and non-Gaussian innovation statistics present many challenges. Ensemble-variational data assimilation (EnVar) systems offer the benefits of flow-dependent background error statistics from an ensemble, and the ability of variational minimization to handle nonlinearity. The specific benefits of ensemble statistics, relative to static background errors more commonly used in variational systems, have not been quantified for the problem of assimilating cloudy radiances. A simple experiment framework is constructed with a regional NWP model and operational variational data assimilation system, to provide the basis understanding the importance of ensemble statistics in cloudy radiance assimilation. Restricting the observations to those corresponding to clouds in the background forecast leads to innovations that are more Gaussian. The number of large innovations is reduced compared to the more general case of all observations, but not eliminated. The Huber norm is investigated to handle the fat tails of the distributions, and allow more observations to be assimilated without the need for strict background checks that eliminate them. Comparing assimilation using only ensemble background error statistics with assimilation using only static background error statistics elucidates the importance of the ensemble statistics. Although the cost functions in both experiments converge to similar values after sufficient outer-loop iterations, the resulting cloud water, ice, and snow content are greater in the ensemble-based analysis. The subsequent forecasts from the ensemble-based analysis also retain more condensed water species, indicating that the local environment is more supportive of clouds. In this presentation we provide details that explain the apparent benefit from using ensembles for cloudy radiance assimilation in an EnVar context.

Repeat-aware modeling and correction of short read errors.

PubMed

Yang, Xiao; Aluru, Srinivas; Dorman, Karin S

2011-02-15

High-throughput short read sequencing is revolutionizing genomics and systems biology research by enabling cost-effective deep coverage sequencing of genomes and transcriptomes. Error detection and correction are crucial to many short read sequencing applications including de novo genome sequencing, genome resequencing, and digital gene expression analysis. Short read error detection is typically carried out by counting the observed frequencies of kmers in reads and validating those with frequencies exceeding a threshold. In case of genomes with high repeat content, an erroneous kmer may be frequently observed if it has few nucleotide differences with valid kmers with multiple occurrences in the genome. Error detection and correction were mostly applied to genomes with low repeat content and this remains a challenging problem for genomes with high repeat content. We develop a statistical model and a computational method for error detection and correction in the presence of genomic repeats. We propose a method to infer genomic frequencies of kmers from their observed frequencies by analyzing the misread relationships among observed kmers. We also propose a method to estimate the threshold useful for validating kmers whose estimated genomic frequency exceeds the threshold. We demonstrate that superior error detection is achieved using these methods. Furthermore, we break away from the common assumption of uniformly distributed errors within a read, and provide a framework to model position-dependent error occurrence frequencies common to many short read platforms. Lastly, we achieve better error correction in genomes with high repeat content. The software is implemented in C++ and is freely available under GNU GPL3 license and Boost Software V1.0 license at "http://aluru-sun.ece.iastate.edu/doku.php?id = redeem". We introduce a statistical framework to model sequencing errors in next-generation reads, which led to promising results in detecting and correcting errors for genomes with high repeat content.

Metrics and Mappings: A Framework for Understanding Real-World Quantitative Estimation.

ERIC Educational Resources Information Center

Brown, Norman R.; Siegler, Robert S.

1993-01-01

A metrics and mapping framework is proposed to account for how heuristics, domain-specific reasoning, and intuitive statistical induction processes are integrated to generate estimates. Results of 4 experiments involving 188 undergraduates illustrate framework usefulness and suggest when people use heuristics and when they emphasize…

A Statistical Framework for the Functional Analysis of Metagenomes

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

Sharon, Itai; Pati, Amrita; Markowitz, Victor

2008-10-01

Metagenomic studies consider the genetic makeup of microbial communities as a whole, rather than their individual member organisms. The functional and metabolic potential of microbial communities can be analyzed by comparing the relative abundance of gene families in their collective genomic sequences (metagenome) under different conditions. Such comparisons require accurate estimation of gene family frequencies. They present a statistical framework for assessing these frequencies based on the Lander-Waterman theory developed originally for Whole Genome Shotgun (WGS) sequencing projects. They also provide a novel method for assessing the reliability of the estimations which can be used for removing seemingly unreliable measurements.more » They tested their method on a wide range of datasets, including simulated genomes and real WGS data from sequencing projects of whole genomes. Results suggest that their framework corrects inherent biases in accepted methods and provides a good approximation to the true statistics of gene families in WGS projects.« less

A Unifying Framework for Teaching Nonparametric Statistical Tests

ERIC Educational Resources Information Center

Bargagliotti, Anna E.; Orrison, Michael E.

2014-01-01

Increased importance is being placed on statistics at both the K-12 and undergraduate level. Research divulging effective methods to teach specific statistical concepts is still widely sought after. In this paper, we focus on best practices for teaching topics in nonparametric statistics at the undergraduate level. To motivate the work, we…

Survey of Native English Speakers and Spanish-Speaking English Language Learners in Tertiary Introductory Statistics

ERIC Educational Resources Information Center

Lesser, Lawrence M.; Wagler, Amy E.; Esquinca, Alberto; Valenzuela, M. Guadalupe

2013-01-01

The framework of linguistic register and case study research on Spanish-speaking English language learners (ELLs) learning statistics informed the construction of a quantitative instrument, the Communication, Language, And Statistics Survey (CLASS). CLASS aims to assess whether ELLs and non-ELLs approach the learning of statistics differently with…

Using GAISE and NCTM Standards as Frameworks for Teaching Probability and Statistics to Pre-Service Elementary and Middle School Mathematics Teachers

ERIC Educational Resources Information Center

Metz, Mary Louise

2010-01-01

Statistics education has become an increasingly important component of the mathematics education of today's citizens. In part to address the call for a more statistically literate citizenship, The "Guidelines for Assessment and Instruction in Statistics Education (GAISE)" were developed in 2005 by the American Statistical Association. These…

Inferring Demographic History Using Two-Locus Statistics.

PubMed

Ragsdale, Aaron P; Gutenkunst, Ryan N

2017-06-01

Population demographic history may be learned from contemporary genetic variation data. Methods based on aggregating the statistics of many single loci into an allele frequency spectrum (AFS) have proven powerful, but such methods ignore potentially informative patterns of linkage disequilibrium (LD) between neighboring loci. To leverage such patterns, we developed a composite-likelihood framework for inferring demographic history from aggregated statistics of pairs of loci. Using this framework, we show that two-locus statistics are more sensitive to demographic history than single-locus statistics such as the AFS. In particular, two-locus statistics escape the notorious confounding of depth and duration of a bottleneck, and they provide a means to estimate effective population size based on the recombination rather than mutation rate. We applied our approach to a Zambian population of Drosophila melanogaster Notably, using both single- and two-locus statistics, we inferred a substantially lower ancestral effective population size than previous works and did not infer a bottleneck history. Together, our results demonstrate the broad potential for two-locus statistics to enable powerful population genetic inference. Copyright © 2017 by the Genetics Society of America.

Global/local methods research using a common structural analysis framework

NASA Technical Reports Server (NTRS)

Knight, Norman F., Jr.; Ransom, Jonathan B.; Griffin, O. H., Jr.; Thompson, Danniella M.

1991-01-01

Methodologies for global/local stress analysis are described including both two- and three-dimensional analysis methods. These methods are being developed within a common structural analysis framework. Representative structural analysis problems are presented to demonstrate the global/local methodologies being developed.

Machine Learning for Detecting Gene-Gene Interactions

PubMed Central

McKinney, Brett A.; Reif, David M.; Ritchie, Marylyn D.; Moore, Jason H.

2011-01-01

Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are ‘the norm’ and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics. PMID:16722772

Quantifying risks with exact analytical solutions of derivative pricing distribution

NASA Astrophysics Data System (ADS)

Zhang, Kun; Liu, Jing; Wang, Erkang; Wang, Jin

2017-04-01

Derivative (i.e. option) pricing is essential for modern financial instrumentations. Despite of the previous efforts, the exact analytical forms of the derivative pricing distributions are still challenging to obtain. In this study, we established a quantitative framework using path integrals to obtain the exact analytical solutions of the statistical distribution for bond and bond option pricing for the Vasicek model. We discuss the importance of statistical fluctuations away from the expected option pricing characterized by the distribution tail and their associations to value at risk (VaR). The framework established here is general and can be applied to other financial derivatives for quantifying the underlying statistical distributions.

Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function. Copyright © 2017. Published by Elsevier Inc.

Computational Analysis for Rocket-Based Combined-Cycle Systems During Rocket-Only Operation

NASA Technical Reports Server (NTRS)

Steffen, C. J., Jr.; Smith, T. D.; Yungster, S.; Keller, D. J.

2000-01-01

A series of Reynolds-averaged Navier-Stokes calculations were employed to study the performance of rocket-based combined-cycle systems operating in an all-rocket mode. This parametric series of calculations were executed within a statistical framework, commonly known as design of experiments. The parametric design space included four geometric and two flowfield variables set at three levels each, for a total of 729 possible combinations. A D-optimal design strategy was selected. It required that only 36 separate computational fluid dynamics (CFD) solutions be performed to develop a full response surface model, which quantified the linear, bilinear, and curvilinear effects of the six experimental variables. The axisymmetric, Reynolds-averaged Navier-Stokes simulations were executed with the NPARC v3.0 code. The response used in the statistical analysis was created from Isp efficiency data integrated from the 36 CFD simulations. The influence of turbulence modeling was analyzed by using both one- and two-equation models. Careful attention was also given to quantify the influence of mesh dependence, iterative convergence, and artificial viscosity upon the resulting statistical model. Thirteen statistically significant effects were observed to have an influence on rocket-based combined-cycle nozzle performance. It was apparent that the free-expansion process, directly downstream of the rocket nozzle, can influence the Isp efficiency. Numerical schlieren images and particle traces have been used to further understand the physical phenomena behind several of the statistically significant results.

SpecBit, DecayBit and PrecisionBit: GAMBIT modules for computing mass spectra, particle decay rates and precision observables

NASA Astrophysics Data System (ADS)

Athron, Peter; Balázs, Csaba; Dal, Lars A.; Edsjö, Joakim; Farmer, Ben; Gonzalo, Tomás E.; Kvellestad, Anders; McKay, James; Putze, Antje; Rogan, Chris; Scott, Pat; Weniger, Christoph; White, Martin

2018-01-01

We present the GAMBIT modules SpecBit, DecayBit and PrecisionBit. Together they provide a new framework for linking publicly available spectrum generators, decay codes and other precision observable calculations in a physically and statistically consistent manner. This allows users to automatically run various combinations of existing codes as if they are a single package. The modular design allows software packages fulfilling the same role to be exchanged freely at runtime, with the results presented in a common format that can easily be passed to downstream dark matter, collider and flavour codes. These modules constitute an essential part of the broader GAMBIT framework, a major new software package for performing global fits. In this paper we present the observable calculations, data, and likelihood functions implemented in the three modules, as well as the conventions and assumptions used in interfacing them with external codes. We also present 3-BIT-HIT, a command-line utility for computing mass spectra, couplings, decays and precision observables in the MSSM, which shows how the three modules can easily be used independently of GAMBIT.

PNNL Report on the Development of Bench-scale CFD Simulations for Gas Absorption across a Wetted Wall Column

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

Wang, Chao; Xu, Zhijie; Lai, Canhai

This report is prepared for the demonstration of hierarchical prediction of carbon capture efficiency of a solvent-based absorption column. A computational fluid dynamics (CFD) model is first developed to simulate the core phenomena of solvent-based carbon capture, i.e., the CO2 physical absorption and chemical reaction, on a simplified geometry of wetted wall column (WWC) at bench scale. Aqueous solutions of ethanolamine (MEA) are commonly selected as a CO2 stream scrubbing liquid. CO2 is captured by both physical and chemical absorption using highly CO2 soluble and reactive solvent, MEA, during the scrubbing process. In order to provide confidence bound on themore » computational predictions of this complex engineering system, a hierarchical calibration and validation framework is proposed. The overall goal of this effort is to provide a mechanism-based predictive framework with confidence bound for overall mass transfer coefficient of the wetted wall column (WWC) with statistical analyses of the corresponding WWC experiments with increasing physical complexity.« less

Statistical physics of language dynamics

NASA Astrophysics Data System (ADS)

Loreto, Vittorio; Baronchelli, Andrea; Mukherjee, Animesh; Puglisi, Andrea; Tria, Francesca

2011-04-01

Language dynamics is a rapidly growing field that focuses on all processes related to the emergence, evolution, change and extinction of languages. Recently, the study of self-organization and evolution of language and meaning has led to the idea that a community of language users can be seen as a complex dynamical system, which collectively solves the problem of developing a shared communication framework through the back-and-forth signaling between individuals. We shall review some of the progress made in the past few years and highlight potential future directions of research in this area. In particular, the emergence of a common lexicon and of a shared set of linguistic categories will be discussed, as examples corresponding to the early stages of a language. The extent to which synthetic modeling is nowadays contributing to the ongoing debate in cognitive science will be pointed out. In addition, the burst of growth of the web is providing new experimental frameworks. It makes available a huge amount of resources, both as novel tools and data to be analyzed, allowing quantitative and large-scale analysis of the processes underlying the emergence of a collective information and language dynamics.

Depth of Teachers' Knowledge: Frameworks for Teachers' Knowledge of Mathematics

ERIC Educational Resources Information Center

Holmes, Vicki-Lynn

2012-01-01

This article describes seven teacher knowledge frameworks and relates these frameworks to the teaching and assessment of elementary teacher's mathematics knowledge. The frameworks classify teachers' knowledge and provide a vocabulary and common language through which knowledge can be discussed and assessed. These frameworks are categorized into…

EPOS--The European E-Portfolio of Languages

ERIC Educational Resources Information Center

Kühn, Bärbel

2016-01-01

Democratic principles and human rights, the core values of the Council of Europe, informed the development of the "Common European Framework of Reference for Languages" (CEFR; Council of Europe 2001. "Common European framework of reference for languages: Learning, teaching, assessment." Cambridge: Cambridge University Press.…

From Interaction to Co-Association —A Fisher r-To-z Transformation-Based Simple Statistic for Real World Genome-Wide Association Study

PubMed Central

Yuan, Zhongshang; Liu, Hong; Zhang, Xiaoshuai; Li, Fangyu; Zhao, Jinghua; Zhang, Furen; Xue, Fuzhong

2013-01-01

Currently, the genetic variants identified by genome wide association study (GWAS) generally only account for a small proportion of the total heritability for complex disease. One crucial reason is the underutilization of gene-gene joint effects commonly encountered in GWAS, which includes their main effects and co-association. However, gene-gene co-association is often customarily put into the framework of gene-gene interaction vaguely. From the causal graph perspective, we elucidate in detail the concept and rationality of gene-gene co-association as well as its relationship with traditional gene-gene interaction, and propose two Fisher r-to-z transformation-based simple statistics to detect it. Three series of simulations further highlight that gene-gene co-association refers to the extent to which the joint effects of two genes differs from the main effects, not only due to the traditional interaction under the nearly independent condition but the correlation between two genes. The proposed statistics are more powerful than logistic regression under various situations, cannot be affected by linkage disequilibrium and can have acceptable false positive rate as long as strictly following the reasonable GWAS data analysis roadmap. Furthermore, an application to gene pathway analysis associated with leprosy confirms in practice that our proposed gene-gene co-association concepts as well as the correspondingly proposed statistics are strongly in line with reality. PMID:23923021

On the Relationship between Molecular Hit Rates in High-Throughput Screening and Molecular Descriptors.

PubMed

Hansson, Mari; Pemberton, John; Engkvist, Ola; Feierberg, Isabella; Brive, Lars; Jarvis, Philip; Zander-Balderud, Linda; Chen, Hongming

2014-06-01

High-throughput screening (HTS) is widely used in the pharmaceutical industry to identify novel chemical starting points for drug discovery projects. The current study focuses on the relationship between molecular hit rate in recent in-house HTS and four common molecular descriptors: lipophilicity (ClogP), size (heavy atom count, HEV), fraction of sp(3)-hybridized carbons (Fsp3), and fraction of molecular framework (f(MF)). The molecular hit rate is defined as the fraction of times the molecule has been assigned as active in the HTS campaigns where it has been screened. Beta-binomial statistical models were built to model the molecular hit rate as a function of these descriptors. The advantage of the beta-binomial statistical models is that the correlation between the descriptors is taken into account. Higher degree polynomial terms of the descriptors were also added into the beta-binomial statistic model to improve the model quality. The relative influence of different molecular descriptors on molecular hit rate has been estimated, taking into account that the descriptors are correlated to each other through applying beta-binomial statistical modeling. The results show that ClogP has the largest influence on the molecular hit rate, followed by Fsp3 and HEV. f(MF) has only a minor influence besides its correlation with the other molecular descriptors. © 2013 Society for Laboratory Automation and Screening.

Toward improved analysis of concentration data: Embracing nondetects.

PubMed

Shoari, Niloofar; Dubé, Jean-Sébastien

2018-03-01

Various statistical tests on concentration data serve to support decision-making regarding characterization and monitoring of contaminated media, assessing exposure to a chemical, and quantifying the associated risks. However, the routine statistical protocols cannot be directly applied because of challenges arising from nondetects or left-censored observations, which are concentration measurements below the detection limit of measuring instruments. Despite the existence of techniques based on survival analysis that can adjust for nondetects, these are seldom taken into account properly. A comprehensive review of the literature showed that managing policies regarding analysis of censored data do not always agree and that guidance from regulatory agencies may be outdated. Therefore, researchers and practitioners commonly resort to the most convenient way of tackling the censored data problem by substituting nondetects with arbitrary constants prior to data analysis, although this is generally regarded as a bias-prone approach. Hoping to improve the interpretation of concentration data, the present article aims to familiarize researchers in different disciplines with the significance of left-censored observations and provides theoretical and computational recommendations (under both frequentist and Bayesian frameworks) for adequate analysis of censored data. In particular, the present article synthesizes key findings from previous research with respect to 3 noteworthy aspects of inferential statistics: estimation of descriptive statistics, hypothesis testing, and regression analysis. Environ Toxicol Chem 2018;37:643-656. © 2017 SETAC. © 2017 SETAC.

Statistical Learning and Language: An Individual Differences Study

ERIC Educational Resources Information Center

Misyak, Jennifer B.; Christiansen, Morten H.

2012-01-01

Although statistical learning and language have been assumed to be intertwined, this theoretical presupposition has rarely been tested empirically. The present study investigates the relationship between statistical learning and language using a within-subject design embedded in an individual-differences framework. Participants were administered…

Dynamic whole body PET parametric imaging: II. Task-oriented statistical estimation

PubMed Central

Karakatsanis, Nicolas A.; Lodge, Martin A.; Zhou, Y.; Wahl, Richard L.; Rahmim, Arman

2013-01-01

In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (~15–20cm) of a single bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. Here, we pursue a transition to dynamic whole body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate Ki and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final Ki parametric images, enabling task-based performance optimization. Overall, whether the observer's task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion study, was employed along with extensive Monte Carlo simulations and an initial clinical FDG patient dataset to validate and demonstrate the potential of the proposed statistical estimation methods. Both simulated and clinical results suggest that hybrid regression in the context of whole-body Patlak Ki imaging considerably reduces MSE without compromising high CNR. Alternatively, for a given CNR, hybrid regression enables larger reductions than OLS in the number of dynamic frames per bed, allowing for even shorter acquisitions of ~30min, thus further contributing to the clinical adoption of the proposed framework. Compared to the SUV approach, whole body parametric imaging can provide better tumor quantification, and can act as a complement to SUV, for the task of tumor detection. PMID:24080994

Dynamic whole-body PET parametric imaging: II. Task-oriented statistical estimation.

PubMed

Karakatsanis, Nicolas A; Lodge, Martin A; Zhou, Y; Wahl, Richard L; Rahmim, Arman

2013-10-21

In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (~15-20 cm) of a single-bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole-body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate Ki and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final Ki parametric images, enabling task-based performance optimization. Overall, whether the observer's task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion study, was employed along with extensive Monte Carlo simulations and an initial clinical (18)F-deoxyglucose patient dataset to validate and demonstrate the potential of the proposed statistical estimation methods. Both simulated and clinical results suggest that hybrid regression in the context of whole-body Patlak Ki imaging considerably reduces MSE without compromising high CNR. Alternatively, for a given CNR, hybrid regression enables larger reductions than OLS in the number of dynamic frames per bed, allowing for even shorter acquisitions of ~30 min, thus further contributing to the clinical adoption of the proposed framework. Compared to the SUV approach, whole-body parametric imaging can provide better tumor quantification, and can act as a complement to SUV, for the task of tumor detection.

[Is "mental health" part of the common good? The sociopolitical framework of psychiatric ethics and the responsibility of health-care elites].

PubMed

Bohlken, Eike

2014-07-01

Psychiatric work can only be that ethical as the framework of a health-care system allows. Thus, the responsibility of the health-care elites to establish a sociopolitical framework that suits psychiatric ethics is discussed on the basis of a theory of the common good and of a philosophical and normative elite theory. "Mental health" is demonstrated to be part of a basic sphere of the common good which cannot be denied to any member of a society. The final section discusses which specific duties can be derived for health-care elites on the ground of the aforementioned conception of "mental health" as a part of the common good. © Georg Thieme Verlag KG Stuttgart · New York.

The human genome as public: Justifications and implications.

PubMed

Bayefsky, Michelle J

2017-03-01

Since the human genome was decoded, great emphasis has been placed on the unique, personal nature of the genome, along with the benefits that personalized medicine can bring to individuals and the importance of safeguarding genetic privacy. As a result, an equally important aspect of the human genome - its common nature - has been underappreciated and underrepresented in the ethics literature and policy dialogue surrounding genetics and genomics. This article will argue that, just as the personal nature of the genome has been used to reinforce individual rights and justify important privacy protections, so too the common nature of the genome can be employed to support protections of the genome at a population level and policies designed to promote the public's wellbeing. In order for public health officials to have the authority to develop genetics policies for the sake of the public good, the genome must have not only a common, but also a public, dimension. This article contends that DNA carries a public dimension through the use of two conceptual frameworks: the common heritage (CH) framework and the common resource (CR) framework. Both frameworks establish a public interest in the human genome, but the CH framework can be used to justify policies aimed at preserving and protecting the genome, while the CR framework can be employed to justify policies for utilizing the genome for the public benefit. A variety of possible policy implications are discussed, with special attention paid to the use of large-scale genomics databases for public health research. © Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Applying Sociocultural Theory to Teaching Statistics for Doctoral Social Work Students

ERIC Educational Resources Information Center

Mogro-Wilson, Cristina; Reeves, Michael G.; Charter, Mollie Lazar

2015-01-01

This article describes the development of two doctoral-level multivariate statistics courses utilizing sociocultural theory, an integrative pedagogical framework. In the first course, the implementation of sociocultural theory helps to support the students through a rigorous introduction to statistics. The second course involves students…

Measuring missing heritability: Inferring the contribution of common variants

PubMed Central

Golan, David; Lander, Eric S.; Rosset, Saharon

2014-01-01

Genome-wide association studies (GWASs), also called common variant association studies (CVASs), have uncovered thousands of genetic variants associated with hundreds of diseases. However, the variants that reach statistical significance typically explain only a small fraction of the heritability. One explanation for the “missing heritability” is that there are many additional disease-associated common variants whose effects are too small to detect with current sample sizes. It therefore is useful to have methods to quantify the heritability due to common variation, without having to identify all causal variants. Recent studies applied restricted maximum likelihood (REML) estimation to case–control studies for diseases. Here, we show that REML considerably underestimates the fraction of heritability due to common variation in this setting. The degree of underestimation increases with the rarity of disease, the heritability of the disease, and the size of the sample. Instead, we develop a general framework for heritability estimation, called phenotype correlation–genotype correlation (PCGC) regression, which generalizes the well-known Haseman–Elston regression method. We show that PCGC regression yields unbiased estimates. Applying PCGC regression to six diseases, we estimate the proportion of the phenotypic variance due to common variants to range from 25% to 56% and the proportion of heritability due to common variants from 41% to 68% (mean 60%). These results suggest that common variants may explain at least half the heritability for many diseases. PCGC regression also is readily applicable to other settings, including analyzing extreme-phenotype studies and adjusting for covariates such as sex, age, and population structure. PMID:25422463

Conducting Human Research

DTIC Science & Technology

2009-08-05

Socio-cultural data acquisition, extraction, and management.??? First the idea of a theoretical framework will be very briefly discussed as well as...SUBJECT TERMS human behavior, theoretical framework , hypothesis development, experimental design, ethical research, statistical power, human laboratory...who throw rocks? • How can we make them stay too far away to throw rocks? UNCLASSIFIED – Approved for Public Release Theoretical Framework / Conceptual

A Modeling Framework for Optimal Computational Resource Allocation Estimation: Considering the Trade-offs between Physical Resolutions, Uncertainty and Computational Costs

NASA Astrophysics Data System (ADS)

Moslehi, M.; de Barros, F.; Rajagopal, R.

2014-12-01

Hydrogeological models that represent flow and transport in subsurface domains are usually large-scale with excessive computational complexity and uncertain characteristics. Uncertainty quantification for predicting flow and transport in heterogeneous formations often entails utilizing a numerical Monte Carlo framework, which repeatedly simulates the model according to a random field representing hydrogeological characteristics of the field. The physical resolution (e.g. grid resolution associated with the physical space) for the simulation is customarily chosen based on recommendations in the literature, independent of the number of Monte Carlo realizations. This practice may lead to either excessive computational burden or inaccurate solutions. We propose an optimization-based methodology that considers the trade-off between the following conflicting objectives: time associated with computational costs, statistical convergence of the model predictions and physical errors corresponding to numerical grid resolution. In this research, we optimally allocate computational resources by developing a modeling framework for the overall error based on a joint statistical and numerical analysis and optimizing the error model subject to a given computational constraint. The derived expression for the overall error explicitly takes into account the joint dependence between the discretization error of the physical space and the statistical error associated with Monte Carlo realizations. The accuracy of the proposed framework is verified in this study by applying it to several computationally extensive examples. Having this framework at hand aims hydrogeologists to achieve the optimum physical and statistical resolutions to minimize the error with a given computational budget. Moreover, the influence of the available computational resources and the geometric properties of the contaminant source zone on the optimum resolutions are investigated. We conclude that the computational cost associated with optimal allocation can be substantially reduced compared with prevalent recommendations in the literature.

Validation of surrogate endpoints in advanced solid tumors: systematic review of statistical methods, results, and implications for policy makers.

PubMed

Ciani, Oriana; Davis, Sarah; Tappenden, Paul; Garside, Ruth; Stein, Ken; Cantrell, Anna; Saad, Everardo D; Buyse, Marc; Taylor, Rod S

2014-07-01

Licensing of, and coverage decisions on, new therapies should rely on evidence from patient-relevant endpoints such as overall survival (OS). Nevertheless, evidence from surrogate endpoints may also be useful, as it may not only expedite the regulatory approval of new therapies but also inform coverage decisions. It is, therefore, essential that candidate surrogate endpoints be properly validated. However, there is no consensus on statistical methods for such validation and on how the evidence thus derived should be applied by policy makers. We review current statistical approaches to surrogate-endpoint validation based on meta-analysis in various advanced-tumor settings. We assessed the suitability of two surrogates (progression-free survival [PFS] and time-to-progression [TTP]) using three current validation frameworks: Elston and Taylor's framework, the German Institute of Quality and Efficiency in Health Care's (IQWiG) framework and the Biomarker-Surrogacy Evaluation Schema (BSES3). A wide variety of statistical methods have been used to assess surrogacy. The strength of the association between the two surrogates and OS was generally low. The level of evidence (observation-level versus treatment-level) available varied considerably by cancer type, by evaluation tools and was not always consistent even within one specific cancer type. Not in all solid tumors the treatment-level association between PFS or TTP and OS has been investigated. According to IQWiG's framework, only PFS achieved acceptable evidence of surrogacy in metastatic colorectal and ovarian cancer treated with cytotoxic agents. Our study emphasizes the challenges of surrogate-endpoint validation and the importance of building consensus on the development of evaluation frameworks.

Supporting children with disabilities at school: implications for the advocate role in professional practice and education

PubMed Central

Ng, Stella L.; Lingard, Lorelei; Hibbert, Kathryn; Regan, Sandra; Phelan, Shanon; Stooke, Rosamund; Meston, Christine; Schryer, Catherine; Manamperi, Madhushani; Friesen, Farah

2015-01-01

Abstract Purpose: School settings are a common practice context for rehabilitation professionals; health advocacy is a common and challenging practice role for professionals in this context. This study explored how pediatric practitioners advocate for children with disabilities at school. Specifically, we examined everyday advocacy in the context of school-based support for children with disabilities. Method: Our theoretical framework and methodological approach were informed by institutional ethnography, which maps and makes visible hidden social coordinators of work processes with a view to improving processes and outcomes. We included families, educators, and health/rehabilitation practitioners from Ontario. Of the 37 consented informants, 27 were interviewed and 15 observed. Documents and texts were collected from the micro-level (e.g. clinician reports) and the macro-level (e.g. policies). Results: Pediatric practitioners' advocacy work included two main work processes: spotlighting invisible disabilities and orienteering the special education terrain. Practitioners advocated indirectly, by proxy, with common proxies being documents and parents. Unintended consequences of advocacy by proxy included conflict and inefficiency, which were often unknown to the practitioner. Conclusions: The findings of this study provide practice-based knowledge about advocacy for children with disabilities, which may be used to inform further development of competency frameworks and continuing education for pediatric practitioners. The findings also show how everyday practices are influenced by policies and social discourses and how rehabilitation professionals may enact change.Implications for RehabilitationRehabilitation professionals frequently perform advocacy work. They may find it beneficial to perform advocacy work that is informed by overarching professional and ethical guidelines, and a nuanced understanding of local processes and structures.Competency frameworks and education for pediatric rehabilitation professionals may be improved by: encouraging professionals to consider how their practices, including their written documents, may affect parental burden, (mis)interpretation by document recipients, and potential unintended consequences.Policies and texts, e.g. privacy legislation and the Diagnostic and Statistical Manual (DSM), influence rehabilitation professionals' actions and interactions when supporting children with disabilities at school.An awareness of the influence of policies and texts may enable practitioners to work more effectively within current systems when supporting individuals with disabilities. PMID:25738906

Data Extraction and Management in Networks of Observational Health Care Databases for Scientific Research: A Comparison of EU-ADR, OMOP, Mini-Sentinel and MATRICE Strategies

PubMed Central

Gini, Rosa; Schuemie, Martijn; Brown, Jeffrey; Ryan, Patrick; Vacchi, Edoardo; Coppola, Massimo; Cazzola, Walter; Coloma, Preciosa; Berni, Roberto; Diallo, Gayo; Oliveira, José Luis; Avillach, Paul; Trifirò, Gianluca; Rijnbeek, Peter; Bellentani, Mariadonata; van Der Lei, Johan; Klazinga, Niek; Sturkenboom, Miriam

2016-01-01

Introduction: We see increased use of existing observational data in order to achieve fast and transparent production of empirical evidence in health care research. Multiple databases are often used to increase power, to assess rare exposures or outcomes, or to study diverse populations. For privacy and sociological reasons, original data on individual subjects can’t be shared, requiring a distributed network approach where data processing is performed prior to data sharing. Case Descriptions and Variation Among Sites: We created a conceptual framework distinguishing three steps in local data processing: (1) data reorganization into a data structure common across the network; (2) derivation of study variables not present in original data; and (3) application of study design to transform longitudinal data into aggregated data sets for statistical analysis. We applied this framework to four case studies to identify similarities and differences in the United States and Europe: Exploring and Understanding Adverse Drug Reactions by Integrative Mining of Clinical Records and Biomedical Knowledge (EU-ADR), Observational Medical Outcomes Partnership (OMOP), the Food and Drug Administration’s (FDA’s) Mini-Sentinel, and the Italian network—the Integration of Content Management Information on the Territory of Patients with Complex Diseases or with Chronic Conditions (MATRICE). Findings: National networks (OMOP, Mini-Sentinel, MATRICE) all adopted shared procedures for local data reorganization. The multinational EU-ADR network needed locally defined procedures to reorganize its heterogeneous data into a common structure. Derivation of new data elements was centrally defined in all networks but the procedure was not shared in EU-ADR. Application of study design was a common and shared procedure in all the case studies. Computer procedures were embodied in different programming languages, including SAS, R, SQL, Java, and C++. Conclusion: Using our conceptual framework we found several areas that would benefit from research to identify optimal standards for production of empirical knowledge from existing databases.an opportunity to advance evidence-based care management. In addition, formalized CM outcomes assessment methodologies will enable us to compare CM effectiveness across health delivery settings. PMID:27014709

SHARE: system design and case studies for statistical health information release

PubMed Central

Gardner, James; Xiong, Li; Xiao, Yonghui; Gao, Jingjing; Post, Andrew R; Jiang, Xiaoqian; Ohno-Machado, Lucila

2013-01-01

Objectives We present SHARE, a new system for statistical health information release with differential privacy. We present two case studies that evaluate the software on real medical datasets and demonstrate the feasibility and utility of applying the differential privacy framework on biomedical data. Materials and Methods SHARE releases statistical information in electronic health records with differential privacy, a strong privacy framework for statistical data release. It includes a number of state-of-the-art methods for releasing multidimensional histograms and longitudinal patterns. We performed a variety of experiments on two real datasets, the surveillance, epidemiology and end results (SEER) breast cancer dataset and the Emory electronic medical record (EeMR) dataset, to demonstrate the feasibility and utility of SHARE. Results Experimental results indicate that SHARE can deal with heterogeneous data present in medical data, and that the released statistics are useful. The Kullback–Leibler divergence between the released multidimensional histograms and the original data distribution is below 0.5 and 0.01 for seven-dimensional and three-dimensional data cubes generated from the SEER dataset, respectively. The relative error for longitudinal pattern queries on the EeMR dataset varies between 0 and 0.3. While the results are promising, they also suggest that challenges remain in applying statistical data release using the differential privacy framework for higher dimensional data. Conclusions SHARE is one of the first systems to provide a mechanism for custodians to release differentially private aggregate statistics for a variety of use cases in the medical domain. This proof-of-concept system is intended to be applied to large-scale medical data warehouses. PMID:23059729

Phenomenology of small violations of Fermi and Bose statistics

NASA Astrophysics Data System (ADS)

Greenberg, O. W.; Mohapatra, Rabindra N.

1989-04-01

In a recent paper, we proposed a ``paronic'' field-theory framework for possible small deviations from the Pauli exclusion principle. This theory cannot be represented in a positive-metric (Hilbert) space. Nonetheless, the issue of possible small violations of the exclusion principle can be addressed in the framework of quantum mechanics, without being connected with a local quantum field theory. In this paper, we discuss the phenomenology of small violations of both Fermi and Bose statistics. We consider the implications of such violations in atomic, nuclear, particle, and condensed-matter physics and in astrophysics and cosmology. We also discuss experiments that can detect small violations of Fermi and Bose statistics or place stringent bounds on their validity.

Sustainable Water Management & Satellite Remote Sensing

EPA Science Inventory

Eutrophication assessment frameworks such as the Australian National Water Quality Management Strategy, Oslo Paris (OSPAR) Commission Common Procedure, Water Framework Directive (WFD) of the European Union, Marine Strategy Framework Directive (MSFD) from the European Commission, ...

Statistical mechanics framework for static granular matter.

PubMed

Henkes, Silke; Chakraborty, Bulbul

2009-06-01

The physical properties of granular materials have been extensively studied in recent years. So far, however, there exists no theoretical framework which can explain the observations in a unified manner beyond the phenomenological jamming diagram. This work focuses on the case of static granular matter, where we have constructed a statistical ensemble which mirrors equilibrium statistical mechanics. This ensemble, which is based on the conservation properties of the stress tensor, is distinct from the original Edwards ensemble and applies to packings of deformable grains. We combine it with a field theoretical analysis of the packings, where the field is the Airy stress function derived from the force and torque balance conditions. In this framework, Point J characterized by a diverging stiffness of the pressure fluctuations. Separately, we present a phenomenological mean-field theory of the jamming transition, which incorporates the mean contact number as a variable. We link both approaches in the context of the marginal rigidity picture proposed by Wyart and others.

Mourning dove hunting regulation strategy based on annual harvest statistics and banding data

USGS Publications Warehouse

Otis, D.L.

2006-01-01

Although managers should strive to base game bird harvest management strategies on mechanistic population models, monitoring programs required to build and continuously update these models may not be in place. Alternatively, If estimates of total harvest and harvest rates are available, then population estimates derived from these harvest data can serve as the basis for making hunting regulation decisions based on population growth rates derived from these estimates. I present a statistically rigorous approach for regulation decision-making using a hypothesis-testing framework and an assumed framework of 3 hunting regulation alternatives. I illustrate and evaluate the technique with historical data on the mid-continent mallard (Anas platyrhynchos) population. I evaluate the statistical properties of the hypothesis-testing framework using the best available data on mourning doves (Zenaida macroura). I use these results to discuss practical implementation of the technique as an interim harvest strategy for mourning doves until reliable mechanistic population models and associated monitoring programs are developed.

A Framework for Establishing Standard Reference Scale of Texture by Multivariate Statistical Analysis Based on Instrumental Measurement and Sensory Evaluation.

PubMed

Zhi, Ruicong; Zhao, Lei; Xie, Nan; Wang, Houyin; Shi, Bolin; Shi, Jingye

2016-01-13

A framework of establishing standard reference scale (texture) is proposed by multivariate statistical analysis according to instrumental measurement and sensory evaluation. Multivariate statistical analysis is conducted to rapidly select typical reference samples with characteristics of universality, representativeness, stability, substitutability, and traceability. The reasonableness of the framework method is verified by establishing standard reference scale of texture attribute (hardness) with Chinese well-known food. More than 100 food products in 16 categories were tested using instrumental measurement (TPA test), and the result was analyzed with clustering analysis, principal component analysis, relative standard deviation, and analysis of variance. As a result, nine kinds of foods were determined to construct the hardness standard reference scale. The results indicate that the regression coefficient between the estimated sensory value and the instrumentally measured value is significant (R(2) = 0.9765), which fits well with Stevens's theory. The research provides reliable a theoretical basis and practical guide for quantitative standard reference scale establishment on food texture characteristics.

Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION

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

Urrego-Blanco, Jorge R.; Hunke, Elizabeth C.; Urban, Nathan M.

Here, we implement a variance-based distance metric (D n) to objectively assess skill of sea ice models when multiple output variables or uncertainties in both model predictions and observations need to be considered. The metric compares observations and model data pairs on common spatial and temporal grids improving upon highly aggregated metrics (e.g., total sea ice extent or volume) by capturing the spatial character of model skill. The D n metric is a gamma-distributed statistic that is more general than the χ 2 statistic commonly used to assess model fit, which requires the assumption that the model is unbiased andmore » can only incorporate observational error in the analysis. The D n statistic does not assume that the model is unbiased, and allows the incorporation of multiple observational data sets for the same variable and simultaneously for different variables, along with different types of variances that can characterize uncertainties in both observations and the model. This approach represents a step to establish a systematic framework for probabilistic validation of sea ice models. The methodology is also useful for model tuning by using the D n metric as a cost function and incorporating model parametric uncertainty as part of a scheme to optimize model functionality. We apply this approach to evaluate different configurations of the standalone Los Alamos sea ice model (CICE) encompassing the parametric uncertainty in the model, and to find new sets of model configurations that produce better agreement than previous configurations between model and observational estimates of sea ice concentration and thickness.« less

Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION

DOE PAGES

Urrego-Blanco, Jorge R.; Hunke, Elizabeth C.; Urban, Nathan M.; ...

2017-04-01

Here, we implement a variance-based distance metric (D n) to objectively assess skill of sea ice models when multiple output variables or uncertainties in both model predictions and observations need to be considered. The metric compares observations and model data pairs on common spatial and temporal grids improving upon highly aggregated metrics (e.g., total sea ice extent or volume) by capturing the spatial character of model skill. The D n metric is a gamma-distributed statistic that is more general than the χ 2 statistic commonly used to assess model fit, which requires the assumption that the model is unbiased andmore » can only incorporate observational error in the analysis. The D n statistic does not assume that the model is unbiased, and allows the incorporation of multiple observational data sets for the same variable and simultaneously for different variables, along with different types of variances that can characterize uncertainties in both observations and the model. This approach represents a step to establish a systematic framework for probabilistic validation of sea ice models. The methodology is also useful for model tuning by using the D n metric as a cost function and incorporating model parametric uncertainty as part of a scheme to optimize model functionality. We apply this approach to evaluate different configurations of the standalone Los Alamos sea ice model (CICE) encompassing the parametric uncertainty in the model, and to find new sets of model configurations that produce better agreement than previous configurations between model and observational estimates of sea ice concentration and thickness.« less

Business Case Analysis for the Versatile Depot Automated Test Station Used in the USAF Warner Robins Air Logistics Center Maintenance Depot

DTIC Science & Technology

2008-06-01

executes the avionics test) can run on the new ATS thus creating the common ATS framework . The system will also enable numerous new functional...Enterprise-level architecture that reflects corporate DoD priorities and requirements for business systems, and provides a common framework to ensure that...entire Business Mission Area (BMA) of the DoD. The BEA also contains a set of integrated Department of Defense Architecture Framework (DoDAF

FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption

PubMed Central

2015-01-01

Background The increasing availability of genome data motivates massive research studies in personalized treatment and precision medicine. Public cloud services provide a flexible way to mitigate the storage and computation burden in conducting genome-wide association studies (GWAS). However, data privacy has been widely concerned when sharing the sensitive information in a cloud environment. Methods We presented a novel framework (FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption) to fully outsource GWAS (i.e., chi-square statistic computation) using homomorphic encryption. The proposed framework enables secure divisions over encrypted data. We introduced two division protocols (i.e., secure errorless division and secure approximation division) with a trade-off between complexity and accuracy in computing chi-square statistics. Results The proposed framework was evaluated for the task of chi-square statistic computation with two case-control datasets from the 2015 iDASH genome privacy protection challenge. Experimental results show that the performance of FORESEE can be significantly improved through algorithmic optimization and parallel computation. Remarkably, the secure approximation division provides significant performance gain, but without missing any significance SNPs in the chi-square association test using the aforementioned datasets. Conclusions Unlike many existing HME based studies, in which final results need to be computed by the data owner due to the lack of the secure division operation, the proposed FORESEE framework support complete outsourcing to the cloud and output the final encrypted chi-square statistics. PMID:26733391

FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption.

PubMed

Zhang, Yuchen; Dai, Wenrui; Jiang, Xiaoqian; Xiong, Hongkai; Wang, Shuang

2015-01-01

The increasing availability of genome data motivates massive research studies in personalized treatment and precision medicine. Public cloud services provide a flexible way to mitigate the storage and computation burden in conducting genome-wide association studies (GWAS). However, data privacy has been widely concerned when sharing the sensitive information in a cloud environment. We presented a novel framework (FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption) to fully outsource GWAS (i.e., chi-square statistic computation) using homomorphic encryption. The proposed framework enables secure divisions over encrypted data. We introduced two division protocols (i.e., secure errorless division and secure approximation division) with a trade-off between complexity and accuracy in computing chi-square statistics. The proposed framework was evaluated for the task of chi-square statistic computation with two case-control datasets from the 2015 iDASH genome privacy protection challenge. Experimental results show that the performance of FORESEE can be significantly improved through algorithmic optimization and parallel computation. Remarkably, the secure approximation division provides significant performance gain, but without missing any significance SNPs in the chi-square association test using the aforementioned datasets. Unlike many existing HME based studies, in which final results need to be computed by the data owner due to the lack of the secure division operation, the proposed FORESEE framework support complete outsourcing to the cloud and output the final encrypted chi-square statistics.

Multivariate Qst–Fst Comparisons: A Neutrality Test for the Evolution of the G Matrix in Structured Populations

PubMed Central

Martin, Guillaume; Chapuis, Elodie; Goudet, Jérôme

2008-01-01

Neutrality tests in quantitative genetics provide a statistical framework for the detection of selection on polygenic traits in wild populations. However, the existing method based on comparisons of divergence at neutral markers and quantitative traits (Qst–Fst) suffers from several limitations that hinder a clear interpretation of the results with typical empirical designs. In this article, we propose a multivariate extension of this neutrality test based on empirical estimates of the among-populations (D) and within-populations (G) covariance matrices by MANOVA. A simple pattern is expected under neutrality: D = 2Fst/(1 − Fst)G, so that neutrality implies both proportionality of the two matrices and a specific value of the proportionality coefficient. This pattern is tested using Flury's framework for matrix comparison [common principal-component (CPC) analysis], a well-known tool in G matrix evolution studies. We show the importance of using a Bartlett adjustment of the test for the small sample sizes typically found in empirical studies. We propose a dual test: (i) that the proportionality coefficient is not different from its neutral expectation [2Fst/(1 − Fst)] and (ii) that the MANOVA estimates of mean square matrices between and among populations are proportional. These two tests combined provide a more stringent test for neutrality than the classic Qst–Fst comparison and avoid several statistical problems. Extensive simulations of realistic empirical designs suggest that these tests correctly detect the expected pattern under neutrality and have enough power to efficiently detect mild to strong selection (homogeneous, heterogeneous, or mixed) when it is occurring on a set of traits. This method also provides a rigorous and quantitative framework for disentangling the effects of different selection regimes and of drift on the evolution of the G matrix. We discuss practical requirements for the proper application of our test in empirical studies and potential extensions. PMID:18245845

Dual-phase evolution in complex adaptive systems

PubMed Central

Paperin, Greg; Green, David G.; Sadedin, Suzanne

2011-01-01

Understanding the origins of complexity is a key challenge in many sciences. Although networks are known to underlie most systems, showing how they contribute to well-known phenomena remains an issue. Here, we show that recurrent phase transitions in network connectivity underlie emergent phenomena in many systems. We identify properties that are typical of systems in different connectivity phases, as well as characteristics commonly associated with the phase transitions. We synthesize these common features into a common framework, which we term dual-phase evolution (DPE). Using this framework, we review the literature from several disciplines to show that recurrent connectivity phase transitions underlie the complex properties of many biological, physical and human systems. We argue that the DPE framework helps to explain many complex phenomena, including perpetual novelty, modularity, scale-free networks and criticality. Our review concludes with a discussion of the way DPE relates to other frameworks, in particular, self-organized criticality and the adaptive cycle. PMID:21247947

Dual-phase evolution in complex adaptive systems.

PubMed

Paperin, Greg; Green, David G; Sadedin, Suzanne

2011-05-06

Understanding the origins of complexity is a key challenge in many sciences. Although networks are known to underlie most systems, showing how they contribute to well-known phenomena remains an issue. Here, we show that recurrent phase transitions in network connectivity underlie emergent phenomena in many systems. We identify properties that are typical of systems in different connectivity phases, as well as characteristics commonly associated with the phase transitions. We synthesize these common features into a common framework, which we term dual-phase evolution (DPE). Using this framework, we review the literature from several disciplines to show that recurrent connectivity phase transitions underlie the complex properties of many biological, physical and human systems. We argue that the DPE framework helps to explain many complex phenomena, including perpetual novelty, modularity, scale-free networks and criticality. Our review concludes with a discussion of the way DPE relates to other frameworks, in particular, self-organized criticality and the adaptive cycle.

Robust group-wise rigid registration of point sets using t-mixture model

NASA Astrophysics Data System (ADS)

Ravikumar, Nishant; Gooya, Ali; Frangi, Alejandro F.; Taylor, Zeike A.

2016-03-01

A probabilistic framework for robust, group-wise rigid alignment of point-sets using a mixture of Students t-distribution especially when the point sets are of varying lengths, are corrupted by an unknown degree of outliers or in the presence of missing data. Medical images (in particular magnetic resonance (MR) images), their segmentations and consequently point-sets generated from these are highly susceptible to corruption by outliers. This poses a problem for robust correspondence estimation and accurate alignment of shapes, necessary for training statistical shape models (SSMs). To address these issues, this study proposes to use a t-mixture model (TMM), to approximate the underlying joint probability density of a group of similar shapes and align them to a common reference frame. The heavy-tailed nature of t-distributions provides a more robust registration framework in comparison to state of the art algorithms. Significant reduction in alignment errors is achieved in the presence of outliers, using the proposed TMM-based group-wise rigid registration method, in comparison to its Gaussian mixture model (GMM) counterparts. The proposed TMM-framework is compared with a group-wise variant of the well-known Coherent Point Drift (CPD) algorithm and two other group-wise methods using GMMs, using both synthetic and real data sets. Rigid alignment errors for groups of shapes are quantified using the Hausdorff distance (HD) and quadratic surface distance (QSD) metrics.

Neural Spike Train Synchronisation Indices: Definitions, Interpretations and Applications.

PubMed

Halliday, D M; Rosenberg, J R

2017-04-24

A comparison of previously defined spike train syncrhonization indices is undertaken within a stochastic point process framework. The second order cumulant density (covariance density) is shown to be common to all the indices. Simulation studies were used to investigate the sampling variability of a single index based on the second order cumulant. The simulations used a paired motoneurone model and a paired regular spiking cortical neurone model. The sampling variability of spike trains generated under identical conditions from the paired motoneurone model varied from 50% { 160% of the estimated value. On theoretical grounds, and on the basis of simulated data a rate dependence is present in all synchronization indices. The application of coherence and pooled coherence estimates to the issue of synchronization indices is considered. This alternative frequency domain approach allows an arbitrary number of spike train pairs to be evaluated for statistically significant differences, and combined into a single population measure. The pooled coherence framework allows pooled time domain measures to be derived, application of this to the simulated data is illustrated. Data from the cortical neurone model is generated over a wide range of firing rates (1 - 250 spikes/sec). The pooled coherence framework correctly characterizes the sampling variability as not significant over this wide operating range. The broader applicability of this approach to multi electrode array data is briefly discussed.

Bridging the gap between biologic, individual, and macroenvironmental factors in cancer: a multilevel approach.

PubMed

Lynch, Shannon M; Rebbeck, Timothy R

2013-04-01

To address the complex nature of cancer occurrence and outcomes, approaches have been developed to simultaneously assess the role of two or more etiologic agents within hierarchical levels including the: (i) macroenvironment level (e.g., health care policy, neighborhood, or family structure); (ii) individual level (e.g., behaviors, carcinogenic exposures, socioeconomic factors, and psychologic responses); and (iii) biologic level (e.g., cellular biomarkers and inherited susceptibility variants). Prior multilevel approaches tend to focus on social and environmental hypotheses, and are thus limited in their ability to integrate biologic factors into a multilevel framework. This limited integration may be related to the limited translation of research findings into the clinic. We propose a "Multi-level Biologic and Social Integrative Construct" (MBASIC) to integrate macroenvironment and individual factors with biology. The goal of this framework is to help researchers identify relationships among factors that may be involved in the multifactorial, complex nature of cancer etiology, to aid in appropriate study design, to guide the development of statistical or mechanistic models to study these relationships, and to position the results of these studies for improved intervention, translation, and implementation. MBASIC allows researchers from diverse fields to develop hypotheses of interest under a common conceptual framework, to guide transdisciplinary collaborations, and to optimize the value of multilevel studies for clinical and public health activities.

A multi-scale framework to link remotely sensed metrics with socioeconomic data

NASA Astrophysics Data System (ADS)

Watmough, Gary; Svenning, Jens-Christian; Palm, Cheryl; Sullivan, Clare; Danylo, Olha; McCallum, Ian

2017-04-01

There is increasing interest in the use of remotely sensed satellite data for estimating human poverty as it can bridge data gaps that prevent fine scale monitoring of development goals across large areas. The ways in which metrics derived from satellite imagery are linked with socioeconomic data are crucial for accurate estimation of poverty. Yet, to date, approaches in the literature linking satellite metrics with socioeconomic data are poorly characterized. Typically, approaches use a GIS approach such as circular buffer zones around a village or household or an administrative boundary such as a district or census enumeration area. These polygons are then used to extract environmental data from satellite imagery and related to the socioeconomic data in statistical analyses. The use of a single polygon to link environment and socioeconomic data is inappropriate in coupled human-natural systems as processes operate over multiple scales. Human interactions with the environment occur at multiple levels from individual (household) access to agricultural plots adjacent to homes, to communal access to common pool resources (CPR) such as forests at the village level. Here, we present a multi-scale framework that explicitly considers how people use the landscape. The framework is presented along with a case study example in Kenya. The multi-scale approach could enhance the modelling of human-environment interactions which will have important consequences for monitoring the sustainable development goals for human livelihoods and biodiversity conservation.

Using the ECD Framework to Support Evidentiary Reasoning in the Context of a Simulation Study for Detecting Learner Differences in Epistemic Games

ERIC Educational Resources Information Center

Sweet, Shauna J.; Rupp, Andre A.

2012-01-01

The "evidence-centered design" (ECD) framework is a powerful tool that supports careful and critical thinking about the identification and accumulation of evidence in assessment contexts. In this paper, we demonstrate how the ECD framework provides critical support for designing simulation studies to investigate statistical methods…

Influence of laser-welding and electroerosion on passive fit of implant-supported prosthesis.

PubMed

Silva, Tatiana Bernardon; De Arruda Nobilo, Mauro Antonio; Pessanha Henriques, Guilherme Elias; Mesquita, Marcelo Ferraz; Guimaraes, Magali Beck

2008-01-01

This study investigated the influence of laser welding and electroerosion procedure on the passive fit of interim fixed implant-supported titanium frameworks. Twenty frameworks were made from a master model, with five parallel placed implants in the inter foramen region, and cast in commercially pure titanium. The frameworks were divided into 4 groups: 10 samples were tested before (G1) and after (G2) electroerosion application; and another 10 were sectioned into five pieces and laser welded before (G3) and after (G4) electroerosion application. The passive fit between the UCLA abutment of the framework and the implant was evaluated using an optical microscope Olympus STM (Olympus Optical Co., Tokyo, Japan) with 0.0005mm of accuracy. Statistical analyses showed significant differences between G1 and G2, G1 and G3, G1 and G4, G2 and G4. However, no statistical difference was observed when comparing G2 and G3. These results indicate that frameworks may show a more precise adaptation if they are sectioned and laser welded. In the same way, electroerosion improves the precision in the framework adaptation.

Language Educational Policy and Language Learning Quality Management: The "Common European Framework of Reference"

ERIC Educational Resources Information Center

Barenfanger, Olaf; Tschirner, Erwin

2008-01-01

The major goal of the Council of Europe to promote and facilitate communication and interaction among Europeans of different mother tongues has led to the development of the "Common European Framework of Reference for Languages: Learning, Teaching, Assessment" (CEFR). Among other things, the CEFR is intended to help language…

The Council of Europe's "Common European Framework of Reference for Languages" (CEFR): Approach, Status, Function and Use

ERIC Educational Resources Information Center

Martyniuk, Waldemar

2012-01-01

The Council of Europe's "Common European Framework of Reference for Languages" is rapidly becoming a powerful instrument for shaping language education policies in Europe and beyond. The task of relating language policies, language curricula, teacher education and training, textbook and course design and content, examinations and…

Segmentation of radiographic images under topological constraints: application to the femur.

PubMed

Gamage, Pavan; Xie, Sheng Quan; Delmas, Patrice; Xu, Wei Liang

2010-09-01

A framework for radiographic image segmentation under topological control based on two-dimensional (2D) image analysis was developed. The system is intended for use in common radiological tasks including fracture treatment analysis, osteoarthritis diagnostics and osteotomy management planning. The segmentation framework utilizes a generic three-dimensional (3D) model of the bone of interest to define the anatomical topology. Non-rigid registration is performed between the projected contours of the generic 3D model and extracted edges of the X-ray image to achieve the segmentation. For fractured bones, the segmentation requires an additional step where a region-based active contours curve evolution is performed with a level set Mumford-Shah method to obtain the fracture surface edge. The application of the segmentation framework to analysis of human femur radiographs was evaluated. The proposed system has two major innovations. First, definition of the topological constraints does not require a statistical learning process, so the method is generally applicable to a variety of bony anatomy segmentation problems. Second, the methodology is able to handle both intact and fractured bone segmentation. Testing on clinical X-ray images yielded an average root mean squared distance (between the automatically segmented femur contour and the manual segmented ground truth) of 1.10 mm with a standard deviation of 0.13 mm. The proposed point correspondence estimation algorithm was benchmarked against three state-of-the-art point matching algorithms, demonstrating successful non-rigid registration for the cases of interest. A topologically constrained automatic bone contour segmentation framework was developed and tested, providing robustness to noise, outliers, deformations and occlusions.

Information management for aged care provision in Australia: development of an aged care minimum dataset and strategies to improve quality and continuity of care.

PubMed

Davis, Jenny; Morgans, Amee; Burgess, Stephen

2016-04-01

Efficient information systems support the provision of multi-disciplinary aged care and a variety of organisational purposes, including quality, funding, communication and continuity of care. Agreed minimum data sets enable accurate communication across multiple care settings. However, in aged care multiple and poorly integrated data collection frameworks are commonly used for client assessment, government reporting and funding purposes. To determine key information needs in aged care settings to improve information quality, information transfer, safety, quality and continuity of care to meet the complex needs of aged care clients. Modified Delphi methods involving five stages were employed by one aged care provider in Victoria, Australia, to establish stakeholder consensus for a derived minimum data set and address barriers to data quality. Eleven different aged care programs were identified; with five related data dictionaries, three minimum data sets, five program standards or quality frameworks. The remaining data collection frameworks related to diseases classification, funding, service activity reporting, and statistical standards and classifications. A total of 170 different data items collected across seven internal information systems were consolidated to a derived set of 60 core data items and aligned with nationally consistent data collection frameworks. Barriers to data quality related to inconsistencies in data items, staff knowledge, workflow, system access and configuration. The development an internal aged care minimum data set highlighted the critical role of primary data quality in the upstream and downstream use of client information; and presents a platform to build national consistency across the sector.

The European Qualifications Framework: A Technical Critique

ERIC Educational Resources Information Center

Lester, Stan

2015-01-01

The European Qualifications Framework (EQF) was introduced in 2008 as a "meta-framework" or common reference point for national qualifications frameworks in Europe, a function for which, with some caveats, it has been pragmatically successful. It has also been used with variable success to support the development or referencing of…

Teaching Statistics with Technology

ERIC Educational Resources Information Center

Prodromou, Theodosia

2015-01-01

The Technological Pedagogical Content Knowledge (TPACK) conceptual framework for teaching mathematics, developed by Mishra and Koehler (2006), emphasises the importance of developing integrated and interdependent understanding of three primary forms of knowledge: technology, pedagogy, and content. The TPACK conceptual framework is based upon the…

Statistical Research of Investment Development of Russian Regions

ERIC Educational Resources Information Center

Burtseva, Tatiana A.; Aleshnikova, Vera I.; Dubovik, Mayya V.; Naidenkova, Ksenya V.; Kovalchuk, Nadezda B.; Repetskaya, Natalia V.; Kuzmina, Oksana G.; Surkov, Anton A.; Bershadskaya, Olga I.; Smirennikova, Anna V.

2016-01-01

This article the article is concerned with a substantiation of procedures ensuring the implementation of statistical research and monitoring of investment development of the Russian regions, which would be pertinent for modern development of the state statistics. The aim of the study is to develop the methodological framework in order to estimate…

Long-term strategy for the statistical design of a forest health monitoring system

Treesearch

Hans T. Schreuder; Raymond L. Czaplewski

1993-01-01

A conceptual framework is given for a broad-scale survey of forest health that accomplishes three objectives: generate descriptive statistics; detect changes in such statistics; and simplify analytical inferences that identify, and possibly establish cause-effect relationships. Our paper discusses the development of sampling schemes to satisfy these three objectives,...

OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records

PubMed Central

Chen, Jonathan H; Podchiyska, Tanya

2016-01-01

Objective: To answer a “grand challenge” in clinical decision support, the authors produced a recommender system that automatically data-mines inpatient decision support from electronic medical records (EMR), analogous to Netflix or Amazon.com’s product recommender. Materials and Methods: EMR data were extracted from 1 year of hospitalizations (>18K patients with >5.4M structured items including clinical orders, lab results, and diagnosis codes). Association statistics were counted for the ∼1.5K most common items to drive an order recommender. The authors assessed the recommender’s ability to predict hospital admission orders and outcomes based on initial encounter data from separate validation patients. Results: Compared to a reference benchmark of using the overall most common orders, the recommender using temporal relationships improves precision at 10 recommendations from 33% to 38% (P < 10−10) for hospital admission orders. Relative risk-based association methods improve inverse frequency weighted recall from 4% to 16% (P < 10−16). The framework yields a prediction receiver operating characteristic area under curve (c-statistic) of 0.84 for 30 day mortality, 0.84 for 1 week need for ICU life support, 0.80 for 1 week hospital discharge, and 0.68 for 30-day readmission. Discussion: Recommender results quantitatively improve on reference benchmarks and qualitatively appear clinically reasonable. The method assumes that aggregate decision making converges appropriately, but ongoing evaluation is necessary to discern common behaviors from “correct” ones. Conclusions: Collaborative filtering recommender algorithms generate clinical decision support that is predictive of real practice patterns and clinical outcomes. Incorporating temporal relationships improves accuracy. Different evaluation metrics satisfy different goals (predicting likely events vs. “interesting” suggestions). PMID:26198303

SOCR Analyses - an Instructional Java Web-based Statistical Analysis Toolkit.

PubMed

Chu, Annie; Cui, Jenny; Dinov, Ivo D

2009-03-01

The Statistical Online Computational Resource (SOCR) designs web-based tools for educational use in a variety of undergraduate courses (Dinov 2006). Several studies have demonstrated that these resources significantly improve students' motivation and learning experiences (Dinov et al. 2008). SOCR Analyses is a new component that concentrates on data modeling and analysis using parametric and non-parametric techniques supported with graphical model diagnostics. Currently implemented analyses include commonly used models in undergraduate statistics courses like linear models (Simple Linear Regression, Multiple Linear Regression, One-Way and Two-Way ANOVA). In addition, we implemented tests for sample comparisons, such as t-test in the parametric category; and Wilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, in the non-parametric category. SOCR Analyses also include several hypothesis test models, such as Contingency tables, Friedman's test and Fisher's exact test.The code itself is open source (http://socr.googlecode.com/), hoping to contribute to the efforts of the statistical computing community. The code includes functionality for each specific analysis model and it has general utilities that can be applied in various statistical computing tasks. For example, concrete methods with API (Application Programming Interface) have been implemented in statistical summary, least square solutions of general linear models, rank calculations, etc. HTML interfaces, tutorials, source code, activities, and data are freely available via the web (www.SOCR.ucla.edu). Code examples for developers and demos for educators are provided on the SOCR Wiki website.In this article, the pedagogical utilization of the SOCR Analyses is discussed, as well as the underlying design framework. As the SOCR project is on-going and more functions and tools are being added to it, these resources are constantly improved. The reader is strongly encouraged to check the SOCR site for most updated information and newly added models.

Applied statistics in ecology: common pitfalls and simple solutions

Treesearch

E. Ashley Steel; Maureen C. Kennedy; Patrick G. Cunningham; John S. Stanovick

2013-01-01

The most common statistical pitfalls in ecological research are those associated with data exploration, the logic of sampling and design, and the interpretation of statistical results. Although one can find published errors in calculations, the majority of statistical pitfalls result from incorrect logic or interpretation despite correct numerical calculations. There...

Markov Logic Networks in the Analysis of Genetic Data

PubMed Central

Sakhanenko, Nikita A.

2010-01-01

Abstract Complex, non-additive genetic interactions are common and can be critical in determining phenotypes. Genome-wide association studies (GWAS) and similar statistical studies of linkage data, however, assume additive models of gene interactions in looking for genotype-phenotype associations. These statistical methods view the compound effects of multiple genes on a phenotype as a sum of influences of each gene and often miss a substantial part of the heritable effect. Such methods do not use any biological knowledge about underlying mechanisms. Modeling approaches from the artificial intelligence (AI) field that incorporate deterministic knowledge into models to perform statistical analysis can be applied to include prior knowledge in genetic analysis. We chose to use the most general such approach, Markov Logic Networks (MLNs), for combining deterministic knowledge with statistical analysis. Using simple, logistic regression-type MLNs we can replicate the results of traditional statistical methods, but we also show that we are able to go beyond finding independent markers linked to a phenotype by using joint inference without an independence assumption. The method is applied to genetic data on yeast sporulation, a complex phenotype with gene interactions. In addition to detecting all of the previously identified loci associated with sporulation, our method identifies four loci with smaller effects. Since their effect on sporulation is small, these four loci were not detected with methods that do not account for dependence between markers due to gene interactions. We show how gene interactions can be detected using more complex models, which can be used as a general framework for incorporating systems biology with genetics. PMID:20958249

Global Sensitivity Analysis of Environmental Systems via Multiple Indices based on Statistical Moments of Model Outputs

NASA Astrophysics Data System (ADS)

Guadagnini, A.; Riva, M.; Dell'Oca, A.

2017-12-01

We propose to ground sensitivity of uncertain parameters of environmental models on a set of indices based on the main (statistical) moments, i.e., mean, variance, skewness and kurtosis, of the probability density function (pdf) of a target model output. This enables us to perform Global Sensitivity Analysis (GSA) of a model in terms of multiple statistical moments and yields a quantification of the impact of model parameters on features driving the shape of the pdf of model output. Our GSA approach includes the possibility of being coupled with the construction of a reduced complexity model that allows approximating the full model response at a reduced computational cost. We demonstrate our approach through a variety of test cases. These include a commonly used analytical benchmark, a simplified model representing pumping in a coastal aquifer, a laboratory-scale tracer experiment, and the migration of fracturing fluid through a naturally fractured reservoir (source) to reach an overlying formation (target). Our strategy allows discriminating the relative importance of model parameters to the four statistical moments considered. We also provide an appraisal of the error associated with the evaluation of our sensitivity metrics by replacing the original system model through the selected surrogate model. Our results suggest that one might need to construct a surrogate model with increasing level of accuracy depending on the statistical moment considered in the GSA. The methodological framework we propose can assist the development of analysis techniques targeted to model calibration, design of experiment, uncertainty quantification and risk assessment.

Mapping SOA Artefacts onto an Enterprise Reference Architecture Framework

NASA Astrophysics Data System (ADS)

Noran, Ovidiu

Currently, there is still no common agreement on the service-Oriented architecture (SOA) definition, or the types and meaning of the artefacts involved in the creation and maintenance of an SOA. Furthermore, the SOA image shift from an infrastructure solution to a business-wide change project may have promoted a perception that SOA is a parallel initiative, a competitor and perhaps a successor of enterprise architecture (EA). This chapter attempts to map several typical SOA artefacts onto an enterprise reference framework commonly used in EA. This is done in order to show that the EA framework can express and structure most of the SOA artefacts and therefore, a framework for SOA could in fact be derived from an EA framework with the ensuing SOA-EA integration benefits.

Self-Regulated Learning: The Continuous-Change Conceptual Framework and a Vision of New Paradigm, Technology System, and Pedagogical Support

ERIC Educational Resources Information Center

Huh, Yeol; Reigeluth, Charles M.

2017-01-01

A modified conceptual framework called the Continuous-Change Framework for self-regulated learning (SRL) is presented. Common elements and limitations among the past frameworks are discussed in relation to the modified conceptual framework. The iterative nature of the goal setting process and overarching presence of self-efficacy and motivational…

SigTree: A Microbial Community Analysis Tool to Identify and Visualize Significantly Responsive Branches in a Phylogenetic Tree.

PubMed

Stevens, John R; Jones, Todd R; Lefevre, Michael; Ganesan, Balasubramanian; Weimer, Bart C

2017-01-01

Microbial community analysis experiments to assess the effect of a treatment intervention (or environmental change) on the relative abundance levels of multiple related microbial species (or operational taxonomic units) simultaneously using high throughput genomics are becoming increasingly common. Within the framework of the evolutionary phylogeny of all species considered in the experiment, this translates to a statistical need to identify the phylogenetic branches that exhibit a significant consensus response (in terms of operational taxonomic unit abundance) to the intervention. We present the R software package SigTree , a collection of flexible tools that make use of meta-analysis methods and regular expressions to identify and visualize significantly responsive branches in a phylogenetic tree, while appropriately adjusting for multiple comparisons.

New statistical scission-point model to predict fission fragment observables

NASA Astrophysics Data System (ADS)

Lemaître, Jean-François; Panebianco, Stefano; Sida, Jean-Luc; Hilaire, Stéphane; Heinrich, Sophie

2015-09-01

The development of high performance computing facilities makes possible a massive production of nuclear data in a full microscopic framework. Taking advantage of the individual potential calculations of more than 7000 nuclei, a new statistical scission-point model, called SPY, has been developed. It gives access to the absolute available energy at the scission point, which allows the use of a parameter-free microcanonical statistical description to calculate the distributions and the mean values of all fission observables. SPY uses the richness of microscopy in a rather simple theoretical framework, without any parameter except the scission-point definition, to draw clear answers based on perfect knowledge of the ingredients involved in the model, with very limited computing cost.

PRECISE:PRivacy-prEserving Cloud-assisted quality Improvement Service in hEalthcare

PubMed Central

Chen, Feng; Wang, Shuang; Mohammed, Noman; Cheng, Samuel; Jiang, Xiaoqian

2015-01-01

Quality improvement (QI) requires systematic and continuous efforts to enhance healthcare services. A healthcare provider might wish to compare local statistics with those from other institutions in order to identify problems and develop intervention to improve the quality of care. However, the sharing of institution information may be deterred by institutional privacy as publicizing such statistics could lead to embarrassment and even financial damage. In this article, we propose a PRivacy-prEserving Cloud-assisted quality Improvement Service in hEalthcare (PRECISE), which aims at enabling cross-institution comparison of healthcare statistics while protecting privacy. The proposed framework relies on a set of state-of-the-art cryptographic protocols including homomorphic encryption and Yao’s garbled circuit schemes. By securely pooling data from different institutions, PRECISE can rank the encrypted statistics to facilitate QI among participating institutes. We conducted experiments using MIMIC II database and demonstrated the feasibility of the proposed PRECISE framework. PMID:26146645

Steganalysis based on reducing the differences of image statistical characteristics

NASA Astrophysics Data System (ADS)

Wang, Ran; Niu, Shaozhang; Ping, Xijian; Zhang, Tao

2018-04-01

Compared with the process of embedding, the image contents make a more significant impact on the differences of image statistical characteristics. This makes the image steganalysis to be a classification problem with bigger withinclass scatter distances and smaller between-class scatter distances. As a result, the steganalysis features will be inseparate caused by the differences of image statistical characteristics. In this paper, a new steganalysis framework which can reduce the differences of image statistical characteristics caused by various content and processing methods is proposed. The given images are segmented to several sub-images according to the texture complexity. Steganalysis features are separately extracted from each subset with the same or close texture complexity to build a classifier. The final steganalysis result is figured out through a weighted fusing process. The theoretical analysis and experimental results can demonstrate the validity of the framework.

PRECISE:PRivacy-prEserving Cloud-assisted quality Improvement Service in hEalthcare.

PubMed

Chen, Feng; Wang, Shuang; Mohammed, Noman; Cheng, Samuel; Jiang, Xiaoqian

2014-10-01

Quality improvement (QI) requires systematic and continuous efforts to enhance healthcare services. A healthcare provider might wish to compare local statistics with those from other institutions in order to identify problems and develop intervention to improve the quality of care. However, the sharing of institution information may be deterred by institutional privacy as publicizing such statistics could lead to embarrassment and even financial damage. In this article, we propose a PRivacy-prEserving Cloud-assisted quality Improvement Service in hEalthcare (PRECISE), which aims at enabling cross-institution comparison of healthcare statistics while protecting privacy. The proposed framework relies on a set of state-of-the-art cryptographic protocols including homomorphic encryption and Yao's garbled circuit schemes. By securely pooling data from different institutions, PRECISE can rank the encrypted statistics to facilitate QI among participating institutes. We conducted experiments using MIMIC II database and demonstrated the feasibility of the proposed PRECISE framework.

Dynamics of person-to-person interactions from distributed RFID sensor networks.

PubMed

Cattuto, Ciro; Van den Broeck, Wouter; Barrat, Alain; Colizza, Vittoria; Pinton, Jean-François; Vespignani, Alessandro

2010-07-15

Digital networks, mobile devices, and the possibility of mining the ever-increasing amount of digital traces that we leave behind in our daily activities are changing the way we can approach the study of human and social interactions. Large-scale datasets, however, are mostly available for collective and statistical behaviors, at coarse granularities, while high-resolution data on person-to-person interactions are generally limited to relatively small groups of individuals. Here we present a scalable experimental framework for gathering real-time data resolving face-to-face social interactions with tunable spatial and temporal granularities. We use active Radio Frequency Identification (RFID) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We analyze the dynamics of person-to-person interaction networks obtained in three high-resolution experiments carried out at different orders of magnitude in community size. The data sets exhibit common statistical properties and lack of a characteristic time scale from 20 seconds to several hours. The association between the number of connections and their duration shows an interesting super-linear behavior, which indicates the possibility of defining super-connectors both in the number and intensity of connections. Taking advantage of scalability and resolution, this experimental framework allows the monitoring of social interactions, uncovering similarities in the way individuals interact in different contexts, and identifying patterns of super-connector behavior in the community. These results could impact our understanding of all phenomena driven by face-to-face interactions, such as the spreading of transmissible infectious diseases and information.

Maximizing Statistical Power When Verifying Probabilistic Forecasts of Hydrometeorological Events

NASA Astrophysics Data System (ADS)

DeChant, C. M.; Moradkhani, H.

2014-12-01

Hydrometeorological events (i.e. floods, droughts, precipitation) are increasingly being forecasted probabilistically, owing to the uncertainties in the underlying causes of the phenomenon. In these forecasts, the probability of the event, over some lead time, is estimated based on some model simulations or predictive indicators. By issuing probabilistic forecasts, agencies may communicate the uncertainty in the event occurring. Assuming that the assigned probability of the event is correct, which is referred to as a reliable forecast, the end user may perform some risk management based on the potential damages resulting from the event. Alternatively, an unreliable forecast may give false impressions of the actual risk, leading to improper decision making when protecting resources from extreme events. Due to this requisite for reliable forecasts to perform effective risk management, this study takes a renewed look at reliability assessment in event forecasts. Illustrative experiments will be presented, showing deficiencies in the commonly available approaches (Brier Score, Reliability Diagram). Overall, it is shown that the conventional reliability assessment techniques do not maximize the ability to distinguish between a reliable and unreliable forecast. In this regard, a theoretical formulation of the probabilistic event forecast verification framework will be presented. From this analysis, hypothesis testing with the Poisson-Binomial distribution is the most exact model available for the verification framework, and therefore maximizes one's ability to distinguish between a reliable and unreliable forecast. Application of this verification system was also examined within a real forecasting case study, highlighting the additional statistical power provided with the use of the Poisson-Binomial distribution.

Flexible Adaptive Paradigms for fMRI Using a Novel Software Package ‘Brain Analysis in Real-Time’ (BART)

PubMed Central

Hellrung, Lydia; Hollmann, Maurice; Zscheyge, Oliver; Schlumm, Torsten; Kalberlah, Christian; Roggenhofer, Elisabeth; Okon-Singer, Hadas; Villringer, Arno; Horstmann, Annette

2015-01-01

In this work we present a new open source software package offering a unified framework for the real-time adaptation of fMRI stimulation procedures. The software provides a straightforward setup and highly flexible approach to adapt fMRI paradigms while the experiment is running. The general framework comprises the inclusion of parameters from subject’s compliance, such as directing gaze to visually presented stimuli and physiological fluctuations, like blood pressure or pulse. Additionally, this approach yields possibilities to investigate complex scientific questions, for example the influence of EEG rhythms or fMRI signals results themselves. To prove the concept of this approach, we used our software in a usability example for an fMRI experiment where the presentation of emotional pictures was dependent on the subject’s gaze position. This can have a significant impact on the results. So far, if this is taken into account during fMRI data analysis, it is commonly done by the post-hoc removal of erroneous trials. Here, we propose an a priori adaptation of the paradigm during the experiment’s runtime. Our fMRI findings clearly show the benefits of an adapted paradigm in terms of statistical power and higher effect sizes in emotion-related brain regions. This can be of special interest for all experiments with low statistical power due to a limited number of subjects, a limited amount of time, costs or available data to analyze, as is the case with real-time fMRI. PMID:25837719

Quantifying mechanical properties in a murine fracture healing system using inverse modeling: preliminary work

NASA Astrophysics Data System (ADS)

Miga, Michael I.; Weis, Jared A.; Granero-Molto, Froilan; Spagnoli, Anna

2010-03-01

Understanding bone remodeling and mechanical property characteristics is important for assessing treatments to accelerate healing or in developing diagnostics to evaluate successful return to function. The murine system whereby mid-diaphaseal tibia fractures are imparted on the subject and fracture healing is assessed at different time points and under different therapeutic conditions is a particularly useful model to study. In this work, a novel inverse geometric nonlinear elasticity modeling framework is proposed that can reconstruct multiple mechanical properties from uniaxial testing data. To test this framework, the Lame' constants were reconstructed within the context of a murine cohort (n=6) where there were no differences in treatment post tibia fracture except that half of the mice were allowed to heal 4 days longer (10 day, and 14 day healing time point, respectively). The properties reconstructed were a shear modulus of G=511.2 +/- 295.6 kPa, and 833.3+/- 352.3 kPa for the 10 day, and 14 day time points respectively. The second Lame' constant reconstructed at λ=1002.9 +/-42.9 kPa, and 14893.7 +/- 863.3 kPa for the 10 day, and 14 day time points respectively. An unpaired Student t-test was used to test for statistically significant differences among the groups. While the shear modulus did not meet our criteria for significance, the second Lame' constant did at a value p<0.0001. Traditional metrics that are commonly used within the bone fracture healing research community were not found to be statistically significant.

Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods

PubMed Central

Dazard, Jean-Eudes; Choe, Michael; LeBlanc, Michael; Rao, J. Sunil

2015-01-01

We introduce a framework to build a survival/risk bump hunting model with a censored time-to-event response. Our Survival Bump Hunting (SBH) method is based on a recursive peeling procedure that uses a specific survival peeling criterion derived from non/semi-parametric statistics such as the hazards-ratio, the log-rank test or the Nelson--Aalen estimator. To optimize the tuning parameter of the model and validate it, we introduce an objective function based on survival or prediction-error statistics, such as the log-rank test and the concordance error rate. We also describe two alternative cross-validation techniques adapted to the joint task of decision-rule making by recursive peeling and survival estimation. Numerical analyses show the importance of replicated cross-validation and the differences between criteria and techniques in both low and high-dimensional settings. Although several non-parametric survival models exist, none addresses the problem of directly identifying local extrema. We show how SBH efficiently estimates extreme survival/risk subgroups unlike other models. This provides an insight into the behavior of commonly used models and suggests alternatives to be adopted in practice. Finally, our SBH framework was applied to a clinical dataset. In it, we identified subsets of patients characterized by clinical and demographic covariates with a distinct extreme survival outcome, for which tailored medical interventions could be made. An R package PRIMsrc (Patient Rule Induction Method in Survival, Regression and Classification settings) is available on CRAN (Comprehensive R Archive Network) and GitHub. PMID:27034730

Generating action descriptions from statistically integrated representations of human motions and sentences.

PubMed

Takano, Wataru; Kusajima, Ikuo; Nakamura, Yoshihiko

2016-08-01

It is desirable for robots to be able to linguistically understand human actions during human-robot interactions. Previous research has developed frameworks for encoding human full body motion into model parameters and for classifying motion into specific categories. For full understanding, the motion categories need to be connected to the natural language such that the robots can interpret human motions as linguistic expressions. This paper proposes a novel framework for integrating observation of human motion with that of natural language. This framework consists of two models; the first model statistically learns the relations between motions and their relevant words, and the second statistically learns sentence structures as word n-grams. Integration of these two models allows robots to generate sentences from human motions by searching for words relevant to the motion using the first model and then arranging these words in appropriate order using the second model. This allows making sentences that are the most likely to be generated from the motion. The proposed framework was tested on human full body motion measured by an optical motion capture system. In this, descriptive sentences were manually attached to the motions, and the validity of the system was demonstrated. Copyright © 2016 Elsevier Ltd. All rights reserved.

Something old, something new, something borrowed, something blue: a framework for the marriage of health econometrics and cost-effectiveness analysis.

PubMed

Hoch, Jeffrey S; Briggs, Andrew H; Willan, Andrew R

2002-07-01

Economic evaluation is often seen as a branch of health economics divorced from mainstream econometric techniques. Instead, it is perceived as relying on statistical methods for clinical trials. Furthermore, the statistic of interest in cost-effectiveness analysis, the incremental cost-effectiveness ratio is not amenable to regression-based methods, hence the traditional reliance on comparing aggregate measures across the arms of a clinical trial. In this paper, we explore the potential for health economists undertaking cost-effectiveness analysis to exploit the plethora of established econometric techniques through the use of the net-benefit framework - a recently suggested reformulation of the cost-effectiveness problem that avoids the reliance on cost-effectiveness ratios and their associated statistical problems. This allows the formulation of the cost-effectiveness problem within a standard regression type framework. We provide an example with empirical data to illustrate how a regression type framework can enhance the net-benefit method. We go on to suggest that practical advantages of the net-benefit regression approach include being able to use established econometric techniques, adjust for imperfect randomisation, and identify important subgroups in order to estimate the marginal cost-effectiveness of an intervention. Copyright 2002 John Wiley & Sons, Ltd.

BEACON: A Summary Framework to Overcome Potential Reimbursement Hurdles.

PubMed

Dunlop, William C N; Mullins, C Daniel; Pirk, Olaf; Goeree, Ron; Postma, Maarten J; Enstone, Ashley; Heron, Louise

2016-10-01

To provide a framework for addressing payers' criteria during the development of pharmaceuticals. A conceptual framework was presented to an international health economic expert panel for discussion. A structured literature search (from 2010 to May 2015), using the following databases in Ovid: Medline(®) and Medline(®) In-Process (PubMed), Embase (Ovid), EconLit (EBSCOhost) and the National Health Service Economic Evaluation Database (NHS EED), and a 'grey literature' search, were conducted to identify existing criteria from the payer perspective. The criteria assessed by existing frameworks and guidelines were collated; the most commonly reported criteria were considered for inclusion in the framework. A mnemonic was conceived as a memory aide to summarise these criteria. Overall, 41 publications were identified as potentially relevant to the objective. Following further screening, 26 were excluded upon full-text review on the basis of no framework presented (n = 13), redundancy (n = 11) or abstract only (n = 2). Frameworks that captured criteria developed for or utilised by the pharmaceutical industry (n = 5) and reimbursement guidance (n = 10) were reviewed. The most commonly identified criteria-unmet need/patient burden, safety, efficacy, quality-of-life outcomes, environment, evidence quality, budget impact and comparator-were incorporated into the summary framework. For ease of communication, the following mnemonic was developed: BEACON (Burden/target population, Environment, Affordability/value, Comparator, Outcomes, Number of studies/quality of evidence). The BEACON framework aims to capture the 'essence' of payer requirements by addressing the most commonly described criteria requested by payers regarding the introduction of a new pharmaceutical.

Manifold parametrization of the left ventricle for a statistical modelling of its complete anatomy

NASA Astrophysics Data System (ADS)

Gil, D.; Garcia-Barnes, J.; Hernández-Sabate, A.; Marti, E.

2010-03-01

Distortion of Left Ventricle (LV) external anatomy is related to some dysfunctions, such as hypertrophy. The architecture of myocardial fibers determines LV electromechanical activation patterns as well as mechanics. Thus, their joined modelling would allow the design of specific interventions (such as peacemaker implantation and LV remodelling) and therapies (such as resynchronization). On one hand, accurate modelling of external anatomy requires either a dense sampling or a continuous infinite dimensional approach, which requires non-Euclidean statistics. On the other hand, computation of fiber models requires statistics on Riemannian spaces. Most approaches compute separate statistical models for external anatomy and fibers architecture. In this work we propose a general mathematical framework based on differential geometry concepts for computing a statistical model including, both, external and fiber anatomy. Our framework provides a continuous approach to external anatomy supporting standard statistics. We also provide a straightforward formula for the computation of the Riemannian fiber statistics. We have applied our methodology to the computation of complete anatomical atlas of canine hearts from diffusion tensor studies. The orientation of fibers over the average external geometry agrees with the segmental description of orientations reported in the literature.

Intelligent Systems Approaches to Product Sound Quality Analysis

NASA Astrophysics Data System (ADS)

Pietila, Glenn M.

As a product market becomes more competitive, consumers become more discriminating in the way in which they differentiate between engineered products. The consumer often makes a purchasing decision based on the sound emitted from the product during operation by using the sound to judge quality or annoyance. Therefore, in recent years, many sound quality analysis tools have been developed to evaluate the consumer preference as it relates to a product sound and to quantify this preference based on objective measurements. This understanding can be used to direct a product design process in order to help differentiate the product from competitive products or to establish an impression on consumers regarding a product's quality or robustness. The sound quality process is typically a statistical tool that is used to model subjective preference, or merit score, based on objective measurements, or metrics. In this way, new product developments can be evaluated in an objective manner without the laborious process of gathering a sample population of consumers for subjective studies each time. The most common model used today is the Multiple Linear Regression (MLR), although recently non-linear Artificial Neural Network (ANN) approaches are gaining popularity. This dissertation will review publicly available published literature and present additional intelligent systems approaches that can be used to improve on the current sound quality process. The focus of this work is to address shortcomings in the current paired comparison approach to sound quality analysis. This research will propose a framework for an adaptive jury analysis approach as an alternative to the current Bradley-Terry model. The adaptive jury framework uses statistical hypothesis testing to focus on sound pairings that are most interesting and is expected to address some of the restrictions required by the Bradley-Terry model. It will also provide a more amicable framework for an intelligent systems approach. Next, an unsupervised jury clustering algorithm is used to identify and classify subgroups within a jury who have conflicting preferences. In addition, a nested Artificial Neural Network (ANN) architecture is developed to predict subjective preference based on objective sound quality metrics, in the presence of non-linear preferences. Finally, statistical decomposition and correlation algorithms are reviewed that can help an analyst establish a clear understanding of the variability of the product sounds used as inputs into the jury study and to identify correlations between preference scores and sound quality metrics in the presence of non-linearities.

Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop.

PubMed

Jagodnik, Kathleen M; Koplev, Simon; Jenkins, Sherry L; Ohno-Machado, Lucila; Paten, Benedict; Schurer, Stephan C; Dumontier, Michel; Verborgh, Ruben; Bui, Alex; Ping, Peipei; McKenna, Neil J; Madduri, Ravi; Pillai, Ajay; Ma'ayan, Avi

2017-07-01

The volume and diversity of data in biomedical research have been rapidly increasing in recent years. While such data hold significant promise for accelerating discovery, their use entails many challenges including: the need for adequate computational infrastructure, secure processes for data sharing and access, tools that allow researchers to find and integrate diverse datasets, and standardized methods of analysis. These are just some elements of a complex ecosystem that needs to be built to support the rapid accumulation of these data. The NIH Big Data to Knowledge (BD2K) initiative aims to facilitate digitally enabled biomedical research. Within the BD2K framework, the Commons initiative is intended to establish a virtual environment that will facilitate the use, interoperability, and discoverability of shared digital objects used for research. The BD2K Commons Framework Pilots Working Group (CFPWG) was established to clarify goals and work on pilot projects that address existing gaps toward realizing the vision of the BD2K Commons. This report reviews highlights from a two-day meeting involving the BD2K CFPWG to provide insights on trends and considerations in advancing Big Data science for biomedical research in the United States. Copyright © 2017 Elsevier Inc. All rights reserved.

Marginal discrepancy of CAD-CAM complete-arch fixed implant-supported frameworks.

PubMed

Yilmaz, Burak; Kale, Ediz; Johnston, William M

2018-02-21

Computer-aided design and computer-aided manufacturing (CAD-CAM) high-density polymers (HDPs) have recently been marketed for the fabrication of long-term interim implant-supported fixed prostheses. However, information regarding the precision of fit of CAD-CAM HDP implant-supported complete-arch screw-retained prostheses is scarce. The purpose of this in vitro study was to evaluate the marginal discrepancy of CAD-CAM HDP complete-arch implant-supported screw-retained fixed prosthesis frameworks and compare them with conventional titanium (Ti) and zirconia (Zir) frameworks. A screw-retained complete-arch acrylic resin prototype with multiunit abutments was fabricated on a typodont model with 2 straight implants in the anterior region and 2 implants with a 30-degree distal tilt in the posterior region. A 3-dimensional (3D) laboratory laser scanner was used to digitize the typodont model with scan bodies and the resin prototype to generate a virtual 3D CAD framework. A CAM milling unit was used to fabricate 5 frameworks from HDP, Ti, and Zir blocks. The 1-screw test was performed by tightening the prosthetic screw in the maxillary left first molar abutment (terminal location) when the frameworks were on the typodont model, and the marginal discrepancy of frameworks was evaluated using an industrial computed tomographic scanner and a 3D volumetric software. The 3D marginal discrepancy at the abutment-framework interface of the maxillary left canine (L1), right canine (L2), and right first molar (L3) sites was measured. The mean values for 3D marginal discrepancy were calculated for each location in a group with 95% confidence limits. The results were analyzed by repeated-measures 2-way ANOVA using the restricted maximum likelihood estimation and the Satterthwaite degrees of freedom methods, which do not require normality and homoscedasticity in the data. The between-subjects factor was material, the within-subjects factor was location, and the interaction was included in the model. Tukey tests were applied to resolve any statistically significant source of variation (overall α=.05). The 3D marginal discrepancy measurement was possible only for L2 and L3 because the L1 values were too small to detect. The mean discrepancy values at L2 were 60 μm for HDP, 74 μm for Ti, and 84 μm for Zir. At the L3 location, the mean discrepancy values were 55 μm for HDP, 102 μm for Ti, and 94 μm for Zir. The ANOVA did not find a statistically significant overall effect for implant location (P=.072) or a statistically significant interaction of location and material (P=.078), but it did find a statistically significant overall effect of material (P=.019). Statistical differences were found overall between HDP and the other 2 materials (P≤.037). When the tested materials were used with the CAD-CAM system, the 3D marginal discrepancy of CAD-CAM HDP frameworks was smaller than that of titanium or zirconia frameworks. Copyright © 2017 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Commonalities between Disaster and Climate Change Risks for Health: A Theoretical Framework.

PubMed

Banwell, Nicola; Rutherford, Shannon; Mackey, Brendan; Street, Roger; Chu, Cordia

2018-03-16

Disasters and climate change have significant implications for human health worldwide. Both climate change and the climate-sensitive hazards that result in disasters, are discussed in terms of direct and indirect impacts on health. A growing body of literature has argued for the need to link disaster risk reduction and climate change adaptation. However, there is limited articulation of the commonalities between these health impacts. Understanding the shared risk pathways is an important starting point for developing joint strategies for adapting to, and reducing, health risks. Therefore, this article discusses the common aspects of direct and indirect health risks of climate change and climate-sensitive disasters. Based on this discussion a theoretical framework is presented for understanding these commonalities. As such, this article hopes to extend the current health impact frameworks and provide a platform for further research exploring opportunities for linked adaptation and risk reduction strategies.

Commonalities between Disaster and Climate Change Risks for Health: A Theoretical Framework

PubMed Central

Banwell, Nicola; Rutherford, Shannon; Mackey, Brendan; Street, Roger; Chu, Cordia

2018-01-01

Disasters and climate change have significant implications for human health worldwide. Both climate change and the climate-sensitive hazards that result in disasters, are discussed in terms of direct and indirect impacts on health. A growing body of literature has argued for the need to link disaster risk reduction and climate change adaptation. However, there is limited articulation of the commonalities between these health impacts. Understanding the shared risk pathways is an important starting point for developing joint strategies for adapting to, and reducing, health risks. Therefore, this article discusses the common aspects of direct and indirect health risks of climate change and climate-sensitive disasters. Based on this discussion a theoretical framework is presented for understanding these commonalities. As such, this article hopes to extend the current health impact frameworks and provide a platform for further research exploring opportunities for linked adaptation and risk reduction strategies. PMID:29547592

AQUATIC STRESSORS: FRAMEWORK AND IMPLEMENTATION PLAN FOR EFFECTS RESEARCH

EPA Science Inventory

This document describes the framework and research implementation plans for ecological effects research on aquatic stressors within the National Health and Environmental Effects Laboratory. The context for the research identified within the framework is the common management goal...

The Common European Framework of Reference (CEFR) in Canada: A Research Agenda

ERIC Educational Resources Information Center

Arnott, Stephanie; Brogden, Lace Marie; Faez, Farahnaz; Péguret, Muriel; Piccardo, Enrica; Rehner, Katherine; Taylor, Shelley K.; Wernicke, Meike

2017-01-01

This article proposes a research agenda for future inquiry into the use of the Common European Framework of Reference (CEFR) in the plurilingual Canadian context. Drawing on data collected from a research forum hosted by the Canadian Association of Second Language Teachers in 2014, as well as a detailed analysis of Canadian empirical studies and…

Empirical Learner Language and the Levels of the "Common European Framework of Reference"

ERIC Educational Resources Information Center

Wisniewski, Katrin

2017-01-01

The "Common European Framework of Reference" (CEFR) is the most widespread reference tool for linking language tests, curricula, and national educational standards to levels of foreign language proficiency in Europe. In spite of this, little is known about how the CEFR levels (A1-C2) relate to empirical learner language(s). This article…

The Species versus Subspecies Conundrum: Quantitative Delimitation from Integrating Multiple Data Types within a Single Bayesian Approach in Hercules Beetles.

PubMed

Huang, Jen-Pan; Knowles, L Lacey

2016-07-01

With the recent attention and focus on quantitative methods for species delimitation, an overlooked but equally important issue regards what has actually been delimited. This study investigates the apparent arbitrariness of some taxonomic distinctions, and in particular how species and subspecies are assigned. Specifically, we use a recently developed Bayesian model-based approach to show that in the Hercules beetles (genus Dynastes) there is no statistical difference in the probability that putative taxa represent different species, irrespective of whether they were given species or subspecies designations. By considering multiple data types, as opposed to relying exclusively on genetic data alone, we also show that both previously recognized species and subspecies represent a variety of points along the speciation spectrum (i.e., previously recognized species are not systematically further along the continuum than subspecies). For example, based on evolutionary models of divergence, some taxa are statistically distinguishable on more than one axis of differentiation (e.g., along both phenotypic and genetic dimensions), whereas other taxa can only be delimited statistically from a single data type. Because both phenotypic and genetic data are analyzed in a common Bayesian framework, our study provides a framework for investigating whether disagreements in species boundaries among data types reflect (i) actual discordance with the actual history of lineage splitting, or instead (ii) differences among data types in the amount of time required for differentiation to become apparent among the delimited taxa. We discuss what the answers to these questions imply about what characters are used to delimit species, as well as the diverse processes involved in the origin and maintenance of species boundaries. With this in mind, we then reflect more generally on how quantitative methods for species delimitation are used to assign taxonomic status. © The Author(s) 2015. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Applied immuno-epidemiological research: an approach for integrating existing knowledge into the statistical analysis of multiple immune markers.

PubMed

Genser, Bernd; Fischer, Joachim E; Figueiredo, Camila A; Alcântara-Neves, Neuza; Barreto, Mauricio L; Cooper, Philip J; Amorim, Leila D; Saemann, Marcus D; Weichhart, Thomas; Rodrigues, Laura C

2016-05-20

Immunologists often measure several correlated immunological markers, such as concentrations of different cytokines produced by different immune cells and/or measured under different conditions, to draw insights from complex immunological mechanisms. Although there have been recent methodological efforts to improve the statistical analysis of immunological data, a framework is still needed for the simultaneous analysis of multiple, often correlated, immune markers. This framework would allow the immunologists' hypotheses about the underlying biological mechanisms to be integrated. We present an analytical approach for statistical analysis of correlated immune markers, such as those commonly collected in modern immuno-epidemiological studies. We demonstrate i) how to deal with interdependencies among multiple measurements of the same immune marker, ii) how to analyse association patterns among different markers, iii) how to aggregate different measures and/or markers to immunological summary scores, iv) how to model the inter-relationships among these scores, and v) how to use these scores in epidemiological association analyses. We illustrate the application of our approach to multiple cytokine measurements from 818 children enrolled in a large immuno-epidemiological study (SCAALA Salvador), which aimed to quantify the major immunological mechanisms underlying atopic diseases or asthma. We demonstrate how to aggregate systematically the information captured in multiple cytokine measurements to immunological summary scores aimed at reflecting the presumed underlying immunological mechanisms (Th1/Th2 balance and immune regulatory network). We show how these aggregated immune scores can be used as predictors in regression models with outcomes of immunological studies (e.g. specific IgE) and compare the results to those obtained by a traditional multivariate regression approach. The proposed analytical approach may be especially useful to quantify complex immune responses in immuno-epidemiological studies, where investigators examine the relationship among epidemiological patterns, immune response, and disease outcomes.

A Framework for Restructuring the Military Retirement System

DTIC Science & Technology

2013-07-01

Associate Professor of Economics in the Social Sciences Department at West Point where he teaches econometrics and labor economics. His areas of...others worth considering, but each should be carefully benchmarked against our proposed framework. 25 ENDNOTES 1. Office of the Actuary , Statistical

Towards a cross-domain interoperable framework for natural hazards and disaster risk reduction information

NASA Astrophysics Data System (ADS)

Tomas, Robert; Harrison, Matthew; Barredo, José I.; Thomas, Florian; Llorente Isidro, Miguel; Cerba, Otakar; Pfeiffer, Manuela

2014-05-01

The vast amount of information and data necessary for comprehensive hazard and risk assessment presents many challenges regarding the lack of accessibility, comparability, quality, organisation and dissemination of natural hazards spatial data. In order to mitigate these limitations an interoperable framework has been developed in the framework of the development of legally binding Implementing rules of the EU INSPIRE Directive1* aiming at the establishment of the European Spatial Data Infrastructure. The interoperability framework is described in the Data Specification on Natural risk zones - Technical Guidelines (DS) document2* that was finalized and published on 10.12. 2013. This framework provides means for facilitating access, integration, harmonisation and dissemination of natural hazard data from different domains and sources. The objective of this paper is twofold. Firstly, the paper demonstrates the applicability of the interoperable framework developed in the DS and highlights the key aspects of the interoperability to the various natural hazards communities. Secondly, the paper "translates" into common language the main features and potentiality of the interoperable framework of the DS for a wider audience of scientists and practitioners in the natural hazards domain. Further in this paper the main five aspects of the interoperable framework will be presented. First, the issue of a common terminology for the natural hazards domain will be addressed. A common data model to facilitate cross domain data integration will follow secondly. Thirdly, the common methodology developed to provide qualitative or quantitative assessments of natural hazards will be presented. Fourthly, the extensible classification schema for natural hazards developed from a literature review and key reference documents from the contributing community of practice will be shown. Finally, the applicability of the interoperable framework for the various stakeholder groups will be also presented. This paper closes discussing open issues and next steps regarding the sustainability and evolution of the interoperable framework and missing aspects such as multi-hazard and multi-risk. --------------- 1*INSPIRE - Infrastructure for spatial information in Europe, http://inspire.ec.europa.eu 2*http://inspire.jrc.ec.europa.eu/documents/Data_Specifications/INSPIRE_DataSpecification_NZ_v3.0.pdf

Understanding common statistical methods, Part I: descriptive methods, probability, and continuous data.

PubMed

Skinner, Carl G; Patel, Manish M; Thomas, Jerry D; Miller, Michael A

2011-01-01

Statistical methods are pervasive in medical research and general medical literature. Understanding general statistical concepts will enhance our ability to critically appraise the current literature and ultimately improve the delivery of patient care. This article intends to provide an overview of the common statistical methods relevant to medicine.

Hidden in the background: a local approach to CMB anomalies

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

Sánchez, Juan C. Bueno, E-mail: juan.c.bueno@correounivalle.edu.co

2016-09-01

We investigate a framework aiming to provide a common origin for the large-angle anomalies detected in the Cosmic Microwave Background (CMB), which are hypothesized as the result of the statistical inhomogeneity developed by different isocurvature fields of mass m ∼ H present during inflation. The inhomogeneity arises as the combined effect of ( i ) the initial conditions for isocurvature fields (obtained after a fast-roll stage finishing many e -foldings before cosmological scales exit the horizon), ( ii ) their inflationary fluctuations and ( iii ) their coupling to other degrees of freedom. Our case of interest is when thesemore » fields (interpreted as the precursors of large-angle anomalies) leave an observable imprint only in isolated patches of the Universe. When the latter intersect the last scattering surface, such imprints arise in the CMB. Nevertheless, due to their statistically inhomogeneous nature, these imprints are difficult to detect, for they become hidden in the background similarly to the Cold Spot. We then compute the probability that a single isocurvature field becomes inhomogeneous at the end of inflation and find that, if the appropriate conditions are given (which depend exclusively on the preexisting fast-roll stage), this probability is at the percent level. Finally, we discuss several mechanisms (including the curvaton and the inhomogeneous reheating) to investigate whether an initial statistically inhomogeneous isocurvature field fluctuation might give rise to some of the observed anomalies. In particular, we focus on the Cold Spot, the power deficit at low multipoles and the breaking of statistical isotropy.« less

Jllumina - A comprehensive Java-based API for statistical Illumina Infinium HumanMethylation450 and Infinium MethylationEPIC BeadChip data processing.

PubMed

Almeida, Diogo; Skov, Ida; Lund, Jesper; Mohammadnejad, Afsaneh; Silva, Artur; Vandin, Fabio; Tan, Qihua; Baumbach, Jan; Röttger, Richard

2016-10-01

Measuring differential methylation of the DNA is the nowadays most common approach to linking epigenetic modifications to diseases (called epigenome-wide association studies, EWAS). For its low cost, its efficiency and easy handling, the Illumina HumanMethylation450 BeadChip and its successor, the Infinium MethylationEPIC BeadChip, is the by far most popular techniques for conduction EWAS in large patient cohorts. Despite the popularity of this chip technology, raw data processing and statistical analysis of the array data remains far from trivial and still lacks dedicated software libraries enabling high quality and statistically sound downstream analyses. As of yet, only R-based solutions are freely available for low-level processing of the Illumina chip data. However, the lack of alternative libraries poses a hurdle for the development of new bioinformatic tools, in particular when it comes to web services or applications where run time and memory consumption matter, or EWAS data analysis is an integrative part of a bigger framework or data analysis pipeline. We have therefore developed and implemented Jllumina, an open-source Java library for raw data manipulation of Illumina Infinium HumanMethylation450 and Infinium MethylationEPIC BeadChip data, supporting the developer with Java functions covering reading and preprocessing the raw data, down to statistical assessment, permutation tests, and identification of differentially methylated loci. Jllumina is fully parallelizable and publicly available at http://dimmer.compbio.sdu.dk/download.html.

Jllumina - A comprehensive Java-based API for statistical Illumina Infinium HumanMethylation450 and MethylationEPIC data processing.

PubMed

Almeida, Diogo; Skov, Ida; Lund, Jesper; Mohammadnejad, Afsaneh; Silva, Artur; Vandin, Fabio; Tan, Qihua; Baumbach, Jan; Röttger, Richard

2016-12-18

Measuring differential methylation of the DNA is the nowadays most common approach to linking epigenetic modifications to diseases (called epigenome-wide association studies, EWAS). For its low cost, its efficiency and easy handling, the Illumina HumanMethylation450 BeadChip and its successor, the Infinium MethylationEPIC BeadChip, is the by far most popular techniques for conduction EWAS in large patient cohorts. Despite the popularity of this chip technology, raw data processing and statistical analysis of the array data remains far from trivial and still lacks dedicated software libraries enabling high quality and statistically sound downstream analyses. As of yet, only R-based solutions are freely available for low-level processing of the Illumina chip data. However, the lack of alternative libraries poses a hurdle for the development of new bioinformatic tools, in particular when it comes to web services or applications where run time and memory consumption matter, or EWAS data analysis is an integrative part of a bigger framework or data analysis pipeline. We have therefore developed and implemented Jllumina, an open-source Java library for raw data manipulation of Illumina Infinium HumanMethylation450 and Infinium MethylationEPIC BeadChip data, supporting the developer with Java functions covering reading and preprocessing the raw data, down to statistical assessment, permutation tests, and identification of differentially methylated loci. Jllumina is fully parallelizable and publicly available at http://dimmer.compbio.sdu.dk/download.html.

Probabilistic Graphical Model Representation in Phylogenetics

PubMed Central

Höhna, Sebastian; Heath, Tracy A.; Boussau, Bastien; Landis, Michael J.; Ronquist, Fredrik; Huelsenbeck, John P.

2014-01-01

Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development. Clear communication and representation of the chosen model is crucial for: (i) reproducibility of an analysis, (ii) model development, and (iii) software design. Moreover, a unified, clear and understandable framework for model representation lowers the barrier for beginners and nonspecialists to grasp complex phylogenetic models, including their assumptions and parameter/variable dependencies. Graphical modeling is a unifying framework that has gained in popularity in the statistical literature in recent years. The core idea is to break complex models into conditionally independent distributions. The strength lies in the comprehensibility, flexibility, and adaptability of this formalism, and the large body of computational work based on it. Graphical models are well-suited to teach statistical models, to facilitate communication among phylogeneticists and in the development of generic software for simulation and statistical inference. Here, we provide an introduction to graphical models for phylogeneticists and extend the standard graphical model representation to the realm of phylogenetics. We introduce a new graphical model component, tree plates, to capture the changing structure of the subgraph corresponding to a phylogenetic tree. We describe a range of phylogenetic models using the graphical model framework and introduce modules to simplify the representation of standard components in large and complex models. Phylogenetic model graphs can be readily used in simulation, maximum likelihood inference, and Bayesian inference using, for example, Metropolis–Hastings or Gibbs sampling of the posterior distribution. [Computation; graphical models; inference; modularization; statistical phylogenetics; tree plate.] PMID:24951559

Putting Cognitive Science behind a Statistics Teacher's Intuition

ERIC Educational Resources Information Center

Jones, Karrie A.; Jones, Jennifer L.; Vermette, Paul J.

2011-01-01

Recent advances in cognitive science have led to an enriched understanding of how people learn. Using a framework presented by Willingham, this article examines instructional best practice from the perspective of conceptual understanding and its implications on statistics education.

Biostatistics primer: part I.

PubMed

Overholser, Brian R; Sowinski, Kevin M

2007-12-01

Biostatistics is the application of statistics to biologic data. The field of statistics can be broken down into 2 fundamental parts: descriptive and inferential. Descriptive statistics are commonly used to categorize, display, and summarize data. Inferential statistics can be used to make predictions based on a sample obtained from a population or some large body of information. It is these inferences that are used to test specific research hypotheses. This 2-part review will outline important features of descriptive and inferential statistics as they apply to commonly conducted research studies in the biomedical literature. Part 1 in this issue will discuss fundamental topics of statistics and data analysis. Additionally, some of the most commonly used statistical tests found in the biomedical literature will be reviewed in Part 2 in the February 2008 issue.

Improved statistical power with a sparse shape model in detecting an aging effect in the hippocampus and amygdala

NASA Astrophysics Data System (ADS)

Chung, Moo K.; Kim, Seung-Goo; Schaefer, Stacey M.; van Reekum, Carien M.; Peschke-Schmitz, Lara; Sutterer, Matthew J.; Davidson, Richard J.

2014-03-01

The sparse regression framework has been widely used in medical image processing and analysis. However, it has been rarely used in anatomical studies. We present a sparse shape modeling framework using the Laplace- Beltrami (LB) eigenfunctions of the underlying shape and show its improvement of statistical power. Tradition- ally, the LB-eigenfunctions are used as a basis for intrinsically representing surface shapes as a form of Fourier descriptors. To reduce high frequency noise, only the first few terms are used in the expansion and higher frequency terms are simply thrown away. However, some lower frequency terms may not necessarily contribute significantly in reconstructing the surfaces. Motivated by this idea, we present a LB-based method to filter out only the significant eigenfunctions by imposing a sparse penalty. For dense anatomical data such as deformation fields on a surface mesh, the sparse regression behaves like a smoothing process, which will reduce the error of incorrectly detecting false negatives. Hence the statistical power improves. The sparse shape model is then applied in investigating the influence of age on amygdala and hippocampus shapes in the normal population. The advantage of the LB sparse framework is demonstrated by showing the increased statistical power.

Screening, management, and treatment of intimate partner violence among women in low-resource settings.

PubMed

Schwab-Reese, Laura M; Renner, Lynette M

2018-01-01

The prevention of intimate partner violence continues to be a high priority for health practitioners and researchers around the world. Screening practices and intervention efforts utilized within high- and/or middle-income areas may not translate effectively to low-resource areas due to differences in financial, social, and physical context. However, little is known about the evidence-base of intervention efforts in such areas. Using the Arksey and O'Malley framework for scoping reviews, the purpose of this review was to synthesize what is known about intimate partner violence screening, management, and treatment in low-resource areas. A total of 31 programs reported across 34 articles were included in this scoping review. The programs incorporated a range of intervention activities, including group-based education and skill-development combined with microfinance to screening and referral to community resources. Slightly less than half of the studies (n = 14) were randomized controlled trials or clustered randomized controlled trials. Many barriers were common across the programs, including limited financial support, lack of community support, and lack of coordination across programs. Despite considerable barriers related to the limited available resources, the literature base had many strengths, such as strong evaluation methodologies, inclusion of a theoretical or conceptual framework to guide the intervention, and community engagement before and during the intervention implementation. However, insufficient statistical power and barriers related to cultural differences or inadequate cultural sensitivity were also common. With a variety of barriers to program implementation noted within the articles, it is important for researchers and practitioners to consider the geographic, social, cultural, and economic contexts when implementing intimate partner violence programs in low-resource areas. Given the significant differences in context across low-resource areas, additional research to establish effective protocols for tailoring and implementing evidence-based programs using a community-engaged framework would be beneficial to future research and practice.

Assessing Cultural Competence in Graduating Students

ERIC Educational Resources Information Center

Kohli, Hermeet K.; Kohli, Amarpreet S.; Huber, Ruth; Faul, Anna C.

2010-01-01

Twofold purpose of this study was to develop a framework to understand cultural competence in graduating social work students, and test that framework for appropriateness and predictability using multivariate statistics. Scale and predictor variables were collected using an online instrument from a nationwide convenience sample of graduating…

An analysis of options available for developing a common laser ray tracing package for Ares and Kull code frameworks

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

Weeratunga, S K

Ares and Kull are mature code frameworks that support ALE hydrodynamics for a variety of HEDP applications at LLNL, using two widely different meshing approaches. While Ares is based on a 2-D/3-D block-structured mesh data base, Kull is designed to support unstructured, arbitrary polygonal/polyhedral meshes. In addition, both frameworks are capable of running applications on large, distributed-memory parallel machines. Currently, both these frameworks separately support assorted collections of physics packages related to HEDP, including one for the energy deposition by laser/ion-beam ray tracing. This study analyzes the options available for developing a common laser/ion-beam ray tracing package that can bemore » easily shared between these two code frameworks and concludes with a set of recommendations for its development.« less

THE DISCOUNTED REPRODUCTIVE NUMBER FOR EPIDEMIOLOGY

PubMed Central

Reluga, Timothy C.; Medlock, Jan; Galvani, Alison

2013-01-01

The basic reproductive number, , and the effective reproductive number, , are commonly used in mathematical epidemiology as summary statistics for the size and controllability of epidemics. However, these commonly used reproductive numbers can be misleading when applied to predict pathogen evolution because they do not incorporate the impact of the timing of events in the life-history cycle of the pathogen. To study evolution problems where the host population size is changing, measures like the ultimate proliferation rate must be used. A third measure of reproductive success, which combines properties of both the basic reproductive number and the ultimate proliferation rate, is the discounted reproductive number . The discounted reproductive number is a measure of reproductive success that is an individual’s expected lifetime offspring production discounted by the background population growth rate. Here, we draw attention to the discounted reproductive number by providing an explicit definition and a systematic application framework. We describe how the discounted reproductive number overcomes the limitations of both the standard reproductive numbers and proliferation rates, and show that is closely connected to Fisher’s reproductive values for different life-history stages PMID:19364158

Fitting distributions to microbial contamination data collected with an unequal probability sampling design.

PubMed

Williams, M S; Ebel, E D; Cao, Y

2013-01-01

The fitting of statistical distributions to microbial sampling data is a common application in quantitative microbiology and risk assessment applications. An underlying assumption of most fitting techniques is that data are collected with simple random sampling, which is often times not the case. This study develops a weighted maximum likelihood estimation framework that is appropriate for microbiological samples that are collected with unequal probabilities of selection. A weighted maximum likelihood estimation framework is proposed for microbiological samples that are collected with unequal probabilities of selection. Two examples, based on the collection of food samples during processing, are provided to demonstrate the method and highlight the magnitude of biases in the maximum likelihood estimator when data are inappropriately treated as a simple random sample. Failure to properly weight samples to account for how data are collected can introduce substantial biases into inferences drawn from the data. The proposed methodology will reduce or eliminate an important source of bias in inferences drawn from the analysis of microbial data. This will also make comparisons between studies and the combination of results from different studies more reliable, which is important for risk assessment applications. © 2012 No claim to US Government works.

Uncertainty Quantification for Polynomial Systems via Bernstein Expansions

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2012-01-01

This paper presents a unifying framework to uncertainty quantification for systems having polynomial response metrics that depend on both aleatory and epistemic uncertainties. The approach proposed, which is based on the Bernstein expansions of polynomials, enables bounding the range of moments and failure probabilities of response metrics as well as finding supersets of the extreme epistemic realizations where the limits of such ranges occur. These bounds and supersets, whose analytical structure renders them free of approximation error, can be made arbitrarily tight with additional computational effort. Furthermore, this framework enables determining the importance of particular uncertain parameters according to the extent to which they affect the first two moments of response metrics and failure probabilities. This analysis enables determining the parameters that should be considered uncertain as well as those that can be assumed to be constants without incurring significant error. The analytical nature of the approach eliminates the numerical error that characterizes the sampling-based techniques commonly used to propagate aleatory uncertainties as well as the possibility of under predicting the range of the statistic of interest that may result from searching for the best- and worstcase epistemic values via nonlinear optimization or sampling.

A generic Transcriptomics Reporting Framework (TRF) for 'omics data processing and analysis.

PubMed

Gant, Timothy W; Sauer, Ursula G; Zhang, Shu-Dong; Chorley, Brian N; Hackermüller, Jörg; Perdichizzi, Stefania; Tollefsen, Knut E; van Ravenzwaay, Ben; Yauk, Carole; Tong, Weida; Poole, Alan

2017-12-01

A generic Transcriptomics Reporting Framework (TRF) is presented that lists parameters that should be reported in 'omics studies used in a regulatory context. The TRF encompasses the processes from transcriptome profiling from data generation to a processed list of differentially expressed genes (DEGs) ready for interpretation. Included within the TRF is a reference baseline analysis (RBA) that encompasses raw data selection; data normalisation; recognition of outliers; and statistical analysis. The TRF itself does not dictate the methodology for data processing, but deals with what should be reported. Its principles are also applicable to sequencing data and other 'omics. In contrast, the RBA specifies a simple data processing and analysis methodology that is designed to provide a comparison point for other approaches and is exemplified here by a case study. By providing transparency on the steps applied during 'omics data processing and analysis, the TRF will increase confidence processing of 'omics data, and regulatory use. Applicability of the TRF is ensured by its simplicity and generality. The TRF can be applied to all types of regulatory 'omics studies, and it can be executed using different commonly available software tools. Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.

One Framework to Unite Them All? Use of the CEFR in European University Entrance Policies

ERIC Educational Resources Information Center

Deygers, Bart; Zeidler, Beate; Vilcu, Dina; Carlsen, Cecilie Hamnes

2018-01-01

Fifteen years after its publication, the Common European Framework of Reference for Languages is a commonly used document in language tests and policies across Europe. This article considers the CEFR's impact on university entrance language tests and policies that are used to regulate the entrance of international L2 students who wish to study in…

Identifying common values among seven health professions: An interprofessional analysis.

PubMed

Grace, Sandra; Innes, Ev; Joffe, Beverly; East, Leah; Coutts, Rosanne; Nancarrow, Susan

2017-05-01

This article reviews the competency frameworks of seven Australian health professions to explore relationships among health professions of similar status as reflected in their competency frameworks and to identify common themes and values across the professions. Frameworks were compared using a constructivist grounded theory approach to identify key themes, against which individual competencies for each profession were mapped and compared. The themes were examined for underlying values and a higher order theoretical framework was developed. In contrast to classical theories of professionalism that foreground differentiation of professions, our study suggests that the professions embrace a common structure and understanding, based on shared underpinning values. We propose a model of two core values that encompass all identified themes: the rights of the client and the capacity of a particular profession to serve the healthcare needs of clients. Interprofessional practice represents the intersection of the rights of the client to receive the best available healthcare and the recognition of the individual contribution of each profession. Recognising that all health professions adhere to a common value base, and exploring professional similarities and differences from that value base, challenges a paradigm that distinguishes professions solely on scope of practice.

Implementing the Biological ConditionGradient Framework for Management of Estuaries and Coasts

EPA Science Inventory

The Biological Condition Gradient (BCG) is a conceptual scientific framework for interpreting biological response to increasing effects of stressors on aquatic ecosystems (U.S. EPA 2016). The framework was developed from common patterns of biological response to stressors observe...

XAL Application Framework and Bricks GUI Builder

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

Pelaia II, Tom

2007-01-01

The XAL [1] Application Framework is a framework for rapidly developing document based Java applications with a common look and feel along with many built-in user interface behaviors. The Bricks GUI builder consists of a modern application and framework for rapidly building user interfaces in support of true Model-View-Controller (MVC) compliant Java applications. Bricks and the XAL Application Framework allow developers to rapidly create quality applications.

Statistical Analysis of CFD Solutions from the Drag Prediction Workshop

NASA Technical Reports Server (NTRS)

Hemsch, Michael J.

2002-01-01

A simple, graphical framework is presented for robust statistical evaluation of results obtained from N-Version testing of a series of RANS CFD codes. The solutions were obtained by a variety of code developers and users for the June 2001 Drag Prediction Workshop sponsored by the AIAA Applied Aerodynamics Technical Committee. The aerodynamic configuration used for the computational tests is the DLR-F4 wing-body combination previously tested in several European wind tunnels and for which a previous N-Version test had been conducted. The statistical framework is used to evaluate code results for (1) a single cruise design point, (2) drag polars and (3) drag rise. The paper concludes with a discussion of the meaning of the results, especially with respect to predictability, Validation, and reporting of solutions.

A general modeling framework for describing spatially structured population dynamics

USGS Publications Warehouse

Sample, Christine; Fryxell, John; Bieri, Joanna; Federico, Paula; Earl, Julia; Wiederholt, Ruscena; Mattsson, Brady; Flockhart, Tyler; Nicol, Sam; Diffendorfer, James E.; Thogmartin, Wayne E.; Erickson, Richard A.; Norris, D. Ryan

2017-01-01

Variation in movement across time and space fundamentally shapes the abundance and distribution of populations. Although a variety of approaches model structured population dynamics, they are limited to specific types of spatially structured populations and lack a unifying framework. Here, we propose a unified network-based framework sufficiently novel in its flexibility to capture a wide variety of spatiotemporal processes including metapopulations and a range of migratory patterns. It can accommodate different kinds of age structures, forms of population growth, dispersal, nomadism and migration, and alternative life-history strategies. Our objective was to link three general elements common to all spatially structured populations (space, time and movement) under a single mathematical framework. To do this, we adopt a network modeling approach. The spatial structure of a population is represented by a weighted and directed network. Each node and each edge has a set of attributes which vary through time. The dynamics of our network-based population is modeled with discrete time steps. Using both theoretical and real-world examples, we show how common elements recur across species with disparate movement strategies and how they can be combined under a unified mathematical framework. We illustrate how metapopulations, various migratory patterns, and nomadism can be represented with this modeling approach. We also apply our network-based framework to four organisms spanning a wide range of life histories, movement patterns, and carrying capacities. General computer code to implement our framework is provided, which can be applied to almost any spatially structured population. This framework contributes to our theoretical understanding of population dynamics and has practical management applications, including understanding the impact of perturbations on population size, distribution, and movement patterns. By working within a common framework, there is less chance that comparative analyses are colored by model details rather than general principles

EFFECTS-BASED CUMULATIVE RISK ASSESSMENT IN A LOW-INCOME URBAN COMMUNITY NEAR A SUPERFUND SITE

EPA Science Inventory

We will introduce into the cumulative risk assessment framework novel methods for non-cancer risk assessment, techniques for dose-response modeling that extend insights from chemical mixtures frameworks to non-chemical stressors, multilevel statistical methods used to address ...

Personality assessment and model comparison with behavioral data: A statistical framework and empirical demonstration with bonobos (Pan paniscus).

PubMed

Martin, Jordan S; Suarez, Scott A

2017-08-01

Interest in quantifying consistent among-individual variation in primate behavior, also known as personality, has grown rapidly in recent decades. Although behavioral coding is the most frequently utilized method for assessing primate personality, limitations in current statistical practice prevent researchers' from utilizing the full potential of their coding datasets. These limitations include the use of extensive data aggregation, not modeling biologically relevant sources of individual variance during repeatability estimation, not partitioning between-individual (co)variance prior to modeling personality structure, the misuse of principal component analysis, and an over-reliance upon exploratory statistical techniques to compare personality models across populations, species, and data collection methods. In this paper, we propose a statistical framework for primate personality research designed to address these limitations. Our framework synthesizes recently developed mixed-effects modeling approaches for quantifying behavioral variation with an information-theoretic model selection paradigm for confirmatory personality research. After detailing a multi-step analytic procedure for personality assessment and model comparison, we employ this framework to evaluate seven models of personality structure in zoo-housed bonobos (Pan paniscus). We find that differences between sexes, ages, zoos, time of observation, and social group composition contributed to significant behavioral variance. Independently of these factors, however, personality nonetheless accounted for a moderate to high proportion of variance in average behavior across observational periods. A personality structure derived from past rating research receives the strongest support relative to our model set. This model suggests that personality variation across the measured behavioral traits is best described by two correlated but distinct dimensions reflecting individual differences in affiliation and sociability (Agreeableness) as well as activity level, social play, and neophilia toward non-threatening stimuli (Openness). These results underscore the utility of our framework for quantifying personality in primates and facilitating greater integration between the behavioral ecological and comparative psychological approaches to personality research. © 2017 Wiley Periodicals, Inc.

Multiple Semantic Matching on Augmented N-partite Graph for Object Co-segmentation.

PubMed

Wang, Chuan; Zhang, Hua; Yang, Liang; Cao, Xiaochun; Xiong, Hongkai

2017-09-08

Recent methods for object co-segmentation focus on discovering single co-occurring relation of candidate regions representing the foreground of multiple images. However, region extraction based only on low and middle level information often occupies a large area of background without the help of semantic context. In addition, seeking single matching solution very likely leads to discover local parts of common objects. To cope with these deficiencies, we present a new object cosegmentation framework, which takes advantages of semantic information and globally explores multiple co-occurring matching cliques based on an N-partite graph structure. To this end, we first propose to incorporate candidate generation with semantic context. Based on the regions extracted from semantic segmentation of each image, we design a merging mechanism to hierarchically generate candidates with high semantic responses. Secondly, all candidates are taken into consideration to globally formulate multiple maximum weighted matching cliques, which complements the discovery of part of the common objects induced by a single clique. To facilitate the discovery of multiple matching cliques, an N-partite graph, which inherently excludes intralinks between candidates from the same image, is constructed to separate multiple cliques without additional constraints. Further, we augment the graph with an additional virtual node in each part to handle irrelevant matches when the similarity between two candidates is too small. Finally, with the explored multiple cliques, we statistically compute pixel-wise co-occurrence map for each image. Experimental results on two benchmark datasets, i.e., iCoseg and MSRC datasets, achieve desirable performance and demonstrate the effectiveness of our proposed framework.

A Common Calibration Source Framework for Fully-Polarimetric and Interferometric Radiometers

NASA Technical Reports Server (NTRS)

Kim, Edward J.; Davis, Brynmor; Piepmeier, Jeff; Zukor, Dorothy J. (Technical Monitor)

2000-01-01

Two types of microwave radiometry--synthetic thinned array radiometry (STAR) and fully-polarimetric (FP) radiometry--have received increasing attention during the last several years. STAR radiometers offer a technological solution to achieving high spatial resolution imaging from orbit without requiring a filled aperture or a moving antenna, and FP radiometers measure extra polarization state information upon which entirely new or more robust geophysical retrieval algorithms can be based. Radiometer configurations used for both STAR and FP instruments share one fundamental feature that distinguishes them from more 'standard' radiometers, namely, they measure correlations between pairs of microwave signals. The calibration requirements for correlation radiometers are broader than those for standard radiometers. Quantities of interest include total powers, complex correlation coefficients, various offsets, and possible nonlinearities. A candidate for an ideal calibration source would be one that injects test signals with precisely controllable correlation coefficients and absolute powers simultaneously into a pair of receivers, permitting all of these calibration quantities to be measured. The complex nature of correlation radiometer calibration, coupled with certain inherent similarities between STAR and FP instruments, suggests significant leverage in addressing both problems together. Recognizing this, a project was recently begun at NASA Goddard Space Flight Center to develop a compact low-power subsystem for spaceflight STAR or FP receiver calibration. We present a common theoretical framework for the design of signals for a controlled correlation calibration source. A statistical model is described, along with temporal and spectral constraints on such signals. Finally, a method for realizing these signals is demonstrated using a Matlab-based implementation.

A Model Independent S/W Framework for Search-Based Software Testing

PubMed Central

Baik, Jongmoon

2014-01-01

In Model-Based Testing (MBT) area, Search-Based Software Testing (SBST) has been employed to generate test cases from the model of a system under test. However, many types of models have been used in MBT. If the type of a model has changed from one to another, all functions of a search technique must be reimplemented because the types of models are different even if the same search technique has been applied. It requires too much time and effort to implement the same algorithm over and over again. We propose a model-independent software framework for SBST, which can reduce redundant works. The framework provides a reusable common software platform to reduce time and effort. The software framework not only presents design patterns to find test cases for a target model but also reduces development time by using common functions provided in the framework. We show the effectiveness and efficiency of the proposed framework with two case studies. The framework improves the productivity by about 50% when changing the type of a model. PMID:25302314

Genomic similarity and kernel methods I: advancements by building on mathematical and statistical foundations.

PubMed

Schaid, Daniel J

2010-01-01

Measures of genomic similarity are the basis of many statistical analytic methods. We review the mathematical and statistical basis of similarity methods, particularly based on kernel methods. A kernel function converts information for a pair of subjects to a quantitative value representing either similarity (larger values meaning more similar) or distance (smaller values meaning more similar), with the requirement that it must create a positive semidefinite matrix when applied to all pairs of subjects. This review emphasizes the wide range of statistical methods and software that can be used when similarity is based on kernel methods, such as nonparametric regression, linear mixed models and generalized linear mixed models, hierarchical models, score statistics, and support vector machines. The mathematical rigor for these methods is summarized, as is the mathematical framework for making kernels. This review provides a framework to move from intuitive and heuristic approaches to define genomic similarities to more rigorous methods that can take advantage of powerful statistical modeling and existing software. A companion paper reviews novel approaches to creating kernels that might be useful for genomic analyses, providing insights with examples [1]. Copyright © 2010 S. Karger AG, Basel.

Preliminary evaluation of a fully automated quantitative framework for characterizing general breast tissue histology via color histogram and color texture analysis

NASA Astrophysics Data System (ADS)

Keller, Brad M.; Gastounioti, Aimilia; Batiste, Rebecca C.; Kontos, Despina; Feldman, Michael D.

2016-03-01

Visual characterization of histologic specimens is known to suffer from intra- and inter-observer variability. To help address this, we developed an automated framework for characterizing digitized histology specimens based on a novel application of color histogram and color texture analysis. We perform a preliminary evaluation of this framework using a set of 73 trichrome-stained, digitized slides of normal breast tissue which were visually assessed by an expert pathologist in terms of the percentage of collagenous stroma, stromal collagen density, duct-lobular unit density and the presence of elastosis. For each slide, our algorithm automatically segments the tissue region based on the lightness channel in CIELAB colorspace. Within each tissue region, a color histogram feature vector is extracted using a common color palette for trichrome images generated with a previously described method. Then, using a whole-slide, lattice-based methodology, color texture maps are generated using a set of color co-occurrence matrix statistics: contrast, correlation, energy and homogeneity. The extracted features sets are compared to the visually assessed tissue characteristics. Overall, the extracted texture features have high correlations to both the percentage of collagenous stroma (r=0.95, p<0.001) and duct-lobular unit density (r=0.71, p<0.001) seen in the tissue samples, and several individual features were associated with either collagen density and/or the presence of elastosis (p<=0.05). This suggests that the proposed framework has promise as a means to quantitatively extract descriptors reflecting tissue-level characteristics and thus could be useful in detecting and characterizing histological processes in digitized histology specimens.

Spatial Modeling for Resources Framework (SMRF): A modular framework for developing spatial forcing data for snow modeling in mountain basins

NASA Astrophysics Data System (ADS)

Havens, Scott; Marks, Danny; Kormos, Patrick; Hedrick, Andrew

2017-12-01

In the Western US and many mountainous regions of the world, critical water resources and climate conditions are difficult to monitor because the observation network is generally very sparse. The critical resource from the mountain snowpack is water flowing into streams and reservoirs that will provide for irrigation, flood control, power generation, and ecosystem services. Water supply forecasting in a rapidly changing climate has become increasingly difficult because of non-stationary conditions. In response, operational water supply managers have begun to move from statistical techniques towards the use of physically based models. As we begin to transition physically based models from research to operational use, we must address the most difficult and time-consuming aspect of model initiation: the need for robust methods to develop and distribute the input forcing data. In this paper, we present a new open source framework, the Spatial Modeling for Resources Framework (SMRF), which automates and simplifies the common forcing data distribution methods. It is computationally efficient and can be implemented for both research and operational applications. We present an example of how SMRF is able to generate all of the forcing data required to a run physically based snow model at 50-100 m resolution over regions of 1000-7000 km2. The approach has been successfully applied in real time and historical applications for both the Boise River Basin in Idaho, USA and the Tuolumne River Basin in California, USA. These applications use meteorological station measurements and numerical weather prediction model outputs as input. SMRF has significantly streamlined the modeling workflow, decreased model set up time from weeks to days, and made near real-time application of a physically based snow model possible.

Estimation and Application of Ecological Memory Functions in Time and Space

NASA Astrophysics Data System (ADS)

Itter, M.; Finley, A. O.; Dawson, A.

2017-12-01

A common goal in quantitative ecology is the estimation or prediction of ecological processes as a function of explanatory variables (or covariates). Frequently, the ecological process of interest and associated covariates vary in time, space, or both. Theory indicates many ecological processes exhibit memory to local, past conditions. Despite such theoretical understanding, few methods exist to integrate observations from the recent past or within a local neighborhood as drivers of these processes. We build upon recent methodological advances in ecology and spatial statistics to develop a Bayesian hierarchical framework to estimate so-called ecological memory functions; that is, weight-generating functions that specify the relative importance of local, past covariate observations to ecological processes. Memory functions are estimated using a set of basis functions in time and/or space, allowing for flexible ecological memory based on a reduced set of parameters. Ecological memory functions are entirely data driven under the Bayesian hierarchical framework—no a priori assumptions are made regarding functional forms. Memory function uncertainty follows directly from posterior distributions for model parameters allowing for tractable propagation of error to predictions of ecological processes. We apply the model framework to simulated spatio-temporal datasets generated using memory functions of varying complexity. The framework is also applied to estimate the ecological memory of annual boreal forest growth to local, past water availability. Consistent with ecological understanding of boreal forest growth dynamics, memory to past water availability peaks in the year previous to growth and slowly decays to zero in five to eight years. The Bayesian hierarchical framework has applicability to a broad range of ecosystems and processes allowing for increased understanding of ecosystem responses to local and past conditions and improved prediction of ecological processes.

A Methodological Framework to Analyze Stakeholder Preferences and Propose Strategic Pathways for a Sustainable University

ERIC Educational Resources Information Center

Turan, Fikret Korhan; Cetinkaya, Saadet; Ustun, Ceyda

2016-01-01

Building sustainable universities calls for participative management and collaboration among stakeholders. Combining analytic hierarchy and network processes (AHP/ANP) with statistical analysis, this research proposes a framework that can be used in higher education institutions for integrating stakeholder preferences into strategic decisions. The…

Developing Sensitivity to Subword Combinatorial Orthographic Regularity (SCORe): A Two-Process Framework

ERIC Educational Resources Information Center

Mano, Quintino R.

2016-01-01

Accumulating evidence suggests that literacy acquisition involves developing sensitivity to the statistical regularities of the textual environment. To organize accumulating evidence and help guide future inquiry, this article integrates data from disparate fields of study and formalizes a new two-process framework for developing sensitivity to…

76 FR 47533 - Fisheries of the Northeastern United States; Monkfish; Framework Adjustment 7

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-05

... FMP). The New England Fishery Management Council and Mid- Atlantic Fishery Management Council (Councils) developed Framework 7 to adjust the annual catch target (ACT) for the Northern Fishery Management... catch (ABC) for monkfish. The New England Council's Scientific and Statistical Committee (SSC) has...

Commentary: Using Potential Outcomes to Understand Causal Mediation Analysis

ERIC Educational Resources Information Center

Imai, Kosuke; Jo, Booil; Stuart, Elizabeth A.

2011-01-01

In this commentary, we demonstrate how the potential outcomes framework can help understand the key identification assumptions underlying causal mediation analysis. We show that this framework can lead to the development of alternative research design and statistical analysis strategies applicable to the longitudinal data settings considered by…

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.

Citizen surveillance for environmental monitoring: combining the efforts of citizen science and crowdsourcing in a quantitative data framework.

PubMed

Welvaert, Marijke; Caley, Peter

2016-01-01

Citizen science and crowdsourcing have been emerging as methods to collect data for surveillance and/or monitoring activities. They could be gathered under the overarching term citizen surveillance . The discipline, however, still struggles to be widely accepted in the scientific community, mainly because these activities are not embedded in a quantitative framework. This results in an ongoing discussion on how to analyze and make useful inference from these data. When considering the data collection process, we illustrate how citizen surveillance can be classified according to the nature of the underlying observation process measured in two dimensions-the degree of observer reporting intention and the control in observer detection effort. By classifying the observation process in these dimensions we distinguish between crowdsourcing, unstructured citizen science and structured citizen science. This classification helps the determine data processing and statistical treatment of these data for making inference. Using our framework, it is apparent that published studies are overwhelmingly associated with structured citizen science, and there are well developed statistical methods for the resulting data. In contrast, methods for making useful inference from purely crowd-sourced data remain under development, with the challenges of accounting for the unknown observation process considerable. Our quantitative framework for citizen surveillance calls for an integration of citizen science and crowdsourcing and provides a way forward to solve the statistical challenges inherent to citizen-sourced data.

Root-Metaphors for Understanding: A Framework for Teachers and Teacher Educators of Information and Communication Technologies

ERIC Educational Resources Information Center

Kilbourn, Brent; Alvarez, Isabel

2008-01-01

This paper argues for understanding ICT from the standpoint of philosophical world views. We present a framework, based on Pepper's root-metaphors (Formism, Contextualism, Mechanism, Organicism, and Animism/Mysticism) and we illustrate the use of the framework by looking at a common example of ICT: e-mail. It is argued that such a framework is…

The "Common European Framework of Reference" Down Under: A Survey of Its Use and Non-Use in Australian Universities

ERIC Educational Resources Information Center

Normand-Marconnet, Nadine; Lo Bianco, Joseph

2015-01-01

Today, the "Common European Framework of Reference for Languages" (CEFR; Council of Europe 2001) is widely recognised as emblematic of globalization in education, both in the realms of policy and in educational practice (Byram et al. 2012a). In Europe the CEFR is regularly cited as a reference point for curriculum planning, and is often…

Teaching Writing within the Common European Framework of Reference (CEFR): A Supplement Asynchronous Blended Learning Approach in an EFL Undergraduate Course in Egypt

ERIC Educational Resources Information Center

Shaarawy, Hanaa Youssef; Lotfy, Nohayer Esmat

2013-01-01

Based on the Common European Framework of Reference (CEFR) and following a blended learning approach (a supplement model), this article reports on a quasi-experiment where writing was taught evenly with other language skills in everyday language contexts and where asynchronous online activities were required from students to extend learning beyond…

ESPC Common Model Architecture Earth System Modeling Framework (ESMF) Software and Application Development

DTIC Science & Technology

2015-09-30

originate from NASA , NOAA , and community modeling efforts, and support for creation of the suite was shared by sponsors from other agencies. ESPS...Framework (ESMF) Software and Application Development Cecelia Deluca NESII/CIRES/ NOAA Earth System Research Laboratory 325 Broadway Boulder, CO...Capability (NUOPC) was established between NOAA and Navy to develop a common software architecture for easy and efficient interoperability. The

Conceptual measurement framework for help-seeking for mental health problems

PubMed Central

Rickwood, Debra; Thomas, Kerry

2012-01-01

Background Despite a high level of research, policy, and practice interest in help-seeking for mental health problems and mental disorders, there is currently no agreed and commonly used definition or conceptual measurement framework for help-seeking. Methods A systematic review of research activity in the field was undertaken to investigate how help-seeking has been conceptualized and measured. Common elements were used to develop a proposed conceptual measurement framework. Results The database search revealed a very high level of research activity and confirmed that there is no commonly applied definition of help-seeking and no psychometrically sound measures that are routinely used. The most common element in the help-seeking research was a focus on formal help-seeking sources, rather than informal sources, although studies did not assess a consistent set of professional sources; rather, each study addressed an idiosyncratic range of sources of professional health and community care. Similarly, the studies considered help-seeking for a range of mental health problems and no consistent terminology was applied. The most common mental health problem investigated was depression, followed by use of generic terms, such as mental health problem, psychological distress, or emotional problem. Major gaps in the consistent measurement of help-seeking were identified. Conclusion It is evident that an agreed definition that supports the comparable measurement of help-seeking is lacking. Therefore, a conceptual measurement framework is proposed to fill this gap. The framework maintains that the essential elements for measurement are: the part of the help-seeking process to be investigated and respective time frame, the source and type of assistance, and the type of mental health concern. It is argued that adopting this framework will facilitate progress in the field by providing much needed conceptual consistency. Results will then be able to be compared across studies and population groups, and this will significantly benefit understanding of policy and practice initiatives aimed at improving access to and engagement with services for people with mental health concerns. PMID:23248576

MONITORING AND ASSESSING THE CONDITION OF AQUATIC RESOURCES: ROLE OF COMPLEX SURVEY DESIGN AND ANALYSIS

EPA Science Inventory

The National Water Quality Monitoring Council (NWQMC) developed a common framework for aquatic resource monitoring. The framework is described in a series of articles published in Water Resources IMPACT, September, 2003. One objective of the framework is to encourage consistenc...

Using core competencies to build an evaluative framework: outcome assessment of the University of Guelph Master of Public Health program.

PubMed

Britten, Nicole; Wallar, Lauren E; McEwen, Scott A; Papadopoulos, Andrew

2014-07-31

Master of Public Health programs have been developed across Canada in response to the need for graduate-level trained professionals to work in the public health sector. The University of Guelph recently conducted a five-year outcome assessment using the Core Competencies for Public Health in Canada as an evaluative framework to determine whether graduates are receiving adequate training, and identify areas for improvement. A curriculum map of core courses and an online survey of University of Guelph Master of Public Health graduates comprised the outcome assessment. The curriculum map was constructed by evaluating course outlines, assignments, and content to determine the extent to which the Core Competencies were covered in each course. Quantitative survey results were characterized using descriptive statistics. Qualitative survey results were analyzed to identify common themes and patterns in open-ended responses. The University of Guelph Master of Public Health program provided a positive learning environment in which graduates gained proficiency across the Core Competencies through core and elective courses, meaningful practicums, and competent faculty. Practice-based learning environments, particularly in collaboration with public health organizations, were deemed to be beneficial to students' learning experiences. The Core Competencies and graduate surveys can be used to conduct a meaningful and informative outcome assessment. We encourage other Master of Public Health programs to conduct their own outcome assessments using a similar framework, and disseminate these results in order to identify best practices and strengthen the Canadian graduate public health education system.

Hazard Analysis and Safety Requirements for Small Drone Operations: To What Extent Do Popular Drones Embed Safety?

PubMed

Plioutsias, Anastasios; Karanikas, Nektarios; Chatzimihailidou, Maria Mikela

2018-03-01

Currently, published risk analyses for drones refer mainly to commercial systems, use data from civil aviation, and are based on probabilistic approaches without suggesting an inclusive list of hazards and respective requirements. Within this context, this article presents: (1) a set of safety requirements generated from the application of the systems theoretic process analysis (STPA) technique on a generic small drone system; (2) a gap analysis between the set of safety requirements and the ones met by 19 popular drone models; (3) the extent of the differences between those models, their manufacturers, and the countries of origin; and (4) the association of drone prices with the extent they meet the requirements derived by STPA. The application of STPA resulted in 70 safety requirements distributed across the authority, manufacturer, end user, or drone automation levels. A gap analysis showed high dissimilarities regarding the extent to which the 19 drones meet the same safety requirements. Statistical results suggested a positive correlation between drone prices and the extent that the 19 drones studied herein met the safety requirements generated by STPA, and significant differences were identified among the manufacturers. This work complements the existing risk assessment frameworks for small drones, and contributes to the establishment of a commonly endorsed international risk analysis framework. Such a framework will support the development of a holistic and methodologically justified standardization scheme for small drone flights. © 2017 Society for Risk Analysis.

Epidemiology Characteristics, Methodological Assessment and Reporting of Statistical Analysis of Network Meta-Analyses in the Field of Cancer

PubMed Central

Ge, Long; Tian, Jin-hui; Li, Xiu-xia; Song, Fujian; Li, Lun; Zhang, Jun; Li, Ge; Pei, Gai-qin; Qiu, Xia; Yang, Ke-hu

2016-01-01

Because of the methodological complexity of network meta-analyses (NMAs), NMAs may be more vulnerable to methodological risks than conventional pair-wise meta-analysis. Our study aims to investigate epidemiology characteristics, conduction of literature search, methodological quality and reporting of statistical analysis process in the field of cancer based on PRISMA extension statement and modified AMSTAR checklist. We identified and included 102 NMAs in the field of cancer. 61 NMAs were conducted using a Bayesian framework. Of them, more than half of NMAs did not report assessment of convergence (60.66%). Inconsistency was assessed in 27.87% of NMAs. Assessment of heterogeneity in traditional meta-analyses was more common (42.62%) than in NMAs (6.56%). Most of NMAs did not report assessment of similarity (86.89%) and did not used GRADE tool to assess quality of evidence (95.08%). 43 NMAs were adjusted indirect comparisons, the methods used were described in 53.49% NMAs. Only 4.65% NMAs described the details of handling of multi group trials and 6.98% described the methods of similarity assessment. The median total AMSTAR-score was 8.00 (IQR: 6.00–8.25). Methodological quality and reporting of statistical analysis did not substantially differ by selected general characteristics. Overall, the quality of NMAs in the field of cancer was generally acceptable. PMID:27848997

Statistical post-processing of seasonal multi-model forecasts: Why is it so hard to beat the multi-model mean?

NASA Astrophysics Data System (ADS)

Siegert, Stefan

2017-04-01

Initialised climate forecasts on seasonal time scales, run several months or even years ahead, are now an integral part of the battery of products offered by climate services world-wide. The availability of seasonal climate forecasts from various modeling centres gives rise to multi-model ensemble forecasts. Post-processing such seasonal-to-decadal multi-model forecasts is challenging 1) because the cross-correlation structure between multiple models and observations can be complicated, 2) because the amount of training data to fit the post-processing parameters is very limited, and 3) because the forecast skill of numerical models tends to be low on seasonal time scales. In this talk I will review new statistical post-processing frameworks for multi-model ensembles. I will focus particularly on Bayesian hierarchical modelling approaches, which are flexible enough to capture commonly made assumptions about collective and model-specific biases of multi-model ensembles. Despite the advances in statistical methodology, it turns out to be very difficult to out-perform the simplest post-processing method, which just recalibrates the multi-model ensemble mean by linear regression. I will discuss reasons for this, which are closely linked to the specific characteristics of seasonal multi-model forecasts. I explore possible directions for improvements, for example using informative priors on the post-processing parameters, and jointly modelling forecasts and observations.

Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application

PubMed Central

Cantor, Rita M.; Lange, Kenneth; Sinsheimer, Janet S.

2010-01-01

Genome-wide association studies (GWAS) have rapidly become a standard method for disease gene discovery. A substantial number of recent GWAS indicate that for most disorders, only a few common variants are implicated and the associated SNPs explain only a small fraction of the genetic risk. This review is written from the viewpoint that findings from the GWAS provide preliminary genetic information that is available for additional analysis by statistical procedures that accumulate evidence, and that these secondary analyses are very likely to provide valuable information that will help prioritize the strongest constellations of results. We review and discuss three analytic methods to combine preliminary GWAS statistics to identify genes, alleles, and pathways for deeper investigations. Meta-analysis seeks to pool information from multiple GWAS to increase the chances of finding true positives among the false positives and provides a way to combine associations across GWAS, even when the original data are unavailable. Testing for epistasis within a single GWAS study can identify the stronger results that are revealed when genes interact. Pathway analysis of GWAS results is used to prioritize genes and pathways within a biological context. Following a GWAS, association results can be assigned to pathways and tested in aggregate with computational tools and pathway databases. Reviews of published methods with recommendations for their application are provided within the framework for each approach. PMID:20074509

Improving information retrieval in functional analysis.

PubMed

Rodriguez, Juan C; González, Germán A; Fresno, Cristóbal; Llera, Andrea S; Fernández, Elmer A

2016-12-01

Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequently used strategies for this purpose are Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology. Several statistical methods have been developed and compared in terms of computational efficiency and/or statistical appropriateness. However, whether their results are similar or complementary, the sensitivity to parameter settings, or possible bias in the analyzed terms has not been addressed so far. Here, two GSEA and four SEA methods and their parameter combinations were evaluated in six datasets by comparing two breast cancer subtypes with well-known differences in genetic background and patient outcomes. We show that GSEA and SEA lead to different results depending on the chosen statistic, model and/or parameters. Both approaches provide complementary results from a biological perspective. Hence, an Integrative Functional Analysis (IFA) tool is proposed to improve information retrieval in FA. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required, since the best SEA/GSEA alternatives are integrated. IFA utility was demonstrated by evaluating four prostate cancer and the TCGA breast cancer microarray datasets, which showed its biological generalization capabilities. Copyright © 2016 Elsevier Ltd. All rights reserved.

Quality of reporting statistics in two Indian pharmacology journals.

PubMed

Jaykaran; Yadav, Preeti

2011-04-01

To evaluate the reporting of the statistical methods in articles published in two Indian pharmacology journals. All original articles published since 2002 were downloaded from the journals' (Indian Journal of Pharmacology (IJP) and Indian Journal of Physiology and Pharmacology (IJPP)) website. These articles were evaluated on the basis of appropriateness of descriptive statistics and inferential statistics. Descriptive statistics was evaluated on the basis of reporting of method of description and central tendencies. Inferential statistics was evaluated on the basis of fulfilling of assumption of statistical methods and appropriateness of statistical tests. Values are described as frequencies, percentage, and 95% confidence interval (CI) around the percentages. Inappropriate descriptive statistics was observed in 150 (78.1%, 95% CI 71.7-83.3%) articles. Most common reason for this inappropriate descriptive statistics was use of mean ± SEM at the place of "mean (SD)" or "mean ± SD." Most common statistical method used was one-way ANOVA (58.4%). Information regarding checking of assumption of statistical test was mentioned in only two articles. Inappropriate statistical test was observed in 61 (31.7%, 95% CI 25.6-38.6%) articles. Most common reason for inappropriate statistical test was the use of two group test for three or more groups. Articles published in two Indian pharmacology journals are not devoid of statistical errors.

"One man's trash is another man's treasure": exploring economic and moral subtexts of the "organ shortage" problem in public views on organ donation.

PubMed

Schicktanz, S; Schweda, M

2009-08-01

The debate over financial incentives and market models for organ procurement represents a key trend in recent bioethics. In this paper, we wish to reassess one of its central premises-the idea of organ shortage. While the problem is often presented as an objective statistical fact that can be taken for granted, we will take a closer look at the underlying framework expressed in the common rhetoric of "scarcity", "shortage" or "unfulfilled demand". On the basis of theoretical considerations as well as a socioempirical examination of public attitudes, we will argue that this rhetoric has an economic subtext that imbues the debate with normative premises that have far-reaching social and ethical consequences and need to be made explicit and discussed.

Bubbles, shocks and elementary technical trading strategies

NASA Astrophysics Data System (ADS)

Fry, John

2014-01-01

In this paper we provide a unifying framework for a set of seemingly disparate models for bubbles, shocks and elementary technical trading strategies in financial markets. Markets operate by balancing intrinsic levels of risk and return. This seemingly simple observation is commonly over-looked by academics and practitioners alike. Our model shares its origins in statistical physics with others. However, under our approach, changes in market regime can be explicitly shown to represent a phase transition from random to deterministic behaviour in prices. This structure leads to an improved physical and econometric model. We develop models for bubbles, shocks and elementary technical trading strategies. The list of empirical applications is both interesting and topical and includes real-estate bubbles and the on-going Eurozone crisis. We close by comparing the results of our model with purely qualitative findings from the finance literature.

Matrix population models from 20 studies of perennial plant populations

USGS Publications Warehouse

Ellis, Martha M.; Williams, Jennifer L.; Lesica, Peter; Bell, Timothy J.; Bierzychudek, Paulette; Bowles, Marlin; Crone, Elizabeth E.; Doak, Daniel F.; Ehrlen, Johan; Ellis-Adam, Albertine; McEachern, Kathryn; Ganesan, Rengaian; Latham, Penelope; Luijten, Sheila; Kaye, Thomas N.; Knight, Tiffany M.; Menges, Eric S.; Morris, William F.; den Nijs, Hans; Oostermeijer, Gerard; Quintana-Ascencio, Pedro F.; Shelly, J. Stephen; Stanley, Amanda; Thorpe, Andrea; Tamara, Ticktin; Valverde, Teresa; Weekley, Carl W.

2012-01-01

Demographic transition matrices are one of the most commonly applied population models for both basic and applied ecological research. The relatively simple framework of these models and simple, easily interpretable summary statistics they produce have prompted the wide use of these models across an exceptionally broad range of taxa. Here, we provide annual transition matrices and observed stage structures/population sizes for 20 perennial plant species which have been the focal species for long-term demographic monitoring. These data were assembled as part of the "Testing Matrix Models" working group through the National Center for Ecological Analysis and Synthesis (NCEAS). In sum, these data represent 82 populations with >460 total population-years of data. It is our hope that making these data available will help promote and improve our ability to monitor and understand plant population dynamics.

Matrix population models from 20 studies of perennial plant populations

USGS Publications Warehouse

Ellis, Martha M.; Williams, Jennifer L.; Lesica, Peter; Bell, Timothy J.; Bierzychudek, Paulette; Bowles, Marlin; Crone, Elizabeth E.; Doak, Daniel F.; Ehrlen, Johan; Ellis-Adam, Albertine; McEachern, Kathryn; Ganesan, Rengaian; Latham, Penelope; Luijten, Sheila; Kaye, Thomas N.; Knight, Tiffany M.; Menges, Eric S.; Morris, William F.; den Nijs, Hans; Oostermeijer, Gerard; Quintana-Ascencio, Pedro F.; Shelly, J. Stephen; Stanley, Amanda; Thorpe, Andrea; Tamara, Ticktin; Valverde, Teresa; Weekley, Carl W.

2012-01-01

Demographic transition matrices are one of the most commonly applied population models for both basic and applied ecological research. The relatively simple framework of these models and simple, easily interpretable summary statistics they produce have prompted the wide use of these models across an exceptionally broad range of taxa. Here, we provide annual transition matrices and observed stage structures/population sizes for 20 perennial plant species which have been the focal species for long-term demographic monitoring. These data were assembled as part of the 'Testing Matrix Models' working group through the National Center for Ecological Analysis and Synthesis (NCEAS). In sum, these data represent 82 populations with >460 total population-years of data. It is our hope that making these data available will help promote and improve our ability to monitor and understand plant population dynamics.

A psychometric evaluation of the digital logic concept inventory

NASA Astrophysics Data System (ADS)

Herman, Geoffrey L.; Zilles, Craig; Loui, Michael C.

2014-10-01

Concept inventories hold tremendous promise for promoting the rigorous evaluation of teaching methods that might remedy common student misconceptions and promote deep learning. The measurements from concept inventories can be trusted only if the concept inventories are evaluated both by expert feedback and statistical scrutiny (psychometric evaluation). Classical Test Theory and Item Response Theory provide two psychometric frameworks for evaluating the quality of assessment tools. We discuss how these theories can be applied to assessment tools generally and then apply them to the Digital Logic Concept Inventory (DLCI). We demonstrate that the DLCI is sufficiently reliable for research purposes when used in its entirety and as a post-course assessment of students' conceptual understanding of digital logic. The DLCI can also discriminate between students across a wide range of ability levels, providing the most information about weaker students' ability levels.

Vertical marginal gap evaluation of conventional cast and computer numeric controlled-milled titanium full-arch implant-supported frameworks.

PubMed

Alfadda, Sara A

2014-01-01

To use a novel approach to measure the amount of vertical marginal gap in computer numeric controlled (CNC)-milled titanium frameworks and conventional cast frameworks. Ten cast frameworks were fabricated on the mandibular master casts of 10 patients. Then, 10 CNC-milled titanium frameworks were fabricated by laser scanning the cast frameworks. The vertical marginal gap was measured and analyzed using the Contura-G2 coordinate measuring machine and special computer software. The CNC-milled titanium frameworks showed an overall reduced mean vertical gap compared with the cast frameworks in all five analogs. This difference was highly statistically significant in the distal analogs. The largest mean gap in the cast framework was recorded in the most distal analogs, and the least amount was in the middle analog. Neither of the two types of frameworks provided a completely gap-free superstructure. The CNCmilled titanium frameworks showed a significantly smaller vertical marginal gap than the cast frameworks.

Alternative Statistical Frameworks for Student Growth Percentile Estimation

ERIC Educational Resources Information Center

Lockwood, J. R.; Castellano, Katherine E.

2015-01-01

This article suggests two alternative statistical approaches for estimating student growth percentiles (SGP). The first is to estimate percentile ranks of current test scores conditional on past test scores directly, by modeling the conditional cumulative distribution functions, rather than indirectly through quantile regressions. This would…

Event coincidence analysis for quantifying statistical interrelationships between event time series. On the role of flood events as triggers of epidemic outbreaks

NASA Astrophysics Data System (ADS)

Donges, J. F.; Schleussner, C.-F.; Siegmund, J. F.; Donner, R. V.

2016-05-01

Studying event time series is a powerful approach for analyzing the dynamics of complex dynamical systems in many fields of science. In this paper, we describe the method of event coincidence analysis to provide a framework for quantifying the strength, directionality and time lag of statistical interrelationships between event series. Event coincidence analysis allows to formulate and test null hypotheses on the origin of the observed interrelationships including tests based on Poisson processes or, more generally, stochastic point processes with a prescribed inter-event time distribution and other higher-order properties. Applying the framework to country-level observational data yields evidence that flood events have acted as triggers of epidemic outbreaks globally since the 1950s. Facing projected future changes in the statistics of climatic extreme events, statistical techniques such as event coincidence analysis will be relevant for investigating the impacts of anthropogenic climate change on human societies and ecosystems worldwide.

[The informational support of statistical observation related to children disability].

PubMed

Son, I M; Polikarpov, A V; Ogrizko, E V; Golubeva, T Yu

2016-01-01

Within the framework of the Convention on rights of the disabled the revision is specified concerning criteria of identification of disability of children and reformation of system of medical social expertise according international standards of indices of health and indices related to health. In connection with it, it is important to consider the relationship between alterations in forms of the Federal statistical monitoring in the part of registration of disabled children in the Russian Federation and classification of health indices and indices related to health applied at identification of disability. The article presents analysis of relationship between alterations in forms of the Federal statistical monitoring in the part of registration of disabled children in the Russian Federation and applied classifications used at identification of disability (International classification of impairments, disabilities and handicap (ICDH), international classification of functioning, disability and health (ICF), international classification of functioning, disability and health, version for children and youth (ICF-CY). The intersectorial interaction is considered within the framework of statistics of children disability.

Environmental statistics and optimal regulation

NASA Astrophysics Data System (ADS)

Sivak, David; Thomson, Matt

2015-03-01

The precision with which an organism can detect its environment, and the timescale for and statistics of environmental change, will affect the suitability of different strategies for regulating protein levels in response to environmental inputs. We propose a general framework--here applied to the enzymatic regulation of metabolism in response to changing nutrient concentrations--to predict the optimal regulatory strategy given the statistics of fluctuations in the environment and measurement apparatus, and the costs associated with enzyme production. We find: (i) relative convexity of enzyme expression cost and benefit influences the fitness of thresholding or graded responses; (ii) intermediate levels of measurement uncertainty call for a sophisticated Bayesian decision rule; and (iii) in dynamic contexts, intermediate levels of uncertainty call for retaining memory of the past. Statistical properties of the environment, such as variability and correlation times, set optimal biochemical parameters, such as thresholds and decay rates in signaling pathways. Our framework provides a theoretical basis for interpreting molecular signal processing algorithms and a classification scheme that organizes known regulatory strategies and may help conceptualize heretofore unknown ones.

Reaching common ground: a patient-family-based conceptual framework of quality EOL care.

PubMed

Howell, Doris; Brazil, Kevin

2005-01-01

Improvement in the quality of end-of-life (EOL) care is a priority health care issue since serious deficiencies in quality of care have been reported across care settings. Increasing pressure is now focused on Canadian health care organizations to be accountable for the quality of palliative and EOL care delivered. Numerous domains of quality EOL care upon which to create accountability frameworks are now published, with some derived from the patient/family perspective. There is a need to reach common ground on the domains of quality EOL care valued by patients and families in order to develop consistent performance measures and set priorities for health care improvement. This paper describes a meta-synthesis study to develop a common conceptual framework of quality EOL care integrating attributes of quality valued by patients and their families.

Fracture behavior of metal-ceramic fixed dental prostheses with frameworks from cast or a newly developed sintered cobalt-chromium alloy.

PubMed

Krug, Klaus-Peter; Knauber, Andreas W; Nothdurft, Frank P

2015-03-01

The aim of this study was to investigate the fracture behavior of metal-ceramic bridges with frameworks from cobalt-chromium-molybdenum (CoCrMo), which are manufactured using conventional casting or a new computer-aided design/computer-aided manufacturing (CAD/CAM) milling and sintering technique. A total of 32 metal-ceramic fixed dental prostheses (FDPs), which are based on a nonprecious metal framework, was produced using a conventional casting process (n = 16) or a new CAD/CAM milling and sintering process (n = 16). Eight unveneered frameworks were manufactured using each of the techniques. After thermal and mechanical aging of half of the restorations, all samples were subjected to a static loading test in a universal testing machine, in which acoustic emission monitoring was performed. Three different critical forces were revealed: the fracture force (F max), the force at the first reduction in force (F decr1), and the force at the critical acoustic event (F acoust1). With the exception of the veneered restorations with cast or sintered metal frameworks without artificial aging, which presented a statistically significant but slightly different F max, no statistically significant differences between cast and CAD/CAM sintered and milled FDPs were detected. Thermal and mechanical loading did not significantly affect the resulting forces. Cast and CAD/CAM milled and sintered metal-ceramic bridges were determined to be comparable with respect to the fracture behavior. FDPs based on CAD/CAM milled and sintered frameworks may be an applicable and less technique-sensitive alternative to frameworks that are based on conventionally cast frameworks.

U.S. History Framework for the 2010 National Assessment of Educational Progress

ERIC Educational Resources Information Center

National Assessment Governing Board, 2009

2009-01-01

This framework identifies the main ideas, major events, key individuals, and unifying themes of American history as a basis for preparing the 2010 assessment. The framework recognizes that U.S. history includes powerful ideas, common and diverse traditions, economic developments, technological and scientific innovations, philosophical debates,…

Combined data preprocessing and multivariate statistical analysis characterizes fed-batch culture of mouse hybridoma cells for rational medium design.

PubMed

Selvarasu, Suresh; Kim, Do Yun; Karimi, Iftekhar A; Lee, Dong-Yup

2010-10-01

We present an integrated framework for characterizing fed-batch cultures of mouse hybridoma cells producing monoclonal antibody (mAb). This framework systematically combines data preprocessing, elemental balancing and statistical analysis technique. Initially, specific rates of cell growth, glucose/amino acid consumptions and mAb/metabolite productions were calculated via curve fitting using logistic equations, with subsequent elemental balancing of the preprocessed data indicating the presence of experimental measurement errors. Multivariate statistical analysis was then employed to understand physiological characteristics of the cellular system. The results from principal component analysis (PCA) revealed three major clusters of amino acids with similar trends in their consumption profiles: (i) arginine, threonine and serine, (ii) glycine, tyrosine, phenylalanine, methionine, histidine and asparagine, and (iii) lysine, valine and isoleucine. Further analysis using partial least square (PLS) regression identified key amino acids which were positively or negatively correlated with the cell growth, mAb production and the generation of lactate and ammonia. Based on these results, the optimal concentrations of key amino acids in the feed medium can be inferred, potentially leading to an increase in cell viability and productivity, as well as a decrease in toxic waste production. The study demonstrated how the current methodological framework using multivariate statistical analysis techniques can serve as a potential tool for deriving rational medium design strategies. Copyright © 2010 Elsevier B.V. All rights reserved.

Recent updates in developing a statistical pseudo-dynamic source-modeling framework to capture the variability of earthquake rupture scenarios

NASA Astrophysics Data System (ADS)

Song, Seok Goo; Kwak, Sangmin; Lee, Kyungbook; Park, Donghee

2017-04-01

It is a critical element to predict the intensity and variability of strong ground motions in seismic hazard assessment. The characteristics and variability of earthquake rupture process may be a dominant factor in determining the intensity and variability of near-source strong ground motions. Song et al. (2014) demonstrated that the variability of earthquake rupture scenarios could be effectively quantified in the framework of 1-point and 2-point statistics of earthquake source parameters, constrained by rupture dynamics and past events. The developed pseudo-dynamic source modeling schemes were also validated against the recorded ground motion data of past events and empirical ground motion prediction equations (GMPEs) at the broadband platform (BBP) developed by the Southern California Earthquake Center (SCEC). Recently we improved the computational efficiency of the developed pseudo-dynamic source-modeling scheme by adopting the nonparametric co-regionalization algorithm, introduced and applied in geostatistics initially. We also investigated the effect of earthquake rupture process on near-source ground motion characteristics in the framework of 1-point and 2-point statistics, particularly focusing on the forward directivity region. Finally we will discuss whether the pseudo-dynamic source modeling can reproduce the variability (standard deviation) of empirical GMPEs and the efficiency of 1-point and 2-point statistics to address the variability of ground motions.

Toward a common theory for learning from reward, affect, and motivation: the SIMON framework.

PubMed

Madan, Christopher R

2013-10-07

While the effects of reward, affect, and motivation on learning have each developed into their own fields of research, they largely have been investigated in isolation. As all three of these constructs are highly related, and use similar experimental procedures, an important advance in research would be to consider the interplay between these constructs. Here we first define each of the three constructs, and then discuss how they may influence each other within a common framework. Finally, we delineate several sources of evidence supporting the framework. By considering the constructs of reward, affect, and motivation within a single framework, we can develop a better understanding of the processes involved in learning and how they interplay, and work toward a comprehensive theory that encompasses reward, affect, and motivation.

Perspectives on the Use of Null Hypothesis Statistical Testing. Part III: the Various Nuts and Bolts of Statistical and Hypothesis Testing

ERIC Educational Resources Information Center

Marmolejo-Ramos, Fernando; Cousineau, Denis

2017-01-01

The number of articles showing dissatisfaction with the null hypothesis statistical testing (NHST) framework has been progressively increasing over the years. Alternatives to NHST have been proposed and the Bayesian approach seems to have achieved the highest amount of visibility. In this last part of the special issue, a few alternative…

Towards people-centred health systems: a multi-level framework for analysing primary health care governance in low- and middle-income countries.

PubMed

Abimbola, Seye; Negin, Joel; Jan, Stephen; Martiniuk, Alexandra

2014-09-01

Although there is evidence that non-government health system actors can individually or collectively develop practical strategies to address primary health care (PHC) challenges in the community, existing frameworks for analysing health system governance largely focus on the role of governments, and do not sufficiently account for the broad range of contribution to PHC governance. This is important because of the tendency for weak governments in low- and middle-income countries (LMICs). We present a multi-level governance framework for use as a thinking guide in analysing PHC governance in LMICs. This framework has previously been used to analyse the governance of common-pool resources such as community fisheries and irrigation systems. We apply the framework to PHC because, like common-pool resources, PHC facilities in LMICs tend to be commonly owned by the community such that individual and collective action is often required to avoid the 'tragedy of the commons'-destruction and degradation of the resource resulting from lack of concern for its continuous supply. In the multi-level framework, PHC governance is conceptualized at three levels, depending on who influences the supply and demand of PHC services in a community and how: operational governance (individuals and providers within the local health market), collective governance (community coalitions) and constitutional governance (governments at different levels and other distant but influential actors). Using the example of PHC governance in Nigeria, we illustrate how the multi-level governance framework offers a people-centred lens on the governance of PHC in LMICs, with a focus on relations among health system actors within and between levels of governance. We demonstrate the potential impact of health system actors functioning at different levels of governance on PHC delivery, and how governance failure at one level can be assuaged by governance at another level. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine © The Author 2014; all rights reserved.

A Probabilistic Framework for Peptide and Protein Quantification from Data-Dependent and Data-Independent LC-MS Proteomics Experiments

PubMed Central

Richardson, Keith; Denny, Richard; Hughes, Chris; Skilling, John; Sikora, Jacek; Dadlez, Michał; Manteca, Angel; Jung, Hye Ryung; Jensen, Ole Nørregaard; Redeker, Virginie; Melki, Ronald; Langridge, James I.; Vissers, Johannes P.C.

2013-01-01

A probability-based quantification framework is presented for the calculation of relative peptide and protein abundance in label-free and label-dependent LC-MS proteomics data. The results are accompanied by credible intervals and regulation probabilities. The algorithm takes into account data uncertainties via Poisson statistics modified by a noise contribution that is determined automatically during an initial normalization stage. Protein quantification relies on assignments of component peptides to the acquired data. These assignments are generally of variable reliability and may not be present across all of the experiments comprising an analysis. It is also possible for a peptide to be identified to more than one protein in a given mixture. For these reasons the algorithm accepts a prior probability of peptide assignment for each intensity measurement. The model is constructed in such a way that outliers of any type can be automatically reweighted. Two discrete normalization methods can be employed. The first method is based on a user-defined subset of peptides, while the second method relies on the presence of a dominant background of endogenous peptides for which the concentration is assumed to be unaffected. Normalization is performed using the same computational and statistical procedures employed by the main quantification algorithm. The performance of the algorithm will be illustrated on example data sets, and its utility demonstrated for typical proteomics applications. The quantification algorithm supports relative protein quantification based on precursor and product ion intensities acquired by means of data-dependent methods, originating from all common isotopically-labeled approaches, as well as label-free ion intensity-based data-independent methods. PMID:22871168

Intensity correlation-based calibration of FRET.

PubMed

Bene, László; Ungvári, Tamás; Fedor, Roland; Sasi Szabó, László; Damjanovich, László

2013-11-05

Dual-laser flow cytometric resonance energy transfer (FCET) is a statistically efficient and accurate way of determining proximity relationships for molecules of cells even under living conditions. In the framework of this algorithm, absolute fluorescence resonance energy transfer (FRET) efficiency is determined by the simultaneous measurement of donor-quenching and sensitized emission. A crucial point is the determination of the scaling factor α responsible for balancing the different sensitivities of the donor and acceptor signal channels. The determination of α is not simple, requiring preparation of special samples that are generally different from a double-labeled FRET sample, or by the use of sophisticated statistical estimation (least-squares) procedures. We present an alternative, free-from-spectral-constants approach for the determination of α and the absolute FRET efficiency, by an extension of the presented framework of the FCET algorithm with an analysis of the second moments (variances and covariances) of the detected intensity distributions. A quadratic equation for α is formulated with the intensity fluctuations, which is proved sufficiently robust to give accurate α-values on a cell-by-cell basis in a wide system of conditions using the same double-labeled sample from which the FRET efficiency itself is determined. This seemingly new approach is illustrated by FRET measurements between epitopes of the MHCI receptor on the cell surface of two cell lines, FT and LS174T. The figures show that whereas the common way of α determination fails at large dye-per-protein labeling ratios of mAbs, this presented-as-new approach has sufficient ability to give accurate results. Although introduced in a flow cytometer, the new approach can also be straightforwardly used with fluorescence microscopes. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

‘N-of-1-pathways’ unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine

PubMed Central

Gardeux, Vincent; Achour, Ikbel; Li, Jianrong; Maienschein-Cline, Mark; Li, Haiquan; Pesce, Lorenzo; Parinandi, Gurunadh; Bahroos, Neil; Winn, Robert; Foster, Ian; Garcia, Joe G N; Lussier, Yves A

2014-01-01

Background The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. Indeed, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery. Method ‘N-of-1-pathways’ is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/biomodules powered by paired samples from the same patient; and (iii) similarity between genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1-pathways predicts the deregulated pathways of each patient. Results Cross-patient N-of-1-pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03). Conclusions The N-of-1-pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies. Software http://lussierlab.org/publications/N-of-1-pathways PMID:25301808

‘N-of-1- pathways ’ unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: Towards precision medicine

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

Gardeux, Vincent; Achour, Ikbel; Li, Jianrong

Background: The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. This research entails, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery. Method: ‘N-of-1- pathways’ is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/biomodules powered by paired samples from the same patient; and (iii) similarity betweenmore » genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1- pathways predicts the deregulated pathways of each patient. Results: Cross-patient N-of-1- pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03). Conclusions: The N-of-1- pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies.« less

‘N-of-1- pathways ’ unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: Towards precision medicine

DOE PAGES

Gardeux, Vincent; Achour, Ikbel; Li, Jianrong; ...

2014-11-01

Background: The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. This research entails, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery. Method: ‘N-of-1- pathways’ is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/biomodules powered by paired samples from the same patient; and (iii) similarity betweenmore » genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1- pathways predicts the deregulated pathways of each patient. Results: Cross-patient N-of-1- pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03). Conclusions: The N-of-1- pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies.« less

Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance

USGS Publications Warehouse

Wilson, T.L.; Odei, J.B.; Hooten, M.B.; Edwards, T.C.

2010-01-01

Conservationists routinely use species distribution models to plan conservation, restoration and development actions, while ecologists use them to infer process from pattern. These models tend to work well for common or easily observable species, but are of limited utility for rare and cryptic species. This may be because honest accounting of known observation bias and spatial autocorrelation are rarely included, thereby limiting statistical inference of resulting distribution maps. We specified and implemented a spatially explicit Bayesian hierarchical model for a cryptic mammal species (pygmy rabbit Brachylagus idahoensis). Our approach used two levels of indirect sign that are naturally hierarchical (burrows and faecal pellets) to build a model that allows for inference on regression coefficients as well as spatially explicit model parameters. We also produced maps of rabbit distribution (occupied burrows) and relative abundance (number of burrows expected to be occupied by pygmy rabbits). The model demonstrated statistically rigorous spatial prediction by including spatial autocorrelation and measurement uncertainty. We demonstrated flexibility of our modelling framework by depicting probabilistic distribution predictions using different assumptions of pygmy rabbit habitat requirements. Spatial representations of the variance of posterior predictive distributions were obtained to evaluate heterogeneity in model fit across the spatial domain. Leave-one-out cross-validation was conducted to evaluate the overall model fit. Synthesis and applications. Our method draws on the strengths of previous work, thereby bridging and extending two active areas of ecological research: species distribution models and multi-state occupancy modelling. Our framework can be extended to encompass both larger extents and other species for which direct estimation of abundance is difficult. ?? 2010 The Authors. Journal compilation ?? 2010 British Ecological Society.

From Sub-basin to Grid Scale Soil Moisture Disaggregation in SMART, A Semi-distributed Hydrologic Modeling Framework

NASA Astrophysics Data System (ADS)

Ajami, H.; Sharma, A.

2016-12-01

A computationally efficient, semi-distributed hydrologic modeling framework is developed to simulate water balance at a catchment scale. The Soil Moisture and Runoff simulation Toolkit (SMART) is based upon the delineation of contiguous and topologically connected Hydrologic Response Units (HRUs). In SMART, HRUs are delineated using thresholds obtained from topographic and geomorphic analysis of a catchment, and simulation elements are distributed cross sections or equivalent cross sections (ECS) delineated in first order sub-basins. ECSs are formulated by aggregating topographic and physiographic properties of the part or entire first order sub-basins to further reduce computational time in SMART. Previous investigations using SMART have shown that temporal dynamics of soil moisture are well captured at a HRU level using the ECS delineation approach. However, spatial variability of soil moisture within a given HRU is ignored. Here, we examined a number of disaggregation schemes for soil moisture distribution in each HRU. The disaggregation schemes are either based on topographic based indices or a covariance matrix obtained from distributed soil moisture simulations. To assess the performance of the disaggregation schemes, soil moisture simulations from an integrated land surface-groundwater model, ParFlow.CLM in Baldry sub-catchment, Australia are used. ParFlow is a variably saturated sub-surface flow model that is coupled to the Common Land Model (CLM). Our results illustrate that the statistical disaggregation scheme performs better than the methods based on topographic data in approximating soil moisture distribution at a 60m scale. Moreover, the statistical disaggregation scheme maintains temporal correlation of simulated daily soil moisture while preserves the mean sub-basin soil moisture. Future work is focused on assessing the performance of this scheme in catchments with various topographic and climate settings.

Pattern Recognition Analysis of Age-Related Retinal Ganglion Cell Signatures in the Human Eye

PubMed Central

Yoshioka, Nayuta; Zangerl, Barbara; Nivison-Smith, Lisa; Khuu, Sieu K.; Jones, Bryan W.; Pfeiffer, Rebecca L.; Marc, Robert E.; Kalloniatis, Michael

2017-01-01

Purpose To characterize macular ganglion cell layer (GCL) changes with age and provide a framework to assess changes in ocular disease. This study used data clustering to analyze macular GCL patterns from optical coherence tomography (OCT) in a large cohort of subjects without ocular disease. Methods Single eyes of 201 patients evaluated at the Centre for Eye Health (Sydney, Australia) were retrospectively enrolled (age range, 20–85); 8 × 8 grid locations obtained from Spectralis OCT macular scans were analyzed with unsupervised classification into statistically separable classes sharing common GCL thickness and change with age. The resulting classes and gridwise data were fitted with linear and segmented linear regression curves. Additionally, normalized data were analyzed to determine regression as a percentage. Accuracy of each model was examined through comparison of predicted 50-year-old equivalent macular GCL thickness for the entire cohort to a true 50-year-old reference cohort. Results Pattern recognition clustered GCL thickness across the macula into five to eight spatially concentric classes. F-test demonstrated segmented linear regression to be the most appropriate model for macular GCL change. The pattern recognition–derived and normalized model revealed less difference between the predicted macular GCL thickness and the reference cohort (average ± SD 0.19 ± 0.92 and −0.30 ± 0.61 μm) than a gridwise model (average ± SD 0.62 ± 1.43 μm). Conclusions Pattern recognition successfully identified statistically separable macular areas that undergo a segmented linear reduction with age. This regression model better predicted macular GCL thickness. The various unique spatial patterns revealed by pattern recognition combined with core GCL thickness data provide a framework to analyze GCL loss in ocular disease. PMID:28632847

The "Common European Framework of Reference for Languages," the European Language Portfolio, and Language Teaching/Learning at University: An Argument and Some Proposals

ERIC Educational Resources Information Center

Little, David

2016-01-01

I begin this article by briefly explaining why I think CercleS should encourage university language centres to align their courses and assessment with the proficiency levels of the "Common European Framework of Reference for Languages" (CEFR) and why they should use a version of the European Language Portfolio (ELP) to support the…

Historical Development of NATO Stanag 6001 Language Standards and Common European Framework (CEF) and the Comparison of Their Current Status

ERIC Educational Resources Information Center

Solak, Ekrem

2011-01-01

The aim of the article is to shed light on the historical development of language studies in military and social context and to compare the current status of NATO Stanag (Standard Agreement) 6001 language scale with Common European Framework (CEF). Language studies in military context date back to World War II and the emergence of Army Specialized…

Integrating prior knowledge in multiple testing under dependence with applications to detecting differential DNA methylation.

PubMed

Kuan, Pei Fen; Chiang, Derek Y

2012-09-01

DNA methylation has emerged as an important hallmark of epigenetics. Numerous platforms including tiling arrays and next generation sequencing, and experimental protocols are available for profiling DNA methylation. Similar to other tiling array data, DNA methylation data shares the characteristics of inherent correlation structure among nearby probes. However, unlike gene expression or protein DNA binding data, the varying CpG density which gives rise to CpG island, shore and shelf definition provides exogenous information in detecting differential methylation. This article aims to introduce a robust testing and probe ranking procedure based on a nonhomogeneous hidden Markov model that incorporates the above-mentioned features for detecting differential methylation. We revisit the seminal work of Sun and Cai (2009, Journal of the Royal Statistical Society: Series B (Statistical Methodology)71, 393-424) and propose modeling the nonnull using a nonparametric symmetric distribution in two-sided hypothesis testing. We show that this model improves probe ranking and is robust to model misspecification based on extensive simulation studies. We further illustrate that our proposed framework achieves good operating characteristics as compared to commonly used methods in real DNA methylation data that aims to detect differential methylation sites. © 2012, The International Biometric Society.

Trap Model for Clogging and Unclogging in Granular Hopper Flows.

PubMed

Nicolas, Alexandre; Garcimartín, Ángel; Zuriguel, Iker

2018-05-11

Granular flows through narrow outlets may be interrupted by the formation of arches or vaults that clog the exit. These clogs may be destroyed by vibrations. A feature which remains elusive is the broad distribution p(τ) of clog lifetimes τ measured under constant vibrations. Here, we propose a simple model for arch breaking, in which the vibrations are formally equivalent to thermal fluctuations in a Langevin equation; the rupture of an arch corresponds to the escape from an energy trap. We infer the distribution of trap depths from experiments made in two-dimensional hoppers. Using this distribution, we show that the model captures the empirically observed heavy tails in p(τ). These heavy tails flatten at large τ, consistently with experimental observations under weak vibrations. But, here, we find that this flattening is systematic, which casts doubt on the ability of gentle vibrations to restore a finite outflow forever. The trap model also replicates recent results on the effect of increasing gravity on the statistics of clog formation in a static silo. Therefore, the proposed framework points to a common physical underpinning to the processes of clogging and unclogging, despite their different statistics.

Demands and resources: parents of school-age children with asthma.

PubMed

Lee, E Juanita; Parker, Veronica; DuBose, Lisa; Gwinn, Jane; Logan, Barbara N

2006-12-01

Asthma is one of the most common chronic diseases in children and is frequently noted as the reason for school absences. The purpose of this pilot study was to determine the differences in demands and resources reported by African American (AA) and European American (EA) parents of school-age children with asthma. A convenience sample of 37 parents participated in the study. Data were collected from 19 AA and 18 EA parents. Family stress theory provided the framework for this study. All subjects completed a demographic questionnaire, the Care of My Child With Asthma Scale, and the Family Inventory of Resources for Management (FIRM). Descriptive statistics were used to analyze the data. The most time-consuming caregiving demand reported by EA parents was providing emotional support for the child. For AA parents, the most time-consuming caregiving demand was managing work or school outside the home and organizing asthma treatments at the same time. AA parents had limited resources in the area of extended family social support. The Mann-Whitney U test found no statistically significant differences between AA and EA parents in relation to demands and resources. Nursing implications are presented.

Hierarchical statistical modeling of xylem vulnerability to cavitation.

PubMed

Ogle, Kiona; Barber, Jarrett J; Willson, Cynthia; Thompson, Brenda

2009-01-01

Cavitation of xylem elements diminishes the water transport capacity of plants, and quantifying xylem vulnerability to cavitation is important to understanding plant function. Current approaches to analyzing hydraulic conductivity (K) data to infer vulnerability to cavitation suffer from problems such as the use of potentially unrealistic vulnerability curves, difficulty interpreting parameters in these curves, a statistical framework that ignores sampling design, and an overly simplistic view of uncertainty. This study illustrates how two common curves (exponential-sigmoid and Weibull) can be reparameterized in terms of meaningful parameters: maximum conductivity (k(sat)), water potential (-P) at which percentage loss of conductivity (PLC) =X% (P(X)), and the slope of the PLC curve at P(X) (S(X)), a 'sensitivity' index. We provide a hierarchical Bayesian method for fitting the reparameterized curves to K(H) data. We illustrate the method using data for roots and stems of two populations of Juniperus scopulorum and test for differences in k(sat), P(X), and S(X) between different groups. Two important results emerge from this study. First, the Weibull model is preferred because it produces biologically realistic estimates of PLC near P = 0 MPa. Second, stochastic embolisms contribute an important source of uncertainty that should be included in such analyses.

Trap Model for Clogging and Unclogging in Granular Hopper Flows

NASA Astrophysics Data System (ADS)

Nicolas, Alexandre; Garcimartín, Ángel; Zuriguel, Iker

2018-05-01

Granular flows through narrow outlets may be interrupted by the formation of arches or vaults that clog the exit. These clogs may be destroyed by vibrations. A feature which remains elusive is the broad distribution p (τ ) of clog lifetimes τ measured under constant vibrations. Here, we propose a simple model for arch breaking, in which the vibrations are formally equivalent to thermal fluctuations in a Langevin equation; the rupture of an arch corresponds to the escape from an energy trap. We infer the distribution of trap depths from experiments made in two-dimensional hoppers. Using this distribution, we show that the model captures the empirically observed heavy tails in p (τ ). These heavy tails flatten at large τ , consistently with experimental observations under weak vibrations. But, here, we find that this flattening is systematic, which casts doubt on the ability of gentle vibrations to restore a finite outflow forever. The trap model also replicates recent results on the effect of increasing gravity on the statistics of clog formation in a static silo. Therefore, the proposed framework points to a common physical underpinning to the processes of clogging and unclogging, despite their different statistics.

Factors to consider when reviewing and reconciling research findings: Methodological, statistical and theoretical.

PubMed

Robinson, Sally J

2017-11-07

Neuroscience is a rapidly evolving interdisciplinary field that is changing the way research is conducted and theories are developed. However, variability between studies and apparently discrepant findings may contribute to difficulties identifying commonalities that can help inform and enhance clinical practice. This article presents a framework to consider when reviewing neuropsychological studies, such that apparent discrepancies in findings may be considered in unison to provide informed theoretical understanding. For illustrative purposes, the article considers the studies of Vargha-Khadem, Salmond, Friston, Gadian, and Mishkin ( 2003 ) and Beauchamp et al. ( 2008 ), which report contrasting memory deficits during development in association with apparently similar bilateral hippocampal damage. The importance of reflecting on participant characteristics, methodological approaches, statistical analysis, and the interpretative value placed on selective test findings are discussed. Factors such as functional brain development, relationships between apparently "typical" functioning and underlying neural structures and networks, the limits of plasticity on the developing cognitive system and clinical implications are also considered. Thus, this article provides a structure that can be applied when reviewing neuropsychological studies and evaluating research inconsistencies, with consideration of the need for greater collaboration between neuroscientists and clinicians to support the development of translational research with real life implications.

The effects of two torque values on the screw preload of implant-supported prostheses with passive fit or misfit.

PubMed

Al-Otaibi, Hanan Nejer; Akeel, Riyadh Fadul

2014-01-01

To determine the effect of increased torque of the abutment screw and retorquing after 10 minutes on implant-supported fixed prostheses. Two strain gauges (SGs) were attached to four implants stabilized on an acrylic resin mandible. Four implant-supported frameworks were constructed to represent passive fit (PF) and different amounts of misfit (MF1, MF2, and MF3). Vertical misfit was measured using a traveling microscope. Each framework was torqued to 35 Ncm (the manufacturer's recommendation) and 40 Ncm, and the preload was recorded immediately and again after retorquing 10 minutes later (torque stage). The smallest gap was observed under the PF framework. Three-way analysis of variance revealed significant effects of the framework, torque value, and torque stage on preload. The PF showed the highest mean preload under both torque values. An independent-sample t test between the torque values revealed a statistically significant difference only for MF1 and MF2. A dependent-sample t test of the torque stage revealed a statistically significant difference at a torque value of 35 Ncm under the PF and MF3 frameworks. Increasing the torque value beyond the manufacturer's recommended amount and retorquing of the screws at 10 minutes after the initial torque did not necessarily lead to a significant increase in preload in full-arch implant-supported fixed prostheses, particularly under non-passively fitting frameworks.

Scene-based nonuniformity correction and enhancement: pixel statistics and subpixel motion.

PubMed

Zhao, Wenyi; Zhang, Chao

2008-07-01

We propose a framework for scene-based nonuniformity correction (NUC) and nonuniformity correction and enhancement (NUCE) that is required for focal-plane array-like sensors to obtain clean and enhanced-quality images. The core of the proposed framework is a novel registration-based nonuniformity correction super-resolution (NUCSR) method that is bootstrapped by statistical scene-based NUC methods. Based on a comprehensive imaging model and an accurate parametric motion estimation, we are able to remove severe/structured nonuniformity and in the presence of subpixel motion to simultaneously improve image resolution. One important feature of our NUCSR method is the adoption of a parametric motion model that allows us to (1) handle many practical scenarios where parametric motions are present and (2) carry out perfect super-resolution in principle by exploring available subpixel motions. Experiments with real data demonstrate the efficiency of the proposed NUCE framework and the effectiveness of the NUCSR method.

A formal framework of scenario creation and analysis of extreme hydrological events

NASA Astrophysics Data System (ADS)

Lohmann, D.

2007-12-01

We are presenting a formal framework for a hydrological risk analysis. Different measures of risk will be introduced, such as average annual loss or occurrence exceedance probability. These are important measures for e.g. insurance companies to determine the cost of insurance. One key aspect of investigating the potential consequences of extreme hydrological events (floods and draughts) is the creation of meteorological scenarios that reflect realistic spatial and temporal patterns of precipitation that also have correct local statistics. 100,000 years of these meteorological scenarios are used in a calibrated rainfall-runoff-flood-loss-risk model to produce flood and draught events that have never been observed. The results of this hazard model are statistically analyzed and linked to socio-economic data and vulnerability functions to show the impact of severe flood events. We are showing results from the Risk Management Solutions (RMS) Europe Flood Model to introduce this formal framework.

A Data Analytical Framework for Improving Real-Time, Decision Support Systems in Healthcare

ERIC Educational Resources Information Center

Yahav, Inbal

2010-01-01

In this dissertation we develop a framework that combines data mining, statistics and operations research methods for improving real-time decision support systems in healthcare. Our approach consists of three main concepts: data gathering and preprocessing, modeling, and deployment. We introduce the notion of offline and semi-offline modeling to…

An Analysis of Variance Framework for Matrix Sampling.

ERIC Educational Resources Information Center

Sirotnik, Kenneth

Significant cost savings can be achieved with the use of matrix sampling in estimating population parameters from psychometric data. The statistical design is intuitively simple, using the framework of the two-way classification analysis of variance technique. For example, the mean and variance are derived from the performance of a certain grade…

Demographic Accounting and Model-Building. Education and Development Technical Reports.

ERIC Educational Resources Information Center

Stone, Richard

This report describes and develops a model for coordinating a variety of demographic and social statistics within a single framework. The framework proposed, together with its associated methods of analysis, serves both general and specific functions. The general aim of these functions is to give numerical definition to the pattern of society and…

Mediation Analysis in a Latent Growth Curve Modeling Framework

ERIC Educational Resources Information Center

von Soest, Tilmann; Hagtvet, Knut A.

2011-01-01

This article presents several longitudinal mediation models in the framework of latent growth curve modeling and provides a detailed account of how such models can be constructed. Logical and statistical challenges that might arise when such analyses are conducted are also discussed. Specifically, we discuss how the initial status (intercept) and…

VALUE - A Framework to Validate Downscaling Approaches for Climate Change Studies

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilke, Renate A. I.

2015-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary downscaling community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. Here, we present the key ingredients of this framework. VALUE's main approach to validation is user-focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur: what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open downscaling intercomparison study, but is intended also to provide general guidance for other validation studies.

VALUE: A framework to validate downscaling approaches for climate change studies

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilcke, Renate A. I.

2015-01-01

VALUE is an open European network to validate and compare downscaling methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary downscaling community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. In this paper, we present the key ingredients of this framework. VALUE's main approach to validation is user- focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur: what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open downscaling intercomparison study, but is intended also to provide general guidance for other validation studies.

The Importance of Statistical Modeling in Data Analysis and Inference

ERIC Educational Resources Information Center

Rollins, Derrick, Sr.

2017-01-01

Statistical inference simply means to draw a conclusion based on information that comes from data. Error bars are the most commonly used tool for data analysis and inference in chemical engineering data studies. This work demonstrates, using common types of data collection studies, the importance of specifying the statistical model for sound…

Deep learning for media analysis in defense scenariosan evaluation of an open source framework for object detection in intelligence related image sets

DTIC Science & Technology

2017-06-01

Training time statistics from Jones’ thesis. . . . . . . . . . . . . . 15 Table 2.2 Evaluation runtime statistics from Camp’s thesis for a single image. 17...Table 2.3 Training and evaluation runtime statistics from Sharpe’s thesis. . . 19 Table 2.4 Sharpe’s screenshot detector results for combinations of...training resources available and time required for each algorithm Jones [15] tested. Table 2.1. Training time statistics from Jones’ [15] thesis. Algorithm

A new framework to increase the efficiency of large-scale solar power plants.

NASA Astrophysics Data System (ADS)

Alimohammadi, Shahrouz; Kleissl, Jan P.

2015-11-01

A new framework to estimate the spatio-temporal behavior of solar power is introduced, which predicts the statistical behavior of power output at utility scale Photo-Voltaic (PV) power plants. The framework is based on spatio-temporal Gaussian Processes Regression (Kriging) models, which incorporates satellite data with the UCSD version of the Weather and Research Forecasting model. This framework is designed to improve the efficiency of the large-scale solar power plants. The results are also validated from measurements of the local pyranometer sensors, and some improvements in different scenarios are observed. Solar energy.

[Effect of Al₂O₃ sandblasting on the bond strength between 3mol% yttrium-stabilized tetragonal zirconium polycrystal zirconia framework and veneering porcelain].

PubMed

Qiang, Zeng; Ning, Li; Yanan, Zhou; Jiazhen, Yan; Wenbo, Liu

2015-12-01

The effect of sandblasting on the bond strength between 3mol% yttrium-stabilized tetragonal zirconium polycrystal (3Y-TZP) zirconia framework and veneering porcelain was evaluated. A total of 21 specimens [(25 ± 1) mm x (3 ± 0.1) mmx (0.5 ± 0.05) mm] were prepared according to ISO 9693. The specimens were then randomly divided into 3 groups. Sandblasting was performed on 2 meshes of Al₂O₃ particles: group A with mesh 110 and group B with mesh 80. Group C, which was not sandblasted, was the control group. The surface roughness of the zirconia framework, as well as the bond strength between 3Y-TZP zirconia framework and veneering porcelain, was measured. The interface microstructure was observed by scanning electron microscope (SEM), and elemental distribution was detected by energy dispersive spectroscopy (EDS). Surface roughness values were (1.272 ± 0.149) μm for group A, (0.622 ± 0.113) μm for group B, and (0.221 ± 0.065) μm for group C. Statistical significance were found among groups (P < 0.05). The bond strength values were (28.21 ± 1.52) MPa for group A, (27.71 ± 1.27) MPa for group B, and (24.87 ± 3.84) MPa for group C. Statistical significance was found between group A and group C (P < 0.05), whereas the other groups had no statistical significance (P > 0.05). Interface adhesion failure was the primary performance. SEM images showed the close interface bonding, and EDS showed that the interface had no obvious element penetration. Al₂O₃ sandblasting can slightly enhance the bond strength between zirconia framework and veneering porcelain.

Predicting weak lensing statistics from halo mass reconstructions - Final Paper

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

Everett, Spencer

2015-08-20

As dark matter does not absorb or emit light, its distribution in the universe must be inferred through indirect effects such as the gravitational lensing of distant galaxies. While most sources are only weakly lensed, the systematic alignment of background galaxies around a foreground lens can constrain the mass of the lens which is largely in the form of dark matter. In this paper, I have implemented a framework to reconstruct all of the mass along lines of sight using a best-case dark matter halo model in which the halo mass is known. This framework is then used to makemore » predictions of the weak lensing of 3,240 generated source galaxies through a 324 arcmin² field of the Millennium Simulation. The lensed source ellipticities are characterized by the ellipticity-ellipticity and galaxy-mass correlation functions and compared to the same statistic for the intrinsic and ray-traced ellipticities. In the ellipticity-ellipticity correlation function, I and that the framework systematically under predicts the shear power by an average factor of 2.2 and fails to capture correlation from dark matter structure at scales larger than 1 arcminute. The model predicted galaxy-mass correlation function is in agreement with the ray-traced statistic from scales 0.2 to 0.7 arcminutes, but systematically underpredicts shear power at scales larger than 0.7 arcminutes by an average factor of 1.2. Optimization of the framework code has reduced the mean CPU time per lensing prediction by 70% to 24 ± 5 ms. Physical and computational shortcomings of the framework are discussed, as well as potential improvements for upcoming work.« less

Density profiles in the Scrape-Off Layer interpreted through filament dynamics

NASA Astrophysics Data System (ADS)

Militello, Fulvio

2017-10-01

We developed a new theoretical framework to clarify the relation between radial Scrape-Off Layer density profiles and the fluctuations that generate them. The framework provides an interpretation of the experimental features of the profiles and of the turbulence statistics on the basis of simple properties of the filaments, such as their radial motion and their draining towards the divertor. L-mode and inter-ELM filaments are described as a Poisson process in which each event is independent and modelled with a wave function of amplitude and width statistically distributed according to experimental observations and evolving according to fluid equations. We will rigorously show that radially accelerating filaments, less efficient parallel exhaust and also a statistical distribution of their radial velocity can contribute to induce flatter profiles in the far SOL and therefore enhance plasma-wall interactions. A quite general result of our analysis is the resiliency of this non-exponential nature of the profiles and the increase of the relative fluctuation amplitude towards the wall, as experimentally observed. According to the framework, profile broadening at high fueling rates can be caused by interactions with neutrals (e.g. charge exchange) in the divertor or by a significant radial acceleration of the filaments. The framework assumptions were tested with 3D numerical simulations of seeded SOL filaments based on a two fluid model. In particular, filaments interact through the electrostatic field they generate only when they are in close proximity (separation comparable to their width in the drift plane), thus justifying our independence hypothesis. In addition, we will discuss how isolated filament motion responds to variations in the plasma conditions, and specifically divertor conditions. Finally, using the theoretical framework we will reproduce and interpret experimental results obtained on JET, MAST and HL-2A.

Statistical modeling of the long-range-dependent structure of barrier island framework geology and surface geomorphology

NASA Astrophysics Data System (ADS)

Weymer, Bradley A.; Wernette, Phillipe; Everett, Mark E.; Houser, Chris

2018-06-01

Shorelines exhibit long-range dependence (LRD) and have been shown in some environments to be described in the wave number domain by a power-law characteristic of scale independence. Recent evidence suggests that the geomorphology of barrier islands can, however, exhibit scale dependence as a result of systematic variations in the underlying framework geology. The LRD of framework geology, which influences island geomorphology and its response to storms and sea level rise, has not been previously examined. Electromagnetic induction (EMI) surveys conducted along Padre Island National Seashore (PAIS), Texas, United States, reveal that the EMI apparent conductivity (σa) signal and, by inference, the framework geology exhibits LRD at scales of up to 101 to 102 km. Our study demonstrates the utility of describing EMI σa and lidar spatial series by a fractional autoregressive integrated moving average (ARIMA) process that specifically models LRD. This method offers a robust and compact way of quantifying the geological variations along a barrier island shoreline using three statistical parameters (p, d, q). We discuss how ARIMA models that use a single parameter d provide a quantitative measure for determining free and forced barrier island evolutionary behavior across different scales. Statistical analyses at regional, intermediate, and local scales suggest that the geologic framework within an area of paleo-channels exhibits a first-order control on dune height. The exchange of sediment amongst nearshore, beach, and dune in areas outside this region are scale independent, implying that barrier islands like PAIS exhibit a combination of free and forced behaviors that affect the response of the island to sea level rise.

The prior statistics of object colors.

PubMed

Koenderink, Jan J

2010-02-01

The prior statistics of object colors is of much interest because extensive statistical investigations of reflectance spectra reveal highly non-uniform structure in color space common to several very different databases. This common structure is due to the visual system rather than to the statistics of environmental structure. Analysis involves an investigation of the proper sample space of spectral reflectance factors and of the statistical consequences of the projection of spectral reflectances on the color solid. Even in the case of reflectance statistics that are translationally invariant with respect to the wavelength dimension, the statistics of object colors is highly non-uniform. The qualitative nature of this non-uniformity is due to trichromacy.

Quality of reporting statistics in two Indian pharmacology journals

PubMed Central

Jaykaran; Yadav, Preeti

2011-01-01

Objective: To evaluate the reporting of the statistical methods in articles published in two Indian pharmacology journals. Materials and Methods: All original articles published since 2002 were downloaded from the journals’ (Indian Journal of Pharmacology (IJP) and Indian Journal of Physiology and Pharmacology (IJPP)) website. These articles were evaluated on the basis of appropriateness of descriptive statistics and inferential statistics. Descriptive statistics was evaluated on the basis of reporting of method of description and central tendencies. Inferential statistics was evaluated on the basis of fulfilling of assumption of statistical methods and appropriateness of statistical tests. Values are described as frequencies, percentage, and 95% confidence interval (CI) around the percentages. Results: Inappropriate descriptive statistics was observed in 150 (78.1%, 95% CI 71.7–83.3%) articles. Most common reason for this inappropriate descriptive statistics was use of mean ± SEM at the place of “mean (SD)” or “mean ± SD.” Most common statistical method used was one-way ANOVA (58.4%). Information regarding checking of assumption of statistical test was mentioned in only two articles. Inappropriate statistical test was observed in 61 (31.7%, 95% CI 25.6–38.6%) articles. Most common reason for inappropriate statistical test was the use of two group test for three or more groups. Conclusion: Articles published in two Indian pharmacology journals are not devoid of statistical errors. PMID:21772766

A data colocation grid framework for big data medical image processing: backend design

NASA Astrophysics Data System (ADS)

Bao, Shunxing; Huo, Yuankai; Parvathaneni, Prasanna; Plassard, Andrew J.; Bermudez, Camilo; Yao, Yuang; Lyu, Ilwoo; Gokhale, Aniruddha; Landman, Bennett A.

2018-03-01

When processing large medical imaging studies, adopting high performance grid computing resources rapidly becomes important. We recently presented a "medical image processing-as-a-service" grid framework that offers promise in utilizing the Apache Hadoop ecosystem and HBase for data colocation by moving computation close to medical image storage. However, the framework has not yet proven to be easy to use in a heterogeneous hardware environment. Furthermore, the system has not yet validated when considering variety of multi-level analysis in medical imaging. Our target design criteria are (1) improving the framework's performance in a heterogeneous cluster, (2) performing population based summary statistics on large datasets, and (3) introducing a table design scheme for rapid NoSQL query. In this paper, we present a heuristic backend interface application program interface (API) design for Hadoop and HBase for Medical Image Processing (HadoopBase-MIP). The API includes: Upload, Retrieve, Remove, Load balancer (for heterogeneous cluster) and MapReduce templates. A dataset summary statistic model is discussed and implemented by MapReduce paradigm. We introduce a HBase table scheme for fast data query to better utilize the MapReduce model. Briefly, 5153 T1 images were retrieved from a university secure, shared web database and used to empirically access an in-house grid with 224 heterogeneous CPU cores. Three empirical experiments results are presented and discussed: (1) load balancer wall-time improvement of 1.5-fold compared with a framework with built-in data allocation strategy, (2) a summary statistic model is empirically verified on grid framework and is compared with the cluster when deployed with a standard Sun Grid Engine (SGE), which reduces 8-fold of wall clock time and 14-fold of resource time, and (3) the proposed HBase table scheme improves MapReduce computation with 7 fold reduction of wall time compare with a naïve scheme when datasets are relative small. The source code and interfaces have been made publicly available.

A Data Colocation Grid Framework for Big Data Medical Image Processing: Backend Design.

PubMed

Bao, Shunxing; Huo, Yuankai; Parvathaneni, Prasanna; Plassard, Andrew J; Bermudez, Camilo; Yao, Yuang; Lyu, Ilwoo; Gokhale, Aniruddha; Landman, Bennett A

2018-03-01

When processing large medical imaging studies, adopting high performance grid computing resources rapidly becomes important. We recently presented a "medical image processing-as-a-service" grid framework that offers promise in utilizing the Apache Hadoop ecosystem and HBase for data colocation by moving computation close to medical image storage. However, the framework has not yet proven to be easy to use in a heterogeneous hardware environment. Furthermore, the system has not yet validated when considering variety of multi-level analysis in medical imaging. Our target design criteria are (1) improving the framework's performance in a heterogeneous cluster, (2) performing population based summary statistics on large datasets, and (3) introducing a table design scheme for rapid NoSQL query. In this paper, we present a heuristic backend interface application program interface (API) design for Hadoop & HBase for Medical Image Processing (HadoopBase-MIP). The API includes: Upload, Retrieve, Remove, Load balancer (for heterogeneous cluster) and MapReduce templates. A dataset summary statistic model is discussed and implemented by MapReduce paradigm. We introduce a HBase table scheme for fast data query to better utilize the MapReduce model. Briefly, 5153 T1 images were retrieved from a university secure, shared web database and used to empirically access an in-house grid with 224 heterogeneous CPU cores. Three empirical experiments results are presented and discussed: (1) load balancer wall-time improvement of 1.5-fold compared with a framework with built-in data allocation strategy, (2) a summary statistic model is empirically verified on grid framework and is compared with the cluster when deployed with a standard Sun Grid Engine (SGE), which reduces 8-fold of wall clock time and 14-fold of resource time, and (3) the proposed HBase table scheme improves MapReduce computation with 7 fold reduction of wall time compare with a naïve scheme when datasets are relative small. The source code and interfaces have been made publicly available.

The social impacts of dams: A new framework for scholarly analysis

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

Kirchherr, Julian, E-mail: julian.kirchherr@sant.ox.ac.uk; Charles, Katrina J., E-mail: katrina.charles@ouce.ox.ac.uk

No commonly used framework exists in the scholarly study of the social impacts of dams. This hinders comparisons of analyses and thus the accumulation of knowledge. The aim of this paper is to unify scholarly understanding of dams' social impacts via the analysis and aggregation of the various frameworks currently used in the scholarly literature. For this purpose, we have systematically analyzed and aggregated 27 frameworks employed by academics analyzing dams' social impacts (found in a set of 217 articles). A key finding of the analysis is that currently used frameworks are often not specific to dams and thus omitmore » key impacts associated with them. The result of our analysis and aggregation is a new framework for scholarly analysis (which we call ‘matrix framework’) specifically on dams' social impacts, with space, time and value as its key dimensions as well as infrastructure, community and livelihood as its key components. Building on the scholarly understanding of this topic enables us to conceptualize the inherently complex and multidimensional issues of dams' social impacts in a holistic manner. If commonly employed in academia (and possibly in practice), this framework would enable more transparent assessment and comparison of projects.« less

Development of a framework to identify research gaps from systematic reviews.

PubMed

Robinson, Karen A; Saldanha, Ian J; McKoy, Naomi A

2011-12-01

Our objective was to develop a framework to identify research gaps from systematic reviews. We reviewed the practices of (1) evidence-based practice centers (EPCs), and (2) other organizations that conduct evidence syntheses. We developed and pilot tested a framework for identifying research gaps. Four (33%) EPCs and three (8%) other organizations reported using an explicit framework to determine research gaps. Variations of the PICO (population, intervention, comparison, outcomes) framework were most common. We developed a framework incorporating both the characterization of the gap using PICOS elements (also including setting) and the identification of the reason(s) why the gap exists as (1) insufficient or imprecise information, (2) biased information, (3) inconsistency or unknown consistency, and (4) not the right information. We mapped each of these reasons to concepts from three common evidence-grading systems. Our framework determines from systematic reviews where the current evidence falls short and why or how the evidence falls short. This explicit identification of research gaps will allow systematic reviews to maximally inform the types of questions that need to be addressed and the types of studies needed to address the research gaps. Copyright © 2011 Elsevier Inc. All rights reserved.

A Social-Cognitive Theoretical Framework for Examining Music Teacher Identity

ERIC Educational Resources Information Center

McClellan, Edward

2017-01-01

The purpose of the study was to examine a diverse range of research literature to provide a social-cognitive theoretical framework as a foundation for definition of identity construction in the music teacher education program. The review of literature may reveal a theoretical framework based around tenets of commonly studied constructs in the…

A Framework for Identifying and Classifying Undergraduate Student Proof Errors

ERIC Educational Resources Information Center

Strickland, S.; Rand, B.

2016-01-01

This paper describes a framework for identifying, classifying, and coding student proofs, modified from existing proof-grading rubrics. The framework includes 20 common errors, as well as categories for interpreting the severity of the error. The coding scheme is intended for use in a classroom context, for providing effective student feedback. In…

A Framework for Credit. Framework Guidelines 2. Learning Outcomes, Units and Modules.

ERIC Educational Resources Information Center

Further Education Development Agency, London (England).

This document refines and develops a 1992 proposal by Great Britain's Further Education Unit (FEU) that all kinds of student achievement be documented within a common framework involving the following procedures: describing adult learners' achievements in terms of learning outcomes; grouping the learning outcomes into coherent units; defining the…

Common and Innovative Visuals: A sparsity modeling framework for video.

PubMed

Abdolhosseini Moghadam, Abdolreza; Kumar, Mrityunjay; Radha, Hayder

2014-05-02

Efficient video representation models are critical for many video analysis and processing tasks. In this paper, we present a framework based on the concept of finding the sparsest solution to model video frames. To model the spatio-temporal information, frames from one scene are decomposed into two components: (i) a common frame, which describes the visual information common to all the frames in the scene/segment, and (ii) a set of innovative frames, which depicts the dynamic behaviour of the scene. The proposed approach exploits and builds on recent results in the field of compressed sensing to jointly estimate the common frame and the innovative frames for each video segment. We refer to the proposed modeling framework by CIV (Common and Innovative Visuals). We show how the proposed model can be utilized to find scene change boundaries and extend CIV to videos from multiple scenes. Furthermore, the proposed model is robust to noise and can be used for various video processing applications without relying on motion estimation and detection or image segmentation. Results for object tracking, video editing (object removal, inpainting) and scene change detection are presented to demonstrate the efficiency and the performance of the proposed model.

Library Statistical Data Base Formats and Definitions.

ERIC Educational Resources Information Center

Jones, Dennis; And Others

Represented are the detailed set of data structures relevant to the categorization of information, terminology, and definitions employed in the design of the library statistical data base. The data base, or management information system, provides administrators with a framework of information and standardized data for library management, planning,…

Visualizing Teacher Education as a Complex System: A Nested Simplex System Approach

ERIC Educational Resources Information Center

Ludlow, Larry; Ell, Fiona; Cochran-Smith, Marilyn; Newton, Avery; Trefcer, Kaitlin; Klein, Kelsey; Grudnoff, Lexie; Haigh, Mavis; Hill, Mary F.

2017-01-01

Our purpose is to provide an exploratory statistical representation of initial teacher education as a complex system comprised of dynamic influential elements. More precisely, we reveal what the system looks like for differently-positioned teacher education stakeholders based on our framework for gathering, statistically analyzing, and graphically…

Statistical Framework for Recreational Water Quality Criteria and Monitoring

EPA Science Inventory

Discussion between the EPA Office of Research and Development (ORD) and the EPA Office of Water (OW), which is charged with setting criteria in accordance with the BEACH Act of 2000, have made it clear that in-depth statistical guidance for such criteria is needed. In January 20...

Theoretical Frameworks for Math Fact Fluency

ERIC Educational Resources Information Center

Arnold, Katherine

2012-01-01

Recent education statistics indicate persistent low math scores for our nation's students. This drop in math proficiency includes deficits in basic number sense and automaticity of math facts. The decrease has been recorded across all grade levels with the elementary levels showing the greatest loss (National Center for Education Statistics,…

A Conceptual Framework for Teaching Statistics from a Distance

ERIC Educational Resources Information Center

Mills, Jamie

2015-01-01

This article discusses important considerations for teachers who teach or may be thinking about teaching statistics online or in a hybrid/blended format. Suggestions from previous research and practical teaching experiences are examined. Moreover, the latest recommendations from the literature are considered in the context of teaching from a…

Cyber Mentoring in an Online Introductory Statistics Course

ERIC Educational Resources Information Center

Rashid, Mamunur; Sarkar, Jyotirmoy

2018-01-01

Students in an online statistics course were prone to become increasingly disengaged as the semester progressed. In Spring 2015, we took a proactive measure to retain student engagement by introducing a cyber mentoring session. We describe the framework, operation and effectiveness of cyber mentoring in improving students' learning experience and…

Commentary to Library Statistical Data Base.

ERIC Educational Resources Information Center

Jones, Dennis; And Others

The National Center for Higher Education Management Systems (NCHEMS) has developed a library statistical data base which concentrates on the management information needs of administrators of public and academic libraries. This document provides an overview of the framework and conceptual approach employed in the design of the data base. The data…

Evaluating handoffs in the context of a communication framework.

PubMed

Hasan, Hani; Ali, Fadwa; Barker, Paul; Treat, Robert; Peschman, Jacob; Mohorek, Matthew; Redlich, Philip; Webb, Travis

2017-03-01

The implementation of mandated restrictions in resident duty hours has led to increased handoffs for patient care and thus more opportunities for errors during transitions of care. Much of the current handoff literature is empiric, with experts recommending the study of handoffs within an established framework. A prospective, single-institution study was conducted evaluating the process of handoffs for the care of surgical patients in the context of a published communication framework. Evaluation tools for the source, receiver, and observer were developed to identify factors impacting the handoff process, and inter-rater correlations were assessed. Data analysis was generated with Pearson/Spearman correlations and multivariate linear regressions. Rater consistency was assessed with intraclass correlations. A total of 126 handoffs were observed. Evaluations were completed by 1 observer (N = 126), 2 observers (N = 23), 2 receivers (N = 39), 1 receiver (N = 82), and 1 source (N = 78). An average (±standard deviation) service handoff included 9.2 (±4.6) patients, lasted 9.1 (±5.4) minutes, and had 4.7 (±3.4) distractions recorded by the observer. The source and receiver(s) recognized distractions in >67% of handoffs, with the most common internal and external distractions being fatigue (60% of handoffs) and extraneous staff entering/exiting the room (31%), respectively. Teams with more patients spent less time per individual patient handoff (r = -0.298; P = .001). Statistically significant intraclass correlations (P ≤ .05) were moderate between observers (r ≥ 0.4) but not receivers (r < 0.4). Intraclass correlation values between different types of raters were inconsistent (P > .05). The quality of the handoff process was affected negatively by presence of active electronic devices (β = -0.565; P = .005), number of teaching discussions (β = -0.417; P = .048), and a sense of hierarchy between source and receiver (β = -0.309; P = .002). Studying the handoff process within an established framework highlights factors that impair communication. Internal and external distractions are common during handoffs and along with the working relationship between the source and receiver impact the quality of the handoff process. This information allows further study and targeted interventions of the handoff process to improve overall effectiveness and patient safety of the handoff. Copyright © 2016 Elsevier Inc. All rights reserved.

Transient flow conditions in probabilistic wellhead protection: importance and ways to manage spatial and temporal uncertainty in capture zone delineation

NASA Astrophysics Data System (ADS)

Enzenhoefer, R.; Rodriguez-Pretelin, A.; Nowak, W.

2012-12-01

"From an engineering standpoint, the quantification of uncertainty is extremely important not only because it allows estimating risk but mostly because it allows taking optimal decisions in an uncertain framework" (Renard, 2007). The most common way to account for uncertainty in the field of subsurface hydrology and wellhead protection is to randomize spatial parameters, e.g. the log-hydraulic conductivity or porosity. This enables water managers to take robust decisions in delineating wellhead protection zones with rationally chosen safety margins in the spirit of probabilistic risk management. Probabilistic wellhead protection zones are commonly based on steady-state flow fields. However, several past studies showed that transient flow conditions may substantially influence the shape and extent of catchments. Therefore, we believe they should be accounted for in the probabilistic assessment and in the delineation process. The aim of our work is to show the significance of flow transients and to investigate the interplay between spatial uncertainty and flow transients in wellhead protection zone delineation. To this end, we advance our concept of probabilistic capture zone delineation (Enzenhoefer et al., 2012) that works with capture probabilities and other probabilistic criteria for delineation. The extended framework is able to evaluate the time fraction that any point on a map falls within a capture zone. In short, we separate capture probabilities into spatial/statistical and time-related frequencies. This will provide water managers additional information on how to manage a well catchment in the light of possible hazard conditions close to the capture boundary under uncertain and time-variable flow conditions. In order to save computational costs, we take advantage of super-positioned flow components with time-variable coefficients. We assume an instantaneous development of steady-state flow conditions after each temporal change in driving forces, following an idea by Festger and Walter, 2002. These quasi steady-state flow fields are cast into a geostatistical Monte Carlo framework to admit and evaluate the influence of parameter uncertainty on the delineation process. Furthermore, this framework enables conditioning on observed data with any conditioning scheme, such as rejection sampling, Ensemble Kalman Filters, etc. To further reduce the computational load, we use the reverse formulation of advective-dispersive transport. We simulate the reverse transport by particle tracking random walk in order to avoid numerical dispersion to account for well arrival times.

A circadian rhythm in skill-based errors in aviation maintenance.

PubMed

Hobbs, Alan; Williamson, Ann; Van Dongen, Hans P A

2010-07-01

In workplaces where activity continues around the clock, human error has been observed to exhibit a circadian rhythm, with a characteristic peak in the early hours of the morning. Errors are commonly distinguished by the nature of the underlying cognitive failure, particularly the level of intentionality involved in the erroneous action. The Skill-Rule-Knowledge (SRK) framework of Rasmussen is used widely in the study of industrial errors and accidents. The SRK framework describes three fundamental types of error, according to whether behavior is under the control of practiced sensori-motor skill routines with minimal conscious awareness; is guided by implicit or explicit rules or expertise; or where the planning of actions requires the conscious application of domain knowledge. Up to now, examinations of circadian patterns of industrial errors have not distinguished between different types of error. Consequently, it is not clear whether all types of error exhibit the same circadian rhythm. A survey was distributed to aircraft maintenance personnel in Australia. Personnel were invited to anonymously report a safety incident and were prompted to describe, in detail, the human involvement (if any) that contributed to it. A total of 402 airline maintenance personnel reported an incident, providing 369 descriptions of human error in which the time of the incident was reported and sufficient detail was available to analyze the error. Errors were categorized using a modified version of the SRK framework, in which errors are categorized as skill-based, rule-based, or knowledge-based, or as procedure violations. An independent check confirmed that the SRK framework had been applied with sufficient consistency and reliability. Skill-based errors were the most common form of error, followed by procedure violations, rule-based errors, and knowledge-based errors. The frequency of errors was adjusted for the estimated proportion of workers present at work/each hour of the day, and the 24 h pattern of each error type was examined. Skill-based errors exhibited a significant circadian rhythm, being most prevalent in the early hours of the morning. Variation in the frequency of rule-based errors, knowledge-based errors, and procedure violations over the 24 h did not reach statistical significance. The results suggest that during the early hours of the morning, maintenance technicians are at heightened risk of "absent minded" errors involving failures to execute action plans as intended.

A framework for conducting mechanistic based reliability assessments of components operating in complex systems

NASA Astrophysics Data System (ADS)

Wallace, Jon Michael

2003-10-01

Reliability prediction of components operating in complex systems has historically been conducted in a statistically isolated manner. Current physics-based, i.e. mechanistic, component reliability approaches focus more on component-specific attributes and mathematical algorithms and not enough on the influence of the system. The result is that significant error can be introduced into the component reliability assessment process. The objective of this study is the development of a framework that infuses the needs and influence of the system into the process of conducting mechanistic-based component reliability assessments. The formulated framework consists of six primary steps. The first three steps, identification, decomposition, and synthesis, are primarily qualitative in nature and employ system reliability and safety engineering principles to construct an appropriate starting point for the component reliability assessment. The following two steps are the most unique. They involve a step to efficiently characterize and quantify the system-driven local parameter space and a subsequent step using this information to guide the reduction of the component parameter space. The local statistical space quantification step is accomplished using two proposed multivariate probability models: Multi-Response First Order Second Moment and Taylor-Based Inverse Transformation. Where existing joint probability models require preliminary distribution and correlation information of the responses, these models combine statistical information of the input parameters with an efficient sampling of the response analyses to produce the multi-response joint probability distribution. Parameter space reduction is accomplished using Approximate Canonical Correlation Analysis (ACCA) employed as a multi-response screening technique. The novelty of this approach is that each individual local parameter and even subsets of parameters representing entire contributing analyses can now be rank ordered with respect to their contribution to not just one response, but the entire vector of component responses simultaneously. The final step of the framework is the actual probabilistic assessment of the component. Although the same multivariate probability tools employed in the characterization step can be used for the component probability assessment, variations of this final step are given to allow for the utilization of existing probabilistic methods such as response surface Monte Carlo and Fast Probability Integration. The overall framework developed in this study is implemented to assess the finite-element based reliability prediction of a gas turbine airfoil involving several failure responses. Results of this implementation are compared to results generated using the conventional 'isolated' approach as well as a validation approach conducted through large sample Monte Carlo simulations. The framework resulted in a considerable improvement to the accuracy of the part reliability assessment and an improved understanding of the component failure behavior. Considerable statistical complexity in the form of joint non-normal behavior was found and accounted for using the framework. Future applications of the framework elements are discussed.

Causal Analysis of Self-tracked Time Series Data Using a Counterfactual Framework for N-of-1 Trials.

PubMed

Daza, Eric J

2018-02-01

Many of an individual's historically recorded personal measurements vary over time, thereby forming a time series (e.g., wearable-device data, self-tracked fitness or nutrition measurements, regularly monitored clinical events or chronic conditions). Statistical analyses of such n-of-1 (i.e., single-subject) observational studies (N1OSs) can be used to discover possible cause-effect relationships to then self-test in an n-of-1 randomized trial (N1RT). However, a principled way of determining how and when to interpret an N1OS association as a causal effect (e.g., as if randomization had occurred) is needed.Our goal in this paper is to help bridge the methodological gap between risk-factor discovery and N1RT testing by introducing a basic counterfactual framework for N1OS design and personalized causal analysis.We introduce and characterize what we call the average period treatment effect (APTE), i.e., the estimand of interest in an N1RT, and build an analytical framework around it that can accommodate autocorrelation and time trends in the outcome, effect carryover from previous treatment periods, and slow onset or decay of the effect. The APTE is loosely defined as a contrast (e.g., difference, ratio) of averages of potential outcomes the individual can theoretically experience under different treatment levels during a given treatment period. To illustrate the utility of our framework for APTE discovery and estimation, two common causal inference methods are specified within the N1OS context. We then apply the framework and methods to search for estimable and interpretable APTEs using six years of the author's self-tracked weight and exercise data, and report both the preliminary findings and the challenges we faced in conducting N1OS causal discovery.Causal analysis of an individual's time series data can be facilitated by an N1RT counterfactual framework. However, for inference to be valid, the veracity of certain key assumptions must be assessed critically, and the hypothesized causal models must be interpretable and meaningful. Schattauer GmbH.

Deep Dyspareunia in Endometriosis: A Proposed Framework Based on Pain Mechanisms and Genito-Pelvic Pain Penetration Disorder.

PubMed

Yong, Paul J

2017-10-01

Endometriosis is a common chronic disease affecting 1 in 10 women of reproductive age, with half of women with endometriosis experiencing deep dyspareunia. A review of research studies on endometriosis indicates a need for a validated question or questionnaire for deep dyspareunia. Moreover, placebo-controlled randomized trials have yet to demonstrate a clear benefit for traditional treatments of endometriosis for the outcome of deep dyspareunia. The reason some patients might not respond to traditional treatments is the multifactorial nature of deep dyspareunia in endometriosis, which can include comorbid conditions (eg, interstitial cystitis and bladder pain syndrome) and central sensitization underlying genito-pelvic pain penetration disorder. In general, there is a lack of a framework that integrates these multifactorial causes to provide a standardized approach to deep dyspareunia in endometriosis. To propose a clinical framework for deep dyspareunia based on a synthesis of pain mechanisms with genito-pelvic pain penetration disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Narrative review after literature search with the terms (endometriosis AND dyspareunia) OR (dyspareunia AND deep) and after analysis of placebo-controlled randomized trials. Deep dyspareunia presence or absence or deep dyspareunia severity on a numeric rating scale or visual analog scale. Four types of deep dyspareunia are proposed in women with endometriosis: type I that is directly due to endometriosis; type II that is related to a comorbid condition; type III in which genito-pelvic pain penetration disorder is primary; and type IV that is secondary to a combination of types I to III. Four types of deep dyspareunia in endometriosis are proposed, which can be used as a framework in research studies and in clinical practice. Research trials could phenotype or stratify patients by each type. The framework also could give rise to more personalized care for patients by targeting appropriate treatments to each deep dyspareunia type. Yong PJ. Deep Dyspareunia in Endometriosis: A Proposed Framework Based on Pain Mechanisms and Genito-Pelvic Pain Penetration Disorder. Sex Med Rev 2017;5:495-507. Copyright © 2017 International Society for Sexual Medicine. Published by Elsevier Inc. All rights reserved.

A review of odour impact criteria in selected countries around the world.

PubMed

Brancher, Marlon; Griffiths, K David; Franco, Davide; de Melo Lisboa, Henrique

2017-02-01

Exposure to environmental odour can result in annoyance, health effects and depreciation of property values. Therefore, many jurisdictions classify odour as an atmospheric pollutant and regulate emissions and/or impacts from odour generating activities at a national, state or municipal level. In this work, a critical review of odour regulations in selected jurisdictions of 28 countries is presented. Individual approaches were identified as: comparing ambient air odour concentration and individual chemicals statistics against impact criteria (maximum impact standard); using fixed and variable separation distances (separation distance standard); maximum emission rate for mixtures of odorants and individual chemical species (maximum emission standard); number of complaints received or annoyance level determined via community surveys (maximum annoyance standard); and requiring use of best available technologies (BAT) to minimize odour emissions (technology standard). The comparison of model-predicted odour concentration statistics against odour impact criteria (OIC) is identified as one of the most common tools used by regulators to evaluate the risk of odour impacts in planning stage assessments and is also used to inform assessment of odour impacts of existing facilities. Special emphasis is given to summarizing OIC (concentration percentile and threshold) and the manner in which they are applied. The way short term odour peak to model time-step mean (peak-to-mean) effects is also captured. Furthermore, the fundamentals of odorant properties, dimensions of nuisance odour, odour sampling and analysis methods and dispersion modelling guidance are provided. Common elements of mature and effective odour regulation frameworks are identified and an integrated multi-tool strategy is recommended. Copyright © 2016 Elsevier Ltd. All rights reserved.

Spatially adapted augmentation of age-specific atlas-based segmentation using patch-based priors

NASA Astrophysics Data System (ADS)

Liu, Mengyuan; Seshamani, Sharmishtaa; Harrylock, Lisa; Kitsch, Averi; Miller, Steven; Chau, Van; Poskitt, Kenneth; Rousseau, Francois; Studholme, Colin

2014-03-01

One of the most common approaches to MRI brain tissue segmentation is to employ an atlas prior to initialize an Expectation- Maximization (EM) image labeling scheme using a statistical model of MRI intensities. This prior is commonly derived from a set of manually segmented training data from the population of interest. However, in cases where subject anatomy varies significantly from the prior anatomical average model (for example in the case where extreme developmental abnormalities or brain injuries occur), the prior tissue map does not provide adequate information about the observed MRI intensities to ensure the EM algorithm converges to an anatomically accurate labeling of the MRI. In this paper, we present a novel approach for automatic segmentation of such cases. This approach augments the atlas-based EM segmentation by exploring methods to build a hybrid tissue segmentation scheme that seeks to learn where an atlas prior fails (due to inadequate representation of anatomical variation in the statistical atlas) and utilize an alternative prior derived from a patch driven search of the atlas data. We describe a framework for incorporating this patch-based augmentation of EM (PBAEM) into a 4D age-specific atlas-based segmentation of developing brain anatomy. The proposed approach was evaluated on a set of MRI brain scans of premature neonates with ages ranging from 27.29 to 46.43 gestational weeks (GWs). Results indicated superior performance compared to the conventional atlas-based segmentation method, providing improved segmentation accuracy for gray matter, white matter, ventricles and sulcal CSF regions.

Probabilistic models in human sensorimotor control

PubMed Central

Wolpert, Daniel M.

2009-01-01

Sensory and motor uncertainty form a fundamental constraint on human sensorimotor control. Bayesian decision theory (BDT) has emerged as a unifying framework to understand how the central nervous system performs optimal estimation and control in the face of such uncertainty. BDT has two components: Bayesian statistics and decision theory. Here we review Bayesian statistics and show how it applies to estimating the state of the world and our own body. Recent results suggest that when learning novel tasks we are able to learn the statistical properties of both the world and our own sensory apparatus so as to perform estimation using Bayesian statistics. We review studies which suggest that humans can combine multiple sources of information to form maximum likelihood estimates, can incorporate prior beliefs about possible states of the world so as to generate maximum a posteriori estimates and can use Kalman filter-based processes to estimate time-varying states. Finally, we review Bayesian decision theory in motor control and how the central nervous system processes errors to determine loss functions and optimal actions. We review results that suggest we plan movements based on statistics of our actions that result from signal-dependent noise on our motor outputs. Taken together these studies provide a statistical framework for how the motor system performs in the presence of uncertainty. PMID:17628731

On the Calculation of Uncertainty Statistics with Error Bounds for CFD Calculations Containing Random Parameters and Fields

NASA Technical Reports Server (NTRS)

Barth, Timothy J.

2016-01-01

This chapter discusses the ongoing development of combined uncertainty and error bound estimates for computational fluid dynamics (CFD) calculations subject to imposed random parameters and random fields. An objective of this work is the construction of computable error bound formulas for output uncertainty statistics that guide CFD practitioners in systematically determining how accurately CFD realizations should be approximated and how accurately uncertainty statistics should be approximated for output quantities of interest. Formal error bounds formulas for moment statistics that properly account for the presence of numerical errors in CFD calculations and numerical quadrature errors in the calculation of moment statistics have been previously presented in [8]. In this past work, hierarchical node-nested dense and sparse tensor product quadratures are used to calculate moment statistics integrals. In the present work, a framework has been developed that exploits the hierarchical structure of these quadratures in order to simplify the calculation of an estimate of the quadrature error needed in error bound formulas. When signed estimates of realization error are available, this signed error may also be used to estimate output quantity of interest probability densities as a means to assess the impact of realization error on these density estimates. Numerical results are presented for CFD problems with uncertainty to demonstrate the capabilities of this framework.

A statistical framework for biomedical literature mining.

PubMed

Chung, Dongjun; Lawson, Andrew; Zheng, W Jim

2017-09-30

In systems biology, it is of great interest to identify new genes that were not previously reported to be associated with biological pathways related to various functions and diseases. Identification of these new pathway-modulating genes does not only promote understanding of pathway regulation mechanisms but also allow identification of novel targets for therapeutics. Recently, biomedical literature has been considered as a valuable resource to investigate pathway-modulating genes. While the majority of currently available approaches are based on the co-occurrence of genes within an abstract, it has been reported that these approaches show only sub-optimal performances because 70% of abstracts contain information only for a single gene. To overcome such limitation, we propose a novel statistical framework based on the concept of ontology fingerprint that uses gene ontology to extract information from large biomedical literature data. The proposed framework simultaneously identifies pathway-modulating genes and facilitates interpreting functions of these new genes. We also propose a computationally efficient posterior inference procedure based on Metropolis-Hastings within Gibbs sampler for parameter updates and the poor man's reversible jump Markov chain Monte Carlo approach for model selection. We evaluate the proposed statistical framework with simulation studies, experimental validation, and an application to studies of pathway-modulating genes in yeast. The R implementation of the proposed model is currently available at https://dongjunchung.github.io/bayesGO/. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback.

PubMed

Rahman, Md Mahmudur; Bhattacharya, Prabir; Desai, Bipin C

2007-01-01

A content-based image retrieval (CBIR) framework for diverse collection of medical images of different imaging modalities, anatomic regions with different orientations and biological systems is proposed. Organization of images in such a database (DB) is well defined with predefined semantic categories; hence, it can be useful for category-specific searching. The proposed framework consists of machine learning methods for image prefiltering, similarity matching using statistical distance measures, and a relevance feedback (RF) scheme. To narrow down the semantic gap and increase the retrieval efficiency, we investigate both supervised and unsupervised learning techniques to associate low-level global image features (e.g., color, texture, and edge) in the projected PCA-based eigenspace with their high-level semantic and visual categories. Specially, we explore the use of a probabilistic multiclass support vector machine (SVM) and fuzzy c-mean (FCM) clustering for categorization and prefiltering of images to reduce the search space. A category-specific statistical similarity matching is proposed in a finer level on the prefiltered images. To incorporate a better perception subjectivity, an RF mechanism is also added to update the query parameters dynamically and adjust the proposed matching functions. Experiments are based on a ground-truth DB consisting of 5000 diverse medical images of 20 predefined categories. Analysis of results based on cross-validation (CV) accuracy and precision-recall for image categorization and retrieval is reported. It demonstrates the improvement, effectiveness, and efficiency achieved by the proposed framework.

A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic

PubMed Central

Qi, Jin-Peng; Qi, Jie; Zhang, Qing

2016-01-01

Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals. PMID:27413364

A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic.

PubMed

Qi, Jin-Peng; Qi, Jie; Zhang, Qing

2016-01-01

Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals.

Questions on universal constants and four-dimensional symmetry from a broad viewpoint. I

NASA Technical Reports Server (NTRS)

Hsu, J. P.

1983-01-01

It is demonstrated that there is a flexibility in clock synchronizations and that four-dimensional symmetry framework can be viewed broadly. The true universality of basic constants is discussed, considering a class of measurement processes based on the velocity = distance/time interval, which always yields some number when used by an observer. The four-dimensional symmetry framework based on common time for all observers is formulated, and related processes of measuring light speed are discussed. Invariant 'action functions' for physical laws in the new four-dimensional symmetry framework with the common time are established to discuss universal constants. Truly universal constants are demonstrated, and it is shown that physics in this new framework and in special relativity are equivalent as far as one-particle systems and the S-matrix in field theories are concerned.

Uncertainty and inference in the world of paleoecological data

NASA Astrophysics Data System (ADS)

McLachlan, J. S.; Dawson, A.; Dietze, M.; Finley, M.; Hooten, M.; Itter, M.; Jackson, S. T.; Marlon, J. R.; Raiho, A.; Tipton, J.; Williams, J.

2017-12-01

Proxy data in paleoecology and paleoclimatology share a common set of biases and uncertainties: spatiotemporal error associated with the taphonomic processes of deposition, preservation, and dating; calibration error between proxy data and the ecosystem states of interest; and error in the interpolation of calibrated estimates across space and time. Researchers often account for this daunting suite of challenges by applying qualitave expert judgment: inferring the past states of ecosystems and assessing the level of uncertainty in those states subjectively. The effectiveness of this approach can be seen by the extent to which future observations confirm previous assertions. Hierarchical Bayesian (HB) statistical approaches allow an alternative approach to accounting for multiple uncertainties in paleo data. HB estimates of ecosystem state formally account for each of the common uncertainties listed above. HB approaches can readily incorporate additional data, and data of different types into estimates of ecosystem state. And HB estimates of ecosystem state, with associated uncertainty, can be used to constrain forecasts of ecosystem dynamics based on mechanistic ecosystem models using data assimilation. Decisions about how to structure an HB model are also subjective, which creates a parallel framework for deciding how to interpret data from the deep past.Our group, the Paleoecological Observatory Network (PalEON), has applied hierarchical Bayesian statistics to formally account for uncertainties in proxy based estimates of past climate, fire, primary productivity, biomass, and vegetation composition. Our estimates often reveal new patterns of past ecosystem change, which is an unambiguously good thing, but we also often estimate a level of uncertainty that is uncomfortably high for many researchers. High levels of uncertainty are due to several features of the HB approach: spatiotemporal smoothing, the formal aggregation of multiple types of uncertainty, and a coarseness in statistical models of taphonomic process. Each of these features provides useful opportunities for statisticians and data-generating researchers to assess what we know about the signal and the noise in paleo data and to improve inference about past changes in ecosystem state.

System Software Framework for System of Systems Avionics

NASA Technical Reports Server (NTRS)

Ferguson, Roscoe C.; Peterson, Benjamin L; Thompson, Hiram C.

2005-01-01

Project Constellation implements NASA's vision for space exploration to expand human presence in our solar system. The engineering focus of this project is developing a system of systems architecture. This architecture allows for the incremental development of the overall program. Systems can be built and connected in a "Lego style" manner to generate configurations supporting various mission objectives. The development of the avionics or control systems of such a massive project will result in concurrent engineering. Also, each system will have software and the need to communicate with other (possibly heterogeneous) systems. Fortunately, this design problem has already been solved during the creation and evolution of systems such as the Internet and the Department of Defense's successful effort to standardize distributed simulation (now IEEE 1516). The solution relies on the use of a standard layered software framework and a communication protocol. A standard framework and communication protocol is suggested for the development and maintenance of Project Constellation systems. The ARINC 653 standard is a great start for such a common software framework. This paper proposes a common system software framework that uses the Real Time Publish/Subscribe protocol for framework-to-framework communication to extend ARINC 653. It is highly recommended that such a framework be established before development. This is important for the success of concurrent engineering. The framework provides an infrastructure for general system services and is designed for flexibility to support a spiral development effort.

Jamming II: Edwards’ statistical mechanics of random packings of hard spheres

NASA Astrophysics Data System (ADS)

Wang, Ping; Song, Chaoming; Jin, Yuliang; Makse, Hernán A.

2011-02-01

The problem of finding the most efficient way to pack spheres has an illustrious history, dating back to the crystalline arrays conjectured by Kepler and the random geometries explored by Bernal in the 1960s. This problem finds applications spanning from the mathematician’s pencil, the processing of granular materials, the jamming and glass transitions, all the way to fruit packing in every grocery. There are presently numerous experiments showing that the loosest way to pack spheres gives a density of ∼55% (named random loose packing, RLP) while filling all the loose voids results in a maximum density of ∼63%-64% (named random close packing, RCP). While those values seem robustly true, to this date there is no well-accepted physical explanation or theoretical prediction for them. Here we develop a common framework for understanding the random packings of monodisperse hard spheres whose limits can be interpreted as the experimentally observed RLP and RCP. The reason for these limits arises from a statistical picture of jammed states in which the RCP can be interpreted as the ground state of the ensemble of jammed matter with zero compactivity, while the RLP arises in the infinite compactivity limit. We combine an extended statistical mechanics approach ‘a la Edwards’ (where the role traditionally played by the energy and temperature in thermal systems is substituted by the volume and compactivity) with a constraint on mechanical stability imposed by the isostatic condition. We show how such approaches can bring results that can be compared to experiments and allow for an exploitation of the statistical mechanics framework. The key result is the use of a relation between the local Voronoi volumes of the constituent grains (denoted the volume function) and the number of neighbors in contact that permits us to simply combine the two approaches to develop a theory of volume fluctuations in jammed matter. Ultimately, our results lead to a phase diagram that provides a unifying view of the disordered hard sphere packing problem and further sheds light on a diverse spectrum of data, including the RLP state. Theoretical results are well reproduced by numerical simulations that confirm the essential role played by friction in determining both the RLP and RCP limits. The RLP values depend on friction, explaining why varied experimental results can be obtained.

Development of the Modes of Collaboration framework

NASA Astrophysics Data System (ADS)

Pawlak, Alanna; Irving, Paul W.; Caballero, Marcos D.

2018-01-01

Group work is becoming increasingly common in introductory physics classrooms. Understanding how students engage in these group learning environments is important for designing and facilitating productive learning opportunities for students. We conducted a study in which we collected video of groups of students working on conceptual electricity and magnetism problems in an introductory physics course. In this setting, students needed to negotiate a common understanding and coordinate group decisions in order to complete the activity successfully. We observed students interacting in several distinct ways while solving these problems. Analysis of these observations focused on identifying the different ways students interacted and articulating what defines and distinguishes them, resulting in the development of the modes of collaboration framework. The modes of collaboration framework defines student interactions along three dimensions: social, discursive, and disciplinary content. This multidimensional approach offers a unique lens through which to consider group work and provides a flexibility that could allow the framework to be adapted for a variety of contexts. We present the framework and several examples of its application here.

Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction.

PubMed

Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

2007-11-01

Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction

NASA Astrophysics Data System (ADS)

Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

2007-11-01

Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

Carbon fibre versus metal framework in full-arch immediate loading rehabilitations of the maxilla - a cohort clinical study.

PubMed

Pera, F; Pesce, P; Solimano, F; Tealdo, T; Pera, P; Menini, M

2017-05-01

Frameworks made of carbon fibre-reinforced composites (CFRC) seem to be a viable alternative to traditional metal frameworks in implant prosthodontics. CFRC provide stiffness, rigidity and optimal biocompatibility. The aim of the present prospective study was to compare carbon fibre frameworks versus metal frameworks used to rigidly splint implants in full-arch immediate loading rehabilitations. Forty-two patients (test group) were rehabilitated with full-arch immediate loading rehabilitations of the upper jaw (total: 170 implants) following the Columbus Bridge Protocol with four to six implants with distal tilted implants. All patients were treated with resin screw-retained full-arch prostheses endowed with carbon fibre frameworks. The mean follow-up was 22 months (range: 18-24). Differences in the absolute change of bone resorption over time between the two implant sides (mesial and distal) were assessed performing a Mann-Whitney U-test. The outcomes were statistically compared with those of patients rehabilitated following the same protocol but using metal frameworks (control group: 34 patients with 163 implants - data reported in Tealdo, Menini, Bevilacqua, Pera, Pesce, Signori, Pera, Int J Prosthodont, 27, 2014, 207). Ten implants failed in the control group (6·1%); none failed in the test group (P = 0·002). A statistically significant difference in the absolute change of bone resorption around the implants was found between the two groups (P = 0·004), with greater mean peri-implant bone resorption in the control group (1 mm) compared to the test group (0·8 mm). Carbon fibre frameworks may be considered as a viable alternative to the metal ones and showed less marginal bone loss around implants and a greater implant survival rate during the observation period. © 2017 John Wiley & Sons Ltd.

Implementing Restricted Maximum Likelihood Estimation in Structural Equation Models

ERIC Educational Resources Information Center

Cheung, Mike W.-L.

2013-01-01

Structural equation modeling (SEM) is now a generic modeling framework for many multivariate techniques applied in the social and behavioral sciences. Many statistical models can be considered either as special cases of SEM or as part of the latent variable modeling framework. One popular extension is the use of SEM to conduct linear mixed-effects…

The Zero to Three Diagnostic System: A Framework for Considering Emotional and Behavioral Problems in Young Children

ERIC Educational Resources Information Center

Evangelista, Nancy; McLellan, Mary J.

2004-01-01

The expansion of early childhood services has brought increasing recognition of the need to address mental health disorders in young children. The transactional perspective of developmental psychopathology is the basis for review of diagnostic frameworks for young children. The Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) is…

Indigenous Peoples and Indicators of Well-Being: Australian Perspectives on United Nations Global Frameworks

ERIC Educational Resources Information Center

Taylor, John

2008-01-01

One of the major tasks of the United Nations Permanent Forum on Indigenous Issues (UNPFII) following its establishment in 2000 has been to establish statistical profiles of the world's Indigenous peoples. As part of this broad task, it has recommended that the Millennium Development Goals and other global reporting frameworks should be assessed…

An Ethical (Descriptive) Framework for Judgment of Actions and Decisions in the Construction Industry and Engineering-Part I.

PubMed

Alkhatib, Omar J; Abdou, Alaa

2018-04-01

The construction industry is usually characterized as a fragmented system of multiple-organizational entities in which members from different technical backgrounds and moral values join together to develop a particular business or project. The greatest challenge in the construction process for the achievement of a successful practice is the development of an outstanding reputation, which is built on identifying and applying an ethical framework. This framework should reflect a common ethical ground for myriad people involved in this process to survive and compete ethically in today's turbulent construction market. This study establishes a framework for ethical judgment of behavior and actions conducted in the construction process. The framework was primarily developed based on the essential attributes of business management identified in the literature review and subsequently incorporates additional attributes identified to prevent breaches in the construction industry and common ethical values related to professional engineering. The proposed judgment framework is based primarily on the ethical dimension of professional responsibility. The Ethical Judgment Framework consists of descriptive approaches involving technical, professional, administrative, and miscellaneous terms. The framework provides the basis for judging actions as either ethical or unethical. Furthermore, the framework can be implemented as a form of preventive ethics, which would help avoid ethical dilemmas and moral allegations. The framework can be considered a decision-making model to guide actions and improve the ethical reasoning process that would help individuals think through possible implications and consequences of ethical dilemmas in the construction industry.

Towards sound epistemological foundations of statistical methods for high-dimensional biology.

PubMed

Mehta, Tapan; Tanik, Murat; Allison, David B

2004-09-01

A sound epistemological foundation for biological inquiry comes, in part, from application of valid statistical procedures. This tenet is widely appreciated by scientists studying the new realm of high-dimensional biology, or 'omic' research, which involves multiplicity at unprecedented scales. Many papers aimed at the high-dimensional biology community describe the development or application of statistical techniques. The validity of many of these is questionable, and a shared understanding about the epistemological foundations of the statistical methods themselves seems to be lacking. Here we offer a framework in which the epistemological foundation of proposed statistical methods can be evaluated.

A general science-based framework for dynamical spatio-temporal models

USGS Publications Warehouse

Wikle, C.K.; Hooten, M.B.

2010-01-01

Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially-explicit processes that evolve over time. Correspondingly, in recent years there has been a significant amount of research on new statistical methodology for such models. Although descriptive models that approach the problem from the second-order (covariance) perspective are important, and innovative work is being done in this regard, many real-world processes are dynamic, and it can be more efficient in some cases to characterize the associated spatio-temporal dependence by the use of dynamical models. The chief challenge with the specification of such dynamical models has been related to the curse of dimensionality. Even in fairly simple linear, first-order Markovian, Gaussian error settings, statistical models are often over parameterized. Hierarchical models have proven invaluable in their ability to deal to some extent with this issue by allowing dependency among groups of parameters. In addition, this framework has allowed for the specification of science based parameterizations (and associated prior distributions) in which classes of deterministic dynamical models (e. g., partial differential equations (PDEs), integro-difference equations (IDEs), matrix models, and agent-based models) are used to guide specific parameterizations. Most of the focus for the application of such models in statistics has been in the linear case. The problems mentioned above with linear dynamic models are compounded in the case of nonlinear models. In this sense, the need for coherent and sensible model parameterizations is not only helpful, it is essential. Here, we present an overview of a framework for incorporating scientific information to motivate dynamical spatio-temporal models. First, we illustrate the methodology with the linear case. We then develop a general nonlinear spatio-temporal framework that we call general quadratic nonlinearity and demonstrate that it accommodates many different classes of scientific-based parameterizations as special cases. The model is presented in a hierarchical Bayesian framework and is illustrated with examples from ecology and oceanography. ?? 2010 Sociedad de Estad??stica e Investigaci??n Operativa.

An emission-weighted proximity model for air pollution exposure assessment.

PubMed

Zou, Bin; Wilson, J Gaines; Zhan, F Benjamin; Zeng, Yongnian

2009-08-15

Among the most common spatial models for estimating personal exposure are Traditional Proximity Models (TPMs). Though TPMs are straightforward to configure and interpret, they are prone to extensive errors in exposure estimates and do not provide prospective estimates. To resolve these inherent problems with TPMs, we introduce here a novel Emission Weighted Proximity Model (EWPM) to improve the TPM, which takes into consideration the emissions from all sources potentially influencing the receptors. EWPM performance was evaluated by comparing the normalized exposure risk values of sulfur dioxide (SO(2)) calculated by EWPM with those calculated by TPM and monitored observations over a one-year period in two large Texas counties. In order to investigate whether the limitations of TPM in potential exposure risk prediction without recorded incidence can be overcome, we also introduce a hybrid framework, a 'Geo-statistical EWPM'. Geo-statistical EWPM is a synthesis of Ordinary Kriging Geo-statistical interpolation and EWPM. The prediction results are presented as two potential exposure risk prediction maps. The performance of these two exposure maps in predicting individual SO(2) exposure risk was validated with 10 virtual cases in prospective exposure scenarios. Risk values for EWPM were clearly more agreeable with the observed concentrations than those from TPM. Over the entire study area, the mean SO(2) exposure risk from EWPM was higher relative to TPM (1.00 vs. 0.91). The mean bias of the exposure risk values of 10 virtual cases between EWPM and 'Geo-statistical EWPM' are much smaller than those between TPM and 'Geo-statistical TPM' (5.12 vs. 24.63). EWPM appears to more accurately portray individual exposure relative to TPM. The 'Geo-statistical EWPM' effectively augments the role of the standard proximity model and makes it possible to predict individual risk in future exposure scenarios resulting in adverse health effects from environmental pollution.

SOCR Analyses – an Instructional Java Web-based Statistical Analysis Toolkit

PubMed Central

Chu, Annie; Cui, Jenny; Dinov, Ivo D.

2011-01-01

The Statistical Online Computational Resource (SOCR) designs web-based tools for educational use in a variety of undergraduate courses (Dinov 2006). Several studies have demonstrated that these resources significantly improve students' motivation and learning experiences (Dinov et al. 2008). SOCR Analyses is a new component that concentrates on data modeling and analysis using parametric and non-parametric techniques supported with graphical model diagnostics. Currently implemented analyses include commonly used models in undergraduate statistics courses like linear models (Simple Linear Regression, Multiple Linear Regression, One-Way and Two-Way ANOVA). In addition, we implemented tests for sample comparisons, such as t-test in the parametric category; and Wilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, in the non-parametric category. SOCR Analyses also include several hypothesis test models, such as Contingency tables, Friedman's test and Fisher's exact test. The code itself is open source (http://socr.googlecode.com/), hoping to contribute to the efforts of the statistical computing community. The code includes functionality for each specific analysis model and it has general utilities that can be applied in various statistical computing tasks. For example, concrete methods with API (Application Programming Interface) have been implemented in statistical summary, least square solutions of general linear models, rank calculations, etc. HTML interfaces, tutorials, source code, activities, and data are freely available via the web (www.SOCR.ucla.edu). Code examples for developers and demos for educators are provided on the SOCR Wiki website. In this article, the pedagogical utilization of the SOCR Analyses is discussed, as well as the underlying design framework. As the SOCR project is on-going and more functions and tools are being added to it, these resources are constantly improved. The reader is strongly encouraged to check the SOCR site for most updated information and newly added models. PMID:21546994

Practical guidance on characterizing availability in resource selection functions under a use-availability design

USGS Publications Warehouse

Northrup, Joseph M.; Hooten, Mevin B.; Anderson, Charles R.; Wittemyer, George

2013-01-01

Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a use-availability framework, whereby animal locations are contrasted with random locations (the availability sample). Although most use-availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics, exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness.

A Mathematical Framework for the Complex System Approach to Group Dynamics: The Case of Recovery House Social Integration.

PubMed

Light, John M; Jason, Leonard A; Stevens, Edward B; Callahan, Sarah; Stone, Ariel

2016-03-01

The complex system conception of group social dynamics often involves not only changing individual characteristics, but also changing within-group relationships. Recent advances in stochastic dynamic network modeling allow these interdependencies to be modeled from data. This methodology is discussed within a context of other mathematical and statistical approaches that have been or could be applied to study the temporal evolution of relationships and behaviors within small- to medium-sized groups. An example model is presented, based on a pilot study of five Oxford House recovery homes, sober living environments for individuals following release from acute substance abuse treatment. This model demonstrates how dynamic network modeling can be applied to such systems, examines and discusses several options for pooling, and shows how results are interpreted in line with complex system concepts. Results suggest that this approach (a) is a credible modeling framework for studying group dynamics even with limited data, (b) improves upon the most common alternatives, and (c) is especially well-suited to complex system conceptions. Continuing improvements in stochastic models and associated software may finally lead to mainstream use of these techniques for the study of group dynamics, a shift already occurring in related fields of behavioral science.

Defense Acquisition Transformation Report to Congress

DTIC Science & Technology

2007-02-01

current initiatives and for the purposes of this report are put into a framework of workforce, acquisition, requirements, budget, industry and organization...requirements. The Capability Portfolio Management Initiative provides a common framework to recognize federated ownership and senior-level teams have been...Performance Trade Off Constraints Risk Based Source Selection techniques are also being piloted to provide an improved framework for

Analysis of Naval NETWAR FORCEnet Enterprise: Implications for Capabilities Based Budgeting

DTIC Science & Technology

2006-12-01

of this background information and projecting how ADNS is likely to succeed in the NNFE framework , two fundamental research questions were addressed...background information and projecting how ADNS is likely to succeed in the NNFE framework , two fundamental research questions were addressed. The...Business Approach ......................................................26 Figure 8. Critical Assumption for Common Analytical Framework

Environmental statistics and optimal regulation.

PubMed

Sivak, David A; Thomson, Matt

2014-09-01

Any organism is embedded in an environment that changes over time. The timescale for and statistics of environmental change, the precision with which the organism can detect its environment, and the costs and benefits of particular protein expression levels all will affect the suitability of different strategies--such as constitutive expression or graded response--for regulating protein levels in response to environmental inputs. We propose a general framework-here specifically applied to the enzymatic regulation of metabolism in response to changing concentrations of a basic nutrient-to predict the optimal regulatory strategy given the statistics of fluctuations in the environment and measurement apparatus, respectively, and the costs associated with enzyme production. We use this framework to address three fundamental questions: (i) when a cell should prefer thresholding to a graded response; (ii) when there is a fitness advantage to implementing a Bayesian decision rule; and (iii) when retaining memory of the past provides a selective advantage. We specifically find that: (i) relative convexity of enzyme expression cost and benefit influences the fitness of thresholding or graded responses; (ii) intermediate levels of measurement uncertainty call for a sophisticated Bayesian decision rule; and (iii) in dynamic contexts, intermediate levels of uncertainty call for retaining memory of the past. Statistical properties of the environment, such as variability and correlation times, set optimal biochemical parameters, such as thresholds and decay rates in signaling pathways. Our framework provides a theoretical basis for interpreting molecular signal processing algorithms and a classification scheme that organizes known regulatory strategies and may help conceptualize heretofore unknown ones.

STAPP: Spatiotemporal analysis of plantar pressure measurements using statistical parametric mapping.

PubMed

Booth, Brian G; Keijsers, Noël L W; Sijbers, Jan; Huysmans, Toon

2018-05-03

Pedobarography produces large sets of plantar pressure samples that are routinely subsampled (e.g. using regions of interest) or aggregated (e.g. center of pressure trajectories, peak pressure images) in order to simplify statistical analysis and provide intuitive clinical measures. We hypothesize that these data reductions discard gait information that can be used to differentiate between groups or conditions. To test the hypothesis of null information loss, we created an implementation of statistical parametric mapping (SPM) for dynamic plantar pressure datasets (i.e. plantar pressure videos). Our SPM software framework brings all plantar pressure videos into anatomical and temporal correspondence, then performs statistical tests at each sampling location in space and time. Novelly, we introduce non-linear temporal registration into the framework in order to normalize for timing differences within the stance phase. We refer to our software framework as STAPP: spatiotemporal analysis of plantar pressure measurements. Using STAPP, we tested our hypothesis on plantar pressure videos from 33 healthy subjects walking at different speeds. As walking speed increased, STAPP was able to identify significant decreases in plantar pressure at mid-stance from the heel through the lateral forefoot. The extent of these plantar pressure decreases has not previously been observed using existing plantar pressure analysis techniques. We therefore conclude that the subsampling of plantar pressure videos - a task which led to the discarding of gait information in our study - can be avoided using STAPP. Copyright © 2018 Elsevier B.V. All rights reserved.

Pattern matching through Chaos Game Representation: bridging numerical and discrete data structures for biological sequence analysis

PubMed Central

2012-01-01

Background Chaos Game Representation (CGR) is an iterated function that bijectively maps discrete sequences into a continuous domain. As a result, discrete sequences can be object of statistical and topological analyses otherwise reserved to numerical systems. Characteristically, CGR coordinates of substrings sharing an L-long suffix will be located within 2-L distance of each other. In the two decades since its original proposal, CGR has been generalized beyond its original focus on genomic sequences and has been successfully applied to a wide range of problems in bioinformatics. This report explores the possibility that it can be further extended to approach algorithms that rely on discrete, graph-based representations. Results The exploratory analysis described here consisted of selecting foundational string problems and refactoring them using CGR-based algorithms. We found that CGR can take the role of suffix trees and emulate sophisticated string algorithms, efficiently solving exact and approximate string matching problems such as finding all palindromes and tandem repeats, and matching with mismatches. The common feature of these problems is that they use longest common extension (LCE) queries as subtasks of their procedures, which we show to have a constant time solution with CGR. Additionally, we show that CGR can be used as a rolling hash function within the Rabin-Karp algorithm. Conclusions The analysis of biological sequences relies on algorithmic foundations facing mounting challenges, both logistic (performance) and analytical (lack of unifying mathematical framework). CGR is found to provide the latter and to promise the former: graph-based data structures for sequence analysis operations are entailed by numerical-based data structures produced by CGR maps, providing a unifying analytical framework for a diversity of pattern matching problems. PMID:22551152

Linking English-Language Test Scores onto the Common European Framework of Reference: An Application of Standard-Setting Methodology. TOEFL iBT Research Report TOEFL iBt-06. ETS RR-08-34

ERIC Educational Resources Information Center

Tannenbaum, Richard J.; Wylie, E. Caroline

2008-01-01

The Common European Framework of Reference (CEFR) describes language proficiency in reading, writing, speaking, and listening on a 6-level scale. In this study, English-language experts from across Europe linked CEFR levels to scores on three tests: the TOEFL® iBT test, the TOEIC® assessment, and the TOEIC "Bridge"™ test.…

The "Common European Framework of Reference for Languages" and the European Language Portfolio: Some History, a View of Language Learner Autonomy, and Some Implications for Language Learning in Higher Education

ERIC Educational Resources Information Center

Little, David

2013-01-01

This article is based on a plenary talk given at the CercleS seminar hosted by the University of Groningen in November 2011 to mark the tenth anniversary of the publication of the "Common European Framework of Reference for Languages" and the launch of the European Language Portfolio. The first part of the article summarizes the history…

Sensor Compromise Detection in Multiple-Target Tracking Systems

PubMed Central

Doucette, Emily A.; Curtis, Jess W.

2018-01-01

Tracking multiple targets using a single estimator is a problem that is commonly approached within a trusted framework. There are many weaknesses that an adversary can exploit if it gains control over the sensors. Because the number of targets that the estimator has to track is not known with anticipation, an adversary could cause a loss of information or a degradation in the tracking precision. Other concerns include the introduction of false targets, which would result in a waste of computational and material resources, depending on the application. In this work, we study the problem of detecting compromised or faulty sensors in a multiple-target tracker, starting with the single-sensor case and then considering the multiple-sensor scenario. We propose an algorithm to detect a variety of attacks in the multiple-sensor case, via the application of finite set statistics (FISST), one-class classifiers and hypothesis testing using nonparametric techniques. PMID:29466314

Affective bias as a rational response to the statistics of rewards and punishments.

PubMed

Pulcu, Erdem; Browning, Michael

2017-10-04

Affective bias, the tendency to differentially prioritise the processing of negative relative to positive events, is commonly observed in clinical and non-clinical populations. However, why such biases develop is not known. Using a computational framework, we investigated whether affective biases may reflect individuals' estimates of the information content of negative relative to positive events. During a reinforcement learning task, the information content of positive and negative outcomes was manipulated independently by varying the volatility of their occurrence. Human participants altered the learning rates used for the outcomes selectively, preferentially learning from the most informative. This behaviour was associated with activity of the central norepinephrine system, estimated using pupilometry, for loss outcomes. Humans maintain independent estimates of the information content of distinct positive and negative outcomes which may bias their processing of affective events. Normalising affective biases using computationally inspired interventions may represent a novel approach to treatment development.

Affective bias as a rational response to the statistics of rewards and punishments

PubMed Central

Pulcu, Erdem

2017-01-01

Affective bias, the tendency to differentially prioritise the processing of negative relative to positive events, is commonly observed in clinical and non-clinical populations. However, why such biases develop is not known. Using a computational framework, we investigated whether affective biases may reflect individuals’ estimates of the information content of negative relative to positive events. During a reinforcement learning task, the information content of positive and negative outcomes was manipulated independently by varying the volatility of their occurrence. Human participants altered the learning rates used for the outcomes selectively, preferentially learning from the most informative. This behaviour was associated with activity of the central norepinephrine system, estimated using pupilometry, for loss outcomes. Humans maintain independent estimates of the information content of distinct positive and negative outcomes which may bias their processing of affective events. Normalising affective biases using computationally inspired interventions may represent a novel approach to treatment development. PMID:28976304

Analyzing force concept inventory with item response theory

NASA Astrophysics Data System (ADS)

Wang, Jing; Bao, Lei

2010-10-01

Item response theory is a popular assessment method used in education. It rests on the assumption of a probability framework that relates students' innate ability and their performance on test questions. Item response theory transforms students' raw test scores into a scaled proficiency score, which can be used to compare results obtained with different test questions. The scaled score also addresses the issues of ceiling effects and guessing, which commonly exist in quantitative assessment. We used item response theory to analyze the force concept inventory (FCI). Our results show that item response theory can be useful for analyzing physics concept surveys such as the FCI and produces results about the individual questions and student performance that are beyond the capability of classical statistics. The theory yields detailed measurement parameters regarding the difficulty, discrimination features, and probability of correct guess for each of the FCI questions.

Modeling error distributions of growth curve models through Bayesian methods.

PubMed

Zhang, Zhiyong

2016-06-01

Growth curve models are widely used in social and behavioral sciences. However, typical growth curve models often assume that the errors are normally distributed although non-normal data may be even more common than normal data. In order to avoid possible statistical inference problems in blindly assuming normality, a general Bayesian framework is proposed to flexibly model normal and non-normal data through the explicit specification of the error distributions. A simulation study shows when the distribution of the error is correctly specified, one can avoid the loss in the efficiency of standard error estimates. A real example on the analysis of mathematical ability growth data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 is used to show the application of the proposed methods. Instructions and code on how to conduct growth curve analysis with both normal and non-normal error distributions using the the MCMC procedure of SAS are provided.

Immersive Interaction, Manipulation and Analysis of Large 3D Datasets for Planetary and Earth Sciences

NASA Astrophysics Data System (ADS)

Pariser, O.; Calef, F.; Manning, E. M.; Ardulov, V.

2017-12-01

We will present implementation and study of several use-cases of utilizing Virtual Reality (VR) for immersive display, interaction and analysis of large and complex 3D datasets. These datasets have been acquired by the instruments across several Earth, Planetary and Solar Space Robotics Missions. First, we will describe the architecture of the common application framework that was developed to input data, interface with VR display devices and program input controllers in various computing environments. Tethered and portable VR technologies will be contrasted and advantages of each highlighted. We'll proceed to presenting experimental immersive analytics visual constructs that enable augmentation of 3D datasets with 2D ones such as images and statistical and abstract data. We will conclude by presenting comparative analysis with traditional visualization applications and share the feedback provided by our users: scientists and engineers.

Bayesian Computation for Log-Gaussian Cox Processes: A Comparative Analysis of Methods

PubMed Central

Teng, Ming; Nathoo, Farouk S.; Johnson, Timothy D.

2017-01-01

The Log-Gaussian Cox Process is a commonly used model for the analysis of spatial point pattern data. Fitting this model is difficult because of its doubly-stochastic property, i.e., it is an hierarchical combination of a Poisson process at the first level and a Gaussian Process at the second level. Various methods have been proposed to estimate such a process, including traditional likelihood-based approaches as well as Bayesian methods. We focus here on Bayesian methods and several approaches that have been considered for model fitting within this framework, including Hamiltonian Monte Carlo, the Integrated nested Laplace approximation, and Variational Bayes. We consider these approaches and make comparisons with respect to statistical and computational efficiency. These comparisons are made through several simulation studies as well as through two applications, the first examining ecological data and the second involving neuroimaging data. PMID:29200537

A Genealogical Interpretation of Principal Components Analysis

PubMed Central

McVean, Gil

2009-01-01

Principal components analysis, PCA, is a statistical method commonly used in population genetics to identify structure in the distribution of genetic variation across geographical location and ethnic background. However, while the method is often used to inform about historical demographic processes, little is known about the relationship between fundamental demographic parameters and the projection of samples onto the primary axes. Here I show that for SNP data the projection of samples onto the principal components can be obtained directly from considering the average coalescent times between pairs of haploid genomes. The result provides a framework for interpreting PCA projections in terms of underlying processes, including migration, geographical isolation, and admixture. I also demonstrate a link between PCA and Wright's fst and show that SNP ascertainment has a largely simple and predictable effect on the projection of samples. Using examples from human genetics, I discuss the application of these results to empirical data and the implications for inference. PMID:19834557

Forecasting seasonal outbreaks of influenza.

PubMed

Shaman, Jeffrey; Karspeck, Alicia

2012-12-11

Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003-2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza.

POWER ANALYSIS FOR COMPLEX MEDIATIONAL DESIGNS USING MONTE CARLO METHODS

PubMed Central

Thoemmes, Felix; MacKinnon, David P.; Reiser, Mark R.

2013-01-01

Applied researchers often include mediation effects in applications of advanced methods such as latent variable models and linear growth curve models. Guidance on how to estimate statistical power to detect mediation for these models has not yet been addressed in the literature. We describe a general framework for power analyses for complex mediational models. The approach is based on the well known technique of generating a large number of samples in a Monte Carlo study, and estimating power as the percentage of cases in which an estimate of interest is significantly different from zero. Examples of power calculation for commonly used mediational models are provided. Power analyses for the single mediator, multiple mediators, three-path mediation, mediation with latent variables, moderated mediation, and mediation in longitudinal designs are described. Annotated sample syntax for Mplus is appended and tabled values of required sample sizes are shown for some models. PMID:23935262

Forecasting seasonal outbreaks of influenza

PubMed Central

Shaman, Jeffrey; Karspeck, Alicia

2012-01-01

Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003–2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza. PMID:23184969

Description and testing of the Geo Data Portal: Data integration framework and Web processing services for environmental science collaboration

USGS Publications Warehouse

Blodgett, David L.; Booth, Nathaniel L.; Kunicki, Thomas C.; Walker, Jordan I.; Viger, Roland J.

2011-01-01

Interest in sharing interdisciplinary environmental modeling results and related data is increasing among scientists. The U.S. Geological Survey Geo Data Portal project enables data sharing by assembling open-standard Web services into an integrated data retrieval and analysis Web application design methodology that streamlines time-consuming and resource-intensive data management tasks. Data-serving Web services allow Web-based processing services to access Internet-available data sources. The Web processing services developed for the project create commonly needed derivatives of data in numerous formats. Coordinate reference system manipulation and spatial statistics calculation components implemented for the Web processing services were confirmed using ArcGIS 9.3.1, a geographic information science software package. Outcomes of the Geo Data Portal project support the rapid development of user interfaces for accessing and manipulating environmental data.

Influence of homology and node age on the growth of protein-protein interaction networks

NASA Astrophysics Data System (ADS)

Bottinelli, Arianna; Bassetti, Bruno; Lagomarsino, Marco Cosentino; Gherardi, Marco

2012-10-01

Proteins participating in a protein-protein interaction network can be grouped into homology classes following their common ancestry. Proteins added to the network correspond to genes added to the classes, so the dynamics of the two objects are intrinsically linked. Here we first introduce a statistical model describing the joint growth of the network and the partitioning of nodes into classes, which is studied through a combined mean-field and simulation approach. We then employ this unified framework to address the specific issue of the age dependence of protein interactions through the definition of three different node wiring or divergence schemes. A comparison with empirical data indicates that an age-dependent divergence move is necessary in order to reproduce the basic topological observables together with the age correlation between interacting nodes visible in empirical data. We also discuss the possibility of nontrivial joint partition and topology observables.

Identifying fMRI Model Violations with Lagrange Multiplier Tests

PubMed Central

Cassidy, Ben; Long, Christopher J; Rae, Caroline; Solo, Victor

2013-01-01

The standard modeling framework in Functional Magnetic Resonance Imaging (fMRI) is predicated on assumptions of linearity, time invariance and stationarity. These assumptions are rarely checked because doing so requires specialised software, although failure to do so can lead to bias and mistaken inference. Identifying model violations is an essential but largely neglected step in standard fMRI data analysis. Using Lagrange Multiplier testing methods we have developed simple and efficient procedures for detecting model violations such as non-linearity, non-stationarity and validity of the common Double Gamma specification for hemodynamic response. These procedures are computationally cheap and can easily be added to a conventional analysis. The test statistic is calculated at each voxel and displayed as a spatial anomaly map which shows regions where a model is violated. The methodology is illustrated with a large number of real data examples. PMID:22542665

A conceptual framework for quality assessment and management of biodiversity data.

PubMed

Veiga, Allan Koch; Saraiva, Antonio Mauro; Chapman, Arthur David; Morris, Paul John; Gendreau, Christian; Schigel, Dmitry; Robertson, Tim James

2017-01-01

The increasing availability of digitized biodiversity data worldwide, provided by an increasing number of institutions and researchers, and the growing use of those data for a variety of purposes have raised concerns related to the "fitness for use" of such data and the impact of data quality (DQ) on the outcomes of analyses, reports, and decisions. A consistent approach to assess and manage data quality is currently critical for biodiversity data users. However, achieving this goal has been particularly challenging because of idiosyncrasies inherent in the concept of quality. DQ assessment and management cannot be performed if we have not clearly established the quality needs from a data user's standpoint. This paper defines a formal conceptual framework to support the biodiversity informatics community allowing for the description of the meaning of "fitness for use" from a data user's perspective in a common and standardized manner. This proposed framework defines nine concepts organized into three classes: DQ Needs, DQ Solutions and DQ Report. The framework is intended to formalize human thinking into well-defined components to make it possible to share and reuse concepts of DQ needs, solutions and reports in a common way among user communities. With this framework, we establish a common ground for the collaborative development of solutions for DQ assessment and management based on data fitness for use principles. To validate the framework, we present a proof of concept based on a case study at the Museum of Comparative Zoology of Harvard University. In future work, we will use the framework to engage the biodiversity informatics community to formalize and share DQ profiles related to DQ needs across the community.

A conceptual framework for quality assessment and management of biodiversity data

PubMed Central

Saraiva, Antonio Mauro; Chapman, Arthur David; Morris, Paul John; Gendreau, Christian; Schigel, Dmitry; Robertson, Tim James

2017-01-01

The increasing availability of digitized biodiversity data worldwide, provided by an increasing number of institutions and researchers, and the growing use of those data for a variety of purposes have raised concerns related to the "fitness for use" of such data and the impact of data quality (DQ) on the outcomes of analyses, reports, and decisions. A consistent approach to assess and manage data quality is currently critical for biodiversity data users. However, achieving this goal has been particularly challenging because of idiosyncrasies inherent in the concept of quality. DQ assessment and management cannot be performed if we have not clearly established the quality needs from a data user’s standpoint. This paper defines a formal conceptual framework to support the biodiversity informatics community allowing for the description of the meaning of "fitness for use" from a data user’s perspective in a common and standardized manner. This proposed framework defines nine concepts organized into three classes: DQ Needs, DQ Solutions and DQ Report. The framework is intended to formalize human thinking into well-defined components to make it possible to share and reuse concepts of DQ needs, solutions and reports in a common way among user communities. With this framework, we establish a common ground for the collaborative development of solutions for DQ assessment and management based on data fitness for use principles. To validate the framework, we present a proof of concept based on a case study at the Museum of Comparative Zoology of Harvard University. In future work, we will use the framework to engage the biodiversity informatics community to formalize and share DQ profiles related to DQ needs across the community. PMID:28658288

Statistics used in current nursing research.

PubMed

Zellner, Kathleen; Boerst, Connie J; Tabb, Wil

2007-02-01

Undergraduate nursing research courses should emphasize the statistics most commonly used in the nursing literature to strengthen students' and beginning researchers' understanding of them. To determine the most commonly used statistics, we reviewed all quantitative research articles published in 13 nursing journals in 2000. The findings supported Beitz's categorization of kinds of statistics. Ten primary statistics used in 80% of nursing research published in 2000 were identified. We recommend that the appropriate use of those top 10 statistics be emphasized in undergraduate nursing education and that the nursing profession continue to advocate for the use of methods (e.g., power analysis, odds ratio) that may contribute to the advancement of nursing research.

Detecting Statistically Significant Communities of Triangle Motifs in Undirected Networks

DTIC Science & Technology

2016-04-26

REPORT TYPE Final 3. DATES COVERED (From - To) 15 Oct 2014 to 14 Jan 2015 4. TITLE AND SUBTITLE Detecting statistically significant clusters of...extend the work of Perry et al. [6] by developing a statistical framework that supports the detection of triangle motif-based clusters in complex...priori, the need for triangle motif-based clustering . 2. Developed an algorithm for clustering undirected networks, where the triangle con guration was

RooStatsCms: A tool for analysis modelling, combination and statistical studies

NASA Astrophysics Data System (ADS)

Piparo, D.; Schott, G.; Quast, G.

2010-04-01

RooStatsCms is an object oriented statistical framework based on the RooFit technology. Its scope is to allow the modelling, statistical analysis and combination of multiple search channels for new phenomena in High Energy Physics. It provides a variety of methods described in literature implemented as classes, whose design is oriented to the execution of multiple CPU intensive jobs on batch systems or on the Grid.

Permutation inference for the general linear model

PubMed Central

Winkler, Anderson M.; Ridgway, Gerard R.; Webster, Matthew A.; Smith, Stephen M.; Nichols, Thomas E.

2014-01-01

Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data. With the availability of fast and inexpensive computing, their main limitation would be some lack of flexibility to work with arbitrary experimental designs. In this paper we report on results on approximate permutation methods that are more flexible with respect to the experimental design and nuisance variables, and conduct detailed simulations to identify the best method for settings that are typical for imaging research scenarios. We present a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and show that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios. We also demonstrate how the inference on glm parameters, originally intended for independent data, can be used in certain special but useful cases in which independence is violated. Detailed examples of common neuroimaging applications are provided, as well as a complete algorithm – the “randomise” algorithm – for permutation inference with the glm. PMID:24530839

Exploring Tree Age & Diameter to Illustrate Sample Design & Inference in Observational Ecology

ERIC Educational Resources Information Center

Casady, Grant M.

2015-01-01

Undergraduate biology labs often explore the techniques of data collection but neglect the statistical framework necessary to express findings. Students can be confused about how to use their statistical knowledge to address specific biological questions. Growth in the area of observational ecology requires that students gain experience in…

A Matched Field Processing Framework for Coherent Detection Over Local and Regional Networks (Postprint)

DTIC Science & Technology

2011-12-30

the term " superresolution "). The single-phase matched field statistic for a given template was also demonstrated to be a viable detection statistic... Superresolution with seismic arrays using empirical matched field processing, Geophys. J. Int. 182: 1455–1477. Kim, K.-H. and Park, Y. (2010): The 20

A Data Warehouse Architecture for DoD Healthcare Performance Measurements.

DTIC Science & Technology

1999-09-01

design, develop, implement, and apply statistical analysis and data mining tools to a Data Warehouse of healthcare metrics. With the DoD healthcare...framework, this thesis defines a methodology to design, develop, implement, and apply statistical analysis and data mining tools to a Data Warehouse...21 F. INABILITY TO CONDUCT HELATHCARE ANALYSIS

Measuring the Impacts of ICT Using Official Statistics. OECD Digital Economy Papers, No. 136

ERIC Educational Resources Information Center

Roberts, Sheridan

2008-01-01

This paper describes the findings of an OECD project examining ICT impact measurement and analyses based on official statistics. Both economic and social impacts are covered and some results are presented. It attempts to place ICT impacts measurement into an Information Society conceptual framework, provides some suggestions for standardising…

Learning Axes and Bridging Tools in a Technology-Based Design for Statistics

ERIC Educational Resources Information Center

Abrahamson, Dor; Wilensky, Uri

2007-01-01

We introduce a design-based research framework, "learning axes and bridging tools," and demonstrate its application in the preparation and study of an implementation of a middle-school experimental computer-based unit on probability and statistics, "ProbLab" (Probability Laboratory, Abrahamson and Wilensky 2002 [Abrahamson, D., & Wilensky, U.…

Information Distribution Practices of Federal Statistical Agencies: The Census Bureau Example.

ERIC Educational Resources Information Center

Gey, Frederick C.

1993-01-01

Describes the current and historical distribution channels of the U.S. Bureau of the Census within a framework of distribution policies and practices for federal statistical information. The issues of reasonable distribution policies and the impact of technological change are discussed, and guidelines are offered. (Contains 26 references.) (EAM)

OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records.

PubMed

Chen, Jonathan H; Podchiyska, Tanya; Altman, Russ B

2016-03-01

To answer a "grand challenge" in clinical decision support, the authors produced a recommender system that automatically data-mines inpatient decision support from electronic medical records (EMR), analogous to Netflix or Amazon.com's product recommender. EMR data were extracted from 1 year of hospitalizations (>18K patients with >5.4M structured items including clinical orders, lab results, and diagnosis codes). Association statistics were counted for the ∼1.5K most common items to drive an order recommender. The authors assessed the recommender's ability to predict hospital admission orders and outcomes based on initial encounter data from separate validation patients. Compared to a reference benchmark of using the overall most common orders, the recommender using temporal relationships improves precision at 10 recommendations from 33% to 38% (P < 10(-10)) for hospital admission orders. Relative risk-based association methods improve inverse frequency weighted recall from 4% to 16% (P < 10(-16)). The framework yields a prediction receiver operating characteristic area under curve (c-statistic) of 0.84 for 30 day mortality, 0.84 for 1 week need for ICU life support, 0.80 for 1 week hospital discharge, and 0.68 for 30-day readmission. Recommender results quantitatively improve on reference benchmarks and qualitatively appear clinically reasonable. The method assumes that aggregate decision making converges appropriately, but ongoing evaluation is necessary to discern common behaviors from "correct" ones. Collaborative filtering recommender algorithms generate clinical decision support that is predictive of real practice patterns and clinical outcomes. Incorporating temporal relationships improves accuracy. Different evaluation metrics satisfy different goals (predicting likely events vs. "interesting" suggestions). Published by Oxford University Press on behalf of the American Medical Informatics Association 2015. This work is written by US Government employees and is in the public domain in the US.

Studying Gene and Gene-Environment Effects of Uncommon and Common Variants on Continuous Traits: A Marker-Set Approach Using Gene-Trait Similarity Regression

PubMed Central

Tzeng, Jung-Ying; Zhang, Daowen; Pongpanich, Monnat; Smith, Chris; McCarthy, Mark I.; Sale, Michèle M.; Worrall, Bradford B.; Hsu, Fang-Chi; Thomas, Duncan C.; Sullivan, Patrick F.

2011-01-01

Genomic association analyses of complex traits demand statistical tools that are capable of detecting small effects of common and rare variants and modeling complex interaction effects and yet are computationally feasible. In this work, we introduce a similarity-based regression method for assessing the main genetic and interaction effects of a group of markers on quantitative traits. The method uses genetic similarity to aggregate information from multiple polymorphic sites and integrates adaptive weights that depend on allele frequencies to accomodate common and uncommon variants. Collapsing information at the similarity level instead of the genotype level avoids canceling signals that have the opposite etiological effects and is applicable to any class of genetic variants without the need for dichotomizing the allele types. To assess gene-trait associations, we regress trait similarities for pairs of unrelated individuals on their genetic similarities and assess association by using a score test whose limiting distribution is derived in this work. The proposed regression framework allows for covariates, has the capacity to model both main and interaction effects, can be applied to a mixture of different polymorphism types, and is computationally efficient. These features make it an ideal tool for evaluating associations between phenotype and marker sets defined by linkage disequilibrium (LD) blocks, genes, or pathways in whole-genome analysis. PMID:21835306

The "con" of concept analysis A discussion paper which explores and critiques the ontological focus, reliability and antecedents of concept analysis frameworks.

PubMed

Beckwith, Sue; Dickinson, Angela; Kendall, Sally

2008-12-01

This paper draws on the work of Paley and Duncan et al in order to extend and engender debate regarding the use of Concept Analysis frameworks. Despite the apparent plethora of Concept Analysis frameworks used in nursing studies we found that over half of those used were derived from the work of one author. This paper explores the suitability and use of these frameworks and is set at a time when the numbers of published concept analysis papers are increasing. For the purpose of this study thirteen commonly used frameworks, identified from the nursing journals 1993 to 2005, were explored to reveal their origins, ontological and philosophical stance, and any common elements. The frameworks were critiqued and links made between their antecedents. It was noted if the articles contained discussion of any possible tensions between the ontological perspective of the framework used, the process of analysis, praxis and possible nursing theory developments. It was found that the thirteen identified frameworks are mainly based on hermeneutic propositions regarding understandings and are interpretive procedures founded on self-reflective modes of discovery. Six frameworks rely on or include the use of casuistry. Seven of the frameworks identified are predicated on, or adapt the work of Wilson, a school master writing for his pupils. Wilson's framework has a simplistic eleven step, binary and reductionist structure. Other frameworks identified include Morse et al's framework which this article suggests employs a contestable theory of concept maturity. Based on the findings revealed through our exploration of the use of concept analysis frameworks in the nursing literature, concerns were raised regarding the unjustified adaptation and alterations and the uncritical use of the frameworks. There is little evidence that these frameworks provide the necessary depth, rigor or replicability to enable the development in nursing theory which they underpin.

A Moral (Normative) Framework for the Judgment of Actions and Decisions in the Construction Industry and Engineering: Part II.

PubMed

Alkhatib, Omar J

2017-12-01

The construction industry is typically characterized as a fragmented, multi-organizational setting in which members from different technical backgrounds and moral values join together to develop a particular business or project. The most challenging obstacle in the construction process is to achieve a successful practice and to identify and apply an ethical framework to manage the behavior of involved specialists and contractors and to ensure the quality of all completed construction activities. The framework should reflect a common moral ground for myriad people involved in this process to survive and compete ethically in today's turbulent construction market. This study establishes a framework for moral judgment of behavior and actions conducted in the construction process. The moral framework provides the basis of judging actions as "moral" or "immoral" based on three levels of moral accountability: personal, professional, and social. The social aspect of the proposed framework is developed primarily from the essential attributes of normative business decision-making models identified in the literature review and subsequently incorporates additional attributes related to professional and personal moral values. The normative decision-making models reviewed are based primarily on social attributes as related to moral theories (e.g., utilitarianism, duty, rights, virtue, etc.). The professional and moral attributes are established by identifying a set of common moral values recognized by professionals in the construction industry and required to prevent common construction breaches. The moral framework presented here is the complementary part of the ethical framework developed in Part I of this article and is based primarily on the personal behavior or the moral aspect of professional responsibility. The framework can be implemented as a form of preventive personal ethics, which would help avoid ethical dilemmas and moral implications in the first place. Furthermore, the moral framework can be considered as a decision-making model to guide actions and improve the moral reasoning process, which would help individuals think through possible implications and the consequences of ethical and moral issues in the construction industry.

A matching framework to improve causal inference in interrupted time-series analysis.

PubMed

Linden, Ariel

2018-04-01

Interrupted time-series analysis (ITSA) is a popular evaluation methodology in which a single treatment unit's outcome is studied over time and the intervention is expected to "interrupt" the level and/or trend of the outcome, subsequent to its introduction. When ITSA is implemented without a comparison group, the internal validity may be quite poor. Therefore, adding a comparable control group to serve as the counterfactual is always preferred. This paper introduces a novel matching framework, ITSAMATCH, to create a comparable control group by matching directly on covariates and then use these matches in the outcomes model. We evaluate the effect of California's Proposition 99 (passed in 1988) for reducing cigarette sales, by comparing California to other states not exposed to smoking reduction initiatives. We compare ITSAMATCH results to 2 commonly used matching approaches, synthetic controls (SYNTH), and regression adjustment; SYNTH reweights nontreated units to make them comparable to the treated unit, and regression adjusts covariates directly. Methods are compared by assessing covariate balance and treatment effects. Both ITSAMATCH and SYNTH achieved covariate balance and estimated similar treatment effects. The regression model found no treatment effect and produced inconsistent covariate adjustment. While the matching framework achieved results comparable to SYNTH, it has the advantage of being technically less complicated, while producing statistical estimates that are straightforward to interpret. Conversely, regression adjustment may "adjust away" a treatment effect. Given its advantages, ITSAMATCH should be considered as a primary approach for evaluating treatment effects in multiple-group time-series analysis. © 2017 John Wiley & Sons, Ltd.

Using core competencies to build an evaluative framework: outcome assessment of the University of Guelph Master of Public Health program

PubMed Central

2014-01-01

Background Master of Public Health programs have been developed across Canada in response to the need for graduate-level trained professionals to work in the public health sector. The University of Guelph recently conducted a five-year outcome assessment using the Core Competencies for Public Health in Canada as an evaluative framework to determine whether graduates are receiving adequate training, and identify areas for improvement. Methods A curriculum map of core courses and an online survey of University of Guelph Master of Public Health graduates comprised the outcome assessment. The curriculum map was constructed by evaluating course outlines, assignments, and content to determine the extent to which the Core Competencies were covered in each course. Quantitative survey results were characterized using descriptive statistics. Qualitative survey results were analyzed to identify common themes and patterns in open-ended responses. Results The University of Guelph Master of Public Health program provided a positive learning environment in which graduates gained proficiency across the Core Competencies through core and elective courses, meaningful practicums, and competent faculty. Practice-based learning environments, particularly in collaboration with public health organizations, were deemed to be beneficial to students’ learning experiences. Conclusions The Core Competencies and graduate surveys can be used to conduct a meaningful and informative outcome assessment. We encourage other Master of Public Health programs to conduct their own outcome assessments using a similar framework, and disseminate these results in order to identify best practices and strengthen the Canadian graduate public health education system. PMID:25078124

A methodology for the design of experiments in computational intelligence with multiple regression models.

PubMed

Fernandez-Lozano, Carlos; Gestal, Marcos; Munteanu, Cristian R; Dorado, Julian; Pazos, Alejandro

2016-01-01

The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

A methodology for the design of experiments in computational intelligence with multiple regression models

PubMed Central

Gestal, Marcos; Munteanu, Cristian R.; Dorado, Julian; Pazos, Alejandro

2016-01-01

The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable. PMID:27920952

Understanding Statistics - Cancer Statistics

Cancer.gov

Annual reports of U.S. cancer statistics including new cases, deaths, trends, survival, prevalence, lifetime risk, and progress toward Healthy People targets, plus statistical summaries for a number of common cancer types.

Teacher Education Preparation and Implementation for Multicultural and Diverse School Environments in the 21st Century: A Mixed Methods Study

ERIC Educational Resources Information Center

Cole, Patricia Ann

2013-01-01

This sequential explanatory mixed methods study investigated 24 college and university syllabi for content consisting of multicultural education that used the framework for multicultural education devised by James A. Banks (2006). This framework was used to analyze data collected using descriptive statistics for quantitative phase one. The four…

When Going Hybrid Is Not Enough: Statistical Analysis of Effectiveness of Blended Courses Piloted within Tempus BLATT Project

ERIC Educational Resources Information Center

Jovanovic, Aleksandar; Jankovic, Anita; Jovanovic, Snezana Markovic; Peric, Vladan; Vitosevic, Biljana; Pavlovic, Milos

2015-01-01

The paper describes the delivery of the courses in the framework of the project implementation and presents the effect the change in the methodology had on student performance as measured by final grade. Methodology: University of Pristina piloted blended courses in 2013 under the framework of the Tempus BLATT project. The blended learning…

Expanding the enablement framework and testing an evaluative instrument for diabetes patient education.

PubMed

Leeseberg Stamler, L; Cole, M M; Patrick, L J

2001-08-01

Strategies to delay or prevent complications from diabetes include diabetes patient education. Diabetes educators seek to provide education that meets the needs of clients and influences positive health outcomes. (1) To expand prior research exploring an enablement framework for patient education by examining perceptions of patient education by persons with diabetes and (2) to test the mastery of stress instrument (MSI) as a potential evaluative instrument for patient education. Triangulated data collection with a convenience sample of adults taking diabetes education classes. Half the sample completed audio-taped semi-structured interviews pre, during and posteducation and all completed the MSI posteducation. Qualitative data were analysed using latent content analysis, descriptive statistics were completed. Qualitative analysis revealed content categories similar to previous work with prenatal participants, supporting the enablement framework. Statistical analyses noted congruence with psychometric findings from development of MSI; secondary qualitative analyses revealed congruency between MSI scores and patient perceptions. Mastery is an outcome congruent with the enablement framework for patient education across content areas. Mastery of stress instrument may be a instrument for identification of patients who are coping well with diabetes self-management, as well as those who are not and who require further nursing interventions.

Integrated framework for developing search and discrimination metrics

NASA Astrophysics Data System (ADS)

Copeland, Anthony C.; Trivedi, Mohan M.

1997-06-01

This paper presents an experimental framework for evaluating target signature metrics as models of human visual search and discrimination. This framework is based on a prototype eye tracking testbed, the Integrated Testbed for Eye Movement Studies (ITEMS). ITEMS determines an observer's visual fixation point while he studies a displayed image scene, by processing video of the observer's eye. The utility of this framework is illustrated with an experiment using gray-scale images of outdoor scenes that contain randomly placed targets. Each target is a square region of a specific size containing pixel values from another image of an outdoor scene. The real-world analogy of this experiment is that of a military observer looking upon the sensed image of a static scene to find camouflaged enemy targets that are reported to be in the area. ITEMS provides the data necessary to compute various statistics for each target to describe how easily the observers located it, including the likelihood the target was fixated or identified and the time required to do so. The computed values of several target signature metrics are compared to these statistics, and a second-order metric based on a model of image texture was found to be the most highly correlated.

A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models

PubMed Central

Wong, Stephen; Gardner, Andrew B.; Krieger, Abba M.; Litt, Brian

2007-01-01

Responsive, implantable stimulation devices to treat epilepsy are now in clinical trials. New evidence suggests that these devices may be more effective when they deliver therapy before seizure onset. Despite years of effort, prospective seizure prediction, which could improve device performance, remains elusive. In large part, this is explained by lack of agreement on a statistical framework for modeling seizure generation and a method for validating algorithm performance. We present a novel stochastic framework based on a three-state hidden Markov model (HMM) (representing interictal, preictal, and seizure states) with the feature that periods of increased seizure probability can transition back to the interictal state. This notion reflects clinical experience and may enhance interpretation of published seizure prediction studies. Our model accommodates clipped EEG segments and formalizes intuitive notions regarding statistical validation. We derive equations for type I and type II errors as a function of the number of seizures, duration of interictal data, and prediction horizon length and we demonstrate the model’s utility with a novel seizure detection algorithm that appeared to predicted seizure onset. We propose this framework as a vital tool for designing and validating prediction algorithms and for facilitating collaborative research in this area. PMID:17021032

Effects of Impression Material, Impression Tray Type, and Type of Partial Edentulism on the Fit of Cobalt-Chromium Partial Denture Frameworks on Initial Clinical Insertion: A Retrospective Clinical Evaluation.

PubMed

Baig, Mirza Rustum; Akbar, Jaber Hussain; Qudeimat, Muawia; Omar, Ridwaan

2018-02-15

To evaluate the effects of impression material, impression tray type, and type of partial edentulism (ie, Kennedy class) on the accuracy of fit of cobalt-chromium (Co-Cr) partial removable dental prostheses (PRDP) in terms of the number of fabricated frameworks required until the attainment of adequate fit. Electronic case documentations of 120 partially edentulous patients provided with Co-Cr PRDP treatment for one or both arches were examined. Statistical analyses of data were performed using analysis of variance and Tukey honest significant difference test to compare the relationships between the different factors and the number of frameworks that needed to be fabricated for each patient (α = .05). Statistical analysis of data derived from 143 records (69 maxillary and 74 mandibular) revealed no significant correlation between impression material, tray type, or Kennedy class and the number of construction attempts for the pooled or individual arch data (P ≥ .05). In PRDP treatment, alginate can be chosen as a first-choice material, and metal stock trays can be a preferred option for making final impressions to fabricate Co-Cr frameworks.

Statistical comparison of a hybrid approach with approximate and exact inference models for Fusion 2+

NASA Astrophysics Data System (ADS)

Lee, K. David; Wiesenfeld, Eric; Gelfand, Andrew

2007-04-01

One of the greatest challenges in modern combat is maintaining a high level of timely Situational Awareness (SA). In many situations, computational complexity and accuracy considerations make the development and deployment of real-time, high-level inference tools very difficult. An innovative hybrid framework that combines Bayesian inference, in the form of Bayesian Networks, and Possibility Theory, in the form of Fuzzy Logic systems, has recently been introduced to provide a rigorous framework for high-level inference. In previous research, the theoretical basis and benefits of the hybrid approach have been developed. However, lacking is a concrete experimental comparison of the hybrid framework with traditional fusion methods, to demonstrate and quantify this benefit. The goal of this research, therefore, is to provide a statistical analysis on the comparison of the accuracy and performance of hybrid network theory, with pure Bayesian and Fuzzy systems and an inexact Bayesian system approximated using Particle Filtering. To accomplish this task, domain specific models will be developed under these different theoretical approaches and then evaluated, via Monte Carlo Simulation, in comparison to situational ground truth to measure accuracy and fidelity. Following this, a rigorous statistical analysis of the performance results will be performed, to quantify the benefit of hybrid inference to other fusion tools.

Some good C++ practices for using the art framework

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

Paterno, Marc

2015-05-01

This document is intended for an audience that has some programming experience, at least beginning familiarity with C++, and at least beginning familiarity with the art2 framework. The intent of this document is to help the reader avoid some of the more common mistakes made by those with little experience in C++, or in use of the art framework, or both.

The Myth of the Rational Decision Maker: A Framework for Applying and Enhancing Heuristic and Intuitive Decision Making by School Leaders

ERIC Educational Resources Information Center

Davis, Stephen H.

2004-01-01

This article takes a critical look at administrative decision making in schools and the extent to which complex decisions conform to normative models and common expectations of rationality. An alternative framework for administrative decision making is presented that is informed, but not driven, by theories of rationality. The framework assumes…

Decision Aids Using Heterogeneous Intelligence Analysis

DTIC Science & Technology

2010-08-20

developing a Geocultural service, a software framework and inferencing engine for the Transparent Urban Structures program. The scope of the effort...has evolved as the program has matured and is including multiple data sources, as well as interfaces out to the ONR architectural framework . Tasks...Interface; Application Program Interface; Application Programmer Interface CAF Common Application Framework EDA Event Driven Architecture a 16. SECURITY