scale structure inference: Topics by Science.gov

Sample records for scale structure inference

Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.

PubMed

Narimani, Zahra; Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger

2017-01-01

Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.
Past and present cosmic structure in the SDSS DR7 main sample

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

Jasche, J.; Leclercq, F.; Wandelt, B.D., E-mail: jasche@iap.fr, E-mail: florent.leclercq@polytechnique.org, E-mail: wandelt@iap.fr

2015-01-01

We present a chrono-cosmography project, aiming at the inference of the four dimensional formation history of the observed large scale structure from its origin to the present epoch. To do so, we perform a full-scale Bayesian analysis of the northern galactic cap of the Sloan Digital Sky Survey (SDSS) Data Release 7 main galaxy sample, relying on a fully probabilistic, physical model of the non-linearly evolved density field. Besides inferring initial conditions from observations, our methodology naturally and accurately reconstructs non-linear features at the present epoch, such as walls and filaments, corresponding to high-order correlation functions generated by late-time structuremore » formation. Our inference framework self-consistently accounts for typical observational systematic and statistical uncertainties such as noise, survey geometry and selection effects. We further account for luminosity dependent galaxy biases and automatic noise calibration within a fully Bayesian approach. As a result, this analysis provides highly-detailed and accurate reconstructions of the present density field on scales larger than ∼ 3 Mpc/h, constrained by SDSS observations. This approach also leads to the first quantitative inference of plausible formation histories of the dynamic large scale structure underlying the observed galaxy distribution. The results described in this work constitute the first full Bayesian non-linear analysis of the cosmic large scale structure with the demonstrated capability of uncertainty quantification. Some of these results will be made publicly available along with this work. The level of detail of inferred results and the high degree of control on observational uncertainties pave the path towards high precision chrono-cosmography, the subject of simultaneously studying the dynamics and the morphology of the inhomogeneous Universe.« less
Rare variation facilitates inferences of fine-scale population structure in humans.

PubMed

O'Connor, Timothy D; Fu, Wenqing; Mychaleckyj, Josyf C; Logsdon, Benjamin; Auer, Paul; Carlson, Christopher S; Leal, Suzanne M; Smith, Joshua D; Rieder, Mark J; Bamshad, Michael J; Nickerson, Deborah A; Akey, Joshua M

2015-03-01

Understanding the genetic structure of human populations has important implications for the design and interpretation of disease mapping studies and reconstructing human evolutionary history. To date, inferences of human population structure have primarily been made with common variants. However, recent large-scale resequencing studies have shown an abundance of rare variation in humans, which may be particularly useful for making inferences of fine-scale population structure. To this end, we used an information theory framework and extensive coalescent simulations to rigorously quantify the informativeness of rare and common variation to detect signatures of fine-scale population structure. We show that rare variation affords unique insights into patterns of recent population structure. Furthermore, to empirically assess our theoretical findings, we analyzed high-coverage exome sequences in 6,515 European and African American individuals. As predicted, rare variants are more informative than common polymorphisms in revealing a distinct cluster of European-American individuals, and subsequent analyses demonstrate that these individuals are likely of Ashkenazi Jewish ancestry. Our results provide new insights into the population structure using rare variation, which will be an important factor to account for in rare variant association studies. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
On the use of Godhavn H-component as an indicator of the interplanetary sector polarity

NASA Technical Reports Server (NTRS)

Svalgaard, L.

1974-01-01

An objective method of inferring the polarity of the interplanetary magnetic field using the H-component at Godhavn is presented. The objectively inferred polarities are compared with a subjective index inferred earlier. It is concluded that no significant difference exists between the two methods. The inferred polarities derived from Godhavn H is biased by the (slp) sub q signature in the sense that during summer prolonged intervals of geomagnetic calm will result in inferred Away polarity regardless of the actual sector polarity. This bias does not significantly alter the large scale structure of the inferred sector structure.
Inferring multi-scale neural mechanisms with brain network modelling

PubMed Central

Schirner, Michael; McIntosh, Anthony Randal; Jirsa, Viktor; Deco, Gustavo

2018-01-01

The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies. PMID:29308767
Inference of scale-free networks from gene expression time series.

PubMed

Daisuke, Tominaga; Horton, Paul

2006-04-01

Quantitative time-series observation of gene expression is becoming possible, for example by cell array technology. However, there are no practical methods with which to infer network structures using only observed time-series data. As most computational models of biological networks for continuous time-series data have a high degree of freedom, it is almost impossible to infer the correct structures. On the other hand, it has been reported that some kinds of biological networks, such as gene networks and metabolic pathways, may have scale-free properties. We hypothesize that the architecture of inferred biological network models can be restricted to scale-free networks. We developed an inference algorithm for biological networks using only time-series data by introducing such a restriction. We adopt the S-system as the network model, and a distributed genetic algorithm to optimize models to fit its simulated results to observed time series data. We have tested our algorithm on a case study (simulated data). We compared optimization under no restriction, which allows for a fully connected network, and under the restriction that the total number of links must equal that expected from a scale free network. The restriction reduced both false positive and false negative estimation of the links and also the differences between model simulation and the given time-series data.
Efficient Exact Inference With Loss Augmented Objective in Structured Learning.

PubMed

Bauer, Alexander; Nakajima, Shinichi; Muller, Klaus-Robert

2016-08-19

Structural support vector machine (SVM) is an elegant approach for building complex and accurate models with structured outputs. However, its applicability relies on the availability of efficient inference algorithms--the state-of-the-art training algorithms repeatedly perform inference to compute a subgradient or to find the most violating configuration. In this paper, we propose an exact inference algorithm for maximizing nondecomposable objectives due to special type of a high-order potential having a decomposable internal structure. As an important application, our method covers the loss augmented inference, which enables the slack and margin scaling formulations of structural SVM with a variety of dissimilarity measures, e.g., Hamming loss, precision and recall, Fβ-loss, intersection over union, and many other functions that can be efficiently computed from the contingency table. We demonstrate the advantages of our approach in natural language parsing and sequence segmentation applications.
Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho

Treesearch

Tzeidle N. Wasserman; Samuel A. Cushman; Michael K. Schwartz; David O. Wallin

2010-01-01

Individual-based analyses relating landscape structure to genetic distances across complex landscapes enable rigorous evaluation of multiple alternative hypotheses linking landscape structure to gene flow. We utilize two extensions to increase the rigor of the individual-based causal modeling approach to inferring relationships between landscape patterns and gene flow...
Functional Inference of Complex Anatomical Tendinous Networks at a Macroscopic Scale via Sparse Experimentation

PubMed Central

Saxena, Anupam; Lipson, Hod; Valero-Cuevas, Francisco J.

2012-01-01

In systems and computational biology, much effort is devoted to functional identification of systems and networks at the molecular-or cellular scale. However, similarly important networks exist at anatomical scales such as the tendon network of human fingers: the complex array of collagen fibers that transmits and distributes muscle forces to finger joints. This network is critical to the versatility of the human hand, and its function has been debated since at least the 16th century. Here, we experimentally infer the structure (both topology and parameter values) of this network through sparse interrogation with force inputs. A population of models representing this structure co-evolves in simulation with a population of informative future force inputs via the predator-prey estimation-exploration algorithm. Model fitness depends on their ability to explain experimental data, while the fitness of future force inputs depends on causing maximal functional discrepancy among current models. We validate our approach by inferring two known synthetic Latex networks, and one anatomical tendon network harvested from a cadaver's middle finger. We find that functionally similar but structurally diverse models can exist within a narrow range of the training set and cross-validation errors. For the Latex networks, models with low training set error [<4%] and resembling the known network have the smallest cross-validation errors [∼5%]. The low training set [<4%] and cross validation [<7.2%] errors for models for the cadaveric specimen demonstrate what, to our knowledge, is the first experimental inference of the functional structure of complex anatomical networks. This work expands current bioinformatics inference approaches by demonstrating that sparse, yet informative interrogation of biological specimens holds significant computational advantages in accurate and efficient inference over random testing, or assuming model topology and only inferring parameters values. These findings also hold clues to both our evolutionary history and the development of versatile machines. PMID:23144601
Functional inference of complex anatomical tendinous networks at a macroscopic scale via sparse experimentation.

PubMed

Saxena, Anupam; Lipson, Hod; Valero-Cuevas, Francisco J

2012-01-01

In systems and computational biology, much effort is devoted to functional identification of systems and networks at the molecular-or cellular scale. However, similarly important networks exist at anatomical scales such as the tendon network of human fingers: the complex array of collagen fibers that transmits and distributes muscle forces to finger joints. This network is critical to the versatility of the human hand, and its function has been debated since at least the 16(th) century. Here, we experimentally infer the structure (both topology and parameter values) of this network through sparse interrogation with force inputs. A population of models representing this structure co-evolves in simulation with a population of informative future force inputs via the predator-prey estimation-exploration algorithm. Model fitness depends on their ability to explain experimental data, while the fitness of future force inputs depends on causing maximal functional discrepancy among current models. We validate our approach by inferring two known synthetic Latex networks, and one anatomical tendon network harvested from a cadaver's middle finger. We find that functionally similar but structurally diverse models can exist within a narrow range of the training set and cross-validation errors. For the Latex networks, models with low training set error [<4%] and resembling the known network have the smallest cross-validation errors [∼5%]. The low training set [<4%] and cross validation [<7.2%] errors for models for the cadaveric specimen demonstrate what, to our knowledge, is the first experimental inference of the functional structure of complex anatomical networks. This work expands current bioinformatics inference approaches by demonstrating that sparse, yet informative interrogation of biological specimens holds significant computational advantages in accurate and efficient inference over random testing, or assuming model topology and only inferring parameters values. These findings also hold clues to both our evolutionary history and the development of versatile machines.
Inverse Problems in Complex Models and Applications to Earth Sciences

NASA Astrophysics Data System (ADS)

Bosch, M. E.

2015-12-01

The inference of the subsurface earth structure and properties requires the integration of different types of data, information and knowledge, by combined processes of analysis and synthesis. To support the process of integrating information, the regular concept of data inversion is evolving to expand its application to models with multiple inner components (properties, scales, structural parameters) that explain multiple data (geophysical survey data, well-logs, core data). The probabilistic inference methods provide the natural framework for the formulation of these problems, considering a posterior probability density function (PDF) that combines the information from a prior information PDF and the new sets of observations. To formulate the posterior PDF in the context of multiple datasets, the data likelihood functions are factorized assuming independence of uncertainties for data originating across different surveys. A realistic description of the earth medium requires modeling several properties and structural parameters, which relate to each other according to dependency and independency notions. Thus, conditional probabilities across model components also factorize. A common setting proceeds by structuring the model parameter space in hierarchical layers. A primary layer (e.g. lithology) conditions a secondary layer (e.g. physical medium properties), which conditions a third layer (e.g. geophysical data). In general, less structured relations within model components and data emerge from the analysis of other inverse problems. They can be described with flexibility via direct acyclic graphs, which are graphs that map dependency relations between the model components. Examples of inverse problems in complex models can be shown at various scales. At local scale, for example, the distribution of gas saturation is inferred from pre-stack seismic data and a calibrated rock-physics model. At regional scale, joint inversion of gravity and magnetic data is applied for the estimation of lithological structure of the crust, with the lithotype body regions conditioning the mass density and magnetic susceptibility fields. At planetary scale, the Earth mantle temperature and element composition is inferred from seismic travel-time and geodetic data.
Nonparametric Bayesian inference of the microcanonical stochastic block model

NASA Astrophysics Data System (ADS)

Peixoto, Tiago P.

2017-01-01

A principled approach to characterize the hidden modular structure of networks is to formulate generative models and then infer their parameters from data. When the desired structure is composed of modules or "communities," a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints, i.e., the generated networks are not allowed to violate the patterns imposed by the model. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: (1) deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, which not only remove limitations that seriously degrade the inference on large networks but also reveal structures at multiple scales; (2) a very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges but also with an unlimited number of modules. We show also how this approach can be used to sample modular hierarchies from the posterior distribution, as well as to perform model selection. We discuss and analyze the differences between sampling from the posterior and simply finding the single parameter estimate that maximizes it. Furthermore, we expose a direct equivalence between our microcanonical approach and alternative derivations based on the canonical SBM.
From broadscale patterns to fine-scale processes: habitat structure influences genetic differentiation in the pitcher plant midge across multiple spatial scales.

PubMed

Rasic, Gordana; Keyghobadi, Nusha

2012-01-01

The spatial scale at which samples are collected and analysed influences the inferences that can be drawn from landscape genetic studies. We examined genetic structure and its landscape correlates in the pitcher plant midge, Metriocnemus knabi, an inhabitant of the purple pitcher plant, Sarracenia purpurea, across several spatial scales that are naturally delimited by the midge's habitat (leaf, plant, cluster of plants, bog and system of bogs). We analysed 11 microsatellite loci in 710 M. knabi larvae from two systems of bogs in Algonquin Provincial Park (Canada) and tested the hypotheses that variables related to habitat structure are associated with genetic differentiation in this midge. Up to 54% of variation in individual-based genetic distances at several scales was explained by broadscale landscape variables of bog size, pitcher plant density within bogs and connectivity of pitcher plant clusters. Our results indicate that oviposition behaviour of females at fine scales, as inferred from the spatial locations of full-sib larvae, and spatially limited gene flow at broad scales represent the important processes underlying observed genetic patterns in M. knabi. Broadscale landscape features (bog size and plant density) appear to influence oviposition behaviour of midges, which in turn influences the patterns of genetic differentiation observed at both fine and broad scales. Thus, we inferred linkages among genetic patterns, landscape patterns and ecological processes across spatial scales in M. knabi. Our results reinforce the value of exploring such links simultaneously across multiple spatial scales and landscapes when investigating genetic diversity within a species. © 2011 Blackwell Publishing Ltd.
Searching for signatures of dark matter-dark radiation interaction in observations of large-scale structure

NASA Astrophysics Data System (ADS)

Pan, Zhen; Kaplinghat, Manoj; Knox, Lloyd

2018-05-01

In this paper, we conduct a search in the latest large-scale structure measurements for signatures of the dark matter-dark radiation interaction proposed by Buen-Abad et al. (2015). We show that prior claims of an inference of this interaction at ˜3 σ significance rely on a use of the Sunyaev-Zeldovich cluster mass function that ignores uncertainty in the mass-observable relationship. Including this uncertainty we find that the inferred level of interaction remains consistent with the data, but so does zero interaction; i.e., there is no longer a preference for nonzero interaction. We also point out that inference of the shape and amplitude of the matter power spectrum from Ly α forest measurements is highly inconsistent with the predictions of the Λ CDM model conditioned on Planck cosmic microwave background temperature, polarization, and lensing power spectra, and that the dark matter-dark radiation model can restore that consistency. We also phenomenologically generalize the model of Buen-Abad et al. (2015) to allow for interaction rates with different scalings with temperature, and find that the original scaling is preferred by the data.
Analysis of a genetically structured variance heterogeneity model using the Box-Cox transformation.

PubMed

Yang, Ye; Christensen, Ole F; Sorensen, Daniel

2011-02-01

Over recent years, statistical support for the presence of genetic factors operating at the level of the environmental variance has come from fitting a genetically structured heterogeneous variance model to field or experimental data in various species. Misleading results may arise due to skewness of the marginal distribution of the data. To investigate how the scale of measurement affects inferences, the genetically structured heterogeneous variance model is extended to accommodate the family of Box-Cox transformations. Litter size data in rabbits and pigs that had previously been analysed in the untransformed scale were reanalysed in a scale equal to the mode of the marginal posterior distribution of the Box-Cox parameter. In the rabbit data, the statistical evidence for a genetic component at the level of the environmental variance is considerably weaker than that resulting from an analysis in the original metric. In the pig data, the statistical evidence is stronger, but the coefficient of correlation between additive genetic effects affecting mean and variance changes sign, compared to the results in the untransformed scale. The study confirms that inferences on variances can be strongly affected by the presence of asymmetry in the distribution of data. We recommend that to avoid one important source of spurious inferences, future work seeking support for a genetic component acting on environmental variation using a parametric approach based on normality assumptions confirms that these are met.
Bayesian analysis of the dynamic cosmic web in the SDSS galaxy survey

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

Leclercq, Florent; Wandelt, Benjamin; Jasche, Jens, E-mail: florent.leclercq@polytechnique.org, E-mail: jasche@iap.fr, E-mail: wandelt@iap.fr

Recent application of the Bayesian algorithm \\textsc(borg) to the Sloan Digital Sky Survey (SDSS) main sample galaxies resulted in the physical inference of the formation history of the observed large-scale structure from its origin to the present epoch. In this work, we use these inferences as inputs for a detailed probabilistic cosmic web-type analysis. To do so, we generate a large set of data-constrained realizations of the large-scale structure using a fast, fully non-linear gravitational model. We then perform a dynamic classification of the cosmic web into four distinct components (voids, sheets, filaments, and clusters) on the basis of themore » tidal field. Our inference framework automatically and self-consistently propagates typical observational uncertainties to web-type classification. As a result, this study produces accurate cosmographic classification of large-scale structure elements in the SDSS volume. By also providing the history of these structure maps, the approach allows an analysis of the origin and growth of the early traces of the cosmic web present in the initial density field and of the evolution of global quantities such as the volume and mass filling fractions of different structures. For the problem of web-type classification, the results described in this work constitute the first connection between theory and observations at non-linear scales including a physical model of structure formation and the demonstrated capability of uncertainty quantification. A connection between cosmology and information theory using real data also naturally emerges from our probabilistic approach. Our results constitute quantitative chrono-cosmography of the complex web-like patterns underlying the observed galaxy distribution.« less
Fine-scale population dynamics in a marine fish species inferred from dynamic state-space models.

PubMed

Rogers, Lauren A; Storvik, Geir O; Knutsen, Halvor; Olsen, Esben M; Stenseth, Nils C

2017-07-01

Identifying the spatial scale of population structuring is critical for the conservation of natural populations and for drawing accurate ecological inferences. However, population studies often use spatially aggregated data to draw inferences about population trends and drivers, potentially masking ecologically relevant population sub-structure and dynamics. The goals of this study were to investigate how population dynamics models with and without spatial structure affect inferences on population trends and the identification of intrinsic drivers of population dynamics (e.g. density dependence). Specifically, we developed dynamic, age-structured, state-space models to test different hypotheses regarding the spatial structure of a population complex of coastal Atlantic cod (Gadus morhua). Data were from a 93-year survey of juvenile (age 0 and 1) cod sampled along >200 km of the Norwegian Skagerrak coast. We compared two models: one which assumes all sampled cod belong to one larger population, and a second which assumes that each fjord contains a unique population with locally determined dynamics. Using the best supported model, we then reconstructed the historical spatial and temporal dynamics of Skagerrak coastal cod. Cross-validation showed that the spatially structured model with local dynamics had better predictive ability. Furthermore, posterior predictive checks showed that a model which assumes one homogeneous population failed to capture the spatial correlation pattern present in the survey data. The spatially structured model indicated that population trends differed markedly among fjords, as did estimates of population parameters including density-dependent survival. Recent biomass was estimated to be at a near-record low all along the coast, but the finer scale model indicated that the decline occurred at different times in different regions. Warm temperatures were associated with poor recruitment, but local changes in habitat and fishing pressure may have played a role in driving local dynamics. More generally, we demonstrated how state-space models can be used to test evidence for population spatial structure based on survey time-series data. Our study shows the importance of considering spatially structured dynamics, as the inferences from such an approach can lead to a different ecological understanding of the drivers of population declines, and fundamentally different management actions to restore populations. © 2017 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.
Approximate Bayesian computation in large-scale structure: constraining the galaxy-halo connection

NASA Astrophysics Data System (ADS)

Hahn, ChangHoon; Vakili, Mohammadjavad; Walsh, Kilian; Hearin, Andrew P.; Hogg, David W.; Campbell, Duncan

2017-08-01

Standard approaches to Bayesian parameter inference in large-scale structure assume a Gaussian functional form (chi-squared form) for the likelihood. This assumption, in detail, cannot be correct. Likelihood free inferences such as approximate Bayesian computation (ABC) relax these restrictions and make inference possible without making any assumptions on the likelihood. Instead ABC relies on a forward generative model of the data and a metric for measuring the distance between the model and data. In this work, we demonstrate that ABC is feasible for LSS parameter inference by using it to constrain parameters of the halo occupation distribution (HOD) model for populating dark matter haloes with galaxies. Using specific implementation of ABC supplemented with population Monte Carlo importance sampling, a generative forward model using HOD and a distance metric based on galaxy number density, two-point correlation function and galaxy group multiplicity function, we constrain the HOD parameters of mock observation generated from selected 'true' HOD parameters. The parameter constraints we obtain from ABC are consistent with the 'true' HOD parameters, demonstrating that ABC can be reliably used for parameter inference in LSS. Furthermore, we compare our ABC constraints to constraints we obtain using a pseudo-likelihood function of Gaussian form with MCMC and find consistent HOD parameter constraints. Ultimately, our results suggest that ABC can and should be applied in parameter inference for LSS analyses.
A statistical approach for inferring the 3D structure of the genome.

PubMed

Varoquaux, Nelle; Ay, Ferhat; Noble, William Stafford; Vert, Jean-Philippe

2014-06-15

Recent technological advances allow the measurement, in a single Hi-C experiment, of the frequencies of physical contacts among pairs of genomic loci at a genome-wide scale. The next challenge is to infer, from the resulting DNA-DNA contact maps, accurate 3D models of how chromosomes fold and fit into the nucleus. Many existing inference methods rely on multidimensional scaling (MDS), in which the pairwise distances of the inferred model are optimized to resemble pairwise distances derived directly from the contact counts. These approaches, however, often optimize a heuristic objective function and require strong assumptions about the biophysics of DNA to transform interaction frequencies to spatial distance, and thereby may lead to incorrect structure reconstruction. We propose a novel approach to infer a consensus 3D structure of a genome from Hi-C data. The method incorporates a statistical model of the contact counts, assuming that the counts between two loci follow a Poisson distribution whose intensity decreases with the physical distances between the loci. The method can automatically adjust the transfer function relating the spatial distance to the Poisson intensity and infer a genome structure that best explains the observed data. We compare two variants of our Poisson method, with or without optimization of the transfer function, to four different MDS-based algorithms-two metric MDS methods using different stress functions, a non-metric version of MDS and ChromSDE, a recently described, advanced MDS method-on a wide range of simulated datasets. We demonstrate that the Poisson models reconstruct better structures than all MDS-based methods, particularly at low coverage and high resolution, and we highlight the importance of optimizing the transfer function. On publicly available Hi-C data from mouse embryonic stem cells, we show that the Poisson methods lead to more reproducible structures than MDS-based methods when we use data generated using different restriction enzymes, and when we reconstruct structures at different resolutions. A Python implementation of the proposed method is available at http://cbio.ensmp.fr/pastis. © The Author 2014. Published by Oxford University Press.
Assessing Clark's nutcracker seed-caching flights using maternally inherited mitochondrial DNA of whitebark pine

Treesearch

Bryce A. Richardson; Ned B. Klopfenstein; Steven J. Brunsfeld

2002-01-01

Maternally inherited mitochondrial DNA haplotypes in whitebark pine (Pinus albicaulis Engelm.) were used to examine the maternal genetic structure at three hierarchical spatial scales: fine scale, coarse scale, and interpopulation. These data were used to draw inferences into Clarkâs nutcracker (Nucifraga columbiana Wilson)...

Inference of neuronal network spike dynamics and topology from calcium imaging data

PubMed Central

Lütcke, Henry; Gerhard, Felipe; Zenke, Friedemann; Gerstner, Wulfram; Helmchen, Fritjof

2013-01-01

Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP) occurrence (“spike trains”) from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR) and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties. PMID:24399936
Model selection and Bayesian inference for high-resolution seabed reflection inversion.

PubMed

Dettmer, Jan; Dosso, Stan E; Holland, Charles W

2009-02-01

This paper applies Bayesian inference, including model selection and posterior parameter inference, to inversion of seabed reflection data to resolve sediment structure at a spatial scale below the pulse length of the acoustic source. A practical approach to model selection is used, employing the Bayesian information criterion to decide on the number of sediment layers needed to sufficiently fit the data while satisfying parsimony to avoid overparametrization. Posterior parameter inference is carried out using an efficient Metropolis-Hastings algorithm for high-dimensional models, and results are presented as marginal-probability depth distributions for sound velocity, density, and attenuation. The approach is applied to plane-wave reflection-coefficient inversion of single-bounce data collected on the Malta Plateau, Mediterranean Sea, which indicate complex fine structure close to the water-sediment interface. This fine structure is resolved in the geoacoustic inversion results in terms of four layers within the upper meter of sediments. The inversion results are in good agreement with parameter estimates from a gravity core taken at the experiment site.
Scale of association: hierarchical linear models and the measurement of ecological systems

Treesearch

Sean M. McMahon; Jeffrey M. Diez

2007-01-01

A fundamental challenge to understanding patterns in ecological systems lies in employing methods that can analyse, test and draw inference from measured associations between variables across scales. Hierarchical linear models (HLM) use advanced estimation algorithms to measure regression relationships and variance-covariance parameters in hierarchically structured...
Specificity of Structural Assessment of Knowledge

ERIC Educational Resources Information Center

Trumpower, David L.; Sharara, Harold; Goldsmith, Timothy E.

2010-01-01

This study examines the specificity of information provided by structural assessment of knowledge (SAK). SAK is a technique which uses the Pathfinder scaling algorithm to transform ratings of concept relatedness into network representations (PFnets) of individuals' knowledge. Inferences about individuals' overall domain knowledge based on the…
Detecting structure of haplotypes and local ancestry

USDA-ARS?s Scientific Manuscript database

We present a two-layer hidden Markov model to detect the structure of haplotypes for unrelated individuals. This allows us to model two scales of linkage disequilibrium (one within a group of haplotypes and one between groups), thereby taking advantage of rich haplotype information to infer local an...
Characterizing the propagation path in moderate to strong optical turbulence.

PubMed

Vetelino, Frida Strömqvist; Clare, Bradley; Corbett, Kerry; Young, Cynthia; Grant, Kenneth; Andrews, Larry

2006-05-20

In February 2005 a joint atmospheric propagation experiment was conducted between the Australian Defence Science and Technology Organisation and the University of Central Florida. A Gaussian beam was propagated along a horizontal 1500 m path near the ground. Scintillation was measured simultaneously at three receivers of diameters 1, 5, and 13 mm. Scintillation theory combined with a numerical scheme was used to infer the structure constant C2n, the inner scale l0, and the outer scale L0 from the optical measurements. At the same time, C2n measurements were taken by a commercial scintillometer, set up parallel to the optical path. The C2n values from the inferred scheme and the commercial scintillometer predict the same behavior, but the inferred scheme consistently gives slightly smaller C2n values.
Folds on Europa: implications for crustal cycling and accommodation of extension.

PubMed

Prockter, L M; Pappalardo, R T

2000-08-11

Regional-scale undulations with associated small-scale secondary structures are inferred to be folds on Jupiter's moon Europa. Formation is consistent with stresses from tidal deformation, potentially triggering compressional instability of a region of locally high thermal gradient. Folds may compensate for extension elsewhere on Europa and then relax away over time.
Using DNA metabarcoding for simultaneous inference of common vampire bat diet and population structure.

PubMed

Bohmann, Kristine; Gopalakrishnan, Shyam; Nielsen, Martin; Nielsen, Luisa Dos Santos Bay; Jones, Gareth; Streicker, Daniel G; Gilbert, M Thomas P

2018-04-19

Metabarcoding diet analysis has become a valuable tool in animal ecology; however, co-amplified predator sequences are not generally used for anything other than to validate predator identity. Exemplified by the common vampire bat, we demonstrate the use of metabarcoding to infer predator population structure alongside diet assessments. Growing populations of common vampire bats impact human, livestock and wildlife health in Latin America through transmission of pathogens, such as lethal rabies viruses. Techniques to determine large-scale variation in vampire bat diet and bat population structure would empower locality- and species-specific projections of disease transmission risks. However, previously used methods are not cost-effective and efficient for large-scale applications. Using bloodmeal and faecal samples from common vampire bats from coastal, Andean and Amazonian regions of Peru, we showcase metabarcoding as a scalable tool to assess vampire bat population structure and feeding preferences. Dietary metabarcoding was highly effective, detecting vertebrate prey in 93.2% of the samples. Bats predominantly preyed on domestic animals, but fed on tapirs at one Amazonian site. In addition, we identified arthropods in 9.3% of samples, likely reflecting consumption of ectoparasites. Using the same data, we document mitochondrial geographic population structure in the common vampire bat in Peru. Such simultaneous inference of vampire bat diet and population structure can enable new insights into the interplay between vampire bat ecology and disease transmission risks. Importantly, the methodology can be incorporated into metabarcoding diet studies of other animals to couple information on diet and population structure. © 2018 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.
The Manhattan Frame Model-Manhattan World Inference in the Space of Surface Normals.

PubMed

Straub, Julian; Freifeld, Oren; Rosman, Guy; Leonard, John J; Fisher, John W

2018-01-01

Objects and structures within man-made environments typically exhibit a high degree of organization in the form of orthogonal and parallel planes. Traditional approaches utilize these regularities via the restrictive, and rather local, Manhattan World (MW) assumption which posits that every plane is perpendicular to one of the axes of a single coordinate system. The aforementioned regularities are especially evident in the surface normal distribution of a scene where they manifest as orthogonally-coupled clusters. This motivates the introduction of the Manhattan-Frame (MF) model which captures the notion of an MW in the surface normals space, the unit sphere, and two probabilistic MF models over this space. First, for a single MF we propose novel real-time MAP inference algorithms, evaluate their performance and their use in drift-free rotation estimation. Second, to capture the complexity of real-world scenes at a global scale, we extend the MF model to a probabilistic mixture of Manhattan Frames (MMF). For MMF inference we propose a simple MAP inference algorithm and an adaptive Markov-Chain Monte-Carlo sampling algorithm with Metropolis-Hastings split/merge moves that let us infer the unknown number of mixture components. We demonstrate the versatility of the MMF model and inference algorithm across several scales of man-made environments.
Unifying Inference of Meso-Scale Structures in Networks.

PubMed

Tunç, Birkan; Verma, Ragini

2015-01-01

Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities) of the brain, as well as its auxiliary characteristics (core-periphery).
Computational analysis of conserved RNA secondary structure in transcriptomes and genomes.

PubMed

Eddy, Sean R

2014-01-01

Transcriptomics experiments and computational predictions both enable systematic discovery of new functional RNAs. However, many putative noncoding transcripts arise instead from artifacts and biological noise, and current computational prediction methods have high false positive rates. I discuss prospects for improving computational methods for analyzing and identifying functional RNAs, with a focus on detecting signatures of conserved RNA secondary structure. An interesting new front is the application of chemical and enzymatic experiments that probe RNA structure on a transcriptome-wide scale. I review several proposed approaches for incorporating structure probing data into the computational prediction of RNA secondary structure. Using probabilistic inference formalisms, I show how all these approaches can be unified in a well-principled framework, which in turn allows RNA probing data to be easily integrated into a wide range of analyses that depend on RNA secondary structure inference. Such analyses include homology search and genome-wide detection of new structural RNAs.
The Dual Role of Vegetation as a Constraint on Mass and Energy Flux into the Critical Zone and as an Emergent Property of Geophysical Critical Zone Structure

NASA Astrophysics Data System (ADS)

Brooks, P. D.; Swetnam, T. L.; Barnard, H. R.; Singha, K.; Harpold, A.; Litvak, M. E.

2017-12-01

Spatial patterns in vegetation long have been used to scale both landsurface-atmosphere exchanges of water and carbon as well as to infer subsurface structure. These pursuits typical proceed in isolation and rarely do inferences gained from one community propagate to related efforts in another. Perhaps more importantly, vegetation often is treated as an emergent property of landscape-climate interactions rather than an active modifier of both critical zone structure and energy fluxes. We posit that vegetation structure and activity are under utilized as a tool towards understanding landscape evolution and present examples that begin to disentangle the role of vegetation as both an emergent property and an active control on critical zone structure and function. As climate change, population growth, and land use changes threaten water resources worldwide, the need for the new insights vegetation can provide becomes not just a basic science priority, but a pressing applied science question with clear societal importance. This presentation will provide an overview of recent efforts to address the dual role of vegetation in both modifying and reflecting critical zone structure in the western North American forests. For example, interactions between topography and stand scale vegetation structure influence both solar radiation and turbulence altering landscape scale partitioning of evaporation vs transpiration with major impacts of surface water supply. Similarly, interactions between topographic shading, lateral redistribution of plant available water, and subsurface storage create a mosaic of drought resistance and resilience across complex terrain. These complex interactions between geophysical and vegetation components of critical zone structure result in predictable patterns in catchment scale hydrologic partitioning within individual watersheds while simultaneously suggesting testable hypotheses for why catchments under similar climate regimes respond so differently to drought stress.
Inferring Markov chains: Bayesian estimation, model comparison, entropy rate, and out-of-class modeling.

PubMed

Strelioff, Christopher C; Crutchfield, James P; Hübler, Alfred W

2007-07-01

Markov chains are a natural and well understood tool for describing one-dimensional patterns in time or space. We show how to infer kth order Markov chains, for arbitrary k , from finite data by applying Bayesian methods to both parameter estimation and model-order selection. Extending existing results for multinomial models of discrete data, we connect inference to statistical mechanics through information-theoretic (type theory) techniques. We establish a direct relationship between Bayesian evidence and the partition function which allows for straightforward calculation of the expectation and variance of the conditional relative entropy and the source entropy rate. Finally, we introduce a method that uses finite data-size scaling with model-order comparison to infer the structure of out-of-class processes.
Structures and lithofacies of inferred silicic conduits in the Paraná-Etendeka LIP, southernmost Brazil

NASA Astrophysics Data System (ADS)

Simões, M. S.; Lima, E. F.; Sommer, C. A.; Rossetti, L. M. M.

2018-04-01

Extensive silicic units in the Paraná-Etendeka LIP have been long interpreted as pyroclastic density currents (rheomorphic ignimbrites) derived from the Messum Complex in Namibia. In recent literature, however, they have been characterized as effusive lava flows and domes. In this paper we describe structures and lithofacies related to postulated silicic lava feeder conduits at Mato Perso, São Marcos and Jaquirana-Cambará do Sul areas in southern Brazil. Inferred conduits are at least 15-25 m in width and the lithofacies include variably vesicular monomictic welded and non-welded breccias in the margins to poorly vesicular, banded, spherulitic and microfractured vitrophyres in the central parts. Flat-lying coherent vitrophyres and massive obsidian are considered to be the subaerial equivalents of the conduits. Large-scale, regional tectonic structures in southern Brazil include the NE-SW aligned Porto Alegre Suture, Leão and Açotea faults besides the Antas Lineament, a curved tectonic feature accompanying the bed of Antas river. South of the Antas Lineament smaller-scale, NW-SE lineaments limit the exposure areas of the inferred conduits. NE-SW and subordinate NW-SE structures within this smaller-scale lineaments are represented by the main postulated conduit outcrops and are parallel to the dominant sub-vertical banding in the widespread banded vitrophyre lithofacies. Upper lava flows display flat-lying foliation, pipe-like and spherical vesicles and have better developed microlites. Petrographic characteristics of the silicic vitrophyres indicate that crystal-poor magmas underwent distinct cooling paths for each inferred conduit area. The vitrophyre chemical composition is defined by the evolution of trachydacitic/dacitic vitrophyres with 62-65 wt% SiO2 to rhyodacite and rhyolite with 66-68 wt% SiO2. The more evolved rocks are assigned to the latest intrusive grey vitrophyre outcropping in the center of the conduits. Degassing pathways formed during fragmentation and fracturing episodes within the conduits may have helped to inhibit the explosivity of the eruptions. Based on the documented lithofacies architecture, we attribute the source of the silicic lava flows in the studied localities to tectonic-controlled, local conduits, rather than pyroclastic density currents from distant vent areas.
A new probe of the magnetic field power spectrum in cosmic web filaments

NASA Astrophysics Data System (ADS)

Hales, Christopher A.; Greiner, Maksim; Ensslin, Torsten A.

2015-08-01

Establishing the properties of magnetic fields on scales larger than galaxy clusters is critical for resolving the unknown origin and evolution of galactic and cluster magnetism. More generally, observations of magnetic fields on cosmic scales are needed for assessing the impacts of magnetism on cosmology, particle physics, and structure formation over the full history of the Universe. However, firm observational evidence for magnetic fields in large scale structure remains elusive. In an effort to address this problem, we have developed a novel statistical method to infer the magnetic field power spectrum in cosmic web filaments using observation of the two-point correlation of Faraday rotation measures from a dense grid of extragalactic radio sources. Here we describe our approach, which embeds and extends the pioneering work of Kolatt (1998) within the context of Information Field Theory (a statistical theory for Bayesian inference on spatially distributed signals; Enfllin et al., 2009). We describe prospects for observation, for example with forthcoming data from the ultra-deep JVLA CHILES Con Pol survey and future surveys with the SKA.
Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness.

PubMed

Conomos, Matthew P; Miller, Michael B; Thornton, Timothy A

2015-05-01

Population structure inference with genetic data has been motivated by a variety of applications in population genetics and genetic association studies. Several approaches have been proposed for the identification of genetic ancestry differences in samples where study participants are assumed to be unrelated, including principal components analysis (PCA), multidimensional scaling (MDS), and model-based methods for proportional ancestry estimation. Many genetic studies, however, include individuals with some degree of relatedness, and existing methods for inferring genetic ancestry fail in related samples. We present a method, PC-AiR, for robust population structure inference in the presence of known or cryptic relatedness. PC-AiR utilizes genome-screen data and an efficient algorithm to identify a diverse subset of unrelated individuals that is representative of all ancestries in the sample. The PC-AiR method directly performs PCA on the identified ancestry representative subset and then predicts components of variation for all remaining individuals based on genetic similarities. In simulation studies and in applications to real data from Phase III of the HapMap Project, we demonstrate that PC-AiR provides a substantial improvement over existing approaches for population structure inference in related samples. We also demonstrate significant efficiency gains, where a single axis of variation from PC-AiR provides better prediction of ancestry in a variety of structure settings than using 10 (or more) components of variation from widely used PCA and MDS approaches. Finally, we illustrate that PC-AiR can provide improved population stratification correction over existing methods in genetic association studies with population structure and relatedness. © 2015 WILEY PERIODICALS, INC.
Decadal- to Centennial-Scale Variations in Anchovy Biomass in the Last 250 Years Inferred From Scales Preserved in Laminated Sediments off the Coast of Pisco, Peru

NASA Astrophysics Data System (ADS)

Salvatteci, R.; Field, D.; Gutierrez, D.; Baumgartner, T.; Ferreira, V.; Velazco, F.; Niquen, M.; Guevara, R.; Sifeddine, A.; Ortlieb, L.

2005-12-01

The highly productive upwelling environment off the coast of Peru sustains one of the world's largest fisheries, the Peruvian anchoveta ( Engraulis ringens), but variability on interannual to decadal timescales results in dramatic variations in catch. We quantified variations in anchovy scale abundance preserved in laminated sediments collected at 300 m depth of the Peruvian margin (near Pisco, central Peru) to infer decadal- to centennial-scale population variability prior to the development of the fishery. High-resolution subsampling of 2.5 - 8.2 mm was done following the laminated structure of the core. A chronology based on downcore excess 210Pb activities and 14C-AMS ages indicate that samples represent an estimated 1-7 years in time. Anchovy scale deposition is correlated with anchovy landings at Pisco, indicating that scale deposition can be used as a proxy of (at least) local biomass. A small, but significant, reduction in anchovy scale width (0.2 mm) after the development of the fishery suggests a small effect of the fishery on anchovy size distributions. While decadal-scale variability in anchovy scale deposition is persistent throughout the record, a dramatic increase in scale flux occurred around 1860 A.D. and persists for approximately a century. Our results indicate that centennial-scale variability composes a large portion of the variability. However, decadal-scale variability associated with the Pacific Decadal Oscillation is not correlated with the inferred biomass variability prior to the development of the fishery. Shifts in the distribution of the population may account for an additional component of the variability in scale deposition.
Exploring seascape genetics and kinship in the reef sponge Stylissa carteri in the Red Sea

PubMed Central

Giles, Emily C; Saenz-Agudelo, Pablo; Hussey, Nigel E; Ravasi, Timothy; Berumen, Michael L

2015-01-01

A main goal of population geneticists is to study patterns of gene flow to gain a better understanding of the population structure in a given organism. To date most efforts have been focused on studying gene flow at either broad scales to identify barriers to gene flow and isolation by distance or at fine spatial scales in order to gain inferences regarding reproduction and local dispersal. Few studies have measured connectivity at multiple spatial scales and have utilized novel tools to test the influence of both environment and geography on shaping gene flow in an organism. Here a seascape genetics approach was used to gain insight regarding geographic and ecological barriers to gene flow of a common reef sponge, Stylissa carteri in the Red Sea. Furthermore, a small-scale (<1 km) analysis was also conducted to infer reproductive potential in this organism. At the broad scale, we found that sponge connectivity is not structured by geography alone, but rather, genetic isolation in the southern Red Sea correlates strongly with environmental heterogeneity. At the scale of a 50-m transect, spatial autocorrelation analyses and estimates of full-siblings revealed that there is no deviation from random mating. However, at slightly larger scales (100–200 m) encompassing multiple transects at a given site, a greater proportion of full-siblings was found within sites versus among sites in a given location suggesting that mating and/or dispersal are constrained to some extent at this spatial scale. This study adds to the growing body of literature suggesting that environmental and ecological variables play a major role in the genetic structure of marine invertebrate populations. PMID:26257865
Synthesis of regional crust and upper-mantle structure from seismic and gravity data

NASA Technical Reports Server (NTRS)

Alexander, S. S.; Lavin, P. M.

1979-01-01

Available seismic and ground based gravity data are combined to infer the three dimensional crust and upper mantle structure in selected regions. This synthesis and interpretation proceeds from large-scale average models suitable for early comparison with high-altitude satellite potential field data to more detailed delineation of structural boundaries and other variations that may be significant in natural resource assessment. Seismic and ground based gravity data are the primary focal point, but other relevant information (e.g. magnetic field, heat flow, Landsat imagery, geodetic leveling, and natural resources maps) is used to constrain the structure inferred and to assist in defining structural domains and boundaries. The seismic data consists of regional refraction lines, limited reflection coverage, surface wave dispersion, teleseismic P and S wave delay times, anelastic absorption, and regional seismicity patterns. The gravity data base consists of available point gravity determinations for the areas considered.
Critical Zone structure inferred from multiscale near surface geophysical and hydrological data across hillslopes at the Eel River CZO

NASA Astrophysics Data System (ADS)

Lee, S. S.; Rempe, D. M.; Holbrook, W. S.; Schmidt, L.; Hahm, W. J.; Dietrich, W. E.

2017-12-01

Except for boreholes and road cut, landslide, and quarry exposures, the subsurface structure of the critical zone (CZ) of weathered bedrock is relatively invisible and unmapped, yet this structure controls the short and long term fluxes of water and solutes. Non-invasive geophysical methods such as seismic refraction are widely applied to image the structure of the CZ at the hillslope scale. However, interpretations of such data are often limited due to heterogeneity and anisotropy contributed from fracturing, moisture content, and mineralogy on the seismic signal. We develop a quantitative framework for using seismic refraction tomography from intersecting geophysical surveys and hydrologic data obtained at the Eel River Critical Zone Observatory (ERCZO) in Northern California to help quantify the nature of subsurface structure across multiple hillslopes of varying topography in the area. To enhance our understanding of modeled velocity gradients and boundaries in relation to lithological properties, we compare refraction tomography results with borehole logs of nuclear magnetic resonance (NMR), gamma and neutron density, standard penetration testing, and observation drilling logs. We also incorporate laboratory scale rock characterization including mineralogical and elemental analyses as well as porosity and density measurements made via pycnometry, helium and mercury porosimetry, and laboratory scale NMR. We evaluate the sensitivity of seismically inferred saprolite-weathered bedrock and weathered-unweathered bedrock boundaries to various velocity and inversion parameters in relation with other macro scale processes such as gravitational and tectonic forces in influencing weathered bedrock velocities. Together, our sensitivity analyses and multi-method data comparison provide insight into the interpretation of seismic refraction tomography for the quantification of CZ structure and hydrologic dynamics.

Teaching machines to find mantle composition

NASA Astrophysics Data System (ADS)

Atkins, Suzanne; Tackley, Paul; Trampert, Jeannot; Valentine, Andrew

2017-04-01

The composition of the mantle affects many geodynamical processes by altering factors such as the density, the location of phase changes, and melting temperature. The inferences we make about mantle composition also determine how we interpret the changes in velocity, reflections, attenuation and scattering seen by seismologists. However, the bulk composition of the mantle is very poorly constrained. Inferences are made from meteorite samples, rock samples from the Earth and inferences made from geophysical data. All of these approaches require significant assumptions and the inferences made are subject to large uncertainties. Here we present a new method for inferring mantle composition, based on pattern recognition machine learning, which uses large scale in situ observations of the mantle to make fully probabilistic inferences of composition for convection simulations. Our method has an advantage over other petrological approaches because we use large scale geophysical observations. This means that we average over much greater length scales and do not need to rely on extrapolating from localised samples of the mantle or planetary disk. Another major advantage of our method is that it is fully probabilistic. This allows us to include all of the uncertainties inherent in the inference process, giving us far more information about the reliability of the result than other methods. Finally our method includes the impact of composition on mantle convection. This allows us to make much more precise inferences from geophysical data than other geophysical approaches, which attempt to invert one observation with no consideration of the relationship between convection and composition. We use a sampling based inversion method, using hundreds of convection simulations run using StagYY with self consistent mineral physics properties calculated using the PerpleX package. The observations from these simulations are used to train a neural network to make a probabilistic inference for major element oxide composition of the mantle. We find we can constrain bulk mantle FeO molar percent, FeO/MgO and FeO/SiO2 using observations of the temperature and density structure of the mantle in convection simulations.
Astrophysical data analysis with information field theory

NASA Astrophysics Data System (ADS)

Enßlin, Torsten

2014-12-01

Non-parametric imaging and data analysis in astrophysics and cosmology can be addressed by information field theory (IFT), a means of Bayesian, data based inference on spatially distributed signal fields. IFT is a statistical field theory, which permits the construction of optimal signal recovery algorithms. It exploits spatial correlations of the signal fields even for nonlinear and non-Gaussian signal inference problems. The alleviation of a perception threshold for recovering signals of unknown correlation structure by using IFT will be discussed in particular as well as a novel improvement on instrumental self-calibration schemes. IFT can be applied to many areas. Here, applications in in cosmology (cosmic microwave background, large-scale structure) and astrophysics (galactic magnetism, radio interferometry) are presented.
RCK: accurate and efficient inference of sequence- and structure-based protein-RNA binding models from RNAcompete data.

PubMed

Orenstein, Yaron; Wang, Yuhao; Berger, Bonnie

2016-06-15

Protein-RNA interactions, which play vital roles in many processes, are mediated through both RNA sequence and structure. CLIP-based methods, which measure protein-RNA binding in vivo, suffer from experimental noise and systematic biases, whereas in vitro experiments capture a clearer signal of protein RNA-binding. Among them, RNAcompete provides binding affinities of a specific protein to more than 240 000 unstructured RNA probes in one experiment. The computational challenge is to infer RNA structure- and sequence-based binding models from these data. The state-of-the-art in sequence models, Deepbind, does not model structural preferences. RNAcontext models both sequence and structure preferences, but is outperformed by GraphProt. Unfortunately, GraphProt cannot detect structural preferences from RNAcompete data due to the unstructured nature of the data, as noted by its developers, nor can it be tractably run on the full RNACompete dataset. We develop RCK, an efficient, scalable algorithm that infers both sequence and structure preferences based on a new k-mer based model. Remarkably, even though RNAcompete data is designed to be unstructured, RCK can still learn structural preferences from it. RCK significantly outperforms both RNAcontext and Deepbind in in vitro binding prediction for 244 RNAcompete experiments. Moreover, RCK is also faster and uses less memory, which enables scalability. While currently on par with existing methods in in vivo binding prediction on a small scale test, we demonstrate that RCK will increasingly benefit from experimentally measured RNA structure profiles as compared to computationally predicted ones. By running RCK on the entire RNAcompete dataset, we generate and provide as a resource a set of protein-RNA structure-based models on an unprecedented scale. Software and models are freely available at http://rck.csail.mit.edu/ bab@mit.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.
Novel probabilistic models of spatial genetic ancestry with applications to stratification correction in genome-wide association studies.

PubMed

Bhaskar, Anand; Javanmard, Adel; Courtade, Thomas A; Tse, David

2017-03-15

Genetic variation in human populations is influenced by geographic ancestry due to spatial locality in historical mating and migration patterns. Spatial population structure in genetic datasets has been traditionally analyzed using either model-free algorithms, such as principal components analysis (PCA) and multidimensional scaling, or using explicit spatial probabilistic models of allele frequency evolution. We develop a general probabilistic model and an associated inference algorithm that unify the model-based and data-driven approaches to visualizing and inferring population structure. Our spatial inference algorithm can also be effectively applied to the problem of population stratification in genome-wide association studies (GWAS), where hidden population structure can create fictitious associations when population ancestry is correlated with both the genotype and the trait. Our algorithm Geographic Ancestry Positioning (GAP) relates local genetic distances between samples to their spatial distances, and can be used for visually discerning population structure as well as accurately inferring the spatial origin of individuals on a two-dimensional continuum. On both simulated and several real datasets from diverse human populations, GAP exhibits substantially lower error in reconstructing spatial ancestry coordinates compared to PCA. We also develop an association test that uses the ancestry coordinates inferred by GAP to accurately account for ancestry-induced correlations in GWAS. Based on simulations and analysis of a dataset of 10 metabolic traits measured in a Northern Finland cohort, which is known to exhibit significant population structure, we find that our method has superior power to current approaches. Our software is available at https://github.com/anand-bhaskar/gap . abhaskar@stanford.edu or ajavanma@usc.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
Using Pathfinder networks to discover alignment between expert and consumer conceptual knowledge from online vaccine content.

PubMed

Amith, Muhammad; Cunningham, Rachel; Savas, Lara S; Boom, Julie; Schvaneveldt, Roger; Tao, Cui; Cohen, Trevor

2017-10-01

This study demonstrates the use of distributed vector representations and Pathfinder Network Scaling (PFNETS) to represent online vaccine content created by health experts and by laypeople. By analyzing a target audience's conceptualization of a topic, domain experts can develop targeted interventions to improve the basic health knowledge of consumers. The underlying assumption is that the content created by different groups reflects the mental organization of their knowledge. Applying automated text analysis to this content may elucidate differences between the knowledge structures of laypeople (heath consumers) and professionals (health experts). This paper utilizes vaccine information generated by laypeople and health experts to investigate the utility of this approach. We used an established technique from cognitive psychology, Pathfinder Network Scaling to infer the structure of the associational networks between concepts learned from online content using methods of distributional semantics. In doing so, we extend the original application of PFNETS to infer knowledge structures from individual participants, to infer the prevailing knowledge structures within communities of content authors. The resulting graphs reveal opportunities for public health and vaccination education experts to improve communication and intervention efforts directed towards health consumers. Our efforts demonstrate the feasibility of using an automated procedure to examine the manifestation of conceptual models within large bodies of free text, revealing evidence of conflicting understanding of vaccine concepts among health consumers as compared with health experts. Additionally, this study provides insight into the differences between consumer and expert abstraction of domain knowledge, revealing vaccine-related knowledge gaps that suggest opportunities to improve provider-patient communication. Copyright © 2017 Elsevier Inc. All rights reserved.
Statistical scaling of pore-scale Lagrangian velocities in natural porous media.

PubMed

Siena, M; Guadagnini, A; Riva, M; Bijeljic, B; Pereira Nunes, J P; Blunt, M J

2014-08-01

We investigate the scaling behavior of sample statistics of pore-scale Lagrangian velocities in two different rock samples, Bentheimer sandstone and Estaillades limestone. The samples are imaged using x-ray computer tomography with micron-scale resolution. The scaling analysis relies on the study of the way qth-order sample structure functions (statistical moments of order q of absolute increments) of Lagrangian velocities depend on separation distances, or lags, traveled along the mean flow direction. In the sandstone block, sample structure functions of all orders exhibit a power-law scaling within a clearly identifiable intermediate range of lags. Sample structure functions associated with the limestone block display two diverse power-law regimes, which we infer to be related to two overlapping spatially correlated structures. In both rocks and for all orders q, we observe linear relationships between logarithmic structure functions of successive orders at all lags (a phenomenon that is typically known as extended power scaling, or extended self-similarity). The scaling behavior of Lagrangian velocities is compared with the one exhibited by porosity and specific surface area, which constitute two key pore-scale geometric observables. The statistical scaling of the local velocity field reflects the behavior of these geometric observables, with the occurrence of power-law-scaling regimes within the same range of lags for sample structure functions of Lagrangian velocity, porosity, and specific surface area.
Impact of Ice Ages on the genetic structure of trees and shrubs.

PubMed Central

Lascoux, Martin; Palmé, Anna E; Cheddadi, Rachid; Latta, Robert G

2004-01-01

Data on the genetic structure of tree and shrub populations on the continental scale have accumulated dramatically over the past decade. However, our ability to make inferences on the impact of the last ice age still depends crucially on the availability of informative palaeoecological data. This is well illustrated by the results from a recent project, during which new pollen fossil maps were established and the variation in chloroplast DNA was studied in 22 European species of trees and shrubs. Species exhibit very different levels of genetic variation between and within populations, and obviously went through very different histories after Ice Ages. However, when palaeoecological data are non-informative, inferences on past history are difficult to draw from entirely genetic data. On the other hand, as illustrated by a study in ponderosa pine, when we can infer the species' history with some certainty, coalescent simulations can be used and new hypotheses can be tested. PMID:15101576
Strike-Slip Faulting Processes on Ganymede: Global Morphological Mapping and Structural Interpretation of Grooved and Transitional Terrains

NASA Astrophysics Data System (ADS)

Burkhard, L. M.; Cameron, M. E.; Smith-Konter, B. R.; Seifert, F.; Pappalardo, R. T.; Collins, G. C.

2015-12-01

Ganymede's fractured surface reveals many large-scale, morphologically distinct regions of inferred distributed shear and strike-slip faulting that may be important to the structural development of its surface and in the transition from dark to light (grooved) materials. To better understand the role of strike-slip tectonism in shaping Ganymede's complex icy surface, we perform a detailed mapping of key examples of strike-slip morphologies (i.e., en echelon structures, strike-slip duplexes, laterally offset pre-existing features, and possible strained craters) from Galileo and Voyager images. We focus on complex structures associated with grooved terrain (e.g. Nun Sulcus, Dardanus Sulcus, Tiamat Sulcus, and Arbela Sulcus) and terrains transitional from dark to light terrain (e.g. the boundary between Nippur Sulcus and Marius Regio, including Byblus Sulcus and Philus Sulcus). Detailed structural interpretations suggest strong evidence of strike-slip faulting in some regions (i.e., Nun and Dardanus Sulcus); however, further investigation of additional strike-slip structures is required of less convincing regions (i.e., Byblus Sulcus). Where applicable, these results are synthesized into a global database representing an inferred sense of shear for many of Ganymede's fractures. Moreover, when combined with existing observations of extensional features, these results help to narrow down the range of possible principal stress directions that could have acted at the regional or global scale to produce grooved terrain on Ganymede.
The large-scale environment from cosmological simulations - I. The baryonic cosmic web

NASA Astrophysics Data System (ADS)

Cui, Weiguang; Knebe, Alexander; Yepes, Gustavo; Yang, Xiaohu; Borgani, Stefano; Kang, Xi; Power, Chris; Staveley-Smith, Lister

2018-01-01

Using a series of cosmological simulations that includes one dark-matter-only (DM-only) run, one gas cooling-star formation-supernova feedback (CSF) run and one that additionally includes feedback from active galactic nuclei (AGNs), we classify the large-scale structures with both a velocity-shear-tensor code (VWEB) and a tidal-tensor code (PWEB). We find that the baryonic processes have almost no impact on large-scale structures - at least not when classified using aforementioned techniques. More importantly, our results confirm that the gas component alone can be used to infer the filamentary structure of the universe practically un-biased, which could be applied to cosmology constraints. In addition, the gas filaments are classified with its velocity (VWEB) and density (PWEB) fields, which can theoretically connect to the radio observations, such as H I surveys. This will help us to bias-freely link the radio observations with dark matter distributions at large scale.
Anisotropy of the Cosmic Microwave Background Radiation on Large and Medium Angular Scales

NASA Technical Reports Server (NTRS)

Houghton, Anthony; Timbie, Peter

1998-01-01

This grant has supported work at Brown University on measurements of the 2.7 K Cosmic Microwave Background Radiation (CMB). The goal has been to characterize the spatial variations in the temperature of the CMB in order to understand the formation of large-scale structure in the universe. We have concurrently pursued two measurements using millimeter-wave telescopes carried aloft by scientific balloons. Both systems operate over a range of wavelengths, chosen to allow spectral removal of foreground sources such as the atmosphere, Galaxy, etc. The angular resolution of approx. 25 arcminutes is near the angular scale at which the most structure is predicted by current models to be visible in the CMB angular power spectrum. The main goal is to determine the angular scale of this structure; in turn we can infer the density parameter, Omega, for the universe as well as other cosmological parameters, such as the Hubble constant.
Anatomically informed mesoscale electrical impedance spectroscopy in southern pine and the electric field distribution for pin-type electric moisture metres

Treesearch

Samuel L. Zelinka; Alex C. Wiedenhoeft; Samuel V. Glass; Flavio Ruffinatto

2015-01-01

Electrical impedance spectra of wood taken at macroscopic scales below the fibre saturation point have led to inferences that the mechanism of charge conduction involves a percolation phenomenon. The pathways responsible for charge conduction would necessarily be influenced by wood structure at a variety of sub-macroscopic scales â at a mesoscale â but these questions...
Investigation of Statistical Inference Methodologies Through Scale Model Propagation Experiments

DTIC Science & Technology

2015-09-30

statistical inference methodologies for ocean- acoustic problems by investigating and applying statistical methods to data collected from scale-model...to begin planning experiments for statistical inference applications. APPROACH In the ocean acoustics community over the past two decades...solutions for waveguide parameters. With the introduction of statistical inference to the field of ocean acoustics came the desire to interpret marginal
Linking physiology and biomineralization processes to ecological inferences on the life history of fishes.

PubMed

Loewen, T N; Carriere, B; Reist, J D; Halden, N M; Anderson, W G

2016-12-01

Biomineral chemistry is frequently used to infer life history events and habitat use in fishes; however, significant gaps remain in our understanding of the underlying mechanisms. Here we have taken a multidisciplinary approach to review the current understanding of element incorporation into biomineralized structures in fishes. Biominerals are primarily composed of calcium-based derivatives such as calcium carbonate found in otoliths and calcium phosphates found in scales, fins and bones. By focusing on non-essential life elements (strontium and barium) and essential life elements (calcium, zinc and magnesium), we attempt to connect several fields of study to synergise how physiology may influence biomineralization and subsequent inference of life history. Data provided in this review indicate that the presence of non-essential elements in biominerals of fish is driven primarily by hypo- and hyper-calcemic environmental conditions. The uptake kinetics between environmental calcium and its competing mimics define what is ultimately incorporated in the biomineral structure. Conversely, circannual hormonally driven variations likely influence essential life elements like zinc that are known to associate with enzyme function. Environmental temperature and pH as well as uptake kinetics for strontium and barium isotopes demonstrate the role of mass fractionation in isotope selection for uptake into fish bony structures. In consideration of calcium mobilisation, the action of osteoclast-like cells on calcium phosphates of scales, fins and bones likely plays a role in fractionation along with transport kinetics. Additional investigations into calcium mobilisation are warranted to understand differing views of strontium, and barium isotope fractionation between calcium phosphates and calcium carbonate structures in fishes. Copyright © 2016 Elsevier Inc. All rights reserved.
Inferring network structure in non-normal and mixed discrete-continuous genomic data.

PubMed

Bhadra, Anindya; Rao, Arvind; Baladandayuthapani, Veerabhadran

2018-03-01

Inferring dependence structure through undirected graphs is crucial for uncovering the major modes of multivariate interaction among high-dimensional genomic markers that are potentially associated with cancer. Traditionally, conditional independence has been studied using sparse Gaussian graphical models for continuous data and sparse Ising models for discrete data. However, there are two clear situations when these approaches are inadequate. The first occurs when the data are continuous but display non-normal marginal behavior such as heavy tails or skewness, rendering an assumption of normality inappropriate. The second occurs when a part of the data is ordinal or discrete (e.g., presence or absence of a mutation) and the other part is continuous (e.g., expression levels of genes or proteins). In this case, the existing Bayesian approaches typically employ a latent variable framework for the discrete part that precludes inferring conditional independence among the data that are actually observed. The current article overcomes these two challenges in a unified framework using Gaussian scale mixtures. Our framework is able to handle continuous data that are not normal and data that are of mixed continuous and discrete nature, while still being able to infer a sparse conditional sign independence structure among the observed data. Extensive performance comparison in simulations with alternative techniques and an analysis of a real cancer genomics data set demonstrate the effectiveness of the proposed approach. © 2017, The International Biometric Society.
Inferring network structure in non-normal and mixed discrete-continuous genomic data

PubMed Central

Bhadra, Anindya; Rao, Arvind; Baladandayuthapani, Veerabhadran

2017-01-01

Inferring dependence structure through undirected graphs is crucial for uncovering the major modes of multivariate interaction among high-dimensional genomic markers that are potentially associated with cancer. Traditionally, conditional independence has been studied using sparse Gaussian graphical models for continuous data and sparse Ising models for discrete data. However, there are two clear situations when these approaches are inadequate. The first occurs when the data are continuous but display non-normal marginal behavior such as heavy tails or skewness, rendering an assumption of normality inappropriate. The second occurs when a part of the data is ordinal or discrete (e.g., presence or absence of a mutation) and the other part is continuous (e.g., expression levels of genes or proteins). In this case, the existing Bayesian approaches typically employ a latent variable framework for the discrete part that precludes inferring conditional independence among the data that are actually observed. The current article overcomes these two challenges in a unified framework using Gaussian scale mixtures. Our framework is able to handle continuous data that are not normal and data that are of mixed continuous and discrete nature, while still being able to infer a sparse conditional sign independence structure among the observed data. Extensive performance comparison in simulations with alternative techniques and an analysis of a real cancer genomics data set demonstrate the effectiveness of the proposed approach. PMID:28437848
Fine-scale genetic structure in populations of the Chagas' disease vector Triatoma infestans (Hemiptera, Reduvidae).

PubMed

Pérez de Rosas, Alicia R; Segura, Elsa L; Fusco, Octavio; Guiñazú, Adolfo L Bareiro; García, Beatriz A

2013-03-01

Fine scale patterns of genetic structure and dispersal in Triatoma infestans populations from Argentina was analysed. A total of 314 insects from 22 domestic and peridomestic sites from the locality of San Martín (Capayán department, Catamarca province) were typed for 10 polymorphic microsatellite loci. The results confirm subdivision of T. infestans populations with restricted dispersal among sampling sites and suggest inbreeding and/or stratification within the different domestic and peridomestic structures. Spatial correlation analysis showed that the scale of structuring is approximately of 400 m, indicating that active dispersal would occur within this distance range. It was detected difference in scale of structuring among sexes, with females dispersing over greater distances than males. This study suggests that insecticide treatment and surveillance should be extended within a radius of 400 m around the infested area, which would help to reduce the probability of reinfestation by covering an area of active dispersal. The inferences made from fine-scale spatial genetic structure analyses of T. infestans populations has demonstrated to be important for community-wide control programs, providing a complementary approach to help improve vector control strategies.
Mantle viscosity structure constrained by joint inversions of seismic velocities and density

NASA Astrophysics Data System (ADS)

Rudolph, M. L.; Moulik, P.; Lekic, V.

2017-12-01

The viscosity structure of Earth's deep mantle affects the thermal evolution of Earth, the ascent of mantle upwellings, sinking of subducted oceanic lithosphere, and the mixing of compositional heterogeneities in the mantle. Modeling the long-wavelength dynamic geoid allows us to constrain the radial viscosity profile of the mantle. Typically, in inversions for the mantle viscosity structure, wavespeed variations are mapped into density variations using a constant- or depth-dependent scaling factor. Here, we use a newly developed joint model of anisotropic Vs, Vp, density and transition zone topographies to generate a suite of solutions for the mantle viscosity structure directly from the seismologically constrained density structure. The density structure used to drive our forward models includes contributions from both thermal and compositional variations, including important contributions from compositionally dense material in the Large Low Velocity Provinces at the base of the mantle. These compositional variations have been neglected in the forward models used in most previous inversions and have the potential to significantly affect large-scale flow and thus the inferred viscosity structure. We use a transdimensional, hierarchical, Bayesian approach to solve the inverse problem, and our solutions for viscosity structure include an increase in viscosity below the base of the transition zone, in the shallow lower mantle. Using geoid dynamic response functions and an analysis of the correlation between the observed geoid and mantle structure, we demonstrate the underlying reason for this inference. Finally, we present a new family of solutions in which the data uncertainty is accounted for using covariance matrices associated with the mantle structure models.
A semiparametric graphical modelling approach for large-scale equity selection.

PubMed

Liu, Han; Mulvey, John; Zhao, Tianqi

2016-01-01

We propose a new stock selection strategy that exploits rebalancing returns and improves portfolio performance. To effectively harvest rebalancing gains, we apply ideas from elliptical-copula graphical modelling and stability inference to select stocks that are as independent as possible. The proposed elliptical-copula graphical model has a latent Gaussian representation; its structure can be effectively inferred using the regularized rank-based estimators. The resulting algorithm is computationally efficient and scales to large data-sets. To show the efficacy of the proposed method, we apply it to conduct equity selection based on a 16-year health care stock data-set and a large 34-year stock data-set. Empirical tests show that the proposed method is superior to alternative strategies including a principal component analysis-based approach and the classical Markowitz strategy based on the traditional buy-and-hold assumption.
A hierarchy of time-scales and the brain.

PubMed

Kiebel, Stefan J; Daunizeau, Jean; Friston, Karl J

2008-11-01

In this paper, we suggest that cortical anatomy recapitulates the temporal hierarchy that is inherent in the dynamics of environmental states. Many aspects of brain function can be understood in terms of a hierarchy of temporal scales at which representations of the environment evolve. The lowest level of this hierarchy corresponds to fast fluctuations associated with sensory processing, whereas the highest levels encode slow contextual changes in the environment, under which faster representations unfold. First, we describe a mathematical model that exploits the temporal structure of fast sensory input to track the slower trajectories of their underlying causes. This model of sensory encoding or perceptual inference establishes a proof of concept that slowly changing neuronal states can encode the paths or trajectories of faster sensory states. We then review empirical evidence that suggests that a temporal hierarchy is recapitulated in the macroscopic organization of the cortex. This anatomic-temporal hierarchy provides a comprehensive framework for understanding cortical function: the specific time-scale that engages a cortical area can be inferred by its location along a rostro-caudal gradient, which reflects the anatomical distance from primary sensory areas. This is most evident in the prefrontal cortex, where complex functions can be explained as operations on representations of the environment that change slowly. The framework provides predictions about, and principled constraints on, cortical structure-function relationships, which can be tested by manipulating the time-scales of sensory input.
Self-fertilization, long-distance flash invasion and biogeography shape the population structure of Pseudosuccinea columella at the worldwide scale.

PubMed

Lounnas, M; Correa, A C; Vázquez, A A; Dia, A; Escobar, J S; Nicot, A; Arenas, J; Ayaqui, R; Dubois, M P; Gimenez, T; Gutiérrez, A; González-Ramírez, C; Noya, O; Prepelitchi, L; Uribe, N; Wisnivesky-Colli, C; Yong, M; David, P; Loker, E S; Jarne, P; Pointier, J P; Hurtrez-Boussès, S

2017-02-01

Population genetic studies are efficient for inferring the invasion history based on a comparison of native and invasive populations, especially when conducted at species scale. An expected outcome in invasive populations is variability loss, and this is especially true in self-fertilizing species. We here focus on the self-fertilizing Pseudosuccinea columella, an invasive hermaphroditic freshwater snail that has greatly expanded its geographic distribution and that acts as intermediate host of Fasciola hepatica, the causative agent of human and veterinary fasciolosis. We evaluated the distribution of genetic diversity at the largest geographic scale analysed to date in this species by surveying 80 populations collected during 16 years from 14 countries, using eight nuclear microsatellites and two mitochondrial genes. As expected, populations from North America, the putative origin area, were strongly structured by selfing and history and harboured much more genetic variability than invasive populations. We found high selfing rates (when it was possible to infer it), none-to-low genetic variability and strong population structure in most invasive populations. Strikingly, we found a unique genotype/haplotype in populations from eight invaded regions sampled all over the world. Moreover, snail populations resistant to infection by the parasite are genetically distinct from susceptible populations. Our results are compatible with repeated introductions in South America and flash worldwide invasion by this unique genotype/haplotype. Our study illustrates the population genetic consequences of biological invasion in a highly selfing species at very large geographic scale. We discuss how such a large-scale flash invasion may affect the spread of fasciolosis. © 2016 John Wiley & Sons Ltd.

Inference and Analysis of Population Structure Using Genetic Data and Network Theory

PubMed Central

Greenbaum, Gili; Templeton, Alan R.; Bar-David, Shirli

2016-01-01

Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance-based approaches such as multidimensional scaling. While existing distance-based approaches suffer from a lack of statistical rigor, model-based approaches entail assumptions of prior conditions such as that the subpopulations are at Hardy-Weinberg equilibria. Here we present a distance-based approach for inference about population structure using genetic data by defining population structure using network theory terminology and methods. A network is constructed from a pairwise genetic-similarity matrix of all sampled individuals. The community partition, a partition of a network to dense subgraphs, is equated with population structure, a partition of the population to genetically related groups. Community-detection algorithms are used to partition the network into communities, interpreted as a partition of the population to subpopulations. The statistical significance of the structure can be estimated by using permutation tests to evaluate the significance of the partition’s modularity, a network theory measure indicating the quality of community partitions. To further characterize population structure, a new measure of the strength of association (SA) for an individual to its assigned community is presented. The strength of association distribution (SAD) of the communities is analyzed to provide additional population structure characteristics, such as the relative amount of gene flow experienced by the different subpopulations and identification of hybrid individuals. Human genetic data and simulations are used to demonstrate the applicability of the analyses. The approach presented here provides a novel, computationally efficient model-free method for inference about population structure that does not entail assumption of prior conditions. The method is implemented in the software NetStruct (available at https://giligreenbaum.wordpress.com/software/). PMID:26888080
Inference and Analysis of Population Structure Using Genetic Data and Network Theory.

PubMed

Greenbaum, Gili; Templeton, Alan R; Bar-David, Shirli

2016-04-01

Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance-based approaches such as multidimensional scaling. While existing distance-based approaches suffer from a lack of statistical rigor, model-based approaches entail assumptions of prior conditions such as that the subpopulations are at Hardy-Weinberg equilibria. Here we present a distance-based approach for inference about population structure using genetic data by defining population structure using network theory terminology and methods. A network is constructed from a pairwise genetic-similarity matrix of all sampled individuals. The community partition, a partition of a network to dense subgraphs, is equated with population structure, a partition of the population to genetically related groups. Community-detection algorithms are used to partition the network into communities, interpreted as a partition of the population to subpopulations. The statistical significance of the structure can be estimated by using permutation tests to evaluate the significance of the partition's modularity, a network theory measure indicating the quality of community partitions. To further characterize population structure, a new measure of the strength of association (SA) for an individual to its assigned community is presented. The strength of association distribution (SAD) of the communities is analyzed to provide additional population structure characteristics, such as the relative amount of gene flow experienced by the different subpopulations and identification of hybrid individuals. Human genetic data and simulations are used to demonstrate the applicability of the analyses. The approach presented here provides a novel, computationally efficient model-free method for inference about population structure that does not entail assumption of prior conditions. The method is implemented in the software NetStruct (available at https://giligreenbaum.wordpress.com/software/). Copyright © 2016 by the Genetics Society of America.
Structure Discovery in Large Semantic Graphs Using Extant Ontological Scaling and Descriptive Statistics

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

al-Saffar, Sinan; Joslyn, Cliff A.; Chappell, Alan R.

As semantic datasets grow to be very large and divergent, there is a need to identify and exploit their inherent semantic structure for discovery and optimization. Towards that end, we present here a novel methodology to identify the semantic structures inherent in an arbitrary semantic graph dataset. We first present the concept of an extant ontology as a statistical description of the semantic relations present amongst the typed entities modeled in the graph. This serves as a model of the underlying semantic structure to aid in discovery and visualization. We then describe a method of ontological scaling in which themore » ontology is employed as a hierarchical scaling filter to infer different resolution levels at which the graph structures are to be viewed or analyzed. We illustrate these methods on three large and publicly available semantic datasets containing more than one billion edges each. Keywords-Semantic Web; Visualization; Ontology; Multi-resolution Data Mining;« less
Multi-agent based control of large-scale complex systems employing distributed dynamic inference engine

NASA Astrophysics Data System (ADS)

Zhang, Daili

Increasing societal demand for automation has led to considerable efforts to control large-scale complex systems, especially in the area of autonomous intelligent control methods. The control system of a large-scale complex system needs to satisfy four system level requirements: robustness, flexibility, reusability, and scalability. Corresponding to the four system level requirements, there arise four major challenges. First, it is difficult to get accurate and complete information. Second, the system may be physically highly distributed. Third, the system evolves very quickly. Fourth, emergent global behaviors of the system can be caused by small disturbances at the component level. The Multi-Agent Based Control (MABC) method as an implementation of distributed intelligent control has been the focus of research since the 1970s, in an effort to solve the above-mentioned problems in controlling large-scale complex systems. However, to the author's best knowledge, all MABC systems for large-scale complex systems with significant uncertainties are problem-specific and thus difficult to extend to other domains or larger systems. This situation is partly due to the control architecture of multiple agents being determined by agent to agent coupling and interaction mechanisms. Therefore, the research objective of this dissertation is to develop a comprehensive, generalized framework for the control system design of general large-scale complex systems with significant uncertainties, with the focus on distributed control architecture design and distributed inference engine design. A Hybrid Multi-Agent Based Control (HyMABC) architecture is proposed by combining hierarchical control architecture and module control architecture with logical replication rings. First, it decomposes a complex system hierarchically; second, it combines the components in the same level as a module, and then designs common interfaces for all of the components in the same module; third, replications are made for critical agents and are organized into logical rings. This architecture maintains clear guidelines for complexity decomposition and also increases the robustness of the whole system. Multiple Sectioned Dynamic Bayesian Networks (MSDBNs) as a distributed dynamic probabilistic inference engine, can be embedded into the control architecture to handle uncertainties of general large-scale complex systems. MSDBNs decomposes a large knowledge-based system into many agents. Each agent holds its partial perspective of a large problem domain by representing its knowledge as a Dynamic Bayesian Network (DBN). Each agent accesses local evidence from its corresponding local sensors and communicates with other agents through finite message passing. If the distributed agents can be organized into a tree structure, satisfying the running intersection property and d-sep set requirements, globally consistent inferences are achievable in a distributed way. By using different frequencies for local DBN agent belief updating and global system belief updating, it balances the communication cost with the global consistency of inferences. In this dissertation, a fully factorized Boyen-Koller (BK) approximation algorithm is used for local DBN agent belief updating, and the static Junction Forest Linkage Tree (JFLT) algorithm is used for global system belief updating. MSDBNs assume a static structure and a stable communication network for the whole system. However, for a real system, sub-Bayesian networks as nodes could be lost, and the communication network could be shut down due to partial damage in the system. Therefore, on-line and automatic MSDBNs structure formation is necessary for making robust state estimations and increasing survivability of the whole system. A Distributed Spanning Tree Optimization (DSTO) algorithm, a Distributed D-Sep Set Satisfaction (DDSSS) algorithm, and a Distributed Running Intersection Satisfaction (DRIS) algorithm are proposed in this dissertation. Combining these three distributed algorithms and a Distributed Belief Propagation (DBP) algorithm in MSDBNs makes state estimations robust to partial damage in the whole system. Combining the distributed control architecture design and the distributed inference engine design leads to a process of control system design for a general large-scale complex system. As applications of the proposed methodology, the control system design of a simplified ship chilled water system and a notional ship chilled water system have been demonstrated step by step. Simulation results not only show that the proposed methodology gives a clear guideline for control system design for general large-scale complex systems with dynamic and uncertain environment, but also indicate that the combination of MSDBNs and HyMABC can provide excellent performance for controlling general large-scale complex systems.
Phylogenic inference using alignment-free methods for applications in microbial community surveys using 16s rRNA gene

PubMed Central

2017-01-01

The diversity of microbiota is best explored by understanding the phylogenetic structure of the microbial communities. Traditionally, sequence alignment has been used for phylogenetic inference. However, alignment-based approaches come with significant challenges and limitations when massive amounts of data are analyzed. In the recent decade, alignment-free approaches have enabled genome-scale phylogenetic inference. Here we evaluate three alignment-free methods: ACS, CVTree, and Kr for phylogenetic inference with 16s rRNA gene data. We use a taxonomic gold standard to compare the accuracy of alignment-free phylogenetic inference with that of common microbiome-wide phylogenetic inference pipelines based on PyNAST and MUSCLE alignments with FastTree and RAxML. We re-simulate fecal communities from Human Microbiome Project data to evaluate the performance of the methods on datasets with properties of real data. Our comparisons show that alignment-free methods are not inferior to alignment-based methods in giving accurate and robust phylogenic trees. Moreover, consensus ensembles of alignment-free phylogenies are superior to those built from alignment-based methods in their ability to highlight community differences in low power settings. In addition, the overall running times of alignment-based and alignment-free phylogenetic inference are comparable. Taken together our empirical results suggest that alignment-free methods provide a viable approach for microbiome-wide phylogenetic inference. PMID:29136663
SHIPS: Spectral Hierarchical Clustering for the Inference of Population Structure in Genetic Studies

PubMed Central

Bouaziz, Matthieu; Paccard, Caroline; Guedj, Mickael; Ambroise, Christophe

2012-01-01

Inferring the structure of populations has many applications for genetic research. In addition to providing information for evolutionary studies, it can be used to account for the bias induced by population stratification in association studies. To this end, many algorithms have been proposed to cluster individuals into genetically homogeneous sub-populations. The parametric algorithms, such as Structure, are very popular but their underlying complexity and their high computational cost led to the development of faster parametric alternatives such as Admixture. Alternatives to these methods are the non-parametric approaches. Among this category, AWclust has proven efficient but fails to properly identify population structure for complex datasets. We present in this article a new clustering algorithm called Spectral Hierarchical clustering for the Inference of Population Structure (SHIPS), based on a divisive hierarchical clustering strategy, allowing a progressive investigation of population structure. This method takes genetic data as input to cluster individuals into homogeneous sub-populations and with the use of the gap statistic estimates the optimal number of such sub-populations. SHIPS was applied to a set of simulated discrete and admixed datasets and to real SNP datasets, that are data from the HapMap and Pan-Asian SNP consortium. The programs Structure, Admixture, AWclust and PCAclust were also investigated in a comparison study. SHIPS and the parametric approach Structure were the most accurate when applied to simulated datasets both in terms of individual assignments and estimation of the correct number of clusters. The analysis of the results on the real datasets highlighted that the clusterings of SHIPS were the more consistent with the population labels or those produced by the Admixture program. The performances of SHIPS when applied to SNP data, along with its relatively low computational cost and its ease of use make this method a promising solution to infer fine-scale genetic patterns. PMID:23077494
EEG functional connectivity is partially predicted by underlying white matter connectivity

PubMed Central

Chu, CJ; Tanaka, N; Diaz, J; Edlow, BL; Wu, O; Hämäläinen, M; Stufflebeam, S; Cash, SS; Kramer, MA.

2015-01-01

Over the past decade, networks have become a leading model to illustrate both the anatomical relationships (structural networks) and the coupling of dynamic physiology (functional networks) linking separate brain regions. The relationship between these two levels of description remains incompletely understood and an area of intense research interest. In particular, it is unclear how cortical currents relate to underlying brain structural architecture. In addition, although theory suggests that brain communication is highly frequency dependent, how structural connections influence overlying functional connectivity in different frequency bands has not been previously explored. Here we relate functional networks inferred from statistical associations between source imaging of EEG activity and underlying cortico-cortical structural brain connectivity determined by probabilistic white matter tractography. We evaluate spontaneous fluctuating cortical brain activity over a long time scale (minutes) and relate inferred functional networks to underlying structural connectivity for broadband signals, as well as in seven distinct frequency bands. We find that cortical networks derived from source EEG estimates partially reflect both direct and indirect underlying white matter connectivity in all frequency bands evaluated. In addition, we find that when structural support is absent, functional connectivity is significantly reduced for high frequency bands compared to low frequency bands. The association between cortical currents and underlying white matter connectivity highlights the obligatory interdependence of functional and structural networks in the human brain. The increased dependence on structural support for the coupling of higher frequency brain rhythms provides new evidence for how underlying anatomy directly shapes emergent brain dynamics at fast time scales. PMID:25534110
Multi-scale integration and predictability in resting state brain activity

PubMed Central

Kolchinsky, Artemy; van den Heuvel, Martijn P.; Griffa, Alessandra; Hagmann, Patric; Rocha, Luis M.; Sporns, Olaf; Goñi, Joaquín

2014-01-01

The human brain displays heterogeneous organization in both structure and function. Here we develop a method to characterize brain regions and networks in terms of information-theoretic measures. We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered. This framework is applied to human brain fMRI recordings of resting-state activity and DSI-inferred structural connectivity. We find that strong functional coupling across large spatial distances distinguishes functional hubs from unimodal low-level areas, and that this long-range functional coupling correlates with structural long-range efficiency on the connectome. We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks. Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales. PMID:25104933
Coronal structures deduced from photospheric magnetic field and He I lambda 10830 observations

NASA Technical Reports Server (NTRS)

Harvey, Karen L.

1995-01-01

The National Solar Observatory synoptic program provides an extensive and unique data base of high-resolution full-disk observations of the line-of-sight photospheric magnetic fields and of the He I lambda 10830 equivalent width. These data have been taken nearly daily for more than 21 years since 1974 and provide the opportunity to investigate the behavior of the magnetic fields in the photosphere and those inferred for the corona spanning on the time scales of a day to that of a solar cycle. The intensity of structures observed in He I lambda 10830 are strongly modulated by overlying coronal radiation; areas with low coronal emission are generally brighter in He I lambda 10830, while areas with high coronal emission are darker. For this reason, He I lambda 10830 was selected in the mid-1970's as way to identify and monitor coronal holes, magnetic fields with an open configuration, and the sources of high-speed solar wind streams. The He I lambda 10830 spectroheliograms also show a wide variety of other structures from small-scale, short-lived dark points (less than 30 arc-sec, hours) to the large-scale, long-lived two 'ribbon' flare events that follow the filament eruptions (1000 arc-sec, days). Such structures provide clues about the connections and changes in the large-scale coronal magnetic fields that are rooted in concentrations of magnetic network and active regions in the photosphere. In this paper, what observations of the photospheric magnetic field and He I lambda 10830 can tell us about the short- and long-term evolution of the coronal magnetic fields will be discussed, focussing on the quiet Sun and coronal holes. These data and what we infer from them will be compared with direct observations of the coronal structure from the Yohkoh Soft X-ray Telescope.
Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen.

PubMed

Nir, Oaz; Bakal, Chris; Perrimon, Norbert; Berger, Bonnie

2010-03-01

Biological networks are highly complex systems, consisting largely of enzymes that act as molecular switches to activate/inhibit downstream targets via post-translational modification. Computational techniques have been developed to perform signaling network inference using some high-throughput data sources, such as those generated from transcriptional and proteomic studies, but comparable methods have not been developed to use high-content morphological data, which are emerging principally from large-scale RNAi screens, to these ends. Here, we describe a systematic computational framework based on a classification model for identifying genetic interactions using high-dimensional single-cell morphological data from genetic screens, apply it to RhoGAP/GTPase regulation in Drosophila, and evaluate its efficacy. Augmented by knowledge of the basic structure of RhoGAP/GTPase signaling, namely, that GAPs act directly upstream of GTPases, we apply our framework for identifying genetic interactions to predict signaling relationships between these proteins. We find that our method makes mediocre predictions using only RhoGAP single-knockdown morphological data, yet achieves vastly improved accuracy by including original data from a double-knockdown RhoGAP genetic screen, which likely reflects the redundant network structure of RhoGAP/GTPase signaling. We consider other possible methods for inference and show that our primary model outperforms the alternatives. This work demonstrates the fundamental fact that high-throughput morphological data can be used in a systematic, successful fashion to identify genetic interactions and, using additional elementary knowledge of network structure, to infer signaling relations.
Aeronomy report no. 74: The Urbana meteor-radar system; design, development, and first observations

NASA Technical Reports Server (NTRS)

Hess, G. C.; Geller, M. A.

1976-01-01

The design, development, and first observations of a high power meteor-radar system located near Urbana, Illinois are described. The roughly five-fold increase in usable echo rate compared to other facilities, along with automated digital data processing and interferometry measurement of echo arrival angles, permits unsurpassed observations of tidal structure and shorter period waves. Such observations are discussed. The technique of using echo decay rates to infer density and scale height and the method of inferring wind shear from radial acceleration are examined. An original experiment to test a theory of the Delta-region winter anomaly is presented.
Inferring personal economic status from social network location

NASA Astrophysics Data System (ADS)

Luo, Shaojun; Morone, Flaviano; Sarraute, Carlos; Travizano, Matías; Makse, Hernán A.

2017-05-01

It is commonly believed that patterns of social ties affect individuals' economic status. Here we translate this concept into an operational definition at the network level, which allows us to infer the economic well-being of individuals through a measure of their location and influence in the social network. We analyse two large-scale sources: telecommunications and financial data of a whole country's population. Our results show that an individual's location, measured as the optimal collective influence to the structural integrity of the social network, is highly correlated with personal economic status. The observed social network patterns of influence mimic the patterns of economic inequality. For pragmatic use and validation, we carry out a marketing campaign that shows a threefold increase in response rate by targeting individuals identified by our social network metrics as compared to random targeting. Our strategy can also be useful in maximizing the effects of large-scale economic stimulus policies.
Inferring personal economic status from social network location.

PubMed

Luo, Shaojun; Morone, Flaviano; Sarraute, Carlos; Travizano, Matías; Makse, Hernán A

2017-05-16

It is commonly believed that patterns of social ties affect individuals' economic status. Here we translate this concept into an operational definition at the network level, which allows us to infer the economic well-being of individuals through a measure of their location and influence in the social network. We analyse two large-scale sources: telecommunications and financial data of a whole country's population. Our results show that an individual's location, measured as the optimal collective influence to the structural integrity of the social network, is highly correlated with personal economic status. The observed social network patterns of influence mimic the patterns of economic inequality. For pragmatic use and validation, we carry out a marketing campaign that shows a threefold increase in response rate by targeting individuals identified by our social network metrics as compared to random targeting. Our strategy can also be useful in maximizing the effects of large-scale economic stimulus policies.
A semiparametric graphical modelling approach for large-scale equity selection

PubMed Central

Liu, Han; Mulvey, John; Zhao, Tianqi

2016-01-01

We propose a new stock selection strategy that exploits rebalancing returns and improves portfolio performance. To effectively harvest rebalancing gains, we apply ideas from elliptical-copula graphical modelling and stability inference to select stocks that are as independent as possible. The proposed elliptical-copula graphical model has a latent Gaussian representation; its structure can be effectively inferred using the regularized rank-based estimators. The resulting algorithm is computationally efficient and scales to large data-sets. To show the efficacy of the proposed method, we apply it to conduct equity selection based on a 16-year health care stock data-set and a large 34-year stock data-set. Empirical tests show that the proposed method is superior to alternative strategies including a principal component analysis-based approach and the classical Markowitz strategy based on the traditional buy-and-hold assumption. PMID:28316507
Structure of the Lithosphere and Upper Mantle Across the Arabian Peninsula

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

Al-Amri, A; Rodgers, A

2007-01-05

Analysis of modern broadband (BB) waveform data allows for the inference of seismic velocity structure of the crust and upper mantle using a variety of techniques. This presentation will report inferences of seismic structure of the Arabian Plate using BB data from various networks. Most data were recorded by the Saudi Arabian National Digital Seismic Network (SANDSN) which consists of 38 (26 BB, 11 SP) stations, mostly located on the Arabian Shield. Additional data were taken from the 1995-7 Saudi Arabian IRIS-PASSCAL Deployment (9 BB stations) and other stations across the Peninsula. Crustal structure, inferred from teleseismic P-wave receiver functions,more » reveals thicker crust in the Arabian Platform (40-45 km) and the interior of the Arabian Shield (35-40 km) and thinner crust along the Red Sea coast. Lithospheric thickness inferred from teleseismic S-wave receiver functions reveals very thin lithosphere (40-80 km) along the Red Sea coast which thickens rapidly toward the interior of the Arabian Shield (100-120 km). We also observe a step of 20-40 km in lithospheric thickness across the Shield-Platform boundary. Seismic velocity structure of the upper mantle inferred from teleseismic P- and S-wave travel time tomography reveals large differences between the Shield and Platform, with the Shield being underlain by slower velocities, {+-}3% for P-waves and {+-}6% for S-waves. Seismic anisotropy was inferred from shear-wave splitting, using teleseismic SKS waveforms. Results reveal a splitting time of approximately 1.4 seconds, with the fast axis slightly east of north. The shear-wave splitting results are consistent across the Peninsula, with a slight clockwise rotation parallel for stations near the Gulf of Aqaba. In summary, these results allow us to make several conclusions about the tectonic evolution and current state of the Arabian Plate. Lithospheric thickness implies that thinning near the Red Sea has accompanied the rupturing of the Arabian-Nubian continental lithosphere. The step in the lithospheric thickness across the Shield-Platform boundary likely reveals a pre-existing difference in the lithospheric structure prior to accretion of the terranes composing the eastern Arabian Shield. Tomographic imaging of upper mantle velocities implies a single large-scale thermal anomaly underlies the Arabian Shield and is associated with Cenozoic uplift and volcanism.« less
Novel Computational Approaches to Drug Discovery

NASA Astrophysics Data System (ADS)

Skolnick, Jeffrey; Brylinski, Michal

2010-01-01

New approaches to protein functional inference based on protein structure and evolution are described. First, FINDSITE, a threading based approach to protein function prediction, is summarized. Then, the results of large scale benchmarking of ligand binding site prediction, ligand screening, including applications to HIV protease, and GO molecular functional inference are presented. A key advantage of FINDSITE is its ability to use low resolution, predicted structures as well as high resolution experimental structures. Then, an extension of FINDSITE to ligand screening in GPCRs using predicted GPCR structures, FINDSITE/QDOCKX, is presented. This is a particularly difficult case as there are few experimentally solved GPCR structures. Thus, we first train on a subset of known binding ligands for a set of GPCRs; this is then followed by benchmarking against a large ligand library. For the virtual ligand screening of a number of Dopamine receptors, encouraging results are seen, with significant enrichment in identified ligands over those found in the training set. Thus, FINDSITE and its extensions represent a powerful approach to the successful prediction of a variety of molecular functions.
Factorial Structure of the Family Values Scale from a Multilevel-Multicultural Perspective

ERIC Educational Resources Information Center

Byrne, Barbara M.; van de Vijver, Fons J. R.

2014-01-01

In cross-cultural research, there is a tendency for researchers to draw inferences at the country level based on individual-level data. Such action implicitly and often mistakenly assumes that both the measuring instrument and its underlying construct(s) are operating equivalently across both levels. Based on responses from 5,482 college students…
Issues on the use of meta-knowledge in expert systems

NASA Technical Reports Server (NTRS)

Facemire, Jon; Chen, Imao

1988-01-01

Meta knowledge is knowledge about knowledge; knowledge that is not domain specific but is concerned instead with its own internal structure. Several past systems have used meta-knowledge to improve the nature of the user interface, to maintain the knowledge base, and to control the inference engine. More extensive use of meta-knowledge is probable for the future as larger scale problems are considered. A proposed system architecture is presented and discussed in terms of meta-knowledge applications. The principle components of this system: the user support subsystem, the control structure, the knowledge base, the inference engine, and a learning facility are all outlined and discussed in light of the use of meta-knowledge. Problems with meta-constructs are also mentioned but it is concluded that the use of meta-knowledge is crucial for increasingly autonomous operations.
Final Report, DOE Early Career Award: Predictive modeling of complex physical systems: new tools for statistical inference, uncertainty quantification, and experimental design

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

Marzouk, Youssef

Predictive simulation of complex physical systems increasingly rests on the interplay of experimental observations with computational models. Key inputs, parameters, or structural aspects of models may be incomplete or unknown, and must be developed from indirect and limited observations. At the same time, quantified uncertainties are needed to qualify computational predictions in the support of design and decision-making. In this context, Bayesian statistics provides a foundation for inference from noisy and limited data, but at prohibitive computional expense. This project intends to make rigorous predictive modeling *feasible* in complex physical systems, via accelerated and scalable tools for uncertainty quantification, Bayesianmore » inference, and experimental design. Specific objectives are as follows: 1. Develop adaptive posterior approximations and dimensionality reduction approaches for Bayesian inference in high-dimensional nonlinear systems. 2. Extend accelerated Bayesian methodologies to large-scale {\\em sequential} data assimilation, fully treating nonlinear models and non-Gaussian state and parameter distributions. 3. Devise efficient surrogate-based methods for Bayesian model selection and the learning of model structure. 4. Develop scalable simulation/optimization approaches to nonlinear Bayesian experimental design, for both parameter inference and model selection. 5. Demonstrate these inferential tools on chemical kinetic models in reacting flow, constructing and refining thermochemical and electrochemical models from limited data. Demonstrate Bayesian filtering on canonical stochastic PDEs and in the dynamic estimation of inhomogeneous subsurface properties and flow fields.« less
Causal inference between bioavailability of heavy metals and environmental factors in a large-scale region.

PubMed

Liu, Yuqiong; Du, Qingyun; Wang, Qi; Yu, Huanyun; Liu, Jianfeng; Tian, Yu; Chang, Chunying; Lei, Jing

2017-07-01

The causation between bioavailability of heavy metals and environmental factors are generally obtained from field experiments at local scales at present, and lack sufficient evidence from large scales. However, inferring causation between bioavailability of heavy metals and environmental factors across large-scale regions is challenging. Because the conventional correlation-based approaches used for causation assessments across large-scale regions, at the expense of actual causation, can result in spurious insights. In this study, a general approach framework, Intervention calculus when the directed acyclic graph (DAG) is absent (IDA) combined with the backdoor criterion (BC), was introduced to identify causation between the bioavailability of heavy metals and the potential environmental factors across large-scale regions. We take the Pearl River Delta (PRD) in China as a case study. The causal structures and effects were identified based on the concentrations of heavy metals (Zn, As, Cu, Hg, Pb, Cr, Ni and Cd) in soil (0-20 cm depth) and vegetable (lettuce) and 40 environmental factors (soil properties, extractable heavy metals and weathering indices) in 94 samples across the PRD. Results show that the bioavailability of heavy metals (Cd, Zn, Cr, Ni and As) was causally influenced by soil properties and soil weathering factors, whereas no causal factor impacted the bioavailability of Cu, Hg and Pb. No latent factor was found between the bioavailability of heavy metals and environmental factors. The causation between the bioavailability of heavy metals and environmental factors at field experiments is consistent with that on a large scale. The IDA combined with the BC provides a powerful tool to identify causation between the bioavailability of heavy metals and environmental factors across large-scale regions. Causal inference in a large system with the dynamic changes has great implications for system-based risk management. Copyright © 2017 Elsevier Ltd. All rights reserved.

3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study.

PubMed

Dolz, Jose; Desrosiers, Christian; Ben Ayed, Ismail

2018-04-15

This study investigates a 3D and fully convolutional neural network (CNN) for subcortical brain structure segmentation in MRI. 3D CNN architectures have been generally avoided due to their computational and memory requirements during inference. We address the problem via small kernels, allowing deeper architectures. We further model both local and global context by embedding intermediate-layer outputs in the final prediction, which encourages consistency between features extracted at different scales and embeds fine-grained information directly in the segmentation process. Our model is efficiently trained end-to-end on a graphics processing unit (GPU), in a single stage, exploiting the dense inference capabilities of fully CNNs. We performed comprehensive experiments over two publicly available datasets. First, we demonstrate a state-of-the-art performance on the ISBR dataset. Then, we report a large-scale multi-site evaluation over 1112 unregistered subject datasets acquired from 17 different sites (ABIDE dataset), with ages ranging from 7 to 64 years, showing that our method is robust to various acquisition protocols, demographics and clinical factors. Our method yielded segmentations that are highly consistent with a standard atlas-based approach, while running in a fraction of the time needed by atlas-based methods and avoiding registration/normalization steps. This makes it convenient for massive multi-site neuroanatomical imaging studies. To the best of our knowledge, our work is the first to study subcortical structure segmentation on such large-scale and heterogeneous data. Copyright © 2017 Elsevier Inc. All rights reserved.
Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm

PubMed Central

Zheng, Ming; Wu, Jia-nan; Huang, Yan-xin; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang

2012-01-01

Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms. PMID:23226565
Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining.

PubMed

Hero, Alfred O; Rajaratnam, Bala

2016-01-01

When can reliable inference be drawn in fue "Big Data" context? This paper presents a framework for answering this fundamental question in the context of correlation mining, wifu implications for general large scale inference. In large scale data applications like genomics, connectomics, and eco-informatics fue dataset is often variable-rich but sample-starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than fue number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for "Big Data". Sample complexity however has received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address fuis gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where fue variable dimension is fixed and fue sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; 3) the purely high dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa cale data dimension. We illustrate this high dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables fua t are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. we demonstrate various regimes of correlation mining based on the unifying perspective of high dimensional learning rates and sample complexity for different structured covariance models and different inference tasks.
Detectability of Granger causality for subsampled continuous-time neurophysiological processes.

PubMed

Barnett, Lionel; Seth, Anil K

2017-01-01

Granger causality is well established within the neurosciences for inference of directed functional connectivity from neurophysiological data. These data usually consist of time series which subsample a continuous-time biophysiological process. While it is well known that subsampling can lead to imputation of spurious causal connections where none exist, less is known about the effects of subsampling on the ability to reliably detect causal connections which do exist. We present a theoretical analysis of the effects of subsampling on Granger-causal inference. Neurophysiological processes typically feature signal propagation delays on multiple time scales; accordingly, we base our analysis on a distributed-lag, continuous-time stochastic model, and consider Granger causality in continuous time at finite prediction horizons. Via exact analytical solutions, we identify relationships among sampling frequency, underlying causal time scales and detectability of causalities. We reveal complex interactions between the time scale(s) of neural signal propagation and sampling frequency. We demonstrate that detectability decays exponentially as the sample time interval increases beyond causal delay times, identify detectability "black spots" and "sweet spots", and show that downsampling may potentially improve detectability. We also demonstrate that the invariance of Granger causality under causal, invertible filtering fails at finite prediction horizons, with particular implications for inference of Granger causality from fMRI data. Our analysis emphasises that sampling rates for causal analysis of neurophysiological time series should be informed by domain-specific time scales, and that state-space modelling should be preferred to purely autoregressive modelling. On the basis of a very general model that captures the structure of neurophysiological processes, we are able to help identify confounds, and offer practical insights, for successful detection of causal connectivity from neurophysiological recordings. Copyright Â© 2016 Elsevier B.V. All rights reserved.
SMALL-SCALE ANISOTROPIES OF COSMIC RAYS FROM RELATIVE DIFFUSION

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

Ahlers, Markus; Mertsch, Philipp

2015-12-10

The arrival directions of multi-TeV cosmic rays show significant anisotropies at small angular scales. It has been argued that this small-scale structure can naturally arise from cosmic ray scattering in local turbulent magnetic fields that distort a global dipole anisotropy set by diffusion. We study this effect in terms of the power spectrum of cosmic ray arrival directions and show that the strength of small-scale anisotropies is related to properties of relative diffusion. We provide a formalism for how these power spectra can be inferred from simulations and motivate a simple analytic extension of the ensemble-averaged diffusion equation that canmore » account for the effect.« less
Scaling up functional traits for ecosystem services with remote sensing: concepts and methods.

PubMed

Abelleira Martínez, Oscar J; Fremier, Alexander K; Günter, Sven; Ramos Bendaña, Zayra; Vierling, Lee; Galbraith, Sara M; Bosque-Pérez, Nilsa A; Ordoñez, Jenny C

2016-07-01

Ecosystem service-based management requires an accurate understanding of how human modification influences ecosystem processes and these relationships are most accurate when based on functional traits. Although trait variation is typically sampled at local scales, remote sensing methods can facilitate scaling up trait variation to regional scales needed for ecosystem service management. We review concepts and methods for scaling up plant and animal functional traits from local to regional spatial scales with the goal of assessing impacts of human modification on ecosystem processes and services. We focus our objectives on considerations and approaches for (1) conducting local plot-level sampling of trait variation and (2) scaling up trait variation to regional spatial scales using remotely sensed data. We show that sampling methods for scaling up traits need to account for the modification of trait variation due to land cover change and species introductions. Sampling intraspecific variation, stratification by land cover type or landscape context, or inference of traits from published sources may be necessary depending on the traits of interest. Passive and active remote sensing are useful for mapping plant phenological, chemical, and structural traits. Combining these methods can significantly improve their capacity for mapping plant trait variation. These methods can also be used to map landscape and vegetation structure in order to infer animal trait variation. Due to high context dependency, relationships between trait variation and remotely sensed data are not directly transferable across regions. We end our review with a brief synthesis of issues to consider and outlook for the development of these approaches. Research that relates typical functional trait metrics, such as the community-weighted mean, with remote sensing data and that relates variation in traits that cannot be remotely sensed to other proxies is needed. Our review narrows the gap between functional trait and remote sensing methods for ecosystem service management.
Pre-treatment analysis of woody vegetation composition and structure on the hardwood ecosystem experiment research units

Treesearch

Michael R. Saunders; Justin E. Arseneault

2013-01-01

In long-term, large-scale forest management studies, documentation of pre-treatment differences among and variability within experimental units is critical for drawing the proper inferences from imposed treatments. We compared pre-treatment overstory and large shrub communities (diameters at breast height >1.5 cm) for the 9 research cores with the Hardwood Ecosystem...
Multi-scale, multi-method geophysical investigations of the Valles Caldera

NASA Astrophysics Data System (ADS)

Barker, J. E.; Daneshvar, S.; Langhans, A.; Okorie, C.; Parapuzha, A.; Perez, N.; Turner, A.; Smith, E.; Carchedi, C. J. W.; Creighton, A.; Folsom, M.; Bedrosian, P.; Pellerin, L.; Feucht, D. W.; Kelly, S.; Ferguson, J. F.; McPhee, D.

2017-12-01

In 2016, the Summer of Applied Geophysical Experience (SAGE) program, in cooperation with the National Park Service, began a multi-year investigation into the structure and evolution of the Valles Caldera in northern New Mexico. The Valles Caldera is a 20-km wide topographic depression in the Jemez Mountains volcanic complex that formed during two massive ignimbrite eruptions at 1.65 and 1.26 Ma. Post-collapse volcanic activity in the caldera includes the rise of Redondo peak, a 1 km high resurgent dome, periodic eruptions of the Valles rhyolite along an inferred ring fracture zone, and the presence of a geothermal reservoir beneath the western caldera with temperatures in excess of 300°C at a mere 2 km depth. Broad sediment-filled valleys associated with lava-dammed Pleistocene lakes occupy much of the northern and southeastern caldera. SAGE activities to date have included collection of new gravity data (>120 stations) throughout the caldera, a transient electromagnetic (TEM) survey of Valle Grande, reprocessing of industrial magnetotelluric (MT) data collected in the 1980s, and new MT data collection both within and outside of the caldera. Gravity modeling provides constraints on the pre-Caldera structure, estimates of the thickness of Caldera fill, and reveals regional structural trends reflected in the geometry of post-Caldera collapse. At a more local scale, TEM-derived resistivity models image rhyolite flows radiating outward from nearby vents into the lacustrine sediments filling Valle Grande. Resistivity models along a 6-km long profile also provide hints of structural dismemberment along the inferred Valles and Toledo ring fracture zones. Preliminary MT modeling at the caldera scale reveals conductive caldera fill, the resistive crystalline basement, and an enigmatic mid-crustal conductor likely related to magmatic activity that post-dates caldera formation.
Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining

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

Hero, Alfred O.; Rajaratnam, Bala

When can reliable inference be drawn in the ‘‘Big Data’’ context? This article presents a framework for answering this fundamental question in the context of correlation mining, with implications for general large-scale inference. In large-scale data applications like genomics, connectomics, and eco-informatics, the data set is often variable rich but sample starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than the number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for ‘‘Big Data.’’ Sample complexity, however, hasmore » received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address this gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where the variable dimension is fixed and the sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; and 3) the purely high-dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa-scale data dimension. We illustrate this high-dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables that are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. We demonstrate various regimes of correlation mining based on the unifying perspective of high-dimensional learning rates and sample complexity for different structured covariance models and different inference tasks.« less
Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining

DOE PAGES

Hero, Alfred O.; Rajaratnam, Bala

2015-12-09

When can reliable inference be drawn in the ‘‘Big Data’’ context? This article presents a framework for answering this fundamental question in the context of correlation mining, with implications for general large-scale inference. In large-scale data applications like genomics, connectomics, and eco-informatics, the data set is often variable rich but sample starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than the number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for ‘‘Big Data.’’ Sample complexity, however, hasmore » received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address this gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where the variable dimension is fixed and the sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; and 3) the purely high-dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa-scale data dimension. We illustrate this high-dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables that are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. We demonstrate various regimes of correlation mining based on the unifying perspective of high-dimensional learning rates and sample complexity for different structured covariance models and different inference tasks.« less
Extreme-Scale Bayesian Inference for Uncertainty Quantification of Complex Simulations

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

Biros, George

Uncertainty quantification (UQ)—that is, quantifying uncertainties in complex mathematical models and their large-scale computational implementations—is widely viewed as one of the outstanding challenges facing the field of CS&E over the coming decade. The EUREKA project set to address the most difficult class of UQ problems: those for which both the underlying PDE model as well as the uncertain parameters are of extreme scale. In the project we worked on these extreme-scale challenges in the following four areas: 1. Scalable parallel algorithms for sampling and characterizing the posterior distribution that exploit the structure of the underlying PDEs and parameter-to-observable map. Thesemore » include structure-exploiting versions of the randomized maximum likelihood method, which aims to overcome the intractability of employing conventional MCMC methods for solving extreme-scale Bayesian inversion problems by appealing to and adapting ideas from large-scale PDE-constrained optimization, which have been very successful at exploring high-dimensional spaces. 2. Scalable parallel algorithms for construction of prior and likelihood functions based on learning methods and non-parametric density estimation. Constructing problem-specific priors remains a critical challenge in Bayesian inference, and more so in high dimensions. Another challenge is construction of likelihood functions that capture unmodeled couplings between observations and parameters. We will create parallel algorithms for non-parametric density estimation using high dimensional N-body methods and combine them with supervised learning techniques for the construction of priors and likelihood functions. 3. Bayesian inadequacy models, which augment physics models with stochastic models that represent their imperfections. The success of the Bayesian inference framework depends on the ability to represent the uncertainty due to imperfections of the mathematical model of the phenomena of interest. This is a central challenge in UQ, especially for large-scale models. We propose to develop the mathematical tools to address these challenges in the context of extreme-scale problems. 4. Parallel scalable algorithms for Bayesian optimal experimental design (OED). Bayesian inversion yields quantified uncertainties in the model parameters, which can be propagated forward through the model to yield uncertainty in outputs of interest. This opens the way for designing new experiments to reduce the uncertainties in the model parameters and model predictions. Such experimental design problems have been intractable for large-scale problems using conventional methods; we will create OED algorithms that exploit the structure of the PDE model and the parameter-to-output map to overcome these challenges. Parallel algorithms for these four problems were created, analyzed, prototyped, implemented, tuned, and scaled up for leading-edge supercomputers, including UT-Austin’s own 10 petaflops Stampede system, ANL’s Mira system, and ORNL’s Titan system. While our focus is on fundamental mathematical/computational methods and algorithms, we will assess our methods on model problems derived from several DOE mission applications, including multiscale mechanics and ice sheet dynamics.« less
Landscape genetics in a changing world: disentangling historical and contemporary influences and inferring change.

PubMed

Epps, Clinton W; Keyghobadi, Nusha

2015-12-01

Landscape genetics seeks to determine the effect of landscape features on gene flow and genetic structure. Often, such analyses are intended to inform conservation and management. However, depending on the many factors that influence the time to reach equilibrium, genetic structure may more strongly represent past rather than contemporary landscapes. This well-known lag between current demographic processes and population genetic structure often makes it challenging to interpret how contemporary landscapes and anthropogenic activity shape gene flow. Here, we review the theoretical framework for factors that influence time lags, summarize approaches to address this temporal disconnect in landscape genetic studies, and evaluate ways to make inferences about landscape change and its effects on species using genetic data alone or in combination with other data. Those approaches include comparing correlation of genetic structure with historical versus contemporary landscapes, using molecular markers with different rates of evolution, contrasting metrics of genetic structure and gene flow that reflect population genetic processes operating at different temporal scales, comparing historical and contemporary samples, combining genetic data with contemporary estimates of species distribution or movement, and controlling for phylogeographic history. We recommend using simulated data sets to explore time lags in genetic structure, and argue that time lags should be explicitly considered both when designing and interpreting landscape genetic studies. We conclude that the time lag problem can be exploited to strengthen inferences about recent landscape changes and to establish conservation baselines, particularly when genetic data are combined with other data. © 2015 John Wiley & Sons Ltd.
A small-scale plasmoid formed during the May 13, 1985, AMPTE magnetotail barium release

NASA Technical Reports Server (NTRS)

Baker, D. N.; Fritz, T. A.; Bernhardt, P. A.

1989-01-01

Plasmoids are closed magnetic-loop structures with entrained hot plasma which are inferred to occur on large spatial scales in space plasma systems. A model is proposed here to explain the brightening and rapid tailward movement of the barium cloud released by the AMPTE IRM spacecraft on May 13, 1985. The model suggests that a small-scale plasmoid was formed due to a predicted development of heavy-ion-induced tearing in the thinned near-tail plasma sheet. Thus, a plasmoid may actually have been imaged due to the emissions of the entrained plasma ions within the plasma bubble.
Large-scale horizontal flows from SOUP observations of solar granulation

NASA Technical Reports Server (NTRS)

November, L. J.; Simon, G. W.; Tarbell, T. D.; Title, A. M.; Ferguson, S. H.

1987-01-01

Using high resolution time sequence photographs of solar granulation from the SOUP experiment on Spacelab 2, large scale horizontal flows were observed in the solar surface. The measurement method is based upon a local spatial cross correlation analysis. The horizontal motions have amplitudes in the range 300 to 1000 m/s. Radial outflow of granulation from a sunspot penumbra into surrounding photosphere is a striking new discovery. Both the supergranulation pattern and cellular structures having the scale of mesogranulation are seen. The vertical flows that are inferred by continuity of mass from these observed horizontal flows have larger upflow amplitudes in cell centers than downflow amplitudes at cell boundaries.
Two radars for AIM mission: A direct observation of the asteroid's structure from deep interior to regolith

NASA Astrophysics Data System (ADS)

Herique, A.; Ciarletti, V.

2015-10-01

Our knowledge of the internal structure of asteroids is, so far, indirect - relying entirely on inferences from remote sensing observations of the surface, and theoretical modeling. What are the bulk properties of the regolith and deep interior? And what are the physical processes that shape their internal structures? Direct measurements are needed to provide answers that will directly improve our ability to understand and model the mechanisms driving Near Earth Asteroids (NEA) for the benefit of science as well as for planetary defense or exploration. Radar tomography is the only technique to characterize internal structure from decimetric scale to global scale. This paper reviews the benefits of direct measurement of the asteroid interior. Then the radar concepts for both deep interior and shallow subsurface are presented and the radar payload proposed for the AIDA/AIM mission is outlined.
Inference of Population Structure using Dense Haplotype Data

PubMed Central

Lawson, Daniel John; Hellenthal, Garrett

2012-01-01

The advent of genome-wide dense variation data provides an opportunity to investigate ancestry in unprecedented detail, but presents new statistical challenges. We propose a novel inference framework that aims to efficiently capture information on population structure provided by patterns of haplotype similarity. Each individual in a sample is considered in turn as a recipient, whose chromosomes are reconstructed using chunks of DNA donated by the other individuals. Results of this “chromosome painting” can be summarized as a “coancestry matrix,” which directly reveals key information about ancestral relationships among individuals. If markers are viewed as independent, we show that this matrix almost completely captures the information used by both standard Principal Components Analysis (PCA) and model-based approaches such as STRUCTURE in a unified manner. Furthermore, when markers are in linkage disequilibrium, the matrix combines information across successive markers to increase the ability to discern fine-scale population structure using PCA. In parallel, we have developed an efficient model-based approach to identify discrete populations using this matrix, which offers advantages over PCA in terms of interpretability and over existing clustering algorithms in terms of speed, number of separable populations, and sensitivity to subtle population structure. We analyse Human Genome Diversity Panel data for 938 individuals and 641,000 markers, and we identify 226 populations reflecting differences on continental, regional, local, and family scales. We present multiple lines of evidence that, while many methods capture similar information among strongly differentiated groups, more subtle population structure in human populations is consistently present at a much finer level than currently available geographic labels and is only captured by the haplotype-based approach. The software used for this article, ChromoPainter and fineSTRUCTURE, is available from http://www.paintmychromosomes.com/. PMID:22291602
Sea surface currents and geographic isolation shape the genetic population structure of a coral reef fish in the Indian Ocean.

PubMed

Huyghe, Filip; Kochzius, Marc

2018-01-01

In this contribution, we determine the genetic population structure in the Skunk Clownfish (Amphiprion akallopsisos) across the Indian Ocean, and on a smaller geographic scale in the Western Indian Ocean (WIO). Highly restricted gene flow was discovered between populations on either side of the Indian Ocean using the control region as a mitochondrial marker (mtDNA). We verify this conclusion using 13 microsatellite markers and infer fine scale genetic structuring within the WIO. In total 387 samples from 21 sites were analysed using mtDNA and 13 microsatellite loci. Analysis included estimation of genetic diversity and population differentiation. A haplotype network was inferred using mtDNA. Nuclear markers were used in Bayesian clustering and a principal component analysis. Both markers confirmed strong genetic differentiation between WIO and Eastern Indian Ocean (EIO) populations, and a shallower population structure among Malagasy and East African mainland populations. Limited gene flow across the Mozambique Channel may be explained by its complex oceanography, which could cause local retention of larvae, limiting dispersal between Madagascar and the East African coast. Two other potential current-mediated barriers to larval dispersal suggested in the WIO, the split of the SEC at approximately 10° S and the convergence of the Somali Current with the East African Coast Current at approximately 3° S, were not found to form a barrier to gene flow in this species.
Sea surface currents and geographic isolation shape the genetic population structure of a coral reef fish in the Indian Ocean

PubMed Central

Kochzius, Marc

2018-01-01

In this contribution, we determine the genetic population structure in the Skunk Clownfish (Amphiprion akallopsisos) across the Indian Ocean, and on a smaller geographic scale in the Western Indian Ocean (WIO). Highly restricted gene flow was discovered between populations on either side of the Indian Ocean using the control region as a mitochondrial marker (mtDNA). We verify this conclusion using 13 microsatellite markers and infer fine scale genetic structuring within the WIO. In total 387 samples from 21 sites were analysed using mtDNA and 13 microsatellite loci. Analysis included estimation of genetic diversity and population differentiation. A haplotype network was inferred using mtDNA. Nuclear markers were used in Bayesian clustering and a principal component analysis. Both markers confirmed strong genetic differentiation between WIO and Eastern Indian Ocean (EIO) populations, and a shallower population structure among Malagasy and East African mainland populations. Limited gene flow across the Mozambique Channel may be explained by its complex oceanography, which could cause local retention of larvae, limiting dispersal between Madagascar and the East African coast. Two other potential current-mediated barriers to larval dispersal suggested in the WIO, the split of the SEC at approximately 10° S and the convergence of the Somali Current with the East African Coast Current at approximately 3° S, were not found to form a barrier to gene flow in this species. PMID:29522547
Factor structure and measurement invariance of the Health Education Impact Questionnaire: Does the subjectivity of the response perspective threaten the contextual validity of inferences?

PubMed

Elsworth, Gerald R; Nolte, Sandra; Osborne, Richard H

2015-01-01

On-going evidence is required to support the validity of inferences about change and group differences in the evaluation of health programs, particularly when self-report scales requiring substantial subjectivity in response generation are used as outcome measures. Following this reasoning, the aim of this study was to replicate the factor structure and investigate the measurement invariance of the latest version of the Health Education Impact Questionnaire, a widely used health program evaluation measure. An archived dataset of responses to the most recent version of the English-language Health Education Impact Questionnaire that uses four rather than six response options (N = 3221) was analysed using exploratory structural equation modelling and confirmatory factor analysis appropriate for ordered categorical data. Metric and scalar invariance were studied following recent recommendations in the literature to apply fully invariant unconditional models with minimum constraints necessary for model identification. The original eight-factor structure was replicated and all but one of the scales (Self Monitoring and Insight) was found to consist of unifactorial items with reliability of ⩾0.8 and satisfactory discriminant validity. Configural, metric and scalar invariance were established across pre-test to post-test and population sub-groups (sex, age, education, ethnic background). The results support the high level of interest in the Health Education Impact Questionnaire, particularly for use as a pre-test/post-test measure in experimental studies, other pre-post evaluation designs and system-level monitoring and evaluation.
Studies of the Intrinsic Complexities of Magnetotail Ion Distributions: Theory and Observations

NASA Technical Reports Server (NTRS)

Ashour-Abdalla, Maha

1998-01-01

This year we have studied the relationship between the structure seen in measured distribution functions and the detailed magnetospheric configuration. Results from our recent studies using time-dependent large-scale kinetic (LSK) calculations are used to infer the sources of the ions in the velocity distribution functions measured by a single spacecraft (Geotail). Our results strongly indicate that the different ion sources and acceleration mechanisms producing a measured distribution function can explain this structure. Moreover, individual structures within distribution functions were traced back to single sources. We also confirmed the fractal nature of ion distributions.

Optimal causal inference: estimating stored information and approximating causal architecture.

PubMed

Still, Susanne; Crutchfield, James P; Ellison, Christopher J

2010-09-01

We introduce an approach to inferring the causal architecture of stochastic dynamical systems that extends rate-distortion theory to use causal shielding--a natural principle of learning. We study two distinct cases of causal inference: optimal causal filtering and optimal causal estimation. Filtering corresponds to the ideal case in which the probability distribution of measurement sequences is known, giving a principled method to approximate a system's causal structure at a desired level of representation. We show that in the limit in which a model-complexity constraint is relaxed, filtering finds the exact causal architecture of a stochastic dynamical system, known as the causal-state partition. From this, one can estimate the amount of historical information the process stores. More generally, causal filtering finds a graded model-complexity hierarchy of approximations to the causal architecture. Abrupt changes in the hierarchy, as a function of approximation, capture distinct scales of structural organization. For nonideal cases with finite data, we show how the correct number of the underlying causal states can be found by optimal causal estimation. A previously derived model-complexity control term allows us to correct for the effect of statistical fluctuations in probability estimates and thereby avoid overfitting.
The FRIGG project: From intermediate galactic scales to self-gravitating cores

NASA Astrophysics Data System (ADS)

Hennebelle, Patrick

2018-03-01

Context. Understanding the detailed structure of the interstellar gas is essential for our knowledge of the star formation process. Aim. The small-scale structure of the interstellar medium (ISM) is a direct consequence of the galactic scales and making the link between the two is essential. Methods: We perform adaptive mesh simulations that aim to bridge the gap between the intermediate galactic scales and the self-gravitating prestellar cores. For this purpose we use stratified supernova regulated ISM magneto-hydrodynamical simulations at the kpc scale to set up the initial conditions. We then zoom, performing a series of concentric uniform refinement and then refining on the Jeans length for the last levels. This allows us to reach a spatial resolution of a few 10-3 pc. The cores are identified using a clump finder and various criteria based on virial analysis. Their most relevant properties are computed and, due to the large number of objects formed in the simulations, reliable statistics are obtained. Results: The cores' properties show encouraging agreements with observations. The mass spectrum presents a clear powerlaw at high masses with an exponent close to ≃-1.3 and a peak at about 1-2 M⊙. The velocity dispersion and the angular momentum distributions are respectively a few times the local sound speed and a few 10-2 pc km s-1. We also find that the distribution of thermally supercritical cores present a range of magnetic mass-to-flux over critical mass-to-flux ratios, typically between ≃0.3 and 3 indicating that they are significantly magnetized. Investigating the time and spatial dependence of these statistical properties, we conclude that they are not significantly affected by the zooming procedure and that they do not present very large fluctuations. The most severe issue appears to be the dependence on the numerical resolution of the core mass function (CMF). While the core definition process may possibly introduce some biases, the peak tends to shift to smaller values when the resolution improves. Conclusions: Our simulations, which use self-consistently generated initial conditions at the kpc scale, produce a large number of prestellar cores from which reliable statistics can be inferred. Preliminary comparisons with observations show encouraging agreements. In particular the inferred CMFs resemble the ones inferred from recent observations. We stress, however, a possible issue with the peak position shifting with numerical resolution.
Contemporary seismicity in and around the Yakima Fold and Thrust Belt in eastern Washington

USGS Publications Warehouse

Gomberg, J.; Sherrod, B.; Trautman, M.; Burns, E.; Snyder, Diane

2012-01-01

We examined characteristics of routinely cataloged seismicity from 1970 to the present in and around the Yakima fold‐and‐thrust belt (YFTB) in eastern Washington to determine if the characteristics of contemporary seismicity provide clues about regional‐scale active tectonics or about more localized, near‐surface processes. We employed new structural and hydrologic models of the Columbia River basalts (CRB) and found that one‐third to one‐half of the cataloged earthquakes occur within the CRB and that these CRB earthquakes exhibit significantly more clustered, and swarmlike, behavior than those outside. These results and inferences from published studies led us to hypothesize that clustered seismicity is likely associated with hydrologic changes in the CRB, which hosts the regional aquifer system. While some general features of the regional groundwater system support this hypothesis, seismicity patterns and mapped long‐term changes in groundwater levels and present‐day irrigation neither support nor refute it. Regional tectonic processes and crustal‐scale structures likely influence the distribution of earthquakes both outside and within the CRB as well. We based this inference on qualitatively assessed alignments between the dominant northwest trends in the geologic structure and the seismicity generally and between specific faults and characteristics of the 2009 Wooded Island swarm and aseismic slip, which is the only cluster studied in detail and the most vigorous since regional monitoring began.
A generative model of whole-brain effective connectivity.

PubMed

Frässle, Stefan; Lomakina, Ekaterina I; Kasper, Lars; Manjaly, Zina M; Leff, Alex; Pruessmann, Klaas P; Buhmann, Joachim M; Stephan, Klaas E

2018-05-25

The development of whole-brain models that can infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. A recently introduced generative model of fMRI data, regression dynamic causal modeling (rDCM), moves towards this goal as it scales gracefully to very large networks. However, large-scale networks with thousands of connections are difficult to interpret; additionally, one typically lacks information (data points per free parameter) for precise estimation of all model parameters. This paper introduces sparsity constraints to the variational Bayesian framework of rDCM as a solution to these problems in the domain of task-based fMRI. This sparse rDCM approach enables highly efficient effective connectivity analyses in whole-brain networks and does not require a priori assumptions about the network's connectivity structure but prunes fully (all-to-all) connected networks as part of model inversion. Following the derivation of the variational Bayesian update equations for sparse rDCM, we use both simulated and empirical data to assess the face validity of the model. In particular, we show that it is feasible to infer effective connection strengths from fMRI data using a network with more than 100 regions and 10,000 connections. This demonstrates the feasibility of whole-brain inference on effective connectivity from fMRI data - in single subjects and with a run-time below 1 min when using parallelized code. We anticipate that sparse rDCM may find useful application in connectomics and clinical neuromodeling - for example, for phenotyping individual patients in terms of whole-brain network structure. Copyright © 2018. Published by Elsevier Inc.
New insights into galaxy structure from GALPHAT- I. Motivation, methodology and benchmarks for Sérsic models

NASA Astrophysics Data System (ADS)

Yoon, Ilsang; Weinberg, Martin D.; Katz, Neal

2011-06-01

We introduce a new galaxy image decomposition tool, GALPHAT (GALaxy PHotometric ATtributes), which is a front-end application of the Bayesian Inference Engine (BIE), a parallel Markov chain Monte Carlo package, to provide full posterior probability distributions and reliable confidence intervals for all model parameters. The BIE relies on GALPHAT to compute the likelihood function. GALPHAT generates scale-free cumulative image tables for the desired model family with precise error control. Interpolation of this table yields accurate pixellated images with any centre, scale and inclination angle. GALPHAT then rotates the image by position angle using a Fourier shift theorem, yielding high-speed, accurate likelihood computation. We benchmark this approach using an ensemble of simulated Sérsic model galaxies over a wide range of observational conditions: the signal-to-noise ratio S/N, the ratio of galaxy size to the point spread function (PSF) and the image size, and errors in the assumed PSF; and a range of structural parameters: the half-light radius re and the Sérsic index n. We characterize the strength of parameter covariance in the Sérsic model, which increases with S/N and n, and the results strongly motivate the need for the full posterior probability distribution in galaxy morphology analyses and later inferences. The test results for simulated galaxies successfully demonstrate that, with a careful choice of Markov chain Monte Carlo algorithms and fast model image generation, GALPHAT is a powerful analysis tool for reliably inferring morphological parameters from a large ensemble of galaxies over a wide range of different observational conditions.
Evaluating Hierarchical Structure in Music Annotations

PubMed Central

McFee, Brian; Nieto, Oriol; Farbood, Morwaread M.; Bello, Juan Pablo

2017-01-01

Music exhibits structure at multiple scales, ranging from motifs to large-scale functional components. When inferring the structure of a piece, different listeners may attend to different temporal scales, which can result in disagreements when they describe the same piece. In the field of music informatics research (MIR), it is common to use corpora annotated with structural boundaries at different levels. By quantifying disagreements between multiple annotators, previous research has yielded several insights relevant to the study of music cognition. First, annotators tend to agree when structural boundaries are ambiguous. Second, this ambiguity seems to depend on musical features, time scale, and genre. Furthermore, it is possible to tune current annotation evaluation metrics to better align with these perceptual differences. However, previous work has not directly analyzed the effects of hierarchical structure because the existing methods for comparing structural annotations are designed for “flat” descriptions, and do not readily generalize to hierarchical annotations. In this paper, we extend and generalize previous work on the evaluation of hierarchical descriptions of musical structure. We derive an evaluation metric which can compare hierarchical annotations holistically across multiple levels. sing this metric, we investigate inter-annotator agreement on the multilevel annotations of two different music corpora, investigate the influence of acoustic properties on hierarchical annotations, and evaluate existing hierarchical segmentation algorithms against the distribution of inter-annotator agreement. PMID:28824514
Quantitative atomic-scale structure characterization of ordered mesoporous carbon materials by solid state NMR

DOE PAGES

Wang, Zhuoran; Opembe, Naftali; Kobayashi, Takeshi; ...

2018-02-03

In this study, solid-state (SS)NMR techniques were applied to characterize the atomic-scale structures of ordered mesoporous carbon (OMC) materials prepared using Pluronic F127 as template with resorcinol and formaldehyde as polymerizing precursors. A rigorous quantitative analysis was developed using a combination of 13C SSNMR spectra acquired with direct polarization and cross polarization on natural abundant and selectively 13C-enriched series of samples pyrolyzed at various temperatures. These experiments identified and counted the key functional groups present in the OMCs at various stages of preparation and thermal treatment. Lastly, the chemical evolution of molecular networks, the average sizes of aromatic clusters andmore » the extended molecular structures of OMCs were then inferred by coupling this information with the elemental analysis results.« less
Quantitative atomic-scale structure characterization of ordered mesoporous carbon materials by solid state NMR

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

Wang, Zhuoran; Opembe, Naftali; Kobayashi, Takeshi

In this study, solid-state (SS)NMR techniques were applied to characterize the atomic-scale structures of ordered mesoporous carbon (OMC) materials prepared using Pluronic F127 as template with resorcinol and formaldehyde as polymerizing precursors. A rigorous quantitative analysis was developed using a combination of 13C SSNMR spectra acquired with direct polarization and cross polarization on natural abundant and selectively 13C-enriched series of samples pyrolyzed at various temperatures. These experiments identified and counted the key functional groups present in the OMCs at various stages of preparation and thermal treatment. Lastly, the chemical evolution of molecular networks, the average sizes of aromatic clusters andmore » the extended molecular structures of OMCs were then inferred by coupling this information with the elemental analysis results.« less
Intrinsic uncertainty on the nature of dark energy

NASA Astrophysics Data System (ADS)

Valkenburg, Wessel; Kunz, Martin; Marra, Valerio

2013-12-01

We argue that there is an intrinsic noise on measurements of the equation of state parameter w = p/ρ from large-scale structure around us. The presence of the large-scale structure leads to an ambiguity in the definition of the background universe and thus there is a maximal precision with which we can determine the equation of state of dark energy. To study the uncertainty due to local structure, we model density perturbations stemming from a standard inflationary power spectrum by means of the exact Lemaître-Tolman-Bondi solution of Einstein’s equation, and show that the usual distribution of matter inhomogeneities in a ΛCDM cosmology causes a variation of w - as inferred from distance measures - of several percent. As we observe only one universe, or equivalently because of the cosmic variance, this uncertainty is systematic in nature.
Mapping Dark Matter in Simulated Galaxy Clusters

NASA Astrophysics Data System (ADS)

Bowyer, Rachel

2018-01-01

Galaxy clusters are the most massive bound objects in the Universe with most of their mass being dark matter. Cosmological simulations of structure formation show that clusters are embedded in a cosmic web of dark matter filaments and large scale structure. It is thought that these filaments are found preferentially close to the long axes of clusters. We extract galaxy clusters from the simulations "cosmo-OWLS" in order to study their properties directly and also to infer their properties from weak gravitational lensing signatures. We investigate various stacking procedures to enhance the signal of the filaments and large scale structure surrounding the clusters to better understand how the filaments of the cosmic web connect with galaxy clusters. This project was supported in part by the NSF REU grant AST-1358980 and by the Nantucket Maria Mitchell Association.
Ion-Scale Structure in Mercury's Magnetopause Reconnection Diffusion Region

NASA Technical Reports Server (NTRS)

Gershman, Daniel J.; Dorelli, John C.; DiBraccio, Gina A.; Raines, Jim M.; Slavin, James A.; Poh, Gangkai; Zurbuchen, Thomas H.

2016-01-01

The strength and time dependence of the electric field in a magnetopause diffusion region relate to the rate of magnetic reconnection between the solar wind and a planetary magnetic field. Here we use approximately 150 milliseconds measurements of energetic electrons from the Mercury Surface, Space Environment, GEochemistry, and Ranging (MESSENGER) spacecraft observed over Mercury's dayside polar cap boundary (PCB) to infer such small-scale changes in magnetic topology and reconnection rates. We provide the first direct measurement of open magnetic topology in flux transfer events at Mercury, structures thought to account for a significant portion of the open magnetic flux transport throughout the magnetosphere. In addition, variations in PCB latitude likely correspond to intermittent bursts of approximately 0.3 to 3 millivolts per meter reconnection electric fields separated by approximately 5 to10 seconds, resulting in average and peak normalized dayside reconnection rates of approximately 0.02 and approximately 0.2, respectively. These data demonstrate that structure in the magnetopause diffusion region at Mercury occurs at the smallest ion scales relevant to reconnection physics.
Causal learning with local computations.

PubMed

Fernbach, Philip M; Sloman, Steven A

2009-05-01

The authors proposed and tested a psychological theory of causal structure learning based on local computations. Local computations simplify complex learning problems via cues available on individual trials to update a single causal structure hypothesis. Structural inferences from local computations make minimal demands on memory, require relatively small amounts of data, and need not respect normative prescriptions as inferences that are principled locally may violate those principles when combined. Over a series of 3 experiments, the authors found (a) systematic inferences from small amounts of data; (b) systematic inference of extraneous causal links; (c) influence of data presentation order on inferences; and (d) error reduction through pretraining. Without pretraining, a model based on local computations fitted data better than a Bayesian structural inference model. The data suggest that local computations serve as a heuristic for learning causal structure. Copyright 2009 APA, all rights reserved.
A Stochastic Evolutionary Model for Protein Structure Alignment and Phylogeny

PubMed Central

Challis, Christopher J.; Schmidler, Scott C.

2012-01-01

We present a stochastic process model for the joint evolution of protein primary and tertiary structure, suitable for use in alignment and estimation of phylogeny. Indels arise from a classic Links model, and mutations follow a standard substitution matrix, whereas backbone atoms diffuse in three-dimensional space according to an Ornstein–Uhlenbeck process. The model allows for simultaneous estimation of evolutionary distances, indel rates, structural drift rates, and alignments, while fully accounting for uncertainty. The inclusion of structural information enables phylogenetic inference on time scales not previously attainable with sequence evolution models. The model also provides a tool for testing evolutionary hypotheses and improving our understanding of protein structural evolution. PMID:22723302
Mantle Circulation Models with variational data assimilation: Inferring past mantle flow and structure from plate motion histories and seismic tomography

NASA Astrophysics Data System (ADS)

Bunge, H.; Hagelberg, C.; Travis, B.

2002-12-01

EarthScope will deliver data on structure and dynamics of continental North America and the underlying mantle on an unprecedented scale. Indeed, the scope of EarthScope makes its mission comparable to the large remote sensing efforts that are transforming the oceanographic and atmospheric sciences today. Arguably the main impact of new solid Earth observing systems is to transform our use of geodynamic models increasingly from conditions that are data poor to an environment that is data rich. Oceanographers and meteorologists already have made substantial progress in adapting to this environment, by developing new approaches of interpreting oceanographic and atmospheric data objectively through data assimilation methods in their models. However, a similarly rigorous theoretical framework for merging EarthScope derived solid Earth data with geodynamic models has yet to be devised. Here we explore the feasibility of data assimilation in mantle convection studies in an attempt to fit global geodynamic model calculations explicitly to tomographic and tectonic constraints. This is an inverse problem not quite unlike the inverse problem of finding optimal seismic velocity structures faced by seismologists. We derive the generalized inverse of mantle convection from a variational approach and present the adjoint equations of mantle flow. The substantial computational burden associated with solutions to the generalized inverse problem of mantle convection is made feasible using a highly efficient finite element approach based on the 3-D spherical fully parallelized mantle dynamics code TERRA, implemented on a cost-effective topical PC-cluster (geowulf) dedicated specifically to large-scale geophysical simulations. This dedicated geophysical modeling computer allows us to investigate global inverse convection problems having a spatial discretization of less than 50 km throughout the mantle. We present a synthetic high-resolution modeling experiment to demonstrate that mid-Cretaceous mantle structure can be inferred accurately from our inverse approach assuming present-day mantle structure is well-known, even if an initial first guess assumption about the mid-Cretaceous mantle involved only a simple 1-D radial temperature profile. We suggest that geodynamic inverse modeling should make it possible to infer a number of flow parameters from observational constraints of the mantle.
A probabilistic model framework for evaluating year-to-year variation in crop productivity

NASA Astrophysics Data System (ADS)

Yokozawa, M.; Iizumi, T.; Tao, F.

2008-12-01

Most models describing the relation between crop productivity and weather condition have so far been focused on mean changes of crop yield. For keeping stable food supply against abnormal weather as well as climate change, evaluating the year-to-year variations in crop productivity rather than the mean changes is more essential. We here propose a new framework of probabilistic model based on Bayesian inference and Monte Carlo simulation. As an example, we firstly introduce a model on paddy rice production in Japan. It is called PRYSBI (Process- based Regional rice Yield Simulator with Bayesian Inference; Iizumi et al., 2008). The model structure is the same as that of SIMRIW, which was developed and used widely in Japan. The model includes three sub- models describing phenological development, biomass accumulation and maturing of rice crop. These processes are formulated to include response nature of rice plant to weather condition. This model inherently was developed to predict rice growth and yield at plot paddy scale. We applied it to evaluate the large scale rice production with keeping the same model structure. Alternatively, we assumed the parameters as stochastic variables. In order to let the model catch up actual yield at larger scale, model parameters were determined based on agricultural statistical data of each prefecture of Japan together with weather data averaged over the region. The posterior probability distribution functions (PDFs) of parameters included in the model were obtained using Bayesian inference. The MCMC (Markov Chain Monte Carlo) algorithm was conducted to numerically solve the Bayesian theorem. For evaluating the year-to-year changes in rice growth/yield under this framework, we firstly iterate simulations with set of parameter values sampled from the estimated posterior PDF of each parameter and then take the ensemble mean weighted with the posterior PDFs. We will also present another example for maize productivity in China. The framework proposed here provides us information on uncertainties, possibilities and limitations on future improvements in crop model as well.
Genetic analysis reveals efficient sexual spore dispersal at a fine spatial scale in Armillaria ostoyae, the causal agent of root-rot disease in conifers.

PubMed

Dutech, Cyril; Labbé, Frédéric; Capdevielle, Xavier; Lung-Escarmant, Brigitte

Armillaria ostoyae (sometimes named Armillaria solidipes) is a fungal species causing root diseases in numerous coniferous forests of the northern hemisphere. The importance of sexual spores for the establishment of new disease centres remains unclear, particularly in the large maritime pine plantations of southwestern France. An analysis of the genetic diversity of a local fungal population distributed over 500 ha in this French forest showed genetic recombination between genotypes to be frequent, consistent with regular sexual reproduction within the population. The estimated spatial genetic structure displayed a significant pattern of isolation by distance, consistent with the dispersal of sexual spores mostly at the spatial scale studied. Using these genetic data, we inferred an effective density of reproductive individuals of 0.1-0.3 individuals/ha, and a second moment of parent-progeny dispersal distance of 130-800 m, compatible with the main models of fungal spore dispersal. These results contrast with those obtained for studies of A. ostoyae over larger spatial scales, suggesting that inferences about mean spore dispersal may be best performed at fine spatial scales (i.e. a few kilometres) for most fungal species. Copyright © 2017 British Mycological Society. Published by Elsevier Ltd. All rights reserved.
Quantifying Biomass from Point Clouds by Connecting Representations of Ecosystem Structure

NASA Astrophysics Data System (ADS)

Hendryx, S. M.; Barron-Gafford, G.

2017-12-01

Quantifying terrestrial ecosystem biomass is an essential part of monitoring carbon stocks and fluxes within the global carbon cycle and optimizing natural resource management. Point cloud data such as from lidar and structure from motion can be effective for quantifying biomass over large areas, but significant challenges remain in developing effective models that allow for such predictions. Inference models that estimate biomass from point clouds are established in many environments, yet, are often scale-dependent, needing to be fitted and applied at the same spatial scale and grid size at which they were developed. Furthermore, training such models typically requires large in situ datasets that are often prohibitively costly or time-consuming to obtain. We present here a scale- and sensor-invariant framework for efficiently estimating biomass from point clouds. Central to this framework, we present a new algorithm, assignPointsToExistingClusters, that has been developed for finding matches between in situ data and clusters in remotely-sensed point clouds. The algorithm can be used for assessing canopy segmentation accuracy and for training and validating machine learning models for predicting biophysical variables. We demonstrate the algorithm's efficacy by using it to train a random forest model of above ground biomass in a shrubland environment in Southern Arizona. We show that by learning a nonlinear function to estimate biomass from segmented canopy features we can reduce error, especially in the presence of inaccurate clusterings, when compared to a traditional, deterministic technique to estimate biomass from remotely measured canopies. Our random forest on cluster features model extends established methods of training random forest regressions to predict biomass of subplots but requires significantly less training data and is scale invariant. The random forest on cluster features model reduced mean absolute error, when evaluated on all test data in leave one out cross validation, by 40.6% from deterministic mesquite allometry and 35.9% from the inferred ecosystem-state allometric function. Our framework should allow for the inference of biomass more efficiently than common subplot methods and more accurately than individual tree segmentation methods in densely vegetated environments.
Modeling Pair Distribution Functions of Rare-Earth Phosphate Glasses Using Principal Component Analysis.

PubMed

Cole, Jacqueline M; Cheng, Xie; Payne, Michael C

2016-11-07

The use of principal component analysis (PCA) to statistically infer features of local structure from experimental pair distribution function (PDF) data is assessed on a case study of rare-earth phosphate glasses (REPGs). Such glasses, codoped with two rare-earth ions (R and R') of different sizes and optical properties, are of interest to the laser industry. The determination of structure-property relationships in these materials is an important aspect of their technological development. Yet, realizing the local structure of codoped REPGs presents significant challenges relative to their singly doped counterparts; specifically, R and R' are difficult to distinguish in terms of establishing relative material compositions, identifying atomic pairwise correlation profiles in a PDF that are associated with each ion, and resolving peak overlap of such profiles in PDFs. This study demonstrates that PCA can be employed to help overcome these structural complications, by statistically inferring trends in PDFs that exist for a restricted set of experimental data on REPGs, and using these as training data to predict material compositions and PDF profiles in unknown codoped REPGs. The application of these PCA methods to resolve individual atomic pairwise correlations in t(r) signatures is also presented. The training methods developed for these structural predictions are prevalidated by testing their ability to reproduce known physical phenomena, such as the lanthanide contraction, on PDF signatures of the structurally simpler singly doped REPGs. The intrinsic limitations of applying PCA to analyze PDFs relative to the quality control of source data, data processing, and sample definition, are also considered. While this case study is limited to lanthanide-doped REPGs, this type of statistical inference may easily be extended to other inorganic solid-state materials and be exploited in large-scale data-mining efforts that probe many t(r) functions.
Feature-to-Feature Inference Under Conditions of Cue Restriction and Dimensional Correlation.

PubMed

Lancaster, Matthew E; Homa, Donald

2017-01-01

The present study explored feature-to-feature and label-to-feature inference in a category task for different category structures. In the correlated condition, each of the 4 dimensions comprising the category was positively correlated to each other and to the category label. In the uncorrelated condition, no correlation existed between the 4 dimensions comprising the category, although the dimension to category label correlation matched that of the correlated condition. After learning, participants made inference judgments of a missing feature, given 1, 2, or 3 feature cues; on half the trials, the category label was also included as a cue. The results showed superior inference of features following training on the correlated structure, with accurate inference when only a single feature was presented. In contrast, a single-feature cue resulted in chance levels of inference for the uncorrelated structure. Feature inference systematically improved with number of cues after training on the correlated structure. Surprisingly, a similar outcome was obtained for the uncorrelated structure, an outcome that must have reflected mediation via the category label. A descriptive model is briefly introduced to explain the results, with a suggestion that this paradigm might be profitably extended to hierarchical structures where the levels of feature-to-feature inference might vary with the depth of the hierarchy.
Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method

PubMed Central

Zhang, Tingting; Kou, S. C.

2010-01-01

Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure. PMID:21258615

Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method.

PubMed

Zhang, Tingting; Kou, S C

2010-01-01

Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure.
Nonparametric weighted stochastic block models

NASA Astrophysics Data System (ADS)

Peixoto, Tiago P.

2018-01-01

We present a Bayesian formulation of weighted stochastic block models that can be used to infer the large-scale modular structure of weighted networks, including their hierarchical organization. Our method is nonparametric, and thus does not require the prior knowledge of the number of groups or other dimensions of the model, which are instead inferred from data. We give a comprehensive treatment of different kinds of edge weights (i.e., continuous or discrete, signed or unsigned, bounded or unbounded), as well as arbitrary weight transformations, and describe an unsupervised model selection approach to choose the best network description. We illustrate the application of our method to a variety of empirical weighted networks, such as global migrations, voting patterns in congress, and neural connections in the human brain.
Dark matter, long-range forces, and large-scale structure

NASA Technical Reports Server (NTRS)

Gradwohl, Ben-Ami; Frieman, Joshua A.

1992-01-01

If the dark matter in galaxies and clusters is nonbaryonic, it can interact with additional long-range fields that are invisible to experimental tests of the equivalence principle. We discuss the astrophysical and cosmological implications of a long-range force coupled only to the dark matter and find rather tight constraints on its strength. If the force is repulsive (attractive), the masses of galaxy groups and clusters (and the mean density of the universe inferred from them) have been systematically underestimated (overestimated). We explore the consequent effects on the two-point correlation function, large-scale velocity flows, and microwave background anisotropies, for models with initial scale-invariant adiabatic perturbations and cold dark matter.
Large-scale horizontal flows from SOUP observations of solar granulation

NASA Astrophysics Data System (ADS)

November, L. J.; Simon, G. W.; Tarbell, T. D.; Title, A. M.; Ferguson, S. H.

1987-09-01

Using high-resolution time-sequence photographs of solar granulation from the SOUP experiment on Spacelab 2 the authors observed large-scale horizontal flows in the solar surface. The measurement method is based upon a local spatial cross correlation analysis. The horizontal motions have amplitudes in the range 300 to 1000 m/s. Radial outflow of granulation from a sunspot penumbra into the surrounding photosphere is a striking new discovery. Both the supergranulation pattern and cellular structures having the scale of mesogranulation are seen. The vertical flows that are inferred by continuity of mass from these observed horizontal flows have larger upflow amplitudes in cell centers than downflow amplitudes at cell boundaries.
Modulated Modularity Clustering as an Exploratory Tool for Functional Genomic Inference

PubMed Central

Stone, Eric A.; Ayroles, Julien F.

2009-01-01

In recent years, the advent of high-throughput assays, coupled with their diminishing cost, has facilitated a systems approach to biology. As a consequence, massive amounts of data are currently being generated, requiring efficient methodology aimed at the reduction of scale. Whole-genome transcriptional profiling is a standard component of systems-level analyses, and to reduce scale and improve inference clustering genes is common. Since clustering is often the first step toward generating hypotheses, cluster quality is critical. Conversely, because the validation of cluster-driven hypotheses is indirect, it is critical that quality clusters not be obtained by subjective means. In this paper, we present a new objective-based clustering method and demonstrate that it yields high-quality results. Our method, modulated modularity clustering (MMC), seeks community structure in graphical data. MMC modulates the connection strengths of edges in a weighted graph to maximize an objective function (called modularity) that quantifies community structure. The result of this maximization is a clustering through which tightly-connected groups of vertices emerge. Our application is to systems genetics, and we quantitatively compare MMC both to the hierarchical clustering method most commonly employed and to three popular spectral clustering approaches. We further validate MMC through analyses of human and Drosophila melanogaster expression data, demonstrating that the clusters we obtain are biologically meaningful. We show MMC to be effective and suitable to applications of large scale. In light of these features, we advocate MMC as a standard tool for exploration and hypothesis generation. PMID:19424432
A Topological Criterion for Filtering Information in Complex Brain Networks

PubMed Central

Latora, Vito; Chavez, Mario

2017-01-01

In many biological systems, the network of interactions between the elements can only be inferred from experimental measurements. In neuroscience, non-invasive imaging tools are extensively used to derive either structural or functional brain networks in-vivo. As a result of the inference process, we obtain a matrix of values corresponding to a fully connected and weighted network. To turn this into a useful sparse network, thresholding is typically adopted to cancel a percentage of the weakest connections. The structural properties of the resulting network depend on how much of the inferred connectivity is eventually retained. However, how to objectively fix this threshold is still an open issue. We introduce a criterion, the efficiency cost optimization (ECO), to select a threshold based on the optimization of the trade-off between the efficiency of a network and its wiring cost. We prove analytically and we confirm through numerical simulations that the connection density maximizing this trade-off emphasizes the intrinsic properties of a given network, while preserving its sparsity. Moreover, this density threshold can be determined a-priori, since the number of connections to filter only depends on the network size according to a power-law. We validate this result on several brain networks, from micro- to macro-scales, obtained with different imaging modalities. Finally, we test the potential of ECO in discriminating brain states with respect to alternative filtering methods. ECO advances our ability to analyze and compare biological networks, inferred from experimental data, in a fast and principled way. PMID:28076353
Multi-scale modelling of the dynamics of cell colonies: insights into cell-adhesion forces and cancer invasion from in silico simulations.

PubMed

Schlüter, Daniela K; Ramis-Conde, Ignacio; Chaplain, Mark A J

2015-02-06

Studying the biophysical interactions between cells is crucial to understanding how normal tissue develops, how it is structured and also when malfunctions occur. Traditional experiments try to infer events at the tissue level after observing the behaviour of and interactions between individual cells. This approach assumes that cells behave in the same biophysical manner in isolated experiments as they do within colonies and tissues. In this paper, we develop a multi-scale multi-compartment mathematical model that accounts for the principal biophysical interactions and adhesion pathways not only at a cell-cell level but also at the level of cell colonies (in contrast to the traditional approach). Our results suggest that adhesion/separation forces between cells may be lower in cell colonies than traditional isolated single-cell experiments infer. As a consequence, isolated single-cell experiments may be insufficient to deduce important biological processes such as single-cell invasion after detachment from a solid tumour. The simulations further show that kinetic rates and cell biophysical characteristics such as pressure-related cell-cycle arrest have a major influence on cell colony patterns and can allow for the development of protrusive cellular structures as seen in invasive cancer cell lines independent of expression levels of pro-invasion molecules.
Multi-scale modelling of the dynamics of cell colonies: insights into cell-adhesion forces and cancer invasion from in silico simulations

PubMed Central

Schlüter, Daniela K.; Ramis-Conde, Ignacio; Chaplain, Mark A. J.

2015-01-01

Studying the biophysical interactions between cells is crucial to understanding how normal tissue develops, how it is structured and also when malfunctions occur. Traditional experiments try to infer events at the tissue level after observing the behaviour of and interactions between individual cells. This approach assumes that cells behave in the same biophysical manner in isolated experiments as they do within colonies and tissues. In this paper, we develop a multi-scale multi-compartment mathematical model that accounts for the principal biophysical interactions and adhesion pathways not only at a cell–cell level but also at the level of cell colonies (in contrast to the traditional approach). Our results suggest that adhesion/separation forces between cells may be lower in cell colonies than traditional isolated single-cell experiments infer. As a consequence, isolated single-cell experiments may be insufficient to deduce important biological processes such as single-cell invasion after detachment from a solid tumour. The simulations further show that kinetic rates and cell biophysical characteristics such as pressure-related cell-cycle arrest have a major influence on cell colony patterns and can allow for the development of protrusive cellular structures as seen in invasive cancer cell lines independent of expression levels of pro-invasion molecules. PMID:25519994
LASSIM-A network inference toolbox for genome-wide mechanistic modeling.

PubMed

Magnusson, Rasmus; Mariotti, Guido Pio; Köpsén, Mattias; Lövfors, William; Gawel, Danuta R; Jörnsten, Rebecka; Linde, Jörg; Nordling, Torbjörn E M; Nyman, Elin; Schulze, Sylvie; Nestor, Colm E; Zhang, Huan; Cedersund, Gunnar; Benson, Mikael; Tjärnberg, Andreas; Gustafsson, Mika

2017-06-01

Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naïve Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases.
The Effect of Star Formation History on the Inferred Stellar Initial Mass Function

NASA Astrophysics Data System (ADS)

Elmegreen, Bruce G.; Scalo, John

2006-01-01

Peaks and lulls in the star formation rate (SFR) over the history of the Galaxy produce plateaus and declines in the present-day mass function (PDMF) where the main-sequence lifetime overlaps the age and duration of the SFR variation. These PDMF features can be misinterpreted as the form of the intrinsic stellar initial mass function (IMF) if the star formation rate is assumed to be constant or slowly varying with time. This effect applies to all regions that have formed stars for longer than the age of the most massive stars, including OB associations, star complexes, and especially galactic field stars. Related problems may apply to embedded clusters. Evidence is summarized for temporal SFR variations from parsec scales to entire galaxies, all of which should contribute to inferred IMF distortions. We give examples of various star formation histories to demonstrate the types of false IMF structures that might be seen. These include short-duration bursts, stochastic histories with lognormal amplitude distributions, and oscillating histories with various periods and phases. The inferred IMF should appear steeper than the intrinsic IMF over mass ranges where the stellar lifetimes correspond to times of decreasing SFRs; shallow portions of the inferred IMF correspond to times of increasing SFRs. If field regions are populated by dispersed clusters and defined by their low current SFRs, then they should have steeper inferred IMFs than the clusters. The SFRs required to give the steep field IMFs in the LMC and SMC are determined. Structure observed in several determinations of the Milky Way field star IMF can be accounted for by a stochastic and bursty star formation history.
Comparative Tectonics of Europa and Ganymede

NASA Astrophysics Data System (ADS)

Pappalardo, R. T.; Collins, G. C.; Prockter, L. M.; Head, J. W.

2000-10-01

Europa and Ganymede are sibling satellites with tectonic similarities and differences. Ganymede's ancient dark terrain is crossed by furrows, probably related to ancient large impacts, and has been normal faulted to various degrees. Bright grooved is pervasively deformed at multiple scales and is locally highly strained, consistent with normal faulting of an ice-rich lithosphere above a ductile asthenosphere, along with minor horizontal shear. Little evidence has been identified for compressional structures. The relative roles of tectonism and icy cryovolcanism in creating bright grooved terrain is an outstanding issue. Some ridge and trough structures within Europa's bands show tectonic similarities to Ganymede's grooved terrain, specifically sawtooth structures resembling normal fault blocks. Small-scale troughs are consistent with widened tension fractures. Shearing has produced transtensional and transpressional structures in Europan bands. Large-scale folds are recognized on Europa, with synclinal small-scale ridges and scarps probably representing folds and/or thrust blocks. Europa's ubiquitous double ridges may have originated as warm ice upwelled along tidally heated fracture zones. The morphological variety of ridges and troughs on Europa imply that care must be taken in inferring their origin. The relative youth of Europa's surface means that the satellite has preserved near-pristine morphologies of many structures, though sputter erosion could have altered the morphology of older topography. Moderate-resolution imaging has revealed lesser apparent diversity in Ganymede's ridge and trough types. Galileo's 28th orbit has brought new 20 m/pixel imaging of Ganymede, allowing direct comparison to Europa's small-scale structures.
Vertical structure of medium-scale traveling ionospheric disturbances

NASA Astrophysics Data System (ADS)

Ssessanga, Nicholas; Kim, Yong Ha; Kim, Eunsol

2015-11-01

We develop an algorithm of computerized ionospheric tomography (CIT) to infer information on the vertical and horizontal structuring of electron density during nighttime medium-scale traveling ionospheric disturbances (MSTIDs). To facilitate digital CIT we have adopted total electron contents (TEC) from a dense Global Positioning System (GPS) receiver network, GEONET, which contains more than 1000 receivers. A multiplicative algebraic reconstruction technique was utilized with a calibrated IRI-2012 model as an initial solution. The reconstructed F2 peak layer varied in altitude with average peak-to-peak amplitude of ~52 km. In addition, the F2 peak layer anticorrelated with TEC variations. This feature supports a theory in which nighttime MSTID is composed of oscillating electric fields due to conductivity variations. Moreover, reconstructed TEC variations over two stations were reasonably close to variations directly derived from the measured TEC data set. Our tomographic analysis may thus help understand three-dimensional structure of MSTIDs in a quantitative way.
Factor structure and measurement invariance of the Health Education Impact Questionnaire: Does the subjectivity of the response perspective threaten the contextual validity of inferences?

PubMed Central

Elsworth, Gerald R; Nolte, Sandra

2015-01-01

Objective: On-going evidence is required to support the validity of inferences about change and group differences in the evaluation of health programs, particularly when self-report scales requiring substantial subjectivity in response generation are used as outcome measures. Following this reasoning, the aim of this study was to replicate the factor structure and investigate the measurement invariance of the latest version of the Health Education Impact Questionnaire, a widely used health program evaluation measure. Methods: An archived dataset of responses to the most recent version of the English-language Health Education Impact Questionnaire that uses four rather than six response options (N = 3221) was analysed using exploratory structural equation modelling and confirmatory factor analysis appropriate for ordered categorical data. Metric and scalar invariance were studied following recent recommendations in the literature to apply fully invariant unconditional models with minimum constraints necessary for model identification. Results: The original eight-factor structure was replicated and all but one of the scales (Self Monitoring and Insight) was found to consist of unifactorial items with reliability of ⩾0.8 and satisfactory discriminant validity. Configural, metric and scalar invariance were established across pre-test to post-test and population sub-groups (sex, age, education, ethnic background). Conclusion: The results support the high level of interest in the Health Education Impact Questionnaire, particularly for use as a pre-test/post-test measure in experimental studies, other pre–post evaluation designs and system-level monitoring and evaluation. PMID:26770785
Learning about the internal structure of categories through classification and feature inference.

PubMed

Jee, Benjamin D; Wiley, Jennifer

2014-01-01

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a "partial inference" condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.
Liquid crystal-based Mueller matrix spectral imaging polarimetry for parameterizing mineral structural organization.

PubMed

Gladish, James C; Duncan, Donald D

2017-01-20

Herein, we discuss the remote assessment of the subwavelength organizational structure of a medium. Specifically, we use spectral imaging polarimetry, as the vector nature of polarized light enables it to interact with optical anisotropies within a medium, while the spectral aspect of polarization is sensitive to small-scale structure. The ability to image these effects allows for inference of spatial structural organization parameters. This work describes a methodology for revealing structural organization by exploiting the Stokes/Mueller formalism and by utilizing measurements from a spectral imaging polarimeter constructed from liquid crystal variable retarders and a liquid crystal tunable filter. We provide results to validate the system and then show results from measurements on a mineral sample.
Angular ellipticity correlations in a composite alignment model for elliptical and spiral galaxies and inference from weak lensing

NASA Astrophysics Data System (ADS)

Tugendhat, Tim M.; Schäfer, Björn Malte

2018-05-01

We investigate a physical, composite alignment model for both spiral and elliptical galaxies and its impact on cosmological parameter estimation from weak lensing for a tomographic survey. Ellipticity correlation functions and angular ellipticity spectra for spiral and elliptical galaxies are derived on the basis of tidal interactions with the cosmic large-scale structure and compared to the tomographic weak-lensing signal. We find that elliptical galaxies cause a contribution to the weak-lensing dominated ellipticity correlation on intermediate angular scales between ℓ ≃ 40 and ℓ ≃ 400 before that of spiral galaxies dominates on higher multipoles. The predominant term on intermediate scales is the negative cross-correlation between intrinsic alignments and weak gravitational lensing (GI-alignment). We simulate parameter inference from weak gravitational lensing with intrinsic alignments unaccounted; the bias induced by ignoring intrinsic alignments in a survey like Euclid is shown to be several times larger than the statistical error and can lead to faulty conclusions when comparing to other observations. The biases generally point into different directions in parameter space, such that in some cases one can observe a partial cancellation effect. Furthermore, it is shown that the biases increase with the number of tomographic bins used for the parameter estimation process. We quantify this parameter estimation bias in units of the statistical error and compute the loss of Bayesian evidence for a model due to the presence of systematic errors as well as the Kullback-Leibler divergence to quantify the distance between the true model and the wrongly inferred one.
Gaussian process based independent analysis for temporal source separation in fMRI.

PubMed

Hald, Ditte Høvenhoff; Henao, Ricardo; Winther, Ole

2017-05-15

Functional Magnetic Resonance Imaging (fMRI) gives us a unique insight into the processes of the brain, and opens up for analyzing the functional activation patterns of the underlying sources. Task-inferred supervised learning with restrictive assumptions in the regression set-up, restricts the exploratory nature of the analysis. Fully unsupervised independent component analysis (ICA) algorithms, on the other hand, can struggle to detect clear classifiable components on single-subject data. We attribute this shortcoming to inadequate modeling of the fMRI source signals by failing to incorporate its temporal nature. fMRI source signals, biological stimuli and non-stimuli-related artifacts are all smooth over a time-scale compatible with the sampling time (TR). We therefore propose Gaussian process ICA (GPICA), which facilitates temporal dependency by the use of Gaussian process source priors. On two fMRI data sets with different sampling frequency, we show that the GPICA-inferred temporal components and associated spatial maps allow for a more definite interpretation than standard temporal ICA methods. The temporal structures of the sources are controlled by the covariance of the Gaussian process, specified by a kernel function with an interpretable and controllable temporal length scale parameter. We propose a hierarchical model specification, considering both instantaneous and convolutive mixing, and we infer source spatial maps, temporal patterns and temporal length scale parameters by Markov Chain Monte Carlo. A companion implementation made as a plug-in for SPM can be downloaded from https://github.com/dittehald/GPICA. Copyright © 2017 Elsevier Inc. All rights reserved.
Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining

PubMed Central

Hero, Alfred O.; Rajaratnam, Bala

2015-01-01

When can reliable inference be drawn in fue “Big Data” context? This paper presents a framework for answering this fundamental question in the context of correlation mining, wifu implications for general large scale inference. In large scale data applications like genomics, connectomics, and eco-informatics fue dataset is often variable-rich but sample-starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than fue number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for “Big Data”. Sample complexity however has received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address fuis gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where fue variable dimension is fixed and fue sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; 3) the purely high dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa cale data dimension. We illustrate this high dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables fua t are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. we demonstrate various regimes of correlation mining based on the unifying perspective of high dimensional learning rates and sample complexity for different structured covariance models and different inference tasks. PMID:27087700
Form and function in hillslope hydrology: in situ imaging and characterization of flow-relevant structures

NASA Astrophysics Data System (ADS)

Jackisch, Conrad; Angermann, Lisa; Allroggen, Niklas; Sprenger, Matthias; Blume, Theresa; Tronicke, Jens; Zehe, Erwin

2017-07-01

The study deals with the identification and characterization of rapid subsurface flow structures through pedo- and geo-physical measurements and irrigation experiments at the point, plot and hillslope scale. Our investigation of flow-relevant structures and hydrological responses refers to the general interplay of form and function, respectively. To obtain a holistic picture of the subsurface, a large set of different laboratory, exploratory and experimental methods was used at the different scales. For exploration these methods included drilled soil core profiles, in situ measurements of infiltration capacity and saturated hydraulic conductivity, and laboratory analyses of soil water retention and saturated hydraulic conductivity. The irrigation experiments at the plot scale were monitored through a combination of dye tracer, salt tracer, soil moisture dynamics, and 3-D time-lapse ground penetrating radar (GPR) methods. At the hillslope scale the subsurface was explored by a 3-D GPR survey. A natural storm event and an irrigation experiment were monitored by a dense network of soil moisture observations and a cascade of 2-D time-lapse GPR trenches. We show that the shift between activated and non-activated state of the flow paths is needed to distinguish structures from overall heterogeneity. Pedo-physical analyses of point-scale samples are the basis for sub-scale structure inference. At the plot and hillslope scale 3-D and 2-D time-lapse GPR applications are successfully employed as non-invasive means to image subsurface response patterns and to identify flow-relevant paths. Tracer recovery and soil water responses from irrigation experiments deliver a consistent estimate of response velocities. The combined observation of form and function under active conditions provides the means to localize and characterize the structures (this study) and the hydrological processes (companion study Angermann et al., 2017, this issue).
Fast and reliable prediction of domain-peptide binding affinity using coarse-grained structure models.

PubMed

Tian, Feifei; Tan, Rui; Guo, Tailin; Zhou, Peng; Yang, Li

2013-07-01

Domain-peptide recognition and interaction are fundamentally important for eukaryotic signaling and regulatory networks. It is thus essential to quantitatively infer the binding stability and specificity of such interaction based upon large-scale but low-accurate complex structure models which could be readily obtained from sophisticated molecular modeling procedure. In the present study, a new method is described for the fast and reliable prediction of domain-peptide binding affinity with coarse-grained structure models. This method is designed to tolerate strong random noises involved in domain-peptide complex structures and uses statistical modeling approach to eliminate systematic bias associated with a group of investigated samples. As a paradigm, this method was employed to model and predict the binding behavior of various peptides to four evolutionarily unrelated peptide-recognition domains (PRDs), i.e. human amph SH3, human nherf PDZ, yeast syh GYF and yeast bmh 14-3-3, and moreover, we explored the molecular mechanism and biological implication underlying the binding of cognate and noncognate peptide ligands to their domain receptors. It is expected that the newly proposed method could be further used to perform genome-wide inference of domain-peptide binding at three-dimensional structure level. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Improving catchment discharge predictions by inferring flow route contributions from a nested-scale monitoring and model setup

NASA Astrophysics Data System (ADS)

van der Velde, Y.; Rozemeijer, J. C.; de Rooij, G. H.; van Geer, F. C.; Torfs, P. J. J. F.; de Louw, P. G. B.

2011-03-01

Identifying effective measures to reduce nutrient loads of headwaters in lowland catchments requires a thorough understanding of flow routes of water and nutrients. In this paper we assess the value of nested-scale discharge and groundwater level measurements for the estimation of flow route volumes and for predictions of catchment discharge. In order to relate field-site measurements to the catchment-scale an upscaling approach is introduced that assumes that scale differences in flow route fluxes originate from differences in the relationship between groundwater storage and the spatial structure of the groundwater table. This relationship is characterized by the Groundwater Depth Distribution (GDD) curve that relates spatial variation in groundwater depths to the average groundwater depth. The GDD-curve was measured for a single field site (0.009 km2) and simple process descriptions were applied to relate groundwater levels to flow route discharges. This parsimonious model could accurately describe observed storage, tube drain discharge, overland flow and groundwater flow simultaneously with Nash-Sutcliff coefficients exceeding 0.8. A probabilistic Monte Carlo approach was applied to upscale field-site measurements to catchment scales by inferring scale-specific GDD-curves from the hydrographs of two nested catchments (0.4 and 6.5 km2). The estimated contribution of tube drain effluent (a dominant source for nitrates) decreased with increasing scale from 76-79% at the field-site to 34-61% and 25-50% for both catchment scales. These results were validated by demonstrating that a model conditioned on nested-scale measurements improves simulations of nitrate loads and predictions of extreme discharges during validation periods compared to a model that was conditioned on catchment discharge only.
Algorithm of OMA for large-scale orthology inference

PubMed Central

Roth, Alexander CJ; Gonnet, Gaston H; Dessimoz, Christophe

2008-01-01

Background OMA is a project that aims to identify orthologs within publicly available, complete genomes. With 657 genomes analyzed to date, OMA is one of the largest projects of its kind. Results The algorithm of OMA improves upon standard bidirectional best-hit approach in several respects: it uses evolutionary distances instead of scores, considers distance inference uncertainty, includes many-to-many orthologous relations, and accounts for differential gene losses. Herein, we describe in detail the algorithm for inference of orthology and provide the rationale for parameter selection through multiple tests. Conclusion OMA contains several novel improvement ideas for orthology inference and provides a unique dataset of large-scale orthology assignments. PMID:19055798
Structural heritage, reactivation and distribution of fault and fracture network in a rifting context: Case study of the western shoulder of the Upper Rhine Graben

NASA Astrophysics Data System (ADS)

Bertrand, Lionel; Jusseaume, Jessie; Géraud, Yves; Diraison, Marc; Damy, Pierre-Clément; Navelot, Vivien; Haffen, Sébastien

2018-03-01

In fractured reservoirs in the basement of extensional basins, fault and fracture parameters like density, spacing and length distribution are key properties for modelling and prediction of reservoir properties and fluids flow. As only large faults are detectable using basin-scale geophysical investigations, these fine-scale parameters need to be inferred from faults and fractures in analogous rocks at the outcrop. In this study, we use the western shoulder of the Upper Rhine Graben as an outcropping analogue of several deep borehole projects in the basement of the graben. Geological regional data, DTM (Digital Terrain Model) mapping and outcrop studies with scanlines are used to determine the spatial arrangement of the faults from the regional to the reservoir scale. The data shows that: 1) The fault network can be hierarchized in three different orders of scale and structural blocks with a characteristic structuration. This is consistent with other basement rocks studies in other rifting system allowing the extrapolation of the important parameters for modelling. 2) In the structural blocks, the fracture network linked to the faults is linked to the interplay between rock facies variation linked to the rock emplacement and the rifting event.
Intermediate scale plasma density irregularities in the polar ionosphere inferred from radio occultation

NASA Astrophysics Data System (ADS)

Shume, E. B.; Komjathy, A.; Langley, R. B.; Verkhoglyadova, O. P.; Butala, M.; Mannucci, A. J.

2014-12-01

In this research, we report intermediate scale plasma density irregularities in the high-latitude ionosphere inferred from high-resolution radio occultation (RO) measurements in the CASSIOPE (CAScade Smallsat and IOnospheric Polar Explorer) - GPS (Global Positioning System) satellites radio link. The high inclination of the CASSIOPE satellite and high rate of signal receptionby the occultation antenna of the GPS Attitude, Positioning and Profiling (GAP) instrument on the Enhanced Polar Outflow Probe platform on CASSIOPE enable a high temporal and spatial resolution investigation of the dynamics of the polar ionosphere, magnetosphere-ionospherecoupling, solar wind effects, etc. with unprecedented details compared to that possible in the past. We have carried out high spatial resolution analysis in altitude and geomagnetic latitude of scintillation-producing plasma density irregularities in the polar ionosphere. Intermediate scale, scintillation-producing plasma density irregularities, which corresponds to 2 to 40 km spatial scales were inferred by applying multi-scale spectral analysis on the RO phase delay measurements. Using our multi-scale spectral analysis approach and Polar Operational Environmental Satellites (POES) and Defense Meteorological Satellite Program (DMSP) observations, we infer that the irregularity scales and phase scintillations have distinct features in the auroral oval and polar cap regions. In specific terms, we found that large length scales and and more intense phase scintillations are prevalent in the auroral oval compared to the polar cap region. Hence, the irregularity scales and phase scintillation characteristics are a function of the solar wind and the magnetospheric forcing. Multi-scale analysis may become a powerful diagnostic tool for characterizing how the ionosphere is dynamically driven by these factors.
Causal Inferences with Large Scale Assessment Data: Using a Validity Framework

ERIC Educational Resources Information Center

Rutkowski, David; Delandshere, Ginette

2016-01-01

To answer the calls for stronger evidence by the policy community, educational researchers and their associated organizations increasingly demand more studies that can yield causal inferences. International large scale assessments (ILSAs) have been targeted as a rich data sources for causal research. It is in this context that we take up a…
Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories.

PubMed

Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas

2016-09-19

Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.
The impact of mating systems and dispersal on fine-scale genetic structure at maternally, paternally and biparentally inherited markers.

PubMed

Shaw, Robyn E; Banks, Sam C; Peakall, Rod

2018-01-01

For decades, studies have focused on how dispersal and mating systems influence genetic structure across populations or social groups. However, we still lack a thorough understanding of how these processes and their interaction shape spatial genetic patterns over a finer scale (tens-hundreds of metres). Using uniparentally inherited markers may help answer these questions, yet their potential has not been fully explored. Here, we use individual-level simulations to investigate the effects of dispersal and mating system on fine-scale genetic structure at autosomal, mitochondrial and Y chromosome markers. Using genetic spatial autocorrelation analysis, we found that dispersal was the major driver of fine-scale genetic structure across maternally, paternally and biparentally inherited markers. However, when dispersal was restricted (mean distance = 100 m), variation in mating behaviour created strong differences in the comparative level of structure detected at maternally and paternally inherited markers. Promiscuity reduced spatial genetic structure at Y chromosome loci (relative to monogamy), whereas structure increased under polygyny. In contrast, mitochondrial and autosomal markers were robust to differences in the specific mating system, although genetic structure increased across all markers when reproductive success was skewed towards fewer individuals. Comparing males and females at Y chromosome vs. mitochondrial markers, respectively, revealed that some mating systems can generate similar patterns to those expected under sex-biased dispersal. This demonstrates the need for caution when inferring ecological and behavioural processes from genetic results. Comparing patterns between the sexes, across a range of marker types, may help us tease apart the processes shaping fine-scale genetic structure. © 2017 John Wiley & Sons Ltd.
Mapping causal functional contributions derived from the clinical assessment of brain damage after stroke

PubMed Central

Zavaglia, Melissa; Forkert, Nils D.; Cheng, Bastian; Gerloff, Christian; Thomalla, Götz; Hilgetag, Claus C.

2015-01-01

Lesion analysis reveals causal contributions of brain regions to mental functions, aiding the understanding of normal brain function as well as rehabilitation of brain-damaged patients. We applied a novel lesion inference technique based on game theory, Multi-perturbation Shapley value Analysis (MSA), to a large clinical lesion dataset. We used MSA to analyze the lesion patterns of 148 acute stroke patients together with their neurological deficits, as assessed by the National Institutes of Health Stroke Scale (NIHSS). The results revealed regional functional contributions to essential behavioral and cognitive functions as reflected in the NIHSS, particularly by subcortical structures. There were also side specific differences of functional contributions between the right and left hemispheric brain regions which may reflect the dominance of the left hemispheric syndrome aphasia in the NIHSS. Comparison of MSA to established lesion inference methods demonstrated the feasibility of the approach for analyzing clinical data and indicated its capability for objectively inferring functional contributions from multiple injured, potentially interacting sites, at the cost of having to predict the outcome of unknown lesion configurations. The analysis of regional functional contributions to neurological symptoms measured by the NIHSS contributes to the interpretation of this widely used standardized stroke scale in clinical practice as well as clinical trials and provides a first approximation of a ‘map of stroke’. PMID:26448908
Mapping causal functional contributions derived from the clinical assessment of brain damage after stroke.

PubMed

Zavaglia, Melissa; Forkert, Nils D; Cheng, Bastian; Gerloff, Christian; Thomalla, Götz; Hilgetag, Claus C

2015-01-01

Lesion analysis reveals causal contributions of brain regions to mental functions, aiding the understanding of normal brain function as well as rehabilitation of brain-damaged patients. We applied a novel lesion inference technique based on game theory, Multi-perturbation Shapley value Analysis (MSA), to a large clinical lesion dataset. We used MSA to analyze the lesion patterns of 148 acute stroke patients together with their neurological deficits, as assessed by the National Institutes of Health Stroke Scale (NIHSS). The results revealed regional functional contributions to essential behavioral and cognitive functions as reflected in the NIHSS, particularly by subcortical structures. There were also side specific differences of functional contributions between the right and left hemispheric brain regions which may reflect the dominance of the left hemispheric syndrome aphasia in the NIHSS. Comparison of MSA to established lesion inference methods demonstrated the feasibility of the approach for analyzing clinical data and indicated its capability for objectively inferring functional contributions from multiple injured, potentially interacting sites, at the cost of having to predict the outcome of unknown lesion configurations. The analysis of regional functional contributions to neurological symptoms measured by the NIHSS contributes to the interpretation of this widely used standardized stroke scale in clinical practice as well as clinical trials and provides a first approximation of a 'map of stroke'.
Supermassive Black Holes and Galaxy Evolution

NASA Technical Reports Server (NTRS)

Merritt, D.

2004-01-01

Supermassive black holes appear to be generic components of galactic nuclei. The formation and growth of black holes is intimately connected with the evolution of galaxies on a wide range of scales. For instance, mergers between galaxies containing nuclear black holes would produce supermassive binaries which eventually coalesce via the emission of gravitational radiation. The formation and decay of these binaries is expected to produce a number of observable signatures in the stellar distribution. Black holes can also affect the large-scale structure of galaxies by perturbing the orbits of stars that pass through the nucleus. Large-scale N-body simulations are beginning to generate testable predictions about these processes which will allow us to draw inferences about the formation history of supermassive black holes.
Bayesian power spectrum inference with foreground and target contamination treatment

NASA Astrophysics Data System (ADS)

Jasche, J.; Lavaux, G.

2017-10-01

This work presents a joint and self-consistent Bayesian treatment of various foreground and target contaminations when inferring cosmological power spectra and three-dimensional density fields from galaxy redshift surveys. This is achieved by introducing additional block-sampling procedures for unknown coefficients of foreground and target contamination templates to the previously presented ARES framework for Bayesian large-scale structure analyses. As a result, the method infers jointly and fully self-consistently three-dimensional density fields, cosmological power spectra, luminosity-dependent galaxy biases, noise levels of the respective galaxy distributions, and coefficients for a set of a priori specified foreground templates. In addition, this fully Bayesian approach permits detailed quantification of correlated uncertainties amongst all inferred quantities and correctly marginalizes over observational systematic effects. We demonstrate the validity and efficiency of our approach in obtaining unbiased estimates of power spectra via applications to realistic mock galaxy observations that are subject to stellar contamination and dust extinction. While simultaneously accounting for galaxy biases and unknown noise levels, our method reliably and robustly infers three-dimensional density fields and corresponding cosmological power spectra from deep galaxy surveys. Furthermore, our approach correctly accounts for joint and correlated uncertainties between unknown coefficients of foreground templates and the amplitudes of the power spectrum. This effect amounts to correlations and anti-correlations of up to 10 per cent across wide ranges in Fourier space.
The Uncertainty of Biomass Estimates from Modeled ICESat-2 Returns Across a Boreal Forest Gradient

NASA Technical Reports Server (NTRS)

Montesano, P. M.; Rosette, J.; Sun, G.; North, P.; Nelson, R. F.; Dubayah, R. O.; Ranson, K. J.; Kharuk, V.

2014-01-01

The Forest Light (FLIGHT) radiative transfer model was used to examine the uncertainty of vegetation structure measurements from NASA's planned ICESat-2 photon counting light detection and ranging (LiDAR) instrument across a synthetic Larix forest gradient in the taiga-tundra ecotone. The simulations demonstrate how measurements from the planned spaceborne mission, which differ from those of previous LiDAR systems, may perform across a boreal forest to non-forest structure gradient in globally important ecological region of northern Siberia. We used a modified version of FLIGHT to simulate the acquisition parameters of ICESat-2. Modeled returns were analyzed from collections of sequential footprints along LiDAR tracks (link-scales) of lengths ranging from 20 m-90 m. These link-scales traversed synthetic forest stands that were initialized with parameters drawn from field surveys in Siberian Larix forests. LiDAR returns from vegetation were compiled for 100 simulated LiDAR collections for each 10 Mg · ha(exp -1) interval in the 0-100 Mg · ha(exp -1) above-ground biomass density (AGB) forest gradient. Canopy height metrics were computed and AGB was inferred from empirical models. The root mean square error (RMSE) and RMSE uncertainty associated with the distribution of inferred AGB within each AGB interval across the gradient was examined. Simulation results of the bright daylight and low vegetation reflectivity conditions for collecting photon counting LiDAR with no topographic relief show that 1-2 photons are returned for 79%-88% of LiDAR shots. Signal photons account for approximately 67% of all LiDAR returns, while approximately 50% of shots result in 1 signal photon returned. The proportion of these signal photon returns do not differ significantly (p greater than 0.05) for AGB intervals greater than 20 Mg · ha(exp -1). The 50m link-scale approximates the finest horizontal resolution (length) at which photon counting LiDAR collection provides strong model fits and minimizes forest structure uncertainty in the synthetic Larix stands. At this link-scale AGB greater than 20 Mg · ha(exp -1) has AGB error from 20-50% at the 95% confidence level. These results suggest that the theoretical sensitivity of ICESat-2 photon counting LiDAR measurements alone lack the ability to consistently discern differences in inferred AGB at 10 Mg · ha(exp -1) intervals in sparse forests characteristic of the taiga-tundra ecotone.
Impact of small-scale vegetation structure on tephra layer preservation

PubMed Central

Cutler, Nick A.; Shears, Olivia M.; Streeter, Richard T.; Dugmore, Andrew J.

2016-01-01

The factors that influence tephra layer taphonomy are poorly understood, but vegetation cover is likely to play a role in the preservation of terrestrial tephra deposits. The impact of vegetation on tephra layer preservation is important because: 1) the morphology of tephra layers could record key characteristics of past land surfaces and 2) vegetation-driven variability in tephra thickness could affect attempts to infer eruption and dispersion parameters. We investigated small- (metre-) scale interactions between vegetation and a thin (<10 cm), recent tephra layer. We conducted surveys of vegetation structure and tephra thickness at two locations which received a similar tephra deposit, but had contrasting vegetation cover (moss vs shrub). The tephra layer was thicker and less variable under shrub cover. Vegetation structure and layer thickness were correlated on the moss site but not under shrub cover, where the canopy reduced the influence of understory vegetation on layer morphology. Our results show that vegetation structure can influence tephra layer thickness on both small and medium (site) scales. These findings suggest that some tephra layers may carry a signal of past vegetation cover. They also have implications for the sampling effort required to reliably estimate the parameters of initial deposits. PMID:27845415
Multi-Scale Multi-Physics Modeling of Matrix Transport Properties in Fractured Shale Reservoirs

NASA Astrophysics Data System (ADS)

Mehmani, A.; Prodanovic, M.

2014-12-01

Understanding the shale matrix flow behavior is imperative in successful reservoir development for hydrocarbon production and carbon storage. Without a predictive model, significant uncertainties in flowback from the formation, the communication between the fracture and matrix as well as proper fracturing practice will ensue. Informed by SEM images, we develop deterministic network models that couple pores from multiple scales and their respective fluid physics. The models are used to investigate sorption hysteresis as an affordable way of inferring the nanoscale pore structure in core scale. In addition, restricted diffusion as a function of pore shape, pore-throat size ratios and network connectivity is computed to make correct interpretation of the 2D NMR maps possible. Our novel pore network models have the ability to match sorption hysteresis measurements without any tuning parameters. The results clarify a common misconception of linking type 3 nitrogen hysteresis curves to only the shale pore shape and show promising sensitivty for nanopore structre inference in core scale. The results on restricted diffusion shed light on the importance of including shape factors in 2D NMR interpretations. A priori "weighting factors" as a function of pore-throat and throat-length ratio are presented and the effect of network connectivity on diffusion is quantitatively assessed. We are currently working on verifying our models with experimental data gathered from the Eagleford formation.
Robust regression for large-scale neuroimaging studies.

PubMed

Fritsch, Virgile; Da Mota, Benoit; Loth, Eva; Varoquaux, Gaël; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Brühl, Rüdiger; Butzek, Brigitte; Conrod, Patricia; Flor, Herta; Garavan, Hugh; Lemaitre, Hervé; Mann, Karl; Nees, Frauke; Paus, Tomas; Schad, Daniel J; Schümann, Gunter; Frouin, Vincent; Poline, Jean-Baptiste; Thirion, Bertrand

2015-05-01

Multi-subject datasets used in neuroimaging group studies have a complex structure, as they exhibit non-stationary statistical properties across regions and display various artifacts. While studies with small sample sizes can rarely be shown to deviate from standard hypotheses (such as the normality of the residuals) due to the poor sensitivity of normality tests with low degrees of freedom, large-scale studies (e.g. >100 subjects) exhibit more obvious deviations from these hypotheses and call for more refined models for statistical inference. Here, we demonstrate the benefits of robust regression as a tool for analyzing large neuroimaging cohorts. First, we use an analytic test based on robust parameter estimates; based on simulations, this procedure is shown to provide an accurate statistical control without resorting to permutations. Second, we show that robust regression yields more detections than standard algorithms using as an example an imaging genetics study with 392 subjects. Third, we show that robust regression can avoid false positives in a large-scale analysis of brain-behavior relationships with over 1500 subjects. Finally we embed robust regression in the Randomized Parcellation Based Inference (RPBI) method and demonstrate that this combination further improves the sensitivity of tests carried out across the whole brain. Altogether, our results show that robust procedures provide important advantages in large-scale neuroimaging group studies. Copyright © 2015 Elsevier Inc. All rights reserved.
Intermediate-scale plasma irregularities in the polar ionosphere inferred from GPS radio occultation

NASA Astrophysics Data System (ADS)

Shume, E. B.; Komjathy, A.; Langley, R. B.; Verkhoglyadova, O.; Butala, M. D.; Mannucci, A. J.

2015-02-01

We report intermediate-scale plasma irregularities in the polar ionosphere inferred from high-resolution radio occultation (RO) measurements using GPS (Global Positioning System) to CASSIOPE (CAScade Smallsat and IOnospheric Polar Explorer) satellite radio links. The high inclination of CASSIOPE and the high rate of signal reception by the GPS Attitude, Positioning, and Profiling RO receiver on CASSIOPE enable a high-resolution investigation of the dynamics of the polar ionosphere with unprecedented detail. Intermediate-scale, scintillation-producing irregularities, which correspond to 1 to 40 km scales, were inferred by applying multiscale spectral analysis on the RO phase measurements. Using our multiscale spectral analysis approach and satellite data (Polar Operational Environmental Satellites and Defense Meteorological Satellite Program), we discovered that the irregularity scales and phase scintillations have distinct features in the auroral oval and polar cap. We found that large length scales and more intense phase scintillations are prevalent in the auroral oval compared to the polar cap implying that the irregularity scales and phase scintillation characteristics are a function of the solar wind and magnetospheric forcings.
The dependence of the strength and thickness of field-aligned currents on solar wind and ionospheric parameters

PubMed Central

Johnson, Jay R.; Wing, Simon

2017-01-01

Sheared plasma flows at the low-latitude boundary layer (LLBL) correlate well with early afternoon auroral arcs and upward field-aligned currents. We present a simple analytic model that relates solar wind and ionospheric parameters to the strength and thickness of field-aligned currents (Λ) in a region of sheared velocity, such as the LLBL. We compare the predictions of the model with DMSP observations and find remarkably good scaling of the upward region 1 currents with solar wind and ionospheric parameters in region located at the boundary layer or open field lines at 1100–1700 magnetic local time. We demonstrate that Λ~nsw−0.5 and Λ ~ L when Λ/L < 5 where L is the auroral electrostatic scale length. The sheared boundary layer thickness (Δm) is inferred to be around 3000 km, which appears to have weak dependence on Vsw. J‖ has dependencies on Δm, Σp, nsw, and Vsw. The analytic model provides a simple way to organize data and to infer boundary layer structures from ionospheric data. PMID:29057194
Detection of multiple damages employing best achievable eigenvectors under Bayesian inference

NASA Astrophysics Data System (ADS)

Prajapat, Kanta; Ray-Chaudhuri, Samit

2018-05-01

A novel approach is presented in this work to localize simultaneously multiple damaged elements in a structure along with the estimation of damage severity for each of the damaged elements. For detection of damaged elements, a best achievable eigenvector based formulation has been derived. To deal with noisy data, Bayesian inference is employed in the formulation wherein the likelihood of the Bayesian algorithm is formed on the basis of errors between the best achievable eigenvectors and the measured modes. In this approach, the most probable damage locations are evaluated under Bayesian inference by generating combinations of various possible damaged elements. Once damage locations are identified, damage severities are estimated using a Bayesian inference Markov chain Monte Carlo simulation. The efficiency of the proposed approach has been demonstrated by carrying out a numerical study involving a 12-story shear building. It has been found from this study that damage scenarios involving as low as 10% loss of stiffness in multiple elements are accurately determined (localized and severities quantified) even when 2% noise contaminated modal data are utilized. Further, this study introduces a term parameter impact (evaluated based on sensitivity of modal parameters towards structural parameters) to decide the suitability of selecting a particular mode, if some idea about the damaged elements are available. It has been demonstrated here that the accuracy and efficiency of the Bayesian quantification algorithm increases if damage localization is carried out a-priori. An experimental study involving a laboratory scale shear building and different stiffness modification scenarios shows that the proposed approach is efficient enough to localize the stories with stiffness modification.
High-Resolution Lithosphere Viscosity and Dynamics Revealed by Magnetotelluric Imaging

NASA Astrophysics Data System (ADS)

Liu, L.; Hasterok, D. P.

2016-12-01

An accurate viscosity structure is critical to truthfully modeling continental lithosphere dynamics, especially at spatial scales of <200 km where active tectonic deformation and volcanism occur. However, the effective viscosity structure of the lithosphere remains a key challenge in geodynamics due to the intimate involvement of viscosity with time and its dependence on many factors including strain rate, plastic failure, composition, and grain size. Current efforts on inferring the detailed lithosphere viscosity structure are sparse and large uncertainties and discrepancies still exist. Here we report an attempt to infer the effective lithospheric viscosity from a high-resolution magnetotelluric (MT) survey across the western United States. The high sensitivity of MT fields to the presence of electrically conductive fluids makes it a promising proxy for determining mechanical strength variations throughout the lithosphere. We demonstrate how a viscosity structure, approximated from electrical resistivity, results in a geodynamic model that successfully predicts short-wavelength surface topography, lithospheric deformation, and mantle upwelling beneath recent volcanism. The results indicate that lithosphere viscosity structure rather than the buoyancy structure is the dominant controlling factor for short-wavelength topography and intra-plate deformation in tectonically active regions. We further show that this viscosity is consistent with and more effective than that derived from laboratory-based rheology. We therefore propose that MT imaging provides a practical observational constraint for quantifying the dynamic evolution of the continental lithosphere.
The electrification of stratiform anvils

NASA Astrophysics Data System (ADS)

Boccippio, Dennis J.

1997-10-01

Stratiform precipitation regions accompany convective activity on many spatial scales. The electrification of these regions is anomalous in a number of ways. Surface and above-cloud fields are often 'inverted' from normal thunderstorm conditions. Unusually large, bright, horizontal 'spider' lightning and high current and charge transfer positive cloud-to-ground (CC) lightning dominates in these regions. Mesospheric 'red sprite' emissions have to date been observed exclusively over stratiform cloud shields. We postulate that a dominant 'inverted dipole' charge structure may account for this anomalous electrification. This is based upon laboratory observations of charge separation which show that in low liquid water content (LWC) environments, or dry but ice- supersaturated environments, precipitation ice tends to charge positively (instead of negatively) upon collision with smaller crystals. Under typical stratiform cloud conditions, liquid water should be depleted and this charging regime favored. An inverted dipole would be the natural consequence of large-scale charge separation (net flux divergence of charged ice), given typical hydrometeor profiles. The inverted dipole hypothesis is tested using radar and electrical observations of four weakly organized, late- stage systems in Orlando, Albuquerque and the Western Pacific. Time-evolving, area-average vertical velocity profiles are inferred from single Doppler radar data. These profiles provide the forcing for a 1-D steady state micro-physical retrieval, which yields vertical hydrometeor profiles and ice/water saturation conditions. The retrieved microphysical parameters are then combined with laboratory charge transfer measurements to infer the instantaneous charging behavior of the systems. Despite limitations in the analysis technique, the retrievals yield useful results. Total charge transfer drops only modestly as the storm enters the late (stratiform) stage, suggesting a continued active generator is plausible. Generator currents show an enhanced lowermost inverted dipole charging structure, which we may infer will result in a comparable inverted dipole charge structure, consistent with surface, in-situ and remote observations. Fine-scale vertical variations in ice and liquid water content may yield multipolar generator current profiles, despite unipolar charge transfer regimes. This suggests that multipoles observed in balloon soundings may not necessarily conflict with the simple ice-ice collisional charge separation mechanism. Overall, the results are consistent with, but not proof of, the inverted dipole model. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253- 1690.)

Emissions of methane in Europe inferred by total column measurements

NASA Astrophysics Data System (ADS)

Wunch, D.; Deutscher, N. M.; Hase, F.; Notholt, J.; Sussmann, R.; Toon, G. C.; Warneke, T.

2017-12-01

Atmospheric total column measurements have been used to infer emissions of methane in urban centres around the world. These measurements have been shown to be useful for verifying city-scale bottom-up inventories, and they can provide both timely and sub-annual emission information. We will present our analysis of atmospheric total column measurements of methane and carbon monoxide to infer annual and seasonal regional emissions of methane within Europe using five long-running atmospheric observatories. These observatories are part of the Total Carbon Column Observing Network, part of a global network that has been carefully designed to measure these gases on a consistent scale. Our inferred emissions will then be used to evaluate gridded emissions inventories in the region.
The impact of category structure and training methodology on learning and generalizing within-category representations.

PubMed

Ell, Shawn W; Smith, David B; Peralta, Gabriela; Hélie, Sébastien

2017-08-01

When interacting with categories, representations focused on within-category relationships are often learned, but the conditions promoting within-category representations and their generalizability are unclear. We report the results of three experiments investigating the impact of category structure and training methodology on the learning and generalization of within-category representations (i.e., correlational structure). Participants were trained on either rule-based or information-integration structures using classification (Is the stimulus a member of Category A or Category B?), concept (e.g., Is the stimulus a member of Category A, Yes or No?), or inference (infer the missing component of the stimulus from a given category) and then tested on either an inference task (Experiments 1 and 2) or a classification task (Experiment 3). For the information-integration structure, within-category representations were consistently learned, could be generalized to novel stimuli, and could be generalized to support inference at test. For the rule-based structure, extended inference training resulted in generalization to novel stimuli (Experiment 2) and inference training resulted in generalization to classification (Experiment 3). These data help to clarify the conditions under which within-category representations can be learned. Moreover, these results make an important contribution in highlighting the impact of category structure and training methodology on the generalization of categorical knowledge.
The relative influence of habitat amount and configuration on genetic structure across multiple spatial scales

PubMed Central

Millette, Katie L; Keyghobadi, Nusha

2015-01-01

Despite strong interest in understanding how habitat spatial structure shapes the genetics of populations, the relative importance of habitat amount and configuration for patterns of genetic differentiation remains largely unexplored in empirical systems. In this study, we evaluate the relative influence of, and interactions among, the amount of habitat and aspects of its spatial configuration on genetic differentiation in the pitcher plant midge, Metriocnemus knabi. Larvae of this species are found exclusively within the water-filled leaves of pitcher plants (Sarracenia purpurea) in a system that is naturally patchy at multiple spatial scales (i.e., leaf, plant, cluster, peatland). Using generalized linear mixed models and multimodel inference, we estimated effects of the amount of habitat, patch size, interpatch distance, and patch isolation, measured at different spatial scales, on genetic differentiation (FST) among larval samples from leaves within plants, plants within clusters, and clusters within peatlands. Among leaves and plants, genetic differentiation appears to be driven by female oviposition behaviors and is influenced by habitat isolation at a broad (peatland) scale. Among clusters, gene flow is spatially restricted and aspects of both the amount of habitat and configuration at the focal scale are important, as is their interaction. Our results suggest that both habitat amount and configuration can be important determinants of genetic structure and that their relative influence is scale dependent. PMID:25628865
The relative influence of habitat amount and configuration on genetic structure across multiple spatial scales.

PubMed

Millette, Katie L; Keyghobadi, Nusha

2015-01-01

Despite strong interest in understanding how habitat spatial structure shapes the genetics of populations, the relative importance of habitat amount and configuration for patterns of genetic differentiation remains largely unexplored in empirical systems. In this study, we evaluate the relative influence of, and interactions among, the amount of habitat and aspects of its spatial configuration on genetic differentiation in the pitcher plant midge, Metriocnemus knabi. Larvae of this species are found exclusively within the water-filled leaves of pitcher plants (Sarracenia purpurea) in a system that is naturally patchy at multiple spatial scales (i.e., leaf, plant, cluster, peatland). Using generalized linear mixed models and multimodel inference, we estimated effects of the amount of habitat, patch size, interpatch distance, and patch isolation, measured at different spatial scales, on genetic differentiation (F ST) among larval samples from leaves within plants, plants within clusters, and clusters within peatlands. Among leaves and plants, genetic differentiation appears to be driven by female oviposition behaviors and is influenced by habitat isolation at a broad (peatland) scale. Among clusters, gene flow is spatially restricted and aspects of both the amount of habitat and configuration at the focal scale are important, as is their interaction. Our results suggest that both habitat amount and configuration can be important determinants of genetic structure and that their relative influence is scale dependent.
Spatial Structure of Seagrass Suggests That Size-Dependent Plant Traits Have a Strong Influence on the Distribution and Maintenance of Tropical Multispecies Meadows

PubMed Central

Ooi, Jillian L. S.; Van Niel, Kimberly P.; Kendrick, Gary A.; Holmes, Karen W.

2014-01-01

Background Seagrass species in the tropics occur in multispecies meadows. How these meadows are maintained through species co-existence and what their ecological drivers may be has been an overarching question in seagrass biogeography. In this study, we quantify the spatial structure of four co-existing species and infer potential ecological processes from these structures. Methods and Results Species presence/absence data were collected using underwater towed and dropped video cameras in Pulau Tinggi, Malaysia. The geostatistical method, utilizing semivariograms, was used to describe the spatial structure of Halophila spp, Halodule uninervis, Syringodium isoetifolium and Cymodocea serrulata. Species had spatial patterns that were oriented in the along-shore and across-shore directions, nested with larger species in meadow interiors, and consisted of multiple structures that indicate the influence of 2–3 underlying processes. The Linear Model of Coregionalization (LMC) was used to estimate the amount of variance contributing to the presence of a species at specific spatial scales. These distances were <2.5 m (micro-scale), 2.5–50 m (fine-scale) and >50 m (broad-scale) in the along-shore; and <2.5 m (micro-scale), 2.5–140 m (fine-scale) and >140 m (broad-scale) in the across-shore. The LMC suggests that smaller species (Halophila spp and H. uninervis) were most influenced by broad-scale processes such as hydrodynamics and water depth whereas large, localised species (S. isoetifolium and C. serrulata) were more influenced by finer-scale processes such as sediment burial, seagrass colonization and growth, and physical disturbance. Conclusion In this study, we provide evidence that spatial structure is distinct even when species occur in well-mixed multispecies meadows, and we suggest that size-dependent plant traits have a strong influence on the distribution and maintenance of tropical marine plant communities. This study offers a contrast from previous spatial models of seagrasses which have largely focused on monospecific temperate meadows. PMID:24497978
Spatial structure of seagrass suggests that size-dependent plant traits have a strong influence on the distribution and maintenance of tropical multispecies meadows.

PubMed

Ooi, Jillian L S; Van Niel, Kimberly P; Kendrick, Gary A; Holmes, Karen W

2014-01-01

Seagrass species in the tropics occur in multispecies meadows. How these meadows are maintained through species co-existence and what their ecological drivers may be has been an overarching question in seagrass biogeography. In this study, we quantify the spatial structure of four co-existing species and infer potential ecological processes from these structures. Species presence/absence data were collected using underwater towed and dropped video cameras in Pulau Tinggi, Malaysia. The geostatistical method, utilizing semivariograms, was used to describe the spatial structure of Halophila spp, Halodule uninervis, Syringodium isoetifolium and Cymodocea serrulata. Species had spatial patterns that were oriented in the along-shore and across-shore directions, nested with larger species in meadow interiors, and consisted of multiple structures that indicate the influence of 2-3 underlying processes. The Linear Model of Coregionalization (LMC) was used to estimate the amount of variance contributing to the presence of a species at specific spatial scales. These distances were <2.5 m (micro-scale), 2.5-50 m (fine-scale) and >50 m (broad-scale) in the along-shore; and <2.5 m (micro-scale), 2.5-140 m (fine-scale) and >140 m (broad-scale) in the across-shore. The LMC suggests that smaller species (Halophila spp and H. uninervis) were most influenced by broad-scale processes such as hydrodynamics and water depth whereas large, localised species (S. isoetifolium and C. serrulata) were more influenced by finer-scale processes such as sediment burial, seagrass colonization and growth, and physical disturbance. In this study, we provide evidence that spatial structure is distinct even when species occur in well-mixed multispecies meadows, and we suggest that size-dependent plant traits have a strong influence on the distribution and maintenance of tropical marine plant communities. This study offers a contrast from previous spatial models of seagrasses which have largely focused on monospecific temperate meadows.
The role of social and ecological processes in structuring animal populations: a case study from automated tracking of wild birds

PubMed Central

Farine, Damien R.; Firth, Josh A.; Aplin, Lucy M.; Crates, Ross A.; Culina, Antica; Garroway, Colin J.; Hinde, Camilla A.; Kidd, Lindall R.; Milligan, Nicole D.; Psorakis, Ioannis; Radersma, Reinder; Verhelst, Brecht; Voelkl, Bernhard; Sheldon, Ben C.

2015-01-01

Both social and ecological factors influence population process and structure, with resultant consequences for phenotypic selection on individuals. Understanding the scale and relative contribution of these two factors is thus a central aim in evolutionary ecology. In this study, we develop a framework using null models to identify the social and spatial patterns that contribute to phenotypic structure in a wild population of songbirds. We used automated technologies to track 1053 individuals that formed 73 737 groups from which we inferred a social network. Our framework identified that both social and spatial drivers contributed to assortment in the network. In particular, groups had a more even sex ratio than expected and exhibited a consistent age structure that suggested local association preferences, such as preferential attachment or avoidance. By contrast, recent immigrants were spatially partitioned from locally born individuals, suggesting differential dispersal strategies by phenotype. Our results highlight how different scales of social decision-making, ranging from post-natal dispersal settlement to fission–fusion dynamics, can interact to drive phenotypic structure in animal populations. PMID:26064644
Social Structure Simulation and Inference Using Artificial Intelligence Techniques

DTIC Science & Technology

2005-06-15

Batagelj and Mrvar , 2003] comes closest to defining a universal interchange format for social network data. PAJEK .net format is defined using a...ObjectStyle, 2005] and in future version of PAJEK[ Batagelj and Mrvar , 2003] GXL[Holt, Winter, and Schürr, 2000][Taentzer, 2001][Winter, 2001] was...Barabási and R. Albert. Emergence of scaling in random networks. Science, 286(5439):509–512, Oct 1999. V. Batagelj and A. Mrvar . Pajek - analysis and
Detecting Selection on Temporal and Spatial Scales: A Genomic Time-Series Assessment of Selective Responses to Devil Facial Tumor Disease

PubMed Central

Brüniche-Olsen, Anna; Austin, Jeremy J.; Jones, Menna E.; Holland, Barbara R.; Burridge, Christopher P.

2016-01-01

Detecting loci under selection is an important task in evolutionary biology. In conservation genetics detecting selection is key to investigating adaptation to the spread of infectious disease. Loci under selection can be detected on a spatial scale, accounting for differences in demographic history among populations, or on a temporal scale, tracing changes in allele frequencies over time. Here we use these two approaches to investigate selective responses to the spread of an infectious cancer—devil facial tumor disease (DFTD)—that since 1996 has ravaged the Tasmanian devil (Sarcophilus harrisii). Using time-series ‘restriction site associated DNA’ (RAD) markers from populations pre- and post DFTD arrival, and DFTD free populations, we infer loci under selection due to DFTD and investigate signatures of selection that are incongruent among methods, populations, and times. The lack of congruence among populations influenced by DFTD with respect to inferred loci under selection, and the direction of that selection, fail to implicate a consistent selective role for DFTD. Instead genetic drift is more likely driving the observed allele frequency changes over time. Our study illustrates the importance of applying methods with different performance optima e.g. accounting for population structure and background selection, and assessing congruence of the results. PMID:26930198
Cortical circuitry implementing graphical models.

PubMed

Litvak, Shai; Ullman, Shimon

2009-11-01

In this letter, we develop and simulate a large-scale network of spiking neurons that approximates the inference computations performed by graphical models. Unlike previous related schemes, which used sum and product operations in either the log or linear domains, the current model uses an inference scheme based on the sum and maximization operations in the log domain. Simulations show that using these operations, a large-scale circuit, which combines populations of spiking neurons as basic building blocks, is capable of finding close approximations to the full mathematical computations performed by graphical models within a few hundred milliseconds. The circuit is general in the sense that it can be wired for any graph structure, it supports multistate variables, and it uses standard leaky integrate-and-fire neuronal units. Following previous work, which proposed relations between graphical models and the large-scale cortical anatomy, we focus on the cortical microcircuitry and propose how anatomical and physiological aspects of the local circuitry may map onto elements of the graphical model implementation. We discuss in particular the roles of three major types of inhibitory neurons (small fast-spiking basket cells, large layer 2/3 basket cells, and double-bouquet neurons), subpopulations of strongly interconnected neurons with their unique connectivity patterns in different cortical layers, and the possible role of minicolumns in the realization of the population-based maximum operation.
Hydraulic head applications of flow logs in the study of heterogeneous aquifers

USGS Publications Warehouse

Paillet, Frederick L.

2001-01-01

Permeability profiles derived from high-resolution flow logs in heterogeneous aquifers provide a limited sample of the most permeable beds or fractures determining the hydraulic properties of those aquifers. This paper demonstrates that flow logs can also be used to infer the large-scale properties of aquifers surrounding boreholes. The analysis is based on the interpretation of the hydraulic head values estimated from the flow log analysis. Pairs of quasi-steady flow profiles obtained under ambient conditions and while either pumping or injecting are used to estimate the hydraulic head in each water-producing zone. Although the analysis yields localized estimates of transmissivity for a few water-producing zones, the hydraulic head estimates apply to the farfield aquifers to which these zones are connected. The hydraulic head data are combined with information from other sources to identify the large-scale structure of heterogeneous aquifers. More complicated cross-borehole flow experiments are used to characterize the pattern of connection between large-scale aquifer units inferred from the hydraulic head estimates. The interpretation of hydraulic heads in situ under steady and transient conditions is illustrated by several case studies, including an example with heterogeneous permeable beds in an unconsolidated aquifer, and four examples with heterogeneous distributions of bedding planes and/or fractures in bedrock aquifers.
The Large-scale Coronal Structure of the 2017 August 21 Great American Eclipse: An Assessment of Solar Surface Flux Transport Model Enabled Predictions and Observations

NASA Astrophysics Data System (ADS)

Nandy, Dibyendu; Bhowmik, Prantika; Yeates, Anthony R.; Panda, Suman; Tarafder, Rajashik; Dash, Soumyaranjan

2018-01-01

On 2017 August 21, a total solar eclipse swept across the contiguous United States, providing excellent opportunities for diagnostics of the Sun’s corona. The Sun’s coronal structure is notoriously difficult to observe except during solar eclipses; thus, theoretical models must be relied upon for inferring the underlying magnetic structure of the Sun’s outer atmosphere. These models are necessary for understanding the role of magnetic fields in the heating of the corona to a million degrees and the generation of severe space weather. Here we present a methodology for predicting the structure of the coronal field based on model forward runs of a solar surface flux transport model, whose predicted surface field is utilized to extrapolate future coronal magnetic field structures. This prescription was applied to the 2017 August 21 solar eclipse. A post-eclipse analysis shows good agreement between model simulated and observed coronal structures and their locations on the limb. We demonstrate that slow changes in the Sun’s surface magnetic field distribution driven by long-term flux emergence and its evolution governs large-scale coronal structures with a (plausibly cycle-phase dependent) dynamical memory timescale on the order of a few solar rotations, opening up the possibility for large-scale, global corona predictions at least a month in advance.
Structure and evolution of the large scale solar and heliospheric magnetic fields. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Hoeksema, J. T.

1984-01-01

Structure and evolution of large scale photospheric and coronal magnetic fields in the interval 1976-1983 were studied using observations from the Stanford Solar Observatory and a potential field model. The solar wind in the heliosphere is organized into large regions in which the magnetic field has a componenet either toward or away from the sun. The model predicts the location of the current sheet separating these regions. Near solar minimum, in 1976, the current sheet lay within a few degrees of the solar equator having two extensions north and south of the equator. Soon after minimum the latitudinal extent began to increase. The sheet reached to at least 50 deg from 1978 through 1983. The complex structure near maximum occasionally included multiple current sheets. Large scale structures persist for up to two years during the entire interval. To minimize errors in determining the structure of the heliospheric field particular attention was paid to decreasing the distorting effects of rapid field evolution, finding the optimum source surface radius, determining the correction to the sun's polar field, and handling missing data. The predicted structure agrees with direct interplanetary field measurements taken near the ecliptic and with coronameter and interplanetary scintillation measurements which infer the three dimensional interplanetary magnetic structure. During most of the solar cycle the heliospheric field cannot be adequately described as a dipole.
Interfacing cellular networks of S. cerevisiae and E. coli: Connecting dynamic and genetic information

PubMed Central

2013-01-01

Background In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored. Results We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes. Conclusions Although the individual networks represent different levels of cellular interactions with global structural and functional dissimilarities, we observe crucial functions of their network interfaces for the assembly of protein complexes, proteolysis, transcription, translation, metabolic and regulatory interactions. Overall, our results shed light on the integrability of these networks and their interfacing biological processes. PMID:23663484
Causal inference in biology networks with integrated belief propagation.

PubMed

Chang, Rui; Karr, Jonathan R; Schadt, Eric E

2015-01-01

Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot.
Upper trophic structure in the Atlantic Patagonian shelf break as inferred from stable isotope analysis

NASA Astrophysics Data System (ADS)

Zhu, Guoping; Zhang, Haiting; Yang, Yang; Wang, Shaoqin; Wei, Lian; Yang, Qingyuan

2017-09-01

The Patagonian Shelf is a very productive region with different ecosystem structures. A long history of fishing in the Southwestern Atlantic Ocean combined with a complex hydrographic structure, with a permanent front over the shelf-break and different coastal frontal regions, and a wide non-frontal area in between have made the food web in this area more complex and have resulted in changes to the spatial-temporal scale. Stable isotopes of carbon and nitrogen were used to determine the trophic structure of the Patagonian shelf break which was previously poorly understood. The results indicated that the average δ15N value of pelagic guild (Illex argentinus) was remarkable lower than those of the other guilds. The δ13C values of almost all species ranged from -17‰ to -18‰, but Stromateus brasiliensis had a significant lower δ13C value. Compared with the southern Patagonian shelf, short food chain length also occurred. The impact of complex oceanographic structures has resulted in food web structure change to the temporal-spatial scale on the Patagonian shelf. The Patagonian shelf break can be considered as a separated ecosystem structure with lower δ15N values.
Persistent millennial-scale shifts in moisture regimes in western Canada during the past six millennia

PubMed Central

Cumming, Brian F.; Laird, Kathleen R.; Bennett, Joseph R.; Smol, John P.; Salomon, Anne K.

2002-01-01

Inferences of past climatic conditions from a sedimentary record from Big Lake, British Columbia, Canada, over the past 5,500 years show strong millennial-scale patterns, which oscillate between periods of wet and drier climatic conditions. Higher frequency decadal- to centennial-scale fluctuations also occur within the dominant millennial-scale patterns. These changes in climatic conditions are based on estimates of changes in lake depth and salinity inferred from diatom assemblages in a well dated sediment core. After periods of relative stability, abrupt shifts in diatom assemblages and inferred climatic conditions occur approximately every 1,220 years. The correspondence of these shifts to millennial-scale variations in records of glacial expansion/recession and ice-rafting events in the Atlantic suggest that abrupt millennial-scale shifts are important to understanding climatic variability in North America during the mid- to late Holocene. Unfortunately, the spatial patterns and mechanisms behind these large and abrupt swings are poorly understood. Similar abrupt and prolonged changes in climatic conditions today could pose major societal challenges for many regions. PMID:12461174
Persistent millennial-scale shifts in moisture regimes in western Canada during the past six millennia.

PubMed

Cumming, Brian F; Laird, Kathleen R; Bennett, Joseph R; Smol, John P; Salomon, Anne K

2002-12-10

Inferences of past climatic conditions from a sedimentary record from Big Lake, British Columbia, Canada, over the past 5,500 years show strong millennial-scale patterns, which oscillate between periods of wet and drier climatic conditions. Higher frequency decadal- to centennial-scale fluctuations also occur within the dominant millennial-scale patterns. These changes in climatic conditions are based on estimates of changes in lake depth and salinity inferred from diatom assemblages in a well dated sediment core. After periods of relative stability, abrupt shifts in diatom assemblages and inferred climatic conditions occur approximately every 1,220 years. The correspondence of these shifts to millennial-scale variations in records of glacial expansionrecession and ice-rafting events in the Atlantic suggest that abrupt millennial-scale shifts are important to understanding climatic variability in North America during the mid- to late Holocene. Unfortunately, the spatial patterns and mechanisms behind these large and abrupt swings are poorly understood. Similar abrupt and prolonged changes in climatic conditions today could pose major societal challenges for many regions.
Some Thoughts on "Using Learning Progressions to Design Vertical Scales That Support Coherent Inferences about Student Growth"

ERIC Educational Resources Information Center

Kingston, Neal M.; Broaddus, Angela; Lao, Hongling

2015-01-01

Briggs and Peck (2015) have written a thought-provoking article on the use of learning progressions in the design of vertical scales that support inferences about student growth. Organized learning models, including learning trajectories, learning progressions, and learning maps have been the subject of research for many years, but more recently…
RegPrecise 3.0--a resource for genome-scale exploration of transcriptional regulation in bacteria.

PubMed

Novichkov, Pavel S; Kazakov, Alexey E; Ravcheev, Dmitry A; Leyn, Semen A; Kovaleva, Galina Y; Sutormin, Roman A; Kazanov, Marat D; Riehl, William; Arkin, Adam P; Dubchak, Inna; Rodionov, Dmitry A

2013-11-01

Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in prokaryotes is one of the critical tasks of modern genomics. Bacteria from different taxonomic groups, whose lifestyles and natural environments are substantially different, possess highly diverged transcriptional regulatory networks. The comparative genomics approaches are useful for in silico reconstruction of bacterial regulons and networks operated by both transcription factors (TFs) and RNA regulatory elements (riboswitches). RegPrecise (http://regprecise.lbl.gov) is a web resource for collection, visualization and analysis of transcriptional regulons reconstructed by comparative genomics. We significantly expanded a reference collection of manually curated regulons we introduced earlier. RegPrecise 3.0 provides access to inferred regulatory interactions organized by phylogenetic, structural and functional properties. Taxonomy-specific collections include 781 TF regulogs inferred in more than 160 genomes representing 14 taxonomic groups of Bacteria. TF-specific collections include regulogs for a selected subset of 40 TFs reconstructed across more than 30 taxonomic lineages. Novel collections of regulons operated by RNA regulatory elements (riboswitches) include near 400 regulogs inferred in 24 bacterial lineages. RegPrecise 3.0 provides four classifications of the reference regulons implemented as controlled vocabularies: 55 TF protein families; 43 RNA motif families; ~150 biological processes or metabolic pathways; and ~200 effectors or environmental signals. Genome-wide visualization of regulatory networks and metabolic pathways covered by the reference regulons are available for all studied genomes. A separate section of RegPrecise 3.0 contains draft regulatory networks in 640 genomes obtained by an conservative propagation of the reference regulons to closely related genomes. RegPrecise 3.0 gives access to the transcriptional regulons reconstructed in bacterial genomes. Analytical capabilities include exploration of: regulon content, structure and function; TF binding site motifs; conservation and variations in genome-wide regulatory networks across all taxonomic groups of Bacteria. RegPrecise 3.0 was selected as a core resource on transcriptional regulation of the Department of Energy Systems Biology Knowledgebase, an emerging software and data environment designed to enable researchers to collaboratively generate, test and share new hypotheses about gene and protein functions, perform large-scale analyses, and model interactions in microbes, plants, and their communities.

The logical primitives of thought: Empirical foundations for compositional cognitive models.

PubMed

Piantadosi, Steven T; Tenenbaum, Joshua B; Goodman, Noah D

2016-07-01

The notion of a compositional language of thought (LOT) has been central in computational accounts of cognition from earliest attempts (Boole, 1854; Fodor, 1975) to the present day (Feldman, 2000; Penn, Holyoak, & Povinelli, 2008; Fodor, 2008; Kemp, 2012; Goodman, Tenenbaum, & Gerstenberg, 2015). Recent modeling work shows how statistical inferences over compositionally structured hypothesis spaces might explain learning and development across a variety of domains. However, the primitive components of such representations are typically assumed a priori by modelers and theoreticians rather than determined empirically. We show how different sets of LOT primitives, embedded in a psychologically realistic approximate Bayesian inference framework, systematically predict distinct learning curves in rule-based concept learning experiments. We use this feature of LOT models to design a set of large-scale concept learning experiments that can determine the most likely primitives for psychological concepts involving Boolean connectives and quantification. Subjects' inferences are most consistent with a rich (nonminimal) set of Boolean operations, including first-order, but not second-order, quantification. Our results more generally show how specific LOT theories can be distinguished empirically. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Analyzing Single-Molecule Time Series via Nonparametric Bayesian Inference

PubMed Central

Hines, Keegan E.; Bankston, John R.; Aldrich, Richard W.

2015-01-01

The ability to measure the properties of proteins at the single-molecule level offers an unparalleled glimpse into biological systems at the molecular scale. The interpretation of single-molecule time series has often been rooted in statistical mechanics and the theory of Markov processes. While existing analysis methods have been useful, they are not without significant limitations including problems of model selection and parameter nonidentifiability. To address these challenges, we introduce the use of nonparametric Bayesian inference for the analysis of single-molecule time series. These methods provide a flexible way to extract structure from data instead of assuming models beforehand. We demonstrate these methods with applications to several diverse settings in single-molecule biophysics. This approach provides a well-constrained and rigorously grounded method for determining the number of biophysical states underlying single-molecule data. PMID:25650922
Modeling pair distribution functions of rare-earth phosphate glasses using principal component analysis

DOE PAGES

Cole, Jacqueline M.; Cheng, Xie; Payne, Michael C.

2016-10-18

The use of principal component analysis (PCA) to statistically infer features of local structure from experimental pair distribution function (PDF) data is assessed on a case study of rare-earth phosphate glasses (REPGs). Such glasses, co-doped with two rare-earth ions (R and R’) of different sizes and optical properties, are of interest to the laser industry. The determination of structure-property relationships in these materials is an important aspect of their technological development. Yet, realizing the local structure of co-doped REPGs presents significant challenges relative to their singly-doped counterparts; specifically, R and R’ are difficult to distinguish in terms of establishing relativemore » material compositions, identifying atomic pairwise correlation profiles in a PDF that are associated with each ion, and resolving peak overlap of such profiles in PDFs. This study demonstrates that PCA can be employed to help overcome these structural complications, by statistically inferring trends in PDFs that exist for a restricted set of experimental data on REPGs, and using these as training data to predict material compositions and PDF profiles in unknown co-doped REPGs. The application of these PCA methods to resolve individual atomic pairwise correlations in t(r) signatures is also presented. The training methods developed for these structural predictions are pre-validated by testing their ability to reproduce known physical phenomena, such as the lanthanide contraction, on PDF signatures of the structurally simpler singly-doped REPGs. The intrinsic limitations of applying PCA to analyze PDFs relative to the quality control of source data, data processing, and sample definition, are also considered. Furthermore, while this case study is limited to lanthanide-doped REPGs, this type of statistical inference may easily be extended to other inorganic solid-state materials, and be exploited in large-scale data-mining efforts that probe many t(r) functions.« less
The relationship between observational scale and explained variance in benthic communities

PubMed Central

Flood, Roger D.; Frisk, Michael G.; Garza, Corey D.; Lopez, Glenn R.; Maher, Nicole P.

2018-01-01

This study addresses the impact of spatial scale on explaining variance in benthic communities. In particular, the analysis estimated the fraction of community variation that occurred at a spatial scale smaller than the sampling interval (i.e., the geographic distance between samples). This estimate is important because it sets a limit on the amount of community variation that can be explained based on the spatial configuration of a study area and sampling design. Six benthic data sets were examined that consisted of faunal abundances, common environmental variables (water depth, grain size, and surficial percent cover), and sonar backscatter treated as a habitat proxy (categorical acoustic provinces). Redundancy analysis was coupled with spatial variograms generated by multiscale ordination to quantify the explained and residual variance at different spatial scales and within and between acoustic provinces. The amount of community variation below the sampling interval of the surveys (< 100 m) was estimated to be 36–59% of the total. Once adjusted for this small-scale variation, > 71% of the remaining variance was explained by the environmental and province variables. Furthermore, these variables effectively explained the spatial structure present in the infaunal community. Overall, no scale problems remained to compromise inferences, and unexplained infaunal community variation had no apparent spatial structure within the observational scale of the surveys (> 100 m), although small-scale gradients (< 100 m) below the observational scale may be present. PMID:29324746
Super-resolution with a positive epsilon multi-quantum-well super-lens

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

Bak, A. O.; Giannini, V.; Maier, S. A.

2013-12-23

We design an anisotropic and dichroic quantum metamaterial that is able to achieve super-resolution without the need for a negative permittivity. When exploring the parameters of the structure, we take into account the limits of semiconductor fabrication technology based on quantum well stacks. By heavily doping the structure with free electrons, we infer an anisotropic effective medium with a prolate ellipsoid dispersion curve which allows for near-diffractionless propagation of light (similar to an epsilon-near-zero hyperbolic lens). This, coupled with low absorption, allows us to resolve images at the sub-wavelength scale at distances 6 times greater than equivalent natural materials.
Using simulation to interpret experimental data in terms of protein conformational ensembles.

PubMed

Allison, Jane R

2017-04-01

In their biological environment, proteins are dynamic molecules, necessitating an ensemble structural description. Molecular dynamics simulations and solution-state experiments provide complimentary information in the form of atomically detailed coordinates and averaged or distributions of structural properties or related quantities. Recently, increases in the temporal and spatial scale of conformational sampling and comparison of the more diverse conformational ensembles thus generated have revealed the importance of sampling rare events. Excitingly, new methods based on maximum entropy and Bayesian inference are promising to provide a statistically sound mechanism for combining experimental data with molecular dynamics simulations. Copyright © 2016 Elsevier Ltd. All rights reserved.
Cavitation and Its Discontents: Opportunities for Resolving Current Controversies1[C

PubMed Central

Rockwell, Fulton E.; Wheeler, James K.; Holbrook, N. Michele

2014-01-01

Cavitation has long been recognized as a key constraint on the structure and functional integrity of the xylem. Yet, recent results call into question how well we understand cavitation in plants. Here, we consider embolism formation in angiosperms at two scales. The first focuses on how air-seeding occurs at the level of pit membranes, raising the question of whether capillary failure is an appropriate physical model. The second addresses methodological uncertainties that affect our ability to infer the formation of embolism and its reversal in plant stems. Overall, our goal is to open up fresh perspectives on the structure-function relationships of xylem. PMID:24501002
Approximation and inference methods for stochastic biochemical kinetics—a tutorial review

NASA Astrophysics Data System (ADS)

Schnoerr, David; Sanguinetti, Guido; Grima, Ramon

2017-03-01

Stochastic fluctuations of molecule numbers are ubiquitous in biological systems. Important examples include gene expression and enzymatic processes in living cells. Such systems are typically modelled as chemical reaction networks whose dynamics are governed by the chemical master equation. Despite its simple structure, no analytic solutions to the chemical master equation are known for most systems. Moreover, stochastic simulations are computationally expensive, making systematic analysis and statistical inference a challenging task. Consequently, significant effort has been spent in recent decades on the development of efficient approximation and inference methods. This article gives an introduction to basic modelling concepts as well as an overview of state of the art methods. First, we motivate and introduce deterministic and stochastic methods for modelling chemical networks, and give an overview of simulation and exact solution methods. Next, we discuss several approximation methods, including the chemical Langevin equation, the system size expansion, moment closure approximations, time-scale separation approximations and hybrid methods. We discuss their various properties and review recent advances and remaining challenges for these methods. We present a comparison of several of these methods by means of a numerical case study and highlight some of their respective advantages and disadvantages. Finally, we discuss the problem of inference from experimental data in the Bayesian framework and review recent methods developed the literature. In summary, this review gives a self-contained introduction to modelling, approximations and inference methods for stochastic chemical kinetics.
Phylogenetic community structure: temporal variation in fish assemblage

PubMed Central

Santorelli, Sergio; Magnusson, William; Ferreira, Efrem; Caramaschi, Erica; Zuanon, Jansen; Amadio, Sidnéia

2014-01-01

Hypotheses about phylogenetic relationships among species allow inferences about the mechanisms that affect species coexistence. Nevertheless, most studies assume that phylogenetic patterns identified are stable over time. We used data on monthly samples of fish from a single lake over 10 years to show that the structure in phylogenetic assemblages varies over time and conclusions depend heavily on the time scale investigated. The data set was organized in guild structures and temporal scales (grouped at three temporal scales). Phylogenetic distance was measured as the mean pairwise distances (MPD) and as mean nearest-neighbor distance (MNTD). Both distances were based on counts of nodes. We compared the observed values of MPD and MNTD with values that were generated randomly using null model independent swap. A serial runs test was used to assess the temporal independence of indices over time. The phylogenetic pattern in the whole assemblage and the functional groups varied widely over time. Conclusions about phylogenetic clustering or dispersion depended on the temporal scales. Conclusions about the frequency with which biotic processes and environmental filters affect the local assembly do not depend only on taxonomic grouping and spatial scales. While these analyzes allow the assertion that all proposed patterns apply to the fish assemblages in the floodplain, the assessment of the relative importance of these processes, and how they vary depending on the temporal scale and functional group studied, cannot be determined with the effort commonly used. It appears that, at least in the system that we studied, the assemblages are forming and breaking continuously, resulting in various phylogeny-related structures that makes summarizing difficult. PMID:25360256
Inferred Lunar Boulder Distributions at Decimeter Scales

NASA Technical Reports Server (NTRS)

Baloga, S. M.; Glaze, L. S.; Spudis, P. D.

2012-01-01

Block size distributions of impact deposits on the Moon are diagnostic of the impact process and environmental effects, such as target lithology and weathering. Block size distributions are also important factors in trafficability, habitability, and possibly the identification of indigenous resources. Lunar block sizes have been investigated for many years for many purposes [e.g., 1-3]. An unresolved issue is the extent to which lunar block size distributions can be extrapolated to scales smaller than limits of resolution of direct measurement. This would seem to be a straightforward statistical application, but it is complicated by two issues. First, the cumulative size frequency distribution of observable boulders rolls over due to resolution limitations at the small end. Second, statistical regression provides the best fit only around the centroid of the data [4]. Confidence and prediction limits splay away from the best fit at the endpoints resulting in inferences in the boulder density at the CPR scale that can differ by many orders of magnitude [4]. These issues were originally investigated by Cintala and McBride [2] using Surveyor data. The objective of this study was to determine whether the measured block size distributions from Lunar Reconnaissance Orbiter Camera - Narrow Angle Camera (LROC-NAC) images (m-scale resolution) can be used to infer the block size distribution at length scales comparable to Mini-RF Circular Polarization Ratio (CPR) scales, nominally taken as 10 cm. This would set the stage for assessing correlations of inferred block size distributions with CPR returns [6].
Martian canyons and African rifts: Structural comparisons and implications

NASA Technical Reports Server (NTRS)

Frey, H. V.

1978-01-01

The resistant parts of the canyon walls of the Martian rift complex Valled Marineris were used to infer an earlier, less eroded reconstruction of the major roughs. The individual canyons were then compared with individual rifts of East Africa. When measured in units of planetary radius, Martian canyons show a distribution of lengths nearly identical to those in Africa, both for individual rifts and for compound rift systems. A common mechanism which scales with planetary radius is suggested. Martian canyons are significantly wider than African rifts. The overall pattern of the rift systems of Africa and Mars are quite different in that the African systems are composed of numerous small faults with highly variable trend. On Mars the trends are less variable; individual scarps are straighter for longer than on earth. This is probably due to the difference in tectonic histories of the two planets: the complex history of the earth and the resulting complicated basement structures influence the development of new rifts. The basement and lithosphere of Mars are inferred to be simple, reflecting a relatively inactive tectonic history prior to the formation of the canyonlands.
Empirical Bayes method for reducing false discovery rates of correlation matrices with block diagonal structure.

PubMed

Pacini, Clare; Ajioka, James W; Micklem, Gos

2017-04-12

Correlation matrices are important in inferring relationships and networks between regulatory or signalling elements in biological systems. With currently available technology sample sizes for experiments are typically small, meaning that these correlations can be difficult to estimate. At a genome-wide scale estimation of correlation matrices can also be computationally demanding. We develop an empirical Bayes approach to improve covariance estimates for gene expression, where we assume the covariance matrix takes a block diagonal form. Our method shows lower false discovery rates than existing methods on simulated data. Applied to a real data set from Bacillus subtilis we demonstrate it's ability to detecting known regulatory units and interactions between them. We demonstrate that, compared to existing methods, our method is able to find significant covariances and also to control false discovery rates, even when the sample size is small (n=10). The method can be used to find potential regulatory networks, and it may also be used as a pre-processing step for methods that calculate, for example, partial correlations, so enabling the inference of the causal and hierarchical structure of the networks.
An evaluation of the precision of fin ray, otolith, and scale age determinations for brook trout

USGS Publications Warehouse

Stolarski, J.T.; Hartman, K.J.

2008-01-01

The ages of brook trout Salvelinus fontinalis are typically estimated using scales despite a lack of research documenting the effectiveness of this technique. The use of scales is often preferred because it is nonlethal and is believed to require less effort than alternative methods. To evaluate the relative effectiveness of different age estimation methodologies for brook trout, we measured the precision and processing times of scale, sagittal otolith, and pectoral fin ray age estimation techniques. Three independent readers, age bias plots, coefficients of variation (CV = 100 x SD/mean), and percent agreement (PA) were used to measure within-reader, among-structure bias and within-structure, among-reader precision. Bias was generally minimal; however, the age estimates derived from scales tended to be lower than those derived from otoliths within older (age > 2) cohorts. Otolith, fin ray, and scale age estimates were within 1 year of each other for 95% of the comparisons. The measures of precision for scales (CV = 6.59; PA = 82.30) and otoliths (CV = 7.45; PA = 81.48) suggest higher agreement between these structures than with fin rays (CV = 11.30; PA = 65.84). The mean per-sample processing times were lower for scale (13.88 min) and otolith techniques (12.23 min) than for fin ray techniques (22.68 min). The comparable processing times of scales and otoliths contradict popular belief and are probably a result of the high proportion of regenerated scales within samples and the ability to infer age from whole (as opposed to sectioned) otoliths. This research suggests that while scales produce age estimates rivaling those of otoliths for younger (age > 3) cohorts, they may be biased within older cohorts and therefore should be used with caution. ?? Copyright by the American Fisheries Society 2008.
DOE Office of Scientific and Technical Information (OSTI.GOV)

G.A> Valentine; F.V. Perry

The distribution and characteristics of individual basaltic volcanoes in the waning Southwestern Nevada Volcanic Field provide insight into the changing physical nature of magmatism and the controls on volcano location. During Pliocene-Pleistocene times the volumes of individual volcanoes have decreased by more than one order of magnitude, as have fissure lengths and inferred lava effusion rates. Eruptions evolved from Hawaiian-style eruptions with extensive lavas to eruptions characterized by small pulses of lava and Strombolian to violent Strombolian mechanisms. These trends indicate progressively decreasing partial melting and length scales, or magmatic footprints, of mantle source zones for individual volcanoes. The locationmore » of each volcano is determined by the location of its magmatic footprint at depth, and only by shallow structural and topographic features that are within that footprint. The locations of future volcanoes in a waning system are less likely to be determined by large-scale topography or structures than were older, larger volume volcanoes.« less
Analysis of alluvial hydrostratigraphy using indicator geostatistics, with examples from Santa Clara Valley, California

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

NONE

1995-03-01

Current trends in hydrogeology seek to enlist sedimentary concepts in the interpretation of permeability structures. However, existing conceptual models of alluvial deposition tend to inadequately account for the heterogeneity caused by complex sedimentological and external factors. This dissertation presents three analyses of alluvial hydrostratigraphy using indicator geostatistics. This approach empirically acknowledges both the random and structured qualities of alluvial structures at scales relevant to site investigations. The first analysis introduces the indicator approach, whereby binary values are assigned to borehole-log intervals on the basis of inferred relative permeability; it presents a case study of indicator variography at a well-documented ground-watermore » contamination site, and uses indicator kriging to interpolate an aquifer-aquitard sequence in three dimensions. The second analysis develops an alluvial-architecture context for interpreting semivariograms, and performs comparative variography for a suite of alluvial sites in Santa Clara Valley, California. The third analysis investigates the use of a water well perforation indicator for assessing large-scale hydrostratigraphic structures within relatively deep production zones.« less
The Use of Weighted Graphs for Large-Scale Genome Analysis

PubMed Central

Zhou, Fang; Toivonen, Hannu; King, Ross D.

2014-01-01

There is an acute need for better tools to extract knowledge from the growing flood of sequence data. For example, thousands of complete genomes have been sequenced, and their metabolic networks inferred. Such data should enable a better understanding of evolution. However, most existing network analysis methods are based on pair-wise comparisons, and these do not scale to thousands of genomes. Here we propose the use of weighted graphs as a data structure to enable large-scale phylogenetic analysis of networks. We have developed three types of weighted graph for enzymes: taxonomic (these summarize phylogenetic importance), isoenzymatic (these summarize enzymatic variety/redundancy), and sequence-similarity (these summarize sequence conservation); and we applied these types of weighted graph to survey prokaryotic metabolism. To demonstrate the utility of this approach we have compared and contrasted the large-scale evolution of metabolism in Archaea and Eubacteria. Our results provide evidence for limits to the contingency of evolution. PMID:24619061
Do Burst-over-threshold Distributions and Structure Functions allow us to Infer the Coexistence of SOC and Intermittent Turbulence in Natural Systems ?

NASA Astrophysics Data System (ADS)

Rosenberg, S.; Watkins, N. W.; Chapman, S.

2008-12-01

Space plasma physics provides an important arena for the study of natural hazards, because of the threat posed by space weather to space-based and ground based communications and other infrastructure. Extreme fluctuations are thus of interest, and there is by now abundant evidence for scaling in many quantities in the coupled solar-terrestrial system (solar wind, magnetosphere and ionosphere). Direct physical explanations for scaling have been sought through descriptions such as low dimensional chaos, intermittent turbulence (IT) and self-organised criticality (SOC). We have however advocated consideration of a complementary approach (Watkins [NPG, 2002]; Watkins et al. [Space Science Reviews, 2005]). This is the use of deliberately oversimplified mathematical "testbeds" to separate the proprties of the diagnostics used to infer IT or SOC from those of the models themselves. To demonstrate the need for this we consider a recent claim by Uritsky et al ([PRL, 2007]; U07) of direct observational evidence for the coexistence of SOC and IT in the magnetized plasma of the solar corona. By analyzing two dimensional (2D) EUV snapshots (typically 3-4000) of the solar corona, U07 found coexisting power law avalanche statistics and multiscaling of the structure functions. Avalanches were defined by "bursts" for which the signal exceeded a given threshold. These properties were asserted to be robust signatures of SOC and IT respectively. U07 took their coexistence to imply new physics with elements of both SOC and IT. We first point out that U07 assumed that their chosen signatures were unique to SOC and IT. We show however i) that a standard 1D multifractal model of IT, the p-model, straightforwardly generates U07's IT and SOC signatures simultaneously, and ii)that a stochastic process, linear fractional stable motion or LFSM can give the IT signatures and nonlinearity in the structure functions. We infer that not only may it not be necessary to invoke SOC to explain U07's observations, but also that our result has wider implications, which will be discussed.
Inferring infection hazard in wildlife populations by linking data across individual and population scales.

PubMed

Pepin, Kim M; Kay, Shannon L; Golas, Ben D; Shriner, Susan S; Gilbert, Amy T; Miller, Ryan S; Graham, Andrea L; Riley, Steven; Cross, Paul C; Samuel, Michael D; Hooten, Mevin B; Hoeting, Jennifer A; Lloyd-Smith, James O; Webb, Colleen T; Buhnerkempe, Michael G

2017-03-01

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease. © 2017 John Wiley & Sons Ltd/CNRS.
Inference of directional selection and mutation parameters assuming equilibrium.

PubMed

Vogl, Claus; Bergman, Juraj

2015-12-01

In a classical study, Wright (1931) proposed a model for the evolution of a biallelic locus under the influence of mutation, directional selection and drift. He derived the equilibrium distribution of the allelic proportion conditional on the scaled mutation rate, the mutation bias and the scaled strength of directional selection. The equilibrium distribution can be used for inference of these parameters with genome-wide datasets of "site frequency spectra" (SFS). Assuming that the scaled mutation rate is low, Wright's model can be approximated by a boundary-mutation model, where mutations are introduced into the population exclusively from sites fixed for the preferred or unpreferred allelic states. With the boundary-mutation model, inference can be partitioned: (i) the shape of the SFS distribution within the polymorphic region is determined by random drift and directional selection, but not by the mutation parameters, such that inference of the selection parameter relies exclusively on the polymorphic sites in the SFS; (ii) the mutation parameters can be inferred from the amount of polymorphic and monomorphic preferred and unpreferred alleles, conditional on the selection parameter. Herein, we derive maximum likelihood estimators for the mutation and selection parameters in equilibrium and apply the method to simulated SFS data as well as empirical data from a Madagascar population of Drosophila simulans. Copyright © 2015 Elsevier Inc. All rights reserved.
Inferring infection hazard in wildlife populations by linking data across individual and population scales

USGS Publications Warehouse

Pepin, Kim M.; Kay, Shannon L.; Golas, Ben D.; Shriner, Susan A.; Gilbert, Amy T.; Miller, Ryan S.; Graham, Andrea L.; Riley, Steven; Cross, Paul C.; Samuel, Michael D.; Hooten, Mevin B.; Hoeting, Jennifer A.; Lloyd-Smith, James O.; Webb, Colleen T.; Buhnerkempe, Michael G.

2017-01-01

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease.

Tree-Structured Infinite Sparse Factor Model

PubMed Central

Zhang, XianXing; Dunson, David B.; Carin, Lawrence

2013-01-01

A tree-structured multiplicative gamma process (TMGP) is developed, for inferring the depth of a tree-based factor-analysis model. This new model is coupled with the nested Chinese restaurant process, to nonparametrically infer the depth and width (structure) of the tree. In addition to developing the model, theoretical properties of the TMGP are addressed, and a novel MCMC sampler is developed. The structure of the inferred tree is used to learn relationships between high-dimensional data, and the model is also applied to compressive sensing and interpolation of incomplete images. PMID:25279389
Nanoscale movements of cellulose microfibrils in primary cell walls.

PubMed

Zhang, Tian; Vavylonis, Dimitrios; Durachko, Daniel M; Cosgrove, Daniel J

2017-04-28

The growing plant cell wall is commonly considered to be a fibre-reinforced structure whose strength, extensibility and anisotropy depend on the orientation of crystalline cellulose microfibrils, their bonding to the polysaccharide matrix and matrix viscoelasticity 1-4 . Structural reinforcement of the wall by stiff cellulose microfibrils is central to contemporary models of plant growth, mechanics and meristem dynamics 4-12 . Although passive microfibril reorientation during wall extension has been inferred from theory and from bulk measurements 13-15 , nanometre-scale movements of individual microfibrils have not been directly observed. Here we combined nanometre-scale imaging of wet cell walls by atomic force microscopy (AFM) with a stretching device and endoglucanase treatment that induces wall stress relaxation and creep, mimicking wall behaviours during cell growth. Microfibril movements during forced mechanical extensions differ from those during creep of the enzymatically loosened wall. In addition to passive angular reorientation, we observed a diverse repertoire of microfibril movements that reveal the spatial scale of molecular connections between microfibrils. Our results show that wall loosening alters microfibril connectivity, enabling microfibril dynamics not seen during mechanical stretch. These insights into microfibril movements and connectivities need to be incorporated into refined models of plant cell wall structure, growth and morphogenesis.
The vertical structure of Jupiter and Saturn zonal winds from nonlinear simulations of major vortices and planetary-scale disturbances

NASA Astrophysics Data System (ADS)

Garcia-Melendo, E.; Legarreta, J.; Sanchez-Lavega, A.

2012-12-01

Direct measurements of the structure of the zonal winds of Jupiter and Saturn below the upper cloud layer are very difficult to retrieve. Except from the vertical profile at a Jupiter hot spot obtained from the Galileo probe in 1995 and measurements from cloud tracking by Cassini instruments just below the upper cloud, no other data are available. We present here our inferences of the vertical structure of Jupiter and Saturn zonal wind across the upper troposphere (deep down to about 10 bar level) obtained from nonlinear simulations using the EPIC code of the stability and interactions of large-scale vortices and planetary-scale disturbances in both planets. Acknowledgements: This work has been funded by Spanish MICIIN AYA2009-10701 with FEDER support, Grupos Gobierno Vasco IT-464-07 and UPV/EHU UFI11/55. [1] García-Melendo E., Sánchez-Lavega A., Dowling T.., Icarus, 176, 272-282 (2005). [2] García-Melendo E., Sánchez-Lavega A., Hueso R., Icarus, 191, 665-677 (2007). [3] Sánchez-Lavega A., et al., Nature, 451, 437- 440 (2008). [4] Sánchez-Lavega A., et al., Nature, 475, 71-74 (2011).
Correlation between the shear-speed structure and thickness of the mantle transition zone

NASA Astrophysics Data System (ADS)

Lebedev, Sergei; Chevrot, Sébastien; van der Hilst, R. D.

2003-04-01

The 410 and 660 km seismic discontinuities that bound the mantle transition zone (TZ) are attributed to phase transformations in olivine structure. This implies that variations in TZ thickness ( HTZ) should correlate with those in TZ temperature. Pertinent seismic evidence has so far been ambiguous, however. We measure converted-wave ( Pd s) differential times tdiff= tP660 s- tP410 s in SE Asia and Australia and compare them with S-velocity ( βTZ) estimates from regional tomographic models. Both tdiff and βTZ vary on a scale of a few hundred kilometers. Inferred variations in HTZ are up to ±30 km over length scales larger than 500 km, implying ±200 K thermal heterogeneity if the effect of composition can be neglected. tdiff and βTZ correlate strongly; the linear dependence of HTZ on the average temperature within the TZ is consistent with olivine Clapeyron slopes. We also show that this relationship holds on a global-scale as well, provided that the scalelengths and uncertainties of the variations in tdiff and βTZ are taken into account. These results confirm that the transformations in olivine structure give rise to the 410 and 660 km discontinuities globally.
Population Explosion in the Yellow-Spined Bamboo Locust Ceracris kiangsu and Inferences for the Impact of Human Activity

PubMed Central

Fan, Zhou; Jiang, Guo-Fang; Liu, Yu-Xiang; He, Qi-Xin; Blanchard, Benjamin

2014-01-01

Geographic distance and geographical barriers likely play a considerable role in structuring genetic variation in species, although some migratory species may have less phylogeographic structure on a smaller spatial scale. Here, genetic diversity and the phylogenetic structure among geographical populations of the yellow-spined bamboo locust, Ceracris kiangsu, were examined with 16S rDNA and amplified fragment length polymorphisms (AFLPs). In this study, no conspicuous phylogeographical structure was discovered from either Maximum parsimony (MP) and Neighbor-joining (NJ) phylogenetic analyses. The effect of geographical isolation was not conspicuous on a large spatial scale.At smaller spatial scales local diversity of some populations within mountainous areas were detected using Nei's genetic distance and AMOVA. There is a high level of genetic diversity and a low genetic differentiation among populations in the C. kiangsu of South and Southeast China. Our analyses indicate that C. kiangsu is a monophyletic group. Our results also support the hypothesis that the C. kiangsu population is in a primary differentiation stage. Given the mismatch distribution, it is likely that a population expansion in C. kiangsu occurred about 0.242 Ma during the Quaternary interglaciation. Based on historical reports, we conjecture that human activities had significant impacts on the C. kiangsu gene flow. PMID:24603526
Direct observation of electrothermal instability structures on intensely Ohmically heated aluminum with current flowing in a surface skin layer

NASA Astrophysics Data System (ADS)

Awe, Thomas

2017-10-01

Implosions on the Z Facility assemble high-energy-density plasmas for radiation effects and ICF experiments, but achievable stagnation pressures and temperatures are degraded by the Magneto-Rayleigh-Taylor (MRT) instability. While the beryllium liners (tubes) used in Magnetized Liner Inertial Fusion (MagLIF) experiments are astonishingly smooth (10 to 50 nm RMS roughness), they also contain distributed micron-scale resistive inclusions, and large MRT amplitudes are observed. Early in the implosion, an electrothermal instability (ETI) may provide a perturbation which greatly exceeds the initial surface roughness of the liner. Resistive inhomogeneities drive nonuniform current density and Joule heating, resulting in locally higher temperature, and thus still higher resistivity. Such unstable temperature and pressure growth produce density perturbations which seed MRT. For MagLIF liners, ETI seeding of MRT has been inferred by evaluating late-time MRT, but a direct observation of ETI is not made. ETI is directly observed on the surface of 1.0-mm-diameter solid Al rods pulsed to 1 MA in 100 ns via high resolution gated optical imaging (2 ns temporal and 3 micron spatial resolution). Aluminum 6061 alloy rods, with micron-scale resistive inclusions, consistently first demonstrate overheating from distinct, 10-micron-scale, sub-eV spots, which 5-10 ns later merge into azimuthally stretched elliptical spots and discrete strata (40-100 microns wide by 10 microns tall). Axial plasma filaments form shortly thereafter. Surface plasma can be suppressed for rods coated with dielectric, enabling extended study of the evolution of stratified ETI structures, and experimental inference of ETI growth rates. This fundamentally new and highly 3-dimensional dataset informs ETI physics, including when the ETI seed of MRT may be initiated.
Summary of types of radiation belt electron precipitation observed by BARREL

NASA Astrophysics Data System (ADS)

Halford, Alexa

2016-07-01

The Balloon Array for Relativistic Radiation belt Electron Loss (BARREL) was able to infer precipitation of radiation belt electrons on multiple time scales and due to multiple loss mechanisms. One storm will be specifically highlighted which occurred on 26 January 2013 when a solar wind shock hit the Earth. Although MeV electrons were observed to be lost due to an EMIC wave event [Zhang et al in prep], and multiple periods of electron loss during substorms were observed [Rae et al submitted JGR, Mann et al in prep], we will consider an event period where loss associated with multiple time scales, and thus possibly different loss mechanisms was observed from 1000 - 1200 UT on 26 January 2013. At about 1005 UT on 26 January 2013 an injection of radiation belt electrons followed by drift echoes for energies of ˜80 - 400 keV. BARREL observed X-rays with energies less than 180 keV associated with multiple temporal structures during the drift echo event period. The Van Allen Probes were at similar L-values but upwards of 2 hours away in MLT. Upper band chorus and ULF waves were observed during the event period. Throughout the beginning of the event period, microbursts were clearly observed. During this time lower band chorus waves as well as time domain structures were observed at Van Allen Probe A located upwards of 2 hours away in MLT. This large difference in MLT meant that neither potential loss mechanism was able to be clearly associated with the microbursts. As the lower band chorus and time domain structures were observed to recede, the microbursts were also observed to subside. ULF time scale modulation of the X-rays was also observed throughout most of the event period. We will examine if the ULF waves are the cause of the precipitation themselves, or are modulating the loss of particles from a secondary loss mechanism [Brito et al 2015 JGR, Rae et al Submitted JGR]. Although the 100s ms and ULF time scales are clearly observed, there is an ˜20 minute overarching structure observed in the X-rays at BARREL. This longer time scale appears to match the drift period of the ˜300 keV electrons observed by the Van Allen probes. However the inferred energy of the precipitating electrons is ˜150 keV. It is unclear what may be causing the ˜20 minute structure in the X-rays. At the time of writing this abstract, it is unclear if the drifting of the 300 keV electrons is related to the precipitation of the lower energy electrons (< 180 keV) or if it is just coincidence that they have the same temporal structure.
Bayesian structural inference for hidden processes.

PubMed

Strelioff, Christopher C; Crutchfield, James P

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ε-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ε-machines, irrespective of estimated transition probabilities. Properties of ε-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.
Bayesian structural inference for hidden processes

NASA Astrophysics Data System (ADS)

Strelioff, Christopher C.; Crutchfield, James P.

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ɛ-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ɛ-machines, irrespective of estimated transition probabilities. Properties of ɛ-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.
Significant demographic and fine-scale genetic structure in expanding and senescing populations of the terrestrial orchid Cymbidium goeringii (Orchidaceae).

PubMed

Chung, Mi Yoon; Nason, John D; Chung, Myong Gi

2011-12-01

Fine-scale genetic structure (FSGS) in plants is influenced by variation in spatial and temporal demographic processes. To determine how demographic structure and FSGS change with stages of population succession, we studied replicate expanding and senescing populations of the Asian terrestrial orchid Cymbidium goeringii. We used spatial autocorrelation methods (O-ring and kinship statistics) to quantify spatial demographic structure and FSGS in two expanding and two senescing populations, also measuring genetic diversity and inbreeding in each. All populations exhibited significant aggregation of individuals and FSGS at short spatial scales. In expanding populations, this finding was associated with high recruitment rates, suggesting restricted seed dispersal. In senescing populations, recruitment was minimal, suggesting alternative mechanisms of aggregation, perhaps including spatial associations with mycorrhizal fungi. All populations had significant evidence of genetic bottlenecks, and inbreeding levels were consistently high. Our results indicate that different successional stages can generate similar patterns of spatial demographic and genetic structure, but as a consequence of different processes. These results contrast with the only other study of senescence effects on population genetic structure in an herbaceous perennial, which found little to no FSGS in senescing populations. With the exception of populations subject to mass collection by orchid sellers, significant FSGS is characteristic of the 16 terrestrial orchid species examined to date. From a conservation perspective, this result suggests that inference of orchid population history will benefit from analyses of both FSGS and demographic structure in combination with other ecological field data.
Probing the Small-scale Structure in Strongly Lensed Systems via Transdimensional Inference

NASA Astrophysics Data System (ADS)

Daylan, Tansu; Cyr-Racine, Francis-Yan; Diaz Rivero, Ana; Dvorkin, Cora; Finkbeiner, Douglas P.

2018-02-01

Strong lensing is a sensitive probe of the small-scale density fluctuations in the Universe. We implement a pipeline to model strongly lensed systems using probabilistic cataloging, which is a transdimensional, hierarchical, and Bayesian framework to sample from a metamodel (union of models with different dimensionality) consistent with observed photon count maps. Probabilistic cataloging allows one to robustly characterize modeling covariances within and across lens models with different numbers of subhalos. Unlike traditional cataloging of subhalos, it does not require model subhalos to improve the goodness of fit above the detection threshold. Instead, it allows the exploitation of all information contained in the photon count maps—for instance, when constraining the subhalo mass function. We further show that, by not including these small subhalos in the lens model, fixed-dimensional inference methods can significantly mismodel the data. Using a simulated Hubble Space Telescope data set, we show that the subhalo mass function can be probed even when many subhalos in the sample catalogs are individually below the detection threshold and would be absent in a traditional catalog. The implemented software, Probabilistic Cataloger (PCAT) is made publicly available at https://github.com/tdaylan/pcat.
Reasoning about Causal Relationships: Inferences on Causal Networks

PubMed Central

Rottman, Benjamin Margolin; Hastie, Reid

2013-01-01

Over the last decade, a normative framework for making causal inferences, Bayesian Probabilistic Causal Networks, has come to dominate psychological studies of inference based on causal relationships. The following causal networks—[X→Y→Z, X←Y→Z, X→Y←Z]—supply answers for questions like, “Suppose both X and Y occur, what is the probability Z occurs?” or “Suppose you intervene and make Y occur, what is the probability Z occurs?” In this review, we provide a tutorial for how normatively to calculate these inferences. Then, we systematically detail the results of behavioral studies comparing human qualitative and quantitative judgments to the normative calculations for many network structures and for several types of inferences on those networks. Overall, when the normative calculations imply that an inference should increase, judgments usually go up; when calculations imply a decrease, judgments usually go down. However, two systematic deviations appear. First, people’s inferences violate the Markov assumption. For example, when inferring Z from the structure X→Y→Z, people think that X is relevant even when Y completely mediates the relationship between X and Z. Second, even when people’s inferences are directionally consistent with the normative calculations, they are often not as sensitive to the parameters and the structure of the network as they should be. We conclude with a discussion of productive directions for future research. PMID:23544658
From seconds to months: an overview of multi-scale dynamics of mobile telephone calls

NASA Astrophysics Data System (ADS)

Saramäki, Jari; Moro, Esteban

2015-06-01

Big Data on electronic records of social interactions allow approaching human behaviour and sociality from a quantitative point of view with unforeseen statistical power. Mobile telephone Call Detail Records (CDRs), automatically collected by telecom operators for billing purposes, have proven especially fruitful for understanding one-to-one communication patterns as well as the dynamics of social networks that are reflected in such patterns. We present an overview of empirical results on the multi-scale dynamics of social dynamics and networks inferred from mobile telephone calls. We begin with the shortest timescales and fastest dynamics, such as burstiness of call sequences between individuals, and "zoom out" towards longer temporal and larger structural scales, from temporal motifs formed by correlated calls between multiple individuals to long-term dynamics of social groups. We conclude this overview with a future outlook.
Butterfly scale form birefringence related to photonics.

PubMed

Vidal, Benedicto de Campos

2011-12-01

Wings of the butterflies Morpho aega and Eryphanis reevesi were investigated in the present study by fluorescence, polarization and infra-red (IR) spectroscopic microscopy with the aim of identifying the oriented organization of their components and morphological details of their substructures. These wings were found to exhibit a strong iridescent glow depending on the angle of the incident light; their isolated scales exhibited blue fluorescence. Parallel columns or ridges extend from the pad and sockets to the dented apical scale's region, and they are perpendicular to the ribs that connect the columnar ridges. The scales reveal linear dichroism (LD) visually, when attached on the wing matrix or isolated on slides. The LD was inferred to be textural and positive and was also demonstrated with IR microscopy. The scale columns and ribs are birefringent structures. Images obtained before and after birefringence compensation allowed a detailed study of the scale morphology. Form and intrinsic birefringence findings here estimated and discussed in the context of nonlinear optical properties, bring to the level of morphology the state of molecular order and periodicity of the wing structure. FT-IR absorption peaks were found at wavenumbers which correspond to symmetric and asymmetric (-N-H) stretching, symmetric (-C-H) stretching, amide I (-CO) stretching, amide II(-N-H), and β-linking. Based on LD results obtained with polarized IR the molecular vibrations of the wing scales of M. aega and E. reevesi are assumed to be oriented with respect to the long axis of these structures. Copyright © 2011 Elsevier Ltd. All rights reserved.
Recent (1999-2003) Canadian research on contemporary processes of river erosion and sedimentation, and river mechanics

NASA Astrophysics Data System (ADS)

de Boer, D. H.; Hassan, M. A.; MacVicar, B.; Stone, M.

2005-01-01

Contributions by Canadian fluvial geomorphologists between 1999 and 2003 are discussed under four major themes: sediment yield and sediment dynamics of large rivers; cohesive sediment transport; turbulent flow structure and sediment transport; and bed material transport and channel morphology. The paper concludes with a section on recent technical advances. During the review period, substantial progress has been made in investigating the details of fluvial processes at relatively small scales. Examples of this emphasis are the studies of flow structure, turbulence characteristics and bedload transport, which continue to form central themes in fluvial research in Canada. Translating the knowledge of small-scale, process-related research to an understanding of the behaviour of large-scale fluvial systems, however, continues to be a formidable challenge. Models play a prominent role in elucidating the link between small-scale processes and large-scale fluvial geomorphology, and, as a result, a number of papers describing models and modelling results have been published during the review period. In addition, a number of investigators are now approaching the problem by directly investigating changes in the system of interest at larger scales, e.g. a channel reach over tens of years, and attempting to infer what processes may have led to the result. It is to be expected that these complementary approaches will contribute to an increased understanding of fluvial systems at a variety of spatial and temporal scales. Copyright
Lithospheric structure of the southern French Alps inferred from broadband analysis

NASA Astrophysics Data System (ADS)

Bertrand, E.; Deschamps, A.

2000-11-01

Broadband receiver functions analysis is commonly used to evaluate the fine-scale S-velocity structure of the lithosphere. We analyse teleseismic P-waves and their coda from 30 selected teleseismic events recorded at three seismological stations of to the French TGRS network in the Alpes Maritimes. Receiver functions are computed in the time domain using an SVD matrix inversion method. Dipping Moho and lateral heterogeneities beneath the array are inferred from the amplitude, arrival time and polarity of locally-generated PS phases. We propose that the Moho dips 11° towards 25°±10°N below station CALF, in the outer part of the Alpine belt. At this station, we determine a Moho depth of about 20±2 km; the same depth is suggested below SAOF station also located in the fold-trust belt. Beneath station STET located in the inner part of the Alpine belt, the Moho depth increases to 30 km and dips towards the N-NW. Moreover, 1D-modelling of summed receiver function from STET station constrains a crustal structure significantly different from that observed at stations located in the outer part of the Alps. Indeed, beneath CALF and SAOF stations we need a 2 km thick shallow low velocity layer to fit best the observed receiver functions whereas this layer seems not to be present beneath STET station. Because recent P-coda studies have shown that near-receiver scattering can dominate teleseismic P-wave recordings in tectonically complicated areas, we account for effect of scattering energy in our records from array measurements. As the array aperture is wide relative to the heterogeneity scale length in the area, the array analysis produces only smooth imaging of scatterers beneath the stations.
Investigating the Cosmic Web with Topological Data Analysis

NASA Astrophysics Data System (ADS)

Cisewski-Kehe, Jessi; Wu, Mike; Fasy, Brittany; Hellwing, Wojciech; Lovell, Mark; Rinaldo, Alessandro; Wasserman, Larry

2018-01-01

Data exhibiting complicated spatial structures are common in many areas of science (e.g. cosmology, biology), but can be difficult to analyze. Persistent homology is a popular approach within the area of Topological Data Analysis that offers a new way to represent, visualize, and interpret complex data by extracting topological features, which can be used to infer properties of the underlying structures. In particular, TDA may be useful for analyzing the large-scale structure (LSS) of the Universe, which is an intricate and spatially complex web of matter. In order to understand the physics of the Universe, theoretical and computational cosmologists develop large-scale simulations that allow for visualizing and analyzing the LSS under varying physical assumptions. Each point in the 3D data set represents a galaxy or a cluster of galaxies, and topological summaries ("persistent diagrams") can be obtained summarizing the different ordered holes in the data (e.g. connected components, loops, voids).The topological summaries are interesting and informative descriptors of the Universe on their own, but hypothesis tests using the topological summaries would provide a way to make more rigorous comparisons of LSS under different theoretical models. For example, the received cosmological model has cold dark matter (CDM); however, while the case is strong for CDM, there are some observational inconsistencies with this theory. Another possibility is warm dark matter (WDM). It is of interest to see if a CDM Universe and WDM Universe produce LSS that is topologically distinct.We present several possible test statistics for two-sample hypothesis tests using the topological summaries, carryout a simulation study to investigate the suitableness of the proposed test statistics using simulated data from a variation of the Voronoi foam model, and finally we apply the proposed inference framework to WDM vs. CDM cosmological simulation data.
Spatio-temporal conditional inference and hypothesis tests for neural ensemble spiking precision

PubMed Central

Harrison, Matthew T.; Amarasingham, Asohan; Truccolo, Wilson

2014-01-01

The collective dynamics of neural ensembles create complex spike patterns with many spatial and temporal scales. Understanding the statistical structure of these patterns can help resolve fundamental questions about neural computation and neural dynamics. Spatio-temporal conditional inference (STCI) is introduced here as a semiparametric statistical framework for investigating the nature of precise spiking patterns from collections of neurons that is robust to arbitrarily complex and nonstationary coarse spiking dynamics. The main idea is to focus statistical modeling and inference, not on the full distribution of the data, but rather on families of conditional distributions of precise spiking given different types of coarse spiking. The framework is then used to develop families of hypothesis tests for probing the spatio-temporal precision of spiking patterns. Relationships among different conditional distributions are used to improve multiple hypothesis testing adjustments and to design novel Monte Carlo spike resampling algorithms. Of special note are algorithms that can locally jitter spike times while still preserving the instantaneous peri-stimulus time histogram (PSTH) or the instantaneous total spike count from a group of recorded neurons. The framework can also be used to test whether first-order maximum entropy models with possibly random and time-varying parameters can account for observed patterns of spiking. STCI provides a detailed example of the generic principle of conditional inference, which may be applicable in other areas of neurostatistical analysis. PMID:25380339
An inference engine for embedded diagnostic systems

NASA Technical Reports Server (NTRS)

Fox, Barry R.; Brewster, Larry T.

1987-01-01

The implementation of an inference engine for embedded diagnostic systems is described. The system consists of two distinct parts. The first is an off-line compiler which accepts a propositional logical statement of the relationship between facts and conclusions and produces data structures required by the on-line inference engine. The second part consists of the inference engine and interface routines which accept assertions of fact and return the conclusions which necessarily follow. Given a set of assertions, it will generate exactly the conclusions which logically follow. At the same time, it will detect any inconsistencies which may propagate from an inconsistent set of assertions or a poorly formulated set of rules. The memory requirements are fixed and the worst case execution times are bounded at compile time. The data structures and inference algorithms are very simple and well understood. The data structures and algorithms are described in detail. The system has been implemented on Lisp, Pascal, and Modula-2.
Utilizing Gravity Methods for Regional Studies in Basin Delineation: Case Study at Jornada del Muerto basin, New Mexico

NASA Astrophysics Data System (ADS)

Villalobos, J. I.

2005-12-01

The modeling of basin structures is an important step in the development of plans and policies for ground water management. To facilitate in the analysis of large scale regional structures, gravity data is implemented to examine the overall structural trend of the region. The gravitational attraction of structures in the upper mantle and crust provide vital information about the possible structure and composition of a region. Improved availability of gravity data via internet has promoted extensive construction and interpretation of gravity maps in the analysis of sub-surface structural anomalies. The utilization of gravity data appears to be particularly worthwhile because it is a non-invasive and inexpensive means of addressing the subsurface tectonic framework of large scale regions. In this paper, the author intends to illustrate 1) acquisition of gravity data and its processing; 2) interpretation of gravity data; and 3) sources of uncertainty and errors by using a case study of the Jornada del Muerto basin in South-Central New Mexico where integrated gravity data inferred several faults, sub-basins and thickness variations within the basins structure. The author also explores the integration of gravity method with other geophysical methods to further refine the delineation of basins.

Probabilistic measures of persistence and extinction in measles (meta)populations.

PubMed

Gunning, Christian E; Wearing, Helen J

2013-08-01

Persistence and extinction are fundamental processes in ecological systems that are difficult to accurately measure due to stochasticity and incomplete observation. Moreover, these processes operate on multiple scales, from individual populations to metapopulations. Here, we examine an extensive new data set of measles case reports and associated demographics in pre-vaccine era US cities, alongside a classic England & Wales data set. We first infer the per-population quasi-continuous distribution of log incidence. We then use stochastic, spatially implicit metapopulation models to explore the frequency of rescue events and apparent extinctions. We show that, unlike critical community size, the inferred distributions account for observational processes, allowing direct comparisons between metapopulations. The inferred distributions scale with population size. We use these scalings to estimate extinction boundary probabilities. We compare these predictions with measurements in individual populations and random aggregates of populations, highlighting the importance of medium-sized populations in metapopulation persistence. © 2013 John Wiley & Sons Ltd/CNRS.
The relation between the column density structures and the magnetic field orientation in the Vela C molecular complex

NASA Astrophysics Data System (ADS)

Soler, J. D.; Ade, P. A. R.; Angilè, F. E.; Ashton, P.; Benton, S. J.; Devlin, M. J.; Dober, B.; Fissel, L. M.; Fukui, Y.; Galitzki, N.; Gandilo, N. N.; Hennebelle, P.; Klein, J.; Li, Z.-Y.; Korotkov, A. L.; Martin, P. G.; Matthews, T. G.; Moncelsi, L.; Netterfield, C. B.; Novak, G.; Pascale, E.; Poidevin, F.; Santos, F. P.; Savini, G.; Scott, D.; Shariff, J. A.; Thomas, N. E.; Tucker, C. E.; Tucker, G. S.; Ward-Thompson, D.

2017-07-01

We statistically evaluated the relative orientation between gas column density structures, inferred from Herschel submillimetre observations, and the magnetic field projected on the plane of sky, inferred from polarized thermal emission of Galactic dust observed by the Balloon-borne Large-Aperture Submillimetre Telescope for Polarimetry (BLASTPol) at 250, 350, and 500 μm, towards the Vela C molecular complex. First, we find very good agreement between the polarization orientations in the three wavelength-bands, suggesting that, at the considered common angular resolution of 3.´0 that corresponds to a physical scale of approximately 0.61 pc, the inferred magnetic field orientation is not significantly affected by temperature or dust grain alignment effects. Second, we find that the relative orientation between gas column density structures and the magnetic field changes progressively with increasing gas column density, from mostly parallel or having no preferred orientation at low column densities to mostly perpendicular at the highest column densities. This observation is in agreement with previous studies by the Planck collaboration towards more nearby molecular clouds. Finally, we find a correspondencebetween (a) the trends in relative orientation between the column density structures and the projected magnetic field; and (b) the shape of the column density probability distribution functions (PDFs). In the sub-regions of Vela C dominated by one clear filamentary structure, or "ridges", where the high-column density tails of the PDFs are flatter, we find a sharp transition from preferentially parallel or having no preferred relative orientation at low column densities to preferentially perpendicular at highest column densities. In the sub-regions of Vela C dominated by several filamentary structures with multiple orientations, or "nests", where the maximum values of the column density are smaller than in the ridge-like sub-regions and the high-column density tails of the PDFs are steeper, such a transition is also present, but it is clearly less sharp than in the ridge-like sub-regions. Both of these results suggest that the magnetic field is dynamically important for the formation of density structures in this region.
Probabilistic Methods for Structural Design and Reliability

NASA Technical Reports Server (NTRS)

Chamis, Christos C.; Whitlow, Woodrow, Jr. (Technical Monitor)

2002-01-01

This report describes a formal method to quantify structural damage tolerance and reliability in the presence of a multitude of uncertainties in turbine engine components. The method is based at the material behavior level where primitive variables with their respective scatter ranges are used to describe behavior. Computational simulation is then used to propagate the uncertainties to the structural scale where damage tolerance and reliability are usually specified. Several sample cases are described to illustrate the effectiveness, versatility, and maturity of the method. Typical results from this method demonstrate, that it is mature and that it can be used to probabilistically evaluate turbine engine structural components. It may be inferred from the results that the method is suitable for probabilistically predicting the remaining life in aging or in deteriorating structures, for making strategic projections and plans, and for achieving better, cheaper, faster products that give competitive advantages in world markets.
Kinematics of Mass Transport Deposits revealed by magnetic fabrics

NASA Astrophysics Data System (ADS)

Weinberger, R.; Levi, T.; Alsop, G. I.; Marco, S.

2017-08-01

The internal deformation and movement directions of Mass Transport Deposits (MTDs) are key factors in understanding the kinematics and dynamics of their emplacement. Although these are relatively easy to recover from well-bedded sediments, they are more difficult to deduce from massive beds without visible strain markers. In order to test the applicability of using anisotropy of magnetic susceptibility (AMS) to determine MTD movement, we compare AMS fabrics, with structural measurements of visible kinematic indicators. Our case study involves the structural analysis of slumped lake sediments extensively exposed in MTDs within the Dead Sea Basin. Structural analyses of MTDs outcropping for >100 km reveal radial transport directions toward the basin depocenter. We show that the AMS fabrics display the same transport directions as inferred from structural analyses. Based on this similarity, we outline a robust procedure to obtain the transport direction of slumped MTDs from AMS fabrics. Variations in the magnetic fabrics and anisotropies in fold-thrust systems within the slumps match the various structural domains. We therefore suggest that magnetic fabrics and anisotropy variations in drill cores may reflect internal deformation within the slumps rather than different slumps. Obtaining magnetic fabrics from MTDs provides a viable way to infer the transport directions and internal deformation of MTDs and reconstruct the basin depocenter in ancient settings. The present results also have implications beyond the kinematics of MTDs, as their geometry resembles fold-thrust systems in other geological settings, scales, and tectonic environments.
Research participant compensation: A matter of statistical inference as well as ethics.

PubMed

Swanson, David M; Betensky, Rebecca A

2015-11-01

The ethics of compensation of research subjects for participation in clinical trials has been debated for years. One ethical issue of concern is variation among subjects in the level of compensation for identical treatments. Surprisingly, the impact of variation on the statistical inferences made from trial results has not been examined. We seek to identify how variation in compensation may influence any existing dependent censoring in clinical trials, thereby also influencing inference about the survival curve, hazard ratio, or other measures of treatment efficacy. In simulation studies, we consider a model for how compensation structure may influence the censoring model. Under existing dependent censoring, we estimate survival curves under different compensation structures and observe how these structures induce variability in the estimates. We show through this model that if the compensation structure affects the censoring model and dependent censoring is present, then variation in that structure induces variation in the estimates and affects the accuracy of estimation and inference on treatment efficacy. From the perspectives of both ethics and statistical inference, standardization and transparency in the compensation of participants in clinical trials are warranted. Copyright © 2015 Elsevier Inc. All rights reserved.
Controls on development and diversity of Early Archean stromatolites

PubMed Central

Allwood, Abigail C.; Grotzinger, John P.; Knoll, Andrew H.; Burch, Ian W.; Anderson, Mark S.; Coleman, Max L.; Kanik, Isik

2009-01-01

The ≈3,450-million-year-old Strelley Pool Formation in Western Australia contains a reef-like assembly of laminated sedimentary accretion structures (stromatolites) that have macroscale characteristics suggestive of biological influence. However, direct microscale evidence of biology—namely, organic microbial remains or biosedimentary fabrics—has to date eluded discovery in the extensively-recrystallized rocks. Recently-identified outcrops with relatively good textural preservation record microscale evidence of primary sedimentary processes, including some that indicate probable microbial mat formation. Furthermore, we find relict fabrics and organic layers that covary with stromatolite morphology, linking morphologic diversity to changes in sedimentation, seafloor mineral precipitation, and inferred microbial mat development. Thus, the most direct and compelling signatures of life in the Strelley Pool Formation are those observed at the microscopic scale. By examining spatiotemporal changes in microscale characteristics it is possible not only to recognize the presence of probable microbial mats during stromatolite development, but also to infer aspects of the biological inputs to stromatolite morphogenesis. The persistence of an inferred biological signal through changing environmental circumstances and stromatolite types indicates that benthic microbial populations adapted to shifting environmental conditions in early oceans. PMID:19515817
PREMER: a Tool to Infer Biological Networks.

PubMed

Villaverde, Alejandro F; Becker, Kolja; Banga, Julio R

2017-10-04

Inferring the structure of unknown cellular networks is a main challenge in computational biology. Data-driven approaches based on information theory can determine the existence of interactions among network nodes automatically. However, the elucidation of certain features - such as distinguishing between direct and indirect interactions or determining the direction of a causal link - requires estimating information-theoretic quantities in a multidimensional space. This can be a computationally demanding task, which acts as a bottleneck for the application of elaborate algorithms to large-scale network inference problems. The computational cost of such calculations can be alleviated by the use of compiled programs and parallelization. To this end we have developed PREMER (Parallel Reverse Engineering with Mutual information & Entropy Reduction), a software toolbox that can run in parallel and sequential environments. It uses information theoretic criteria to recover network topology and determine the strength and causality of interactions, and allows incorporating prior knowledge, imputing missing data, and correcting outliers. PREMER is a free, open source software tool that does not require any commercial software. Its core algorithms are programmed in FORTRAN 90 and implement OpenMP directives. It has user interfaces in Python and MATLAB/Octave, and runs on Windows, Linux and OSX (https://sites.google.com/site/premertoolbox/).
Geographical ecology of the palms (Arecaceae): determinants of diversity and distributions across spatial scales

PubMed Central

Eiserhardt, Wolf L.; Svenning, Jens-Christian; Kissling, W. Daniel; Balslev, Henrik

2011-01-01

Background The palm family occurs in all tropical and sub-tropical regions of the world. Palms are of high ecological and economical importance, and display complex spatial patterns of species distributions and diversity. Scope This review summarizes empirical evidence for factors that determine palm species distributions, community composition and species richness such as the abiotic environment (climate, soil chemistry, hydrology and topography), the biotic environment (vegetation structure and species interactions) and dispersal. The importance of contemporary vs. historical impacts of these factors and the scale at which they function is discussed. Finally a hierarchical scale framework is developed to guide predictor selection for future studies. Conclusions Determinants of palm distributions, composition and richness vary with spatial scale. For species distributions, climate appears to be important at landscape and broader scales, soil, topography and vegetation at landscape and local scales, hydrology at local scales, and dispersal at all scales. For community composition, soil appears important at regional and finer scales, hydrology, topography and vegetation at landscape and local scales, and dispersal again at all scales. For species richness, climate and dispersal appear to be important at continental to global scales, soil at landscape and broader scales, and topography at landscape and finer scales. Some scale–predictor combinations have not been studied or deserve further attention, e.g. climate on regional to finer scales, and hydrology and topography on landscape and broader scales. The importance of biotic interactions – apart from general vegetation structure effects – for the geographic ecology of palms is generally underexplored. Future studies should target scale–predictor combinations and geographic domains not studied yet. To avoid biased inference, one should ideally include at least all predictors previously found important at the spatial scale of investigation. PMID:21712297
A method for estimating 2D Wrinkle Ridge Strain from application of fault displacement scaling to the Yakima Folds, Washington

NASA Astrophysics Data System (ADS)

Mège, Daniel; Reidel, Stephen P.

The Yakima folds on the central Columbia Plateau are a succession of thrusted anticlines thought to be analogs of planetary wrinkle ridges. They provide a unique opportunity to understand wrinkle ridge structure. Field data and length-displacement scaling are used to demonstrate a method for estimating two-dimensional horizontal contractional strain at wrinkle ridges. Strain is given as a function of ridge length, and depends on other parameters that can be inferred from the Yakima folds and fault population displacement studies. Because ridge length can be readily obtained from orbital imagery, the method can be applied to any wrinkle ridge population, and helps constrain quantitative tectonic models on other planets.
Statistical Inference at Work: Statistical Process Control as an Example

ERIC Educational Resources Information Center

Bakker, Arthur; Kent, Phillip; Derry, Jan; Noss, Richard; Hoyles, Celia

2008-01-01

To characterise statistical inference in the workplace this paper compares a prototypical type of statistical inference at work, statistical process control (SPC), with a type of statistical inference that is better known in educational settings, hypothesis testing. Although there are some similarities between the reasoning structure involved in…
Benchmarking Inverse Statistical Approaches for Protein Structure and Design with Exactly Solvable Models.

PubMed

Jacquin, Hugo; Gilson, Amy; Shakhnovich, Eugene; Cocco, Simona; Monasson, Rémi

2016-05-01

Inverse statistical approaches to determine protein structure and function from Multiple Sequence Alignments (MSA) are emerging as powerful tools in computational biology. However the underlying assumptions of the relationship between the inferred effective Potts Hamiltonian and real protein structure and energetics remain untested so far. Here we use lattice protein model (LP) to benchmark those inverse statistical approaches. We build MSA of highly stable sequences in target LP structures, and infer the effective pairwise Potts Hamiltonians from those MSA. We find that inferred Potts Hamiltonians reproduce many important aspects of 'true' LP structures and energetics. Careful analysis reveals that effective pairwise couplings in inferred Potts Hamiltonians depend not only on the energetics of the native structure but also on competing folds; in particular, the coupling values reflect both positive design (stabilization of native conformation) and negative design (destabilization of competing folds). In addition to providing detailed structural information, the inferred Potts models used as protein Hamiltonian for design of new sequences are able to generate with high probability completely new sequences with the desired folds, which is not possible using independent-site models. Those are remarkable results as the effective LP Hamiltonians used to generate MSA are not simple pairwise models due to the competition between the folds. Our findings elucidate the reasons for the success of inverse approaches to the modelling of proteins from sequence data, and their limitations.
Evaluating the Influence of the Microsatellite Marker Set on the Genetic Structure Inferred in Pyrus communis L.

PubMed Central

Urrestarazu, Jorge; Royo, José B.; Santesteban, Luis G.; Miranda, Carlos

2015-01-01

Fingerprinting information can be used to elucidate in a robust manner the genetic structure of germplasm collections, allowing a more rational and fine assessment of genetic resources. Bayesian model-based approaches are nowadays majorly preferred to infer genetic structure, but it is still largely unresolved how marker sets should be built in order to obtain a robust inference. The objective was to evaluate, in Pyrus germplasm collections, the influence of the SSR marker set size on the genetic structure inferred, also evaluating the influence of the criterion used to select those markers. Inferences were performed considering an increasing number of SSR markers that ranged from just two up to 25, incorporated one at a time into the analysis. The influence of the number of SSR markers used was evaluated comparing the number of populations and the strength of the signal detected, and also the similarity of the genotype assignments to populations between analyses. In order to test if those results were influenced by the criterion used to select the SSRs, several choosing scenarios based on the discrimination power or the fixation index values of the SSRs were tested. Our results indicate that population structure could be inferred accurately once a certain SSR number threshold was reached, which depended on the underlying structure within the genotypes, but the method used to select the markers included on each set appeared not to be very relevant. The minimum number of SSRs required to provide robust structure inferences and adequate measurements of the differentiation, even when low differentiation levels exist within populations, was proved similar to that of the complete list of recommended markers for fingerprinting. When a SSR set size similar to the minimum marker sets recommended for fingerprinting it is used, only major divisions or moderate (F ST>0.05) differentiation of the germplasm are detected. PMID:26382618
Dark matter and the equivalence principle

NASA Technical Reports Server (NTRS)

Frieman, Joshua A.; Gradwohl, Ben-Ami

1991-01-01

If the dark matter in galaxies and clusters is nonbaryonic, it can interact with additional long-range fields that are invisible to experimental tests of the equivalence principle. The astrophysical and cosmological implications of a long-range force coupled only to the dark matter are discussed and rather tight constraints on its strength are found. If the force is repulsive (attractive), the masses of galaxy groups and clusters (and the mean density of the universe inferred from them) have been systematically underestimated (overestimated). Such an interaction also has unusual implications for the growth of large-scale structure.
On regreening and degradation in Sahelian watersheds.

PubMed

Kaptué, Armel T; Prihodko, Lara; Hanan, Niall P

2015-09-29

Over many decades our understanding of the impacts of intermittent drought in water-limited environments like the West African Sahel has been influenced by a narrative of overgrazing and human-induced desertification. The desertification narrative has persisted in both scientific and popular conception, such that recent regional-scale recovery ("regreening") and local success stories (community-led conservation efforts) in the Sahel, following the severe droughts of the 1970s-1980s, are sometimes ignored. Here we report a study of watershed-scale vegetation dynamics in 260 watersheds, sampled in four regions of Senegal, Mali, and Niger from 1983-2012, using satellite-derived vegetation indices as a proxy for net primary production. In response to earlier controversy, we first examine the shape of the rainfall-net primary production relationship and how it impacts conclusions regarding greening or degradation. We conclude that the choice of functional relationship has little quantitative impact on our ability to infer greening or degradation trends. We then present an approach to analyze changes in long-term (decade-scale) average rain-use efficiency (an indicator of slowly responding vegetation structural changes) relative to changes in interannual-scale rainfall sensitivity (an indicator of landscape ability to respond rapidly to rainfall variability) to infer trends in greening/degradation of the watersheds in our sample regions. The predominance of increasing rain-use efficiency in our data supports earlier reports of a "greening" trend across the Sahel. However, there are strong regional differences in the extent and direction of change, and in the apparent role of changing woody and herbaceous components in driving those temporal trends.
On regreening and degradation in Sahelian watersheds

PubMed Central

Kaptué, Armel T.; Prihodko, Lara; Hanan, Niall P.

2015-01-01

Over many decades our understanding of the impacts of intermittent drought in water-limited environments like the West African Sahel has been influenced by a narrative of overgrazing and human-induced desertification. The desertification narrative has persisted in both scientific and popular conception, such that recent regional-scale recovery (“regreening”) and local success stories (community-led conservation efforts) in the Sahel, following the severe droughts of the 1970s–1980s, are sometimes ignored. Here we report a study of watershed-scale vegetation dynamics in 260 watersheds, sampled in four regions of Senegal, Mali, and Niger from 1983–2012, using satellite-derived vegetation indices as a proxy for net primary production. In response to earlier controversy, we first examine the shape of the rainfall–net primary production relationship and how it impacts conclusions regarding greening or degradation. We conclude that the choice of functional relationship has little quantitative impact on our ability to infer greening or degradation trends. We then present an approach to analyze changes in long-term (decade-scale) average rain-use efficiency (an indicator of slowly responding vegetation structural changes) relative to changes in interannual-scale rainfall sensitivity (an indicator of landscape ability to respond rapidly to rainfall variability) to infer trends in greening/degradation of the watersheds in our sample regions. The predominance of increasing rain-use efficiency in our data supports earlier reports of a “greening” trend across the Sahel. However, there are strong regional differences in the extent and direction of change, and in the apparent role of changing woody and herbaceous components in driving those temporal trends. PMID:26371296
Fractal Interrelationships in Field and Seismic Data

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

Wilson, T.H.; Dominic, Jovita; Halverson, Joel

1997-10-01

Size scaling interrelationships are evaluated in this study using a fractal model. Fractal models of several geologic variables are examined and include fracture patterns, reflection travel times, structural relief, drainage, topographic relief and active fault patterns. The fractal properties of structural relief inferred from seismic data and structural cross sections provide a quantitative means to characterize and compare complex structural patterns. Studies were conducted using seismic data from the Granny Creek oil field in the Appalachian Plateau. Previous studies of the field reveal that subtle detached structures present on the limb of a larger structure are associated with enhanced productionmore » from the field. Vertical increases of fractal dimension across the zone of detachment provide a measure of the extent to which detachment has occurred. The increases of fractal dimension are greatest in the more productive areas of the field. A result with equally important ramifications is that fracture systems do not appear to be intrinsically fractal as is often suggested in the literature. While examples of nearly identical patterns can be found at different scales supporting the idea of self-similarity, these examples are often taken from different areas and from different lithologies. Examination of fracture systems at different scales in the Valley and Ridge Province suggest that their distribution become increasingly sparse with scale reduction, and therefore are dissimilar or non-fractal. Box counting data in all cases failed to yield a fractal regime. The results obtained from this analysis bring into question the general applicability of reservoir simulations employing fractal models of fracture distribution. The same conclusions were obtained from the analysis of 1D fracture patterns such as those that might appear in a horizontal well.« less
Network diffusion accurately models the relationship between structural and functional brain connectivity networks

PubMed Central

Abdelnour, Farras; Voss, Henning U.; Raj, Ashish

2014-01-01

The relationship between anatomic connectivity of large-scale brain networks and their functional connectivity is of immense importance and an area of active research. Previous attempts have required complex simulations which model the dynamics of each cortical region, and explore the coupling between regions as derived by anatomic connections. While much insight is gained from these non-linear simulations, they can be computationally taxing tools for predicting functional from anatomic connectivities. Little attention has been paid to linear models. Here we show that a properly designed linear model appears to be superior to previous non-linear approaches in capturing the brain’s long-range second order correlation structure that governs the relationship between anatomic and functional connectivities. We derive a linear network of brain dynamics based on graph diffusion, whereby the diffusing quantity undergoes a random walk on a graph. We test our model using subjects who underwent diffusion MRI and resting state fMRI. The network diffusion model applied to the structural networks largely predicts the correlation structures derived from their fMRI data, to a greater extent than other approaches. The utility of the proposed approach is that it can routinely be used to infer functional correlation from anatomic connectivity. And since it is linear, anatomic connectivity can also be inferred from functional data. The success of our model confirms the linearity of ensemble average signals in the brain, and implies that their long-range correlation structure may percolate within the brain via purely mechanistic processes enacted on its structural connectivity pathways. PMID:24384152
Inference of multi-Gaussian property fields by probabilistic inversion of crosshole ground penetrating radar data using an improved dimensionality reduction

NASA Astrophysics Data System (ADS)

Hunziker, Jürg; Laloy, Eric; Linde, Niklas

2016-04-01

Deterministic inversion procedures can often explain field data, but they only deliver one final subsurface model that depends on the initial model and regularization constraints. This leads to poor insights about the uncertainties associated with the inferred model properties. In contrast, probabilistic inversions can provide an ensemble of model realizations that accurately span the range of possible models that honor the available calibration data and prior information allowing a quantitative description of model uncertainties. We reconsider the problem of inferring the dielectric permittivity (directly related to radar velocity) structure of the subsurface by inversion of first-arrival travel times from crosshole ground penetrating radar (GPR) measurements. We rely on the DREAM_(ZS) algorithm that is a state-of-the-art Markov chain Monte Carlo (MCMC) algorithm. Such algorithms need several orders of magnitude more forward simulations than deterministic algorithms and often become infeasible in high parameter dimensions. To enable high-resolution imaging with MCMC, we use a recently proposed dimensionality reduction approach that allows reproducing 2D multi-Gaussian fields with far fewer parameters than a classical grid discretization. We consider herein a dimensionality reduction from 5000 to 257 unknowns. The first 250 parameters correspond to a spectral representation of random and uncorrelated spatial fluctuations while the remaining seven geostatistical parameters are (1) the standard deviation of the data error, (2) the mean and (3) the variance of the relative electric permittivity, (4) the integral scale along the major axis of anisotropy, (5) the anisotropy angle, (6) the ratio of the integral scale along the minor axis of anisotropy to the integral scale along the major axis of anisotropy and (7) the shape parameter of the Matérn function. The latter essentially defines the type of covariance function (e.g., exponential, Whittle, Gaussian). We present an improved formulation of the dimensionality reduction, and numerically show how it reduces artifacts in the generated models and provides better posterior estimation of the subsurface geostatistical structure. We next show that the results of the method compare very favorably against previous deterministic and stochastic inversion results obtained at the South Oyster Bacterial Transport Site in Virginia, USA. The long-term goal of this work is to enable MCMC-based full waveform inversion of crosshole GPR data.
Network inference using informative priors.

PubMed

Mukherjee, Sach; Speed, Terence P

2008-09-23

Recent years have seen much interest in the study of systems characterized by multiple interacting components. A class of statistical models called graphical models, in which graphs are used to represent probabilistic relationships between variables, provides a framework for formal inference regarding such systems. In many settings, the object of inference is the network structure itself. This problem of "network inference" is well known to be a challenging one. However, in scientific settings there is very often existing information regarding network connectivity. A natural idea then is to take account of such information during inference. This article addresses the question of incorporating prior information into network inference. We focus on directed models called Bayesian networks, and use Markov chain Monte Carlo to draw samples from posterior distributions over network structures. We introduce prior distributions on graphs capable of capturing information regarding network features including edges, classes of edges, degree distributions, and sparsity. We illustrate our approach in the context of systems biology, applying our methods to network inference in cancer signaling.
Characterization of the seismically imaged Tuscarora fold system and implications for layer parallel shortening in the Pennsylvania salient

NASA Astrophysics Data System (ADS)

Mount, Van S.; Wilkins, Scott; Comiskey, Cody S.

2017-12-01

The Tuscarora fold system (TFS) is located in the Pennsylvania salient in the foreland of the Valley and Ridge province. The TFS is imaged in high quality 3D seismic data and comprises a system of small-scale folds within relatively flat-lying Lower Silurian Tuscarora Formation strata. We characterize the TFS structures and infer layer parallel shortening (LPS) directions and magnitudes associated with deformation during the Alleghany Orogeny. Previously reported LPS data in our study area are from shallow Devonian and Carboniferous strata (based on outcrop and core analyses) above the shallowest of three major detachments recognized in the region. Seismic data allows us to characterize LPS at depth in strata beneath the shallow detachment. Our LPS data (orientations and inferred magnitudes) are consistent with the shallow data leading us to surmise that LPS during Alleghanian deformation fanned around the salient and was distributed throughout the stratigraphic section - and not isolated to strata above the shallow detachment. We propose that a NW-SE oriented Alleghanian maximum principal stress was perturbed by deep structure associated with the non-linear margin of Laurentia resulting in fanning of shortening directions within the salient.

Genomes as documents of evolutionary history: a probabilistic macrosynteny model for the reconstruction of ancestral genomes

PubMed Central

Nakatani, Yoichiro; McLysaght, Aoife

2017-01-01

Abstract Motivation: It has been argued that whole-genome duplication (WGD) exerted a profound influence on the course of evolution. For the purpose of fully understanding the impact of WGD, several formal algorithms have been developed for reconstructing pre-WGD gene order in yeast and plant. However, to the best of our knowledge, those algorithms have never been successfully applied to WGD events in teleost and vertebrate, impeded by extensive gene shuffling and gene losses. Results: Here, we present a probabilistic model of macrosynteny (i.e. conserved linkage or chromosome-scale distribution of orthologs), develop a variational Bayes algorithm for inferring the structure of pre-WGD genomes, and study estimation accuracy by simulation. Then, by applying the method to the teleost WGD, we demonstrate effectiveness of the algorithm in a situation where gene-order reconstruction algorithms perform relatively poorly due to a high rate of rearrangement and extensive gene losses. Our high-resolution reconstruction reveals previously overlooked small-scale rearrangements, necessitating a revision to previous views on genome structure evolution in teleost and vertebrate. Conclusions: We have reconstructed the structure of a pre-WGD genome by employing a variational Bayes approach that was originally developed for inferring topics from millions of text documents. Interestingly, comparison of the macrosynteny and topic model algorithms suggests that macrosynteny can be regarded as documents on ancestral genome structure. From this perspective, the present study would seem to provide a textbook example of the prevalent metaphor that genomes are documents of evolutionary history. Availability and implementation: The analysis data are available for download at http://www.gen.tcd.ie/molevol/supp_data/MacrosyntenyTGD.zip, and the software written in Java is available upon request. Contact: yoichiro.nakatani@tcd.ie or aoife.mclysaght@tcd.ie Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28881993
Genomes as documents of evolutionary history: a probabilistic macrosynteny model for the reconstruction of ancestral genomes.

PubMed

Nakatani, Yoichiro; McLysaght, Aoife

2017-07-15

It has been argued that whole-genome duplication (WGD) exerted a profound influence on the course of evolution. For the purpose of fully understanding the impact of WGD, several formal algorithms have been developed for reconstructing pre-WGD gene order in yeast and plant. However, to the best of our knowledge, those algorithms have never been successfully applied to WGD events in teleost and vertebrate, impeded by extensive gene shuffling and gene losses. Here, we present a probabilistic model of macrosynteny (i.e. conserved linkage or chromosome-scale distribution of orthologs), develop a variational Bayes algorithm for inferring the structure of pre-WGD genomes, and study estimation accuracy by simulation. Then, by applying the method to the teleost WGD, we demonstrate effectiveness of the algorithm in a situation where gene-order reconstruction algorithms perform relatively poorly due to a high rate of rearrangement and extensive gene losses. Our high-resolution reconstruction reveals previously overlooked small-scale rearrangements, necessitating a revision to previous views on genome structure evolution in teleost and vertebrate. We have reconstructed the structure of a pre-WGD genome by employing a variational Bayes approach that was originally developed for inferring topics from millions of text documents. Interestingly, comparison of the macrosynteny and topic model algorithms suggests that macrosynteny can be regarded as documents on ancestral genome structure. From this perspective, the present study would seem to provide a textbook example of the prevalent metaphor that genomes are documents of evolutionary history. The analysis data are available for download at http://www.gen.tcd.ie/molevol/supp_data/MacrosyntenyTGD.zip , and the software written in Java is available upon request. yoichiro.nakatani@tcd.ie or aoife.mclysaght@tcd.ie. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
Implementation of structure-mapping inference by event-file binding and action planning: a model of tool-improvisation analogies.

PubMed

Fields, Chris

2011-03-01

Structure-mapping inferences are generally regarded as dependent upon relational concepts that are understood and expressible in language by subjects capable of analogical reasoning. However, tool-improvisation inferences are executed by members of a variety of non-human primate and other species. Tool improvisation requires correctly inferring the motion and force-transfer affordances of an object; hence tool improvisation requires structure mapping driven by relational properties. Observational and experimental evidence can be interpreted to indicate that structure-mapping analogies in tool improvisation are implemented by multi-step manipulation of event files by binding and action-planning mechanisms that act in a language-independent manner. A functional model of language-independent event-file manipulations that implement structure mapping in the tool-improvisation domain is developed. This model provides a mechanism by which motion and force representations commonly employed in tool-improvisation structure mappings may be sufficiently reinforced to be available to inwardly directed attention and hence conceptualization. Predictions and potential experimental tests of this model are outlined.
Role of Utility and Inference in the Evolution of Functional Information

PubMed Central

Sharov, Alexei A.

2009-01-01

Functional information means an encoded network of functions in living organisms from molecular signaling pathways to an organism’s behavior. It is represented by two components: code and an interpretation system, which together form a self-sustaining semantic closure. Semantic closure allows some freedom between components because small variations of the code are still interpretable. The interpretation system consists of inference rules that control the correspondence between the code and the function (phenotype) and determines the shape of the fitness landscape. The utility factor operates at multiple time scales: short-term selection drives evolution towards higher survival and reproduction rate within a given fitness landscape, and long-term selection favors those fitness landscapes that support adaptability and lead to evolutionary expansion of certain lineages. Inference rules make short-term selection possible by shaping the fitness landscape and defining possible directions of evolution, but they are under control of the long-term selection of lineages. Communication normally occurs within a set of agents with compatible interpretation systems, which I call communication system. Functional information cannot be directly transferred between communication systems with incompatible inference rules. Each biological species is a genetic communication system that carries unique functional information together with inference rules that determine evolutionary directions and constraints. This view of the relation between utility and inference can resolve the conflict between realism/positivism and pragmatism. Realism overemphasizes the role of inference in evolution of human knowledge because it assumes that logic is embedded in reality. Pragmatism substitutes usefulness for truth and therefore ignores the advantage of inference. The proposed concept of evolutionary pragmatism rejects the idea that logic is embedded in reality; instead, inference rules are constructed within each communication system to represent reality and they evolve towards higher adaptability on a long time scale. PMID:20160960
Wavefield complexity and stealth structures: Resolution constraints by wave physics

NASA Astrophysics Data System (ADS)

Nissen-Meyer, T.; Leng, K.

2017-12-01

Imaging the Earth's interior relies on understanding how waveforms encode information from heterogeneous multi-scale structure. This relation is given by elastodynamics, but forward modeling in the context of tomography primarily serves to deliver synthetic waveforms and gradients for the inversion procedure. While this is entirely appropriate, it depreciates a wealth of complementary inference that can be obtained from the complexity of the wavefield. Here, we are concerned with the imprint of realistic multi-scale Earth structure on the wavefield, and the question on the inherent physical resolution limit of structures encoded in seismograms. We identify parameter and scattering regimes where structures remain invisible as a function of seismic wavelength, structural multi-scale geometry, scattering strength, and propagation path. Ultimately, this will aid in interpreting tomographic images by acknowledging the scope of "forgotten" structures, and shall offer guidance for optimising the selection of seismic data for tomography. To do so, we use our novel 3D modeling method AxiSEM3D which tackles global wave propagation in visco-elastic, anisotropic 3D structures with undulating boundaries at unprecedented resolution and efficiency by exploiting the inherent azimuthal smoothness of wavefields via a coupled Fourier expansion-spectral-element approach. The method links computational cost to wavefield complexity and thereby lends itself well to exploring the relation between waveforms and structures. We will show various examples of multi-scale heterogeneities which appear or disappear in the waveform, and argue that the nature of the structural power spectrum plays a central role in this. We introduce the concept of wavefield learning to examine the true wavefield complexity for a complexity-dependent modeling framework and discriminate which scattering structures can be retrieved by surface measurements. This leads to the question of physical invisibility and the tomographic resolution limit, and offers insight as to why tomographic images still show stark differences for smaller-scale heterogeneities despite progress in modeling and data resolution. Finally, we give an outlook on how we expand this modeling framework towards an inversion procedure guided by wavefield complexity.
Multi-scale habitat selection modeling: A review and outlook

Treesearch

Kevin McGarigal; Ho Yi Wan; Kathy A. Zeller; Brad C. Timm; Samuel A. Cushman

2016-01-01

Scale is the lens that focuses ecological relationships. Organisms select habitat at multiple hierarchical levels and at different spatial and/or temporal scales within each level. Failure to properly address scale dependence can result in incorrect inferences in multi-scale habitat selection modeling studies.
Computational Morphometry for Detecting Changes in Brain Structure Due to Development, Aging, Learning, Disease and Evolution

PubMed Central

Mietchen, Daniel; Gaser, Christian

2009-01-01

The brain, like any living tissue, is constantly changing in response to genetic and environmental cues and their interaction, leading to changes in brain function and structure, many of which are now in reach of neuroimaging techniques. Computational morphometry on the basis of Magnetic Resonance (MR) images has become the method of choice for studying macroscopic changes of brain structure across time scales. Thanks to computational advances and sophisticated study designs, both the minimal extent of change necessary for detection and, consequently, the minimal periods over which such changes can be detected have been reduced considerably during the last few years. On the other hand, the growing availability of MR images of more and more diverse brain populations also allows more detailed inferences about brain changes that occur over larger time scales, way beyond the duration of an average research project. On this basis, a whole range of issues concerning the structures and functions of the brain are now becoming addressable, thereby providing ample challenges and opportunities for further contributions from neuroinformatics to our understanding of the brain and how it changes over a lifetime and in the course of evolution. PMID:19707517
Scale Mixture Models with Applications to Bayesian Inference

NASA Astrophysics Data System (ADS)

Qin, Zhaohui S.; Damien, Paul; Walker, Stephen

2003-11-01

Scale mixtures of uniform distributions are used to model non-normal data in time series and econometrics in a Bayesian framework. Heteroscedastic and skewed data models are also tackled using scale mixture of uniform distributions.
Constraining the baryon-dark matter relative velocity with the large-scale 3-point correlation function of the SDSS BOSS DR12 CMASS galaxies

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

Slepian, Zachary; Slosar, Anze; Eisenstein, Daniel J.

We search for a galaxy clustering bias due to a modulation of galaxy number with the baryon-dark matter relative velocity resulting from recombination-era physics. We find no detected signal and place the constraint bv <0.01 on the relative velocity bias for the CMASS galaxies. This bias is an important potential systematic of Baryon Acoustic Oscillation (BAO) method measurements of the cosmic distance scale using the 2-point clustering. Our limit on the relative velocity bias indicates a systematic shift of no more than 0.3% rms in the distance scale inferred from the BAO feature in the BOSS 2-point clustering, well belowmore » the 1% statistical error of this measurement. In conclusion, this constraint is the most stringent currently available and has important implications for the ability of upcoming large-scale structure surveys such as DESI to self-protect against the relative velocity as a possible systematic.« less
Constraining the baryon-dark matter relative velocity with the large-scale 3-point correlation function of the SDSS BOSS DR12 CMASS galaxies

DOE PAGES

Slepian, Zachary; Slosar, Anze; Eisenstein, Daniel J.; ...

2017-10-24

We search for a galaxy clustering bias due to a modulation of galaxy number with the baryon-dark matter relative velocity resulting from recombination-era physics. We find no detected signal and place the constraint bv <0.01 on the relative velocity bias for the CMASS galaxies. This bias is an important potential systematic of Baryon Acoustic Oscillation (BAO) method measurements of the cosmic distance scale using the 2-point clustering. Our limit on the relative velocity bias indicates a systematic shift of no more than 0.3% rms in the distance scale inferred from the BAO feature in the BOSS 2-point clustering, well belowmore » the 1% statistical error of this measurement. In conclusion, this constraint is the most stringent currently available and has important implications for the ability of upcoming large-scale structure surveys such as DESI to self-protect against the relative velocity as a possible systematic.« less
Constraining the baryon-dark matter relative velocity with the large-scale three-point correlation function of the SDSS BOSS DR12 CMASS galaxies

NASA Astrophysics Data System (ADS)

Slepian, Zachary; Eisenstein, Daniel J.; Blazek, Jonathan A.; Brownstein, Joel R.; Chuang, Chia-Hsun; Gil-Marín, Héctor; Ho, Shirley; Kitaura, Francisco-Shu; McEwen, Joseph E.; Percival, Will J.; Ross, Ashley J.; Rossi, Graziano; Seo, Hee-Jong; Slosar, Anže; Vargas-Magaña, Mariana

2018-02-01

We search for a galaxy clustering bias due to a modulation of galaxy number with the baryon-dark matter relative velocity resulting from recombination-era physics. We find no detected signal and place the constraint bv < 0.01 on the relative velocity bias for the CMASS galaxies. This bias is an important potential systematic of baryon acoustic oscillation (BAO) method measurements of the cosmic distance scale using the two-point clustering. Our limit on the relative velocity bias indicates a systematic shift of no more than 0.3 per cent rms in the distance scale inferred from the BAO feature in the BOSS two-point clustering, well below the 1 per cent statistical error of this measurement. This constraint is the most stringent currently available and has important implications for the ability of upcoming large-scale structure surveys such as the Dark Energy Spectroscopic Instrument (DESI) to self-protect against the relative velocity as a possible systematic.
Damage Tolerance and Reliability of Turbine Engine Components

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

1999-01-01

This report describes a formal method to quantify structural damage tolerance and reliability in the presence of a multitude of uncertainties in turbine engine components. The method is based at the material behavior level where primitive variables with their respective scatter ranges are used to describe behavior. Computational simulation is then used to propagate the uncertainties to the structural scale where damage tolerance and reliability are usually specified. Several sample cases are described to illustrate the effectiveness, versatility, and maturity of the method. Typical results from this method demonstrate that it is mature and that it can be used to probabilistically evaluate turbine engine structural components. It may be inferred from the results that the method is suitable for probabilistically predicting the remaining life in aging or deteriorating structures, for making strategic projections and plans, and for achieving better, cheaper, faster products that give competitive advantages in world markets.
Shigaraki UAV-Radar Experiment (ShUREX): overview of the campaign with some preliminary results

NASA Astrophysics Data System (ADS)

Kantha, Lakshmi; Lawrence, Dale; Luce, Hubert; Hashiguchi, Hiroyuki; Tsuda, Toshitaka; Wilson, Richard; Mixa, Tyler; Yabuki, Masanori

2017-12-01

The Shigaraki unmanned aerial vehicle (UAV)-Radar Experiment (ShUREX) is an international (USA-Japan-France) observational campaign, whose overarching goal is to demonstrate the utility of small, lightweight, inexpensive, autonomous UAVs in probing and monitoring the lower troposphere and to promote synergistic use of UAVs and very high frequency (VHF) radars. The 2-week campaign lasting from June 1 to June 14, 2015, was carried out at the Middle and Upper Atmosphere (MU) Observatory in Shigaraki, Japan. During the campaign, the DataHawk UAV, developed at the University of Colorado, Boulder, and equipped with high-frequency response cold wire and pitot tube sensors (as well as an iMET radiosonde), was flown near and over the VHF-band MU radar. Measurements in the atmospheric column in the immediate vicinity of the radar were obtained. Simultaneous and continuous operation of the radar in range imaging mode enabled fine-scale structures in the atmosphere to be visualized by the radar. It also permitted the UAV to be commanded to sample interesting structures, guided in near real time by the radar images. This overview provides a description of the ShUREX campaign and some interesting but preliminary results of the very first simultaneous and intensive probing of turbulent structures by UAVs and the MU radar. The campaign demonstrated the validity and utility of the radar range imaging technique in obtaining very high vertical resolution ( 20 m) images of echo power in the atmospheric column, which display evolving fine-scale atmospheric structures in unprecedented detail. The campaign also permitted for the very first time the evaluation of the consistency of turbulent kinetic energy dissipation rates in turbulent structures inferred from the spectral broadening of the backscattered radar signal and direct, in situ measurements by the high-frequency response velocity sensor on the UAV. The data also enabled other turbulence parameters such as the temperature structure function parameter {C}_T^2 and refractive index structure function parameter {C}_n^2 to be measured by sensors on the UAV, along with radar-inferred refractive index structure function parameter {C}_{n,radar}^2 . The comprehensive dataset collected during the campaign (from the radar, the UAV, the boundary layer lidar, the ceilometer, and radiosondes) is expected to help obtain a better understanding of turbulent atmospheric structures, as well as arrive at a better interpretation of the radar data.
Ancestry estimation and control of population stratification for sequence-based association studies.

PubMed

Wang, Chaolong; Zhan, Xiaowei; Bragg-Gresham, Jennifer; Kang, Hyun Min; Stambolian, Dwight; Chew, Emily Y; Branham, Kari E; Heckenlively, John; Fulton, Robert; Wilson, Richard K; Mardis, Elaine R; Lin, Xihong; Swaroop, Anand; Zöllner, Sebastian; Abecasis, Gonçalo R

2014-04-01

Estimating individual ancestry is important in genetic association studies where population structure leads to false positive signals, although assigning ancestry remains challenging with targeted sequence data. We propose a new method for the accurate estimation of individual genetic ancestry, based on direct analysis of off-target sequence reads, and implement our method in the publicly available LASER software. We validate the method using simulated and empirical data and show that the method can accurately infer worldwide continental ancestry when used with sequencing data sets with whole-genome shotgun coverage as low as 0.001×. For estimates of fine-scale ancestry within Europe, the method performs well with coverage of 0.1×. On an even finer scale, the method improves discrimination between exome-sequenced study participants originating from different provinces within Finland. Finally, we show that our method can be used to improve case-control matching in genetic association studies and to reduce the risk of spurious findings due to population structure.
Cosmology and the neutrino mass ordering

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

Hannestad, Steen; Schwetz, Thomas, E-mail: sth@phys.au.dk, E-mail: schwetz@kit.edu

We propose a simple method to quantify a possible exclusion of the inverted neutrino mass ordering from cosmological bounds on the sum of the neutrino masses. The method is based on Bayesian inference and allows for a calculation of the posterior odds of normal versus inverted ordering. We apply the method for a specific set of current data from Planck CMB data and large-scale structure surveys, providing an upper bound on the sum of neutrino masses of 0.14 eV at 95% CL. With this analysis we obtain posterior odds for normal versus inverted ordering of about 2:1. If cosmological datamore » is combined with data from oscillation experiments the odds reduce to about 3:2. For an exclusion of the inverted ordering from cosmology at more than 95% CL, an accuracy of better than 0.02 eV is needed for the sum. We demonstrate that such a value could be reached with planned observations of large scale structure by analysing artificial mock data for a EUCLID-like survey.« less
Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor

DOE PAGES

Faulon, Jean-Loup; Misra, Milind; Martin, Shawn; ...

2007-11-23

Motivation: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. Additionally, there is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein–chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. Results: Our method relies on expressing proteins and chemicals with a common cheminformaticsmore » representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Lastly, such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets.« less
Observations of height-dependent pressure-perturbation structure of a strong mesoscale gravity wave

NASA Technical Reports Server (NTRS)

Starr, David O'C.; Korb, C. L.; Schwemmer, Geary K.; Weng, Chi Y.

1992-01-01

Airborne observations using a downward-looking, dual-frequency, near-infrared, differential absorption lidar system provide the first measurements of the height-dependent pressure-perturbation field associated with a strong mesoscale gravity wave. A pressure-perturbation amplitude of 3.5 mb was measured within the lowest 1.6 km of the atmosphere over a 52-km flight line. Corresponding vertical displacements of 250-500 m were inferred from lidar-observed displacement of aerosol layers. Accounting for probable wave orientation, a horizontal wavelength of about 40 km was estimated. Satellite observations reveal wave structure of a comparable scale in concurrent cirrus cloud fields over an extended area. Smaller-scale waves were also observed. Local meteorological soundings are analyzed to confirm the existence of a suitable wave duct. Potential wave-generation mechanisms are examined and discussed. The large pressure-perturbation wave is attributed to rapid amplification or possible wave breaking of a gravity wave as it propagated offshore and interacted with a very stable marine boundary layer capped by a strong shear layer.
Black hole mass measurement using molecular gas kinematics: what ALMA can do

NASA Astrophysics Data System (ADS)

Yoon, Ilsang

2017-04-01

We study the limits of the spatial and velocity resolution of radio interferometry to infer the mass of supermassive black holes (SMBHs) in galactic centres using the kinematics of circum-nuclear molecular gas, by considering the shapes of the galaxy surface brightness profile, signal-to-noise ratios (S/Ns) of the position-velocity diagram (PVD) and systematic errors due to the spatial and velocity structure of the molecular gas. We argue that for fixed galaxy stellar mass and SMBH mass, the spatial and velocity scales that need to be resolved increase and decrease, respectively, with decreasing Sérsic index of the galaxy surface brightness profile. We validate our arguments using simulated PVDs for varying beam size and velocity channel width. Furthermore, we consider the systematic effects on the inference of the SMBH mass by simulating PVDs including the spatial and velocity structure of the molecular gas, which demonstrates that their impacts are not significant for a PVD with good S/N unless the spatial and velocity scale associated with the systematic effects are comparable to or larger than the angular resolution and velocity channel width of the PVD from pure circular motion. Also, we caution that a bias in a galaxy surface brightness profile owing to the poor resolution of a galaxy photometric image can largely bias the SMBH mass by an order of magnitude. This study shows the promise and the limits of ALMA observations for measuring SMBH mass using molecular gas kinematics and provides a useful technical justification for an ALMA proposal with the science goal of measuring SMBH mass.
Data analysis using scale-space filtering and Bayesian probabilistic reasoning

NASA Technical Reports Server (NTRS)

Kulkarni, Deepak; Kutulakos, Kiriakos; Robinson, Peter

1991-01-01

This paper describes a program for analysis of output curves from Differential Thermal Analyzer (DTA). The program first extracts probabilistic qualitative features from a DTA curve of a soil sample, and then uses Bayesian probabilistic reasoning to infer the mineral in the soil. The qualifier module employs a simple and efficient extension of scale-space filtering suitable for handling DTA data. We have observed that points can vanish from contours in the scale-space image when filtering operations are not highly accurate. To handle the problem of vanishing points, perceptual organizations heuristics are used to group the points into lines. Next, these lines are grouped into contours by using additional heuristics. Probabilities are associated with these contours using domain-specific correlations. A Bayes tree classifier processes probabilistic features to infer the presence of different minerals in the soil. Experiments show that the algorithm that uses domain-specific correlation to infer qualitative features outperforms a domain-independent algorithm that does not.
Inference of reactive transport model parameters using a Bayesian multivariate approach

NASA Astrophysics Data System (ADS)

Carniato, Luca; Schoups, Gerrit; van de Giesen, Nick

2014-08-01

Parameter estimation of subsurface transport models from multispecies data requires the definition of an objective function that includes different types of measurements. Common approaches are weighted least squares (WLS), where weights are specified a priori for each measurement, and weighted least squares with weight estimation (WLS(we)) where weights are estimated from the data together with the parameters. In this study, we formulate the parameter estimation task as a multivariate Bayesian inference problem. The WLS and WLS(we) methods are special cases in this framework, corresponding to specific prior assumptions about the residual covariance matrix. The Bayesian perspective allows for generalizations to cases where residual correlation is important and for efficient inference by analytically integrating out the variances (weights) and selected covariances from the joint posterior. Specifically, the WLS and WLS(we) methods are compared to a multivariate (MV) approach that accounts for specific residual correlations without the need for explicit estimation of the error parameters. When applied to inference of reactive transport model parameters from column-scale data on dissolved species concentrations, the following results were obtained: (1) accounting for residual correlation between species provides more accurate parameter estimation for high residual correlation levels whereas its influence for predictive uncertainty is negligible, (2) integrating out the (co)variances leads to an efficient estimation of the full joint posterior with a reduced computational effort compared to the WLS(we) method, and (3) in the presence of model structural errors, none of the methods is able to identify the correct parameter values.

Assessing population genetic structure via the maximisation of genetic distance

PubMed Central

2009-01-01

Background The inference of the hidden structure of a population is an essential issue in population genetics. Recently, several methods have been proposed to infer population structure in population genetics. Methods In this study, a new method to infer the number of clusters and to assign individuals to the inferred populations is proposed. This approach does not make any assumption on Hardy-Weinberg and linkage equilibrium. The implemented criterion is the maximisation (via a simulated annealing algorithm) of the averaged genetic distance between a predefined number of clusters. The performance of this method is compared with two Bayesian approaches: STRUCTURE and BAPS, using simulated data and also a real human data set. Results The simulations show that with a reduced number of markers, BAPS overestimates the number of clusters and presents a reduced proportion of correct groupings. The accuracy of the new method is approximately the same as for STRUCTURE. Also, in Hardy-Weinberg and linkage disequilibrium cases, BAPS performs incorrectly. In these situations, STRUCTURE and the new method show an equivalent behaviour with respect to the number of inferred clusters, although the proportion of correct groupings is slightly better with the new method. Re-establishing equilibrium with the randomisation procedures improves the precision of the Bayesian approaches. All methods have a good precision for FST ≥ 0.03, but only STRUCTURE estimates the correct number of clusters for FST as low as 0.01. In situations with a high number of clusters or a more complex population structure, MGD performs better than STRUCTURE and BAPS. The results for a human data set analysed with the new method are congruent with the geographical regions previously found. Conclusion This new method used to infer the hidden structure in a population, based on the maximisation of the genetic distance and not taking into consideration any assumption about Hardy-Weinberg and linkage equilibrium, performs well under different simulated scenarios and with real data. Therefore, it could be a useful tool to determine genetically homogeneous groups, especially in those situations where the number of clusters is high, with complex population structure and where Hardy-Weinberg and/or linkage equilibrium are present. PMID:19900278
Temperature-induced local and average structural changes in BaTiO3-xBi(Zn1/2Ti1/2)O3 solid solutions: The origin of high temperature dielectric permittivity

NASA Astrophysics Data System (ADS)

Hou, Dong; Usher, Tedi-Marie; Zhou, Hanhan; Raengthon, Natthaphon; Triamnak, Narit; Cann, David P.; Forrester, Jennifer S.; Jones, Jacob L.

2017-08-01

The existence of local tetragonal distortions is evidenced in the BaTiO3-xBi(Zn1/2Ti1/2)O3 (BT-xBZT) relaxor dielectric material system at elevated temperatures. The local and average structures of BT-xBZT with different compositions are characterized using in situ high temperature total scattering techniques. Using the box-car fitting method, it is inferred that there are tetragonal polar clusters embedded in a non-polar pseudocubic matrix for BT-xBZT relaxors. The diameter of these polar clusters is estimated as 2-3 nm at room temperature. Sequential temperature series fitting shows the persistence of the tetragonal distortion on the local scale, while the average structure transforms to a pseudocubic paraelectric phase at high temperatures. The fundamental origin of the temperature stable permittivity of BT-xBZT and the relationship with the unique local scale structures are discussed. This systematic structural study of the BT-xBZT system provides both insight into the nature of lead-free perovskite relaxors, and advances the development of a wide range of electronics with reliable high temperature performance.
Mapping 3D plasma structure in the solar wind with the L1 constellation: joint observations from Wind, ACE, DSCOVR, and SoHO

NASA Astrophysics Data System (ADS)

Stevens, M. L.; Kasper, J. C.; Case, A. W.; Korreck, K. E.; Szabo, A.; Biesecker, D. A.; Prchlik, J.

2017-12-01

At this moment in time, four observatories with similar instrumentation- Wind, ACE, DSCOVR, and SoHO- are stationed directly upstream of the Earth and making continuous observations. They are separated by drift-time baselines of seconds to minutes, timescales on which MHD instabilities in the solar wind are known to grow and evolve, and spatial baselines of tens to 200 earth radii, length scales relevant to the Earth's magnetosphere. By comparing measurements of matched solar wind structures from the four vantage points, the form of structures and associated dynamics on these scales is illuminated. Our targets include shocks and MHD discontinuities, stream fronts, locii of reconnection and exhaust flow boundary layers, plasmoids, and solitary structures born of nonlinear instability. We use the tetrahedral quality factors and other conventions adopted for Cluster to identify periods where the WADS constellation is suitably non-degenerate and arranged in such a way as to enable specific types of spatial, temporal, or spatiotemporal inferences. We present here an overview of the geometries accessible to the L1 constellation and timing-based and plasma-based observations of solar wind structures from 2016-17. We discuss the unique potential of the constellation approach for space physics and space weather forecasting at 1 AU.
Temperature-induced local and average structural changes in BaTiO 3- xBi(Zn 1/2Ti 1/2)O 3 solid solutions: The origin of high temperature dielectric permittivity

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

Hou, Dong; Usher, Tedi -Marie; Zhou, Hanhan

The existence of local tetragonal distortions is evidenced in the BaTiO 3–xBi(Zn 1/2Ti 1/2)O 3 (BT–xBZT) relaxor dielectric material system at elevated temperatures. The local and average structures of BT-xBZT with different compositions are characterized using in situ high temperature total scattering techniques. Using the box-car fitting method, it is inferred that there are tetragonal polar clusters embedded in a non-polar pseudocubic matrix for BT-xBZT relaxors. The diameter of these polar clusters is estimated as 2–3 nm at room temperature. Sequential temperature series fitting shows the persistence of the tetragonal distortion on the local scale, while the average structure transformsmore » to a pseudocubic paraelectric phase at high temperatures. The fundamental origin of the temperature stable permittivity of BT-xBZT and the relationship with the unique local scale structures are discussed. This systematic structural study of the BT-xBZT system provides both insight into the nature of lead-free perovskite relaxors, and advances the development of a wide range of electronics with reliable high temperature performance.« less
Temperature-induced local and average structural changes in BaTiO 3- xBi(Zn 1/2Ti 1/2)O 3 solid solutions: The origin of high temperature dielectric permittivity

DOE PAGES

Hou, Dong; Usher, Tedi -Marie; Zhou, Hanhan; ...

2017-08-11

The existence of local tetragonal distortions is evidenced in the BaTiO 3–xBi(Zn 1/2Ti 1/2)O 3 (BT–xBZT) relaxor dielectric material system at elevated temperatures. The local and average structures of BT-xBZT with different compositions are characterized using in situ high temperature total scattering techniques. Using the box-car fitting method, it is inferred that there are tetragonal polar clusters embedded in a non-polar pseudocubic matrix for BT-xBZT relaxors. The diameter of these polar clusters is estimated as 2–3 nm at room temperature. Sequential temperature series fitting shows the persistence of the tetragonal distortion on the local scale, while the average structure transformsmore » to a pseudocubic paraelectric phase at high temperatures. The fundamental origin of the temperature stable permittivity of BT-xBZT and the relationship with the unique local scale structures are discussed. This systematic structural study of the BT-xBZT system provides both insight into the nature of lead-free perovskite relaxors, and advances the development of a wide range of electronics with reliable high temperature performance.« less
A systematic search for dwarf counterparts to ultra compact high velocity clouds

NASA Astrophysics Data System (ADS)

Bennet, Paul; Sand, David J.; Crnojevic, Denija; Strader, Jay

2015-01-01

Observations of the Universe on scales smaller than typical, massive galaxies challenge the standard Lambda Cold Dark Matter paradigm for structure formation. It is thus imperative to discover and characterize the faintest dwarf galaxy systems, not just within the Local Group, but in relatively isolated environments as well in order to properly connect them with models of structure formation. Here we report on a systematic search of public ultraviolet and optical archives for dwarf galaxy counterparts to so-called Ultra Compact High Velocity Clouds (UCHVCs), which are compact, isolated HI sources recently found in the Galactic Arecibo L-band Feed Array-HI (GALFA-HI) and Arecibo Legacy Fast ALFA (ALFALFA-HI) surveys. Our search has uncovered at least three strong dwarf galaxy candidates, and we present their inferred star formation rate and structural properties here.
Critical perspectives on causality and inference in brain networks: Allusions, illusions, solutions?. Comment on: "Foundational perspectives on causality in large-scale brain networks" by M. Mannino and S.L. Bressler

NASA Astrophysics Data System (ADS)

Diwadkar, Vaibhav A.

2015-12-01

The human brain is an impossibly difficult cartographic landscape to map out. Within it's convoluted and labyrinthine structure is folded a million years of phylogeny, somehow expressed in the ontogeny of the specific organism; an ontogeny that conceals idiosyncratic effects of countless genes, and then the (perhaps) countably infinite effects of processes of the organism's lifespan subsequently resulting in remarkable heterogeneity [1,2]. The physical brain itself is therefore a nearly un-decodable ;time machine; motivating more questions than frameworks for answering those questions: Why has evolution endowed it with the general structure that is possesses [3]; Is there regularity in macroscopic metrics of structure across species [4]; What are the most meaningful structural units in the brain: molecules, neurons, cortical columns or cortical maps [5]? Remarkably, understanding the intricacies of structure is perhaps not even the most difficult aspect of understanding the human brain. In fact, and as recently argued, a central issue lies in resolving the dialectic between structure and function: how does dynamic function arises from static (at least at the time scales at which human brain function is experimentally studied) brain structures [6]? In other words, if the mind is the brain ;in action;, how does it arise?
Latitudinal variability of large-scale coronal temperature and its association with the density and the global magnetic field

NASA Technical Reports Server (NTRS)

Guhathakurta, M.; Fisher, R. R.

1994-01-01

In this paper we utilize the latitiude distribution of the coronal temperature during the period 1984-1992 that was derived in a paper by Guhathakurta et al, 1993, utilizing ground-based intensity observations of the green (5303 A Fe XIV) and red (6374 A Fe X) coronal forbidden lines from the National Solar Observatory at Sacramento Peak, and establish it association with the global magnetic field and the density distributions in the corona. A determination of plasma temperature, T, was estimated from the intensity ratio Fe X/Fe XIV (where T is inversely proportional to the ratio), since both emission lines come from ionized states of Fe, and the ratio is only weakly dependent on density. We observe that there is a large-scale organization of the inferred coronal temperature distribution that is associated with the large-scale, weak magnetic field structures and bright coronal features; this organization tends to persist through most of the magnetic activity cycle. These high-temperature structures exhibit time-space characteristics which are similar to those of the polar crown filaments. This distribution differs in spatial and temporal characterization from the traditional picture of sunspot and active region evolution over the range of the sunspot cycle, which are manifestations of the small-scale, strong magnetic field regions.
Spatially extensive uniform stress fields on Venus inferred from radial dike swarm geometries: The Aphrodite Terra example

NASA Technical Reports Server (NTRS)

Grosfils, Eric B.; Head, James W.

1993-01-01

The high resolution and near global coverage of Magellan radar images is facilitating attempts to systematically investigate the stresses that have deformed the venusian crust. Here we continue earlier efforts to utilize approximately 170 large, radially lineated structures interpreted as dike swarms to assess the orientation of the regional maximum horizontal compressive stress (MHCS) which existed in their vicinities during emplacement. Examination of swarms near the equator reveals a link to broad scale regional structures, such as Aphrodite Terra, across distances in excess of 1000 km, suggesting the existence of first order stress fields which affect areas of more than 10(exp 6) sq km in a uniform fashion. Focusing further upon the Aphrodite Terra region, the MHCS field in the surrounding lowlands inferred from radial swarms is oriented approximately normal to the slope of the highland topography. This stress configuration appears, at a simple level, to be incompatible with that expected during either upwelling or downwelling construction of the highlands. In addition, the relatively undeformed geometry of the radial structures within the highlands implies that these dike swarm features formed more recently than their highly deformed surroundings. We conclude that the differential stresses which existed during emplacement of the dike swarms within and adjacent to the Aphrodite Terra highlands are related to the gravitational relaxation of pre-existing topography.
Population structure and demographic history of a tropical lowland rainforest tree species Shorea parvifolia (Dipterocarpaceae) from Southeastern Asia.

PubMed

Iwanaga, Hiroko; Teshima, Kosuke M; Khatab, Ismael A; Inomata, Nobuyuki; Finkeldey, Reiner; Siregar, Iskandar Z; Siregar, Ulfah J; Szmidt, Alfred E

2012-07-01

Distribution of tropical rainforests in Southeastern Asia has changed over geo-logical time scale, due to movement of tectonic plates and/or global climatic changes. Shorea parvifolia is one of the most common tropical lowland rainforest tree species in Southeastern Asia. To infer population structure and demographic history of S. parvifolia, as indicators of temporal changes in the distribution and extent of tropical rainforest in this region, we studied levels and patterns of nucleotide polymorphism in the following five nuclear gene regions: GapC, GBSSI, PgiC, SBE2, and SODH. Seven populations from peninsular Malaysia, Sumatra, and eastern Borneo were included in the analyses. STRUCTURE analysis revealed that the investigated populations are divided into two groups: Sumatra-Malay and Borneo. Furthermore, each group contained one admixed population. Under isolation with migration model, divergence of the two groups was estimated to occur between late Pliocene (2.6 MYA) and middle Pleistocene (0.7 MYA). The log-likelihood ratio tests of several demographic models strongly supported model with population expansion and low level of migration after divergence of the Sumatra-Malay and Borneo groups. The inferred demographic history of S. parvifolia suggested the presence of a scarcely forested land bridge on the Sunda Shelf during glacial periods in the Pleistocene and predominance of tropical lowland rainforest at least in Sumatra and eastern Borneo.
Investigating Segmentation in Cascadia: Anisotropic Crustal Structure and Mantle Wedge Serpentinization from Receiver Functions

NASA Astrophysics Data System (ADS)

Krueger, Hannah E.; Wirth, Erin A.

2017-10-01

The Cascadia subduction zone exhibits along-strike segmentation in structure, processes, and seismogenic behavior. While characterization of seismic anisotropy can constrain deformation processes at depth, the character of seismic anisotropy in Cascadia remains poorly understood. This is primarily due to a lack of seismicity in the subducting Juan de Fuca slab, which limits shear wave splitting and other seismological analyses that interrogate the fine-scale anisotropic structure of the crust and mantle wedge. We investigate lower crustal anisotropy and mantle wedge structure by computing P-to-S receiver functions at 12 broadband seismic stations along the Cascadia subduction zone. We observe P-to-SV converted energy consistent with previously estimated Moho depths. Several stations exhibit evidence of an "inverted Moho" (i.e., a downward velocity decrease across the crust-mantle boundary), indicative of a serpentinized mantle wedge. Stations with an underlying hydrated mantle wedge appear prevalent from northern Washington to central Oregon, but sparse in southern Oregon and northern California. Transverse component receiver functions are complex, suggesting anisotropic and/or dipping crustal structure. To constrain the orientation of crustal anisotropy we compute synthetic receiver functions using manual forward modeling. We determine that the lower crust shows variable orientations of anisotropy along-strike, with highly complex anisotropy in northern Cascadia, and generally NW-SE and NE-SW orientations of slow-axis anisotropy in central and southern Cascadia, respectively. The orientations of anisotropy from this work generally agree with those inferred from shear wave splitting of tremor studies at similar locations, lending confidence to this relatively new method of inferring seismic anisotropy from slow earthquakes.
Causal learning and inference as a rational process: the new synthesis.

PubMed

Holyoak, Keith J; Cheng, Patricia W

2011-01-01

Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with bayesian probabilistic inference. We describe this new synthesis, which views causal learning and inference as a fundamentally rational process, and review a sample of the empirical findings that support the causal framework over associative alternatives. Causal events, like all events in the distal world as opposed to our proximal perceptual input, are inherently unobservable. A central assumption of the causal approach is that humans (and potentially nonhuman animals) have been designed in such a way as to infer the most invariant causal relations for achieving their goals based on observed events. In contrast, the associative approach assumes that learners only acquire associations among important observed events, omitting the representation of the distal relations. By incorporating bayesian inference over distributions of causal strength and causal structures, along with noisy-logical (i.e., causal) functions for integrating the influences of multiple causes on a single effect, human judgments about causal strength and structure can be predicted accurately for relatively simple causal structures. Dynamic models of learning based on the causal framework can explain patterns of acquisition observed with serial presentation of contingency data and are consistent with available neuroimaging data. The approach has been extended to a diverse range of inductive tasks, including category-based and analogical inferences.
Geodynamic constraints on deep-mantle buoyancy: Implications for thermochemical structure of LLSVP and large-scale upwellings under the Pacific Ocean.

NASA Astrophysics Data System (ADS)

Forte, A. M.; Glisovic, P.; Grand, S. P.; Lu, C.; Simmons, N. A.; Rowley, D. B.

2015-12-01

Convection-related data constrain lower-mantle density anomalies that contribute to mantle convective flow. These include global gravity and topography anomalies, plate motions and excess ellipticity of the core-mantle boundary (CMB). Each datum possesses differing wavelength and depth dependent resolution of heterogeneity and thus the strongest constraints on density anomalies are obtained by jointly inverting all data in combination. The joint-inversions employ viscous response functions (i.e. geodynamic kernels) for a flowing mantle. Non-uniqueness is greatly reduced by including seismic and mineral physics data into the joint inversions. We present the results of inversions where seismic and geodynamic data are singly and jointly inverted to map density anomalies. Employing mineral physical data we estimate thermal and compositional contributions to density anomalies. We evaluate the extent to which "Large Low Shear Velocity Provinces" (LLSVP) are anomalous and we determine their impact on the global pattern of convective flow. The inversions yield consistent maps of lower-mantle flow (see figure) that are dominated by two large upwellings, under the Western Pacific (next to the Caroline microplate) and Eastern Pacific (under the East Pacific Rise). These hot upwellings effectively delimit the margins of the Pacific LLSVP, suggesting intrinsic negative buoyancy within this structure impedes large-scale upwellings in the mantle above. These two upwellings do not resemble classical mantle "plumes" found in simple isoviscous and isochemical convection models but their contribution to mass and heat transport across the lower mantle is significant and thus behave similarly to plumes. The large scale of these upwellings may be understood in terms of the high viscosity in the lower mantle, inferred from geodynamic constraints on mantle rheology. Very-long time convection simulations initiated with present-day structure inferred from these inversions show the two Pacific upwellings possess remarkable geographic fixity and longevity extending over several hundred million years, again a consequence of the high viscosity in the lower mantle. These upwellings are fed by large heat flux across the CMB (from 12 to 20 TW) and should play a major role in the thermal evolution of the mantle.
Population genetics of the understory fishtail palm Chamaedorea ernesti-augusti in Belize: high genetic connectivity with local differentiation

PubMed Central

Cibrián-Jaramillo, Angélica; Bacon, Christine D; Garwood, Nancy C; Bateman, Richard M; Thomas, Meredith M; Russell, Steve; Bailey, C Donovan; Hahn, William J; Bridgewater, Samuel GM; DeSalle, Rob

2009-01-01

Background Developing a greater understanding of population genetic structure in lowland tropical plant species is highly relevant to our knowledge of increasingly fragmented forests and to the conservation of threatened species. Specific studies are particularly needed for taxa whose population dynamics are further impacted by human harvesting practices. One such case is the fishtail or xaté palm (Chamaedorea ernesti-augusti) of Central America, whose wild-collected leaves are becoming progressively more important to the global ornamental industry. We use microsatellite markers to describe the population genetics of this species in Belize and test the effects of climate change and deforestation on its recent and historical effective population size. Results We found high levels of inbreeding coupled with moderate or high allelic diversity within populations. Overall high gene flow was observed, with a north and south gradient and ongoing differentiation at smaller spatial scales. Immigration rates among populations were more difficult to discern, with minimal evidence for isolation by distance. We infer a tenfold reduction in effective population size ca. 10,000 years ago, but fail to detect changes attributable to Mayan or contemporary deforestation. Conclusion Populations of C. ernesti-augusti are genetically heterogeneous demes at a local spatial scale, but are widely connected at a regional level in Belize. We suggest that the inferred patterns in population genetic structure are the result of the colonization of this species into Belize following expansion of humid forests in combination with demographic and mating patterns. Within populations, we hypothesize that low aggregated population density over large areas, short distance pollen dispersal via thrips, low adult survival, and low fruiting combined with early flowering may contribute towards local inbreeding via genetic drift. Relatively high levels of regional connectivity are likely the result of animal-mediated long-distance seed dispersal. The greatest present threat to the species is the potential onset of inbreeding depression as the result of increased human harvesting activities. Future genetic studies in understory palms should focus on both fine-scale and landscape-level genetic structure. PMID:19818141
Combining epidemiological and genetic networks signifies the importance of early treatment in HIV-1 transmission.

PubMed

Zarrabi, Narges; Prosperi, Mattia; Belleman, Robert G; Colafigli, Manuela; De Luca, Andrea; Sloot, Peter M A

2012-01-01

Inferring disease transmission networks is important in epidemiology in order to understand and prevent the spread of infectious diseases. Reconstruction of the infection transmission networks requires insight into viral genome data as well as social interactions. For the HIV-1 epidemic, current research either uses genetic information of patients' virus to infer the past infection events or uses statistics of sexual interactions to model the network structure of viral spreading. Methods for a reliable reconstruction of HIV-1 transmission dynamics, taking into account both molecular and societal data are still lacking. The aim of this study is to combine information from both genetic and epidemiological scales to characterize and analyse a transmission network of the HIV-1 epidemic in central Italy.We introduce a novel filter-reduction method to build a network of HIV infected patients based on their social and treatment information. The network is then combined with a genetic network, to infer a hypothetical infection transmission network. We apply this method to a cohort study of HIV-1 infected patients in central Italy and find that patients who are highly connected in the network have longer untreated infection periods. We also find that the network structures for homosexual males and heterosexual populations are heterogeneous, consisting of a majority of 'peripheral nodes' that have only a few sexual interactions and a minority of 'hub nodes' that have many sexual interactions. Inferring HIV-1 transmission networks using this novel combined approach reveals remarkable correlations between high out-degree individuals and longer untreated infection periods. These findings signify the importance of early treatment and support the potential benefit of wide population screening, management of early diagnoses and anticipated antiretroviral treatment to prevent viral transmission and spread. The approach presented here for reconstructing HIV-1 transmission networks can have important repercussions in the design of intervention strategies for disease control.
Statistics, Computation, and Modeling in Cosmology

NASA Astrophysics Data System (ADS)

Jewell, Jeff; Guiness, Joe; SAMSI 2016 Working Group in Cosmology

2017-01-01

Current and future ground and space based missions are designed to not only detect, but map out with increasing precision, details of the universe in its infancy to the present-day. As a result we are faced with the challenge of analyzing and interpreting observations from a wide variety of instruments to form a coherent view of the universe. Finding solutions to a broad range of challenging inference problems in cosmology is one of the goals of the “Statistics, Computation, and Modeling in Cosmology” workings groups, formed as part of the year long program on ‘Statistical, Mathematical, and Computational Methods for Astronomy’, hosted by the Statistical and Applied Mathematical Sciences Institute (SAMSI), a National Science Foundation funded institute. Two application areas have emerged for focused development in the cosmology working group involving advanced algorithmic implementations of exact Bayesian inference for the Cosmic Microwave Background, and statistical modeling of galaxy formation. The former includes study and development of advanced Markov Chain Monte Carlo algorithms designed to confront challenging inference problems including inference for spatial Gaussian random fields in the presence of sources of galactic emission (an example of a source separation problem). Extending these methods to future redshift survey data probing the nonlinear regime of large scale structure formation is also included in the working group activities. In addition, the working group is also focused on the study of ‘Galacticus’, a galaxy formation model applied to dark matter-only cosmological N-body simulations operating on time-dependent halo merger trees. The working group is interested in calibrating the Galacticus model to match statistics of galaxy survey observations; specifically stellar mass functions, luminosity functions, and color-color diagrams. The group will use subsampling approaches and fractional factorial designs to statistically and computationally efficiently explore the Galacticus parameter space. The group will also use the Galacticus simulations to study the relationship between the topological and physical structure of the halo merger trees and the properties of the resulting galaxies.
Feature Inference Learning and Eyetracking

ERIC Educational Resources Information Center

Rehder, Bob; Colner, Robert M.; Hoffman, Aaron B.

2009-01-01

Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of…
Neural correlates of species-typical illogical cognitive bias in human inference.

PubMed

Ogawa, Akitoshi; Yamazaki, Yumiko; Ueno, Kenichi; Cheng, Kang; Iriki, Atsushi

2010-09-01

The ability to think logically is a hallmark of human intelligence, yet our innate inferential abilities are marked by implicit biases that often lead to illogical inference. For example, given AB ("if A then B"), people frequently but fallaciously infer the inverse, BA. This mode of inference, called symmetry, is logically invalid because, although it may be true, it is not necessarily true. Given pairs of conditional relations, such as AB and BC, humans reflexively perform two additional modes of inference: transitivity, whereby one (validly) infers AC; and equivalence, whereby one (invalidly) infers CA. In sharp contrast, nonhuman animals can handle transitivity but can rarely be made to acquire symmetry or equivalence. In the present study, human subjects performed logical and illogical inferences about the relations between abstract, visually presented figures while their brain activation was monitored with fMRI. The prefrontal, medial frontal, and intraparietal cortices were activated during all modes of inference. Additional activation in the precuneus and posterior parietal cortex was observed during transitivity and equivalence, which may reflect the need to retrieve the intermediate stimulus (B) from memory. Surprisingly, the patterns of brain activation in illogical and logical inference were very similar. We conclude that the observed inference-related fronto-parietal network is adapted for processing categorical, but not logical, structures of association among stimuli. Humans might prefer categorization over the memorization of logical structures in order to minimize the cognitive working memory load when processing large volumes of information.
Inferring properties of disordered chains from FRET transfer efficiencies

NASA Astrophysics Data System (ADS)

Zheng, Wenwei; Zerze, Gül H.; Borgia, Alessandro; Mittal, Jeetain; Schuler, Benjamin; Best, Robert B.

2018-03-01

Förster resonance energy transfer (FRET) is a powerful tool for elucidating both structural and dynamic properties of unfolded or disordered biomolecules, especially in single-molecule experiments. However, the key observables, namely, the mean transfer efficiency and fluorescence lifetimes of the donor and acceptor chromophores, are averaged over a broad distribution of donor-acceptor distances. The inferred average properties of the ensemble therefore depend on the form of the model distribution chosen to describe the distance, as has been widely recognized. In addition, while the distribution for one type of polymer model may be appropriate for a chain under a given set of physico-chemical conditions, it may not be suitable for the same chain in a different environment so that even an apparently consistent application of the same model over all conditions may distort the apparent changes in chain dimensions with variation of temperature or solution composition. Here, we present an alternative and straightforward approach to determining ensemble properties from FRET data, in which the polymer scaling exponent is allowed to vary with solution conditions. In its simplest form, it requires either the mean FRET efficiency or fluorescence lifetime information. In order to test the accuracy of the method, we have utilized both synthetic FRET data from implicit and explicit solvent simulations for 30 different protein sequences, and experimental single-molecule FRET data for an intrinsically disordered and a denatured protein. In all cases, we find that the inferred radii of gyration are within 10% of the true values, thus providing higher accuracy than simpler polymer models. In addition, the scaling exponents obtained by our procedure are in good agreement with those determined directly from the molecular ensemble. Our approach can in principle be generalized to treating other ensemble-averaged functions of intramolecular distances from experimental data.
Multiresolution analysis of characteristic length scales with high-resolution topographic data

NASA Astrophysics Data System (ADS)

Sangireddy, Harish; Stark, Colin P.; Passalacqua, Paola

2017-07-01

Characteristic length scales (CLS) define landscape structure and delimit geomorphic processes. Here we use multiresolution analysis (MRA) to estimate such scales from high-resolution topographic data. MRA employs progressive terrain defocusing, via convolution of the terrain data with Gaussian kernels of increasing standard deviation, and calculation at each smoothing resolution of (i) the probability distributions of curvature and topographic index (defined as the ratio of slope to area in log scale) and (ii) characteristic spatial patterns of divergent and convergent topography identified by analyzing the curvature of the terrain. The MRA is first explored using synthetic 1-D and 2-D signals whose CLS are known. It is then validated against a set of MARSSIM (a landscape evolution model) steady state landscapes whose CLS were tuned by varying hillslope diffusivity and simulated noise amplitude. The known CLS match the scales at which the distributions of topographic index and curvature show scaling breaks, indicating that the MRA can identify CLS in landscapes based on the scaling behavior of topographic attributes. Finally, the MRA is deployed to measure the CLS of five natural landscapes using meter resolution digital terrain model data. CLS are inferred from the scaling breaks of the topographic index and curvature distributions and equated with (i) small-scale roughness features and (ii) the hillslope length scale.

A knowledge based software engineering environment testbed

NASA Technical Reports Server (NTRS)

Gill, C.; Reedy, A.; Baker, L.

1985-01-01

The Carnegie Group Incorporated and Boeing Computer Services Company are developing a testbed which will provide a framework for integrating conventional software engineering tools with Artifical Intelligence (AI) tools to promote automation and productivity. The emphasis is on the transfer of AI technology to the software development process. Experiments relate to AI issues such as scaling up, inference, and knowledge representation. In its first year, the project has created a model of software development by representing software activities; developed a module representation formalism to specify the behavior and structure of software objects; integrated the model with the formalism to identify shared representation and inheritance mechanisms; demonstrated object programming by writing procedures and applying them to software objects; used data-directed and goal-directed reasoning to, respectively, infer the cause of bugs and evaluate the appropriateness of a configuration; and demonstrated knowledge-based graphics. Future plans include introduction of knowledge-based systems for rapid prototyping or rescheduling; natural language interfaces; blackboard architecture; and distributed processing
Assessing adolescents' personality with the NEO PI-R.

PubMed

De Fruyt, F; Mervielde, I; Hoekstra, H A; Rolland, J P

2000-12-01

The suitability of the Revised NEO Personality Inventory (NEO PI-R) to assess adolescents' personality traits was investigated in an unselected heterogeneous sample of 469 adolescents aged 12 to 17 years. They were further administered the Hierarchical Personality Inventory for Children (HiPIC) to allow an examination of convergent and discriminant validity. The adult NEO PI-R factor structure proved to be highly replicable in the sample of adolescents, with all facet scales primarily loading on the expected factors, independent of the age group. Domain and facet internal consistency coefficients were comparable to those obtained in adult samples, with less than 12% of the items showing corrected item-facet correlations below absolute value .20. Although, in general, adolescents reported few difficulties with the comprehensibility of the items, they tend to report more problems with the Openness to Ideas (05) and Openness to Values (06) items. Correlations between NEO PI-R and HiPIC scales underscored the convergent and discriminant validity of the NEO facets and HiPIC scales. It was concluded that the NEO PI-R in its present form is useful for assessing adolescents' traits at the primary level, but additional research is necessary to infer the most appropriate facet level structure.
Multiscale field-aligned current analyzer

NASA Astrophysics Data System (ADS)

Bunescu, C.; Marghitu, O.; Constantinescu, D.; Narita, Y.; Vogt, J.; Blǎgǎu, A.

2015-11-01

The magnetosphere-ionosphere coupling is achieved, essentially, by a superposition of quasi-stationary and time-dependent field-aligned currents (FACs), over a broad range of spatial and temporal scales. The planarity of the FAC structures observed by satellite data and the orientation of the planar FAC sheets can be investigated by the well-established minimum variance analysis (MVA) of the magnetic perturbation. However, such investigations are often constrained to a predefined time window, i.e., to a specific scale of the FAC. The multiscale field-aligned current analyzer, introduced here, relies on performing MVA continuously and over a range of scales by varying the width of the analyzing window, appropriate for the complexity of the magnetic field signatures above the auroral oval. The proposed technique provides multiscale information on the planarity and orientation of the observed FACs. A new approach, based on the derivative of the largest eigenvalue of the magnetic variance matrix with respect to the length of the analysis window, makes possible the inference of the current structures' location (center) and scale (thickness). The capabilities of the FAC analyzer are explored analytically for the magnetic field profile of the Harris sheet and tested on synthetic FAC structures with uniform current density and infinite or finite geometry in the cross-section plane of the FAC. The method is illustrated with data observed by the Cluster spacecraft on crossing the nightside auroral region, and the results are cross checked with the optical observations from the Time History of Events and Macroscale Interactions during Substorms ground network.
Adaptive neuro fuzzy inference system-based power estimation method for CMOS VLSI circuits

NASA Astrophysics Data System (ADS)

Vellingiri, Govindaraj; Jayabalan, Ramesh

2018-03-01

Recent advancements in very large scale integration (VLSI) technologies have made it feasible to integrate millions of transistors on a single chip. This greatly increases the circuit complexity and hence there is a growing need for less-tedious and low-cost power estimation techniques. The proposed work employs Back-Propagation Neural Network (BPNN) and Adaptive Neuro Fuzzy Inference System (ANFIS), which are capable of estimating the power precisely for the complementary metal oxide semiconductor (CMOS) VLSI circuits, without requiring any knowledge on circuit structure and interconnections. The ANFIS to power estimation application is relatively new. Power estimation using ANFIS is carried out by creating initial FIS modes using hybrid optimisation and back-propagation (BP) techniques employing constant and linear methods. It is inferred that ANFIS with the hybrid optimisation technique employing the linear method produces better results in terms of testing error that varies from 0% to 0.86% when compared to BPNN as it takes the initial fuzzy model and tunes it by means of a hybrid technique combining gradient descent BP and mean least-squares optimisation algorithms. ANFIS is the best suited for power estimation application with a low RMSE of 0.0002075 and a high coefficient of determination (R) of 0.99961.
Neural mechanisms underlying valence inferences to sound: The role of the right angular gyrus.

PubMed

Bravo, Fernando; Cross, Ian; Hawkins, Sarah; Gonzalez, Nadia; Docampo, Jorge; Bruno, Claudio; Stamatakis, Emmanuel Andreas

2017-07-28

We frequently infer others' intentions based on non-verbal auditory cues. Although the brain underpinnings of social cognition have been extensively studied, no empirical work has yet examined the impact of musical structure manipulation on the neural processing of emotional valence during mental state inferences. We used a novel sound-based theory-of-mind paradigm in which participants categorized stimuli of different sensory dissonance level in terms of positive/negative valence. Whilst consistent with previous studies which propose facilitated encoding of consonances, our results demonstrated that distinct levels of consonance/dissonance elicited differential influences on the right angular gyrus, an area implicated in mental state attribution and attention reorienting processes. Functional and effective connectivity analyses further showed that consonances modulated a specific inhibitory interaction from associative memory to mental state attribution substrates. Following evidence suggesting that individuals with autism may process social affective cues differently, we assessed the relationship between participants' task performance and self-reported autistic traits in clinically typical adults. Higher scores on the social cognition scales of the AQ were associated with deficits in recognising positive valence in consonant sound cues. These findings are discussed with respect to Bayesian perspectives on autistic perception, which highlight a functional failure to optimize precision in relation to prior beliefs. Copyright © 2017 Elsevier Ltd. All rights reserved.
Network inference using informative priors

PubMed Central

Mukherjee, Sach; Speed, Terence P.

2008-01-01

Recent years have seen much interest in the study of systems characterized by multiple interacting components. A class of statistical models called graphical models, in which graphs are used to represent probabilistic relationships between variables, provides a framework for formal inference regarding such systems. In many settings, the object of inference is the network structure itself. This problem of “network inference” is well known to be a challenging one. However, in scientific settings there is very often existing information regarding network connectivity. A natural idea then is to take account of such information during inference. This article addresses the question of incorporating prior information into network inference. We focus on directed models called Bayesian networks, and use Markov chain Monte Carlo to draw samples from posterior distributions over network structures. We introduce prior distributions on graphs capable of capturing information regarding network features including edges, classes of edges, degree distributions, and sparsity. We illustrate our approach in the context of systems biology, applying our methods to network inference in cancer signaling. PMID:18799736
Oscillating red giants in eclipsing binary systems: empirical reference value for asteroseismic scaling relation

NASA Astrophysics Data System (ADS)

Themeßl, N.; Hekker, S.; Southworth, J.; Beck, P. G.; Pavlovski, K.; Tkachenko, A.; Angelou, G. C.; Ball, W. H.; Barban, C.; Corsaro, E.; Elsworth, Y.; Handberg, R.; Kallinger, T.

2018-05-01

The internal structures and properties of oscillating red-giant stars can be accurately inferred through their global oscillation modes (asteroseismology). Based on 1460 days of Kepler observations we perform a thorough asteroseismic study to probe the stellar parameters and evolutionary stages of three red giants in eclipsing binary systems. We present the first detailed analysis of individual oscillation modes of the red-giant components of KIC 8410637, KIC 5640750 and KIC 9540226. We obtain estimates of their asteroseismic masses, radii, mean densities and logarithmic surface gravities by using the asteroseismic scaling relations as well as grid-based modelling. As these red giants are in double-lined eclipsing binaries, it is possible to derive their independent dynamical masses and radii from the orbital solution and compare it with the seismically inferred values. For KIC 5640750 we compute the first spectroscopic orbit based on both components of this system. We use high-resolution spectroscopic data and light curves of the three systems to determine up-to-date values of the dynamical stellar parameters. With our comprehensive set of stellar parameters we explore consistencies between binary analysis and asteroseismic methods, and test the reliability of the well-known scaling relations. For the three red giants under study, we find agreement between dynamical and asteroseismic stellar parameters in cases where the asteroseismic methods account for metallicity, temperature and mass dependence as well as surface effects. We are able to attain agreement from the scaling laws in all three systems if we use Δνref, emp = 130.8 ± 0.9 μHz instead of the usual solar reference value.
Forming Ganymede's grooves at smaller strain: Toward a self-consistent local and global strain history for Ganymede

NASA Astrophysics Data System (ADS)

Bland, Michael T.; McKinnon, William B.

2015-01-01

The ubiquity of tectonic features formed in extension, and the apparent absence of ones formed in contraction, has led to the hypothesis that Ganymede has undergone global expansion in its past. Determining the magnitude of such expansion is challenging however, and extrapolation of locally or regionally inferred strains to global scales often results in strain estimates that exceed those based on global constraints. Here we use numerical simulations of groove terrain formation to develop a strain history for Ganymede that is generally consistent at local, regional, and global scales. These simulations reproduce groove-like amplitudes, wavelengths, and average slopes at modest regional extensions (10-15%). The modest strains are more consistent with global constraints on Ganymede's expansion. Yet locally, we also find that surface strains can be much larger (30-60%) in the same simulations, consistent with observations of highly-extended impact craters. Thus our simulations satisfy both the smallest-scale and largest-scale inferences of strain on Ganymede. The growth rate of the topography is consistent with (or exceeds) predictions of analytical models, and results from the use of a non-associated plastic rheology that naturally permits localization of brittle failure (plastic strain) into linear fault-like shear zones. These fault-like zones are organized into periodically-spaced graben-like structures with stepped, steeply-dipping faults. As in previous work, groove amplitudes and wavelengths depend on both the imposed heat flux and surface temperature, but because our brittle strength increases with depth, we find (for the parameters explored) that the growth rate of topography is initially faster for lower heat flows. We observe a transition to narrow rifting for higher heat flows and larger strains, which is a potential pathway for breakaway margin or band formation.
Forming Ganymede’s grooves at smaller strain: Toward a self-consistent local and global strain history for Ganymede

USGS Publications Warehouse

Bland, Michael T.; McKinnon, W. B.

2015-01-01

The ubiquity of tectonic features formed in extension, and the apparent absence of ones formed in contraction, has led to the hypothesis that Ganymede has undergone global expansion in its past. Determining the magnitude of such expansion is challenging however, and extrapolation of locally or regionally inferred strains to global scales often results in strain estimates that exceed those based on global constraints. Here we use numerical simulations of groove terrain formation to develop a strain history for Ganymede that is generally consistent at local, regional, and global scales. These simulations reproduce groove-like amplitudes, wavelengths, and average slopes at modest regional extensions (10–15%). The modest strains are more consistent with global constraints on Ganymede’s expansion. Yet locally, we also find that surface strains can be much larger (30–60%) in the same simulations, consistent with observations of highly-extended impact craters. Thus our simulations satisfy both the smallest-scale and largest-scale inferences of strain on Ganymede. The growth rate of the topography is consistent with (or exceeds) predictions of analytical models, and results from the use of a non-associated plastic rheology that naturally permits localization of brittle failure (plastic strain) into linear fault-like shear zones. These fault-like zones are organized into periodically-spaced graben-like structures with stepped, steeply-dipping faults. As in previous work, groove amplitudes and wavelengths depend on both the imposed heat flux and surface temperature, but because our brittle strength increases with depth, we find (for the parameters explored) that the growth rate of topography is initially faster for lower heat flows. We observe a transition to narrow rifting for higher heat flows and larger strains, which is a potential pathway for breakaway margin or band formation.
Convergent evolution of gene networks by single-gene duplications in higher eukaryotes.

PubMed

Amoutzias, Gregory D; Robertson, David L; Oliver, Stephen G; Bornberg-Bauer, Erich

2004-03-01

By combining phylogenetic, proteomic and structural information, we have elucidated the evolutionary driving forces for the gene-regulatory interaction networks of basic helix-loop-helix transcription factors. We infer that recurrent events of single-gene duplication and domain rearrangement repeatedly gave rise to distinct networks with almost identical hub-based topologies, and multiple activators and repressors. We thus provide the first empirical evidence for scale-free protein networks emerging through single-gene duplications, the dominant importance of molecular modularity in the bottom-up construction of complex biological entities, and the convergent evolution of networks.
Sparse Bayesian Inference and the Temperature Structure of the Solar Corona

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

Warren, Harry P.; Byers, Jeff M.; Crump, Nicholas A.

Measuring the temperature structure of the solar atmosphere is critical to understanding how it is heated to high temperatures. Unfortunately, the temperature of the upper atmosphere cannot be observed directly, but must be inferred from spectrally resolved observations of individual emission lines that span a wide range of temperatures. Such observations are “inverted” to determine the distribution of plasma temperatures along the line of sight. This inversion is ill posed and, in the absence of regularization, tends to produce wildly oscillatory solutions. We introduce the application of sparse Bayesian inference to the problem of inferring the temperature structure of themore » solar corona. Within a Bayesian framework a preference for solutions that utilize a minimum number of basis functions can be encoded into the prior and many ad hoc assumptions can be avoided. We demonstrate the efficacy of the Bayesian approach by considering a test library of 40 assumed temperature distributions.« less
Oblique reactivation of lithosphere-scale lineaments controls rift physiography - the upper-crustal expression of the Sorgenfrei-Tornquist Zone, offshore southern Norway

NASA Astrophysics Data System (ADS)

Phillips, Thomas B.; Jackson, Christopher A.-L.; Bell, Rebecca E.; Duffy, Oliver B.

2018-04-01

Pre-existing structures within sub-crustal lithosphere may localise stresses during subsequent tectonic events, resulting in complex fault systems at upper-crustal levels. As these sub-crustal structures are difficult to resolve at great depths, the evolution of kinematically and perhaps geometrically linked upper-crustal fault populations can offer insights into their deformation history, including when and how they reactivate and accommodate stresses during later tectonic events. In this study, we use borehole-constrained 2-D and 3-D seismic reflection data to investigate the structural development of the Farsund Basin, offshore southern Norway. We use throw-length (T-x) analysis and fault displacement backstripping techniques to determine the geometric and kinematic evolution of N-S- and E-W-striking upper-crustal fault populations during the multiphase evolution of the Farsund Basin. N-S-striking faults were active during the Triassic, prior to a period of sinistral strike-slip activity along E-W-striking faults during the Early Jurassic, which represented a hitherto undocumented phase of activity in this area. These E-W-striking upper-crustal faults are later obliquely reactivated under a dextral stress regime during the Early Cretaceous, with new faults also propagating away from pre-existing ones, representing a switch to a predominantly dextral sense of motion. The E-W faults within the Farsund Basin are interpreted to extend through the crust to the Moho and link with the Sorgenfrei-Tornquist Zone, a lithosphere-scale lineament, identified within the sub-crustal lithosphere, that extends > 1000 km across central Europe. Based on this geometric linkage, we infer that the E-W-striking faults represent the upper-crustal component of the Sorgenfrei-Tornquist Zone and that the Sorgenfrei-Tornquist Zone represents a long-lived lithosphere-scale lineament that is periodically reactivated throughout its protracted geological history. The upper-crustal component of the lineament is reactivated in a range of tectonic styles, including both sinistral and dextral strike-slip motions, with the geometry and kinematics of these faults often inconsistent with what may otherwise be inferred from regional tectonics alone. Understanding these different styles of reactivation not only allows us to better understand the influence of sub-crustal lithospheric structure on rifting but also offers insights into the prevailing stress field during regional tectonic events.
An inference method from multi-layered structure of biomedical data.

PubMed

Kim, Myungjun; Nam, Yonghyun; Shin, Hyunjung

2017-05-18

Biological system is a multi-layered structure of omics with genome, epigenome, transcriptome, metabolome, proteome, etc., and can be further stretched to clinical/medical layers such as diseasome, drugs, and symptoms. One advantage of omics is that we can figure out an unknown component or its trait by inferring from known omics components. The component can be inferred by the ones in the same level of omics or the ones in different levels. To implement the inference process, an algorithm that can be applied to the multi-layered complex system is required. In this study, we develop a semi-supervised learning algorithm that can be applied to the multi-layered complex system. In order to verify the validity of the inference, it was applied to the prediction problem of disease co-occurrence with a two-layered network composed of symptom-layer and disease-layer. The symptom-disease layered network obtained a fairly high value of AUC, 0.74, which is regarded as noticeable improvement when comparing 0.59 AUC of single-layered disease network. If further stretched to whole layered structure of omics, the proposed method is expected to produce more promising results. This research has novelty in that it is a new integrative algorithm that incorporates the vertical structure of omics data, on contrary to other existing methods that integrate the data in parallel fashion. The results can provide enhanced guideline for disease co-occurrence prediction, thereby serve as a valuable tool for inference process of multi-layered biological system.
Ecosystem Food Web Lift-The-Flap Pages

ERIC Educational Resources Information Center

Atwood-Blaine, Dana; Rule, Audrey C.; Morgan, Hannah

2016-01-01

In the lesson on which this practical article is based, third grade students constructed a "lift-the-flap" page to explore food webs on the prairie. The moveable papercraft focused student attention on prairie animals' external structures and how the inferred functions of those structures could support further inferences about the…
Hierarchical Fragmentation in the Perseus Molecular Cloud: From the Cloud Scale to Protostellar Objects

NASA Astrophysics Data System (ADS)

Pokhrel, Riwaj; Myers, Philip C.; Dunham, Michael M.; Stephens, Ian W.; Sadavoy, Sarah I.; Zhang, Qizhou; Bourke, Tyler L.; Tobin, John J.; Lee, Katherine I.; Gutermuth, Robert A.; Offner, Stella S. R.

2018-01-01

We present a study of hierarchical structure in the Perseus molecular cloud, from the scale of the entire cloud (≳ 10 pc) to smaller clumps (∼1 pc), cores (∼0.05–0.1 pc), envelopes (∼300–3000 au), and protostellar objects (∼15 au). We use new observations from the Submillimeter Array (SMA) large project “Mass Assembly of Stellar Systems and their Evolution with the SMA (MASSES)” to probe the envelopes, and recent single-dish and interferometric observations from the literature for the remaining scales. This is the first study to analyze hierarchical structure over five scales in the same cloud complex. We compare the number of fragments with the number of Jeans masses in each scale to calculate the Jeans efficiency, or the ratio of observed to expected number of fragments. The velocity dispersion is assumed to arise either from purely thermal motions or from combined thermal and non-thermal motions inferred from observed spectral line widths. For each scale, thermal Jeans fragmentation predicts more fragments than observed, corresponding to inefficient thermal Jeans fragmentation. For the smallest scale, thermal plus non-thermal Jeans fragmentation also predicts too many protostellar objects. However, at each of the larger scales thermal plus non-thermal Jeans fragmentation predicts fewer than one fragment, corresponding to no fragmentation into envelopes, cores, and clumps. Over all scales, the results are inconsistent with complete Jeans fragmentation based on either thermal or thermal plus non-thermal motions. They are more nearly consistent with inefficient thermal Jeans fragmentation, where the thermal Jeans efficiency increases from the largest to the smallest scale.
Relative information content of polymorphic microsatellites and mitochondrial DNA for inferring dispersal and population genetic structure in the olive sea snake, Aipysurus laevis.

PubMed

Lukoschek, V; Waycott, M; Keogh, J S

2008-07-01

Polymorphic microsatellites are widely considered more powerful for resolving population structure than mitochondrial DNA (mtDNA) markers, particularly for recently diverged lineages or geographically proximate populations. Weaker population subdivision for biparentally inherited nuclear markers than maternally inherited mtDNA may signal male-biased dispersal but can also be attributed to marker-specific evolutionary characteristics and sampling properties. We discriminated between these competing explanations with a population genetic study on olive sea snakes, Aipysurus laevis. A previous mtDNA study revealed strong regional population structure for A. laevis around northern Australia, where Pleistocene sea-level fluctuations have influenced the genetic signatures of shallow-water marine species. Divergences among phylogroups dated to the Late Pleistocene, suggesting recent range expansions by previously isolated matrilines. Fine-scale population structure within regions was, however, poorly resolved for mtDNA. In order to improve estimates of fine-scale genetic divergence and to compare population structure between nuclear and mtDNA, 354 olive sea snakes (previously sequenced for mtDNA) were genotyped for five microsatellite loci. F statistics and Bayesian multilocus genotype clustering analyses found similar regional population structure as mtDNA and, after standardizing microsatellite F statistics for high heterozygosities, regional divergence estimates were quantitatively congruent between marker classes. Over small spatial scales, however, microsatellites recovered almost no genetic structure and standardized F statistics were orders of magnitude smaller than for mtDNA. Three tests for male-biased dispersal were not significant, suggesting that recent demographic expansions to the typically large population sizes of A. laevis have prevented microsatellites from reaching mutation-drift equilibrium and local populations may still be diverging.
Metamorphic core complexes: Expression of crustal extension by ductile-brittle shearing of the geologic column

NASA Technical Reports Server (NTRS)

Davis, G. H.

1985-01-01

Metamorphic core complexes and detachment fault terranes in the American Southwest are products of stretching of continental crust in the Tertiary. The physical and geometric properties of the structures, fault rocks, and contact relationships that developed as a consequence of the extension are especially well displayed in southeastern Arizona. The structures and fault rocks, as a system, reflect a ductile-through-brittle continuum of deformation, with individual structures and faults rocks showing remarkably coordinated strain and displacement patterns. Careful mapping and analysis of the structural system has led to the realization that strain and displacement were partitioned across a host of structures, through a spectrum of scales, in rocks of progressively changing rheology. By integrating observations made in different parts of the extensional system, especially at different inferred depth levels, it has been possible to construct a descriptive/kinematic model of the progressive deformation that achieved continental crustal extension in general, and the development of metamorphic core complexes in particular.
Climate-induced changes in lake ecosystem structure inferred from coupled neo- and paleoecological approaches

USGS Publications Warehouse

Saros, Jasmine E.; Stone, Jeffery R.; Pederson, Gregory T.; Slemmons, Krista; Spanbauer, Trisha; Schliep, Anna; Cahl, Douglas; Williamson, Craig E.; Engstrom, Daniel R.

2015-01-01

Over the 20th century, surface water temperatures have increased in many lake ecosystems around the world, but long-term trends in the vertical thermal structure of lakes remain unclear, despite the strong control that thermal stratification exerts on the biological response of lakes to climate change. Here we used both neo- and paleoecological approaches to develop a fossil-based inference model for lake mixing depths and thereby refine understanding of lake thermal structure change. We focused on three common planktonic diatom taxa, the distributions of which previous research suggests might be affected by mixing depth. Comparative lake surveys and growth rate experiments revealed that these species respond to lake thermal structure when nitrogen is sufficient, with species optima ranging from shallower to deeper mixing depths. The diatom-based mixing depth model was applied to sedimentary diatom profiles extending back to 1750 AD in two lakes with moderate nitrate concentrations but differing climate settings. Thermal reconstructions were consistent with expected changes, with shallower mixing depths inferred for an alpine lake where treeline has advanced, and deeper mixing depths inferred for a boreal lake where wind strength has increased. The inference model developed here provides a new tool to expand and refine understanding of climate-induced changes in lake ecosystems.
Brittle fracture in structural steels: perspectives at different size-scales.

PubMed

Knott, John

2015-03-28

This paper describes characteristics of transgranular cleavage fracture in structural steel, viewed at different size-scales. Initially, consideration is given to structures and the service duty to which they are exposed at the macroscale, highlighting failure by plastic collapse and failure by brittle fracture. This is followed by sections describing the use of fracture mechanics and materials testing in carrying-out assessments of structural integrity. Attention then focuses on the microscale, explaining how values of the local fracture stress in notched bars or of fracture toughness in pre-cracked test-pieces are related to features of the microstructure: carbide thicknesses in wrought material; the sizes of oxide/silicate inclusions in weld metals. Effects of a microstructure that is 'heterogeneous' at the mesoscale are treated briefly, with respect to the extraction of test-pieces from thick sections and to extrapolations of data to low failure probabilities. The values of local fracture stress may be used to infer a local 'work-of-fracture' that is found experimentally to be a few times greater than that of two free surfaces. Reasons for this are discussed in the conclusion section on nano-scale events. It is suggested that, ahead of a sharp crack, it is necessary to increase the compliance by a cooperative movement of atoms (involving extra work) to allow the crack-tip bond to displace sufficiently for the energy of attraction between the atoms to reduce to zero. © 2015 The Author(s) Published by the Royal Society. All rights reserved.
Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data

PubMed Central

Emmert-Streib, Frank; Glazko, Galina V.; Altay, Gökmen; de Matos Simoes, Ricardo

2012-01-01

In this paper, we present a systematic and conceptual overview of methods for inferring gene regulatory networks from observational gene expression data. Further, we discuss two classic approaches to infer causal structures and compare them with contemporary methods by providing a conceptual categorization thereof. We complement the above by surveying global and local evaluation measures for assessing the performance of inference algorithms. PMID:22408642

Learning Progressions as Evolving Tools in Joint Enterprises for Educational Improvement

ERIC Educational Resources Information Center

Penuel, William R.

2015-01-01

In their article, "Using Learning Progressions to Design Vertical Scales that Support Coherent Inferences about Student Growth," Briggs and Peck (this issue) argue that an important goal of assessment should be "to support coherent and actionable inferences of growth." They suggest that current approaches to test design rely on…
Learning large-scale dynamic discrete choice models of spatio-temporal preferences with application to migratory pastoralism in East Africa

USDA-ARS?s Scientific Manuscript database

Understanding spatio-temporal resource preferences is paramount in the design of policies for sustainable development. Unfortunately, resource preferences are often unknown to policy-makers and have to be inferred from data. In this paper we consider the problem of inferring agents’ preferences fro...
STRIDE: Species Tree Root Inference from Gene Duplication Events.

PubMed

Emms, David M; Kelly, Steven

2017-12-01

The correct interpretation of any phylogenetic tree is dependent on that tree being correctly rooted. We present STRIDE, a fast, effective, and outgroup-free method for identification of gene duplication events and species tree root inference in large-scale molecular phylogenetic analyses. STRIDE identifies sets of well-supported in-group gene duplication events from a set of unrooted gene trees, and analyses these events to infer a probability distribution over an unrooted species tree for the location of its root. We show that STRIDE correctly identifies the root of the species tree in multiple large-scale molecular phylogenetic data sets spanning a wide range of timescales and taxonomic groups. We demonstrate that the novel probability model implemented in STRIDE can accurately represent the ambiguity in species tree root assignment for data sets where information is limited. Furthermore, application of STRIDE to outgroup-free inference of the origin of the eukaryotic tree resulted in a root probability distribution that provides additional support for leading hypotheses for the origin of the eukaryotes. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
Use of Bayesian Inference in Crystallographic Structure Refinement via Full Diffraction Profile Analysis

PubMed Central

Fancher, Chris M.; Han, Zhen; Levin, Igor; Page, Katharine; Reich, Brian J.; Smith, Ralph C.; Wilson, Alyson G.; Jones, Jacob L.

2016-01-01

A Bayesian inference method for refining crystallographic structures is presented. The distribution of model parameters is stochastically sampled using Markov chain Monte Carlo. Posterior probability distributions are constructed for all model parameters to properly quantify uncertainty by appropriately modeling the heteroskedasticity and correlation of the error structure. The proposed method is demonstrated by analyzing a National Institute of Standards and Technology silicon standard reference material. The results obtained by Bayesian inference are compared with those determined by Rietveld refinement. Posterior probability distributions of model parameters provide both estimates and uncertainties. The new method better estimates the true uncertainties in the model as compared to the Rietveld method. PMID:27550221
Sensing, Measuring and Modelling the Mechanical Properties of Sandstone

NASA Astrophysics Data System (ADS)

Antony, S. J.; Olugbenga, A.; Ozerkan, N. G.

2018-02-01

We present a hybrid framework for simulating the strength and dilation characteristics of sandstone. Where possible, the grain-scale properties of sandstone are evaluated experimentally in detail. Also, using photo-stress analysis, we sense the deviator stress (/strain) distribution at the micro-scale and its components along the orthogonal directions on the surface of a V-notch sandstone sample under mechanical loading. Based on this measurement and applying a grain-scale model, the optical anisotropy index K 0 is inferred at the grain scale. This correlated well with the grain contact stiffness ratio K evaluated using ultrasound sensors independently. Thereafter, in addition to other experimentally characterised structural and grain-scale properties of sandstone, K is fed as an input into the discrete element modelling of fracture strength and dilation of the sandstone samples. Physical bulk-scale experiments are also conducted to evaluate the load-displacement relation, dilation and bulk fracture strength characteristics of sandstone samples under compression and shear. A good level of agreement is obtained between the results of the simulations and experiments. The current generic framework could be applied to understand the internal and bulk mechanical properties of such complex opaque and heterogeneous materials more realistically in future.
Development and validation of the trait and state versions of the Post-Event Processing Inventory.

PubMed

Blackie, Rebecca A; Kocovski, Nancy L

2017-03-01

Post-event processing (PEP) refers to negative and prolonged rumination following anxiety-provoking social situations. Although there are scales to assess PEP, they are situation-specific, some targeting only public-speaking situations. Furthermore, there are no trait measures to assess the tendency to engage in PEP. The purpose of this research was to create a new measure of PEP, the Post-Event Processing Inventory (PEPI), which can be employed following all types of social situations and includes both trait and state forms. Over two studies (study 1, N = 220; study 2, N = 199), we explored and confirmed the factor structure of the scale with student samples. For each form of the scale, we found and confirmed that a higher-order, general PEP factor could be inferred from three sub-domains (intensity, frequency, and self-judgment). We also found preliminary evidence for the convergent, concurrent, discriminant/divergent, incremental, and predictive validity for each version of the scale. Both forms of the scale demonstrated excellent internal consistency and the trait form had excellent two-week test-retest reliability. Given the utility and versatility of the scale, the PEPI may provide a useful alternative to existing measures of PEP and rumination.
Method of fuzzy inference for one class of MISO-structure systems with non-singleton inputs

NASA Astrophysics Data System (ADS)

Sinuk, V. G.; Panchenko, M. V.

2018-03-01

In fuzzy modeling, the inputs of the simulated systems can receive both crisp values and non-Singleton. Computational complexity of fuzzy inference with fuzzy non-Singleton inputs corresponds to an exponential. This paper describes a new method of inference, based on the theorem of decomposition of a multidimensional fuzzy implication and a fuzzy truth value. This method is considered for fuzzy inputs and has a polynomial complexity, which makes it possible to use it for modeling large-dimensional MISO-structure systems.
Local topography shapes fine-scale spatial genetic structure in the Arkansas Valley evening primrose, Oenothera harringtonii (Onagraceae).

PubMed

Rhodes, Matthew K; Fant, Jeremie B; Skogen, Krissa A

2014-01-01

Identifying factors that shape the spatial distribution of genetic variation is crucial to understanding many population- and landscape-level processes. In this study, we explore fine-scale spatial genetic structure in Oenothera harringtonii (Onagraceae), an insect-pollinated, gravity-dispersed herb endemic to the grasslands of south-central and southeastern Colorado, USA. We genotyped 315 individuals with 11 microsatellite markers and utilized a combination of spatial autocorrelation analyses and landscape genetic models to relate life history traits and landscape features to dispersal processes. Spatial genetic structure was consistent with theoretical expectations of isolation by distance, but this pattern was weak (Sp = 0.00374). Anisotropic analyses indicated that spatial genetic structure was markedly directional, in this case consistent with increased dispersal along prominent slopes. Landscape genetic models subsequently confirmed that spatial genetic variation was significantly influenced by local topographic heterogeneity, specifically that geographic distance, elevation and aspect were important predictors of spatial genetic structure. Among these variables, geographic distance was ~68% more important than elevation in describing spatial genetic variation, and elevation was ~42% more important than aspect after removing the effect of geographic distance. From these results, we infer a mechanism of hydrochorous seed dispersal along major drainages aided by seasonal monsoon rains. Our findings suggest that landscape features may shape microevolutionary processes at much finer spatial scales than typically considered, and stress the importance of considering how particular dispersal vectors are influenced by their environmental context. © The American Genetic Association 2014. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
A review of causal inference for biomedical informatics

PubMed Central

Kleinberg, Samantha; Hripcsak, George

2011-01-01

Causality is an important concept throughout the health sciences and is particularly vital for informatics work such as finding adverse drug events or risk factors for disease using electronic health records. While philosophers and scientists working for centuries on formalizing what makes something a cause have not reached a consensus, new methods for inference show that we can make progress in this area in many practical cases. This article reviews core concepts in understanding and identifying causality and then reviews current computational methods for inference and explanation, focusing on inference from large-scale observational data. While the problem is not fully solved, we show that graphical models and Granger causality provide useful frameworks for inference and that a more recent approach based on temporal logic addresses some of the limitations of these methods. PMID:21782035
Scale dependent inference in landscape genetics

Treesearch

Samuel A. Cushman; Erin L. Landguth

2010-01-01

Ecological relationships between patterns and processes are highly scale dependent. This paper reports the first formal exploration of how changing scale of research away from the scale of the processes governing gene flow affects the results of landscape genetic analysis. We used an individual-based, spatially explicit simulation model to generate patterns of genetic...
Field-induced polarization rotation and phase transitions in 0.70 Pb ( M g 1 / 3 N b 2 / 3 ) O 3 – 0.30 PbTi O 3 piezoceramics observed by in situ high-energy x-ray scattering

DOE PAGES

Hou, Dong; Usher, Tedi -Marie; Fulanovic, Lovro; ...

2018-06-12

Changes to the crystal structure of 0.70Pb(Mg 1/3Nb 2/3)O 3–0.30PbTiO 3 (PMN-0.30PT) piezoceramic under application of electric fields at the long-range and local scale are revealed by in situ high-energy x-ray diffraction (XRD) and pair-distribution function (PDF) analyses, respectively. The crystal structure of unpoled samples is identified as monoclinic Cm at both the long-range and local scale. In situ XRD results suggest that field-induced polarization rotation and phase transitions occur at specific field strengths. A polarization rotation pathway is proposed based on the Bragg-peak behaviors and the Le Bail fitting results of the in situ XRD patterns. The PDF resultsmore » show systematic changes to the structures at the local scale, which is in agreement with the changes inferred from the in situ XRD study. More importantly, our results prove that polarization rotation can be detected and determined in a polycrystalline relaxor ferroelectric. Furthermore, this study supports the idea that multiple contributions, specifically ferroelectric-ferroelectric phase transition and polarization rotation, are responsible for the high piezoelectric properties at the morphotropic phase boundary of PMN-xPT piezoceramics.« less
Seaglider surveys at Ocean Station Papa: Circulation and water mass properties in a meander of the North Pacific Current

NASA Astrophysics Data System (ADS)

Pelland, Noel A.; Eriksen, Charles C.; Cronin, Meghan F.

2016-09-01

A Seaglider autonomous underwater vehicle augmented the Ocean Station Papa (OSP; 50°N, 145°W) surface mooring, measuring spatial structure on scales relevant to the monthly evolution of the moored time series. During each of three missions from June 2008 to January 2010, a Seaglider made biweekly 50 km × 50 km surveys in a bowtie-shaped survey track. Horizontal temperature and salinity gradients measured by these surveys were an order of magnitude stronger than climatological values and sometimes of opposite sign. Geostrophically inferred circulation was corroborated by moored acoustic Doppler current profiler measurements and AVISO satellite altimetry estimates of surface currents, confirming that glider surveys accurately resolved monthly scale mesoscale spatial structure. In contrast to climatological North Pacific Current circulation, upper-ocean flow was modestly northward during the first half of the 18 month survey period, and weakly westward during its latter half, with Rossby number O>(0.01>). This change in circulation coincided with a shift from cool and fresh to warm, saline, oxygen-rich water in the upper-ocean halocline, and an increase in vertical fine structure there and in the lower pycnocline. The anomalous flow and abrupt water mass transition were due to the slow growth of an anticyclonic meander within the North Pacific Current with radius comparable to the scale of the survey pattern, originating to the southeast of OSP.
Field-induced polarization rotation and phase transitions in 0.70 Pb ( M g 1 / 3 N b 2 / 3 ) O 3 – 0.30 PbTi O 3 piezoceramics observed by in situ high-energy x-ray scattering

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

Hou, Dong; Usher, Tedi -Marie; Fulanovic, Lovro

Changes to the crystal structure of 0.70Pb(Mg 1/3Nb 2/3)O 3–0.30PbTiO 3 (PMN-0.30PT) piezoceramic under application of electric fields at the long-range and local scale are revealed by in situ high-energy x-ray diffraction (XRD) and pair-distribution function (PDF) analyses, respectively. The crystal structure of unpoled samples is identified as monoclinic Cm at both the long-range and local scale. In situ XRD results suggest that field-induced polarization rotation and phase transitions occur at specific field strengths. A polarization rotation pathway is proposed based on the Bragg-peak behaviors and the Le Bail fitting results of the in situ XRD patterns. The PDF resultsmore » show systematic changes to the structures at the local scale, which is in agreement with the changes inferred from the in situ XRD study. More importantly, our results prove that polarization rotation can be detected and determined in a polycrystalline relaxor ferroelectric. Furthermore, this study supports the idea that multiple contributions, specifically ferroelectric-ferroelectric phase transition and polarization rotation, are responsible for the high piezoelectric properties at the morphotropic phase boundary of PMN-xPT piezoceramics.« less
Field-induced polarization rotation and phase transitions in 0.70 Pb (M g1 /3N b2 /3 ) O3-0.30 PbTi O3 piezoceramics observed by in situ high-energy x-ray scattering

NASA Astrophysics Data System (ADS)

Hou, Dong; Usher, Tedi-Marie; Fulanovic, Lovro; Vrabelj, Marko; Otonicar, Mojca; Ursic, Hana; Malic, Barbara; Levin, Igor; Jones, Jacob L.

2018-06-01

Changes to the crystal structure of 0.70 Pb (M g1 /3N b2 /3 ) O3-0.30 PbTi O3 (PMN-0.30PT) piezoceramic under application of electric fields at the long-range and local scale are revealed by in situ high-energy x-ray diffraction (XRD) and pair-distribution function (PDF) analyses, respectively. The crystal structure of unpoled samples is identified as monoclinic C m at both the long-range and local scale. In situ XRD results suggest that field-induced polarization rotation and phase transitions occur at specific field strengths. A polarization rotation pathway is proposed based on the Bragg-peak behaviors and the Le Bail fitting results of the in situ XRD patterns. The PDF results show systematic changes to the structures at the local scale, which is in agreement with the changes inferred from the in situ XRD study. More importantly, our results prove that polarization rotation can be detected and determined in a polycrystalline relaxor ferroelectric. This study supports the idea that multiple contributions, specifically ferroelectric-ferroelectric phase transition and polarization rotation, are responsible for the high piezoelectric properties at the morphotropic phase boundary of PMN-x PT piezoceramics.
Magnetohydrodynamic Simulations for Studying Solar Flare Trigger Mechanism

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

Muhamad, J.; Kusano, K.; Inoue, S.

In order to understand the flare trigger mechanism, we conduct three-dimensional magnetohydrodynamic simulations using a coronal magnetic field model derived from data observed by the Hinode satellite. Several types of magnetic bipoles are imposed into the photospheric boundary of the Nonlinear Force-free Field model of Active Region (AR) NOAA 10930 on 2006 December 13, to investigate what kind of magnetic disturbance may trigger the flare. As a result, we confirm that certain small bipole fields, which emerge into the highly sheared global magnetic field of an AR, can effectively trigger a flare. These bipole fields can be classified into twomore » groups based on their orientation relative to the polarity inversion line: the so-called opposite polarity, and reversed shear structures, as suggested by Kusano et al. We also investigate the structure of the footpoints of reconnected field lines. By comparing the distribution of reconstructed field lines and observed flare ribbons, the trigger structure of the flare can be inferred. Our simulation suggests that the data-constrained simulation, taking into account both the large-scale magnetic structure and small-scale magnetic disturbance (such as emerging fluxes), is a good way to discover a flare-producing AR, which can be applied to space weather prediction.« less
Introduction to bioinformatics.

PubMed

Can, Tolga

2014-01-01

Bioinformatics is an interdisciplinary field mainly involving molecular biology and genetics, computer science, mathematics, and statistics. Data intensive, large-scale biological problems are addressed from a computational point of view. The most common problems are modeling biological processes at the molecular level and making inferences from collected data. A bioinformatics solution usually involves the following steps: Collect statistics from biological data. Build a computational model. Solve a computational modeling problem. Test and evaluate a computational algorithm. This chapter gives a brief introduction to bioinformatics by first providing an introduction to biological terminology and then discussing some classical bioinformatics problems organized by the types of data sources. Sequence analysis is the analysis of DNA and protein sequences for clues regarding function and includes subproblems such as identification of homologs, multiple sequence alignment, searching sequence patterns, and evolutionary analyses. Protein structures are three-dimensional data and the associated problems are structure prediction (secondary and tertiary), analysis of protein structures for clues regarding function, and structural alignment. Gene expression data is usually represented as matrices and analysis of microarray data mostly involves statistics analysis, classification, and clustering approaches. Biological networks such as gene regulatory networks, metabolic pathways, and protein-protein interaction networks are usually modeled as graphs and graph theoretic approaches are used to solve associated problems such as construction and analysis of large-scale networks.
On Galactic Density Modeling in the Presence of Dust Extinction

NASA Astrophysics Data System (ADS)

Bovy, Jo; Rix, Hans-Walter; Green, Gregory M.; Schlafly, Edward F.; Finkbeiner, Douglas P.

2016-02-01

Inferences about the spatial density or phase-space structure of stellar populations in the Milky Way require a precise determination of the effective survey volume. The volume observed by surveys such as Gaia or near-infrared spectroscopic surveys, which have good coverage of the Galactic midplane region, is highly complex because of the abundant small-scale structure in the three-dimensional interstellar dust extinction. We introduce a novel framework for analyzing the importance of small-scale structure in the extinction. This formalism demonstrates that the spatially complex effect of extinction on the selection function of a pencil-beam or contiguous sky survey is equivalent to a low-pass filtering of the extinction-affected selection function with the smooth density field. We find that the angular resolution of current 3D extinction maps is sufficient for analyzing Gaia sub-samples of millions of stars. However, the current distance resolution is inadequate and needs to be improved by an order of magnitude, especially in the inner Galaxy. We also present a practical and efficient method for properly taking the effect of extinction into account in analyses of Galactic structure through an effective selection function. We illustrate its use with the selection function of red-clump stars in APOGEE using and comparing a variety of current 3D extinction maps.
Crustal structure across the Altyn Tagh Range at the northern margin of the Tibetan Plateau and tectonic implications

USGS Publications Warehouse

Zhao, J.; Mooney, W.D.; Zhang, X.; Li, Z.; Jin, Z.; Okaya, N.

2006-01-01

We present new seismic refraction/wide-angle-reflection data across the Altyn Tagh Range and its adjacent basins. We find that the crustal velocity structure, and by inference, the composition of the crust changes abruptly beneath the Cherchen fault, i.e., ???100 km north of the northern margin of the Tibetan plateau. North of the Cherchen fault, beneath the Tarim basin, a platform-type crust is evident. In contrast, south the Cherchen fault the crust is characterized by a missing high-velocity lower-crustal layer. Our seismic model indicates that the high topography (???3 km) of the Altyn Tagh Range is supported by a wedge-shaped region with a seismic velocity of 7.6-7.8 km/s that we interpret as a zone of crust-mantle mix. We infer that the Altyn Tagh Range formed by crustal-scale strike-slip motion along the North Altyn Tagh fault and northeast-southwest contraction over the range. The contraction is accommodated by (1) crustal thickening via upper-crustal thrusting and lower-crustal flow (i.e., creep), and (2) slip-parallel (SW-directed) underthrusting of only the lower crust and mantle of the eastern Tarim basin beneath the Altyn Tagh Range. ?? 2005 Elsevier B.V. All rights reserved.
On imputing function to structure from the behavioural effects of brain lesions.

PubMed

Young, M P; Hilgetag, C C; Scannell, J W

2000-01-29

What is the link, if any, between the patterns of connections in the brain and the behavioural effects of localized brain lesions? We explored this question in four related ways. First, we investigated the distribution of activity decrements that followed simulated damage to elements of the thalamocortical network, using integrative mechanisms that have recently been used to successfully relate connection data to information on the spread of activation, and to account simultaneously for a variety of lesion effects. Second, we examined the consequences of the patterns of decrement seen in the simulation for each type of inference that has been employed to impute function to structure on the basis of the effects of brain lesions. Every variety of conventional inference, including double dissociation, readily misattributed function to structure. Third, we tried to derive a more reliable framework of inference for imputing function to structure, by clarifying concepts of function, and exploring a more formal framework, in which knowledge of connectivity is necessary but insufficient, based on concepts capable of mathematical specification. Fourth, we applied this framework to inferences about function relating to a simple network that reproduces intact, lesioned and paradoxically restored orientating behaviour. Lesion effects could be used to recover detailed and reliable information on which structures contributed to particular functions in this simple network. Finally, we explored how the effects of brain lesions and this formal approach could be used in conjunction with information from multiple neuroscience methodologies to develop a practical and reliable approach to inferring the functional roles of brain structures.
Modeling Protein Expression and Protein Signaling Pathways

PubMed Central

Telesca, Donatello; Müller, Peter; Kornblau, Steven M.; Suchard, Marc A.; Ji, Yuan

2015-01-01

High-throughput functional proteomic technologies provide a way to quantify the expression of proteins of interest. Statistical inference centers on identifying the activation state of proteins and their patterns of molecular interaction formalized as dependence structure. Inference on dependence structure is particularly important when proteins are selected because they are part of a common molecular pathway. In that case, inference on dependence structure reveals properties of the underlying pathway. We propose a probability model that represents molecular interactions at the level of hidden binary latent variables that can be interpreted as indicators for active versus inactive states of the proteins. The proposed approach exploits available expert knowledge about the target pathway to define an informative prior on the hidden conditional dependence structure. An important feature of this prior is that it provides an instrument to explicitly anchor the model space to a set of interactions of interest, favoring a local search approach to model determination. We apply our model to reverse-phase protein array data from a study on acute myeloid leukemia. Our inference identifies relevant subpathways in relation to the unfolding of the biological process under study. PMID:26246646

Resistivity structures across the Humboldt River basin, north-central Nevada

USGS Publications Warehouse

Rodriguez, Brian D.; Williams, Jackie M.

2002-01-01

Magnetotelluric data collected along five profiles show deep resistivity structures beneath the Battle Mountain-Eureka and Carlin gold trends in north-central Nevada, which appear consistent with tectonic breaks in the crust that possibly served as channels for hydrothermal fluids. It seems likely that gold deposits along these linear trends were, therefore, controlled by deep regional crustal fault systems. Two-dimensional resistivity modeling of the magnetotelluric data generally show resistive (30 to 1,000 ohm-m) crustal blocks broken by sub-vertical, two-dimensional, conductive (1 to 10 ohmm) zones that are indicative of large-scale crustal fault zones. These inferred fault zones are regional in scale, trend northeast-southwest, north-south, and northwest-southeast, and extend to mid-crustal (20 km) depths. The conductors are about 2- to 15-km wide, extend from about 1 to 4 km below the surface to about 20 km depth, and show two-dimensional electrical structure. By connecting the locations of similar trending conductors together, individual regional crustal fault zones within the upper crust can be inferred that range from about 4- to 10-km wide and about 30- to 150-km long. One of these crustal fault zones coincides with the Battle Mountain-Eureka mineral trend. The interpreted electrical property sections also show regional changes in the resistive crust from south to north. Most of the subsurface in the upper 20 km beneath Reese River Valley and southern Boulder Valley are underlain by rock that is generally more conductive than the subsurface beneath Kelly Creek Basin and northern Boulder Valley. This suggests that either elevated-temperature or high-salinity fluids, alteration, or carbonaceous rocks are more pervasive in the more conductive area (Battle Mountain Heat-Flow High), which implies that the crust beneath these valleys is either more fractured or has more carbonaceous rocks than in the area surveyed along the 41st parallel.
About recent star formation rates inferences

NASA Astrophysics Data System (ADS)

Cerviño, M.; Bongiovanni, A.; Hidalgo, S.

2017-03-01

Star Formation Rate (SFR) inferences are based in the so-called constant SFR approximation, where synthesis models are require to provide a calibration; we aims to study the key points of such approximation to produce accurate SFR inferences. We use the intrinsic algebra used in synthesis models, and we explore how SFR can be inferred from the integrated light without any assumption about the underling Star Formation history (SFH). We show that the constant SFR approximation is actually a simplified expression of more deeper characteristics of synthesis models: It is a characterization of the evolution of single stellar populations (SSPs), acting the SSPs as sensitivity curve over different measures of the SFH can be obtained. As results, we find that (1) the best age to calibrate SFR indices is the age of the observed system (i.e. about 13 Gyr for z = 0 systems); (2) constant SFR and steady-state luminosities are not requirements to calibrate the SFR ; (3) it is not possible to define a SFR single time scale over which the recent SFH is averaged, and we suggest to use typical SFR indices (ionizing flux, UV fluxes) together with no typical ones (optical/IR fluxes) to correct the SFR from the contribution of the old component of the SFH, we show how to use galaxy colors to quote age ranges where the recent component of the SFH is stronger/softer than the older component. Particular values of SFR calibrations are (almost) not affect by this work, but the meaning of what is obtained by SFR inferences does. In our framework, results as the correlation of SFR time scales with galaxy colors, or the sensitivity of different SFR indices to sort and long scale variations in the SFH, fit naturally. In addition, the present framework provides a theoretical guideline to optimize the available information from data/numerical experiments to improve the accuracy of SFR inferences. More info en Cerviño, Bongiovanni & Hidalgo A&A 588, 108C (2016)
Inferred Rheology and Petrology of Southern California and Northwest Mexico Mantle from Postseismic Deformation following the 2010 El Mayor-Cucapah Earthquake

NASA Astrophysics Data System (ADS)

Freed, A. M.; Dickinson, H.; Huang, M. H.; Fielding, E. J.; Burgmann, R.; Andronicos, C.

2015-12-01

The Mw 7.2 El Mayor-Cucapah (EMC) earthquake ruptured a ~120 km long series of faults striking northwest from the Gulf of California to the Sierra Cucapah. Five years after the EMC event, a dense network of GPS stations in southern California and a sparse array of sites installed after the earthquake in northern Mexico measure ongoing surface deformation as coseismic stresses relax. We use 3D finite element models of seismically inferred crustal and mantle structure with earthquake slip constrained by GPS, InSAR range change and SAR and SPOT image sub-pixel offset measurements to infer the rheologic structure of the region. Model complexity, including 3D Moho structure and distinct geologic regions such as the Peninsular Ranges and Salton Trough, enable us to explore vertical and lateral heterogeneities of crustal and mantle rheology. We find that postseismic displacements can be explained by relaxation of a laterally varying, stratified rheologic structure controlled by temperature and crustal thickness. In the Salton Trough region, particularly large postseismic displacements require a relatively weak mantle column that weakens with depth, consistent with a strong but thin (22 km thick) crust and high regional temperatures. In contrast, beneath the neighboring Peninsular Ranges a strong, thick (up to 35 km) crust and cooler temperatures lead to a rheologically stronger mantle column. Thus, we find that the inferred rheologic structure corresponds with observed seismic structure and thermal variations. Significant afterslip is not required to explain postseismic displacements, but cannot be ruled out. Combined with isochemical phase diagrams, our results enable us to go beyond rheologic structure and infer some basic properties about the regional mantle, including composition, water content, and the degree of partial melting.
Neutrino footprint in large scale structure

NASA Astrophysics Data System (ADS)

Garay, Carlos Peña; Verde, Licia; Jimenez, Raul

2017-03-01

Recent constrains on the sum of neutrino masses inferred by analyzing cosmological data, show that detecting a non-zero neutrino mass is within reach of forthcoming cosmological surveys. Such a measurement will imply a direct determination of the absolute neutrino mass scale. Physically, the measurement relies on constraining the shape of the matter power spectrum below the neutrino free streaming scale: massive neutrinos erase power at these scales. However, detection of a lack of small-scale power from cosmological data could also be due to a host of other effects. It is therefore of paramount importance to validate neutrinos as the source of power suppression at small scales. We show that, independent on hierarchy, neutrinos always show a footprint on large, linear scales; the exact location and properties are fully specified by the measured power suppression (an astrophysical measurement) and atmospheric neutrinos mass splitting (a neutrino oscillation experiment measurement). This feature cannot be easily mimicked by systematic uncertainties in the cosmological data analysis or modifications in the cosmological model. Therefore the measurement of such a feature, up to 1% relative change in the power spectrum for extreme differences in the mass eigenstates mass ratios, is a smoking gun for confirming the determination of the absolute neutrino mass scale from cosmological observations. It also demonstrates the synergy between astrophysics and particle physics experiments.
Searching for 3D Viscosity Models of Glacial Isostatic Adjustment in Support of the Global ICE-6G_C Ice History Model

NASA Astrophysics Data System (ADS)

LI, T., II; Wu, P.; Steffen, H.; Wang, H.

2017-12-01

The global ice history model ICE-6G_C was constructed based on the laterally homogeneous earth model VM5a. The combined model of glacial isostatic adjustment (GIA) called ICE-6G_C (VM5a) fits global observations of GIA simultaneously well. However, seismic and geological observations clearly show that the Earth's mantle is laterally heterogeneous. Our aim therefore is to search for the best laterally heterogeneous viscosity models with ICE-6G_C ice history that is able to fit the global relative sea-level (RSL) data, the peak uplift rates (from GNSS) and peak g-dot rates (from the GRACE satellite mission) in Laurentia and Fennoscandia simultaneously. The Coupled Laplace-Finite Element Method is used to compute gravitationally self-consistent sea levels with time dependent coastlines and rotational feedback in addition to changes in deformation, gravity and the state of stress. As a start, the VM5a Earth model is used as the radial background viscosity structure but other radial background viscosity models will also be investigated. Lateral mantle viscosity structure is obtained by the superposition of the radial background viscosity and the lateral viscosity perturbations logarithmically. The latter is inferred from a seismic tomography model using a scaling relationship that takes into account the effects of anharmonicity, anelasticity and non-thermal effects. We will show that several laterally heterogeneous mantle viscosity models can fit the global sea level, GPS and GRACE data better than laterally homogeneous models, provided that the scaling relationship for mantle heterogeneity under northern Europe is allowed to be different from that under Laurentia. In addition, the effects of laterally heterogeneous lithosphere, as inferred from seismic tomography, and the lateral changes in sub-lithospheric properties will also be presented.
Extensive sampling of polar bears (Ursus maritimus) in the Northwest Passage (Canadian Arctic Archipelago) reveals population differentiation across multiple spatial and temporal scales.

PubMed

Campagna, Leonardo; Van Coeverden de Groot, Peter J; Saunders, Brenda L; Atkinson, Stephen N; Weber, Diana S; Dyck, Markus G; Boag, Peter T; Lougheed, Stephen C

2013-09-01

As global warming accelerates the melting of Arctic sea ice, polar bears (Ursus maritimus) must adapt to a rapidly changing landscape. This process will necessarily alter the species distribution together with population dynamics and structure. Detailed knowledge of these changes is crucial to delineating conservation priorities. Here, we sampled 361 polar bears from across the center of the Canadian Arctic Archipelago spanning the Gulf of Boothia (GB) and M'Clintock Channel (MC). We use DNA microsatellites and mitochondrial control region sequences to quantify genetic differentiation, estimate gene flow, and infer population history. Two populations, roughly coincident with GB and MC, are significantly differentiated at both nuclear (F ST = 0.01) and mitochondrial (ΦST = 0.47; F ST = 0.29) loci, allowing Bayesian clustering analyses to assign individuals to either group. Our data imply that the causes of the mitochondrial and nuclear genetic patterns differ. Analysis of mtDNA reveals the matrilineal structure dates at least to the Holocene, and is common to individuals throughout the species' range. These mtDNA differences probably reflect both genetic drift and historical colonization dynamics. In contrast, the differentiation inferred from microsatellites is only on the scale of hundreds of years, possibly reflecting contemporary impediments to gene flow. Taken together, our data suggest that gene flow is insufficient to homogenize the GB and MC populations and support the designation of GB and MC as separate polar bear conservation units. Our study also provide a striking example of how nuclear DNA and mtDNA capture different aspects of a species demographic history.
Estimation and modeling of forest attributes across large spatial scales using BiomeBGC, high-resolution imagery, LiDAR data, and inventory data

NASA Astrophysics Data System (ADS)

Golinkoff, Jordan Seth

The accurate estimation of forest attributes at many different spatial scales is a critical problem. Forest landowners may be interested in estimating timber volume, forest biomass, and forest structure to determine their forest's condition and value. Counties and states may be interested to learn about their forests to develop sustainable management plans and policies related to forests, wildlife, and climate change. Countries and consortiums of countries need information about their forests to set global and national targets to deal with issues of climate change and deforestation as well as to set national targets and understand the state of their forest at a given point in time. This dissertation approaches these questions from two perspectives. The first perspective uses the process model Biome-BGC paired with inventory and remote sensing data to make inferences about a current forest state given known climate and site variables. Using a model of this type, future climate data can be used to make predictions about future forest states as well. An example of this work applied to a forest in northern California is presented. The second perspective of estimating forest attributes uses high resolution aerial imagery paired with light detection and ranging (LiDAR) remote sensing data to develop statistical estimates of forest structure. Two approaches within this perspective are presented: a pixel based approach and an object based approach. Both approaches can serve as the platform on which models (either empirical growth and yield models or process models) can be run to generate inferences about future forest state and current forest biogeochemical cycling.
Inferring field-scale properties of a fractured aquifer from ground surface deformation during a well test

NASA Astrophysics Data System (ADS)

Schuite, Jonathan; Longuevergne, Laurent; Bour, Olivier; Boudin, Frédérick; Durand, Stéphane; Lavenant, Nicolas

2015-12-01

Fractured aquifers which bear valuable water resources are often difficult to characterize with classical hydrogeological tools due to their intrinsic heterogeneities. Here we implement ground surface deformation tools (tiltmetry and optical leveling) to monitor groundwater pressure changes induced by a classical hydraulic test at the Ploemeur observatory. By jointly analyzing complementary time constraining data (tilt) and spatially constraining data (vertical displacement), our results strongly suggest that the use of these surface deformation observations allows for estimating storativity and structural properties (dip, root depth, and lateral extension) of a large hydraulically active fracture, in good agreement with previous studies. Hence, we demonstrate that ground surface deformation is a useful addition to traditional hydrogeological techniques and opens possibilities for characterizing important large-scale properties of fractured aquifers with short-term well tests as a controlled forcing.
Large-scale solar magnetic fields and H-alpha patterns

NASA Technical Reports Server (NTRS)

Mcintosh, P. S.

1972-01-01

Coronal and interplanetary magnetic fields computed from measurements of large-scale photospheric magnetic fields suffer from interruptions in day-to-day observations and the limitation of using only measurements made near the solar central meridian. Procedures were devised for inferring the lines of polarity reversal from H-alpha solar patrol photographs that map the same large-scale features found on Mt. Wilson magnetograms. These features may be monitored without interruption by combining observations from the global network of observatories associated with NOAA's Space Environment Services Center. The patterns of inferred magnetic fields may be followed accurately as far as 60 deg from central meridian. Such patterns will be used to improve predictions of coronal features during the next solar eclipse.
Sector structure of the interplanetary magnetic field in the second half of the 19th century inferred from ground-based magnetometers

NASA Astrophysics Data System (ADS)

Vokhmyanin, M.; Ponyavin, D. I.

2012-12-01

Interplanetary magnetic field (IMF) polarities can be inferred in the pre-satellite era using Svalgaard-Mansurov effect, according to which different IMF directions lead to different geomagnetic variations at polar stations. Basing on this effect we propose a method to derive a sector structure of the IMF when only ground based data are available. Details of the method and results have been presented in our recent paper: Vokhmyanin, M. V., and D. I. Ponyavin (2012), Inferring interplanetary magnetic field polarities from geomagnetic variations, J. Geophys. Res., 117, A06102, doi:10.1029/2011JA017060. Using data from eight stations: Sitka, Sodankyla, Godhavn, Lerwick, Thule, Baker Lake, Vostok and Mirny, we reconstructed sector structure back to 1905. The quality of inferring from 1965 to 2005 ranges between 78% and 90% depending on the used set of stations. Our results show both high success rate and good agreement with the well-known Russell-McPherron and Rosenberg-Coleman effects. In the current study we applied the technique to historical data of Helsinki observatory where digital versions of hourly geomagnetic components are available from 1844 to 1897. Helsinki station stopped operates at the beginning of 20th century. Thus, to create a model describing the local Svalgaard-Mansurov effect we analyzed data from Nurmijarvi station located near the same region. The success rate of reconstruction from 1965 to 2005 is around 82%. So we assume that the IMF polarities obtained for the period 1869-1889 have sufficient quality. Inferred sector structure at this time consists of two sectors typically for all declining phases of solar activity cycle. Catalogue of IMF proxies seem to be important in analyzing structure and dynamics of solar magnetic fields in the past.; Left: Bartels diagram of IMF sector structure inferred from Helsinki data. Right: sunspot number indicating solar cycles.
Simulating multi-spacecraft Heliospheric Imager observations for tomographic reconstruction of interplanetary CMEs

NASA Astrophysics Data System (ADS)

Barnes, D.

2017-12-01

The multiple, spatially separated vantage points afforded by the STEREO and SOHO missions provide physicists with a means to infer the three-dimensional structure of the solar corona via tomographic imaging. The reconstruction process combines these multiple projections of the optically thin plasma to constrain its three-dimensional density structure and has been successfully applied to the low corona using the STEREO and SOHO coronagraphs. However, the technique is also possible at larger, inter-planetary distances using wide-angle imagers, such as the STEREO Heliospheric Imagers (HIs), to observe faint solar wind plasma and Coronal Mass Ejections (CMEs). Limited small-scale structure may be inferred from only three, or fewer, viewpoints and the work presented here is done so with the aim of establishing techniques for observing CMEs with upcoming and future HI-like technology. We use simulated solar wind densities to compute realistic white-light HI observations, with which we explore the requirements of such instruments for determining solar wind plasma density structure via tomography. We exploit this information to investigate the optimal orbital characteristics, such as spacecraft number, separation, inclination and eccentricity, necessary to perform the technique with HIs. Further to this we argue that tomography may be greatly enhanced by means of improved instrumentation; specifically, the use of wide-angle imagers capable of measuring polarised light. This work has obvious space weather applications, serving as a demonstration for potential future missions (such as at L1 and L5) and will prove timely in fully exploiting the science return from the upcoming Solar Orbiter and Parker Solar Probe missions.
INFERRING THE MAGNETOHYDRODYNAMIC STRUCTURE OF SOLAR FLARE SUPRA-ARCADE PLASMAS FROM A DATA-ASSIMILATED FIELD TRANSPORT MODEL

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

Scott, Roger B.; McKenzie, David E.; Longcope, Dana W.

2016-03-01

Supra-arcade fans are highly dynamic structures that form in the region above post-reconnection flare arcades. In these features the plasma density and temperature evolve on the scale of a few seconds, despite the much slower dynamics of the underlying arcade. Further, the motion of supra-arcade plasma plumes appears to be inconsistent with the low-beta conditions that are often assumed to exist in the solar corona. In order to understand the nature of these highly debated structures, it is, therefore, important to investigate the interplay of the magnetic field with the plasma. Here we present a technique for inferring the underlying magnetohydrodynamicmore » processes that might lead to the types of motions seen in supra-arcade structures. Taking as a case study the 2011 October 22 event, we begin with extreme-ultraviolet observations and develop a time-dependent velocity field that is consistent with both continuity and local correlation tracking. We then assimilate this velocity field into a simplified magnetohydrodynamic simulation, which deals simultaneously with regions of high and low signal-to-noise ratio, thereby allowing the magnetic field to evolve self-consistently with the fluid. Ultimately, we extract the missing contributions from the momentum equation in order to estimate the relative strength of the various forcing terms. In this way we are able to make estimates of the plasma beta, as well as predict the spectral character and total power of Alfvén waves radiated from the supra-arcade region.« less
Ionospheric Scintillation Explorer (ISX)

NASA Astrophysics Data System (ADS)

Iuliano, J.; Bahcivan, H.

2015-12-01

NSF has recently selected Ionospheric Scintillation Explorer (ISX), a 3U Cubesat mission to explore the three-dimensional structure of scintillation-scale ionospheric irregularities associated with Equatorial Spread F (ESF). ISX is a collaborative effort between SRI International and Cal Poly. This project addresses the science question: To what distance along a flux tube does an irregularity of certain transverse-scale extend? It has been difficult to measure the magnetic field-alignment of scintillation-scale turbulent structures because of the difficulty of sampling a flux tube at multiple locations within a short time. This measurement is now possible due to the worldwide transition to DTV, which presents unique signals of opportunity for remote sensing of ionospheric irregularities from numerous vantage points. DTV spectra, in various formats, contain phase-stable, narrowband pilot carrier components that are transmitted simultaneously. A 4-channel radar receiver will simultaneously record up to 4 spatially separated transmissions from the ground. Correlations of amplitude and phase scintillation patterns corresponding to multiple points on the same flux tube will be a measure of the spatial extent of the structures along the magnetic field. A subset of geometries where two or more transmitters are aligned with the orbital path will be used to infer the temporal development of the structures. ISX has the following broad impact. Scintillation of space-based radio signals is a space weather problem that is intensively studied. ISX is a step toward a CubeSat constellation to monitor worldwide TEC variations and radio wave distortions on thousands of ionospheric paths. Furthermore, the rapid sampling along spacecraft orbits provides a unique dataset to deterministically reconstruct ionospheric irregularities at scintillation-scale resolution using diffraction radio tomography, a technique that enables prediction of scintillations at other radio frequencies, and potentially, mitigation of phase distortions.
Inference of Expanded Lrp-Like Feast/Famine Transcription Factor Targets in a Non-Model Organism Using Protein Structure-Based Prediction

PubMed Central

Ashworth, Justin; Plaisier, Christopher L.; Lo, Fang Yin; Reiss, David J.; Baliga, Nitin S.

2014-01-01

Widespread microbial genome sequencing presents an opportunity to understand the gene regulatory networks of non-model organisms. This requires knowledge of the binding sites for transcription factors whose DNA-binding properties are unknown or difficult to infer. We adapted a protein structure-based method to predict the specificities and putative regulons of homologous transcription factors across diverse species. As a proof-of-concept we predicted the specificities and transcriptional target genes of divergent archaeal feast/famine regulatory proteins, several of which are encoded in the genome of Halobacterium salinarum. This was validated by comparison to experimentally determined specificities for transcription factors in distantly related extremophiles, chromatin immunoprecipitation experiments, and cis-regulatory sequence conservation across eighteen related species of halobacteria. Through this analysis we were able to infer that Halobacterium salinarum employs a divergent local trans-regulatory strategy to regulate genes (carA and carB) involved in arginine and pyrimidine metabolism, whereas Escherichia coli employs an operon. The prediction of gene regulatory binding sites using structure-based methods is useful for the inference of gene regulatory relationships in new species that are otherwise difficult to infer. PMID:25255272
Inference of expanded Lrp-like feast/famine transcription factor targets in a non-model organism using protein structure-based prediction.

PubMed

Ashworth, Justin; Plaisier, Christopher L; Lo, Fang Yin; Reiss, David J; Baliga, Nitin S

2014-01-01

Widespread microbial genome sequencing presents an opportunity to understand the gene regulatory networks of non-model organisms. This requires knowledge of the binding sites for transcription factors whose DNA-binding properties are unknown or difficult to infer. We adapted a protein structure-based method to predict the specificities and putative regulons of homologous transcription factors across diverse species. As a proof-of-concept we predicted the specificities and transcriptional target genes of divergent archaeal feast/famine regulatory proteins, several of which are encoded in the genome of Halobacterium salinarum. This was validated by comparison to experimentally determined specificities for transcription factors in distantly related extremophiles, chromatin immunoprecipitation experiments, and cis-regulatory sequence conservation across eighteen related species of halobacteria. Through this analysis we were able to infer that Halobacterium salinarum employs a divergent local trans-regulatory strategy to regulate genes (carA and carB) involved in arginine and pyrimidine metabolism, whereas Escherichia coli employs an operon. The prediction of gene regulatory binding sites using structure-based methods is useful for the inference of gene regulatory relationships in new species that are otherwise difficult to infer.
Diurnal Transcriptome and Gene Network Represented through Sparse Modeling in Brachypodium distachyon.

PubMed

Koda, Satoru; Onda, Yoshihiko; Matsui, Hidetoshi; Takahagi, Kotaro; Yamaguchi-Uehara, Yukiko; Shimizu, Minami; Inoue, Komaki; Yoshida, Takuhiro; Sakurai, Tetsuya; Honda, Hiroshi; Eguchi, Shinto; Nishii, Ryuei; Mochida, Keiichi

2017-01-01

We report the comprehensive identification of periodic genes and their network inference, based on a gene co-expression analysis and an Auto-Regressive eXogenous (ARX) model with a group smoothly clipped absolute deviation (SCAD) method using a time-series transcriptome dataset in a model grass, Brachypodium distachyon . To reveal the diurnal changes in the transcriptome in B. distachyon , we performed RNA-seq analysis of its leaves sampled through a diurnal cycle of over 48 h at 4 h intervals using three biological replications, and identified 3,621 periodic genes through our wavelet analysis. The expression data are feasible to infer network sparsity based on ARX models. We found that genes involved in biological processes such as transcriptional regulation, protein degradation, and post-transcriptional modification and photosynthesis are significantly enriched in the periodic genes, suggesting that these processes might be regulated by circadian rhythm in B. distachyon . On the basis of the time-series expression patterns of the periodic genes, we constructed a chronological gene co-expression network and identified putative transcription factors encoding genes that might be involved in the time-specific regulatory transcriptional network. Moreover, we inferred a transcriptional network composed of the periodic genes in B. distachyon , aiming to identify genes associated with other genes through variable selection by grouping time points for each gene. Based on the ARX model with the group SCAD regularization using our time-series expression datasets of the periodic genes, we constructed gene networks and found that the networks represent typical scale-free structure. Our findings demonstrate that the diurnal changes in the transcriptome in B. distachyon leaves have a sparse network structure, demonstrating the spatiotemporal gene regulatory network over the cyclic phase transitions in B. distachyon diurnal growth.
Record of continental to marine transition from the Mesoproterozoic Ampani basin, Central India: An exercise of process-based sedimentology in a structurally deformed basin

NASA Astrophysics Data System (ADS)

Chakraborty, Partha Pratim; Saha, Subhojit; Das, Kaushik

2017-08-01

The Mesoproterozoic Ampani Group of rocks, a structurally deformed sedimentary package hosted within the Bastar Craton in central India, was studied for process-based facies and paleoenvironmental analyses. Outcrop mapping on 1:1500 scale, deconvolution of deformation pattern, and process-based facies analyses have led to the identification of fifteen facies types, clubbed under four facies associations. A range of paleoenvironmental settings varying from continental fluvial to distal marine shelf is inferred. Deductive paleohydrology revealed poorly-efficient 'dirty river' character for the Ampani River system with low water discharge. However, at times of catastrophic sheet floods release of sediments trapped at the river mouth in form of hyperpycnal underflows triggered formation of river mouth delta. Reworking of delta front sediment in wave-dominated coastline resulted development of beach-foreshore and shoreface (proximal to distal). Variation in the relative proportion of bar and interbar products within the shoreface successions exposed at different studied sections is interpreted as signature of relative bathymetric variation. The pro-deltaic Ampani shelf was storm infested. Tectonic perturbance in the basin hinterland in course of Ampani sedimentation is inferred from occurrence of a disparately thick lobate high-density flow deposit towards the top of shoreface succession and increase in feldspar content upward within the shoreface succession. Addition of detritus from a ∼1600 Ma Mesoproterozoic provenance in upper part of shoreface also strengthen the contention. Deconvolution of deformation pattern and delineation of environmental products ranging between continental and deep marine allowed us to infer the Ampani sediment package as fining-upward in character evolved in a transgressive mode.
Modeling urban air pollution with optimized hierarchical fuzzy inference system.

PubMed

Tashayo, Behnam; Alimohammadi, Abbas

2016-10-01

Environmental exposure assessments (EEA) and epidemiological studies require urban air pollution models with appropriate spatial and temporal resolutions. Uncertain available data and inflexible models can limit air pollution modeling techniques, particularly in under developing countries. This paper develops a hierarchical fuzzy inference system (HFIS) to model air pollution under different land use, transportation, and meteorological conditions. To improve performance, the system treats the issue as a large-scale and high-dimensional problem and develops the proposed model using a three-step approach. In the first step, a geospatial information system (GIS) and probabilistic methods are used to preprocess the data. In the second step, a hierarchical structure is generated based on the problem. In the third step, the accuracy and complexity of the model are simultaneously optimized with a multiple objective particle swarm optimization (MOPSO) algorithm. We examine the capabilities of the proposed model for predicting daily and annual mean PM2.5 and NO2 and compare the accuracy of the results with representative models from existing literature. The benefits provided by the model features, including probabilistic preprocessing, multi-objective optimization, and hierarchical structure, are precisely evaluated by comparing five different consecutive models in terms of accuracy and complexity criteria. Fivefold cross validation is used to assess the performance of the generated models. The respective average RMSEs and coefficients of determination (R (2)) for the test datasets using proposed model are as follows: daily PM2.5 = (8.13, 0.78), annual mean PM2.5 = (4.96, 0.80), daily NO2 = (5.63, 0.79), and annual mean NO2 = (2.89, 0.83). The obtained results demonstrate that the developed hierarchical fuzzy inference system can be utilized for modeling air pollution in EEA and epidemiological studies.
Bayesian estimation of the transmissivity spatial structure from pumping test data

NASA Astrophysics Data System (ADS)

Demir, Mehmet Taner; Copty, Nadim K.; Trinchero, Paolo; Sanchez-Vila, Xavier

2017-06-01

Estimating the statistical parameters (mean, variance, and integral scale) that define the spatial structure of the transmissivity or hydraulic conductivity fields is a fundamental step for the accurate prediction of subsurface flow and contaminant transport. In practice, the determination of the spatial structure is a challenge because of spatial heterogeneity and data scarcity. In this paper, we describe a novel approach that uses time drawdown data from multiple pumping tests to determine the transmissivity statistical spatial structure. The method builds on the pumping test interpretation procedure of Copty et al. (2011) (Continuous Derivation method, CD), which uses the time-drawdown data and its time derivative to estimate apparent transmissivity values as a function of radial distance from the pumping well. A Bayesian approach is then used to infer the statistical parameters of the transmissivity field by combining prior information about the parameters and the likelihood function expressed in terms of radially-dependent apparent transmissivities determined from pumping tests. A major advantage of the proposed Bayesian approach is that the likelihood function is readily determined from randomly generated multiple realizations of the transmissivity field, without the need to solve the groundwater flow equation. Applying the method to synthetically-generated pumping test data, we demonstrate that, through a relatively simple procedure, information on the spatial structure of the transmissivity may be inferred from pumping tests data. It is also shown that the prior parameter distribution has a significant influence on the estimation procedure, given the non-uniqueness of the estimation procedure. Results also indicate that the reliability of the estimated transmissivity statistical parameters increases with the number of available pumping tests.
Radiation breakage of DNA: a model based on random-walk chromatin structure

NASA Technical Reports Server (NTRS)

Ponomarev, A. L.; Sachs, R. K.

2001-01-01

Monte Carlo computer software, called DNAbreak, has recently been developed to analyze observed non-random clustering of DNA double strand breaks in chromatin after exposure to densely ionizing radiation. The software models coarse-grained configurations of chromatin and radiation tracks, small-scale details being suppressed in order to obtain statistical results for larger scales, up to the size of a whole chromosome. We here give an analytic counterpart of the numerical model, useful for benchmarks, for elucidating the numerical results, for analyzing the assumptions of a more general but less mechanistic "randomly-located-clusters" formalism, and, potentially, for speeding up the calculations. The equations characterize multi-track DNA fragment-size distributions in terms of one-track action; an important step in extrapolating high-dose laboratory results to the much lower doses of main interest in environmental or occupational risk estimation. The approach can utilize the experimental information on DNA fragment-size distributions to draw inferences about large-scale chromatin geometry during cell-cycle interphase.

Visual completion from 2D cross-sections: Implications for visual theory and STEM education and practice.

PubMed

Gagnier, Kristin Michod; Shipley, Thomas F

2016-01-01

Accurately inferring three-dimensional (3D) structure from only a cross-section through that structure is not possible. However, many observers seem to be unaware of this fact. We present evidence for a 3D amodal completion process that may explain this phenomenon and provide new insights into how the perceptual system processes 3D structures. Across four experiments, observers viewed cross-sections of common objects and reported whether regions visible on the surface extended into the object. If they reported that the region extended, they were asked to indicate the orientation of extension or that the 3D shape was unknowable from the cross-section. Across Experiments 1, 2, and 3, participants frequently inferred 3D forms from surface views, showing a specific prior to report that regions in the cross-section extend straight back into the object, with little variance in orientation. In Experiment 3, we examined whether 3D visual inferences made from cross-sections are similar to other cases of amodal completion by examining how the inferences were influenced by observers' knowledge of the objects. Finally, in Experiment 4, we demonstrate that these systematic visual inferences are unlikely to result from demand characteristics or response biases. We argue that these 3D visual inferences have been largely unrecognized by the perception community, and have implications for models of 3D visual completion and science education.
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.
Can measurements of 2HDM parameters provide hints for high scale supersymmetry?

NASA Astrophysics Data System (ADS)

Bhattacharyya, Gautam; Das, Dipankar; Pérez, M. Jay; Saha, Ipsita; Santamaria, Arcadi; Vives, Oscar

2018-05-01

Two-Higgs-doublet models (2HDMs) are minimal extensions of the Standard Model (SM) that may still be discovered at the LHC. The quartic couplings of their potentials can be determined from the measurement of the masses and branching ratios of their extended scalar sectors. We show that the evolution of these couplings through renormalization group equations can determine whether the observed 2HDM is a low energy manifestation of a more fundamental theory, as for instance, supersymmetry, which fixes the quartic couplings in terms of the gauge couplings. At leading order, the minimal supersymmetric extension of the SM (MSSM) dictates all the quartic couplings, which can be translated into a predictive structure for the scalar masses and mixings at the weak scale. Running these couplings to higher scales, one can check if they converge to their MSSM values, and more interestingly, whether one can infer the supersymmetry breaking scale. Although we study this question in the context of supersymmetry, this strategy could be applied to any theory whose ultraviolet completion unambiguously predicts all scalar quartic couplings.
Towards an eco-phylogenetic framework for infectious disease ecology.

PubMed

Fountain-Jones, Nicholas M; Pearse, William D; Escobar, Luis E; Alba-Casals, Ana; Carver, Scott; Davies, T Jonathan; Kraberger, Simona; Papeş, Monica; Vandegrift, Kurt; Worsley-Tonks, Katherine; Craft, Meggan E

2018-05-01

Identifying patterns and drivers of infectious disease dynamics across multiple scales is a fundamental challenge for modern science. There is growing awareness that it is necessary to incorporate multi-host and/or multi-parasite interactions to understand and predict current and future disease threats better, and new tools are needed to help address this task. Eco-phylogenetics (phylogenetic community ecology) provides one avenue for exploring multi-host multi-parasite systems, yet the incorporation of eco-phylogenetic concepts and methods into studies of host pathogen dynamics has lagged behind. Eco-phylogenetics is a transformative approach that uses evolutionary history to infer present-day dynamics. Here, we present an eco-phylogenetic framework to reveal insights into parasite communities and infectious disease dynamics across spatial and temporal scales. We illustrate how eco-phylogenetic methods can help untangle the mechanisms of host-parasite dynamics from individual (e.g. co-infection) to landscape scales (e.g. parasite/host community structure). An improved ecological understanding of multi-host and multi-pathogen dynamics across scales will increase our ability to predict disease threats. © 2017 Cambridge Philosophical Society.
Aspects of turbulent-shear-layer dynamics and mixing

NASA Astrophysics Data System (ADS)

Slessor, Michael David

Experiments have been conducted in the GALCIT Supersonic Shear Layer Facility to investigate some aspects of high-Reynolds-number, turbulent, shear-layer flows in both incompressible- and compressible-flow regimes. Experiments designed to address several issues were performed; effects of inflow boundary conditions, freestream conditions (supersonic/subsonic flow), and compressibility, on both large-scale dynamics and small-scale mixing, are described. Chemically-reacting and non-reacting flows were investigated, the former relying on the (Hsb2 + NO)/Fsb2 chemical system, in the fast-kinetic regime, to infer the structure and amount of molecular-scale mixing through use of "flip" experiments. A variety of experimental techniques, including a color-schlieren visualization system developed as part of this work, were used to study the flows. Both inflow conditions and compressibility are found to have significant effects on the flow. In particular, inflow conditions are "remembered" for long distances downstream, a sensitivity similar to that observed in low-dimensionality, non-linear (chaotic) systems. The global flowfields (freestreams coupled by the shear layer) of transonic flows exhibit a sensitivity to imposed boundary conditions, a.e., local area ratios. A previously-proposed mode-selection rule for turbulent-structure convection speeds, based on the presence of a lab-frame subsonic freestream, was experimentally demonstrated to be incorrect. Compressibility, when decoupled from ail other parameters, e.g., Reynolds number, velocity and density ratios, etc., reduces large-scale entrainment and turbulent growth, but slightly enhances small-scale mixing, with an associated change in the structure of the molecularly-mixed fluid. This reduction in shear-layer growth rate is examined and a new parameter that interprets compressibility as an energy-exchange mechanism is proposed. The parameter reconciles and collapses experimentally-observed growth rates.
Global scale stratospheric processes as measured by the infrasound IMS network

NASA Astrophysics Data System (ADS)

Le Pichon, A.; Ceranna, L.; Kechut, P.

2012-12-01

IMS infrasound array data are routinely processed at the International Data Center (IDC). The wave parameters of the detected signals are estimated with the Progressive Multi-Channel Correlation method (PMCC). We have processed continuous recordings from 41 certified IMS stations from 2005 to 2010 in the 0.01-5 Hz frequency band using a new implementation of the PMCC algorithm. Microbaroms are the dominant source of signals near-continuously and globally detected. The observed azimuthal seasonal trend correlates well with the variation of the effective sound speed ratio (Veff-ratio) which is a proxy for the combined effects of refraction due to sound speed gradients and advection due to along-path stratospheric wind on infrasound propagation. Systematic correlations between infrasound parameters (e.g. number of detections, amplitude) and Veff-ratio calculated at different ranges of altitudes are performed. Combined with propagation modeling, we show that such an analysis enables a characterization of the wind and temperature structure above the stratosphere and may provide detailed information on upper atmospheric processes (e.g., large-scale planetary waves, stratospheric warming effects) from the seasonal trend to short time scale variability. We discuss the potential benefit of long-term infrasound monitoring to infer stratospheric processes for the first time on a global scale. This study suggests poorly resolved stratospheric wind fluctuations at low latitude regions with strengths of horizontal wind structures underestimated by at least ~10 m/s. It is expected that this correlation between infrasound observations and the state-of-the-art atmospheric specifications will allow to statistically quantify the spatial and temporal resolutions of the wind structures at different ranges of altitudes, latitudes and time scales.
Missing data imputation and haplotype phase inference for genome-wide association studies

PubMed Central

Browning, Sharon R.

2009-01-01

Imputation of missing data and the use of haplotype-based association tests can improve the power of genome-wide association studies (GWAS). In this article, I review methods for haplotype inference and missing data imputation, and discuss their application to GWAS. I discuss common features of the best algorithms for haplotype phase inference and missing data imputation in large-scale data sets, as well as some important differences between classes of methods, and highlight the methods that provide the highest accuracy and fastest computational performance. PMID:18850115
Infrasound data inversion for atmospheric sounding

NASA Astrophysics Data System (ADS)

Lalande, J.-M.; Sèbe, O.; Landès, M.; Blanc-Benon, Ph.; Matoza, R. S.; Le Pichon, A.; Blanc, E.

2012-07-01

The International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) continuously records acoustic waves in the 0.01-10 Hz frequency band, known as infrasound. These waves propagate through the layered structure of the atmosphere. Coherent infrasonic waves are produced by a variety of anthropogenic and natural sources and their propagation is controlled by spatiotemporal variations of temperature and wind velocity. Natural stratification of atmospheric properties (e.g. temperature, density and winds) forms waveguides, allowing long-range propagation of infrasound waves. However, atmospheric specifications used in infrasound propagation modelling suffer from lack and sparsity of available data above an altitude of 50 km. As infrasound can propagate in the upper atmosphere up to 120 km, we assume that infrasonic data could be used for sounding the atmosphere, analogous to the use of seismic data to infer solid Earth structure and the use of hydroacoustic data to infer oceanic structure. We therefore develop an inversion scheme for vertical atmospheric wind profiles in the framework of an iterative linear inversion. The forward problem is treated in the high-frequency approximation using a Hamiltonian formulation and complete first-order ray perturbation theory is developed to construct the Fréchet derivatives matrix. We introduce a specific parametrization for the unknown model parameters based on Principal Component Analysis. Finally, our algorithm is tested on synthetic data cases spanning different seasonal periods and network configurations. The results show that our approach is suitable for infrasound atmospheric sounding on a regional scale.
Flexural analysis of uplifted rift flanks on Venus

NASA Technical Reports Server (NTRS)

Evans, Susan A.; Simons, Mark; Solomon, Sean C.

1992-01-01

Knowledge of the thermal structure of a planet is vital to a thorough understanding of its general scheme of tectonics. Since no direct measurements of heat flow or thermal gradient are available for Venus, most estimates have been derived from theoretical considerations or by analog with the Earth. The flexural response of the lithosphere to applied loads is sensitive to regional thermal structure. Under the assumption that the yield strength as a function of depth can be specified, the temperature gradient can be inferred from the effective elastic plate thickness. Previous estimates of the effective elastic plate thickness of Venus range from 11-18 km for the foredeep north of Uorsar Rupes to 30-60 km for the annular troughs around several coronae. Thermal gradients inferred for these regions are 14-23 K km(exp -1) and 4-9 K km(exp -1) respectively. In this study, we apply the same techniques to investigate the uplifted flanks of an extensional rift. Hypotheses for the origin of uplifted rift flanks on Earth include lateral transport of heat from the center of the rift, vertical transport of heat by small-scale convection, differential thinning of the lithosphere, dynamical uplift, and isostatic response to mechanical uploading of the lithosphere. The 1st hypothesis is considered the dominant contributor to terrestrial rift flanks lacking evidence for volcanic activity, particularly for rift structures that are no longer active. In this study, we model the uplifted flanks of a venusian rift as the flexural response to a vertical end load.
Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing.

PubMed

Pajak, Bozena; Fine, Alex B; Kleinschmidt, Dave F; Jaeger, T Florian

2016-12-01

We present a framework of second and additional language (L2/L n ) acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/L n learning uses the same type of socio-indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy, and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/L n acquisition as probabilistic inference under uncertainty over socio-indexical structure. It also offers a new perspective on crosslinguistic influences during L2/L n learning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa.
Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing

PubMed Central

Pajak, Bozena; Fine, Alex B.; Kleinschmidt, Dave F.; Jaeger, T. Florian

2015-01-01

We present a framework of second and additional language (L2/Ln) acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/Ln learning uses the same type of socio-indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy, and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/Ln acquisition as probabilistic inference under uncertainty over socio-indexical structure. It also offers a new perspective on crosslinguistic influences during L2/Ln learning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa. PMID:28348442
Developing JSequitur to Study the Hierarchical Structure of Biological Sequences in a Grammatical Inference Framework of String Compression Algorithms.

PubMed

Galbadrakh, Bulgan; Lee, Kyung-Eun; Park, Hyun-Seok

2012-12-01

Grammatical inference methods are expected to find grammatical structures hidden in biological sequences. One hopes that studies of grammar serve as an appropriate tool for theory formation. Thus, we have developed JSequitur for automatically generating the grammatical structure of biological sequences in an inference framework of string compression algorithms. Our original motivation was to find any grammatical traits of several cancer genes that can be detected by string compression algorithms. Through this research, we could not find any meaningful unique traits of the cancer genes yet, but we could observe some interesting traits in regards to the relationship among gene length, similarity of sequences, the patterns of the generated grammar, and compression rate.
3-D S-velocity structure in the lowermost mantle beneath the Northern Pacific

NASA Astrophysics Data System (ADS)

Suzuki, Y.; Kawai, K.; Geller, R. J.; Borgeaud, A. F. E.; Konishi, K.

2017-12-01

We previously (Suzuki et al., EPS, 2016) reported the results of waveform inversion to infer the three-dimensional (3-D) S-velocity structure in the lowermost 400 km of the mantle (the Dʺ region) beneath the Northern Pacific region. Our dataset consists of about 20,000 transverse component broadband body-wave seismograms observed at North American stations (mainly USArray) for 131 intermediate and deep earthquakes which occurred beneath the western Pacific subduction region. Synthetic resolution tests indicate that our methods and dataset can resolve the velocity structure in the target region with a horizontal scale of about 150 km and a vertical scale of about 50 km. The 3-D S-velocity model obtained in that study shows three prominent features: (i) horizontal high-velocity anomalies up to about 3 per cent faster than the Preliminary Reference Earth Model (PREM) with a thickness of a few hundred km and a lower boundary which is at most about 150 km above the core-mantle boundary (CMB), (ii) low-velocity anomalies about 2.5 per cent slower than PREM beneath the high-velocity anomalies at the base of the lower mantle, (iii) a thin (about 150 km) low-velocity structure continuous from the base of the low-velocity zone to at least 400 km above the CMB. We interpret these features respectively as: (i) remnants of slab material where the Mg-perovskite to Mg-post-perovskite phase transition could have occurred within the slab, (ii, iii) large amounts of hot and less dense materials beneath the cold Kula or Pacific slab remnants immediately above the CMB which ascend and form a passive plume upwelling at the edge of the slab remnants. Since our initial work we subsequently conducted waveform inversion using both the transverse- and radial-component horizontal waveform data to infer the isotropic shear velocity structure in the lowermost mantle beneath the Northern Pacific in more detail. We also compute partial derivatives with respect to the 5 independent elastic constants (A, C, F, L, N) of a transversely isotropy (TI) medium, and conduct a synthetic resolution test to examine the ability of our methods and dataset to resolve the anisotropic structure in this region using two-component waveform data.
Reliability of Multi-Category Rating Scales

ERIC Educational Resources Information Center

Parker, Richard I.; Vannest, Kimberly J.; Davis, John L.

2013-01-01

The use of multi-category scales is increasing for the monitoring of IEP goals, classroom and school rules, and Behavior Improvement Plans (BIPs). Although they require greater inference than traditional data counting, little is known about the inter-rater reliability of these scales. This simulation study examined the performance of nine…
Computational Cosmology at the Bleeding Edge

NASA Astrophysics Data System (ADS)

Habib, Salman

2013-04-01

Large-area sky surveys are providing a wealth of cosmological information to address the mysteries of dark energy and dark matter. Observational probes based on tracking the formation of cosmic structure are essential to this effort, and rely crucially on N-body simulations that solve the Vlasov-Poisson equation in an expanding Universe. As statistical errors from survey observations continue to shrink, and cosmological probes increase in number and complexity, simulations are entering a new regime in their use as tools for scientific inference. Changes in supercomputer architectures provide another rationale for developing new parallel simulation and analysis capabilities that can scale to computational concurrency levels measured in the millions to billions. In this talk I will outline the motivations behind the development of the HACC (Hardware/Hybrid Accelerated Cosmology Code) extreme-scale cosmological simulation framework and describe its essential features. By exploiting a novel algorithmic structure that allows flexible tuning across diverse computer architectures, including accelerated and many-core systems, HACC has attained a performance of 14 PFlops on the IBM BG/Q Sequoia system at 69% of peak, using more than 1.5 million cores.
Cosmological constraint on the light gravitino mass from CMB lensing and cosmic shear

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

Osato, Ken; Yoshida, Naoki; Sekiguchi, Toyokazu

2016-06-01

Light gravitinos of mass ∼< O (10) eV are of particular interest in cosmology, offering various baryogenesis scenarios without suffering from the cosmological gravitino problem. The gravitino may contribute considerably to the total matter content of the Universe and affect structure formation from early to present epochs. After the gravitinos decouple from other particles in the early Universe, they free-stream and consequently suppress density fluctuations of (sub-)galactic length scales. Observations of structure at the relevant length-scales can be used to infer or constrain the mass and the abundance of light gravitinos. We derive constraints on the light gravitino mass usingmore » the data of cosmic microwave background (CMB) lensing from Planck and of cosmic shear from the Canada France Hawaii Lensing Survey survey, combined with analyses of the primary CMB anisotropies and the signature of baryon acoustic oscillations in galaxy distributions. The obtained constraint on the gravitino mass is m {sub 3/2} < 4.7 eV (95 % C.L.), which is substantially tighter than the previous constraint from clustering analysis of Ly-α forests.« less
Polarity, cell division, and out-of-equilibrium dynamics control the growth of epithelial structures

PubMed Central

Cerruti, Benedetta; Puliafito, Alberto; Shewan, Annette M.; Yu, Wei; Combes, Alexander N.; Little, Melissa H.; Chianale, Federica; Primo, Luca; Serini, Guido; Mostov, Keith E.; Celani, Antonio

2013-01-01

The growth of a well-formed epithelial structure is governed by mechanical constraints, cellular apico-basal polarity, and spatially controlled cell division. Here we compared the predictions of a mathematical model of epithelial growth with the morphological analysis of 3D epithelial structures. In both in vitro cyst models and in developing epithelial structures in vivo, epithelial growth could take place close to or far from mechanical equilibrium, and was determined by the hierarchy of time-scales of cell division, cell–cell rearrangements, and lumen dynamics. Equilibrium properties could be inferred by the analysis of cell–cell contact topologies, and the nonequilibrium phenotype was altered by inhibiting ROCK activity. The occurrence of an aberrant multilumen phenotype was linked to fast nonequilibrium growth, even when geometric control of cell division was correctly enforced. We predicted and verified experimentally that slowing down cell division partially rescued a multilumen phenotype induced by altered polarity. These results improve our understanding of the development of epithelial organs and, ultimately, of carcinogenesis. PMID:24145168
Velocity Structure of the Subducted Yakutat Terrane, Alaska: Insights from Guided Waves

NASA Astrophysics Data System (ADS)

Coulson, S.; Garth, T.; Rietbrock, A.

2017-12-01

Subduction zone guided wave arrivals from intermediate depth earthquakes provide insight into the fine scale velocity structure of the subducting oceanic crust as it dehydrates. These observations can be used to determine the average velocity and thickness of the crustal low velocity layer (LVL) at depth, allowing inferences to be drawn about composition and degree of hydration. We constrain guided wave dispersion by comparing waveforms recorded in the subduction forearc with simulated waveforms, produced using a 2D finite difference waveform propagation model. The structure of the Aleutian arc is complex due to the accretion of the Yakutat Terrane (YT) to the east, which is partially coupled with the subducting Pacific plate. An unusually thick LVL associated with the YT has been inferred down to 140 km depth by receiver function studies and travel time tomography. Focussing on a profile running NNW-SSE close to Anchorage, we constrain slab geometry using global and local catalogues, as well as the curvature inferred from receiver functions (Kim et al., 2014). P-wave arrivals from 41 earthquakes (2012-2015) show significant guided wave dispersion on at least one station; high frequency (>1-3 Hz) energy is delayed by up to 2-3 seconds. Choosing the clearest dispersion observations, we systematically vary both LVL width and P-wave velocity, to find the lowest misfit between the observed and synthetic waveforms. Multiple modelled events show the thickness of the LVL associated with subducted YT to be 6-10 km, significantly thinner than inferred by receiver function studies. Most events are accounted for by an LVL velocity contrast of 12.5-15% with overriding mantle material, however, observations of the deepest event in the northern corner of the YT require a velocity contrast of 6%. Lower velocities in the shallower slab (70-120 km) cannot be accounted for by reacted or unreacted MORB or gabbro compositions. We postulate the presence of interbedded sediments within the YT reducing the bulk velocity of the LVL. Increased velocities seen at the northern edge of the YT can be explained by reacted MORB or gabbro assemblages. This may be explained by a lack of interbedded sediments in this part of the YT, or the warmer conditions at the edge of the subducted terrane causing a faster pace of metamorphic reaction in this part of the slab.
Breaking Ground on the Moon and Mars: Reconstructing Lunar Tectonic Evolution and Martian Central Pit Crater Formation

NASA Astrophysics Data System (ADS)

Williams, Nathan Robert

Understanding the structural evolution of planetary surfaces provides key insights to their physical properties and processes. On the Moon, large-scale tectonism was thought to have ended over a billion years ago. However, new Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) high resolution images show the Moon's surface in unprecedented detail and show many previously unidentified tectonic landforms, forcing a re-assessment of our views of lunar tectonism. I mapped lobate scarps, wrinkle ridges, and graben across Mare Frigoris -- selected as a type area due to its excellent imaging conditions, abundance of tectonic landforms, and range of inferred structural controls. The distribution, morphology, and crosscutting relationships of these newly identified populations of tectonic landforms imply a more complex and longer-lasting history of deformation that continues to today. I also performed additional numerical modeling of lobate scarp structures that indicates the upper kilometer of the lunar surface has experienced 3.5-18.6 MPa of differential stress in the recent past, likely due to global compression from radial thermal contraction. Central pit craters on Mars are another instance of intriguing structures that probe subsurface physical properties. These kilometer-scale pits are nested in the centers of many impact craters on Mars as well as on icy satellites. They are inferred to form in the presence of a water-ice rich substrate; however, the process(es) responsible for their formation is still debated. Previous models invoke origins by either explosive excavation of potentially water-bearing crustal material, or by subsurface drainage of meltwater and/or collapse. I assessed radial trends in grain size around central pits using thermal inertias calculated from Thermal Emission Imaging System (THEMIS) thermal infrared images. Average grain size decreases with radial distance from pit rims -- consistent with pit-derived ejecta but not expected for collapse models. I present a melt-contact model that might enable a delayed explosion, in which a central uplift brings ice-bearing substrate into contact with impact melt to generate steam explosions and excavate central pits during the impact modification stage.
Trajectories of thermospheric air parcels flowing over Alaska, reconstructed from ground-based wind measurements

NASA Astrophysics Data System (ADS)

Dhadly, Manbharat; Conde, Mark

2017-06-01

It is widely presumed that the convective stability and enormous kinematic viscosity of Earth's upper thermosphere hinders development of both horizontal and vertical wind shears and other gradients. Any strong local structure (over scale sizes of several hundreds of kilometers) that might somehow form would be expected to dissipate rapidly. Air flow in such an atmosphere should be relatively simple, and transport effects only slowly disperse and mix air masses. However, our observations show that wind fields in Earth's thermosphere have much more local-scale structure than usually predicated by current modeling techniques, at least at auroral latitudes; they complicate air parcel trajectories enormously, relative to typical expectations. For tracing air parcels, we used wind measurements of an all-sky Scanning Doppler Fabry-Perot interferometer and reconstructed time-resolved two-dimensional maps of the horizontal vector wind field to infer forward and backward air parcel trajectories over time. This is the first comprehensive study to visualize the complex motions of thermospheric air parcels carried through the actual observed local-scale structures in the high-latitude winds. Results show that thermospheric air parcel transport is a very difficult observational problem, because the trajectories followed are very sensitive to the detailed features of the driving wind field. To reconstruct the actual motion of a given air parcel requires wind measurements everywhere along the trajectory followed, with spatial resolutions of 100 km or less, and temporal resolutions of a few minutes or better. Understanding such transport is important, for example, in predicting the global-scale impacts of aurorally generated composition perturbations.

Pacifiplex: an ancestry-informative SNP panel centred on Australia and the Pacific region.

PubMed

Santos, Carla; Phillips, Christopher; Fondevila, Manuel; Daniel, Runa; van Oorschot, Roland A H; Burchard, Esteban G; Schanfield, Moses S; Souto, Luis; Uacyisrael, Jolame; Via, Marc; Carracedo, Ángel; Lareu, Maria V

2016-01-01

The analysis of human population variation is an area of considerable interest in the forensic, medical genetics and anthropological fields. Several forensic single nucleotide polymorphism (SNP) assays provide ancestry-informative genotypes in sensitive tests designed to work with limited DNA samples, including a 34-SNP multiplex differentiating African, European and East Asian ancestries. Although assays capable of differentiating Oceanian ancestry at a global scale have become available, this study describes markers compiled specifically for differentiation of Oceanian populations. A sensitive multiplex assay, termed Pacifiplex, was developed and optimized in a small-scale test applicable to forensic analyses. The Pacifiplex assay comprises 29 ancestry-informative marker SNPs (AIM-SNPs) selected to complement the 34-plex test, that in a combined set distinguish Africans, Europeans, East Asians and Oceanians. Nine Pacific region study populations were genotyped with both SNP assays, then compared to four reference population groups from the HGDP-CEPH human diversity panel. STRUCTURE analyses estimated population cluster membership proportions that aligned with the patterns of variation suggested for each study population's currently inferred demographic histories. Aboriginal Taiwanese and Philippine samples indicated high East Asian ancestry components, Papua New Guinean and Aboriginal Australians samples were predominantly Oceanian, while other populations displayed cluster patterns explained by the distribution of divergence amongst Melanesians, Polynesians and Micronesians. Genotype data from Pacifiplex and 34-plex tests is particularly well suited to analysis of Australian Aboriginal populations and when combined with Y and mitochondrial DNA variation will provide a powerful set of markers for ancestry inference applied to modern Australian demographic profiles. On a broader geographic scale, Pacifiplex adds highly informative data for inferring the ancestry of individuals from Oceanian populations. The sensitivity of Pacifiplex enabled successful genotyping of population samples from 50-year-old serum samples obtained from several Oceanian regions that would otherwise be unlikely to produce useful population data. This indicates tests primarily developed for forensic ancestry analysis also provide an important contribution to studies of populations where useful samples are in limited supply. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Theory of Mind: An Overview and Behavioral Perspective

ERIC Educational Resources Information Center

Schlinger, Henry D., Jr.

2009-01-01

Theory of mind (ToM) refers to the ability of an individual to make inferences about what others may be thinking or feeling and to predict what they may do in a given situation based on those inferences. Discussions of ToM focus almost exclusively on inferred cognitive structures and processes and shed little light on the actual behaviors…
Processing Conversational Implicatures: Alternatives and Counterfactual Reasoning.

PubMed

van Tiel, Bob; Schaeken, Walter

2017-05-01

In a series of experiments, Bott and Noveck (2004) found that the computation of scalar inferences, a variety of conversational implicature, caused a delay in response times. In order to determine what aspect of the inferential process that underlies scalar inferences caused this delay, we extended their paradigm to three other kinds of inferences: free choice inferences, conditional perfection, and exhaustivity in "it"-clefts. In contrast to scalar inferences, the computation of these three kinds of inferences facilitated response times. Following a suggestion made by Chemla and Bott (2014), we propose that the time it takes to compute a conversational implicature depends on the structural characteristics of the required alternatives. Copyright © 2016 Cognitive Science Society, Inc.
A Priori Method of Using Photon Activation Analysis to Determine Unknown Trace Element Concentrations in NIST Standards

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

Green, Jaromy; Sun Zaijing; Wells, Doug

2009-03-10

Photon activation analysis detected elements in two NIST standards that did not have reported concentration values. A method is currently being developed to infer these concentrations by using scaling parameters and the appropriate known quantities within the NIST standard itself. Scaling parameters include: threshold, peak and endpoint energies; photo-nuclear cross sections for specific isotopes; Bremstrahlung spectrum; target thickness; and photon flux. Photo-nuclear cross sections and energies from the unknown elements must also be known. With these quantities, the same integral was performed for both the known and unknown elements resulting in an inference of the concentration of the un-reported elementmore » based on the reported value. Since Rb and Mn were elements that were reported in the standards, and because they had well-identified peaks, they were used as the standards of inference to determine concentrations of the unreported elements of As, I, Nb, Y, and Zr. This method was tested by choosing other known elements within the standards and inferring a value based on the stated procedure. The reported value of Mn in the first NIST standard was 403{+-}15 ppm and the reported value of Ca in the second NIST standard was 87000 ppm (no reported uncertainty). The inferred concentrations were 370{+-}23 ppm and 80200{+-}8700 ppm respectively.« less
Bayesian pedigree inference with small numbers of single nucleotide polymorphisms via a factor-graph representation.

PubMed

Anderson, Eric C; Ng, Thomas C

2016-02-01

We develop a computational framework for addressing pedigree inference problems using small numbers (80-400) of single nucleotide polymorphisms (SNPs). Our approach relaxes the assumptions, which are commonly made, that sampling is complete with respect to the pedigree and that there is no genotyping error. It relies on representing the inferred pedigree as a factor graph and invoking the Sum-Product algorithm to compute and store quantities that allow the joint probability of the data to be rapidly computed under a large class of rearrangements of the pedigree structure. This allows efficient MCMC sampling over the space of pedigrees, and, hence, Bayesian inference of pedigree structure. In this paper we restrict ourselves to inference of pedigrees without loops using SNPs assumed to be unlinked. We present the methodology in general for multigenerational inference, and we illustrate the method by applying it to the inference of full sibling groups in a large sample (n=1157) of Chinook salmon typed at 95 SNPs. The results show that our method provides a better point estimate and estimate of uncertainty than the currently best-available maximum-likelihood sibling reconstruction method. Extensions of this work to more complex scenarios are briefly discussed. Published by Elsevier Inc.
Influence of eye biometrics and corneal micro-structure on noncontact tonometry.

PubMed

Jesus, Danilo A; Majewska, Małgorzata; Krzyżanowska-Berkowska, Patrycja; Iskander, D Robert

2017-01-01

Tonometry is widely used as the main screening tool supporting glaucoma diagnosis. Still, its accuracy could be improved if full knowledge about the variation of the corneal biomechanical properties was available. In this study, Optical Coherence Tomography (OCT) speckle statistics are used to infer the organisation of the corneal micro-structure and hence, to analyse its influence on intraocular pressure (IOP) measurements. Fifty-six subjects were recruited for this prospective study. Macro and micro-structural corneal parameters as well as subject age were considered. Macro-structural analysis included the parameters that are associated with the ocular anatomy, such as central corneal thickness (CCT), corneal radius, axial length, anterior chamber depth and white-to-white corneal diameter. Micro-structural parameters which included OCT speckle statistics were related to the internal organisation of the corneal tissue and its physiological changes during lifetime. The corneal speckle obtained from OCT was modelled with the Generalised Gamma (GG) distribution that is characterised with a scale parameter and two shape parameters. In macro-structure analysis, only CCT showed a statistically significant correlation with IOP (R2 = 0.25, p<0.001). The scale parameter and the ratio of the shape parameters of GG distribution showed statistically significant correlation with IOP (R2 = 0.19, p<0.001 and R2 = 0.17, p<0.001, respectively). For the studied group, a weak, although significant correlation was found between age and IOP (R2 = 0.053, p = 0.04). Forward stepwise regression showed that CCT and the scale parameter of the Generalised Gamma distribution can be combined in a regression model (R2 = 0.39, p<0.001) to study the role of the corneal structure on IOP. We show, for the first time, that corneal micro-structure influences the IOP measurements obtained from noncontact tonometry. OCT speckle statistics can be employed to learn about the corneal micro-structure and hence, to further calibrate the IOP measurements.
Influence of eye biometrics and corneal micro-structure on noncontact tonometry

PubMed Central

Majewska, Małgorzata; Krzyżanowska-Berkowska, Patrycja; Iskander, D. Robert

2017-01-01

Purpose Tonometry is widely used as the main screening tool supporting glaucoma diagnosis. Still, its accuracy could be improved if full knowledge about the variation of the corneal biomechanical properties was available. In this study, Optical Coherence Tomography (OCT) speckle statistics are used to infer the organisation of the corneal micro-structure and hence, to analyse its influence on intraocular pressure (IOP) measurements. Methods Fifty-six subjects were recruited for this prospective study. Macro and micro-structural corneal parameters as well as subject age were considered. Macro-structural analysis included the parameters that are associated with the ocular anatomy, such as central corneal thickness (CCT), corneal radius, axial length, anterior chamber depth and white-to-white corneal diameter. Micro-structural parameters which included OCT speckle statistics were related to the internal organisation of the corneal tissue and its physiological changes during lifetime. The corneal speckle obtained from OCT was modelled with the Generalised Gamma (GG) distribution that is characterised with a scale parameter and two shape parameters. Results In macro-structure analysis, only CCT showed a statistically significant correlation with IOP (R2 = 0.25, p<0.001). The scale parameter and the ratio of the shape parameters of GG distribution showed statistically significant correlation with IOP (R2 = 0.19, p<0.001 and R2 = 0.17, p<0.001, respectively). For the studied group, a weak, although significant correlation was found between age and IOP (R2 = 0.053, p = 0.04). Forward stepwise regression showed that CCT and the scale parameter of the Generalised Gamma distribution can be combined in a regression model (R2 = 0.39, p<0.001) to study the role of the corneal structure on IOP. Conclusions We show, for the first time, that corneal micro-structure influences the IOP measurements obtained from noncontact tonometry. OCT speckle statistics can be employed to learn about the corneal micro-structure and hence, to further calibrate the IOP measurements. PMID:28472178
Models of earth structure inferred from neodymium and strontium isotopic abundances

PubMed Central

Wasserburg, G. J.; DePaolo, D. J.

1979-01-01

A simplified model of earth structure based on the Nd and Sr isotopic characteristics of oceanic and continental tholeiitic flood basalts is presented, taking into account the motion of crustal plates and a chemical balance for trace elements. The resulting structure that is inferred consists of a lower mantle that is still essentially undifferentiated, overlain by an upper mantle that is the residue of the original source from which the continents were derived. PMID:16592688
A comparison of algorithms for inference and learning in probabilistic graphical models.

PubMed

Frey, Brendan J; Jojic, Nebojsa

2005-09-01

Research into methods for reasoning under uncertainty is currently one of the most exciting areas of artificial intelligence, largely because it has recently become possible to record, store, and process large amounts of data. While impressive achievements have been made in pattern classification problems such as handwritten character recognition, face detection, speaker identification, and prediction of gene function, it is even more exciting that researchers are on the verge of introducing systems that can perform large-scale combinatorial analyses of data, decomposing the data into interacting components. For example, computational methods for automatic scene analysis are now emerging in the computer vision community. These methods decompose an input image into its constituent objects, lighting conditions, motion patterns, etc. Two of the main challenges are finding effective representations and models in specific applications and finding efficient algorithms for inference and learning in these models. In this paper, we advocate the use of graph-based probability models and their associated inference and learning algorithms. We review exact techniques and various approximate, computationally efficient techniques, including iterated conditional modes, the expectation maximization (EM) algorithm, Gibbs sampling, the mean field method, variational techniques, structured variational techniques and the sum-product algorithm ("loopy" belief propagation). We describe how each technique can be applied in a vision model of multiple, occluding objects and contrast the behaviors and performances of the techniques using a unifying cost function, free energy.
Inferring ontology graph structures using OWL reasoning.

PubMed

Rodríguez-García, Miguel Ángel; Hoehndorf, Robert

2018-01-05

Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies' semantic content remains a challenge. We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph . Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.
Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff

DOE PAGES

Chua, Xin-Yi; Buckingham, Lawrence; Hogan, James M.; ...

2015-06-01

The advent of Next Generation Sequencing (NGS) technologies has seen explosive growth in genomic datasets, and dense coverage of related organisms, supporting study of subtle, strain-specific variations as a determinant of function. Such data collections present fresh and complex challenges for bioinformatics, those of comparing models of complex relationships across hundreds and even thousands of sequences. Transcriptional Regulatory Network (TRN) structures document the influence of regulatory proteins called Transcription Factors (TFs) on associated Target Genes (TGs). TRNs are routinely inferred from model systems or iterative search, and analysis at these scales requires simultaneous displays of multiple networks well beyond thosemore » of existing network visualisation tools [1]. In this paper we describe TRNDiff, an open source system supporting the comparative analysis and visualization of TRNs (and similarly structured data) from many genomes, allowing rapid identification of functional variations within species. The approach is demonstrated through a small scale multiple TRN analysis of the Fur iron-uptake system of Yersinia, suggesting a number of candidate virulence factors; and through a larger study exploiting integration with the RegPrecise database (http://regprecise.lbl.gov; [2]) - a collection of hundreds of manually curated and predicted transcription factor regulons drawn from across the entire spectrum of prokaryotic organisms.« less
Comparative Phylogeography of Direct-Developing Frogs (Anura: Craugastoridae: Pristimantis) in the Southern Andes of Colombia

PubMed Central

García-R, Juan C.; Crawford, Andrew J.; Mendoza, Ángela María; Ospina, Oscar; Cardenas, Heiber; Castro, Fernando

2012-01-01

The Andes of South America hosts perhaps the highest amphibian species diversity in the world, and a sizable component of that diversity is comprised of direct-developing frogs of the genus Pristimantis (Anura: Craugastoridae). In order to better understand the initial stages of species formation in these frogs, this study quantified local-scale spatial genetic structuring in three species of Pristimantis. DNA sequences of two mitochondrial gene fragments (16S and COI) were obtained from P. brevifrons, P. palmeri and P. jubatus at different locations in the Cordillera Occidental. We found high levels of genetic diversity in the three species, with highly structured populations (as measured by F ST) in P. brevifrons and P. palmeri while P. jubatus showed panmixia. Large effective population sizes, inferred from the high levels of genetic diversity, were found in the three species and two highly divergent lineages were detected within P. jubatus and P. palmeri. Estimated divergence times among populations within P. brevifrons and P. palmeri coincide with the Pleistocene, perhaps due to similar responses to climatic cycling or recent geological history. Such insights have important implications for linking alpha and beta diversity, suggesting regional scale patterns may be associated with local scale processes in promoting differentiation among populations in the Andes. PMID:23049941
Partially acoustic dark matter, interacting dark radiation, and large scale structure

NASA Astrophysics Data System (ADS)

Chacko, Zackaria; Cui, Yanou; Hong, Sungwoo; Okui, Takemichi; Tsai, Yuhsinz

2016-12-01

The standard paradigm of collisionless cold dark matter is in tension with measurements on large scales. In particular, the best fit values of the Hubble rate H 0 and the matter density perturbation σ 8 inferred from the cosmic microwave background seem inconsistent with the results from direct measurements. We show that both problems can be solved in a framework in which dark matter consists of two distinct components, a dominant component and a subdominant component. The primary component is cold and collisionless. The secondary component is also cold, but interacts strongly with dark radiation, which itself forms a tightly coupled fluid. The growth of density perturbations in the subdominant component is inhibited by dark acoustic oscillations due to its coupling to the dark radiation, solving the σ 8 problem, while the presence of tightly coupled dark radiation ameliorates the H 0 problem. The subdominant component of dark matter and dark radiation continue to remain in thermal equilibrium until late times, inhibiting the formation of a dark disk. We present an example of a simple model that naturally realizes this scenario in which both constituents of dark matter are thermal WIMPs. Our scenario can be tested by future stage-IV experiments designed to probe the CMB and large scale structure.
Partially acoustic dark matter, interacting dark radiation, and large scale structure

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

Chacko, Zackaria; Cui, Yanou; Hong, Sungwoo

The standard paradigm of collisionless cold dark matter is in tension with measurements on large scales. In particular, the best fit values of the Hubble rate H 0 and the matter density perturbation σ 8 inferred from the cosmic microwave background seem inconsistent with the results from direct measurements. We show that both problems can be solved in a framework in which dark matter consists of two distinct components, a dominant component and a subdominant component. The primary component is cold and collisionless. The secondary component is also cold, but interacts strongly with dark radiation, which itself forms a tightlymore » coupled fluid. The growth of density perturbations in the subdominant component is inhibited by dark acoustic oscillations due to its coupling to the dark radiation, solving the σ 8 problem, while the presence of tightly coupled dark radiation ameliorates the H 0 problem. The subdominant component of dark matter and dark radiation continue to remain in thermal equilibrium until late times, inhibiting the formation of a dark disk. We present an example of a simple model that naturally realizes this scenario in which both constituents of dark matter are thermal WIMPs. Our scenario can be tested by future stage-IV experiments designed to probe the CMB and large scale structure.« less
Partially acoustic dark matter, interacting dark radiation, and large scale structure

DOE PAGES

Chacko, Zackaria; Cui, Yanou; Hong, Sungwoo; ...

2016-12-21

The standard paradigm of collisionless cold dark matter is in tension with measurements on large scales. In particular, the best fit values of the Hubble rate H 0 and the matter density perturbation σ 8 inferred from the cosmic microwave background seem inconsistent with the results from direct measurements. We show that both problems can be solved in a framework in which dark matter consists of two distinct components, a dominant component and a subdominant component. The primary component is cold and collisionless. The secondary component is also cold, but interacts strongly with dark radiation, which itself forms a tightlymore » coupled fluid. The growth of density perturbations in the subdominant component is inhibited by dark acoustic oscillations due to its coupling to the dark radiation, solving the σ 8 problem, while the presence of tightly coupled dark radiation ameliorates the H 0 problem. The subdominant component of dark matter and dark radiation continue to remain in thermal equilibrium until late times, inhibiting the formation of a dark disk. We present an example of a simple model that naturally realizes this scenario in which both constituents of dark matter are thermal WIMPs. Our scenario can be tested by future stage-IV experiments designed to probe the CMB and large scale structure.« less
Boolean network inference from time series data incorporating prior biological knowledge.

PubMed

Haider, Saad; Pal, Ranadip

2012-01-01

Numerous approaches exist for modeling of genetic regulatory networks (GRNs) but the low sampling rates often employed in biological studies prevents the inference of detailed models from experimental data. In this paper, we analyze the issues involved in estimating a model of a GRN from single cell line time series data with limited time points. We present an inference approach for a Boolean Network (BN) model of a GRN from limited transcriptomic or proteomic time series data based on prior biological knowledge of connectivity, constraints on attractor structure and robust design. We applied our inference approach to 6 time point transcriptomic data on Human Mammary Epithelial Cell line (HMEC) after application of Epidermal Growth Factor (EGF) and generated a BN with a plausible biological structure satisfying the data. We further defined and applied a similarity measure to compare synthetic BNs and BNs generated through the proposed approach constructed from transitions of various paths of the synthetic BNs. We have also compared the performance of our algorithm with two existing BN inference algorithms. Through theoretical analysis and simulations, we showed the rarity of arriving at a BN from limited time series data with plausible biological structure using random connectivity and absence of structure in data. The framework when applied to experimental data and data generated from synthetic BNs were able to estimate BNs with high similarity scores. Comparison with existing BN inference algorithms showed the better performance of our proposed algorithm for limited time series data. The proposed framework can also be applied to optimize the connectivity of a GRN from experimental data when the prior biological knowledge on regulators is limited or not unique.
Analysis and Design of Complex Network Environments

DTIC Science & Technology

2014-02-01

entanglements among un- measured variables. This “potential entanglement ” type of network complexity is previously unaddressed in the literature, yet it...Appreciating the power of structural representations that allow for potential entanglement among unmeasured variables to simplify network inference problems...rely on the idea of subsystems and allows for potential entanglement among unmeasured states. As a result, inferring a system’s signal structure
Boosting Bayesian parameter inference of nonlinear stochastic differential equation models by Hamiltonian scale separation.

PubMed

Albert, Carlo; Ulzega, Simone; Stoop, Ruedi

2016-04-01

Parameter inference is a fundamental problem in data-driven modeling. Given observed data that is believed to be a realization of some parameterized model, the aim is to find parameter values that are able to explain the observed data. In many situations, the dominant sources of uncertainty must be included into the model for making reliable predictions. This naturally leads to stochastic models. Stochastic models render parameter inference much harder, as the aim then is to find a distribution of likely parameter values. In Bayesian statistics, which is a consistent framework for data-driven learning, this so-called posterior distribution can be used to make probabilistic predictions. We propose a novel, exact, and very efficient approach for generating posterior parameter distributions for stochastic differential equation models calibrated to measured time series. The algorithm is inspired by reinterpreting the posterior distribution as a statistical mechanics partition function of an object akin to a polymer, where the measurements are mapped on heavier beads compared to those of the simulated data. To arrive at distribution samples, we employ a Hamiltonian Monte Carlo approach combined with a multiple time-scale integration. A separation of time scales naturally arises if either the number of measurement points or the number of simulation points becomes large. Furthermore, at least for one-dimensional problems, we can decouple the harmonic modes between measurement points and solve the fastest part of their dynamics analytically. Our approach is applicable to a wide range of inference problems and is highly parallelizable.
Quantifying spatial scaling patterns and their local and regional correlates in headwater streams: Implications for resilience

USGS Publications Warehouse

Gothe, Emma; Sandin, Leonard; Allen, Craig R.; Angeler, David G.

2014-01-01

The distribution of functional traits within and across spatiotemporal scales has been used to quantify and infer the relative resilience across ecosystems. We use explicit spatial modeling to evaluate within- and cross-scale redundancy in headwater streams, an ecosystem type with a hierarchical and dendritic network structure. We assessed the cross-scale distribution of functional feeding groups of benthic invertebrates in Swedish headwater streams during two seasons. We evaluated functional metrics, i.e., Shannon diversity, richness, and evenness, and the degree of redundancy within and across modeled spatial scales for individual feeding groups. We also estimated the correlates of environmental versus spatial factors of both functional composition and the taxonomic composition of functional groups for each spatial scale identified. Measures of functional diversity and within-scale redundancy of functions were similar during both seasons, but both within- and cross-scale redundancy were low. This apparent low redundancy was partly attributable to a few dominant taxa explaining the spatial models. However, rare taxa with stochastic spatial distributions might provide additional information and should therefore be considered explicitly for complementing future resilience assessments. Otherwise, resilience may be underestimated. Finally, both environmental and spatial factors correlated with the scale-specific functional and taxonomic composition. This finding suggests that resilience in stream networks emerges as a function of not only local conditions but also regional factors such as habitat connectivity and invertebrate dispersal.
Inferring the relative resilience of alternative states

USGS Publications Warehouse

Angeler, David G.; Allen, Craig R.; Rojo, Carmen; Alvarez-Cobelas, Miguel; Rodrigo, Maria A.; Sanchez-Carrillo, Salvador

2013-01-01

Ecological systems may occur in alternative states that differ in ecological structures, functions and processes. Resilience is the measure of disturbance an ecological system can absorb before changing states. However, how the intrinsic structures and processes of systems that characterize their states affects their resilience remains unclear. We analyzed time series of phytoplankton communities at three sites in a floodplain in central Spain to assess the dominant frequencies or “temporal scales” in community dynamics and compared the patterns between a wet and a dry alternative state. The identified frequencies and cross-scale structures are expected to arise from positive feedbacks that are thought to reinforce processes in alternative states of ecological systems and regulate emergent phenomena such as resilience. Our analyses show a higher species richness and diversity but lower evenness in the dry state. Time series modeling revealed a decrease in the importance of short-term variability in the communities, suggesting that community dynamics slowed down in the dry relative to the wet state. The number of temporal scales at which community dynamics manifested, and the explanatory power of time series models, was lower in the dry state. The higher diversity, reduced number of temporal scales and the lower explanatory power of time series models suggest that species dynamics tended to be more stochastic in the dry state. From a resilience perspective our results highlight a paradox: increasing species richness may not necessarily enhance resilience. The loss of cross-scale structure (i.e. the lower number of temporal scales) in community dynamics across sites suggests that resilience erodes during drought. Phytoplankton communities in the dry state are therefore likely less resilient than in the wet state. Our case study demonstrates the potential of time series modeling to assess attributes that mediate resilience. The approach is useful for assessing resilience of alternative states across ecological and other complex systems.

FuncPatch: a web server for the fast Bayesian inference of conserved functional patches in protein 3D structures.

PubMed

Huang, Yi-Fei; Golding, G Brian

2015-02-15

A number of statistical phylogenetic methods have been developed to infer conserved functional sites or regions in proteins. Many methods, e.g. Rate4Site, apply the standard phylogenetic models to infer site-specific substitution rates and totally ignore the spatial correlation of substitution rates in protein tertiary structures, which may reduce their power to identify conserved functional patches in protein tertiary structures when the sequences used in the analysis are highly similar. The 3D sliding window method has been proposed to infer conserved functional patches in protein tertiary structures, but the window size, which reflects the strength of the spatial correlation, must be predefined and is not inferred from data. We recently developed GP4Rate to solve these problems under the Bayesian framework. Unfortunately, GP4Rate is computationally slow. Here, we present an intuitive web server, FuncPatch, to perform a fast approximate Bayesian inference of conserved functional patches in protein tertiary structures. Both simulations and four case studies based on empirical data suggest that FuncPatch is a good approximation to GP4Rate. However, FuncPatch is orders of magnitudes faster than GP4Rate. In addition, simulations suggest that FuncPatch is potentially a useful tool complementary to Rate4Site, but the 3D sliding window method is less powerful than FuncPatch and Rate4Site. The functional patches predicted by FuncPatch in the four case studies are supported by experimental evidence, which corroborates the usefulness of FuncPatch. The software FuncPatch is freely available at the web site, http://info.mcmaster.ca/yifei/FuncPatch golding@mcmaster.ca Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Scale issues in soil hydrology related to measurement and simulation: A case study in Colorado

USDA-ARS?s Scientific Manuscript database

State variables, such as soil water content (SWC), are typically measured or inferred at very small scales while being simulated at larger scales relevant to spatial management or hillslope areas. Thus there is an implicit spatial disparity that is often ignored. Surface runoff, on the other hand, ...
Effects of seed bank disturbance on the fine-scale genetic structure of populations of the rare shrub Grevillea macleayana.

PubMed

England, P R; Whelan, R J; Ayre, D J

2003-11-01

Dispersal in most plants is mediated by the movement of seeds and pollen, which move genes across the landscape differently. Grevillea macleayana is a rare, fire-dependent Australian shrub with large seeds lacking adaptations for dispersal; yet it produces inflorescences adapted to pollination by highly mobile vertebrates (eg birds). Interpreting fine-scale genetic structure in the light of these two processes is confounded by the recent imposition of anthropogenic disturbances with potentially contrasting genetic consequences: (1) the unusual foraging behaviour of exotic honeybees and 2. widespread disturbance of the soil-stored seedbank by road building and quarrying. To test for evidence of fine-scale genetic structure within G. macleayana populations and to test the prediction that such structure might be masked by disturbance of the seed bank, we sampled two sites in undisturbed habitat and compared their genetic structure with two sites that had been strongly affected by road building using a test for spatial autocorrelation of genotypes. High selfing levels inferred from genotypes at all four sites implies that pollen dispersal is limited. Consistent with this, we observed substantial spatial clustering of genes at 10 m or less in the two undisturbed populations and argue that this reflects the predicted effects of both high selfing levels and limited seed dispersal. In contrast, at the two sites disturbed by road building, spatial autocorrelation was weak. This suggests there has been mixing of the seed bank, counteracting the naturally low dispersal and elevated selfing due to honeybees. Pollination between near neighbours with reduced relatedness potentially has fitness consequences for G. macleayana in disturbed sites.
An expert system shell for inferring vegetation characteristics: Implementation of additional techniques (task E)

NASA Technical Reports Server (NTRS)

Harrison, P. Ann

1992-01-01

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG subgoal PROPORTION.GROUND.COVER has been completed and a number of additional techniques that infer the proportion ground cover of a sample have been implemented. Some techniques operate on sample data at a single wavelength. The techniques previously incorporated in VEG for other subgoals operated on data at a single wavelength so implementing the additional single wavelength techniques required no changes to the structure of VEG. Two techniques which use data at multiple wavelengths to infer proportion ground cover were also implemented. This work involved modifying the structure of VEG so that multiple wavelength techniques could be incorporated. All the new techniques were tested using both the VEG 'Research Mode' and the 'Automatic Mode.'
Statistical Inference for Big Data Problems in Molecular Biophysics

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

Ramanathan, Arvind; Savol, Andrej; Burger, Virginia

2012-01-01

We highlight the role of statistical inference techniques in providing biological insights from analyzing long time-scale molecular simulation data. Technologi- cal and algorithmic improvements in computation have brought molecular simu- lations to the forefront of techniques applied to investigating the basis of living systems. While these longer simulations, increasingly complex reaching petabyte scales presently, promise a detailed view into microscopic behavior, teasing out the important information has now become a true challenge on its own. Mining this data for important patterns is critical to automating therapeutic intervention discovery, improving protein design, and fundamentally understanding the mech- anistic basis of cellularmore » homeostasis.« less
Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners

PubMed Central

Feinauer, Christoph; Procaccini, Andrea; Zecchina, Riccardo; Weigt, Martin; Pagnani, Andrea

2014-01-01

In the course of evolution, proteins show a remarkable conservation of their three-dimensional structure and their biological function, leading to strong evolutionary constraints on the sequence variability between homologous proteins. Our method aims at extracting such constraints from rapidly accumulating sequence data, and thereby at inferring protein structure and function from sequence information alone. Recently, global statistical inference methods (e.g. direct-coupling analysis, sparse inverse covariance estimation) have achieved a breakthrough towards this aim, and their predictions have been successfully implemented into tertiary and quaternary protein structure prediction methods. However, due to the discrete nature of the underlying variable (amino-acids), exact inference requires exponential time in the protein length, and efficient approximations are needed for practical applicability. Here we propose a very efficient multivariate Gaussian modeling approach as a variant of direct-coupling analysis: the discrete amino-acid variables are replaced by continuous Gaussian random variables. The resulting statistical inference problem is efficiently and exactly solvable. We show that the quality of inference is comparable or superior to the one achieved by mean-field approximations to inference with discrete variables, as done by direct-coupling analysis. This is true for (i) the prediction of residue-residue contacts in proteins, and (ii) the identification of protein-protein interaction partner in bacterial signal transduction. An implementation of our multivariate Gaussian approach is available at the website http://areeweb.polito.it/ricerca/cmp/code. PMID:24663061
First order augmentation to tensor voting for boundary inference and multiscale analysis in 3D.

PubMed

Tong, Wai-Shun; Tang, Chi-Keung; Mordohai, Philippos; Medioni, Gérard

2004-05-01

Most computer vision applications require the reliable detection of boundaries. In the presence of outliers, missing data, orientation discontinuities, and occlusion, this problem is particularly challenging. We propose to address it by complementing the tensor voting framework, which was limited to second order properties, with first order representation and voting. First order voting fields and a mechanism to vote for 3D surface and volume boundaries and curve endpoints in 3D are defined. Boundary inference is also useful for a second difficult problem in grouping, namely, automatic scale selection. We propose an algorithm that automatically infers the smallest scale that can preserve the finest details. Our algorithm then proceeds with progressively larger scales to ensure continuity where it has not been achieved. Therefore, the proposed approach does not oversmooth features or delay the handling of boundaries and discontinuities until model misfit occurs. The interaction of smooth features, boundaries, and outliers is accommodated by the unified representation, making possible the perceptual organization of data in curves, surfaces, volumes, and their boundaries simultaneously. We present results on a variety of data sets to show the efficacy of the improved formalism.
Active subspace uncertainty quantification for a polydomain ferroelectric phase-field model

NASA Astrophysics Data System (ADS)

Leon, Lider S.; Smith, Ralph C.; Miles, Paul; Oates, William S.

2018-03-01

Quantum-informed ferroelectric phase field models capable of predicting material behavior, are necessary for facilitating the development and production of many adaptive structures and intelligent systems. Uncertainty is present in these models, given the quantum scale at which calculations take place. A necessary analysis is to determine how the uncertainty in the response can be attributed to the uncertainty in the model inputs or parameters. A second analysis is to identify active subspaces within the original parameter space, which quantify directions in which the model response varies most dominantly, thus reducing sampling effort and computational cost. In this investigation, we identify an active subspace for a poly-domain ferroelectric phase-field model. Using the active variables as our independent variables, we then construct a surrogate model and perform Bayesian inference. Once we quantify the uncertainties in the active variables, we obtain uncertainties for the original parameters via an inverse mapping. The analysis provides insight into how active subspace methodologies can be used to reduce computational power needed to perform Bayesian inference on model parameters informed by experimental or simulated data.
Inferring transposons activity chronology by TRANScendence - TEs database and de-novo mining tool.

PubMed

Startek, Michał Piotr; Nogły, Jakub; Gromadka, Agnieszka; Grzebelus, Dariusz; Gambin, Anna

2017-10-16

The constant progress in sequencing technology leads to ever increasing amounts of genomic data. In the light of current evidence transposable elements (TEs for short) are becoming useful tools for learning about the evolution of host genome. Therefore the software for genome-wide detection and analysis of TEs is of great interest. Here we describe the computational tool for mining, classifying and storing TEs from newly sequenced genomes. This is an online, web-based, user-friendly service, enabling users to upload their own genomic data, and perform de-novo searches for TEs. The detected TEs are automatically analyzed, compared to reference databases, annotated, clustered into families, and stored in TEs repository. Also, the genome-wide nesting structure of found elements are detected and analyzed by new method for inferring evolutionary history of TEs. We illustrate the functionality of our tool by performing a full-scale analyses of TE landscape in Medicago truncatula genome. TRANScendence is an effective tool for the de-novo annotation and classification of transposable elements in newly-acquired genomes. Its streamlined interface makes it well-suited for evolutionary studies.
Short-term cyclic variations and diurnal variations of the Venus upper atmosphere

NASA Technical Reports Server (NTRS)

Keating, G. M.; Taylor, F. W.; Nicholson, J. Y.; Hinson, E. W.

1979-01-01

The vertical structure of the nighttime thermosphere and exosphere of Venus was discussed. A comparison of the day and nighttime profiles indicates, contrary to the model of Dickinson and Riley (1977), that densities (principally atomic oxygen) dropped sharply from day to night. It was suggested either that the lower estimates were related to cooler exospheric temperatures at night or that the atomic bulge was flatter than expected at lower altitudes. Large periodic oscillations, in both density and inferred exospheric temperatures, were detected with periods of 5 to 6 days. The possibility that cyclic variations in the thermosphere and stratosphere were caused by planetary-scale waves, propagated upward from the lower atmosphere, was investigated using simultaneous temperature measurements obtained by the Venus radiometric temperature experiment (VORTEX). Inferred exospheric temperatures in the morning were found to be lower than in the evening as if the atmosphere rotated in the direction of the planet's rotation, similar to that of earth. Superrotation of the thermosphere and exosphere was discussed as a possible extension of the 4-day cyclic atmospheric rotation near the cloud tops.
How does the cosmic large-scale structure bias the Hubble diagram?

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

Fleury, Pierre; Clarkson, Chris; Maartens, Roy, E-mail: pierre.fleury@uct.ac.za, E-mail: chris.clarkson@qmul.ac.uk, E-mail: roy.maartens@gmail.com

2017-03-01

The Hubble diagram is one of the cornerstones of observational cosmology. It is usually analysed assuming that, on average, the underlying relation between magnitude and redshift matches the prediction of a Friedmann-Lemaître-Robertson-Walker model. However, the inhomogeneity of the Universe generically biases these observables, mainly due to peculiar velocities and gravitational lensing, in a way that depends on the notion of average used in theoretical calculations. In this article, we carefully derive the notion of average which corresponds to the observation of the Hubble diagram. We then calculate its bias at second-order in cosmological perturbations, and estimate the consequences on themore » inference of cosmological parameters, for various current and future surveys. We find that this bias deeply affects direct estimations of the evolution of the dark-energy equation of state. However, errors in the standard inference of cosmological parameters remain smaller than observational uncertainties, even though they reach percent level on some parameters; they reduce to sub-percent level if an optimal distance indicator is used.« less
Study of the ammonia ice cloud layer in the north tropical zone of Jupiter from the infrared interferometric experiment on Voyager

NASA Technical Reports Server (NTRS)

Shaffer, William A.; Samuelson, Robert E.; Conrath, Barney J.

1986-01-01

An average of 51 Voyager 1 IRIS spectra of Jupiter's North Tropical Zone was analyzed to infer the abundance, vertical extent, and size distribution of the particles making up the ammonia cloud in this region. It is assumed that the cloud base coincides with the level at which 100% saturation of ammonia vapor occurs. The vertical distribution of particulates above this level is determined by assuming a constant total ammonia mixing ratio and adjusting the two phases so that the vapor is saturated throughout the cloud. A constant scaling factor then adjusts the base number density. A radiative transfer program is used that includes the effects of absorption and emission of all relevant gases as well as anisotropic scattering by cloud particles. Mie scattering from a gaussian particle size distribution is assumed. The vertical thermal structure is inferred from a temperature retrieval program that utilizes the collision induced S(0) and S(1) molecular hydrogen lines between 300 and 700.cm, and the 1304.cm methane band.
Case study of the 9 May 2003 windstorm in southwestern Slovakia

NASA Astrophysics Data System (ADS)

Kaňák, Ján; Benko, Martin; Simon, André; Sokol, Alois

2007-02-01

May 9, 2003 thunderstorm in southwest Slovakia is considered one of the most severe convective events to have happened in Slovakia during the past ten years. The majority of the reported damage was caused by very strong outflowing winds and hail. The downburst (macroburst) nature of the event was confirmed by a damage survey carried out in the area hit by the thunderstorm. The supercell nature of the storm was inferred from radar measurements, with the fields of radar reflectivity and radial Doppler velocity showing typical supercell features (e.g. BWER echo). The satellite imagery (from METEOSAT 7) indicated a large-scale dry air intrusion as a possible factor of downdraft enhancement. Aspects of the storm environment were inferred from soundings, numerical analysis of the ALADIN model and Velocity Azimuth Display data from radar. The results enable comparison of the outputs of several instability indices, such as CAPE, DCAPE and Storm to Relative Environmental Helicity (SREH). It was concluded based on structure and development that the storm showed many similarities to the so called High Precipitation (HP) supercell type.
The occultation of 28 Sgr by Titan

NASA Technical Reports Server (NTRS)

Hubbard, W. B.; Sicardy, Bruno; Miles, R.; Hollis, A. J.; Forrest, R. W.; Nicolson, I. K. M.; Appleby, G.; Beisker, W.; Bittner, C.; Bode, H.-J.

1993-01-01

We present a comprehensive analysis of data obtained during the 1989 July 3 occultation of 28 Sgr by Titan. The data set includes 23 lightcurves from 15 separate stations, spanning wavelengths from 0.36 to 0.89 micron. A detailed model of the structure of Titan's atmosphere in the altitude range 250 to 450 km is developed, giving the distribution of temperature, pressure, haze optical depth, and zonal wind velocity as a function of altitude and latitude. Haze layers detected in Titan's stratosphere are about one scale height higher than inferred from Voyager data, and show a wavelength dependence indicative of particle sizes on the order of 0.1 micron. A marked north-south dichotomy in haze density is observed with a transition to lower density south of about -20 deg latitude. Zonal wind speeds are inferred from global distortions from spherical symmetry and are of the order of 100 m/s with significant increase toward higher latitudes. Titan's high atmosphere shows substantial axial symmetry; the position angle of the symmetry axis is equal to the position angle of Saturn's spin axis to within about 1 deg.
Fitting Flux Ropes to a Global MHD Solution: A Comparison of Techniques. Appendix 1

NASA Technical Reports Server (NTRS)

Riley, Pete; Linker, J. A.; Lionello, R.; Mikic, Z.; Odstrcil, D.; Hidalgo, M. A.; Cid, C.; Hu, Q.; Lepping, R. P.; Lynch, B. J.

2004-01-01

Flux rope fitting (FRF) techniques are an invaluable tool for extracting information about the properties of a subclass of CMEs in the solar wind. However, it has proven difficult to assess their accuracy since the underlying global structure of the CME cannot be independently determined from the data. In contrast, large-scale MHD simulations of CME evolution can provide both a global view as well as localized time series at specific points in space. In this study we apply 5 different fitting techniques to 2 hypothetical time series derived from MHD simulation results. Independent teams performed the analysis of the events in "blind tests", for which no information, other than the time series, was provided. F rom the results, we infer the following: (1) Accuracy decreases markedly with increasingly glancing encounters; (2) Correct identification of the boundaries of the flux rope can be a significant limiter; and (3) Results from techniques that infer global morphology must be viewed with caution. In spite of these limitations, FRF techniques remain a useful tool for describing in situ observations of flux rope CMEs.
The earth and the moon /Harold Jeffreys Lecture/.

NASA Technical Reports Server (NTRS)

Press, F.

1971-01-01

The internal structures of the earth and the moon are compared in the light of the latest extensive data on the earth structure, mobility of the earth outer layers, and the properties of lunar crust. The Monte Carlo method is applied to develop an earth model by a stepwise process beginning with a random distribution of two elastic velocities and the density as a function of de pth. Lunar seismic, magnetic, and rock analysis data are used to infer the properties of the moon. The marked planetological contrast between the earth and the moon is shown to consist in that the earth is highly differentiated and still undergoes a large-scale differentiation, while the moon has lost its volatiles in its early history and has a cold dynamically inactive shell which has been without basic changes for three billion years.
Probing Mantle Heterogeneity Across Spatial Scales

NASA Astrophysics Data System (ADS)

Hariharan, A.; Moulik, P.; Lekic, V.

2017-12-01

Inferences of mantle heterogeneity in terms of temperature, composition, grain size, melt and crystal structure may vary across local, regional and global scales. Probing these scale-dependent effects require quantitative comparisons and reconciliation of tomographic models that vary in their regional scope, parameterization, regularization and observational constraints. While a range of techniques like radial correlation functions and spherical harmonic analyses have revealed global features like the dominance of long-wavelength variations in mantle heterogeneity, they have limited applicability for specific regions of interest like subduction zones and continental cratons. Moreover, issues like discrepant 1-D reference Earth models and related baseline corrections have impeded the reconciliation of heterogeneity between various regional and global models. We implement a new wavelet-based approach that allows for structure to be filtered simultaneously in both the spectral and spatial domain, allowing us to characterize heterogeneity on a range of scales and in different geographical regions. Our algorithm extends a recent method that expanded lateral variations into the wavelet domain constructed on a cubed sphere. The isolation of reference velocities in the wavelet scaling function facilitates comparisons between models constructed with arbitrary 1-D reference Earth models. The wavelet transformation allows us to quantify the scale-dependent consistency between tomographic models in a region of interest and investigate the fits to data afforded by heterogeneity at various dominant wavelengths. We find substantial and spatially varying differences in the spectrum of heterogeneity between two representative global Vp models constructed using different data and methodologies. Applying the orthonormality of the wavelet expansion, we isolate detailed variations in velocity from models and evaluate additional fits to data afforded by adding such complexities to long-wavelength variations. Our method provides a way to probe and evaluate localized features in a multi-scale description of mantle heterogeneity.
Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems.

PubMed

Yu, Michael Ku; Kramer, Michael; Dutkowski, Janusz; Srivas, Rohith; Licon, Katherine; Kreisberg, Jason; Ng, Cherie T; Krogan, Nevan; Sharan, Roded; Ideker, Trey

2016-02-24

Accurately translating genotype to phenotype requires accounting for the functional impact of genetic variation at many biological scales. Here we present a strategy for genotype-phenotype reasoning based on existing knowledge of cellular subsystems. These subsystems and their hierarchical organization are defined by the Gene Ontology or a complementary ontology inferred directly from previously published datasets. Guided by the ontology's hierarchical structure, we organize genotype data into an "ontotype," that is, a hierarchy of perturbations representing the effects of genetic variation at multiple cellular scales. The ontotype is then interpreted using logical rules generated by machine learning to predict phenotype. This approach substantially outperforms previous, non-hierarchical methods for translating yeast genotype to cell growth phenotype, and it accurately predicts the growth outcomes of two new screens of 2,503 double gene knockouts impacting DNA repair or nuclear lumen. Ontotypes also generalize to larger knockout combinations, setting the stage for interpreting the complex genetics of disease.
Classification-based reasoning

NASA Technical Reports Server (NTRS)

Gomez, Fernando; Segami, Carlos

1991-01-01

A representation formalism for N-ary relations, quantification, and definition of concepts is described. Three types of conditions are associated with the concepts: (1) necessary and sufficient properties, (2) contingent properties, and (3) necessary properties. Also explained is how complex chains of inferences can be accomplished by representing existentially quantified sentences, and concepts denoted by restrictive relative clauses as classification hierarchies. The representation structures that make possible the inferences are explained first, followed by the reasoning algorithms that draw the inferences from the knowledge structures. All the ideas explained have been implemented and are part of the information retrieval component of a program called Snowy. An appendix contains a brief session with the program.
Accurate Structural Correlations from Maximum Likelihood Superpositions

PubMed Central

Theobald, Douglas L; Wuttke, Deborah S

2008-01-01

The cores of globular proteins are densely packed, resulting in complicated networks of structural interactions. These interactions in turn give rise to dynamic structural correlations over a wide range of time scales. Accurate analysis of these complex correlations is crucial for understanding biomolecular mechanisms and for relating structure to function. Here we report a highly accurate technique for inferring the major modes of structural correlation in macromolecules using likelihood-based statistical analysis of sets of structures. This method is generally applicable to any ensemble of related molecules, including families of nuclear magnetic resonance (NMR) models, different crystal forms of a protein, and structural alignments of homologous proteins, as well as molecular dynamics trajectories. Dominant modes of structural correlation are determined using principal components analysis (PCA) of the maximum likelihood estimate of the correlation matrix. The correlations we identify are inherently independent of the statistical uncertainty and dynamic heterogeneity associated with the structural coordinates. We additionally present an easily interpretable method (“PCA plots”) for displaying these positional correlations by color-coding them onto a macromolecular structure. Maximum likelihood PCA of structural superpositions, and the structural PCA plots that illustrate the results, will facilitate the accurate determination of dynamic structural correlations analyzed in diverse fields of structural biology. PMID:18282091

Boosting Bayesian parameter inference of stochastic differential equation models with methods from statistical physics

NASA Astrophysics Data System (ADS)

Albert, Carlo; Ulzega, Simone; Stoop, Ruedi

2016-04-01

Measured time-series of both precipitation and runoff are known to exhibit highly non-trivial statistical properties. For making reliable probabilistic predictions in hydrology, it is therefore desirable to have stochastic models with output distributions that share these properties. When parameters of such models have to be inferred from data, we also need to quantify the associated parametric uncertainty. For non-trivial stochastic models, however, this latter step is typically very demanding, both conceptually and numerically, and always never done in hydrology. Here, we demonstrate that methods developed in statistical physics make a large class of stochastic differential equation (SDE) models amenable to a full-fledged Bayesian parameter inference. For concreteness we demonstrate these methods by means of a simple yet non-trivial toy SDE model. We consider a natural catchment that can be described by a linear reservoir, at the scale of observation. All the neglected processes are assumed to happen at much shorter time-scales and are therefore modeled with a Gaussian white noise term, the standard deviation of which is assumed to scale linearly with the system state (water volume in the catchment). Even for constant input, the outputs of this simple non-linear SDE model show a wealth of desirable statistical properties, such as fat-tailed distributions and long-range correlations. Standard algorithms for Bayesian inference fail, for models of this kind, because their likelihood functions are extremely high-dimensional intractable integrals over all possible model realizations. The use of Kalman filters is illegitimate due to the non-linearity of the model. Particle filters could be used but become increasingly inefficient with growing number of data points. Hamiltonian Monte Carlo algorithms allow us to translate this inference problem to the problem of simulating the dynamics of a statistical mechanics system and give us access to most sophisticated methods that have been developed in the statistical physics community over the last few decades. We demonstrate that such methods, along with automated differentiation algorithms, allow us to perform a full-fledged Bayesian inference, for a large class of SDE models, in a highly efficient and largely automatized manner. Furthermore, our algorithm is highly parallelizable. For our toy model, discretized with a few hundred points, a full Bayesian inference can be performed in a matter of seconds on a standard PC.
Pelagic Life and Depth: Coastal Physical Features in West Africa Shape the Genetic Structure of the Bonga Shad, Ethmalosa fimbriata

PubMed Central

Durand, Jean-Dominique; Guinand, Bruno; Dodson, Julian J.; Lecomte, Frédéric

2013-01-01

The bonga shad, Ethmalosa fimbriata, is a West African pelagic species still abundant in most habitats of its distribution range and thought to be only recently affected by anthropogenic pressure (habitat destruction or fishing pressure). Its presence in a wide range of coastal habitats characterised by different hydrodynamic processes, represents a case study useful for evaluating the importance of physical structure of the west African shoreline on the genetic structure of a small pelagic species. To investigate this question, the genetic diversity of E. fimbriata was assessed at both regional and species range scales, using mitochondrial (mt) and nuclear DNA markers. Whereas only three panmictic units were identified with mtDNA at the large spatial scale, nuclear genetic markers (EPIC: exon-primed intron-crossing) indicated a more complex genetic pattern at the regional scale. In the northern-most section of shad’s distribution range, up to 4 distinct units were identified. Bayesian inference as well as spatial autocorrelation methods provided evidence that gene flow is impeded by the presence of deep-water areas near the coastline (restricting the width of the coastal shelf), such as the Cap Timiris and the Kayar canyons in Mauritania and Senegal, respectively. The added discriminatory power provided by the use of EPIC markers proved to be essential to detect the influence of more subtle, contemporary processes (e.g. gene flow, barriers, etc.) acting within the glacial refuges identified previously by mtDNA. PMID:24130890
Genetic analysis across different spatial scales reveals multiple dispersal mechanisms for the invasive hydrozoan Cordylophora in the Great Lakes.

PubMed

Darling, John A; Folino-Rorem, Nadine C

2009-12-01

Discerning patterns of post-establishment spread by invasive species is critically important for the design of effective management strategies and the development of appropriate theoretical models predicting spatial expansion of introduced populations. The globally invasive colonial hydrozoan Cordylophora produces propagules both sexually and vegetatively and is associated with multiple potential dispersal mechanisms, making it a promising system to investigate complex patterns of population structure generated throughout the course of rapid range expansion. Here, we explore genetic patterns associated with the spread of this taxon within the North American Great Lakes basin. We collected intensively from eight harbours in the Chicago area in order to conduct detailed investigation of local population expansion. In addition, we collected from Lakes Michigan, Erie, and Ontario, as well as Lake Cayuga in the Finger Lakes of upstate New York in order to assess genetic structure on a regional scale. Based on data from eight highly polymorphic microsatellite loci we examined the spatial extent of clonal genotypes, assessed levels of neutral genetic diversity, and explored patterns of migration and dispersal at multiple spatial scales through assessment of population level genetic differentiation (pairwise F(ST) and factorial correspondence analysis), Bayesian inference of population structure, and assignment tests on individual genotypes. Results of these analyses indicate that Cordylophora populations in this region spread predominantly through sexually produced propagules, and that while limited natural larval dispersal can drive expansion locally, regional expansion likely relies on anthropogenic dispersal vectors.
Geography of Genetic Structure in Barley Wild Relative Hordeum vulgare subsp. spontaneum in Jordan.

PubMed

Thormann, Imke; Reeves, Patrick; Reilley, Ann; Engels, Johannes M M; Lohwasser, Ulrike; Börner, Andreas; Pillen, Klaus; Richards, Christopher M

2016-01-01

Informed collecting, conservation, monitoring and utilization of genetic diversity requires knowledge of the distribution and structure of the variation occurring in a species. Hordeum vulgare subsp. spontaneum (K. Koch) Thell., a primary wild relative of barley, is an important source of genetic diversity for barley improvement and co-occurs with the domesticate within the center of origin. We studied the current distribution of genetic diversity and population structure in H. vulgare subsp. spontaneum in Jordan and investigated whether it is correlated with either spatial or climatic variation inferred from publically available climate layers commonly used in conservation and ecogeographical studies. The genetic structure of 32 populations collected in 2012 was analyzed with 37 SSRs. Three distinct genetic clusters were identified. Populations were characterized by admixture and high allelic richness, and genetic diversity was concentrated in the northern part of the study area. Genetic structure, spatial location and climate were not correlated. This may point out a limitation in using large scale climatic data layers to predict genetic diversity, especially as it is applied to regional genetic resources collections in H. vulgare subsp. spontaneum.
Geography of Genetic Structure in Barley Wild Relative Hordeum vulgare subsp. spontaneum in Jordan

PubMed Central

Reeves, Patrick; Reilley, Ann; Engels, Johannes M. M.; Lohwasser, Ulrike; Börner, Andreas; Pillen, Klaus; Richards, Christopher M.

2016-01-01

Informed collecting, conservation, monitoring and utilization of genetic diversity requires knowledge of the distribution and structure of the variation occurring in a species. Hordeum vulgare subsp. spontaneum (K. Koch) Thell., a primary wild relative of barley, is an important source of genetic diversity for barley improvement and co-occurs with the domesticate within the center of origin. We studied the current distribution of genetic diversity and population structure in H. vulgare subsp. spontaneum in Jordan and investigated whether it is correlated with either spatial or climatic variation inferred from publically available climate layers commonly used in conservation and ecogeographical studies. The genetic structure of 32 populations collected in 2012 was analyzed with 37 SSRs. Three distinct genetic clusters were identified. Populations were characterized by admixture and high allelic richness, and genetic diversity was concentrated in the northern part of the study area. Genetic structure, spatial location and climate were not correlated. This may point out a limitation in using large scale climatic data layers to predict genetic diversity, especially as it is applied to regional genetic resources collections in H. vulgare subsp. spontaneum. PMID:27513459
Inference of Transmission Network Structure from HIV Phylogenetic Trees

DOE PAGES

Giardina, Federica; Romero-Severson, Ethan Obie; Albert, Jan; ...

2017-01-13

Phylogenetic inference is an attractive means to reconstruct transmission histories and epidemics. However, there is not a perfect correspondence between transmission history and virus phylogeny. Both node height and topological differences may occur, depending on the interaction between within-host evolutionary dynamics and between-host transmission patterns. To investigate these interactions, we added a within-host evolutionary model in epidemiological simulations and examined if the resulting phylogeny could recover different types of contact networks. To further improve realism, we also introduced patient-specific differences in infectivity across disease stages, and on the epidemic level we considered incomplete sampling and the age of the epidemic.more » Second, we implemented an inference method based on approximate Bayesian computation (ABC) to discriminate among three well-studied network models and jointly estimate both network parameters and key epidemiological quantities such as the infection rate. Our ABC framework used both topological and distance-based tree statistics for comparison between simulated and observed trees. Overall, our simulations showed that a virus time-scaled phylogeny (genealogy) may be substantially different from the between-host transmission tree. This has important implications for the interpretation of what a phylogeny reveals about the underlying epidemic contact network. In particular, we found that while the within-host evolutionary process obscures the transmission tree, the diversification process and infectivity dynamics also add discriminatory power to differentiate between different types of contact networks. We also found that the possibility to differentiate contact networks depends on how far an epidemic has progressed, where distance-based tree statistics have more power early in an epidemic. Finally, we applied our ABC inference on two different outbreaks from the Swedish HIV-1 epidemic.« less
Inference of Transmission Network Structure from HIV Phylogenetic Trees

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

Giardina, Federica; Romero-Severson, Ethan Obie; Albert, Jan

Phylogenetic inference is an attractive means to reconstruct transmission histories and epidemics. However, there is not a perfect correspondence between transmission history and virus phylogeny. Both node height and topological differences may occur, depending on the interaction between within-host evolutionary dynamics and between-host transmission patterns. To investigate these interactions, we added a within-host evolutionary model in epidemiological simulations and examined if the resulting phylogeny could recover different types of contact networks. To further improve realism, we also introduced patient-specific differences in infectivity across disease stages, and on the epidemic level we considered incomplete sampling and the age of the epidemic.more » Second, we implemented an inference method based on approximate Bayesian computation (ABC) to discriminate among three well-studied network models and jointly estimate both network parameters and key epidemiological quantities such as the infection rate. Our ABC framework used both topological and distance-based tree statistics for comparison between simulated and observed trees. Overall, our simulations showed that a virus time-scaled phylogeny (genealogy) may be substantially different from the between-host transmission tree. This has important implications for the interpretation of what a phylogeny reveals about the underlying epidemic contact network. In particular, we found that while the within-host evolutionary process obscures the transmission tree, the diversification process and infectivity dynamics also add discriminatory power to differentiate between different types of contact networks. We also found that the possibility to differentiate contact networks depends on how far an epidemic has progressed, where distance-based tree statistics have more power early in an epidemic. Finally, we applied our ABC inference on two different outbreaks from the Swedish HIV-1 epidemic.« less
Inferring Regulatory Networks from Experimental Morphological Phenotypes: A Computational Method Reverse-Engineers Planarian Regeneration

PubMed Central

Lobo, Daniel; Levin, Michael

2015-01-01

Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures, and reveal what external signals will trigger desired changes of large-scale pattern. Despite recent advances in bioinformatics, extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation. The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex, self-regulating pattern to emerge. To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning, it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes. For example, planarian regeneration has been studied for over a century, but despite increasing insight into the pathways that control its stem cells, no constructive, mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations. We present a method to infer the molecular products, topology, and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic, surgical, and pharmacological experiments. We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together, our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration. This method provides an automated, highly generalizable framework for identifying the underlying control mechanisms responsible for the dynamic regulation of growth and form. PMID:26042810
The causal structure of utility conditionals.

PubMed

Bonnefon, Jean-François; Sloman, Steven A

2013-01-01

The psychology of reasoning is increasingly considering agents' values and preferences, achieving greater integration with judgment and decision making, social cognition, and moral reasoning. Some of this research investigates utility conditionals, ''if p then q'' statements where the realization of p or q or both is valued by some agents. Various approaches to utility conditionals share the assumption that reasoners make inferences from utility conditionals based on the comparison between the utility of p and the expected utility of q. This article introduces a new parameter in this analysis, the underlying causal structure of the conditional. Four experiments showed that causal structure moderated utility-informed conditional reasoning. These inferences were strongly invited when the underlying structure of the conditional was causal, and significantly less so when the underlying structure of the conditional was diagnostic. This asymmetry was only observed for conditionals in which the utility of q was clear, and disappeared when the utility of q was unclear. Thus, an adequate account of utility-informed inferences conditional reasoning requires three components: utility, probability, and causal structure. Copyright © 2012 Cognitive Science Society, Inc.
PyClone: statistical inference of clonal population structure in cancer.

PubMed

Roth, Andrew; Khattra, Jaswinder; Yap, Damian; Wan, Adrian; Laks, Emma; Biele, Justina; Ha, Gavin; Aparicio, Samuel; Bouchard-Côté, Alexandre; Shah, Sohrab P

2014-04-01

We introduce PyClone, a statistical model for inference of clonal population structures in cancers. PyClone is a Bayesian clustering method for grouping sets of deeply sequenced somatic mutations into putative clonal clusters while estimating their cellular prevalences and accounting for allelic imbalances introduced by segmental copy-number changes and normal-cell contamination. Single-cell sequencing validation demonstrates PyClone's accuracy.
Soil-Moisture Retention Curves, Capillary Pressure Curves, and Mercury Porosimetry: A Theoretical and Computational Investigation of the Determination of the Geometric Properties of the Pore Space

NASA Astrophysics Data System (ADS)

Strand, T. E.; Wang, H. F.

2003-12-01

Immiscible displacement protocols have long been used to infer the geometric properties of the void space in granular porous media. The three most commonly used experimental techniques are the measurement of soil-moisture retention curves and relative permeability-capillary pressure-saturation relations, as well as mercury intrusion porosimetry experiments. A coupled theoretical and computational investigation was performed that provides insight into the limitations associated with each technique and quantifies the relationship between experimental observations and the geometric properties of the void space. It is demonstrated that the inference of the pore space geometry from both mercury porosimetry experiments and measurements of capillary pressure curves is influenced by trapping/mobilization phenomena and subject to scaling behavior. In addition, both techniques also assume that the capillary pressure at a location on the meniscus can be approximated by a pressure difference across a region or sample. For example, when performing capillary pressure measurements, the capillary pressure, taken to be the difference between the injected fluid pressure at the inlet and the defending fluid pressure at the outlet, is increased in a series of small steps and the fluid saturation is measured each time the system reaches steady. Regions of defending fluid that become entrapped by the invading fluid can be subsequently mobilized at higher flow rates (capillary pressures), contributing to a scale-dependence of the capillary pressure-saturation curve that complicates the determination of the properties of the pore space. This scale-dependence is particularly problematic for measurements performed at the core scale. Mercury porosimetry experiments are subject to similar limitations. Trapped regions of defending fluid are also present during the measurement of soil-moisture retention curves, but the effects of scaling behavior on the evaluation of the pore space properties from the immiscible displacement structure are much simpler to account for due to the control of mobilization phenomena. Some mobilization may occur due to film flow, but this can be limited by keeping time scales relatively small or exploited at longer time scales in order to quantify the rate of film flow. Computer simulations of gradient-stabilized drainage and imbibition to the (respective) equilibrium positions were performed using a pore-scale modified invasion percolation (MIP) model in order to quantify the relationship between the saturation profile and the geometric properties of the void space. These simulations are similar to the experimental measurement of soil-moisture retention curves. Results show that the equilibrium height and the width of the equilibrium fringe depend on two length scale distributions, one controlling the imbibition equilibrium structure and the other controlling the drainage structure. The equilibrium height is related to the mean value of the appropriate distribution as described by Jurin's law, and the width of the equilibrium fringe scales as a function of a combined parameter, the Bond number, Bo, divided by the coefficient of variation (cov). Simulations also demonstrate that the apparent radius distribution obtained from saturation profiles using direct inversion by Jurin's law is a subset of the actual distribution in the porous medium. The relationship between the apparent and actual radius distributions is quantified in terms of the combined parameter, Bo/cov, and the mean coordination number of the porous medium.
AzTEC Millimetre Survey of the COSMOS field - II. Source count overdensity and correlations with large-scale structure

NASA Astrophysics Data System (ADS)

Austermann, J. E.; Aretxaga, I.; Hughes, D. H.; Kang, Y.; Kim, S.; Lowenthal, J. D.; Perera, T. A.; Sanders, D. B.; Scott, K. S.; Scoville, N.; Wilson, G. W.; Yun, M. S.

2009-03-01

We report an overdensity of bright submillimetre galaxies (SMGs) in the 0.15 deg2 AzTEC/COSMOS survey and a spatial correlation between the SMGs and the optical-IR galaxy density at z <~ 1.1. This portion of the COSMOS field shows a ~3σ overdensity of robust SMG detections when compared to a background, or `blank-field', population model that is consistent with SMG surveys of fields with no extragalactic bias. The SMG overdensity is most significant in the number of very bright detections (14 sources with measured fluxes S1.1mm > 6 mJy), which is entirely incompatible with sample variance within our adopted blank-field number densities and infers an overdensity significance of >> 4σ. We find that the overdensity and spatial correlation to optical-IR galaxy density are most consistent with lensing of a background SMG population by foreground mass structures along the line of sight, rather than physical association of the SMGs with the z <~ 1.1 galaxies/clusters. The SMG positions are only weakly correlated with weak-lensing maps, suggesting that the dominant sources of correlation are individual galaxies and the more tenuous structures in the survey region, and not the massive and compact clusters. These results highlight the important roles cosmic variance and large-scale structure can play in the study of SMGs.
ON GALACTIC DENSITY MODELING IN THE PRESENCE OF DUST EXTINCTION

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

Bovy, Jo; Rix, Hans-Walter; Schlafly, Edward F.

Inferences about the spatial density or phase-space structure of stellar populations in the Milky Way require a precise determination of the effective survey volume. The volume observed by surveys such as Gaia or near-infrared spectroscopic surveys, which have good coverage of the Galactic midplane region, is highly complex because of the abundant small-scale structure in the three-dimensional interstellar dust extinction. We introduce a novel framework for analyzing the importance of small-scale structure in the extinction. This formalism demonstrates that the spatially complex effect of extinction on the selection function of a pencil-beam or contiguous sky survey is equivalent to amore » low-pass filtering of the extinction-affected selection function with the smooth density field. We find that the angular resolution of current 3D extinction maps is sufficient for analyzing Gaia sub-samples of millions of stars. However, the current distance resolution is inadequate and needs to be improved by an order of magnitude, especially in the inner Galaxy. We also present a practical and efficient method for properly taking the effect of extinction into account in analyses of Galactic structure through an effective selection function. We illustrate its use with the selection function of red-clump stars in APOGEE using and comparing a variety of current 3D extinction maps.« less
Inferring and analysis of social networks using RFID check-in data in China

PubMed Central

Liu, Tao; Liu, Shouyin; Ge, Shuangkui

2017-01-01

Social networks play an important role in our daily lives. However, social ties are rather elusive to quantify, especially for large groups of subjects over prolonged periods of time. In this work, we first propose a methodology for extracting social ties from long spatio-temporal data streams, where the subjects are 17,795 undergraduates from a university of China and the data streams are the 9,147,106 time-stamped RFID check-in records left behind by them during one academic year. By several metrics mentioned below, we then analyze the structure of the social network. Our results center around three main observations. First, we characterize the global structure of the network, and we confirm the small-world phenomenon on a global scale. Second, we find that the network shows clear community structure. And we observe that younger students at lower levels tend to form large communities, while students at higher levels mostly form smaller communities. Third, we characterize the assortativity patterns by studying the basic demographic and network properties of users. We observe clear degree assortativity on a global scale. Furthermore, we find a strong effect of grade and school on tie formation preference, but we do not find any strong region homophily. Our research may help us to elucidate the structural characteristics and the preference of the formation of social ties in college students’ social network. PMID:28570586
Inference of financial networks using the normalised mutual information rate.

PubMed

Goh, Yong Kheng; Hasim, Haslifah M; Antonopoulos, Chris G

2018-01-01

In this paper, we study data from financial markets, using the normalised Mutual Information Rate. We show how to use it to infer the underlying network structure of interrelations in the foreign currency exchange rates and stock indices of 15 currency areas. We first present the mathematical method and discuss its computational aspects, and apply it to artificial data from chaotic dynamics and to correlated normal-variates data. We then apply the method to infer the structure of the financial system from the time-series of currency exchange rates and stock indices. In particular, we study and reveal the interrelations among the various foreign currency exchange rates and stock indices in two separate networks, of which we also study their structural properties. Our results show that both inferred networks are small-world networks, sharing similar properties and having differences in terms of assortativity. Importantly, our work shows that global economies tend to connect with other economies world-wide, rather than creating small groups of local economies. Finally, the consistent interrelations depicted among the 15 currency areas are further supported by a discussion from the viewpoint of economics.
Inference of financial networks using the normalised mutual information rate

PubMed Central

2018-01-01

In this paper, we study data from financial markets, using the normalised Mutual Information Rate. We show how to use it to infer the underlying network structure of interrelations in the foreign currency exchange rates and stock indices of 15 currency areas. We first present the mathematical method and discuss its computational aspects, and apply it to artificial data from chaotic dynamics and to correlated normal-variates data. We then apply the method to infer the structure of the financial system from the time-series of currency exchange rates and stock indices. In particular, we study and reveal the interrelations among the various foreign currency exchange rates and stock indices in two separate networks, of which we also study their structural properties. Our results show that both inferred networks are small-world networks, sharing similar properties and having differences in terms of assortativity. Importantly, our work shows that global economies tend to connect with other economies world-wide, rather than creating small groups of local economies. Finally, the consistent interrelations depicted among the 15 currency areas are further supported by a discussion from the viewpoint of economics. PMID:29420644
Object segmentation controls image reconstruction from natural scenes

PubMed Central

2017-01-01

The structure of the physical world projects images onto our eyes. However, those images are often poorly representative of environmental structure: well-defined boundaries within the eye may correspond to irrelevant features of the physical world, while critical features of the physical world may be nearly invisible at the retinal projection. The challenge for the visual cortex is to sort these two types of features according to their utility in ultimately reconstructing percepts and interpreting the constituents of the scene. We describe a novel paradigm that enabled us to selectively evaluate the relative role played by these two feature classes in signal reconstruction from corrupted images. Our measurements demonstrate that this process is quickly dominated by the inferred structure of the environment, and only minimally controlled by variations of raw image content. The inferential mechanism is spatially global and its impact on early visual cortex is fast. Furthermore, it retunes local visual processing for more efficient feature extraction without altering the intrinsic transduction noise. The basic properties of this process can be partially captured by a combination of small-scale circuit models and large-scale network architectures. Taken together, our results challenge compartmentalized notions of bottom-up/top-down perception and suggest instead that these two modes are best viewed as an integrated perceptual mechanism. PMID:28827801
Lithospheric structure of northwest Africa: Insights into the tectonic history and influence of mantle flow on large-scale deformation

NASA Astrophysics Data System (ADS)

Miller, Meghan S.; Becker, Thorsten

2014-05-01

Northwest Africa is affected by late stage convergence of Africa with Eurasia, the Canary Island hotspot, and bounded by the Proterozoic-age West African craton. We present seismological evidence from receiver functions and shear-wave splitting along with geodynamic modeling to show how the interactions of these tectonic features resulted in dramatic deformation of the lithosphere. We interpret seismic discontinuities from the receiver functions and find evidence for localized, near vertical-offset deformation of both crust-mantle and lithosphere-asthenosphere interfaces at the flanks of the High Atlas. These offsets coincide with the locations of Jurassic-aged normal faults that have been reactivated during the Cenozoic, further suggesting that inherited, lithospheric-scale zones of weakness were involved in the formation of the Atlas. Another significant step in lithospheric thickness is inferred within the Middle Atlas. Its location corresponds to the source of regional Quaternary alkali volcanism, where the influx of melt induced by the shallow asthenosphere appears restricted to a lithospheric-scale fault on the northern side of the mountain belt. Inferred stretching axes from shear-wave splitting are aligned with the topographic grain in the High Atlas, suggesting along-strike asthenospheric shearing in a mantle channel guided by the lithospheric topography. Isostatic modeling based on our improved lithospheric constraints indicates that lithospheric thinning alone does not explain the anomalous Atlas topography. Instead, an mantle upwelling induced by a hot asthenospheric anomaly appears required, likely guided by the West African craton and perhaps sucked northward by subducted lithosphere beneath the Alboran. This dynamic support scenario for the Atlas also suggests that the timing of uplift is contemporaneous with the recent volcanismin the Middle Atlas.
Long-term dynamics of hawaiian volcanoes inferred by large-scale relative relocations of earthquakes

NASA Astrophysics Data System (ADS)

Got, J.-L.; Okubo, P.

2003-04-01

We investigated the microseismicity recorded in an active volcano to infer information concerning the volcano structure and long-term dynamics, by using relative relocations and focal mechanisms of microearthquakes. 32000 earthquakes of Mauna Loa and Kilauea volcanoes were recorded by more than 8 stations of the Hawaiian Volcano Observatory seismic network between 1988 and 1999. We studied 17000 of these events and relocated more than 70% with an accuracy ranging from 10 to 500 meters. About 75% of these relocated events are located in the vicinity of subhorizontal decollement planes, at 8 to 11 km depth. However, the striking features revealed by these relocation results are steep south-east dipping fault planes working as reverse faults, clearly located below the decollement plane and which intersect it. If this decollement plane coincides with the pre-Mauna Loa seafloor, as hypothesized by numerous authors, such reverse faults rupture the pre-Mauna Loa oceanic crust. The weight of the volcano and pressure in the magma storage system are possible causes of these ruptures, fully compatible with the local stress tensor computed by Gillard et al. (1996). Reverse faults are suspected of producing scarps revealed by km-long horizontal slip-perpendicular lineations along the decollement surface, and therefore large-scale roughness, asperities and normal stress variations. These are capable of generating stick-slip, large magnitude earthquakes, the spatial microseismic pattern observed in the south flank of Kilauea volcano, and Hilina-type instabilities. Ruptures intersecting the decollement surface, causing its large-scale roughness, may be an important parameter controlling the growth of Hawaiian volcanoes. Are there more or less rough decollement planes existing near the base of other volcanoes, such as Piton de la Fournaise or Etna, and able to explain part of their deformation and seismicity ?
The interplay between local ecology, divergent selection, and genetic drift in population divergence of a sexually antagonistic female trait.

PubMed

Green, Kristina Karlsson; Svensson, Erik I; Bergsten, Johannes; Härdling, Roger; Hansson, Bengt

2014-07-01

Genetically polymorphic species offer the possibility to study maintenance of genetic variation and the potential role for genetic drift in population divergence. Indirect inference of the selection regimes operating on polymorphic traits can be achieved by comparing population divergence in neutral genetic markers with population divergence in trait frequencies. Such an approach could further be combined with ecological data to better understand agents of selection. Here, we infer the selective regimes acting on a polymorphic mating trait in an insect group; the dorsal structures (either rough or smooth) of female diving beetles. Our recent work suggests that the rough structures have a sexually antagonistic function in reducing male mating attempts. For two species (Dytiscus lapponicus and Graphoderus zonatus), we could not reject genetic drift as an explanation for population divergence in morph frequencies, whereas for the third (Hygrotus impressopunctatus) we found that divergent selection pulls morph frequencies apart across populations. Furthermore, population morph frequencies in H. impressopunctatus were significantly related to local bioclimatic factors, providing an additional line of evidence for local adaptation in this species. These data, therefore, suggest that local ecological factors and sexual conflict interact over larger spatial scales to shape population divergence in the polymorphism. © 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.

Mathematical inference and control of molecular networks from perturbation experiments

NASA Astrophysics Data System (ADS)

Mohammed-Rasheed, Mohammed

One of the main challenges facing biologists and mathematicians in the post genomic era is to understand the behavior of molecular networks and harness this understanding into an educated intervention of the cell. The cell maintains its function via an elaborate network of interconnecting positive and negative feedback loops of genes, RNA and proteins that send different signals to a large number of pathways and molecules. These structures are referred to as genetic regulatory networks (GRNs) or molecular networks. GRNs can be viewed as dynamical systems with inherent properties and mechanisms, such as steady-state equilibriums and stability, that determine the behavior of the cell. The biological relevance of the mathematical concepts are important as they may predict the differentiation of a stem cell, the maintenance of a normal cell, the development of cancer and its aberrant behavior, and the design of drugs and response to therapy. Uncovering the underlying GRN structure from gene/protein expression data, e.g., microarrays or perturbation experiments, is called inference or reverse engineering of the molecular network. Because of the high cost and time consuming nature of biological experiments, the number of available measurements or experiments is very small compared to the number of molecules (genes, RNA and proteins). In addition, the observations are noisy, where the noise is due to the measurements imperfections as well as the inherent stochasticity of genetic expression levels. Intra-cellular activities and extra-cellular environmental attributes are also another source of variability. Thus, the inference of GRNs is, in general, an under-determined problem with a highly noisy set of observations. The ultimate goal of GRN inference and analysis is to be able to intervene within the network, in order to force it away from undesirable cellular states and into desirable ones. However, it remains a major challenge to design optimal intervention strategies in order to affect the time evolution of molecular activity in a desirable manner. In this proposal, we address both the inference and control problems of GRNs. In the first part of the thesis, we consider the control problem. We assume that we are given a general topology network structure, whose dynamics follow a discrete-time Markov chain model. We subsequently develop a comprehensive framework for optimal perturbation control of the network. The aim of the perturbation is to drive the network away from undesirable steady-states and to force it to converge to a unique desirable steady-state. The proposed framework does not make any assumptions about the topology of the initial network (e.g., ergodicity, weak and strong connectivity), and is thus applicable to general topology networks. We define the optimal perturbation as the minimum-energy perturbation measured in terms of the Frobenius norm between the initial and perturbed networks. We subsequently demonstrate that there exists at most one optimal perturbation that forces the network into the desirable steady-state. In the event where the optimal perturbation does not exist, we construct a family of sub-optimal perturbations that approximate the optimal solution arbitrarily closely. In the second part of the thesis, we address the inference problem of GRNs from time series data. We model the dynamics of the molecules using a system of ordinary differential equations corrupted by additive white noise. For large-scale networks, we formulate the inference problem as a constrained maximum likelihood estimation problem. We derive the molecular interactions that maximize the likelihood function while constraining the network to be sparse. We further propose a procedure to recover weak interactions based on the Bayesian information criterion. For small-size networks, we investigated the inference of a globally stable 7-gene melanoma genetic regulatory network from genetic perturbation experiments. We considered five melanoma cell lines, who exhibit different motility/invasion behavior under the same perturbation experiment of gene Wnt5a. The results of the simulations validate both the steady state levels and the experimental data of the perturbation experiments of all five cell lines. The goal of this study is to answer important questions that link the response of the network to perturbations, as measured by the experiments, to its structure, i.e., connectivity. Answers to these questions shed novel insights on the structure of networks and how they react to perturbations.
Log-Normal Turbulence Dissipation in Global Ocean Models

NASA Astrophysics Data System (ADS)

Pearson, Brodie; Fox-Kemper, Baylor

2018-03-01

Data from turbulent numerical simulations of the global ocean demonstrate that the dissipation of kinetic energy obeys a nearly log-normal distribution even at large horizontal scales O (10 km ) . As the horizontal scales of resolved turbulence are larger than the ocean is deep, the Kolmogorov-Yaglom theory for intermittency in 3D homogeneous, isotropic turbulence cannot apply; instead, the down-scale potential enstrophy cascade of quasigeostrophic turbulence should. Yet, energy dissipation obeys approximate log-normality—robustly across depths, seasons, regions, and subgrid schemes. The distribution parameters, skewness and kurtosis, show small systematic departures from log-normality with depth and subgrid friction schemes. Log-normality suggests that a few high-dissipation locations dominate the integrated energy and enstrophy budgets, which should be taken into account when making inferences from simplified models and inferring global energy budgets from sparse observations.
The use of large scale datasets for understanding traffic network state.

DOT National Transportation Integrated Search

2013-09-01

The goal of this proposal is to develop novel modeling techniques to infer individual activity patterns from the large scale cell phone : datasets and taxi data from NYC. As such this research offers a paradigm shift from traditional transportation m...
Using multi-scale distribution and movement effects along a montane highway to identify optimal crossing locations for a large-bodied mammal community.

PubMed

Schuster, Richard; Römer, Heinrich; Germain, Ryan R

2013-01-01

Roads are a major cause of habitat fragmentation that can negatively affect many mammal populations. Mitigation measures such as crossing structures are a proposed method to reduce the negative effects of roads on wildlife, but the best methods for determining where such structures should be implemented, and how their effects might differ between species in mammal communities is largely unknown. We investigated the effects of a major highway through south-eastern British Columbia, Canada on several mammal species to determine how the highway may act as a barrier to animal movement, and how species may differ in their crossing-area preferences. We collected track data of eight mammal species across two winters, along both the highway and pre-marked transects, and used a multi-scale modeling approach to determine the scale at which habitat characteristics best predicted preferred crossing sites for each species. We found evidence for a severe barrier effect on all investigated species. Freely-available remotely-sensed habitat landscape data were better than more costly, manually-digitized microhabitat maps in supporting models that identified preferred crossing sites; however, models using both types of data were better yet. Further, in 6 of 8 cases models which incorporated multiple spatial scales were better at predicting preferred crossing sites than models utilizing any single scale. While each species differed in terms of the landscape variables associated with preferred/avoided crossing sites, we used a multi-model inference approach to identify locations along the highway where crossing structures may benefit all of the species considered. By specifically incorporating both highway and off-highway data and predictions we were able to show that landscape context plays an important role for maximizing mitigation measurement efficiency. Our results further highlight the need for mitigation measures along major highways to improve connectivity between mammal populations, and illustrate how multi-scale data can be used to identify preferred crossing sites for different species within a mammal community.
Mineral resource potential map of the Raywood Flat Roadless Areas, Riverside and San Bernardino counties, California

USGS Publications Warehouse

Matti, Jonathan C.; Cox, Brett F.; Iverson, Stephen R.

1983-01-01

The area having moderate potential for base-metal resources forms a small zone in the eastern part of the recommended wilderness (A5-187). Within this zone, evidence provided by stream-sediment geochemistry suggests that crystalline bedrocks in several drainages contain concentrations of metallic elements. Because the terrain is inaccessible and covered with dense brush, most of the bedrock in the specific drainages containing the geochemical anomalies could not be examined. Thus, although we infer that mineral occurrences exist in the drainage basins, we have little data on which to base an estimate of their extent and quality. Locally, the crystalline rocks probably contain hydrothermal veins or disseminated occurrences where lead, copper, molybdenum, tin, cobalt, bismuth, and arsenic have been concentrated. However, the geochemical anomalies for these metals are small, and the stream drainages also are relatively small. Therefore, the inferred occurrences of metallic minerals probably are small scale, scattered, and low grade. There is only low probability that the inferred mineral occurrences are large scale.
Climate variations of Central Asia on orbital to millennial timescales.

PubMed

Cheng, Hai; Spötl, Christoph; Breitenbach, Sebastian F M; Sinha, Ashish; Wassenburg, Jasper A; Jochum, Klaus Peter; Scholz, Denis; Li, Xianglei; Yi, Liang; Peng, Youbing; Lv, Yanbin; Zhang, Pingzhong; Votintseva, Antonina; Loginov, Vadim; Ning, Youfeng; Kathayat, Gayatri; Edwards, R Lawrence

2016-11-11

The extent to which climate variability in Central Asia is causally linked to large-scale changes in the Asian monsoon on varying timescales remains a longstanding question. Here we present precisely dated high-resolution speleothem oxygen-carbon isotope and trace element records of Central Asia's hydroclimate variability from Tonnel'naya cave, Uzbekistan, and Kesang cave, western China. On orbital timescales, the supra-regional climate variance, inferred from our oxygen isotope records, exhibits a precessional rhythm, punctuated by millennial-scale abrupt climate events, suggesting a close coupling with the Asian monsoon. However, the local hydroclimatic variability at both cave sites, inferred from carbon isotope and trace element records, shows climate variations that are distinctly different from their supra-regional modes. Particularly, hydroclimatic changes in both Tonnel'naya and Kesang areas during the Holocene lag behind the supra-regional climate variability by several thousand years. These observations may reconcile the apparent out-of-phase hydroclimatic variability, inferred from the Holocene lake proxy records, between Westerly Central Asia and Monsoon Asia.
Climate variations of Central Asia on orbital to millennial timescales

PubMed Central

Cheng, Hai; Spötl, Christoph; Breitenbach, Sebastian F. M.; Sinha, Ashish; Wassenburg, Jasper A.; Jochum, Klaus Peter; Scholz, Denis; Li, Xianglei; Yi, Liang; Peng, Youbing; Lv, Yanbin; Zhang, Pingzhong; Votintseva, Antonina; Loginov, Vadim; Ning, Youfeng; Kathayat, Gayatri; Edwards, R. Lawrence

2016-01-01

The extent to which climate variability in Central Asia is causally linked to large-scale changes in the Asian monsoon on varying timescales remains a longstanding question. Here we present precisely dated high-resolution speleothem oxygen-carbon isotope and trace element records of Central Asia’s hydroclimate variability from Tonnel’naya cave, Uzbekistan, and Kesang cave, western China. On orbital timescales, the supra-regional climate variance, inferred from our oxygen isotope records, exhibits a precessional rhythm, punctuated by millennial-scale abrupt climate events, suggesting a close coupling with the Asian monsoon. However, the local hydroclimatic variability at both cave sites, inferred from carbon isotope and trace element records, shows climate variations that are distinctly different from their supra-regional modes. Particularly, hydroclimatic changes in both Tonnel’naya and Kesang areas during the Holocene lag behind the supra-regional climate variability by several thousand years. These observations may reconcile the apparent out-of-phase hydroclimatic variability, inferred from the Holocene lake proxy records, between Westerly Central Asia and Monsoon Asia. PMID:27833133
Scaling up spike-and-slab models for unsupervised feature learning.

PubMed

Goodfellow, Ian J; Courville, Aaron; Bengio, Yoshua

2013-08-01

We describe the use of two spike-and-slab models for modeling real-valued data, with an emphasis on their applications to object recognition. The first model, which we call spike-and-slab sparse coding (S3C), is a preexisting model for which we introduce a faster approximate inference algorithm. We introduce a deep variant of S3C, which we call the partially directed deep Boltzmann machine (PD-DBM) and extend our S3C inference algorithm for use on this model. We describe learning procedures for each. We demonstrate that our inference procedure for S3C enables scaling the model to unprecedented large problem sizes, and demonstrate that using S3C as a feature extractor results in very good object recognition performance, particularly when the number of labeled examples is low. We show that the PD-DBM generates better samples than its shallow counterpart, and that unlike DBMs or DBNs, the PD-DBM may be trained successfully without greedy layerwise training.
Inference of segmented color and texture description by tensor voting.

PubMed

Jia, Jiaya; Tang, Chi-Keung

2004-06-01

A robust synthesis method is proposed to automatically infer missing color and texture information from a damaged 2D image by (N)D tensor voting (N > 3). The same approach is generalized to range and 3D data in the presence of occlusion, missing data and noise. Our method translates texture information into an adaptive (N)D tensor, followed by a voting process that infers noniteratively the optimal color values in the (N)D texture space. A two-step method is proposed. First, we perform segmentation based on insufficient geometry, color, and texture information in the input, and extrapolate partitioning boundaries by either 2D or 3D tensor voting to generate a complete segmentation for the input. Missing colors are synthesized using (N)D tensor voting in each segment. Different feature scales in the input are automatically adapted by our tensor scale analysis. Results on a variety of difficult inputs demonstrate the effectiveness of our tensor voting approach.
An argument for mechanism-based statistical inference in cancer

PubMed Central

Ochs, Michael; Price, Nathan D.; Tomasetti, Cristian; Younes, Laurent

2015-01-01

Cancer is perhaps the prototypical systems disease, and as such has been the focus of extensive study in quantitative systems biology. However, translating these programs into personalized clinical care remains elusive and incomplete. In this perspective, we argue that realizing this agenda—in particular, predicting disease phenotypes, progression and treatment response for individuals—requires going well beyond standard computational and bioinformatics tools and algorithms. It entails designing global mathematical models over network-scale configurations of genomic states and molecular concentrations, and learning the model parameters from limited available samples of high-dimensional and integrative omics data. As such, any plausible design should accommodate: biological mechanism, necessary for both feasible learning and interpretable decision making; stochasticity, to deal with uncertainty and observed variation at many scales; and a capacity for statistical inference at the patient level. This program, which requires a close, sustained collaboration between mathematicians and biologists, is illustrated in several contexts, including learning bio-markers, metabolism, cell signaling, network inference and tumorigenesis. PMID:25381197
Inferring action structure and causal relationships in continuous sequences of human action.

PubMed

Buchsbaum, Daphna; Griffiths, Thomas L; Plunkett, Dillon; Gopnik, Alison; Baldwin, Dare

2015-02-01

In the real world, causal variables do not come pre-identified or occur in isolation, but instead are embedded within a continuous temporal stream of events. A challenge faced by both human learners and machine learning algorithms is identifying subsequences that correspond to the appropriate variables for causal inference. A specific instance of this problem is action segmentation: dividing a sequence of observed behavior into meaningful actions, and determining which of those actions lead to effects in the world. Here we present a Bayesian analysis of how statistical and causal cues to segmentation should optimally be combined, as well as four experiments investigating human action segmentation and causal inference. We find that both people and our model are sensitive to statistical regularities and causal structure in continuous action, and are able to combine these sources of information in order to correctly infer both causal relationships and segmentation boundaries. Copyright © 2014. Published by Elsevier Inc.
Hierarchical modeling and inference in ecology: The analysis of data from populations, metapopulations and communities

USGS Publications Warehouse

Royle, J. Andrew; Dorazio, Robert M.

2008-01-01

A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics.
Inverse Bayesian inference as a key of consciousness featuring a macroscopic quantum logical structure.

PubMed

Gunji, Yukio-Pegio; Shinohara, Shuji; Haruna, Taichi; Basios, Vasileios

2017-02-01

To overcome the dualism between mind and matter and to implement consciousness in science, a physical entity has to be embedded with a measurement process. Although quantum mechanics have been regarded as a candidate for implementing consciousness, nature at its macroscopic level is inconsistent with quantum mechanics. We propose a measurement-oriented inference system comprising Bayesian and inverse Bayesian inferences. While Bayesian inference contracts probability space, the newly defined inverse one relaxes the space. These two inferences allow an agent to make a decision corresponding to an immediate change in their environment. They generate a particular pattern of joint probability for data and hypotheses, comprising multiple diagonal and noisy matrices. This is expressed as a nondistributive orthomodular lattice equivalent to quantum logic. We also show that an orthomodular lattice can reveal information generated by inverse syllogism as well as the solutions to the frame and symbol-grounding problems. Our model is the first to connect macroscopic cognitive processes with the mathematical structure of quantum mechanics with no additional assumptions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Inductive reasoning about causally transmitted properties.

PubMed

Shafto, Patrick; Kemp, Charles; Bonawitz, Elizabeth Baraff; Coley, John D; Tenenbaum, Joshua B

2008-11-01

Different intuitive theories constrain and guide inferences in different contexts. Formalizing simple intuitive theories as probabilistic processes operating over structured representations, we present a new computational model of category-based induction about causally transmitted properties. A first experiment demonstrates undergraduates' context-sensitive use of taxonomic and food web knowledge to guide reasoning about causal transmission and shows good qualitative agreement between model predictions and human inferences. A second experiment demonstrates strong quantitative and qualitative fits to inferences about a more complex artificial food web. A third experiment investigates human reasoning about complex novel food webs where species have known taxonomic relations. Results demonstrate a double-dissociation between the predictions of our causal model and a related taxonomic model [Kemp, C., & Tenenbaum, J. B. (2003). Learning domain structures. In Proceedings of the 25th annual conference of the cognitive science society]: the causal model predicts human inferences about diseases but not genes, while the taxonomic model predicts human inferences about genes but not diseases. We contrast our framework with previous models of category-based induction and previous formal instantiations of intuitive theories, and outline challenges in developing a complete model of context-sensitive reasoning.
Decorrelation Times of Photospheric Fields and Flows

NASA Technical Reports Server (NTRS)

Welsch, B. T.; Kusano, K.; Yamamoto, T. T.; Muglach, K.

2012-01-01

We use autocorrelation to investigate evolution in flow fields inferred by applying Fourier Local Correlation Tracking (FLCT) to a sequence of high-resolution (0.3 "), high-cadence (approx = 2 min) line-of-sight magnetograms of NOAA active region (AR) 10930 recorded by the Narrowband Filter Imager (NFI) of the Solar Optical Telescope (SOT) aboard the Hinode satellite over 12 - 13 December 2006. To baseline the timescales of flow evolution, we also autocorrelated the magnetograms, at several spatial binnings, to characterize the lifetimes of active region magnetic structures versus spatial scale. Autocorrelation of flow maps can be used to optimize tracking parameters, to understand tracking algorithms f susceptibility to noise, and to estimate flow lifetimes. Tracking parameters varied include: time interval Delta t between magnetogram pairs tracked, spatial binning applied to the magnetograms, and windowing parameter sigma used in FLCT. Flow structures vary over a range of spatial and temporal scales (including unresolved scales), so tracked flows represent a local average of the flow over a particular range of space and time. We define flow lifetime to be the flow decorrelation time, tau . For Delta t > tau, tracking results represent the average velocity over one or more flow lifetimes. We analyze lifetimes of flow components, divergences, and curls as functions of magnetic field strength and spatial scale. We find a significant trend of increasing lifetimes of flow components, divergences, and curls with field strength, consistent with Lorentz forces partially governing flows in the active photosphere, as well as strong trends of increasing flow lifetime and decreasing magnitudes with increases in both spatial scale and Delta t.
De Novo Protein Structure Prediction

NASA Astrophysics Data System (ADS)

Hung, Ling-Hong; Ngan, Shing-Chung; Samudrala, Ram

An unparalleled amount of sequence data is being made available from large-scale genome sequencing efforts. The data provide a shortcut to the determination of the function of a gene of interest, as long as there is an existing sequenced gene with similar sequence and of known function. This has spurred structural genomic initiatives with the goal of determining as many protein folds as possible (Brenner and Levitt, 2000; Burley, 2000; Brenner, 2001; Heinemann et al., 2001). The purpose of this is twofold: First, the structure of a gene product can often lead to direct inference of its function. Second, since the function of a protein is dependent on its structure, direct comparison of the structures of gene products can be more sensitive than the comparison of sequences of genes for detecting homology. Presently, structural determination by crystallography and NMR techniques is still slow and expensive in terms of manpower and resources, despite attempts to automate the processes. Computer structure prediction algorithms, while not providing the accuracy of the traditional techniques, are extremely quick and inexpensive and can provide useful low-resolution data for structure comparisons (Bonneau and Baker, 2001). Given the immense number of structures which the structural genomic projects are attempting to solve, there would be a considerable gain even if the computer structure prediction approach were applicable to a subset of proteins.
INFERRING THE STRUCTURE OF THE SOLAR CORONA AND INNER HELIOSPHERE DURING THE MAUNDER MINIMUM USING GLOBAL THERMODYNAMIC MAGNETOHYDRODYNAMIC SIMULATIONS

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

Riley, Pete; Lionello, Roberto; Linker, Jon A., E-mail: pete@predsci.com, E-mail: lionel@predsci.com, E-mail: linkerj@predsci.com

Observations of the Sun’s corona during the space era have led to a picture of relatively constant, but cyclically varying solar output and structure. Longer-term, more indirect measurements, such as from {sup 10}Be, coupled by other albeit less reliable contemporaneous reports, however, suggest periods of significant departure from this standard. The Maunder Minimum was one such epoch where: (1) sunspots effectively disappeared for long intervals during a 70 yr period; (2) eclipse observations suggested the distinct lack of a visible K-corona but possible appearance of the F-corona; (3) reports of aurora were notably reduced; and (4) cosmic ray intensities atmore » Earth were inferred to be substantially higher. Using a global thermodynamic MHD model, we have constructed a range of possible coronal configurations for the Maunder Minimum period and compared their predictions with these limited observational constraints. We conclude that the most likely state of the corona during—at least—the later portion of the Maunder Minimum was not merely that of the 2008/2009 solar minimum, as has been suggested recently, but rather a state devoid of any large-scale structure, driven by a photospheric field composed of only ephemeral regions, and likely substantially reduced in strength. Moreover, we suggest that the Sun evolved from a 2008/2009-like configuration at the start of the Maunder Minimum toward an ephemeral-only configuration by the end of it, supporting a prediction that we may be on the cusp of a new grand solar minimum.« less
Alignment-free genome tree inference by learning group-specific distance metrics.

PubMed

Patil, Kaustubh R; McHardy, Alice C

2013-01-01

Understanding the evolutionary relationships between organisms is vital for their in-depth study. Gene-based methods are often used to infer such relationships, which are not without drawbacks. One can now attempt to use genome-scale information, because of the ever increasing number of genomes available. This opportunity also presents a challenge in terms of computational efficiency. Two fundamentally different methods are often employed for sequence comparisons, namely alignment-based and alignment-free methods. Alignment-free methods rely on the genome signature concept and provide a computationally efficient way that is also applicable to nonhomologous sequences. The genome signature contains evolutionary signal as it is more similar for closely related organisms than for distantly related ones. We used genome-scale sequence information to infer taxonomic distances between organisms without additional information such as gene annotations. We propose a method to improve genome tree inference by learning specific distance metrics over the genome signature for groups of organisms with similar phylogenetic, genomic, or ecological properties. Specifically, our method learns a Mahalanobis metric for a set of genomes and a reference taxonomy to guide the learning process. By applying this method to more than a thousand prokaryotic genomes, we showed that, indeed, better distance metrics could be learned for most of the 18 groups of organisms tested here. Once a group-specific metric is available, it can be used to estimate the taxonomic distances for other sequenced organisms from the group. This study also presents a large scale comparison between 10 methods--9 alignment-free and 1 alignment-based.
Model-based Bayesian inference for ROC data analysis

NASA Astrophysics Data System (ADS)

Lei, Tianhu; Bae, K. Ty

2013-03-01

This paper presents a study of model-based Bayesian inference to Receiver Operating Characteristics (ROC) data. The model is a simple version of general non-linear regression model. Different from Dorfman model, it uses a probit link function with a covariate variable having zero-one two values to express binormal distributions in a single formula. Model also includes a scale parameter. Bayesian inference is implemented by Markov Chain Monte Carlo (MCMC) method carried out by Bayesian analysis Using Gibbs Sampling (BUGS). Contrast to the classical statistical theory, Bayesian approach considers model parameters as random variables characterized by prior distributions. With substantial amount of simulated samples generated by sampling algorithm, posterior distributions of parameters as well as parameters themselves can be accurately estimated. MCMC-based BUGS adopts Adaptive Rejection Sampling (ARS) protocol which requires the probability density function (pdf) which samples are drawing from be log concave with respect to the targeted parameters. Our study corrects a common misconception and proves that pdf of this regression model is log concave with respect to its scale parameter. Therefore, ARS's requirement is satisfied and a Gaussian prior which is conjugate and possesses many analytic and computational advantages is assigned to the scale parameter. A cohort of 20 simulated data sets and 20 simulations from each data set are used in our study. Output analysis and convergence diagnostics for MCMC method are assessed by CODA package. Models and methods by using continuous Gaussian prior and discrete categorical prior are compared. Intensive simulations and performance measures are given to illustrate our practice in the framework of model-based Bayesian inference using MCMC method.
Exploring differences in pain beliefs within and between a large nonclinical (workplace) population and a clinical (chronic low back pain) population using the pain beliefs questionnaire.

PubMed

Baird, Andrew J; Haslam, Roger A

2013-12-01

Beliefs, cognitions, and behaviors relating to pain can be associated with a range of negative outcomes. In patients, certain beliefs are associated with increased levels of pain and related disability. There are few data, however, showing the extent to which beliefs of patients differ from those of the general population. This study explored pain beliefs in a large nonclinical population and a chronic low back pain (CLBP) sample using the Pain Beliefs Questionnaire (PBQ) to identify differences in scores and factor structures between and within the samples. This was a cross-sectional study. The samples comprised patients attending a rehabilitation program and respondents to a workplace survey. Pain beliefs were assessed using the PBQ, which incorporates 2 scales: organic and psychological. Exploratory factor analysis was used to explore variations in factor structure within and between samples. The relationship between the 2 scales also was examined. Patients reported higher organic scores and lower psychological scores than the nonclinical sample. Within the nonclinical sample, those who reported frequent pain scored higher on the organic scale than those who did not. Factor analysis showed variations in relation to the presence of pain. The relationship between scales was stronger in those not reporting frequent pain. This was a cross-sectional study; therefore, no causal inferences can be made. Patients experiencing CLBP adopt a more biomedical perspective on pain than nonpatients. The presence of pain is also associated with increased biomedical thinking in a nonclinical sample. However, the impact is not only on the strength of beliefs, but also on the relationship between elements of belief and the underlying belief structure.

Planck data versus large scale structure: Methods to quantify discordance

NASA Astrophysics Data System (ADS)

Charnock, Tom; Battye, Richard A.; Moss, Adam

2017-06-01

Discordance in the Λ cold dark matter cosmological model can be seen by comparing parameters constrained by cosmic microwave background (CMB) measurements to those inferred by probes of large scale structure. Recent improvements in observations, including final data releases from both Planck and SDSS-III BOSS, as well as improved astrophysical uncertainty analysis of CFHTLenS, allows for an update in the quantification of any tension between large and small scales. This paper is intended, primarily, as a discussion on the quantifications of discordance when comparing the parameter constraints of a model when given two different data sets. We consider Kullback-Leibler divergence, comparison of Bayesian evidences and other statistics which are sensitive to the mean, variance and shape of the distributions. However, as a byproduct, we present an update to the similar analysis in [R. A. Battye, T. Charnock, and A. Moss, Phys. Rev. D 91, 103508 (2015), 10.1103/PhysRevD.91.103508], where we find that, considering new data and treatment of priors, the constraints from the CMB and from a combination of large scale structure (LSS) probes are in greater agreement and any tension only persists to a minor degree. In particular, we find the parameter constraints from the combination of LSS probes which are most discrepant with the Planck 2015 +Pol +BAO parameter distributions can be quantified at a ˜2.55 σ tension using the method introduced in [R. A. Battye, T. Charnock, and A. Moss, Phys. Rev. D 91, 103508 (2015), 10.1103/PhysRevD.91.103508]. If instead we use the distributions constrained by the combination of LSS probes which are in greatest agreement with those from Planck 2015 +Pol +BAO this tension is only 0.76 σ .
Aspects of turbulent-shear-layer dynamics and mixing

NASA Astrophysics Data System (ADS)

Slessor, Michael David

Experiments have been conducted in the GALCIT Supersonic Shear Layer Facility to investigate some aspects of high-Reynolds-number, turbulent, shearlayer flows in both incompressible- and compressible-flow regimes. Experiments designed to address several issues were performed; effects of inflow boundary conditions, freestream conditions (supersonic/subsonic flow), and compressibility, on both large-scale dynamics and small-scale mixing, are described. Chemically-reacting and non-reacting flows were investigated, the former relying on the (H2 + NO/F2) chemical system, in the fast-kinetic regime, to infer the structure and amount of molecular-scale mixing through use of "flip" experiments. A variety of experimental techniques, including a color-schlieren visualization system developed as part of this work, were used to study the flows. Both inflow conditions and compressibility are found to have significant effects on the flow. In particular, inflow conditions are "remembered" for long distances downstream, a sensitivity similar to that observed in low-dimensionality, non-linear (chaotic) systems. The global flowfields (freestreams coupled by the shear layer) of transonic flows exhibit a sensitivity to imposed boundary conditions, i. e., local area ratios. A previously-proposed mode-selection rule for turbulent-structure convection speeds, based on the presence of a lab-frame subsonic freestream, was experimentally demonstrated to be incorrect. Compressibility, when decoupled from all other parameters, e.g., Reynolds number, velocity and density ratios, etc., reduces laxge-scale entrainment and turbulent growth, but slightly enhances smallscale mixing, with an associated change in the structure of the molecularly-mixed fluid. This reduction in shear-layer growth rate is examined and a new parameter that interprets compressibility as an energy-exchange mechanism is proposed. The parameter reconciles and collapses experimentally-observed growth rates.
Cavitation erosion prediction based on analysis of flow dynamics and impact load spectra

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

Mihatsch, Michael S., E-mail: michael.mihatsch@aer.mw.tum.de; Schmidt, Steffen J.; Adams, Nikolaus A.

2015-10-15

Cavitation erosion is the consequence of repeated collapse-induced high pressure-loads on a material surface. The present paper assesses the prediction of impact load spectra of cavitating flows, i.e., the rate and intensity distribution of collapse events based on a detailed analysis of flow dynamics. Data are obtained from a numerical simulation which employs a density-based finite volume method, taking into account the compressibility of both phases, and resolves collapse-induced pressure waves. To determine the spectrum of collapse events in the fluid domain, we detect and quantify the collapse of isolated vapor structures. As reference configuration we consider the expansion ofmore » a liquid into a radially divergent gap which exhibits unsteady sheet and cloud cavitation. Analysis of simulation data shows that global cavitation dynamics and dominant flow events are well resolved, even though the spatial resolution is too coarse to resolve individual vapor bubbles. The inviscid flow model recovers increasingly fine-scale vapor structures and collapses with increasing resolution. We demonstrate that frequency and intensity of these collapse events scale with grid resolution. Scaling laws based on two reference lengths are introduced for this purpose. We show that upon applying these laws impact load spectra recorded on experimental and numerical pressure sensors agree with each other. Furthermore, correlation between experimental pitting rates and collapse-event rates is found. Locations of high maximum wall pressures and high densities of collapse events near walls obtained numerically agree well with areas of erosion damage in the experiment. The investigation shows that impact load spectra of cavitating flows can be inferred from flow data that captures the main vapor structures and wave dynamics without the need for resolving all flow scales.« less
Multi-scale characterization of topographic anisotropy

NASA Astrophysics Data System (ADS)

Roy, S. G.; Koons, P. O.; Osti, B.; Upton, P.; Tucker, G. E.

2016-05-01

We present the every-direction variogram analysis (EVA) method for quantifying orientation and scale dependence of topographic anisotropy to aid in differentiation of the fluvial and tectonic contributions to surface evolution. Using multi-directional variogram statistics to track the spatial persistence of elevation values across a landscape, we calculate anisotropy as a multiscale, direction-sensitive variance in elevation between two points on a surface. Tectonically derived topographic anisotropy is associated with the three-dimensional kinematic field, which contributes (1) differential surface displacement and (2) crustal weakening along fault structures, both of which amplify processes of surface erosion. Based on our analysis, tectonic displacements dominate the topographic field at the orogenic scale, while a combination of the local displacement and strength fields are well represented at the ridge and valley scale. Drainage network patterns tend to reflect the geometry of underlying active or inactive tectonic structures due to the rapid erosion of faults and differential uplift associated with fault motion. Regions that have uniform environmental conditions and have been largely devoid of tectonic strain, such as passive coastal margins, have predominantly isotropic topography with typically dendritic drainage network patterns. Isolated features, such as stratovolcanoes, are nearly isotropic at their peaks but exhibit a concentric pattern of anisotropy along their flanks. The methods we provide can be used to successfully infer the settings of past or present tectonic regimes, and can be particularly useful in predicting the location and orientation of structural features that would otherwise be impossible to elude interpretation in the field. Though we limit the scope of this paper to elevation, EVA can be used to quantify the anisotropy of any spatially variable property.
Solving the small-scale structure puzzles with dissipative dark matter

NASA Astrophysics Data System (ADS)

Foot, Robert; Vagnozzi, Sunny

2016-07-01

Small-scale structure is studied in the context of dissipative dark matter, arising for instance in models with a hidden unbroken Abelian sector, so that dark matter couples to a massless dark photon. The dark sector interacts with ordinary matter via gravity and photon-dark photon kinetic mixing. Mirror dark matter is a theoretically constrained special case where all parameters are fixed except for the kinetic mixing strength, epsilon. In these models, the dark matter halo around spiral and irregular galaxies takes the form of a dissipative plasma which evolves in response to various heating and cooling processes. It has been argued previously that such dynamics can account for the inferred cored density profiles of galaxies and other related structural features. Here we focus on the apparent deficit of nearby small galaxies (``missing satellite problem"), which these dissipative models have the potential to address through small-scale power suppression by acoustic and diffusion damping. Using a variant of the extended Press-Schechter formalism, we evaluate the halo mass function for the special case of mirror dark matter. Considering a simplified model where Mbaryons propto Mhalo, we relate the halo mass function to more directly observable quantities, and find that for epsilon ≈ 2 × 10-10 such a simplified description is compatible with the measured galaxy luminosity and velocity functions. On scales Mhalo lesssim 108 Msolar, diffusion damping exponentially suppresses the halo mass function, suggesting a nonprimordial origin for dwarf spheroidal satellite galaxies, which we speculate were formed via a top-down fragmentation process as the result of nonlinear dissipative collapse of larger density perturbations. This could explain the planar orientation of satellite galaxies around Andromeda and the Milky Way.
Growth through Levels

ERIC Educational Resources Information Center

Thissen, David

2015-01-01

In "Using Learning Progressions to Design Vertical Scales that Support Coherent Inferences about Student Growth" (hereafter ULR), Briggs and Peck suggest that learning progressions could be used as the basis of vertical scales with naturally benchmarked descriptions of student proficiency. They propose and provide a single example of a…
Temporal Information Partitioning Networks (TIPNets): A process network approach to infer ecohydrologic shifts

NASA Astrophysics Data System (ADS)

Goodwell, Allison E.; Kumar, Praveen

2017-07-01

In an ecohydrologic system, components of atmospheric, vegetation, and root-soil subsystems participate in forcing and feedback interactions at varying time scales and intensities. The structure of this network of complex interactions varies in terms of connectivity, strength, and time scale due to perturbations or changing conditions such as rainfall, drought, or land use. However, characterization of these interactions is difficult due to multivariate and weak dependencies in the presence of noise, nonlinearities, and limited data. We introduce a framework for Temporal Information Partitioning Networks (TIPNets), in which time-series variables are viewed as nodes, and lagged multivariate mutual information measures are links. These links are partitioned into synergistic, unique, and redundant information components, where synergy is information provided only jointly, unique information is only provided by a single source, and redundancy is overlapping information. We construct TIPNets from 1 min weather station data over several hour time windows. From a comparison of dry, wet, and rainy conditions, we find that information strengths increase when solar radiation and surface moisture are present, and surface moisture and wind variability are redundant and synergistic influences, respectively. Over a growing season, network trends reveal patterns that vary with vegetation and rainfall patterns. The framework presented here enables us to interpret process connectivity in a multivariate context, which can lead to better inference of behavioral shifts due to perturbations in ecohydrologic systems. This work contributes to more holistic characterizations of system behavior, and can benefit a wide variety of studies of complex systems.
Measurements of the cosmic background radiation

NASA Technical Reports Server (NTRS)

Lubin, P.; Villela, T.

1987-01-01

Maps of the large scale structure (theta is greater than 6 deg) of the cosmic background radiation covering 90 percent of the sky are now available. The data show a very strong 50-100 sigma (statistical error) dipole component, interpreted as being due to our motion, with a direction of alpha = 11.5 + or - 0.15 hours, sigma = -5.6 + or - 2.0 deg. The inferred direction of the velocity of our galaxy relative to the cosmic background radiation is alpha = 10.6 + or - 0.3 hours, sigma = -2.3 + or - 5 deg. This is 44 deg from the center of the Virgo cluster. After removing the dipole component, the data show a galactic signature but no apparent residual structure. An autocorrelation of the residual data, after substraction of the galactic component from a combined Berkeley (3 mm) and Princeton (12 mm) data sets, show no apparent structure from 10 to 180 deg with a rms of 0.01 mK(sup 2). At 90 percent confidence level limit of .00007 is placed on a quadrupole component.
Individual-scale inference to anticipate climate-change vulnerability of biodiversity.

PubMed

Clark, James S; Bell, David M; Kwit, Matthew; Stine, Anne; Vierra, Ben; Zhu, Kai

2012-01-19

Anticipating how biodiversity will respond to climate change is challenged by the fact that climate variables affect individuals in competition with others, but interest lies at the scale of species and landscapes. By omitting the individual scale, models cannot accommodate the processes that determine future biodiversity. We demonstrate how individual-scale inference can be applied to the problem of anticipating vulnerability of species to climate. The approach places climate vulnerability in the context of competition for light and soil moisture. Sensitivities to climate and competition interactions aggregated from the individual tree scale provide estimates of which species are vulnerable to which variables in different habitats. Vulnerability is explored in terms of specific demographic responses (growth, fecundity and survival) and in terms of the synthetic response (the combination of demographic rates), termed climate tracking. These indices quantify risks for individuals in the context of their competitive environments. However, by aggregating in specific ways (over individuals, years, and other input variables), we provide ways to summarize and rank species in terms of their risks from climate change.
Quantifying the scale- and process- dependent reorganization of landscape under climatic change: inferences from an experimental landscape

NASA Astrophysics Data System (ADS)

Singh, A.; Tejedor, A.; Grimaud, J. L.; Zaliapin, I. V.; Foufoula-Georgiou, E.

2016-12-01

Knowledge of the dynamics of evolving landscapes in terms of their geomorphic and topologic re-organization in response to changing climatic or tectonic forcing is of scientific and practical interest. Although several studies have addressed the large-scale response (e.g., change in mean relief), studies on the smaller-scale drainage pattern re-organization and quantification of landscape vulnerability to the timing, magnitude, and frequency of changing forcing are lacking. The reason is the absence of data for such an analysis. To that goal, a series of controlled laboratory experiments were conducted at the St. Anthony Falls laboratory of the University of Minnesota to study the effect of changing precipitation patterns on landscape evolution at the short and long-time scales. High resolution digital elevation (DEM) both in space and time were measured for a range of rainfall patterns and uplift rates. Results from our study show a distinct signature of the precipitation increase on the probabilistic and geometrical structure of landscape features, evident in widening and deepening of channels and valleys, change in drainage patterns within sub-basins and change in the space-time structure of erosional and depositional events. A spatially explicit analysis of the locus of these erosional and depositional events suggests a regime shift, during the onset of the transient state, from supply-limited to transport-limited fluvial channels. We document a characteristic scale-dependent signature of erosion at steady state (which we term the "E50-area curve") and show that during reorganization, its evolving shape reflects process and scales of geomorphic change. Finally, we document changes in the longitudinal river profiles, in response to increased precipitation rate, with the formation of abrupt gradient (knickpoints) that migrate upstream as time proceeds.
Constraints on inflation with LSS surveys: features in the primordial power spectrum

NASA Astrophysics Data System (ADS)

Palma, Gonzalo A.; Sapone, Domenico; Sypsas, Spyros

2018-06-01

We analyse the efficiency of future large scale structure surveys to unveil the presence of scale dependent features in the primordial spectrum—resulting from cosmic inflation—imprinted in the distribution of galaxies. Features may appear as a consequence of non-trivial dynamics during cosmic inflation, in which one or more background quantities experienced small but rapid deviations from their characteristic slow-roll evolution. We consider two families of features: localised features and oscillatory extended features. To characterise them we employ various possible templates parametrising their scale dependence and provide forecasts on the constraints on these parametrisations for LSST like surveys. We perform a Fisher matrix analysis for three observables: cosmic microwave background (CMB), galaxy clustering and weak lensing. We find that the combined data set of these observables will be able to limit the presence of features down to levels that are more restrictive than current constraints coming from CMB observations only. In particular, we address the possibility of gaining information on currently known deviations from scale invariance inferred from CMB data, such as the feature appearing at the l ~ 20 multipole (which is the main contribution to the low-l deficit) and another one around l ~ 800.
An Illustrative Guide to the Minerva Framework

NASA Astrophysics Data System (ADS)

Flom, Erik; Leonard, Patrick; Hoeffel, Udo; Kwak, Sehyun; Pavone, Andrea; Svensson, Jakob; Krychowiak, Maciej; Wendelstein 7-X Team Collaboration

2017-10-01

Modern phsyics experiments require tracking and modelling data and their associated uncertainties on a large scale, as well as the combined implementation of multiple independent data streams for sophisticated modelling and analysis. The Minerva Framework offers a centralized, user-friendly method of large-scale physics modelling and scientific inference. Currently used by teams at multiple large-scale fusion experiments including the Joint European Torus (JET) and Wendelstein 7-X (W7-X), the Minerva framework provides a forward-model friendly architecture for developing and implementing models for large-scale experiments. One aspect of the framework involves so-called data sources, which are nodes in the graphical model. These nodes are supplied with engineering and physics parameters. When end-user level code calls a node, it is checked network-wide against its dependent nodes for changes since its last implementation and returns version-specific data. Here, a filterscope data node is used as an illustrative example of the Minerva Framework's data management structure and its further application to Bayesian modelling of complex systems. This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 under Grant Agreement No. 633053.
Irradiation direction from texture

NASA Astrophysics Data System (ADS)

Koenderink, Jan J.; Pont, Sylvia C.

2003-10-01

We present a theory of image texture resulting from the shading of corrugated (three-dimensional textured) surfaces, Lambertian on the micro scale, in the domain of geometrical optics. The derivation applies to isotropic Gaussian random surfaces, under collimated illumination, in normal view. The theory predicts the structure tensors from either the gradient or the Hessian of the image intensity and allows inferences of the direction of irradiation of the surface. Although the assumptions appear prima facie rather restrictive, even for surfaces that are not at all Gaussian, with the bidirectional reflectance distribution function far from Lambertian and vignetting and multiple scattering present, we empirically recover the direction of irradiation with an accuracy of a few degrees.
Characterization of the Shuttle Landing Facility as a laser range for testing and evaluation of EO systems

NASA Astrophysics Data System (ADS)

Stromqvist Vetelino, Frida; Borbath, Michael R.; Andrews, Larry C.; Phillips, Ronald L.; Burdge, Geoffrey L.; Chin, Peter G.; Galus, Darren J.; Wayne, David; Pescatore, Robert; Cowan, Doris; Thomas, Frederick

2005-08-01

The Shuttle Landing Facility runway at the Kennedy Space Center in Cape Canaveral, Florida is almost 5 km long and 100 m wide. Its homogeneous environment makes it a unique and ideal place for testing and evaluating EO systems. An experiment, with the goal of characterizing atmospheric parameters on the runway, was conducted in June 2005. Weather data was collected and the refractive index structure parameter was measured with a commercial scintillometer. The inner scale of turbulence was inferred from wind speed measurements and surface roughness. Values of the crosswind speed obtained from the scintillometer were compared with wind measurements taken by a weather station.
Total meltwater volume since the Last Glacial Maximum and viscosity structure of Earth's mantle inferred from relative sea level changes at Barbados and Bonaparte Gulf and GIA-induced J˙2

NASA Astrophysics Data System (ADS)

Nakada, Masao; Okuno, Jun'ichi; Yokoyama, Yusuke

2016-02-01

Inference of globally averaged eustatic sea level (ESL) rise since the Last Glacial Maximum (LGM) highly depends on the interpretation of relative sea level (RSL) observations at Barbados and Bonaparte Gulf, Australia, which are sensitive to the viscosity structure of Earth's mantle. Here we examine the RSL changes at the LGM for Barbados and Bonaparte Gulf ({{RSL}}_{{L}}^{{{Bar}}} and {{RSL}}_{{L}}^{{{Bon}}}), differential RSL for both sites (Δ {{RSL}}_{{L}}^{{{Bar}},{{Bon}}}) and rate of change of degree-two harmonics of Earth's geopotential due to glacial isostatic adjustment (GIA) process (GIA-induced J˙2) to infer the ESL component and viscosity structure of Earth's mantle. Differential RSL, Δ {{RSL}}_{{L}}^{{{Bar}},{{Bon}}} and GIA-induced J˙2 are dominantly sensitive to the lower-mantle viscosity, and nearly insensitive to the upper-mantle rheological structure and GIA ice models with an ESL component of about (120-130) m. The comparison between the predicted and observationally derived Δ {{RSL}}_{{L}}^{{{Bar}},{{Bon}}} indicates the lower-mantle viscosity higher than ˜2 × 1022 Pa s, and the observationally derived GIA-induced J˙2 of -(6.0-6.5) × 10-11 yr-1 indicates two permissible solutions for the lower mantle, ˜1022 and (5-10) × 1022 Pa s. That is, the effective lower-mantle viscosity inferred from these two observational constraints is (5-10) × 1022 Pa s. The LGM RSL changes at both sites, {{RSL}}_{{L}}^{{{Bar}}} and {{RSL}}_{{L}}^{{{Bon}}}, are also sensitive to the ESL component and upper-mantle viscosity as well as the lower-mantle viscosity. The permissible upper-mantle viscosity increases with decreasing ESL component due to the sensitivity of the LGM sea level at Bonaparte Gulf ({{RSL}}_{{L}}^{{{Bon}}}) to the upper-mantle viscosity, and inferred upper-mantle viscosity for adopted lithospheric thicknesses of 65 and 100 km is (1-3) × 1020 Pa s for ESL˜130 m and (4-10) × 1020 Pa s for ESL˜125 m. The former solution of (1-3) × 1020 Pa s is consistent with the inferences from the postglacial differential RSL changes in the Australian region and also inversion study of far-field sea-level data. The inference of the viscosity structure based on these four observational constraints is, however, relatively insensitive to the viscosity structure of D″ layer.
Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation.

PubMed

Witoonchart, Peerajak; Chongstitvatana, Prabhas

2017-08-01

In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer. Thus, we obtain a new type of convolutional neural network called an Structured SVM convolutional neural network, which we applied to the human pose estimation problem. This new neural network can be used as the final layers in deep learning. Our method jointly learns the structural model parameters and the appearance model parameters. We implemented our method as a new layer in the existing Caffe library. Copyright © 2017 Elsevier Ltd. All rights reserved.
A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks

PubMed Central

Yin, Junming; Ho, Qirong; Xing, Eric P.

2014-01-01

We propose a scalable approach for making inference about latent spaces of large networks. With a succinct representation of networks as a bag of triangular motifs, a parsimonious statistical model, and an efficient stochastic variational inference algorithm, we are able to analyze real networks with over a million vertices and hundreds of latent roles on a single machine in a matter of hours, a setting that is out of reach for many existing methods. When compared to the state-of-the-art probabilistic approaches, our method is several orders of magnitude faster, with competitive or improved accuracy for latent space recovery and link prediction. PMID:25400487
Dimension reduction and multiscaling law through source extraction

NASA Astrophysics Data System (ADS)

Capobianco, Enrico

2003-04-01

Through the empirical analysis of financial return generating processes one may find features that are common to other research fields, such as internet data from network traffic, physiological studies about human heart beat, speech and sleep recorded time series, geophysics signals, just to mention well-known cases of study. In particular, long range dependence, intermittency, heteroscedasticity are clearly appearing, and consequently power laws and multi-scaling behavior result typical signatures of either the spectral or the time correlation diagnostics. We study these features and the dynamics underlying financial volatility, which can respectively be detected and inferred from high frequency realizations of stock index returns, and show that they vary according to the resolution levels used for both the analysis and the synthesis of the available information. Discovering whether the volatility dynamics are subject to changes in scaling regimes requires the consideration of a model embedding scale-dependent information packets, thus accounting for possible heterogeneous activity occurring in financial markets. Independent component analysis result to be an important tool for reducing the dimension of the problem and calibrating greedy approximation techniques aimed to learn the structure of the underlying volatility.
Strain analysis in the Sanandaj-Sirjan HP-LT Metamorphic Belt, SW Iran: Insights from small-scale faults and associated drag folds

NASA Astrophysics Data System (ADS)

Sarkarinejad, Khalil; Keshavarz, Saeede; Faghih, Ali

2015-05-01

This study is aimed at quantifying the kinematics of deformation using a population of drag fold structures associated with small-scale faults in deformed quartzites from Seh-Ghalatoun area within the HP-LT Sanandaj-Sirjan Metamorphic Belt, SW Iran. A total 30 small-scale faults in the quartzite layers were examined to determine the deformation characteristics. Obtained data revealed α0 (initial fault angle) and ω (angle between flow apophyses) are equal to 83° and 32°, respectively. These data yield mean kinematic vorticity number (Wm) equal to 0.79 and mean finite strain (Rs) of 2.32. These results confirm the relative contribution of ∼43% pure shear and ∼57% simple shear components, respectively. The strain partitioning inferred from this quantitative analysis is consistent with a sub-simple or general shear deformation pattern associated with a transpressional flow regime in the study area as a part of the Zagros Orogen. This type of deformation resulted from oblique convergence between the Afro-Arabian and Central-Iranian plates.
Examining Underlying Relationships between the Supports Intensity Scale-Adult Version and the Supports Intensity Scale-Children's Version

ERIC Educational Resources Information Center

Seo, Hyojeong; Wehmeyer, Michael L.; Shogren, Karrie A.; Hughes, Carolyn; Thompson, James R.; Little, Todd D.; Palmer, Susan B.

2017-01-01

Given the growing importance of support needs assessment in the field of intellectual disability, it is imperative to develop assessments of support needs whose scores and inferences demonstrate reliability and validity. The purpose of this study was to examine the criterion validity of scores on the "Supports Intensity Scale-Children's…

Statistical inference of protein structural alignments using information and compression.

PubMed

Collier, James H; Allison, Lloyd; Lesk, Arthur M; Stuckey, Peter J; Garcia de la Banda, Maria; Konagurthu, Arun S

2017-04-01

Structural molecular biology depends crucially on computational techniques that compare protein three-dimensional structures and generate structural alignments (the assignment of one-to-one correspondences between subsets of amino acids based on atomic coordinates). Despite its importance, the structural alignment problem has not been formulated, much less solved, in a consistent and reliable way. To overcome these difficulties, we present here a statistical framework for the precise inference of structural alignments, built on the Bayesian and information-theoretic principle of Minimum Message Length (MML). The quality of any alignment is measured by its explanatory power-the amount of lossless compression achieved to explain the protein coordinates using that alignment. We have implemented this approach in MMLigner , the first program able to infer statistically significant structural alignments. We also demonstrate the reliability of MMLigner 's alignment results when compared with the state of the art. Importantly, MMLigner can also discover different structural alignments of comparable quality, a challenging problem for oligomers and protein complexes. Source code, binaries and an interactive web version are available at http://lcb.infotech.monash.edu.au/mmligner . arun.konagurthu@monash.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
What Do the Hitomi Observations Tell Us About the Turbulent Velocities in the Perseus Cluster?

NASA Astrophysics Data System (ADS)

ZuHone, John A.; Miller, Eric D.; Bulbul, Esra; Zhuravleva, Irina

2017-08-01

Recently, the Hitomi X-ray Observatory provided the first-ever direct measurements of Doppler line shifting and broadening from the hot plasma in clusters of galaxies via its observations of the Perseus Cluster. It has been reported that these observations demonstrate that the ICM in Perseus is "quiescent". It is indisputable that the velocities inferred from the measured line shifts and broadening are low, but what do these observations imply about the structure of the velocity field on scales smaller than the Hitomi PSF? We use hydrodynamic simulations of gas motions in a cool-core cluster in combination with synthetic Hitomi observations in order to compare the observed line-of-sight velocities to the 3D velocity structure of the ICM, and assess the impact of Hitomi's spatial resolution and the effects of varying the underlying ICM physics.
Effects of spanwise rotation on the structure of two-dimensional fully developed turbulent channel flow.

NASA Technical Reports Server (NTRS)

Johnston, J. P.; Halleen, R. M.; Lezius, D. K.

1972-01-01

Experiments on fully developed turbulent flow in a channel which is rotating at a steady rate about a spanwise axis are described. The Coriolis force components in the region of two-dimensional mean flow affect both local and global stability. Three stability-related phenomena were observed or inferred: (1) the reduction (increase) of the rate of wall-layer streak bursting in locally stabilized (destabilized) wall layers; (2) the total suppression of transition to turbulence in a stabilized layer; (3) the development of large-scale roll cells on the destabilized side of the channel by growth of a Taylor-Gortler vortex instability. Local effects of rotational stabilization, such as reduction of the turbulent stress in wall layers, can be related to the local Richardson number in a simple way. This paper not only investigates this effect, but also, by methods of flow visualization, exposes some of the underlying structure changes caused by rotation.-
Multitemporal diurnal AVIRIS images of a forested ecosystem

NASA Technical Reports Server (NTRS)

Ustin, Susan L.; Smith, Milton O.; Adams, John B.

1992-01-01

Both physiological and ecosystem structural information may be derived from diurnal images. Structural information may be inferred from changes in canopy shadows between images and from changes in spectral composition due to changes in proportions of subpixel mixing resulting from the differences in sun/view angles. Physiological processes having diurnal scales also may be measurable if a stable basis for spectral comparison can be established. Six diurnal images of an area east of Mt. Shasta, CA were acquired on 22 Sep. 1989. This unique diurnal data set provided an opportunity to test the consistency of endmember fractions and residuals. It was expected that shade endmember abundances would show the greatest change as lighting geometry changed and less change in the normalized fractional proportion of other endmembers. Diurnal changes in spectral features related to physiological characteristics may be identifiable as changes in wavelength specific residuals.
Reconstruction of the IMF polarity using midlatitude geomagnetic observations in the nineteenth century

NASA Astrophysics Data System (ADS)

Vokhmyanin, M. V.; Ponyavin, D. I.

2016-12-01

The interplanetary magnetic field (IMF) By component affects the configuration of field-aligned currents (FAC) whose geomagnetic response is observed from high to low latitudes. The ground magnetic perturbations induced by FACs are opposite on the dawnside and duskside and depend upon the IMF By polarity. Based on the multilinear regression analysis, we show that this effect is presented at the midlatitude observatories, Niemegk and Arti, in the X and Y components of the geomagnetic field. This allows us to infer the IMF sector structure from the old geomagnetic records made at Ekaterinburg and Potsdam since 1850 and 1890, respectively. Geomagnetic data from various stations provide proxies of the IMF polarity which coincide for the most part of the nineteenth and twentieth centuries. This supports their reliabilities and makes them suitable for studying the large-scale IMF sector structure in the past.
Inferring topological features of proteins from amino acid residue networks

NASA Astrophysics Data System (ADS)

Alves, Nelson Augusto; Martinez, Alexandre Souto

2007-02-01

Topological properties of native folds are obtained from statistical analysis of 160 low homology proteins covering the four structural classes. This is done analyzing one, two and three-vertex joint distribution of quantities related to the corresponding network of amino acid residues. Emphasis on the amino acid residue hydrophobicity leads to the definition of their center of mass as vertices in this contact network model with interactions represented by edges. The network analysis helps us to interpret experimental results such as hydrophobic scales and fraction of buried accessible surface area in terms of the network connectivity. Moreover, those networks show assortative mixing by degree. To explore the vertex-type dependent correlations, we build a network of hydrophobic and polar vertices. This procedure presents the wiring diagram of the topological structure of globular proteins leading to the following attachment probabilities between hydrophobic-hydrophobic 0.424(5), hydrophobic-polar 0.419(2) and polar-polar 0.157(3) residues.
Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses

PubMed Central

Stephen, Emily P.; Lepage, Kyle Q.; Eden, Uri T.; Brunner, Peter; Schalk, Gerwin; Brumberg, Jonathan S.; Guenther, Frank H.; Kramer, Mark A.

2014-01-01

The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty—both in the functional network edges and the corresponding aggregate measures of network topology—are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here—appropriate for static and dynamic network inference and different statistical measures of coupling—permits the evaluation of confidence in network measures in a variety of settings common to neuroscience. PMID:24678295
Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.

PubMed

Stephen, Emily P; Lepage, Kyle Q; Eden, Uri T; Brunner, Peter; Schalk, Gerwin; Brumberg, Jonathan S; Guenther, Frank H; Kramer, Mark A

2014-01-01

The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty-both in the functional network edges and the corresponding aggregate measures of network topology-are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here-appropriate for static and dynamic network inference and different statistical measures of coupling-permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.
Effects of Demographic History on the Detection of Recombination Hotspots from Linkage Disequilibrium

PubMed Central

Dapper, Amy L; Payseur, Bret A

2018-01-01

Abstract In some species, meiotic recombination is concentrated in small genomic regions. These “recombination hotspots” leave signatures in fine-scale patterns of linkage disequilibrium, raising the prospect that the genomic landscape of hotspots can be characterized from sequence variation. This approach has led to the inference that hotspots evolve rapidly in some species, but are conserved in others. Historic demographic events, such as population bottlenecks, are known to affect patterns of linkage disequilibrium across the genome, violating population genetic assumptions of this approach. Although such events are prevalent, demographic history is generally ignored when making inferences about the evolution of recombination hotspots. To determine the effect of demography on the detection of recombination hotspots, we use the coalescent to simulate haplotypes with a known recombination landscape. We measure the ability of popular linkage disequilibrium-based programs to detect hotspots across a range of demographic histories, including population bottlenecks, hidden population structure, population expansions, and population contractions. We find that demographic events have the potential to greatly reduce the power and increase the false positive rate of hotspot discovery. Neither the power nor the false positive rate of hotspot detection can be predicted without also knowing the demographic history of the sample. Our results suggest that ignoring demographic history likely overestimates the power to detect hotspots and therefore underestimates the degree of hotspot sharing between species. We suggest strategies for incorporating demographic history into population genetic inferences about recombination hotspots. PMID:29045724
An Algebraic Approach to Inference in Complex Networked Structures

DTIC Science & Technology

2015-07-09

44], [45],[46] where the shift is the elementary non-trivial filter that generates, under an appropriate notion of shift invariance, all linear ... elementary filter, and its output is a graph signal with the value at vertex n of the graph given approximately by a weighted linear combination of...AFRL-AFOSR-VA-TR-2015-0265 An Algebraic Approach to Inference in Complex Networked Structures Jose Moura CARNEGIE MELLON UNIVERSITY Final Report 07
Stand, species, and individual traits impact transpiration in historically disturbed forests.

NASA Astrophysics Data System (ADS)

Blakely, B.; Rocha, A. V.; McLachlan, J. S.

2017-12-01

Historic logging disturbances have changed the structure and species composition of most Northern temperate forests. These changes impact the process of transpiration - which in turn impacts canopy surface temperature - but the links among structure, composition, and transpiration remain unclear. For this reason, ecosystem models typically use simplified structure and composition to simulate the impact of disturbances on forest transpiration. However, such simplifications ignore real variability among stands, species, and individual trees that may strongly influence transpiration across spatial and temporal scales. To capture this variability, we monitored transpiration in 48 individual trees of multiple species in both undisturbed (400+ yr) and historically logged (80 - 120 yr) forests. Using modern and historic forest surveys, we upscaled our observations to stand and regional scales to identify the key changes impacting transpiration. We extended these inferences by establishing a relationship between transpiration and measured surface temperature, linking disturbance-induced changes in structure and composition to local and regional climate. Despite greater potential evapotranspiration and basal area, undisturbed forest transpired less than disturbed (logged) forest. Transpiration was a strong predictor of surface temperature, and the canopy surface was warmer in undisturbed forest. Transpiration differences among disturbed and undisturbed forests resulted from (1) lesser transpiration and dampened seasonality in evergreen species (2) greater transpiration in younger individuals within a species, and (3) strong transpiration by large individuals. When transpiration was scaled to the stand or regional level in a simplified manner (e.g. a single transpiration rate for all deciduous individuals), the resulting estimates differed markedly from the original. Stand- species- and individual-level traits are therefore essential for understanding how transpiration and surface temperature respond to disturbance. Without consideration of such traits, current ecosystem models may struggle to capture the true impact of logging disturbances on forest transpiration.
Feral pig populations are structured at fine spatial scales in tropical Queensland, Australia.

PubMed

Lopez, Jobina; Hurwood, David; Dryden, Bart; Fuller, Susan

2014-01-01

Feral pigs occur throughout tropical far north Queensland, Australia and are a significant threat to biodiversity and World Heritage values, agriculture and are a vector of infectious diseases. One of the constraints on long-lasting, local eradication of feral pigs is the process of reinvasion into recently controlled areas. This study examined the population genetic structure of feral pigs in far north Queensland to identify the extent of movement and the scale at which demographically independent management units exist. Genetic analysis of 328 feral pigs from the Innisfail to Tully region of tropical Queensland was undertaken. Seven microsatellite loci were screened and Bayesian clustering methods used to infer population clusters. Sequence variation at the mitochondrial DNA control region was examined to identify pig breed. Significant population structure was identified in the study area at a scale of 25 to 35 km, corresponding to three demographically independent management units (MUs). Distinct natural or anthropogenic barriers were not found, but environmental features such as topography and land use appear to influence patterns of gene flow. Despite the strong, overall pattern of structure, some feral pigs clearly exhibited ancestry from a MU outside of that from which they were sampled indicating isolated long distance dispersal or translocation events. Furthermore, our results suggest that gene flow is restricted among pigs of domestic Asian and European origin and non-random mating influences management unit boundaries. We conclude that the three MUs identified in this study should be considered as operational units for feral pig control in far north Queensland. Within a MU, coordinated and simultaneous control is required across farms, rainforest areas and National Park Estates to prevent recolonisation from adjacent localities.
Late-time mixing and turbulent behavior in high-energy-density shear experiments at high Atwood numbers

DOE PAGES

Flippo, K. A.; Doss, F. W.; Merritt, E. C.; ...

2018-05-30

The LANL Shear Campaign uses millimeter-scale initially solid shock tubes on the National Ignition Facility to conduct high-energy-density hydrodynamic plasma experiments, capable of reaching energy densities exceeding 100 kJ/cm 3. These shock-tube experiments have for the first time reproduced spontaneously emergent coherent structures due to shear-based fluid instabilities [i.e., Kelvin-Helmholtz (KH)], demonstrating hydrodynamic scaling over 8 orders of magnitude in time and velocity. The KH vortices, referred to as “rollers,” and the secondary instabilities, referred to as “ribs,” are used to understand the turbulent kinetic energy contained in the system. Their evolution is used to understand the transition to turbulencemore » and that transition's dependence on initial conditions. Experimental results from these studies are well modeled by the RAGE (Radiation Adaptive Grid Eulerian) hydro-code using the Besnard-Harlow-Rauenzahn turbulent mix model. Information inferred from both the experimental data and the mix model allows us to demonstrate that the specific Turbulent Kinetic Energy (sTKE) in the layer, as calculated from the plan-view structure data, is consistent with the mixing width growth and the RAGE simulations of sTKE.« less
Lateral variation in upper mantle temperature and composition beneath mid-ocean ridges inferred from shear-wave propagation, geoid, and bathymetry. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Sheehan, Anne Francis

1991-01-01

Resolution of both the extent and mechanism of lateral heterogeneity in the upper mantle constraints the nature and scales of mantle convection. Oceanic regions are of particular interest as they are likely to provide the closest glimpse at the patterns of temperature anomalies and convective flow in the upper mantle because of their young age and simple crustal structure relative to continental regions. Lateral variations were determined in the seismic velocity and attenuation structure of the lithosphere and astenosphere beneath the oceans, and these seismological observations were combined with the data and theory of geoid and bathymetry anomalies in order to test and improve current models for seafloor spreading and mantle convection. Variations were determined in mantle properties on a scale of about 1000 km, comparable to the thickness of the upper mantle. Seismic velocity, geoid, and bathymetry anomalies are all sensitive to variations in upper mantle density, and inversions were formulated to combine quantitatively these different data and to search for a common origin. Variations in mantle density can be either of thermal or compositional origin and are related to mantle convection or differentiation.
Late-time mixing and turbulent behavior in high-energy-density shear experiments at high Atwood numbers

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

Flippo, K. A.; Doss, F. W.; Merritt, E. C.

The LANL Shear Campaign uses millimeter-scale initially solid shock tubes on the National Ignition Facility to conduct high-energy-density hydrodynamic plasma experiments, capable of reaching energy densities exceeding 100 kJ/cm 3. These shock-tube experiments have for the first time reproduced spontaneously emergent coherent structures due to shear-based fluid instabilities [i.e., Kelvin-Helmholtz (KH)], demonstrating hydrodynamic scaling over 8 orders of magnitude in time and velocity. The KH vortices, referred to as “rollers,” and the secondary instabilities, referred to as “ribs,” are used to understand the turbulent kinetic energy contained in the system. Their evolution is used to understand the transition to turbulencemore » and that transition's dependence on initial conditions. Experimental results from these studies are well modeled by the RAGE (Radiation Adaptive Grid Eulerian) hydro-code using the Besnard-Harlow-Rauenzahn turbulent mix model. Information inferred from both the experimental data and the mix model allows us to demonstrate that the specific Turbulent Kinetic Energy (sTKE) in the layer, as calculated from the plan-view structure data, is consistent with the mixing width growth and the RAGE simulations of sTKE.« less
From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation Among Gene Classes from Large-Scale Expression Data

NASA Technical Reports Server (NTRS)

Mjolsness, Eric; Castano, Rebecca; Mann, Tobias; Wold, Barbara

2000-01-01

We provide preliminary evidence that existing algorithms for inferring small-scale gene regulation networks from gene expression data can be adapted to large-scale gene expression data coming from hybridization microarrays. The essential steps are (I) clustering many genes by their expression time-course data into a minimal set of clusters of co-expressed genes, (2) theoretically modeling the various conditions under which the time-courses are measured using a continuous-time analog recurrent neural network for the cluster mean time-courses, (3) fitting such a regulatory model to the cluster mean time courses by simulated annealing with weight decay, and (4) analysing several such fits for commonalities in the circuit parameter sets including the connection matrices. This procedure can be used to assess the adequacy of existing and future gene expression time-course data sets for determining transcriptional regulatory relationships such as coregulation.
Estimating mountain basin-mean precipitation from streamflow using Bayesian inference

NASA Astrophysics Data System (ADS)

Henn, Brian; Clark, Martyn P.; Kavetski, Dmitri; Lundquist, Jessica D.

2015-10-01

Estimating basin-mean precipitation in complex terrain is difficult due to uncertainty in the topographical representativeness of precipitation gauges relative to the basin. To address this issue, we use Bayesian methodology coupled with a multimodel framework to infer basin-mean precipitation from streamflow observations, and we apply this approach to snow-dominated basins in the Sierra Nevada of California. Using streamflow observations, forcing data from lower-elevation stations, the Bayesian Total Error Analysis (BATEA) methodology and the Framework for Understanding Structural Errors (FUSE), we infer basin-mean precipitation, and compare it to basin-mean precipitation estimated using topographically informed interpolation from gauges (PRISM, the Parameter-elevation Regression on Independent Slopes Model). The BATEA-inferred spatial patterns of precipitation show agreement with PRISM in terms of the rank of basins from wet to dry but differ in absolute values. In some of the basins, these differences may reflect biases in PRISM, because some implied PRISM runoff ratios may be inconsistent with the regional climate. We also infer annual time series of basin precipitation using a two-step calibration approach. Assessment of the precision and robustness of the BATEA approach suggests that uncertainty in the BATEA-inferred precipitation is primarily related to uncertainties in hydrologic model structure. Despite these limitations, time series of inferred annual precipitation under different model and parameter assumptions are strongly correlated with one another, suggesting that this approach is capable of resolving year-to-year variability in basin-mean precipitation.
The "fault of the Pool" along the Congo River between Kinshasa and Brazzaville, R(D)Congo is no more a myth: Paleostress from small-scale brittle structures

NASA Astrophysics Data System (ADS)

Delvaux, Damien; Ganza, Gloire; Kongota, Elvis; Fukiabantu, Guilain; Mbokola, Dim; Boudzoumou, Florent; Miyouna, Timothée; Gampio, Urbain; Nkodia, Hardy

2017-04-01

Small-scale brittle structures such as shear fractures and tension joints are well developed in the indurated Paleozoic Inkisi red sandstones of the West-Congo Supergroup in the "pool" region of Kinshasa and Brazzaville, along the Congo River. They appear to be related to the evolution of intraplate stresses during the late Cretaceous-Paleogene period, possibly related to the opening of the South Atlantic. However, inferring paleostresses from such structures is difficult due to the lack of clear kinematic indicators, so we used mainly the geometry, architecture and sequence of the joint systems to infer paleostresses. A limited number of kinematic indicators for slip sense (displaced pebbles, irregularities on striated surfaces, slickensides) or extension (plume joints) confirm the general conclusions of the joint architecture analysis. We found evidence for two major brittle deformation systems, leading to almost orthogonal fracture sets. They both started by the development of plume joints, which progressively evolved into open tension joints, isolated shear fractures and long (up to several hundred meters) brittle shear zones. The first system started to develop under NE-SW extension and evolved into strike-slip with NNW-SSE horizontal compression while the second (and later), started to develop under NW-SE extension and evolved into strike-slip with NNE-SSW horizontal compression. The second brittle deformation episode was associated with fluid flow as shown by the presence of palygorskite-calcite veins in the most prominent fractures of the second fracture system. Along the NE-SW brittle shear zones which run parallel to the Congo River, carbonate-rich fault-gauge lenses are filled by sand derived from the crushed adjacent walls and calcite vein fragments injected at a high fluid pressure, with late precipitation of palygorskite. Our study demonstrates the existence of two fault systems between Kinshasa and Brazzaville, the first one orthogonal to the trend of the Congo River and the second one, orthogonal to it. This reconciles the different views on the suspected presence of a major fault in the Pool.
Genome-wide population structure and evolutionary history of the Frizarta dairy sheep.

PubMed

Kominakis, A; Hager-Theodorides, A L; Saridaki, A; Antonakos, G; Tsiamis, G

2017-10-01

In the present study, we used genomic data, generated with a medium density single nucleotide polymorphisms (SNP) array, to acquire more information on the population structure and evolutionary history of the synthetic Frizarta dairy sheep. First, two typical measures of linkage disequilibrium (LD) were estimated at various physical distances that were then used to make inferences on the effective population size at key past time points. Population structure was also assessed by both multidimensional scaling analysis and k-means clustering on the distance matrix obtained from the animals' genomic relationships. The Wright's fixation F ST index was also employed to assess herds' genetic homogeneity and to indirectly estimate past migration rates. The Wright's fixation F IS index and genomic inbreeding coefficients based on the genomic relationship matrix as well as on runs of homozygosity were also estimated. The Frizarta breed displays relatively low LD levels with r 2 and |D'| equal to 0.18 and 0.50, respectively, at an average inter-marker distance of 31 kb. Linkage disequilibrium decayed rapidly by distance and persisted over just a few thousand base pairs. Rate of LD decay (β) varied widely among the 26 autosomes with larger values estimated for shorter chromosomes (e.g. β=0.057, for OAR6) and smaller values for longer ones (e.g. β=0.022, for OAR2). The inferred effective population size at the beginning of the breed's formation was as high as 549, was then reduced to 463 in 1981 (end of the breed's formation) and further declined to 187, one generation ago. Multidimensional scaling analysis and k-means clustering suggested a genetically homogenous population, F ST estimates indicated relatively low genetic differentiation between herds, whereas a heat map of the animals' genomic kinship relationships revealed a stratified population, at a herd level. Estimates of genomic inbreeding coefficients suggested that most recent parental relatedness may have been a major determinant of the current effective population size. A denser than the 50k SNP panel may be more beneficial when performing genome wide association studies in the breed.
Multi-Scale Thermal Heat Tracer Tests for Characterizing Transport Processes and Flow Channelling in Fractured Media: Theory and Field Experiments

NASA Astrophysics Data System (ADS)

de La Bernardie, J.; Klepikova, M.; Bour, O.; Le Borgne, T.; Dentz, M.; Guihéneuf, N.; Gerard, M. F.; Lavenant, N.

2017-12-01

The characterization of flow and transport in fractured media is particularly challenging because hydraulic conductivity and transport properties are often strongly dependent on the geometric structure of the fracture surfaces. Here we show how thermal tracer tests may be an excellent complement to conservative solute tracer tests to infer fracture geometry and flow channeling. We performed a series of thermal tracer tests at different scales in a crystalline rock aquifer at the experimental site of Ploemeur (H+ observatory network). The first type of thermal tracer tests are push-pull tracer tests at different scales. The temporal and spatial scaling of heat recovery, measured from thermal breakthrough curves, shows a clear signature of flow channeling. In particular, the late time tailing of heat recovery under channeled flow is shown to diverge from the T(t) α t-1,5 behavior expected for the classical parallel plate model and follow the scaling T(t) α 1/t(logt)2 for a simple channel modeled as a tube. Flow channeling is also manifested on the spatial scaling of heat recovery as flow channeling affects the decay of the thermal breakthrough peak amplitude and the increase of the peak time with scale. The second type of thermal tracer tests are flow-through tracer tests where a pulse of hot water was injected in a fracture isolated by a double straddle packer while pumping at the same flow rate in another fracture at a distance of about 10 meters to create a dipole flow field. Comparison with a solute tracer test performed under the same conditions also present a clear signature of flow channeling. We derive analytical expressions for the retardation and decay of the thermal breakthrough peak amplitude for different fracture geometries and show that the observed differences between thermal and solute breakthrough can be explained only by channelized flow. These results suggest that heat transport is much more sensitive to fracture heterogeneity and flow channeling than conservative solute transport. These findings, which bring new insights on the effect of flow channeling on heat transfer in fractured rocks, show how heat recovery in geothermal systems may be controlled by fracture geometry. This highlights the interest of thermal tracer tests as a complement to solute tracers tests to infer fracture aperture and geometry.

Testing the gravitational instability hypothesis?

NASA Technical Reports Server (NTRS)

Babul, Arif; Weinberg, David H.; Dekel, Avishai; Ostriker, Jeremiah P.

1994-01-01

We challenge a widely accepted assumption of observational cosmology: that successful reconstruction of observed galaxy density fields from measured galaxy velocity fields (or vice versa), using the methods of gravitational instability theory, implies that the observed large-scale structures and large-scale flows were produced by the action of gravity. This assumption is false, in that there exist nongravitational theories that pass the reconstruction tests and gravitational theories with certain forms of biased galaxy formation that fail them. Gravitational instability theory predicts specific correlations between large-scale velocity and mass density fields, but the same correlations arise in any model where (a) structures in the galaxy distribution grow from homogeneous initial conditions in a way that satisfies the continuity equation, and (b) the present-day velocity field is irrotational and proportional to the time-averaged velocity field. We demonstrate these assertions using analytical arguments and N-body simulations. If large-scale structure is formed by gravitational instability, then the ratio of the galaxy density contrast to the divergence of the velocity field yields an estimate of the density parameter Omega (or, more generally, an estimate of beta identically equal to Omega(exp 0.6)/b, where b is an assumed constant of proportionality between galaxy and mass density fluctuations. In nongravitational scenarios, the values of Omega or beta estimated in this way may fail to represent the true cosmological values. However, even if nongravitational forces initiate and shape the growth of structure, gravitationally induced accelerations can dominate the velocity field at late times, long after the action of any nongravitational impulses. The estimated beta approaches the true value in such cases, and in our numerical simulations the estimated beta values are reasonably accurate for both gravitational and nongravitational models. Reconstruction tests that show correlations between galaxy density and velocity fields can rule out some physically interesting models of large-scale structure. In particular, successful reconstructions constrain the nature of any bias between the galaxy and mass distributions, since processes that modulate the efficiency of galaxy formation on large scales in a way that violates the continuity equation also produce a mismatch between the observed galaxy density and the density inferred from the peculiar velocity field. We obtain successful reconstructions for a gravitational model with peaks biasing, but we also show examples of gravitational and nongravitational models that fail reconstruction tests because of more complicated modulations of galaxy formation.
Glacial Refugia in Pathogens: European Genetic Structure of Anther Smut Pathogens on Silene latifolia and Silene dioica

PubMed Central

Vercken, Elodie; Fontaine, Michael C.; Gladieux, Pierre; Hood, Michael E.; Jonot, Odile; Giraud, Tatiana

2010-01-01

Climate warming is predicted to increase the frequency of invasions by pathogens and to cause the large-scale redistribution of native host species, with dramatic consequences on the health of domesticated and wild populations of plants and animals. The study of historic range shifts in response to climate change, such as during interglacial cycles, can help in the prediction of the routes and dynamics of infectious diseases during the impending ecosystem changes. Here we studied the population structure in Europe of two Microbotryum species causing anther smut disease on the plants Silene latifolia and Silene dioica. Clustering analyses revealed the existence of genetically distinct groups for the pathogen on S. latifolia, providing a clear-cut example of European phylogeography reflecting recolonization from southern refugia after glaciation. The pathogen genetic structure was congruent with the genetic structure of its host species S. latifolia, suggesting dependence of the migration pathway of the anther smut fungus on its host. The fungus, however, appeared to have persisted in more numerous and smaller refugia than its host and to have experienced fewer events of large-scale dispersal. The anther smut pathogen on S. dioica also showed a strong phylogeographic structure that might be related to more northern glacial refugia. Differences in host ecology probably played a role in these differences in the pathogen population structure. Very high selfing rates were inferred in both fungal species, explaining the low levels of admixture between the genetic clusters. The systems studied here indicate that migration patterns caused by climate change can be expected to include pathogen invasions that follow the redistribution of their host species at continental scales, but also that the recolonization by pathogens is not simply a mirror of their hosts, even for obligate biotrophs, and that the ecology of hosts and pathogen mating systems likely affects recolonization patterns. PMID:21187901
A full-Bayesian approach to parameter inference from tracer travel time moments and investigation of scale effects at the Cape Cod experimental site

USGS Publications Warehouse

Woodbury, Allan D.; Rubin, Yoram

2000-01-01

A method for inverting the travel time moments of solutes in heterogeneous aquifers is presented and is based on peak concentration arrival times as measured at various samplers in an aquifer. The approach combines a Lagrangian [Rubin and Dagan, 1992] solute transport framework with full‐Bayesian hydrogeological parameter inference. In the full‐Bayesian approach the noise values in the observed data are treated as hyperparameters, and their effects are removed by marginalization. The prior probability density functions (pdfs) for the model parameters (horizontal integral scale, velocity, and log K variance) and noise values are represented by prior pdfs developed from minimum relative entropy considerations. Analysis of the Cape Cod (Massachusetts) field experiment is presented. Inverse results for the hydraulic parameters indicate an expected value for the velocity, variance of log hydraulic conductivity, and horizontal integral scale of 0.42 m/d, 0.26, and 3.0 m, respectively. While these results are consistent with various direct‐field determinations, the importance of the findings is in the reduction of confidence range about the various expected values. On selected control planes we compare observed travel time frequency histograms with the theoretical pdf, conditioned on the observed travel time moments. We observe a positive skew in the travel time pdf which tends to decrease as the travel time distance grows. We also test the hypothesis that there is no scale dependence of the integral scale λ with the scale of the experiment at Cape Cod. We adopt two strategies. The first strategy is to use subsets of the full data set and then to see if the resulting parameter fits are different as we use different data from control planes at expanding distances from the source. The second approach is from the viewpoint of entropy concentration. No increase in integral scale with distance is inferred from either approach over the range of the Cape Cod tracer experiment.
"HOOF-Print" Genotyping and Haplotype Inference Discriminates among Brucella spp Isolates From a Small Spatial Scale

USDA-ARS?s Scientific Manuscript database

We demonstrate that the “HOOF-Print” assay provides high power to discriminate among Brucella isolates collected on a small spatial scale (within Portugal). Additionally, we illustrate how haplotype identification using non-random association among markers allows resolution of B. melitensis biovars ...
Challenges on the Path to Implementation

ERIC Educational Resources Information Center

Martineau, Joseph A.; Wyse, Adam E.

2015-01-01

This article is a commentary of a paper by Derek C. Briggs and Frederick A. Peck, "Using Learning Progressions to Design Vertical Scales That Support Coherent Inferences about Student Growth," which describes an elegant potential framework for at least beginning to address three priorities in large-scale assessment that have not been…
Orientation Uncertainty of Structures Measured in Cored Boreholes: Methodology and Case Study of Swedish Crystalline Rock

NASA Astrophysics Data System (ADS)

Stigsson, Martin

2016-11-01

Many engineering applications in fractured crystalline rocks use measured orientations of structures such as rock contact and fractures, and lineated objects such as foliation and rock stress, mapped in boreholes as their foundation. Despite that these measurements are afflicted with uncertainties, very few attempts to quantify their magnitudes and effects on the inferred orientations have been reported. Only relying on the specification of tool imprecision may considerably underestimate the actual uncertainty space. The present work identifies nine sources of uncertainties, develops inference models of their magnitudes, and points out possible implications for the inference on orientation models and thereby effects on downstream models. The uncertainty analysis in this work builds on a unique data set from site investigations, performed by the Swedish Nuclear Fuel and Waste Management Co. (SKB). During these investigations, more than 70 boreholes with a maximum depth of 1 km were drilled in crystalline rock with a cumulative length of more than 34 km including almost 200,000 single fracture intercepts. The work presented, hence, relies on orientation of fractures. However, the techniques to infer the magnitude of orientation uncertainty may be applied to all types of structures and lineated objects in boreholes. The uncertainties are not solely detrimental, but can be valuable, provided that the reason for their presence is properly understood and the magnitudes correctly inferred. The main findings of this work are as follows: (1) knowledge of the orientation uncertainty is crucial in order to be able to infer correct orientation model and parameters coupled to the fracture sets; (2) it is important to perform multiple measurements to be able to infer the actual uncertainty instead of relying on the theoretical uncertainty provided by the manufacturers; (3) it is important to use the most appropriate tool for the prevailing circumstances; and (4) the single most important parameter to decrease the uncertainty space is to avoid drilling steeper than about -80°.
A Multi-Scale, Integrated Approach to Representing Watershed Systems

NASA Astrophysics Data System (ADS)

Ivanov, Valeriy; Kim, Jongho; Fatichi, Simone; Katopodes, Nikolaos

2014-05-01

Understanding and predicting process dynamics across a range of scales are fundamental challenges for basic hydrologic research and practical applications. This is particularly true when larger-spatial-scale processes, such as surface-subsurface flow and precipitation, need to be translated to fine space-time scale dynamics of processes, such as channel hydraulics and sediment transport, that are often of primary interest. Inferring characteristics of fine-scale processes from uncertain coarse-scale climate projection information poses additional challenges. We have developed an integrated model simulating hydrological processes, flow dynamics, erosion, and sediment transport, tRIBS+VEGGIE-FEaST. The model targets to take the advantage of the current generation of wealth of data representing watershed topography, vegetation, soil, and landuse, as well as to explore the hydrological effects of physical factors and their feedback mechanisms over a range of scales. We illustrate how the modeling system connects precipitation-hydrologic runoff partition process to the dynamics of flow, erosion, and sedimentation, and how the soil's substrate condition can impact the latter processes, resulting in a non-unique response. We further illustrate an approach to using downscaled climate change information with a process-based model to infer the moments of hydrologic variables in future climate conditions and explore the impact of climate information uncertainty.
Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology

NASA Astrophysics Data System (ADS)

Alsing, Justin; Wandelt, Benjamin; Feeney, Stephen

2018-07-01

Many statistical models in cosmology can be simulated forwards but have intractable likelihood functions. Likelihood-free inference methods allow us to perform Bayesian inference from these models using only forward simulations, free from any likelihood assumptions or approximations. Likelihood-free inference generically involves simulating mock data and comparing to the observed data; this comparison in data space suffers from the curse of dimensionality and requires compression of the data to a small number of summary statistics to be tractable. In this paper, we use massive asymptotically optimal data compression to reduce the dimensionality of the data space to just one number per parameter, providing a natural and optimal framework for summary statistic choice for likelihood-free inference. Secondly, we present the first cosmological application of Density Estimation Likelihood-Free Inference (DELFI), which learns a parametrized model for joint distribution of data and parameters, yielding both the parameter posterior and the model evidence. This approach is conceptually simple, requires less tuning than traditional Approximate Bayesian Computation approaches to likelihood-free inference and can give high-fidelity posteriors from orders of magnitude fewer forward simulations. As an additional bonus, it enables parameter inference and Bayesian model comparison simultaneously. We demonstrate DELFI with massive data compression on an analysis of the joint light-curve analysis supernova data, as a simple validation case study. We show that high-fidelity posterior inference is possible for full-scale cosmological data analyses with as few as ˜104 simulations, with substantial scope for further improvement, demonstrating the scalability of likelihood-free inference to large and complex cosmological data sets.
Disentangling the phylogenetic and ecological components of spider phenotypic variation.

PubMed

Gonçalves-Souza, Thiago; Diniz-Filho, José Alexandre Felizola; Romero, Gustavo Quevedo

2014-01-01

An understanding of how the degree of phylogenetic relatedness influences the ecological similarity among species is crucial to inferring the mechanisms governing the assembly of communities. We evaluated the relative importance of spider phylogenetic relationships and ecological niche (plant morphological variables) to the variation in spider body size and shape by comparing spiders at different scales: (i) between bromeliads and dicot plants (i.e., habitat scale) and (ii) among bromeliads with distinct architectural features (i.e., microhabitat scale). We partitioned the interspecific variation in body size and shape into phylogenetic (that express trait values as expected by phylogenetic relationships among species) and ecological components (that express trait values independent of phylogenetic relationships). At the habitat scale, bromeliad spiders were larger and flatter than spiders associated with the surrounding dicots. At this scale, plant morphology sorted out close related spiders. Our results showed that spider flatness is phylogenetically clustered at the habitat scale, whereas it is phylogenetically overdispersed at the microhabitat scale, although phylogenic signal is present in both scales. Taken together, these results suggest that whereas at the habitat scale selective colonization affect spider body size and shape, at fine scales both selective colonization and adaptive evolution determine spider body shape. By partitioning the phylogenetic and ecological components of phenotypic variation, we were able to disentangle the evolutionary history of distinct spider traits and show that plant architecture plays a role in the evolution of spider body size and shape. We also discussed the relevance in considering multiple scales when studying phylogenetic community structure.
Disentangling the Phylogenetic and Ecological Components of Spider Phenotypic Variation

PubMed Central

Gonçalves-Souza, Thiago; Diniz-Filho, José Alexandre Felizola; Romero, Gustavo Quevedo

2014-01-01

An understanding of how the degree of phylogenetic relatedness influences the ecological similarity among species is crucial to inferring the mechanisms governing the assembly of communities. We evaluated the relative importance of spider phylogenetic relationships and ecological niche (plant morphological variables) to the variation in spider body size and shape by comparing spiders at different scales: (i) between bromeliads and dicot plants (i.e., habitat scale) and (ii) among bromeliads with distinct architectural features (i.e., microhabitat scale). We partitioned the interspecific variation in body size and shape into phylogenetic (that express trait values as expected by phylogenetic relationships among species) and ecological components (that express trait values independent of phylogenetic relationships). At the habitat scale, bromeliad spiders were larger and flatter than spiders associated with the surrounding dicots. At this scale, plant morphology sorted out close related spiders. Our results showed that spider flatness is phylogenetically clustered at the habitat scale, whereas it is phylogenetically overdispersed at the microhabitat scale, although phylogenic signal is present in both scales. Taken together, these results suggest that whereas at the habitat scale selective colonization affect spider body size and shape, at fine scales both selective colonization and adaptive evolution determine spider body shape. By partitioning the phylogenetic and ecological components of phenotypic variation, we were able to disentangle the evolutionary history of distinct spider traits and show that plant architecture plays a role in the evolution of spider body size and shape. We also discussed the relevance in considering multiple scales when studying phylogenetic community structure. PMID:24651264
Analytic prediction of baryonic effects from the EFT of large scale structures

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

Lewandowski, Matthew; Perko, Ashley; Senatore, Leonardo, E-mail: mattlew@stanford.edu, E-mail: perko@stanford.edu, E-mail: senatore@stanford.edu

2015-05-01

The large scale structures of the universe will likely be the next leading source of cosmological information. It is therefore crucial to understand their behavior. The Effective Field Theory of Large Scale Structures provides a consistent way to perturbatively predict the clustering of dark matter at large distances. The fact that baryons move distances comparable to dark matter allows us to infer that baryons at large distances can be described in a similar formalism: the backreaction of short-distance non-linearities and of star-formation physics at long distances can be encapsulated in an effective stress tensor, characterized by a few parameters. Themore » functional form of baryonic effects can therefore be predicted. In the power spectrum the leading contribution goes as ∝ k{sup 2} P(k), with P(k) being the linear power spectrum and with the numerical prefactor depending on the details of the star-formation physics. We also perform the resummation of the contribution of the long-wavelength displacements, allowing us to consistently predict the effect of the relative motion of baryons and dark matter. We compare our predictions with simulations that contain several implementations of baryonic physics, finding percent agreement up to relatively high wavenumbers such as k ≅ 0.3 hMpc{sup −1} or k ≅ 0.6 hMpc{sup −1}, depending on the order of the calculation. Our results open a novel way to understand baryonic effects analytically, as well as to interface with simulations.« less
Genome-scale rates of evolutionary change in bacteria

PubMed Central

Duchêne, Sebastian; Holt, Kathryn E.; Weill, François-Xavier; Le Hello, Simon; Hawkey, Jane; Edwards, David J.; Fourment, Mathieu

2016-01-01

Estimating the rates at which bacterial genomes evolve is critical to understanding major evolutionary and ecological processes such as disease emergence, long-term host–pathogen associations and short-term transmission patterns. The surge in bacterial genomic data sets provides a new opportunity to estimate these rates and reveal the factors that shape bacterial evolutionary dynamics. For many organisms estimates of evolutionary rate display an inverse association with the time-scale over which the data are sampled. However, this relationship remains unexplored in bacteria due to the difficulty in estimating genome-wide evolutionary rates, which are impacted by the extent of temporal structure in the data and the prevalence of recombination. We collected 36 whole genome sequence data sets from 16 species of bacterial pathogens to systematically estimate and compare their evolutionary rates and assess the extent of temporal structure in the absence of recombination. The majority (28/36) of data sets possessed sufficient clock-like structure to robustly estimate evolutionary rates. However, in some species reliable estimates were not possible even with ‘ancient DNA’ data sampled over many centuries, suggesting that they evolve very slowly or that they display extensive rate variation among lineages. The robustly estimated evolutionary rates spanned several orders of magnitude, from approximately 10−5 to 10−8 nucleotide substitutions per site year−1. This variation was negatively associated with sampling time, with this relationship best described by an exponential decay curve. To avoid potential estimation biases, such time-dependency should be considered when inferring evolutionary time-scales in bacteria. PMID:28348834
Topology, divergence dates, and macroevolutionary inferences vary between different tip-dating approaches applied to fossil theropods (Dinosauria).

PubMed

Bapst, D W; Wright, A M; Matzke, N J; Lloyd, G T

2016-07-01

Dated phylogenies of fossil taxa allow palaeobiologists to estimate the timing of major divergences and placement of extinct lineages, and to test macroevolutionary hypotheses. Recently developed Bayesian 'tip-dating' methods simultaneously infer and date the branching relationships among fossil taxa, and infer putative ancestral relationships. Using a previously published dataset for extinct theropod dinosaurs, we contrast the dated relationships inferred by several tip-dating approaches and evaluate potential downstream effects on phylogenetic comparative methods. We also compare tip-dating analyses to maximum-parsimony trees time-scaled via alternative a posteriori approaches including via the probabilistic cal3 method. Among tip-dating analyses, we find opposing but strongly supported relationships, despite similarity in inferred ancestors. Overall, tip-dating methods infer divergence dates often millions (or tens of millions) of years older than the earliest stratigraphic appearance of that clade. Model-comparison analyses of the pattern of body-size evolution found that the support for evolutionary mode can vary across and between tree samples from cal3 and tip-dating approaches. These differences suggest that model and software choice in dating analyses can have a substantial impact on the dated phylogenies obtained and broader evolutionary inferences. © 2016 The Author(s).
Abnormal agency experiences in schizophrenia patients: Examining the role of psychotic symptoms and familial risk.

PubMed

Prikken, Merel; van der Weiden, Anouk; Renes, Robert A; Koevoets, Martijn G J C; Heering, Henriette D; Kahn, René S; Aarts, Henk; van Haren, Neeltje E M

2017-04-01

Experiencing self-agency over one's own action outcomes is essential for social functioning. Recent research revealed that patients with schizophrenia do not use implicitly available information about their action-outcomes (i.e., prime-based agency inference) to arrive at self-agency experiences. Here, we examined whether this is related to symptoms and/or familial risk to develop the disease. Fifty-four patients, 54 controls, and 19 unaffected (and unrelated) siblings performed an agency inference task, in which experienced agency was measured over action-outcomes that matched or mismatched outcome-primes that were presented before action performance. The Positive and Negative Syndrome Scale (PANSS) and Comprehensive Assessment of Symptoms and History (CASH) were administered to assess psychopathology. Impairments in prime-based inferences did not differ between patients with symptoms of over- and underattribution. However, patients with agency underattribution symptoms reported significantly lower overall self-agency experiences. Siblings displayed stronger prime-based agency inferences than patients, but weaker prime-based inferences than healthy controls. However, these differences were not statistically significant. Findings suggest that impairments in prime-based agency inferences may be a trait characteristic of schizophrenia. Moreover, this study may stimulate further research on the familial basis and the clinical relevance of impairments in implicit agency inferences. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.
Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data.

PubMed

Bhaskar, Anand; Wang, Y X Rachel; Song, Yun S

2015-02-01

With the recent increase in study sample sizes in human genetics, there has been growing interest in inferring historical population demography from genomic variation data. Here, we present an efficient inference method that can scale up to very large samples, with tens or hundreds of thousands of individuals. Specifically, by utilizing analytic results on the expected frequency spectrum under the coalescent and by leveraging the technique of automatic differentiation, which allows us to compute gradients exactly, we develop a very efficient algorithm to infer piecewise-exponential models of the historical effective population size from the distribution of sample allele frequencies. Our method is orders of magnitude faster than previous demographic inference methods based on the frequency spectrum. In addition to inferring demography, our method can also accurately estimate locus-specific mutation rates. We perform extensive validation of our method on simulated data and show that it can accurately infer multiple recent epochs of rapid exponential growth, a signal that is difficult to pick up with small sample sizes. Lastly, we use our method to analyze data from recent sequencing studies, including a large-sample exome-sequencing data set of tens of thousands of individuals assayed at a few hundred genic regions. © 2015 Bhaskar et al.; Published by Cold Spring Harbor Laboratory Press.
Nanometer-scale characterization of exceptionally preserved bacterial fossils in Paleocene phosphorites from Ouled Abdoun (Morocco).

PubMed

Cosmidis, J; Benzerara, K; Gheerbrant, E; Estève, I; Bouya, B; Amaghzaz, M

2013-03-01

Micrometer-sized spherical and rod-shaped forms have been reported in many phosphorites and often interpreted as microbes fossilized by apatite, based on their morphologic resemblance with modern bacteria inferred by scanning electron microscopy (SEM) observations. This interpretation supports models involving bacteria in the formation of phosphorites. Here, we studied a phosphatic coprolite of Paleocene age originating from the Ouled Abdoun phosphate basin (Morocco) down to the nanometer-scale using focused ion beam milling, transmission electron microscopy (TEM), and scanning transmission x-ray microscopy (STXM) coupled with x-ray absorption near-edge structure spectroscopy (XANES). The coprolite, exclusively composed of francolite (a carbonate-fluroapatite), is formed by the accumulation of spherical objects, delimited by a thin envelope, and whose apparent diameters are between 0.5 and 3 μm. The envelope of the spheres is composed of a continuous crown dense to electrons, which measures 20-40 nm in thickness. It is surrounded by two thinner layers that are more porous and transparent to electrons and enriched in organic carbon. The observed spherical objects are very similar with bacteria encrusting in hydroxyapatite as observed in laboratory experiments. We suggest that they are Gram-negative bacteria fossilized by francolite, the precipitation of which started within the periplasm of the cells. We discuss the role of bacteria in the fossilization mechanism and propose that they could have played an active role in the formation of francolite. This study shows that ancient phosphorites can contain fossil biological subcellular structures as fine as a bacterial periplasm. Moreover, we demonstrate that while morphological information provided by SEM analyses is valuable, the use of additional nanoscale analyses is a powerful approach to help inferring the biogenicity of biomorphs found in phosphorites. A more systematic use of this approach could considerably improve our knowledge and understanding of the microfossils present in the geological record. © 2012 Blackwell Publishing Ltd.
Assessing mental health clinicians' intentions to adopt evidence-based treatments: reliability and validity testing of the evidence-based treatment intentions scale.

PubMed

Williams, Nathaniel J

2016-05-05

Intentions play a central role in numerous empirically supported theories of behavior and behavior change and have been identified as a potentially important antecedent to successful evidence-based treatment (EBT) implementation. Despite this, few measures of mental health clinicians' EBT intentions exist and available measures have not been subject to thorough psychometric evaluation or testing. This paper evaluates the psychometric properties of the evidence-based treatment intentions (EBTI) scale, a new measure of mental health clinicians' intentions to adopt EBTs. The study evaluates the reliability and validity of inferences made with the EBTI using multi-method, multi-informant criterion variables collected over 12 months from a sample of 197 mental health clinicians delivering services in 13 mental health agencies. Structural, predictive, and discriminant validity evidence is assessed. Findings support the EBTI's factor structure (χ (2) = 3.96, df = 5, p = .556) and internal consistency reliability (α = .80). Predictive validity evidence was provided by robust and significant associations between EBTI scores and clinicians' observer-reported attendance at a voluntary EBT workshop at a 1-month follow-up (OR = 1.92, p < .05), self-reported EBT adoption at a 12-month follow-up (R (2) = .17, p < .001), and self-reported use of EBTs with clients at a 12-month follow-up (R (2) = .25, p < .001). Discriminant validity evidence was provided by small associations with clinicians' concurrently measured psychological work climate perceptions of functionality (R (2) = .06, p < .05), engagement (R (2) = .06, p < .05), and stress (R (2) = .00, ns). The EBTI is a practical and theoretically grounded measure of mental health clinicians' EBT intentions. Scores on the EBTI provide a basis for valid inferences regarding mental health clinicians' intentions to adopt EBTs. Discussion focuses on research and practice applications.
The gamut of alkoxy radicals

NASA Astrophysics Data System (ADS)

Box, Harold C.; Budzinski, Edwin E.; Freund, Harold G.

1984-12-01

It is shown that various radicals exhibiting diverse ESR and ENDOR spectral characteristics are nonetheless a closely related family of alkoxy radicals. The relationship is established by correlating the g tensor with crystal structure and by relating dihedral angles inferred from proton hyperfine couplings to dihedral angles inferred from the g tensor and crystal structure. The analysis also serves to demonstrate that an ESR absorption observed in x-irradiated single crystals of uridine 5'-monophosphate is due to an alkoxy radical.
The southern Whidbey Island fault: An active structure in the Puget Lowland, Washington

USGS Publications Warehouse

Johnson, S.Y.; Potter, C.J.; Armentrout, J.M.; Miller, J.J.; Finn, C.; Weaver, C.S.

1996-01-01

Information from seismic-reflection profiles, outcrops, boreholes, and potential field surveys is used to interpret the structure and history of the southern Whidbey Island fault in the Puget Lowland of western Washington. This northwest-trending fault comprises a broad (as wide as 6-11 km), steep, northeast-dipping zone that includes several splays with inferred strike-slip, reverse, and thrust displacement. Transpressional deformation along the southern Whidbey Island fault is indicated by alongstrike variations in structural style and geometry, positive flower structure, local unconformities, out-of-plane displacements, and juxtaposition of correlative sedimentary units with different histories. The southern Whidbey Island fault represents a segment of a boundary between two major crustal blocks. The Cascade block to the northeast is floored by diverse assemblages of pre-Tertiary rocks; the Coast Range block to the southwest is floored by lower Eocene marine basaltic rocks of the Crescent Formation. The fault probably originated during the early Eocene as a dextral strike-slip fault along the eastern side of a continental-margin rift. Bending of the fault and transpressional deformation began during the late middle Eocene and continues to the present. Oblique convergence and clockwise rotation along the continental margin are the inferred driving forces for ongoing deformation. Evidence for Quaternary movement on the southern Whidbey Island fault includes (1) offset and disrupted upper Quaternary strata imaged on seismic-reflection profiles; (2) borehole data that suggests as much as 420 m of structural relief on the Tertiary-Quaternary boundary in the fault zone; (3) several meters of displacement along exposed faults in upper Quaternary sediments; (4) late Quaternary folds with limb dips of as much as ???9??; (5) large-scale liquefaction features in upper Quaternary sediments within the fault zone; and (6) minor historical seismicity. The southern Whidbey Island fault should be considered capable of generating large earthquakes (Ms ???7) and represents a potential seismic hazard to residents of the Puget Lowland.
The Value of Large-Scale Randomised Control Trials in System-Wide Improvement: The Case of the Reading Catch-Up Programme

ERIC Educational Resources Information Center

Fleisch, Brahm; Taylor, Stephen; Schöer, Volker; Mabogoane, Thabo

2017-01-01

This article illustrates the value of large-scale impact evaluations with counterfactual components. It begins by exploring the limitations of small-scale impact studies, which do not allow reliable inference to a wider population or which do not use valid comparison groups. The paper then describes the design features of a recent large-scale…

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